Event Begins: Thursday, November 14, 2024 3:00pm
Location: Medical Science Unit II
Organized By: Department of Molecular, Cellular, and Developmental Biology

Mentor: Kristen Verhey

He described J&K as “a repository of Indian creative traditions

StarWind, Intel, Mellanox, and Supermicro present the industry's fastest hyperconverged cluster built to demonstrate outstanding hardware utilization efficiency.

Tradition has it that where big businesses are rooted, vibrant small business communities follow. It happened in Harrison, N.Y., when Mastercard and PepsiCo set up shop. It happened in Redmond, Wash. after Microsoft moved in. It is happening in Greenville, S.C., where BMW built an SUV manufacturing plant.

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(EMAILWIRE.COM, November 01, 2024 ) The global Digital Instrument Cluster market is witnessing rapid growth, fueled by increasing demand for advanced automotive displays and enhanced in-car experiences. Driven by advancements in digital interfaces, these clusters provide customizable, high-resolution...

been on this for as issues for about 5 months abs light has been intermittently n and off for last year and a half then cluster went out truck runs fine gauges on edge screen all read fine...

Collaborative Filtering(CF) is a well-known technique in recommender systems. CF exploits relationships between users and recommends items to the active user according to the ratings of his/her neighbors. CF suffers from the data sparsity problem, where users only rate a small set of items. That makes the computation of similarity between users imprecise and consequently reduces the accuracy of CF algorithms. In this article, we propose a clustering approach based on the social information of users to derive the recommendations. We study the application of this approach in two application scenarios: academic venue recommendation based on collaboration information and trust-based recommendation. Using the data from DBLP digital library and Epinion, the evaluation shows that our clustering technique based CF performs better than traditional CF algorithms.

Due to the problems of low clustering accuracy and efficiency in traditional similar resource clustering methods, this paper studies an English MOOC similar resource clustering method based on grey correlation. Principal component analysis was used to extract similar resource features of English MOOC, and feature selection methods was used to pre-process similar resource features of English MOOC. On this basis, based on the grey correlation method, the pre-processed English MOOC similar resource features are standardised, and the correlation degree between different English MOOC similar resource features is calculated. The English MOOC similar resource correlation matrix is constructed to achieve English MOOC similar resource clustering. The experimental results show that the contour coefficient of the proposed method is closer to one, and the clustering accuracy of similar resources in English MOOC is as high as 94.2%, with a clustering time of only 22.3 ms.

Aim/Purpose: This study examines the influence of ambidextrous knowledge sharing in industrial clusters on innovation performance from the perspective of knowledge-based dynamic capabilities. Background: The key factor to improving innovation performance in an enterprise is to share knowledge with other enterprises in the same cluster and use dynamic capabilities to absorb, integrate, and create knowledge. However, the relationships among these concepts remain unclear. Based on the dynamic capability theory, this study empirically reveals how enterprises drive innovation performance through knowledge sharing. Methodology: Survey data from 238 cluster enterprises were used in this study. The sample was collected from industrial clusters in China’s Fujian province that belong to the automobile, optoelectronic, and microwave communications industries. Through structural equation modeling, this study assessed the relationships among ambidextrous knowledge sharing, dynamic capabilities, and innovation performance. Contribution: This study contributes to the burgeoning literature on knowledge management in China, an important emerging economy. It also enriches the exploration of innovation performance in the cluster context and expands research on the dynamic mechanism from a knowledge perspective. Findings: Significant relationships are found between ambidextrous knowledge sharing and innovation performance. First, ambidextrous knowledge sharing positively influences the innovation performance of cluster enterprises. Further, knowledge absorption and knowledge generation capabilities play a mediating role in this relationship, which confirms that dynamic capabilities are a partial mediator in the relationship between ambidextrous knowledge sharing and innovation performance. Recommendations for Practitioners: The results highlight the crucial role of knowledge management in contributing to cluster innovation and management practices. They indicate that cluster enterprises should consider the importance of knowledge sharing and dynamic capabilities for improving innovation performance and establish a multi-agent knowledge sharing platform. Recommendation for Researchers: Researchers could further explore the role of other mediating variables (e.g., organizational agility, industry growth) as well as moderating variables (e.g., environmental uncertainty, learning orientation). Impact on Society: This study provides a reference for enterprises in industrial clusters to use knowledge-based capabilities to enhance their competitive advantage. Future Research: Future research could collect data from various countries and regions to test the research model and conduct a comparative analysis of industrial clusters.

Aim/Purpose: This article proposes a methodology for selecting the initial sets for clustering categorical data. The main idea is to combine all the different values of every single criterion or attribute, to form the first proposal of the so-called multiclusters, obtaining in this way the maximum number of clusters for the whole dataset. The multiclusters thus obtained, are themselves clustered in a second step, according to the desired final number of clusters. Background: Popular cluster methods for categorical data, such as the well-known K-Modes, usually select the initial sets by means of some random process. This fact introduces some randomness in the final results of the algorithms. We explore a different application of the clustering methodology for categorical data that overcomes the instability problems and ultimately provides a greater clustering efficiency. Methodology: For assessing the performance of the proposed algorithm and its comparison with K-Modes, we apply both of them to categorical databases where the response variable is known but not used in the analysis. In our examples, that response variable can be identified to the real clusters or classes to which the observations belong. With every data set, we perform a two-step analysis. In the first step we perform the clustering analysis on data where the response variable (the real clusters) has been omitted, and in the second step we use that omitted information to check the efficiency of the clustering algorithm (by comparing the real clusters to those given by the algorithm). Contribution: Simplicity, efficiency and stability are the main advantages of the multicluster method. Findings: The experimental results attained with real databases show that the multicluster algorithm has greater precision and a better grouping effect than the classical K-modes algorithm. Recommendations for Practitioners: The method can be useful for those researchers working with small and medium size datasets, allowing them to detect the underlying structure of the data in an intuitive and reasonable way. Recommendation for Researchers: The proposed algorithm is slower than K-Modes, since it devotes a lot of time to the calculation of the initial combinations of attributes. The reduction of the computing time is therefore an important research topic. Future Research: We are concerned with the scalability of the algorithm to large and complex data sets, as well as the application to mixed data sets with both quantitative and qualitative attributes.

Aim/Purpose: The clustering techniques are normally considered to determine the significant and meaningful subclasses purposed in datasets. It is an unsupervised type of Machine Learning (ML) where the objective is to form groups from objects based on their similarity and used to determine the implicit relationships between the different features of the data. Cluster Analysis is considered a significant problem area in data exploration when dealing with arbitrary shape problems in different datasets. Clustering on large data sets has the following challenges: (1) clusters with arbitrary shapes; (2) less knowledge discovery process to decide the possible input features; (3) scalability for large data sizes. Density-based clustering has been known as a dominant method for determining the arbitrary-shape clusters. Background: Existing density-based clustering methods commonly cited in the literature have been examined in terms of their behavior with data sets that contain nested clusters of varying density. The existing methods are not enough or ideal for such data sets, because they typically partition the data into clusters that cannot be nested. Methodology: A density-based approach on traditional center-based clustering is introduced that assigns a weight to each cluster. The weights are then utilized in calculating the distances from data vectors to centroids by multiplying the distance by the centroid weight. Contribution: In this paper, we have examined different density-based clustering methods for data sets with nested clusters of varying density. Two such data sets were used to evaluate some of the commonly cited algorithms found in the literature. Nested clusters were found to be challenging for the existing algorithms. In utmost cases, the targeted algorithms either did not detect the largest clusters or simply divided large clusters into non-overlapping regions. But, it may be possible to detect all clusters by doing multiple runs of the algorithm with different inputs and then combining the results. This work considered three challenges of clustering methods. Findings: As a result, a center with a low weight will attract objects from further away than a centroid with higher weight. This allows dense clusters inside larger clusters to be recognized. The methods are tested experimentally using the K-means, DBSCAN, TURN*, and IDCUP algorithms. The experimental results with different data sets showed that IDCUP is more robust and produces better clusters than DBSCAN, TURN*, and K-means. Finally, we compare K-means, DBSCAN, TURN*, and to deal with arbitrary shapes problems at different datasets. IDCUP shows better scalability compared to TURN*. Future Research: As future recommendations of this research, we are concerned with the exploration of further available challenges of the knowledge discovery process in clustering along with complex data sets with more time. A hybrid approach based on density-based and model-based clustering algorithms needs to compare to achieve maximum performance accuracy and avoid the arbitrary shapes related problems including optimization. It is anticipated that the comparable kind of the future suggested process will attain improved performance with analogous precision in identification of clustering shapes.

This paper proposes an efficient solution for personalised web directories recommendation using fast FCM+DQN. At first, web directory usage file obtained from given dataset is fed into the accretion matrix computation module, where visitor chain matrix, visitor chain binary matrix, directory chain matrix and directory chain binary matrix are formulated. In this, directory grouping is accomplished based on fast FCM and matching among query and group is conducted based on Kumar Hassebrook and Kulczynski similarity. The user preferred directory is restored at this stage and at last, personalised web directories are recommended to the visitors by means of DQN. The proposed approach has received superior results with respect to maximum accuracy of 0.910, minimum mean squared error (MSE) of 0.0206 and root mean squared error (RMSE) of 0.144. Although the system offered magnificent outcomes, it failed to order web directories in the form of highly, medium and low interested directories.

After a successful networking session in Bangkok, Thailand the SUSTAIN EU-ASEAN project is organising another networking session for interested institutions and research projects in Brussels, Belgium. The event will take place on 28 March 2014 in Brussels, Belgium.This meeting is helpful if you

• want to get to know and network with fellow environment research projects (in the areas of climate change, resource efficiency and raw materials) with an ASEAN focus and/or relevant research results for the region
• want to identify and harness synergies with other projects (possible future cooperation; sharing of results and work, etc.)
• want to get in touch with the SUSTAIN team, communicating what kind of support your research efforts would benefit from

The SUSTAIN EU-ASEAN project generally has to offer

• networking and project twinning support: find and coordinate with similar projects!
• R&D services for results take-up: get support in approaching potential investors!
• communication and exposure support: make your results visible for potential users!

Get in touch here, if you are interested in joining the meeting. The pre-meeting agenda is available as an attachment below,

Read more about the event on the SUSTAIN EU-ASEAN website.

• Meet and get to know fellow projects working on similar topics of relevance to ASEAN
• Discuss results and synergies, exchange on methodologies, plan for joint events, applications, publications, etc. or simply see what's going on in the other region in this topic.
• Plan concrete next joint steps with projects in the cluster.
• Benefit from SUSTAIN EU-ASEAN support (e.g. in your dissemination, exploitation and development activities) and inputs (on the funding environment)
• Share your views on thematic gaps in current research - SUSTAIN is mandated to carry the message to the European Commission

Bakery On Main is expanding its diverse portfolio of gluten-free cereals and snacks with the addition of a certified Paleo and Keto Grain-Free Cluster Trail Trek Blend snack line.

The fixed effects model is a useful alternative to the mixed effects model for analyzing stepped-wedge cluster randomized trials (SW-CRTs). It controls for all time-invariant cluster-level confounders and has … Read the full article ›

The post Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials with binary and count outcomes was curated by information for practice.

Three-dimensional electron diffraction (3D-ED) is a powerful technique for crystallographic characterization of nanometre-sized crystals that are too small for X-ray diffraction. For accurate crystal structure refinement, however, it is important that the Bragg diffracted intensities are treated dynamically. Bloch wave simulations are often used in 3D-ED, but can be computationally expensive for large unit cell crystals due to the large number of diffracted beams. Proposed here is an alternative method, the `scattering cluster algorithm' (SCA), that replaces the eigen-decomposition operation in Bloch waves with a simpler matrix multiplication. The underlying principle of SCA is that the intensity of a given Bragg reflection is largely determined by intensity transfer (i.e. `scattering') from a cluster of neighbouring diffracted beams. However, the penalty for using matrix multiplication is that the sample must be divided into a series of thin slices and the diffracted beams calculated iteratively, similar to the multislice approach. Therefore, SCA is more suitable for thin specimens. The accuracy and speed of SCA are demonstrated on tri-iso­propyl silane (TIPS) pentacene and rubrene, two exemplar organic materials with large unit cells.

A novel automated high-throughput screening approach, ClusterFinder, is reported for finding candidate structures for atomic pair distribution function (PDF) structural refinements. Finding starting models for PDF refinements is notoriously difficult when the PDF originates from nanoclusters or small nanoparticles. The reported ClusterFinder algorithm can screen 104 to 105 candidate structures from structural databases such as the Inorganic Crystal Structure Database (ICSD) in minutes, using the crystal structures as templates in which it looks for atomic clusters that result in a PDF similar to the target measured PDF. The algorithm returns a rank-ordered list of clusters for further assessment by the user. The algorithm has performed well for simulated and measured PDFs of metal–oxido clusters such as Keggin clusters. This is therefore a powerful approach to finding structural cluster candidates in a modelling campaign for PDFs of nanoparticles and nanoclusters.

A 6D structure model for face-centred icosahedral quasicrystals consisting of so-called pseudo-Mackay and mini-Bergman-type atomic clusters is proposed based on the structure model of the Al69.1Pd22Cr2.1Fe6.8 3/2 cubic approximant crystal (with space group Pa3, a = 40.5 Å) [Fujita et al. (2013). Acta Cryst. A69, 322–340]. The cluster centres form an icosahedral close sphere packing generated by the occupation domains similar to those in the model proposed by Katz & Gratias [J. Non-Cryst. Solids (1993), 153–154, 187–195], but their size is smaller by a factor τ2 [τ = (1 + (5)1/2)/2]. The clusters cover approximately 99.46% of the atomic structure, and the cluster arrangement exhibits 15 and 19 different local configurations, respectively, for the pseudo-Mackay and mini-Bergman-type clusters. The occupation domains that generate cluster shells are modelled and discussed in terms of structural disorder and local reorganization of the cluster arrangements (phason flip).

During the automatic processing of crystallographic diffraction experiments, beamstop shadows are often unaccounted for or only partially masked. As a result of this, outlier reflection intensities are integrated, which is a known issue. Traditional statistical diagnostics have only limited effectiveness in identifying these outliers, here termed Not-Excluded-unMasked-Outliers (NEMOs). The diagnostic tool AUSPEX allows visual inspection of NEMOs, where they form a typical pattern: clusters at the low-resolution end of the AUSPEX plots of intensities or amplitudes versus resolution. To automate NEMO detection, a new algorithm was developed by combining data statistics with a density-based clustering method. This approach demonstrates a promising performance in detecting NEMOs in merged data sets without disrupting existing data-reduction pipelines. Re-refinement results indicate that excluding the identified NEMOs can effectively enhance the quality of subsequent structure-determination steps. This method offers a prospective automated means to assess the efficacy of a beamstop mask, as well as highlighting the potential of modern pattern-recognition techniques for automating outlier exclusion during data processing, facilitating future adaptation to evolving experimental strategies.

The mol­ecular and crystal structure of a discrete [Ni8(μ4-OH)6(μ-4-Rpz)12]2− (R = H; pz = pyrazolate anion, C3H3N2−) cluster with an unprecedented, perfectly cubic arrangement of its eight Ni centers is reported, along with its lower-symmetry alkyl-functionalized (R = methyl and n-oct­yl) derivatives. Crystals of the latter two were obtained with two identical counter-ions (Bu4N+), whereas the crystal of the complex with the parent pyrazole ligand has one Me4N+ and one Bu4N+ counter-ion. The methyl derivative incorporates 1,2-di­chloro­ethane solvent mol­ecules in its crystal structure, whereas the other two are solvent-free. The compounds are tetra­butyl­aza­nium tetra­methyl­aza­nium hexa-μ4-hydroxido-dodeca-μ2-pyrazolato-hexa­hedro-octa­nickel, (C16H36N)(C4H12N)[Ni8(C3H3N2)12(OH)6] or (Bu4N)(Me4N)[Ni8(μ4-OH)6(μ-pz)12] (1), bis­(tetra­butyl­aza­nium) hexa-μ4-hydroxido-dodeca-μ2-(4-methyl­pyrazolato)-hexa­hedro-octa­nickel 1,2-di­chloro­ethane 7.196-solvate, (C16H36N)2[Ni8(C4H5N2)12(OH)6]·7.196C2H4Cl2 or (Bu4N)2[Ni8(μ4-OH)6(μ-4-Mepz)12]·7.196(ClCH2CH2Cl) (2), and bis­(tetra­butyl­aza­nium) hexa-μ4-hydroxido-dodeca-μ2-(4-octylpyrazolato)-hexa­hedro-octa­nickel, (C16H36N)2[Ni8(C11H19N2)12(OH)6] or (Bu4N)2[Ni8(μ4-OH)6(μ-4-nOctpz)12] (3). All counter-ions are disordered (with the exception of one Bu4N+ in 3). Some of the octyl chains of 3 (the crystal is twinned by non-merohedry) are also disordered. Various structural features are discussed and contrasted with those of other known [Ni8(μ4-OH)6(μ-4-Rpz)12]2− complexes, including extended three-dimensional metal–organic frameworks. In all three structures, the Ni8 units are lined up in columns.

The title compound, tetra­ethyl­ammonium tri­azido­tri-μ3-sulfido-[μ3-(tri­methyl­sil­yl)aza­nediido][tris­(3,5-di­methyl­pyrazol-1-yl)hydro­borato]triiron(+2.33)molybdenum(IV), (C8H20N)[Fe3MoS3(C15H22BN6)(C3H9NSi)(N3)3] or (Et4N)[(Tp*)MoFe3S3(μ3-NSiMe3)(N3)3] [Tp* = tris­(3,5-di­methyl­pyrazol-1-yl)hydro­bor­ate(1−)], crystallizes as needle-like black crystals in space group Poverline{1}. In this cluster, the Mo site is in a distorted octa­hedral coordination model, coordinating three N atoms on the Tp* ligand and three μ3-bridging S atoms in the core. The Fe sites are in a distorted tetra­hedral coordination model, coordinating two μ3-bridging S atoms, one μ3-bridging N atom from Me3SiN2−, and another N atom on the terminal azide ligand. This type of heterometallic and heteroleptic single cubane cluster represents a typical example within the Mo–Fe–S cluster family, which may be a good reference for understanding the structure and function of the nitro­genase FeMo cofactor. The residual electron density of disordered solvent mol­ecules in the void space could not be reasonably modeled, thus the SQUEEZE [Spek (2015). Acta Cryst. C71, 9–18] function was applied. The solvent contribution is not included in the reported mol­ecular weight and density.

Single crystals of the mol­ecular compound, {Cu20Ir6Cl8(C21H24N2)6(C4H4N2)3]·3.18CH3OH or [({Cu10Ir3}Cl4(IMes)3(pyrazine))2(pyrazine)]·3.18CH3OH [where IMes is 1,3-bis­(2,4,6-trimethylphen­yl)imidazol-2-yl­idene], with a unique heterometallic cluster have been prepared and the structure revealed using single-crystal X-ray diffraction. The mol­ecule is centrosymmetric with two {Cu10Ir3} cores bridged by a pyrazine ligand. The polymetallic cluster contains three stabilizing N-heterocyclic carbenes, four Cl ligands, and a non-bridging pyrazine ligand. Notably, the Cu—Ir core is arranged in an unusual shape containing 13 vertices, 22 faces, and 32 sides. The atoms within the trideca­metallic cluster are arranged in four planes, with 2, 4, 4, 3 metals in each plane. Ir atoms are present in alternate planes with an Ir atom featuring in the peripheral bimetallic plane, and two Ir atoms featuring on opposite sides of the non-adjacent tetra­metallic plane. The crystal contains two disordered methanol solvent mol­ecules with an additional region of non-modelled electron density corrected for using the SQUEEZE routine in PLATON [Spek (2015). Acta Cryst. C71, 9–18]. The given chemical formula and other crystal data do not take into account the unmodelled methanol solvent mol­ecule(s).

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.

Scientists at Iowa State University have developed a new formulation that helps to explain the self-assembly of atoms into nanoclusters and to advance the scientific understanding of related nanotechnologies. Their research offers a theoretical framework to explain the relationship between the distribution of "capture zones," the regions that surround the nanoscale "islands" formed by deposition on surfaces, and the underlying nucleation or formation process.

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The Karnataka government is planning to open two new tech clusters in Shivamogga and AICC president Mallikarjun Kharge's hometown Kalaburagi by the end of this year, the state’s industry ecosystem accelerator Karnataka Digital Economy Mission chairman Naidu BV told ET.

The bite-sized snacks are filled with hemp hearts, pumpkin seeds, sunflower seeds, chai, flax, and quinoa, and are Manitoba’s debut into the snacking category.

In this conversation Oracle's Jim Grisanzio talks with Oracle engineer Chris Bensen about the massive Raspberry Pi cluster he was showing at JavaOne & Oracle CloudWorld in Las Vegas. The cluster was connected to Oracle Cloud and ran a variety of technologies, such as Java, Linux, Database, and more.

Chris Bensen, Oracle Developer Relations
https://twitter.com/chrisbensen

Jim Grisanzio, Duke's Corner Podcast Host
https://twitter.com/jimgris

Podcast Video
https://youtu.be/gNkSQIJfxjw

Dev Java
https://dev.java/

Inside Java
https://inside.java/

Emperor penguins huddle together to share warmth and protect each other during the intense winds of the harsh Antarctic storms. Fortunately, it’s not just penguins that can benefit from huddling together. Here, Jonathan Wilkins, marketing director of obsolete industrial parts supplier EU Automation, explains why manufacturers form clusters around the world.

Numerous iron-sulfur (Fe-S) proteins with diverse functions are present in the matrix and respiratory chain complexes of mitochondria. Although [4Fe-4S] clusters are the most common type of Fe-S cluster in mitochondria, the molecular mechanism of [4Fe-4S] cluster assembly and insertion into target proteins by the mitochondrial iron-sulfur cluster (ISC) maturation system is not well-understood. Here we report a detailed characterization of two late-acting Fe-S cluster-carrier proteins from Arabidopsis thaliana, NFU4 and NFU5. Yeast two-hybrid and bimolecular fluorescence complementation studies demonstrated interaction of both the NFU4 and NFU5 proteins with the ISCA class of Fe-S carrier proteins. Recombinant NFU4 and NFU5 were purified as apo-proteins after expression in Escherichia coli. In vitro Fe-S cluster reconstitution led to the insertion of one [4Fe-4S]2+ cluster per homodimer as determined by UV-visible absorption/CD, resonance Raman and EPR spectroscopy, and analytical studies. Cluster transfer reactions, monitored by UV-visible absorption and CD spectroscopy, showed that a [4Fe-4S]2+ cluster-bound ISCA1a/2 heterodimer is effective in transferring [4Fe-4S]2+ clusters to both NFU4 and NFU5 with negligible back reaction. In addition, [4Fe-4S]2+ cluster-bound ISCA1a/2, NFU4, and NFU5 were all found to be effective [4Fe-4S]2+ cluster donors for maturation of the mitochondrial apo-aconitase 2 as assessed by enzyme activity measurements. The results demonstrate rapid, unidirectional, and quantitative [4Fe-4S]2+ cluster transfer from ISCA1a/2 to NFU4 or NFU5 that further delineates their respective positions in the plant ISC machinery and their contributions to the maturation of client [4Fe-4S] cluster-containing proteins.

R. Greg Stacey
Nov 4, 2020; 0:RA120.002275v1-mcp.RA120.002275
Research

Biological functions emerge from complex and dynamic networks of protein-protein interactions. Because these protein-protein interaction networks, or interactomes, represent pairwise connections within a hierarchically organized system, it is often useful to identify higher-order associations embedded within them, such as multi-member protein complexes. Graph-based clustering techniques are widely used to accomplish this goal, and dozens of field-specific and general clustering algorithms exist. However, interactomes can be prone to errors, especially when inferred from high-throughput biochemical assays. Therefore, robustness to network-level noise is an important criterion for any clustering algorithm that aims to generate robust, reproducible clusters. Here, we tested the robustness of a range of graph-based clustering algorithms in the presence of noise, including algorithms common across domains and those specific to protein networks. Strikingly, we found that all of the clustering algorithms tested here markedly amplified noise within the underlying protein interaction network. Randomly rewiring only 1% of network edges yielded more than a 50% change in clustering results, indicating that clustering markedly amplified network-level noise. Moreover, we found the impact of network noise on individual clusters was not uniform: some clusters were consistently robust to injected noise while others were not. To assist in assessing this, we developed the clust.perturb R package and Shiny web application to measure the reproducibility of clusters by randomly perturbing the network. We show that clust.perturb results are predictive of real-world cluster stability: poorly reproducible clusters as identified by clust.perturb are significantly less likely to be reclustered across experiments. We conclude that graph-based clustering amplifies noise in protein interaction networks, but quantifying the robustness of a cluster to network noise can separate stable protein complexes from spurious associations.

Aug. 19, 2024 — SURF’s innovation department recently collaborated with Cornelis Networks to advance networking capabilities for high-performance computing (HPC) clusters. The collaboration marks a new step forward for SURF […]

The post Cornelis Networks Partners with SURF to Enhance HPC Cluster Networking Capabilities appeared first on HPCwire.

The precise regulation of Ca²⁺ signals plays a crucial role in the physiological functions of neurons. Here, we investigated the rapid effect of glucocorticoids on Ca²⁺ signals in cultured hippocampal neurons from both female and male rats. In cultured hippocampal neurons, glucocorticoids inhibited the spontaneous somatic Ca²⁺ spikes generated by Kv2.1-organized Ca²⁺ microdomains. Furthermore, glucocorticoids rapidly reduced the cell surface expressions of Kv2.1 and Ca_V1.2 channels in hippocampal neurons. In HEK293 cells transfected with Kv2.1 alone, glucocorticoids significantly reduced the surface expression of Kv2.1 with little effect on K⁺ currents. In HEK293 cells transfected with Ca_V1.2 alone, glucocorticoids inhibited Ca_V1.2 currents but had no effect on the cell surface expression of Ca_V1.2. Notably, in the presence of wild-type Kv2.1, glucocorticoids caused a decrease in the surface expression of Ca_V1.2 channels in HEK293 cells. However, this effect was not observed in the presence of nonclustering Kv2.1S586A mutant channels. Live-cell imaging showed that glucocorticoids rapidly decreased Kv2.1 clusters on the plasma membrane. Correspondingly, Western blot results indicated a significant increase in the cytoplasmic level of Kv2.1, suggesting the endocytosis of Kv2.1 clusters. Glucocorticoids rapidly decreased the intracellular cAMP concentration and the phosphorylation level of PKA in hippocampal neurons. The PKA inhibitor H89 mimicked the effect of glucocorticoids on Kv2.1, while the PKA agonist forskolin abrogated the effect. In conclusion, glucocorticoids rapidly suppress Ca_V1.2-mediated Ca²⁺ signals in hippocampal neurons by promoting the endocytosis of Kv2.1 channel clusters through reducing PKA activity.

Title: Nasal Spray May Ease Cluster Headaches
Category: Health News
Created: 8/29/2007 12:00:00 AM
Last Editorial Review: 8/29/2007 12:00:00 AM

PURPOSE

We undertook a trial to test the efficacy of a technology-assisted health coaching intervention for weight management, called Goals for Eating and Moving (GEM), within primary care.

This stunning new image from the NASA/ESA Hubble Space Telescope is one of the best ever views of a galaxy duo called MCG+05-31-045.

The post Hubble Spots Pair of Interacting Galaxies in Coma Cluster appeared first on Sci.News: Breaking Science News.