Nov. 13, 2024 — When researchers use high-performance computing (HPC)—to model electric vehicle infrastructure or to test the performance of sustainable technologies, for example—they can see how advanced computing impacts their […]

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Aim/Purpose: Over the past few years, new approaches to introducing young children to computational thinking have grown in popularity. This paper examines the role that user interfaces have on children’s mastery of computational thinking concepts and positive interpersonal behaviors. Background: There is a growing pressure to begin teaching computational thinking at a young age. This study explores the affordances of two very different programming interfaces for teaching computational thinking: a graphical coding application on the iPad (ScratchJr) and tangible programmable robotics kit (KIBO). Methodology : This study used a mixed-method approach to explore the learning experiences that young children have with tangible and graphical coding interfaces. A sample of children ages four to seven (N = 28) participated. Findings: Results suggest that type of user interface does have an impact on children’s learning, but is only one of many factors that affect positive academic and socio-emotional experiences. Tangible and graphical interfaces each have qualities that foster different types of learning

Aim/Purpose: This research aims to describe and demonstrate the results of an intervention through educational robotics to improve the computational thinking of student teachers. Background: Educational robotics has been increasing in school classrooms for the development of computational thinking and digital competence. However, there is a lack of research on how to prepare future teachers of Kindergarten and Elementary School in the didactic use of computational thinking, as part of their necessary digital teaching competence. Methodology: Following the Design-Based Research methodology, we designed an intervention with educational robots that includes unplugged, playing, making and remixing activities. Participating in this study were 114 Spanish university students of education. Contribution: This research helps to improve the initial training of student teachers, especially in the field of educational robotics. Findings: The student teachers consider themselves digital competent, especially in the dimensions related to social and multimedia aspects, and to a lesser extent in the technological dimension. The results obtained also confirm the effectiveness of the intervention through educational robotics in the development of computational thinking of these students, especially among male students. Recommendations for Practitioners: Teacher trainers could introduce robotics following these steps: (1) initiation and unplugged activities, (2) gamified activities of initiation to the programming and test of the robots, (3) initiation activities to Scratch, and (4) design and resolution of a challenge. Recommendation for Researchers: Researchers could examine how interventions with educational robots helps to improve the computational thinking of student teachers, and thoroughly analyze gender-differences. Impact on Society: Computational thinking and robotics are one of the emerging educational trends. Despite the rise of this issue, there are still few investigations that systematize and collect evidence in this regard. This study allows to visualize an educational intervention that favors the development of the computational thinking of student teachers. Future Research: Researchers could evaluate not only the computational thinking of student teachers, but also their didactics, their ability to teach or create didactic activities to develop computational thinking in their future students.

Aim/Purpose: This study presents some activities that integrate computational thinking (CT) into mathematics lessons utilizing GeoGebra to promote constructionist learning. Background: CT activities in the Indonesian curriculum are dominated by worked examples with less plugged-mode activities that might hinder students from acquiring CT skills. Therefore, we developed mathematics and CT (math+CT) lessons to promote students’ constructionist key behaviors while learning. Methodology: The researchers utilized an educational design research (EDR) to guide the lesson’s development. The lesson featured 11 applets and 22 short questions developed in GeoGebra. To improve the lesson, it was sent to eight mathematics teachers and an expert in educational technology for feedback, and the lesson was improved accordingly. The improved lessons were then piloted with 17 students, during which the collaborating mathematics teachers taught the lessons. Data were collected through the students’ work on GeoGebra, screen recording when they approached the activities, and interviews. We used content analysis to analyze the qualitative data and presented descriptive statistics to quantitative data. Contribution: This study provided an example and insight into how CT can be enhanced in mathematics lessons in a constructionist manner. Findings: Students were active in learning mathematics and CT, especially when they were engaged in programming and debugging tasks. Recommendations for Practitioners: Educators are recommended to use familiar mathematics software such as GeoGebra to support students’ CT skills while learning mathematics. Additionally, our applets are better run on big-screen devices to optimize students’ CT programming and debugging skills. Moreover, it is recommended that students work collaboratively to benefit from peer feedback and discussion. Recommendation for Researchers: Collaboration with teachers will help researchers better understand the situation in the classroom and how the students will respond to the activities. Additionally, it is important to provide more time for students to get familiar with GeoGebra and start with fewer errors to debug. Future Research: Further research can explore more mathematics topics when integrating CT utilizing GeoGebra or other mathematics software or implement the lessons with a larger classroom size to provide a more generalizable result and deeper understanding.

Aim/Purpose: The primary objective of this research is to build an enhanced framework for Applied and Computational Math. This framework allows a variety of applied math concepts to be organized into a meaningful whole. Background: The framework can help students grasp new mathematical applications by comparing them to a common reference model. Methodology: In this research, we measure the most frequent words used in a sample of Math and Computer Science books. We combine these words with those obtained in an earlier study, from which we constructed our original Computational Math scale. Contribution: The enhanced framework improves the Computational Math scale by integrating selected concepts from the field of Data Science. Findings: The resulting enhanced framework better explains how abstract mathematical models and algorithms are tied to real world applications and computer implementations. Future Research: We want to empirically test our enhanced Applied and Computational Math framework in a classroom setting. Our goal is to measure how effective the use of this framework is in improving students’ understanding of newly introduced Math concepts.

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.

In the title compound, C19H20Cl2N2O, the seven-membered 1,4-diazepane ring adopts a chair conformation while the 4-chloro­phenyl substituents adopt equatorial orientations. The chloro­phenyl ring at position 7 is disordered over two positions [site occupancies 0.480 (16):0.520 (16)]. The dihedral angle between the two benzene rings is 63.0 (4)°. The methyl groups at position 3 have an axial and an equatorial orientation. The compound exists as a dimer exhibiting inter­molecular N—H⋯O hydrogen bonding with R22(8) graph-set motifs. The crystal structure is further stabilized by C—H⋯O hydrogen bonds together with two C—Cl⋯π (ring) inter­actions. The geometry was optimized by DFT using the B3LYP/6–31 G(d,p) level basis set. In addition, the HOMO and LUMO energies, chemical reactivity parameters and mol­ecular electrostatic potential were calculated at the same level of theory. Hirshfeld surface analysis indicated that the most important contributions to the crystal packing are from H⋯H (45.6%), Cl⋯H/H⋯Cl (23.8%), H⋯C/C⋯H (12.6%), H⋯O/O⋯H (8.7%) and C⋯Cl/Cl⋯C (7.1%) inter­actions. Analysis of the inter­action energies showed that the dispersion energy is greater than the electrostatic energy. A crystal void volume of 237.16 Å3 is observed. A mol­ecular docking study with the human oestrogen receptor 3ERT protein revealed good docking with a score of −8.9 kcal mol−1.

Analysis of small-angle scattering (SAS) data requires intensive modeling to infer and characterize the structures present in a sample. This iterative improvement of models is a time-consuming process. Presented here is Scattering Equation Builder (SEB), a C++ library that derives exact analytic expressions for the form factors of complex composite structures. The user writes a small program that specifies how the sub-units should be linked to form a composite structure and calls SEB to obtain an expression for the form factor. SEB supports e.g. Gaussian polymer chains and loops, thin rods and circles, solid spheres, spherical shells and cylinders, and many different options for how these can be linked together. The formalism behind SEB is presented and simple case studies are given, such as block copolymers with different types of linkage, as well as more complex examples, such as a random walk model of 100 linked sub-units, dendrimers, polymers and rods attached to the surfaces of geometric objects, and finally the scattering from a linear chain of five stars, where each star is built up of four diblock copolymers. These examples illustrate how SEB can be used to develop complex models and hence reduce the cost of analyzing SAS data.

Minglecon (CEO Helen Jean) develops gamified AI education through its Minglebot AI platform. It combines AI with computational thinking and is expanding globally, securing contracts in Indonesia and exploring markets in Taiwan, Vietnam, and Dubai.

When investigating how we frame technology in the design process,... more

Scientists have used computational tools, including machine learning, to differentiate between subtypes of rheumatoid arthritis.

Scientists have used computational tools, including machine learning, to differentiate between subtypes of rheumatoid arthritis.

Scientists have used computational tools, including machine learning, to differentiate between subtypes of rheumatoid arthritis.

Location: Electronic Resource- 

Location: Electronic Resource- 

Location: Electronic Resource- 

Location: Electronic Resource- 

Location: Electronic Resource- 

Location: Electronic Resource- 

Location: Electronic Resource- 

Location: Electronic Resource- 

Location: Electronic Resource- 

Location: Electronic Resource- 

The QT interval is a recording of cardiac electrical activity. Previous genome-wide association studies identified genetic variants that modify the QT interval upstream of LITAF (lipopolysaccharide-induced tumor necrosis factor-α factor), a protein encoding a regulator of endosomal trafficking. However, it was not clear how LITAF might impact cardiac excitation. We investigated the effect of LITAF on the voltage-gated sodium channel Nav1.5, which is critical for cardiac depolarization. We show that overexpressed LITAF resulted in a significant increase in the density of Nav1.5-generated voltage-gated sodium current INa and Nav1.5 surface protein levels in rabbit cardiomyocytes and in HEK cells stably expressing Nav1.5. Proximity ligation assays showed co-localization of endogenous LITAF and Nav1.5 in cardiomyocytes, whereas co-immunoprecipitations confirmed they are in the same complex when overexpressed in HEK cells. In vitro data suggest that LITAF interacts with the ubiquitin ligase NEDD4-2, a regulator of Nav1.5. LITAF overexpression down-regulated NEDD4-2 in cardiomyocytes and HEK cells. In HEK cells, LITAF increased ubiquitination and proteasomal degradation of co-expressed NEDD4-2 and significantly blunted the negative effect of NEDD4-2 on INa. We conclude that LITAF controls cardiac excitability by promoting degradation of NEDD4-2, which is essential for removal of surface Nav1.5. LITAF-knockout zebrafish showed increased variation in and a nonsignificant 15% prolongation of action potential duration. Computer simulations using a rabbit-cardiomyocyte model demonstrated that changes in Ca2+ and Na+ homeostasis are responsible for the surprisingly modest action potential duration shortening. These computational data thus corroborate findings from several genome-wide association studies that associated LITAF with QT interval variation.

Mammalian circadian clocks are driven by transcription/translation feedback loops composed of positive transcriptional activators (BMAL1 and CLOCK) and negative repressors (CRYPTOCHROMEs (CRYs) and PERIODs (PERs)). CRYs, in complex with PERs, bind to the BMAL1/CLOCK complex and repress E-box–driven transcription of clock-associated genes. There are two individual CRYs, with CRY1 exhibiting higher affinity to the BMAL1/CLOCK complex than CRY2. It is known that this differential binding is regulated by a dynamic serine-rich loop adjacent to the secondary pocket of both CRYs, but the underlying features controlling loop dynamics are not known. Here we report that allosteric regulation of the serine-rich loop is mediated by Arg-293 of CRY1, identified as a rare CRY1 SNP in the Ensembl and 1000 Genomes databases. The p.Arg293His CRY1 variant caused a shortened circadian period in a Cry1−/−Cry2−/− double knockout mouse embryonic fibroblast cell line. Moreover, the variant displayed reduced repressor activity on BMAL1/CLOCK driven transcription, which is explained by reduced affinity to BMAL1/CLOCK in the absence of PER2 compared with CRY1. Molecular dynamics simulations revealed that the p.Arg293His CRY1 variant altered a communication pathway between Arg-293 and the serine loop by reducing its dynamicity. Collectively, this study provides direct evidence that allosterism in CRY1 is critical for the regulation of circadian rhythm.

Centrioles are key eukaryotic organelles that are responsible for the formation of cilia and flagella, and for organizing the microtubule network and the mitotic spindle in animals. Centriole assembly requires oligomerization of the essential protein spindle assembly abnormal 6 (SAS-6), which forms a structural scaffold templating the organization of further organelle components. A dimerization interaction between SAS-6 N-terminal “head” domains was previously shown to be essential for protein oligomerization in vitro and for function in centriole assembly. Here, we developed a pharmacophore model allowing us to assemble a library of low-molecular-weight ligands predicted to bind the SAS-6 head domain and inhibit protein oligomerization. We demonstrate using NMR spectroscopy that a ligand from this family binds at the head domain dimerization site of algae, nematode, and human SAS-6 variants, but also that another ligand specifically recognizes human SAS-6. Atomistic molecular dynamics simulations starting from SAS-6 head domain crystallographic structures, including that of the human head domain which we now resolve, suggest that ligand specificity derives from favorable Van der Waals interactions with a hydrophobic cavity at the dimerization site.

The tenovins are a frequently studied class of compounds capable of inhibiting sirtuin activity, which is thought to result in increased acetylation and protection of the tumor suppressor p53 from degradation. However, as we and other laboratories have shown previously, certain tenovins are also capable of inhibiting autophagic flux, demonstrating the ability of these compounds to engage with more than one target. In this study, we present two additional mechanisms by which tenovins are able to activate p53 and kill tumor cells in culture. These mechanisms are the inhibition of a key enzyme of the de novo pyrimidine synthesis pathway, dihydroorotate dehydrogenase (DHODH), and the blockage of uridine transport into cells. These findings hold a 3-fold significance: first, we demonstrate that tenovins, and perhaps other compounds that activate p53, may activate p53 by more than one mechanism; second, that work previously conducted with certain tenovins as SirT1 inhibitors should additionally be viewed through the lens of DHODH inhibition as this is a major contributor to the mechanism of action of the most widely used tenovins; and finally, that small changes in the structure of a small molecule can lead to a dramatic change in the target profile of the molecule even when the phenotypic readout remains static.

COLUMBUS, Ohio, Nov. 7, 2024 — Alexander Hoover, an assistant professor in the Department of Mathematics and Statistics at Cleveland State University, has always been fascinated by marine organisms—the way […]

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WALLISELLEN, Switzerland, Nov. 15, 2023 — Hewlett Packard Enterprise (HPE) today announced that Sauber Motorsport AG is partnering with HPE to advance aerodynamics of its Formula One race cars. The […]

The post HPE Unveils Advanced Computational Fluid Dynamics Solutions for Sauber Motorsport appeared first on HPCwire.

Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification of new targets for effective treatments. Recently available large-scale multiomics datasets provide opportunities to use computational approaches for such studies. Here, we devised a novel disease gene identification (digID) computational framework that consists of a semi-supervised deep learning classifier to predict AD-associated genes and a protein–protein interaction (PPI) network-based analysis to prioritize the importance of these predicted genes in AD. digID predicted 1,529 AD-associated genes and revealed potentially new AD molecular mechanisms and therapeutic targets including GNAI1 and GNB1, two G-protein subunits that regulate cell signaling, and KNG1, an upstream modulator of CDC42 small G-protein signaling and mediator of inflammation and candidate coregulator of amyloid precursor protein (APP). Analysis of mRNA expression validated their dysregulation in AD brains but further revealed the significant spatial patterns in different brain regions as well as among different subregions of the frontal cortex and hippocampi. Super-resolution STochastic Optical Reconstruction Microscopy (STORM) further demonstrated their subcellular colocalization and molecular interactions with APP in a transgenic mouse model of both sexes with AD-like mutations. These studies support the predictions made by digID while highlighting the importance of concurrent biological validation of computationally identified gene clusters as potential new AD therapeutic targets.

The Penn State Institute for Computational and Data Sciences announced two new co-hires: Dana Calacci, assistant professor in the College of Information Sciences and Technology, and Enrico Casella, assistant professor of data science for animal systems in the College of Agricultural Sciences. 

The Indian healthcare industry is looking to invest further in computational infrastructure as data integration frameworks and regulatory compliance are pivotal to ensure intelligent clinical support

Berlin ; Boston : De Gruyter, [2023]

IEEE Computer Society