The validation of structural models obtained by macromolecular X-ray crystallography against experimental diffraction data, whether before deposition into the PDB or after, is typically carried out exclusively against the merged data that are eventually archived along with the atomic coordinates. It is shown here that the availability of unmerged reflection data enables valuable additional analyses to be performed that yield improvements in the final models, and tools are presented to implement them, together with examples of the results to which they give access. The first example is the automatic identification and removal of image ranges affected by loss of crystal centering or by excessive decay of the diffraction pattern as a result of radiation damage. The second example is the `reflection-auditing' process, whereby individual merged data items showing especially poor agreement with model predictions during refinement are investigated thanks to the specific metadata (such as image number and detector position) that are available for the corresponding unmerged data, potentially revealing previously undiagnosed instrumental, experimental or processing problems. The third example is the calculation of so-called F(early) − F(late) maps from carefully selected subsets of unmerged amplitude data, which can not only highlight the location and extent of radiation damage but can also provide guidance towards suitable fine-grained parametrizations to model the localized effects of such damage.

Data acquisition and processing for cryo-electron tomography can be a significant bottleneck for users. To simplify and streamline the cryo-ET workflow, Tomo Live, an on-the-fly solution that automates the alignment and reconstruction of tilt-series data, enabling real-time data-quality assessment, has been developed. Through the integration of Tomo Live into the data-acquisition workflow for cryo-ET, motion correction is performed directly after each of the acquired tilt angles. Immediately after the tilt-series acquisition has completed, an unattended tilt-series alignment and reconstruction into a 3D volume is performed. The results are displayed in real time in a dedicated remote web platform that runs on the microscope hardware. Through this web platform, users can review the acquired data (aligned stack and 3D volume) and several quality metrics that are obtained during the alignment and reconstruction process. These quality metrics can be used for fast feedback for subsequent acquisitions to save time. Parameters such as Alignment Accuracy, Deleted Tilts and Tilt Axis Correction Angle are visualized as graphs and can be used as filters to export only the best tomograms (raw data, reconstruction and intermediate data) for further processing. Here, the Tomo Live algorithms and workflow are described and representative results on several biological samples are presented. The Tomo Live workflow is accessible to both expert and non-expert users, making it a valuable tool for the continued advancement of structural biology, cell biology and histology.

A considerable bottleneck in serial crystallography at XFEL and synchrotron sources is the efficient production of large quantities of homogenous, well diffracting microcrystals. Efficient high-throughput screening of batch-grown microcrystals and the determination of ground-state structures from different conditions is thus of considerable value in the early stages of a project. Here, a highly sample-efficient methodology to measure serial crystallography data from microcrystals by raster scanning within standard in situ 96-well crystallization plates is described. Structures were determined from very small quantities of microcrystal suspension and the results were compared with those from other sample-delivery methods. The analysis of a two-dimensional batch crystallization screen using this method is also described as a useful guide for further optimization and the selection of appropriate conditions for scaling up microcrystallization.

Metalloproteins are ubiquitous in all living organisms and take part in a very wide range of biological processes. For this reason, their experimental characterization is crucial to obtain improved knowledge of their structure and biological functions. The three-dimensional structure represents highly relevant information since it provides insight into the interaction between the metal ion(s) and the protein fold. Such interactions determine the chemical reactivity of the bound metal. The available PDB structures can contain errors due to experimental factors such as poor resolution and radiation damage. A lack of use of distance restraints during the refinement and validation process also impacts the structure quality. Here, the aim was to obtain a thorough overview of the distribution of the distances between metal ions and their donor atoms through the statistical analysis of a data set based on more than 115 000 metal-binding sites in proteins. This analysis not only produced reference data that can be used by experimentalists to support the structure-determination process, for example as refinement restraints, but also resulted in an improved insight into how protein coordination occurs for different metals and the nature of their binding interactions. In particular, the features of carboxylate coordination were inspected, which is the only type of interaction that is commonly present for nearly all metals.

For cryo-electron tomography (cryo-ET) of beam-sensitive biological specimens, a planar sample geometry is typically used. As the sample is tilted, the effective thickness of the sample along the direction of the electron beam increases and the signal-to-noise ratio concomitantly decreases, limiting the transfer of information at high tilt angles. In addition, the tilt range where data can be collected is limited by a combination of various sample-environment constraints, including the limited space in the objective lens pole piece and the possible use of fixed conductive braids to cool the specimen. Consequently, most tilt series are limited to a maximum of ±70°, leading to the presence of a missing wedge in Fourier space. The acquisition of cryo-ET data without a missing wedge, for example using a cylindrical sample geometry, is hence attractive for volumetric analysis of low-symmetry structures such as organelles or vesicles, lysis events, pore formation or filaments for which the missing information cannot be compensated by averaging techniques. Irrespective of the geometry, electron-beam damage to the specimen is an issue and the first images acquired will transfer more high-resolution information than those acquired last. There is also an inherent trade-off between higher sampling in Fourier space and avoiding beam damage to the sample. Finally, the necessity of using a sufficient electron fluence to align the tilt images means that this fluence needs to be fractionated across a small number of images; therefore, the order of data acquisition is also a factor to consider. Here, an n-helix tilt scheme is described and simulated which uses overlapping and interleaved tilt series to maximize the use of a pillar geometry, allowing the entire pillar volume to be reconstructed as a single unit. Three related tilt schemes are also evaluated that extend the continuous and classic dose-symmetric tilt schemes for cryo-ET to pillar samples to enable the collection of isotropic information across all spatial frequencies. A fourfold dose-symmetric scheme is proposed which provides a practical compromise between uniform information transfer and complexity of data acquisition.

Protein crystallography is an established method to study the atomic structures of macromolecules and their complexes. A prerequisite for successful structure determination is diffraction-quality crystals, which may require extensive optimization of both the protein and the conditions, and hence projects can stretch over an extended period, with multiple users being involved. The workflow from crystallization and crystal treatment to deposition and publication is well defined, and therefore an electronic laboratory information management system (LIMS) is well suited to management of the data. Completion of the project requires key information on all the steps being available and this information should also be made available according to the FAIR principles. As crystallized samples are typically shipped between facilities, a key feature to be captured in the LIMS is the exchange of metadata between the crystallization facility of the home laboratory and, for example, synchrotron facilities. On completion, structures are deposited in the Protein Data Bank (PDB) and the LIMS can include the PDB code in its database, completing the chain of custody from crystallization to structure deposition and publication. A LIMS designed for macromolecular crystallography, IceBear, is available as a standalone installation and as a hosted service, and the implementation of key features for the capture of metadata in IceBear is discussed as an example.

Proteins frequently undergo covalent modification at the post-translational level, which involves the covalent attachment of chemical groups onto amino acids. This can entail the singular or multiple addition of small groups, such as phosphorylation; long-chain modifications, such as glycosylation; small proteins, such as ubiquitination; as well as the interconversion of chemical groups, such as the formation of pyroglutamic acid. These post-translational modifications (PTMs) are essential for the normal functioning of cells, as they can alter the physicochemical properties of amino acids and therefore influence enzymatic activity, protein localization, protein–protein interactions and protein stability. Despite their inherent importance, accurately depicting PTMs in experimental studies of protein structures often poses a challenge. This review highlights the role of PTMs in protein structures, as well as the prevalence of PTMs in the Protein Data Bank, directing the reader to accurately built examples suitable for use as a modelling reference.

Most scientific facilities produce large amounts of heterogeneous data at a rapid pace. Managing users, instruments, reports and invoices presents additional challenges. To address these challenges, EMhub, a web platform designed to support the daily operations and record-keeping of a scientific facility, has been introduced. EMhub enables the easy management of user information, instruments, bookings and projects. The application was initially developed to meet the needs of a cryoEM facility, but its functionality and adaptability have proven to be broad enough to be extended to other data-generating centers. The expansion of EMHub is enabled by the modular nature of its core functionalities. The application allows external processes to be connected via a REST API, automating tasks such as folder creation, user and password generation, and the execution of real-time data-processing pipelines. EMhub has been used for several years at the Swedish National CryoEM Facility and has been installed in the CryoEM center at the Structural Biology Department at St. Jude Children's Research Hospital. A fully automated single-particle pipeline has been implemented for on-the-fly data processing and analysis. At St. Jude, the X-Ray Crystallography Center and the Single-Molecule Imaging Center have already expanded the platform to support their operational and data-management workflows.

Serial electron diffraction (SerialED), which applies a snapshot data acquisition strategy for each crystal, was introduced to tackle the problem of radiation damage in the structure determination of beam-sensitive materials by three-dimensional electron diffraction (3DED). The snapshot data acquisition in SerialED can be realized using both transmission and scanning transmission electron microscopes (TEM/STEM). However, the current SerialED workflow based on STEM setups requires special external devices and software, which limits broader adoption. Here, we present a simplified experimental implementation of STEM-based SerialED on Thermo Fisher Scientific STEMs using common proprietary software interfaced through Python scripts to automate data collection. Specifically, we utilize TEM Imaging and Analysis (TIA) scripting and TEM scripting to access the STEM functionalities of the microscope, and DigitalMicrograph scripting to control the camera for snapshot data acquisition. Data analysis adapts the existing workflow using the software CrystFEL, which was developed for serial X-ray crystallography. Our workflow for STEM SerialED can be used on any Gatan or Thermo Fisher Scientific camera. We apply this workflow to collect high-resolution STEM SerialED data from two aluminosilicate zeolites, zeolite Y and ZSM-25. We demonstrate, for the first time, ab initio structure determination through direct methods using STEM SerialED data. Zeolite Y is relatively stable under the electron beam, and STEM SerialED data extend to 0.60 Å. We show that the structural model obtained using STEM SerialED data merged from 358 crystals is nearly identical to that using continuous rotation electron diffraction data from one crystal. This demonstrates that accurate structures can be obtained from STEM SerialED. Zeolite ZSM-25 is very beam-sensitive and has a complex structure. We show that STEM SerialED greatly improves the data resolution of ZSM-25, compared with serial rotation electron diffraction (SerialRED), from 1.50 to 0.90 Å. This allows, for the first time, the use of standard phasing methods, such as direct methods, for the ab initio structure determination of ZSM-25.

Our study compares short-range order parameters refined from the diffuse scattering in single-crystal X-ray and single-crystal electron diffraction data. Nb0.84CoSb was chosen as a reference material. The correlations between neighbouring vacancies and the displacements of Sb and Co atoms were refined from the diffuse scattering using a Monte Carlo refinement in DISCUS. The difference between the Sb and Co displacements refined from the diffuse scattering and the Sb and Co displacements refined from the Bragg reflections in single-crystal X-ray diffraction data is 0.012 (7) Å for the refinement on diffuse scattering in single-crystal X-ray diffraction data and 0.03 (2) Å for the refinement on the diffuse scattering in single-crystal electron diffraction data. As electron diffraction requires much smaller crystals than X-ray diffraction, this opens up the possibility of refining short-range order parameters in many technologically relevant materials for which no crystals large enough for single-crystal X-ray diffraction are available.

Interoperability of crystallographic data with other disciplines is essential for the smooth and rapid progress of structure-based science in the computer age. Within crystallography and closely related subject areas, there is already a high level of conformance to the generally accepted FAIR principles (that data be findable, accessible, interoperable and reusable) through the adoption of common information exchange protocols by databases, publishers, instrument vendors, experimental facilities and software authors. Driven by the success within these domains, the IUCr has worked closely with CODATA (the Committee on Data of the International Science Council) to help develop the latter's commitment to cross-domain integration of discipline-specific data. The IUCr has, in particular, emphasized the need for standards relating to data quality and completeness as an adjunct to the FAIR data landscape. This can ensure definitive reusable data, which in turn can aid interoperability across domains. A microsymposium at the IUCr 2023 Congress provided an up-to-date survey of data interoperability within and outside of crystallography, expounded using a broad range of examples.

Serial crystallography (SX) has become an established technique for protein structure determination, especially when dealing with small or radiation-sensitive crystals and investigating fast or irreversible protein dynamics. The advent of newly developed multi-megapixel X-ray area detectors, capable of capturing over 1000 images per second, has brought about substantial benefits. However, this advancement also entails a notable increase in the volume of collected data. Today, up to 2 PB of data per experiment could be easily obtained under efficient operating conditions. The combined costs associated with storing data from multiple experiments provide a compelling incentive to develop strategies that effectively reduce the amount of data stored on disk while maintaining the quality of scientific outcomes. Lossless data-compression methods are designed to preserve the information content of the data but often struggle to achieve a high compression ratio when applied to experimental data that contain noise. Conversely, lossy compression methods offer the potential to greatly reduce the data volume. Nonetheless, it is vital to thoroughly assess the impact of data quality and scientific outcomes when employing lossy compression, as it inherently involves discarding information. The evaluation of lossy compression effects on data requires proper data quality metrics. In our research, we assess various approaches for both lossless and lossy compression techniques applied to SX data, and equally importantly, we describe metrics suitable for evaluating SX data quality.

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for the deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and the resulting consensus recommendations. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules (e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.

The hardware for data archiving has expanded capacities for digital storage enormously in the past decade or more. The IUCr evaluated the costs and benefits of this within an official working group which advised that raw data archiving would allow ground truth reproducibility in published studies. Consultations of the IUCr's Commissions ensued via a newly constituted standing advisory committee, the Committee on Data. At all stages, the IUCr financed workshops to facilitate community discussions and possible methods of raw data archiving implementation. The recent launch of the IUCrData journal's Raw Data Letters is a milestone in the implementation of raw data archiving beyond the currently published studies: it includes diffraction patterns that have not been fully interpreted, if at all. The IUCr 75th Congress in Melbourne included a workshop on raw data reuse, discussing the successes and ongoing challenges of raw data reuse. This article charts the efforts of the IUCr to facilitate discussions and plans relating to raw data archiving and reuse within the various communities of crystallography, diffraction and scattering.

Stimulated by informal conversations at the XVII International Small Angle Scattering (SAS) conference (Traverse City, 2017), an international team of experts undertook a round-robin exercise to produce a large dataset from proteins under standard solution conditions. These data were used to generate consensus SAS profiles for xylose isomerase, urate oxidase, xylanase, lysozyme and ribonuclease A. Here, we apply a new protocol using maximum likelihood with a larger number of the contributed datasets to generate improved consensus profiles. We investigate the fits of these profiles to predicted profiles from atomic coordinates that incorporate different models to account for the contribution to the scattering of water molecules of hydration surrounding proteins in solution. Programs using an implicit, shell-type hydration layer generally optimize fits to experimental data with the aid of two parameters that adjust the volume of the bulk solvent excluded by the protein and the contrast of the hydration layer. For these models, we found the error-weighted residual differences between the model and the experiment generally reflected the subsidiary maxima and minima in the consensus profiles that are determined by the size of the protein plus the hydration layer. By comparison, all-atom solute and solvent molecular dynamics (MD) simulations are without the benefit of adjustable parameters and, nonetheless, they yielded at least equally good fits with residual differences that are less reflective of the structure in the consensus profile. Further, where MD simulations accounted for the precise solvent composition of the experiment, specifically the inclusion of ions, the modelled radius of gyration values were significantly closer to the experiment. The power of adjustable parameters to mask real differences between a model and the structure present in solution is demonstrated by the results for the conformationally dynamic ribonuclease A and calculations with pseudo-experimental data. This study shows that, while methods invoking an implicit hydration layer have the unequivocal advantage of speed, care is needed to understand the influence of the adjustable parameters. All-atom solute and solvent MD simulations are slower but are less susceptible to false positives, and can account for thermal fluctuations in atomic positions, and more accurately represent the water molecules of hydration that contribute to the scattering profile.

In the folded state, biomolecules exchange between multiple conformational states crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules accurately, we must move towards modeling and predicting structural ensembles. Information about structural ensembles exists within experimental data from X-ray crystallography and cryo-electron microscopy. Although new tools are available to detect conformational and compositional heterogeneity within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity. We propose modifications to the macromolecular crystallographic information file (mmCIF) to improve the representation and interrelation of conformational and compositional heterogeneity. These modifications will enable the capture of macromolecular ensembles in a human and machine-interpretable way, potentially catalyzing breakthroughs for ensemble–function predictions, analogous to the achievements of AlphaFold with single-structure prediction.

This study examines various methods for modelling the electron density and, thus, the electrostatic potential of an organometallic complex for use in crystal structure refinement against 3D electron diffraction (ED) data. It focuses on modelling the scattering factors of iron(III), considering the electron density distribution specific for coordination with organic linkers. We refined the structural model of the metal–organic complex, iron(III) acetyl­acetonate (FeAcAc), using both the independent atom model (IAM) and the transferable aspherical atom model (TAAM). TAAM refinement initially employed multipolar parameters from the MATTS databank for acetyl­acetonate, while iron was modelled with a spherical and neutral approach (TAAM ligand). Later, custom-made TAAM scattering factors for Fe—O coordination were derived from DFT calculations [TAAM-ligand-Fe(III)]. Our findings show that, in this compound, the TAAM scattering factor corresponding to Fe3+ has a lower scattering amplitude than the Fe3+ charged scattering factor described by IAM. When using scattering factors corresponding to the oxidation state of iron, IAM inaccurately represents electrostatic potential maps and overestimates the scattering potential of the iron. In addition, TAAM significantly improved the fitting of the model to the data, shown by improved R1 values, goodness-of-fit (GooF) and reduced noise in the Fourier difference map (based on the residual distribution analysis). For 3D ED, R1 values improved from 19.36% (IAM) to 17.44% (TAAM-ligand) and 17.49% (TAAM-ligand-Fe3+), and for single-crystal X-ray diffraction (SCXRD) from 3.82 to 2.03% and 1.98%, respectively. For 3D ED, the most significant R1 reductions occurred in the low-resolution region (8.65–2.00 Å), dropping from 20.19% (IAM) to 14.67% and 14.89% for TAAM-ligand and TAAM-ligand-Fe(III), respectively, with less improvement in high-resolution ranges (2.00–0.85 Å). This indicates that the major enhancements are due to better scattering modelling in low-resolution zones. Furthermore, when using TAAM instead of IAM, there was a noticeable improvement in the shape of the thermal ellipsoids, which more closely resembled those of an SCXRD-refined model. This study demonstrates the applicability of more sophisticated scattering factors to improve the refinement of metal–organic complexes against 3D ED data, suggesting the need for more accurate modelling methods and highlighting the potential of TAAM in examining the charge distribution of large molecular structures using 3D ED.

Reaching beyond the commonly used spherical atomic electron density model allows one to greatly improve the accuracy of hydrogen atom structural param­eters derived from X-ray data. However, the effects of atomic asphericity are less explored for electron diffraction data. In this work, Hirshfeld atom refinement (HAR), a method that uses an accurate description of electron density by quantum mechanical calculation for a system of interest, was applied for the first time to the kinematical refinement of electron diffraction data. This approach was applied here to derive the structure of ordinary hexagonal ice (Ih). The effect of introducing HAR is much less noticeable than in the case of X-ray refinement and it is largely overshadowed by dynamical scattering effects. It led to only a slight change in the O—H bond lengths (shortening by 0.01 Å) compared with the independent atom model (IAM). The average absolute differences in O—H bond lengths between the kinematical refinements and the reference neutron structure were much larger: 0.044 for IAM and 0.046 Å for HAR. The refinement results changed considerably when dynamical scattering effects were modelled – with extinction correction or with dynamical refinement. The latter led to an improvement of the O—H bond length accuracy to 0.021 Å on average (with IAM refinement). Though there is a potential for deriving more accurate structures using HAR for electron diffraction, modelling of dynamical scattering effects seems to be a necessary step to achieve this. However, at present there is no software to support both HAR and dynamical refinement.

Identifying and characterizing metal-binding sites (MBS) within macromolecular structures is imperative for elucidating their biological functions. CheckMyMetal (CMM) is a web based tool that facilitates the interactive valid­ation of MBS in structures determined through X-ray crystallography and cryo-electron microscopy (cryo-EM). Recent updates to CMM have significantly enhanced its capability to efficiently handle large datasets generated from cryo-EM structural analyses. In this study, we address various challenges inherent in validating MBS within both X-ray and cryo-EM structures. Specifically, we examine the difficulties associated with accurately identifying metals and modeling their coordination environments by considering the ongoing reproducibility challenges in structural biology and the critical importance of well annotated, high-quality experimental data. CMM employs a sophisticated framework of rules rooted in the valence bond theory for MBS validation. We explore how CMM validation parameters correlate with the resolution of experimentally derived structures of macromolecules and their complexes. Additionally, we showcase the practical utility of CMM by analyzing a representative cryo-EM structure. Through a comprehensive examination of experimental data, we demonstrate the capability of CMM to advance MBS characterization and identify potential instances of metal misassignment.

The absence of solvent molecules in high-resolution protein crystal structure models deposited in the Protein Data Bank (PDB) contradicts the fact that, for proteins crystallized from aqueous media, water molecules are always expected to bind to the protein surface, as well as to some sites in the protein interior. An analysis of the contents of the PDB indicated that the expected ratio of the number of water molecules to the number of amino-acid residues exceeds 1.5 in atomic resolution structures, decreasing to 0.25 at around 2.5 Å resolution. Nevertheless, almost 800 protein crystal structures determined at a resolution of 2.5 Å or higher are found in the current release of the PDB without any water molecules, whereas some other depositions have unusually low or high occupancies of modeled solvent. Detailed analysis of these depositions revealed that the lack of solvent molecules might be an indication of problems with either the diffraction data, the refinement protocol, the deposition process or a combination of these factors. It is postulated that problems with solvent structure should be flagged by the PDB and addressed by the depositors.

In recent years, there is a significant interest from the crystallographic and materials science communities to have access to raw diffraction data. The effort in archiving raw data for access by the user community is spearheaded by the International Union of Crystallography (IUCr) Committee on Data. In materials science, where powder diffraction is extensively used, the challenge in archiving raw data is different to that from single crystal data, owing to the very nature of the contributions involved. Powder diffraction (X-ray or neutron) data consist of contributions from the material under study as well as instrument specific parameters. Having raw powder diffraction data can be essential in cases of analysing materials with poor crystallinity, disorder, micro structure (size/strain) etc. Here, the initiative and progress made by the International Centre for Diffraction Data (ICDDR) in archiving powder X-ray diffraction raw data in the Powder Diffraction FileTM (PDFR) database is outlined. The upcoming 2025 release of the PDF-5+ database will have more than 20 800 raw powder diffraction patterns that are available for reference.

An exhaustive search for missing symmetry was performed for 223 076 entries in the ICSD (2023-2 release). Approximately 0.65% of them can be described with higher symmetry than reported. Out of the identified noncentrosymmetric entries, ∼74% can be described by centrosymmetric space groups; this has implications for compatible physical properties. It is proposed that the information on the correct space group is included in the ICSD.

The crystal structure of the title compound {systematic name: (4S,4aS,5aR,12aR)-4,7-bis­(di­methyl­amino)-9-[(2,2-di­methyl­propyl­amino)­meth­yl]-1,10,11,12a-tetra­hydroxy-3,12-dioxo-4a,5,5a,6-tetra­hydro-4H-tetra­cene-2-carb­oxamide dihydrate, C29H40N4O7·2H2O} has been solved and refined using synchrotron X-ray powder diffraction data: it crystallizes in space group R3 with a = 24.34430 (7), c = 14.55212 (4) Å, V = 7468.81 (2) Å3 and Z = 9. Most of the hydrogen bonds are intra­molecular, but two classical N—H⋯O inter­molecular hydrogen bonds (along with probable weak C—H⋯O and C—H⋯N hydrogen bonds) link the mol­ecules into a three-dimensional framework. The framework contains voids, which contain disordered water mol­ecules. Keto–enol tautomerism is apparently important in this mol­ecule, and the exact mol­ecular structure is ambiguous.

The International Tables Symmetry Database (https://symmdb.iucr.org/), which is part of International Tables for Crystallography, is a collection of individual databases of crystallographic space-group and point-group information with associated programs. The programs let the user access and in some cases interactively visualize the data, and some also allow new data to be calculated `on the fly'. Together these databases and programs expand upon and complement the symmetry information provided in International Tables for Crystallography Volume A, Space-Group Symmetry, and Volume A1, Symmetry Relations between Space Groups. The Symmetry Database allows users to learn about and explore the space and point groups, and facilitates the study of group–subgroup relations between space groups, with applications in determining crystal-structure relationships, in studying phase transitions and in domain-structure analysis. The use of the International Tables Symmetry Database in all these areas is demonstrated using several examples.

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

BioXTAS RAW is a free open-source program for reduction, analysis and modelling of biological small-angle scattering data. Here, the new developments in RAW version 2 are described. These include improved data reduction using pyFAI; updated automated Guinier fitting and Dmax finding algorithms; automated series (e.g. size-exclusion chromatography coupled small-angle X-ray scattering or SEC-SAXS) buffer- and sample-region finding algorithms; linear and integral baseline correction for series; deconvolution of series data using regularized alternating least squares (REGALS); creation of electron-density reconstructions using electron density via solution scattering (DENSS); a comparison window showing residuals, ratios and statistical comparisons between profiles; and generation of PDF reports with summary plots and tables for all analysis. Furthermore, there is now a RAW API, which can be used without the graphical user interface (GUI), providing full access to all of the functionality found in the GUI. In addition to these new capabilities, RAW has undergone significant technical updates, such as adding Python 3 compatibility, and has entirely new documentation available both online and in the program.

INSIGHT is a Python-based software tool for processing and reducing 2D grazing-incidence wide- and small-angle X-ray scattering (GIWAXS/GISAXS) data. It offers the geometric transformation of the 2D GIWAXS/GISAXS detector image to reciprocal space, including vectorized and parallelized pixel-wise intensity correction calculations. An explicit focus on efficient data management and batch processing enables full control of large time-resolved synchrotron and laboratory data sets for a detailed analysis of kinetic GIWAXS/GISAXS studies of thin films. It processes data acquired with arbitrarily rotated detectors and performs vertical, horizontal, azimuthal and radial cuts in reciprocal space. It further allows crystallographic indexing and GIWAXS pattern simulation, and provides various plotting and export functionalities. Customized scripting offers a one-step solution to reduce, process, analyze and export findings of large in situ and operando data sets.

Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real-time data classification pipeline is essential to differentiate reliably between useful and non-useful images, typically known as `hit' and `miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature-point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real-time feature extraction algorithm called modified and parallelized FAST (MP-FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field-programmable gate arrays are implemented and their performances compared. Finally, MP-FAST-based image classification is evaluated using a multi-layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers.

Due to the ambiguity related to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This ambiguity poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this challenge, a novel training procedure has been designed which incorporates dynamic prior boundaries for each physical parameter as additional inputs to the neural network. In this manner, the neural network can be trained simultaneously on all well-posed subintervals of a larger parameter space in which the inverse problem is underdetermined. During inference, users can flexibly input their own prior knowledge about the physical system to constrain the neural network prediction to distinct target subintervals in the parameter space. The effectiveness of the method is demonstrated in various scenarios, including multilayer structures with a box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. In contrast to previous methods, this approach scales favourably when increasing the complexity of the inverse problem, working properly even for a five-layer multilayer model and a periodic multilayer model with up to 17 open parameters.

The pair angle distribution function (PADF) is a three- and four-atom correlation function that characterizes the local angular structure of disordered materials, particles or nanocrystalline materials. The PADF can be measured using X-ray or electron fluctuation diffraction data, which can be collected by scanning or flowing a structurally disordered sample through a focused beam. It is a natural generalization of established pair distribution methods, which do not provide angular information. The software package pypadf provides tools to calculate the PADF from fluctuation diffraction data. The package includes tools for calculating the intensity correlation function, which is a necessary step in the PADF calculation and also the basis for other fluctuation scattering analysis techniques.

The capillary wave model of a liquid surface predicts both the X-ray specular reflection and the diffuse scattering around it. A quantitative method is presented to obtain the X-ray reflectivity (XRR) from a liquid surface through the diffuse scattering data around the specular reflection measured using a grazing incidence X-ray off-specular scattering (GIXOS) geometry at a fixed horizontal offset angle with respect to the plane of incidence. With this approach the entire Qz-dependent reflectivity profile can be obtained at a single, fixed incident angle. This permits a much faster acquisition of the profile than with conventional reflectometry, where the incident angle must be scanned point by point to obtain a Qz-dependent profile. The XRR derived from the GIXOS-measured diffuse scattering, referred to in this paper as pseudo-reflectivity, provides a larger Qz range compared with the reflectivity measured by conventional reflectometry. Transforming the GIXOS-measured diffuse scattering profile to pseudo-XRR opens up the GIXOS method to widely available specular XRR analysis software tools. Here the GIXOS-derived pseudo-XRR is compared with the XRR measured by specular reflectometry from two simple vapor–liquid interfaces at different surface tension, and from a hexadecyltri­methyl­ammonium bromide monolayer on a water surface. For the simple liquids, excellent agreement (beyond 11 orders of magnitude in signal) is found between the two methods, supporting the approach of using GIXOS-measured diffuse scattering to derive reflectivities. Pseudo-XRR obtained at different horizontal offset angles with respect to the plane of incidence yields indistinguishable results, and this supports the robustness of the GIXOS-XRR approach. The pseudo-XRR method can be extended to soft thin films on a liquid surface, and criteria are established for the applicability of the approach.

Recent developments in synchrotron radiation facilities have increased the amount of data generated during acquisitions considerably, requiring fast and efficient data processing techniques. Here, the application of dense neural networks (DNNs) to data treatment of X-ray diffraction computed tomography (XRD-CT) experiments is presented. Processing involves mapping the phases in a tomographic slice by predicting the phase fraction in each individual pixel. DNNs were trained on sets of calculated XRD patterns generated using a Python algorithm developed in-house. An initial Rietveld refinement of the tomographic slice sum pattern provides additional information (peak widths and integrated intensities for each phase) to improve the generation of simulated patterns and make them closer to real data. A grid search was used to optimize the network architecture and demonstrated that a single fully connected dense layer was sufficient to accurately determine phase proportions. This DNN was used on the XRD-CT acquisition of a mock-up and a historical sample of highly heterogeneous multi-layered decoration of a late medieval statue, called `applied brocade'. The phase maps predicted by the DNN were in good agreement with other methods, such as non-negative matrix factorization and serial Rietveld refinements performed with TOPAS, and outperformed them in terms of speed and efficiency. The method was evaluated by regenerating experimental patterns from predictions and using the R-weighted profile as the agreement factor. This assessment allowed us to confirm the accuracy of the results.

Presented and discussed here is the implementation of a software solution that provides prompt X-ray diffraction data analysis during fast dynamic compression experiments conducted within the dynamic diamond anvil cell technique. It includes efficient data collection, streaming of data and metadata to a high-performance cluster (HPC), fast azimuthal data integration on the cluster, and tools for controlling the data processing steps and visualizing the data using the DIOPTAS software package. This data processing pipeline is invaluable for a great number of studies. The potential of the pipeline is illustrated with two examples of data collected on ammonia–water mixtures and multiphase mineral assemblies under high pressure. The pipeline is designed to be generic in nature and could be readily adapted to provide rapid feedback for many other X-ray diffraction techniques, e.g. large-volume press studies, in situ stress/strain studies, phase transformation studies, chemical reactions studied with high-resolution diffraction etc.

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics (e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

High-pressure neutron powder diffraction data from PbNCN were collected on the high-pressure diffraction beamline SNAP located at the Spallation Neutron Source (SNS) of Oak Ridge National Laboratory (Tennessee, USA). The diffraction data were analyzed using the novel method of multidimensional (two dimensions for now, potentially more in the future) Rietveld refinement and, for comparison, employing the conventional Rietveld method. To achieve two-dimensional analysis, a detailed description of the SNAP instrument characteristics was created, serving as an instrument parameter file, and then yielding both cell and spatial parameters as refined under pressure for the first time for solid-state cyanamides/carbodi­imides. The bulk modulus B0 = 25.1 (15) GPa and its derivative B'0 = 11.1 (8) were extracted for PbNCN following the Vinet equation of state. Surprisingly, an internal transition was observed beyond 2.0 (2) GPa, resulting from switching the bond multiplicities (and bending direction) of the NCN2− complex anion. The results were corroborated using electronic structure calculation from first principles, highlighting both local structural and chemical bonding details.

This article presents a web-based framework to build a database without in-depth programming knowledge given a set of CIF dictionaries and a collection of CIFs. The framework consists of two main elements: the public site that displays the information contained in the CIFs in an ordered manner, and the restricted administrative site which defines how that information is stored, processed and, eventually, displayed. Thus, the web application allows users to easily explore, filter and access the data, download the original CIFs, and visualize the structures via JSmol. The modulated structures open database B-IncStrDB, the official International Union of Crystallography repository for this type of material and available through the Bilbao Crystallographic Server, has been re-implemented following the proposed framework.

Presented here is an effective approach to desmearing slit ultra-small-angle neutron scattering (USANS) data, based on complementary small-angle neutron scattering (SANS) measurements, leading to a seamless merging of these data sets. The study focuses on the methodological aspects of desmearing USANS data, which can then be presented in the conventional manner of SANS, enabling a broader pool of data analysis methods. The key innovation lies in the use of smeared SANS data for extrapolating slit USANS, offering a self-consistent integrand function for desmearing with Lake's iterative method. The proposed approach is validated through experimental data on porous anodized aluminium oxide membranes, showcasing its applicability and benefits. The findings emphasize the importance of accurate desmearing for merging USANS and SANS data in the crossover q region, which is particularly crucial for complex scattering patterns.

Here, a recently commissioned five-analyzer Johann spectrometer at the Inner Shell Spectroscopy beamline (8-ID) at the National Synchrotron Light Source II (NSLS-II) is presented. Designed for hard X-ray photon-in/photon-out spectroscopy, the spectrometer achieves a resolution in the 0.5–2 eV range, depending on the element and/or emission line, providing detailed insights into the local electronic and geometric structure of materials. It serves a diverse user community, including fields such as physical, chemical, biological, environmental and materials sciences. This article details the mechanical design, alignment procedures and data-acquisition scheme of the spectrometer, with a particular focus on the continuous asynchronous data-acquisition approach that significantly enhances experimental efficiency.

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Scientists have recovered the first genetic data from an extinct bird in the Caribbean, thanks to the remarkably preserved bones of a Creighton's caracara in a flooded sinkhole on Great Abaco Island in the Bahamas. Studies of ancient DNA from tropical birds have faced two formidable obstacles. Organic material quickly degrades when exposed to heat, light and oxygen. And birds' lightweight, hollow bones break easily, accelerating the decay of the DNA within. But the dark, oxygen-free depths of a 100-foot blue hole known as Sawmill Sink provided ideal preservation conditions for the bones of Caracara creightoni, a species of large carrion-eating falcon that disappeared soon after humans arrived in the Bahamas about 1,000 years ago. Florida Museum of Natural History researcher Jessica Oswald and her colleagues extracted and sequenced genetic material from the 2,500-year-old C. creightoni femur. Because ancient DNA is often fragmented or missing, the team had modest expectations for what they would find –- maybe one or two genes. But instead, the bone yielded 98.7% of the bird's mitochondrial genome, the DNA most living things inherit from their mothers. The mitochondrial genome showed that C. creightoni is closely related to the two remaining caracara species alive today: the crested caracara and the southern caracara. The three species last shared a common ancestor between 1.2 and 0.4 million years ago. "This project enhanced our understanding of the ecological and evolutionary implications of extinction, forged strong international partnerships, and trained the next generation of researchers," says Jessica Robin, a program director in National Science Foundation's Office of International Science and Engineering, which funded the study.

Image credit: Florida Museum photo by Kristen Grace

Lithuania-based iDenfy has introduced an AI-powered Data Crossmatch feature aimed at improving the Know Your Business (KYB) compliance process.

For two decades now, and counting, the LANDFIRE program continues to assemble the most easy-to-use, intuitive and complete clearinghouse of remote sensing data products for wildland fire managers. 

NASA released a workshop report on the UNBOUND-FEW workshop series, which was facilitated in part by Tribal Resilience Learning Network staff from the Alaska CASC. The workshop report reveals key recommendations for making data tools more useful for climate adaptation planning.

Source: Dr. Ram Selvaraju 10/18/2024

H.C. Wainwright & Co. analyst Dr. Ram Selvaraju, in a research report published on October 18, 2024, reiterated a Buy rating on Anavex Life Sciences Corp. (AVXL:NASDAQ) with a price target of US$40.00. The report follows Anavex's announcement of encouraging preliminary electroencephalography (EEG) biomarker results from Part A of the ongoing Phase 2 clinical study of ANAVEX3-71 for schizophrenia treatment.

Selvaraju highlighted the significance of these results, stating, "Preliminary results demonstrated a dose-dependent effect of ANAVEX3-71 on two key EEG biomarkers in patients with schizophrenia. Treatment with ANAVEX3-71 vs. placebo resulted in improvements in 40 Hz Auditory Steady-State Response (ASSR) Inter Trial Coherence (ITC) and Resting State Alpha Power."

The analyst viewed these developments positively, noting, "These results provide evidence of CNS target engagement and potential therapeutic effects of ANAVEX3-71 in schizophrenia. The observed changes reversed known EEG and ERP biomarker abnormalities associated with schizophrenia."

Regarding Anavex's lead candidate, blarcamesine, Selvaraju stated, "Anavex remains committed to completing the Marketing Authorization Application (MAA) submission to the European Medicines Agency (EMA) under the Centralized Procedure petitioning for approval of blarcamesine for treatment of Alzheimer's disease (AD) in 4Q24."

The report also highlighted Anavex's progress with other clinical programs, including a pivotal Phase 2b/3 trial in Parkinson's disease and potential trials in Rett syndrome and Fragile X Syndrome.

Selvaraju's valuation methodology for Anavex Life Sciences is based on a discounted cash flow (DCF) approach. He explained, "We utilize a discounted cash flow (DCF)-driven methodology, which ascribes a total value of roughly US$3.25B to blarcamesine alone without ascribing value to any other pipeline assets. We employ a 50% probability of approval in Rett syndrome; 60% in Parkinson's disease dementia (PDD); and 50% in AD."

The analyst added, "Further, we apply a 12% discount rate and 1% terminal growth rate. We derive a total firm value of ~US$3.4B, which yields a 12-month price objective of US$40 per share, assuming 84.8M shares outstanding as of end-F2Q25."

Selvaraju also outlined several risk factors, including potential negative clinical data, regulatory approval challenges, and commercialization difficulties.

In conclusion, H.C. Wainwright & Co.'s maintenance of a Buy rating and US$40 price target reflects a positive outlook on Anavex Life Sciences' clinical progress and potential in developing treatments for neurological disorders. The share price at the time of the report of US$5.51 represents a potential return of approximately 626% to the analyst's target price, highlighting the significant upside potential if the company's clinical development plans prove successful.

Important Disclosures:

1. This article does not constitute investment advice and is not a solicitation for any investment. Streetwise Reports does not render general or specific investment advice and the information on Streetwise Reports should not be considered a recommendation to buy or sell any security. Each reader is encouraged to consult with his or her personal financial adviser and perform their own comprehensive investment research. By opening this page, each reader accepts and agrees to Streetwise Reports' terms of use and full legal disclaimer. Streetwise Reports does not endorse or recommend the business, products, services or securities of any company.
2. This article does not constitute medical advice. Officers, employees and contributors to Streetwise Reports are not licensed medical professionals. Readers should always contact their healthcare professionals for medical advice.

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Disclosures for H.C. Wainwright & Co., Anavex Life Sciences Corp., October 18, 2024.

This material is confidential and intended for use by Institutional Accounts as defined in FINRA Rule 4512(c). It may also be privileged or otherwise protected by work product immunity or other legal rules. If you have received it by mistake, please let us know by e-mail reply to unsubscribe@hcwresearch.com and delete it from your system; you may not copy this message or disclose its contents to anyone. The integrity and security of this message cannot be guaranteed on the Internet. H.C. WAINWRIGHT & CO, LLC RATING SYSTEM: H.C. Wainwright employs a three tier rating system for evaluating both the potential return and risk associated with owning common equity shares of rated firms. The expected return of any given equity is measured on a RELATIVE basis of other companies in the same sector. The price objective is calculated to estimate the potential movements in price that a given equity could reach provided certain targets are met over a defined time horizon. Price objectives are subject to external factors including industry events and market volatility.

H.C. Wainwright & Co, LLC (the “Firm”) is a member of FINRA and SIPC and a registered U.S. Broker-Dealer. I, Raghuram Selvaraju, Ph.D. , certify that 1) all of the views expressed in this report accurately reflect my personal views about any and all subject securities or issuers discussed; and 2) no part of my compensation was, is, or will be directly or indirectly related to the specific recommendation or views expressed in this research report; and 3) neither myself nor any members of my household is an officer, director or advisory board member of these companies. None of the research analysts or the research analyst’s household has a financial interest in the securities of Anavex Life Sciences Corp. (including, without limitation, any option, right, warrant, future, long or short position). As of September 30, 2024 neither the Firm nor its affiliates beneficially own 1% or more of any class of common equity securities of Anavex Life Sciences Corp.. Neither the research analyst nor the Firm knows or has reason to know of any other material conflict of interest at the time of publication of this research report.

The research analyst principally responsible for preparation of the report does not receive compensation that is based upon any specific investment banking services or transaction but is compensated based on factors including total revenue and profitability of the Firm, a substantial portion of which is derived from investment banking services. Mr. Selvaraju, who is [the][an] author of this report, is the Chairman of and receives compensation from Relief Therapeutics Holding SA, a Swiss, commercial-stage biopharmaceutical company identifying, developing and commercializing novel, patent protected products in selected specialty, rare and ultra-rare disease areas on a global basis ("Relief"). You should consider Mr. Selvaraju's position with Relief when reading this research report. The firm or its affiliates received compensation from Anavex Life Sciences Corp. for non-investment banking services in the previous 12 months. The Firm or its affiliates did not receive compensation from Anavex Life Sciences Corp. for investment banking services within twelve months before, but will seek compensation from the companies mentioned in this report for investment banking services within three months following publication of the research report. The Firm does not make a market in Anavex Life Sciences Corp. as of the date of this research report. The securities of the company discussed in this report may be unsuitable for investors depending on their specific investment objectives and financial position. Past performance is no guarantee of future results. This report is offered for informational purposes only, and does not constitute an offer or solicitation to buy or sell any securities discussed herein in any jurisdiction where such would be prohibited. This research report is not intended to provide tax advice or to be used to provide tax advice to any person. Electronic versions of H.C. Wainwright & Co., LLC research reports are made available to all clients simultaneously. No part of this report may be reproduced in any form without the expressed permission of H.C. Wainwright & Co., LLC. Additional information available upon request. H.C. Wainwright & Co., LLC does not provide individually tailored investment advice in research reports. This research report is not intended to provide personal investment advice and it does not take into account the specific investment objectives, financial situation and the particular needs of any specific person. Investors should seek financial advice regarding the appropriateness of investing in financial instruments and implementing investment strategies discussed or recommended in this research report. H.C. Wainwright & Co., LLC’s and its affiliates’ salespeople, traders, and other professionals may provide oral or written market commentary or trading strategies that reflect opinions that are contrary to the opinions expressed in this research report. H.C. Wainwright & Co., LLC and its affiliates, officers, directors, and employees, excluding its analysts, will from time to time have long or short positions in, act as principal in, and buy or sell, the securities or derivatives (including options and warrants) thereof of covered companies referred to in this research report. The information contained herein is based on sources which we believe to be reliable but is not guaranteed by us as being accurate and does not purport to be a complete statement or summary of the available data on the company, industry or security discussed in the report. All opinions and estimates included in this report constitute the analyst’s judgment as of the date of this report and are subject to change without notice. Securities and other financial instruments discussed in this research report: may lose value; are not insured by the Federal Deposit Insurance Corporation; and are subject to investment risks, including possible loss of the principal amount invested.

( Companies Mentioned: AVXL:NASDAQ, )

Source: Dr. Douglas Loe 10/31/2024

Leede Financial analysts Dr. Douglas Loe and Siew Ching Yeo, in a research report published on October 30, 2024, maintained their Speculative Buy rating on ProMIS Neurosciences Inc. (PMN:TSX; PMN:NCM) with a price target of US$9.50. The report follows ProMIS's presentation of interim Phase I data for PMN310, its Alzheimer's disease (AD) candidate, at the Clinical Trials on Alzheimer's Disease (CTAD) conference.

The analysts highlighted the positive safety and pharmacokinetic (PK) data, stating, "We were encouraged (though not overly surprised) to see that the mAb was well-tolerated at all five test doses ranging from 2.5mg/kg-to-40mg/kg." They added, "PK analysis of all of these patient cohorts in this single-ascending dose (SAD) trial suggests that once-monthly dosing may be sufficient to sustain mAb levels both in plasma and in cerebrospinal fluid over time."

Regarding dosing efficacy, the analysts noted, "Importantly, ProMIS indicated in the Jul/24 update that even at 2.5mg/kg dosing, PMN310 levels in CSF were over 100x higher than predicted to be necessary to bind to all beta-amyloid oligomers that could accumulate in CSF in diseased patients."

The analysts emphasized the significance of recent industry developments, particularly AbbVie's acquisition of Aliada Therapeutics, stating, "AbbVie's tangible interest in Phase I-stage AD assets shows us that ProMIS could itself be attractive to future suitors if/when it can document direct impact on cognitive impairment in diseased patients."

The report highlighted ProMIS's financial position following its recent equity offering, noting that the company raised US$30.3M with multiple layers of warrant coverage tied to development milestones.

Leede Financial's valuation methodology combines multiple approaches. The analysts explained, "We are maintaining our Speculative Buy rating and one-year PT of US$9.50 on PMN, with our valuation still based on NPV (30% discount rate) and multiples of our F2029 EBITDA/fd EPS forecasts."

They added, "By direct comparison to Aliada's US$1.4B value, PMN shares would notionally be valued on a fully-diluted basis at US$17.65/shr."

In conclusion, Leede Financial's maintenance of their Speculative Buy rating and US$9.50 price target reflects confidence in ProMIS's development of PMN310 and its potential in the Alzheimer's disease market. The share price at the time of the report of US$1.03 represents a potential return of approximately 822% to the analysts' target price, highlighting the significant upside potential if the company's clinical development plans prove successful.

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Important Disclosures:

1. As of the date of this article, officers and/or employees of Streetwise Reports LLC (including members of their household) own securities of ProMIS Neurosciences Inc.
2. This article does not constitute investment advice and is not a solicitation for any investment. Streetwise Reports does not render general or specific investment advice and the information on Streetwise Reports should not be considered a recommendation to buy or sell any security. Each reader is encouraged to consult with his or her personal financial adviser and perform their own comprehensive investment research. By opening this page, each reader accepts and agrees to Streetwise Reports' terms of use and full legal disclaimer. Streetwise Reports does not endorse or recommend the business, products, services or securities of any company.
3. This article does not constitute medical advice. Officers, employees and contributors to Streetwise Reports are not licensed medical professionals. Readers should always contact their healthcare professionals for medical advice.

For additional disclosures, please click here.

Disclosures for Leede Financial Inc., ProMIS Neurosciences Inc., October 30, 2024

Important Information and Legal Disclaimers Leede Financial Inc. (Leede) is a member of the Canadian Investment Regulatory Organization (CIRO) and a member of the Canadian Investor Protection Fund (CIPF). This document is not an offer to buy or sell or a solicitation of an offer to buy or sell any security or instrument or to participate in any particular investing strategy. Data from various sources were used in the preparation of these documents; the information is believed but in no way warranted to be reliable, accurate and appropriate. All information is as of the date of publication and is subject to change without notice. Any opinions or recommendations expressed herein do not necessarily reflect those of Leede. Leede cannot accept any trading instructions via e-mail as the timely receipt of e-mail messages, or their integrity over the Internet, cannot be guaranteed. Dividend yields change as stock prices change, and companies may change or cancel dividend payments in the future. All securities involve varying amounts of risk, and their values will fluctuate, and the fluctuation of foreign currency exchange rates will also impact your investment returns if measured in Canadian Dollars. Past performance does not guarantee future returns, investments may increase or decrease in value, and you may lose money. Leede employees may buy and sell shares of the companies that are recommended for their own accounts and for the accounts of other clients. Disclosure codes are used in accordance with Policy 3600 of CIRO.

Description of Disclosure Codes 1. Leede and its affiliates collectively beneficially own 1% or more of any class of equity securities of the company as of the end of the preceding month or the month prior to the preceding month if the report was issued prior to the 10th. 2. The analyst or any associate of the analyst responsible for the report or public comment hold shares or is short any of the company's securities directly or through derivatives. 3. Leede or a director or officer of Leede or any analyst provided services to the company for remuneration other than normal investment advisory or trade execution services within the preceding 12 months. 4. Leede provided investment banking services for the company during the 12 months preceding the publication of the research report. 5. Leede expects to receive or intends to seek compensation for investment banking services in the next three months. 6. The analyst preparing the report received compensation based upon Leede investment banking revenues for this issuer within the preceding 12 months. 7. The director, officer, employee, or research analyst is an officer, director or employee of the company, or serves in an advisory capacity to the company. 8. Leede acts as a market maker of the company. 9. The analyst has conducted a site visit and has viewed a major facility or operation of the issuer. 10. The company has paid for all, or a material portion, of the travel costs associated with the site visit by the analyst.

Dissemination All final research reports are disseminated to existing and potential institutional clients of Leede Financial Inc. (Leede) in electronic form to intended recipients thorough e-mail and third-party aggregators. Research reports are posted to the Leede website and are accessible to customers who are entitled to the firm’s research. Reproduction of this report in whole or in part without permission is prohibited.

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Canadian Disclosures This research has been approved by Leede Financial Inc. (Leede), which accepts sole responsibility for this research and its dissemination in Canada. Leede is registered and regulated by the Canadian Investment Regulatory Organization (CIRO) and is a member of the Canadian Investor Protection Fund (CIPF). Canadian clients wishing to effect transactions in any designated investment discussed should do so through a Leede Registered Representative.

U.S. Disclosures This research report was prepared by Leede Financial Inc. (Leede). Leede is registered and regulated by the Canadian Investment Regulatory Organization (CIRO) and is a member of the Canadian Investor Protection Fund (CIPF). This report does not constitute an offer to sell or the solicitation of an offer to buy any of the securities discussed herein. Leede is not registered as a broker-dealer in the United States and is not subject to U.S. rules regarding the preparation of research reports and the independence of research analysts. Any resulting transactions should be effected through a U.S. broker-dealer.

( Companies Mentioned: PMN:TSX; PMN:NCM, )

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People come to Catawba County’s web site for many reasons. One is for the information and data that they find there.  The site has always been rich in information about the county and services that are provided. In recent years, as more and more people wanted data in digital format, many datasets were moved to [...]

In the fall of 2009, as a part of the Catawba Aging Leadership Planning Project, Catawba County, along with aging service providers, volunteers and local municipalities developed a �Livable & Senior Friendly Community Survey�. This survey was widely distributed throughout Catawba County and targeted the County�s Senior Community, their caregivers and professionals in the aging field. The purpose of the survey was to obtain current and relevant information on the quality of life of seniors. Obtaining this information is important when looking toward the future of Catawba County.

Bed Bath & Beyond Inc., headquartered in Union NJ, has chosen to locate one of its data center facilities in Claremont, Catawba County. The company will locate the data center in the 48,000 square foot Center Point shell building in the Claremont International Business Park on Kelly Drive. Bed Bath & Beyond�s investment is expected to equal or exceed $36,800,000 and the new facility will create a minimum of 7 new jobs by the end of 2018.