Interactive Analysis Tool Research Articles

GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks.

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and users. It is therefore difficult to diagnose possible errors of GNNs. Despite many visual analytics studies being done on CNNs and RNNs, little research has addressed the challenges for GNNs. This paper fills the research gap with an interactive visual analysis tool, GNNLens, to assist model developers and users in understanding and analyzing GNNs. Specifically, Parallel Sets View and Projection View enable users to quickly identify and validate error patterns in the set of wrong predictions; Graph View and Feature Matrix View offer a detailed analysis of individual nodes to assist users in forming hypotheses about the error patterns. Since GNNs jointly model the graph structure and the node features, we reveal the relative influences of the two types of information by comparing the predictions of three models: GNN, Multi-Layer Perceptron (MLP), and GNN Without Using Features (GNNWUF). Two case studies and interviews with domain experts demonstrate the effectiveness of GNNLens in facilitating the understanding of GNN models and their errors.

Analysis of spatial conflicts of large scale salmonid aquaculture with coastal fisheries and other interests in a Norwegian fjord environment, using the novel GIS-tool SEAGRID and stakeholder surveys

The expansion of the Norwegian aquaculture industry has generated a need for balancing aquaculture with other societal interests in the coastal zone.The interactions, conflicts and synergies of different uses of a Norwegian coastal region heavily influenced by large-scale salmonid aquaculture was analyzed and mapped by means of systematic stakeholder participatory approach and a GIS-based spatial interaction analysis tool, SEAGRID. Our study focused on spatial conflicts/synergies, whereas non-spatial interactions were taken into account in order to elucidate the spatial effects.The questionnaire analysis showed that there was a large agreement across the respondents that aquaculture and fisheries compete for access to sea areas, which is also in agreement with the SEAGRID analysis. All but one of the interviewees thought that conservation issues will become more important in the future. We found that societal interests and infrastructure obstruct or displace private enterprises and economic interests, whereas environmental protection measures do so to a lesser extent; an exception are coral reefs which seem to be well protected against both fishing operations as well as new aquaculture facilities. Nature protection was not found to affect fisheries nor aquaculture to a large extent, with the exception of the single salmon fjord, which is protected from salmonid aquaculture, but otherwise open to other activities, such as fisheries, tourist fisheries and tourism in general. The restricted military areas had a limited extent, and were not viewed as a cause of conflicts.Stakeholder consultations, like our participatory GIS approach, combined with GIS-based tools for analysis of spatial conflicts/synergies may be useful in identifying areas where aquaculture production can increase with little increase in degree of conflict with fisheries, although some interactions are not strictly spatial. Our results indicated a certain degree of mistrust of management authorities since neither fishermen nor aquaculturists perceived that their own sector was given priority by management authorities, but thought the other sector was prioritized.

An Interactive Spectral Analysis Tool for Chemical Identification and Quantification of Gas-Phase Species in Complex Spectra.

A spectral analysis tool has been developed to interactively identify and quantify individual gas-phase species from complex infrared absorbance spectra obtained from laboratory or field data. The SpecQuant program has an intuitive graphical interface that accommodates both reference and experimental data with varying resolution and instrumental lineshape, as well as algorithms to readily align the wavenumber axis of a sample spectrum with the raster of a reference spectrum. Using a classical least squares model in conjunction with reference spectra such as those from the Pacific Northwest National Laboratory (PNNL) gas-phase infrared database or simulated spectra derived from the HITRAN line-by-line database, the mixing ratio of each identified species is determined along with its associated estimation error. After correcting the wavelength and intensity of the field data, SpecQuant displays the calculated mixing ratio versus the experimental data for each analyte along with the residual spectrum with any or all analyte fits subtracted for visual inspection of the fit and residuals. The software performance for multianalyte quantification was demonstrated using moderate resolution (0.5 cm-1) infrared spectra that were collected during the time-resolved infrared photolysis of methyl iodide.

Abstract 2087: Sherlock-Genome: a R shiny app for genomic analysis and visualization

Abstract Over the last few decades, next generation sequencing technologies, including whole genome sequencing (WGS), have been increasingly used in cancer genomics studies. However, there is no efficient way to manage, visualize and integratively analyze sample-level WGS results across different bioinformatic pipelines, particularly for non-bioinformaticians. This greatly limits the usability of WGS data for biological discovery. Following the FAIR principles (findability, accessibility, interoperability, and reusability), we developed Sherlock-Genome, a R Shiny app designed for data harmonization, visualization and integrative analyses for WGS-based cancer genomics studies. Specifically, Sherlock-Genome provides pipelines for WGS analyses coupled with detailed guidelines on data preparation. The users can download the pipelines on their local computer, run the analyses and then upload the sample-level results into the Sherlock-Genome for data harmonization and integrative analyses across different genomic features. Sherlock-Genome also implements rigorous WGS-related data QC steps based on NGSpurity, a workflow that estimates the tumor purity, ploidy and clonal architecture by integrating somatic copy number alterations (SCNA), single nucleotide variants (SNV) and cancer cell fraction. Currently, Sherlock-Genome includes a few independent modules that allow users to generate a variety of interactive data inspection and visualization images, and perform many integrative analyses also using clinical and epidemiological data. These modules include study information and sample-level data QC (study overview, manifest information, NGSpurity), summaries of major genomics alterations (mutations, SCNA, structural variants), reports of advanced genomics analyses (mutational signatures, genomic landscape, clonal evolution) and functions related to different types of integrative analyses (survival analysis, integrative analysis). In addition, all plots in the Sherlock-Genome app can be downloaded as publication-ready figures from each module. Sherlock-Genome can be deployed in both local and cloud environments, allowing users to share sample-level analytical results together with the related publications. Sherlock-Genome has the potential to be widely used in cancer genomics allowing sample-level integrative analyses across multiple WGS and additional multi-omics features. A Sherlock-Genome demo with major module functions could be presented to allow users to know about this new interactive user-friendly tool for data analysis and visualization that can greatly advance biological discoveries. Citation Format: Alyssa Klein, Maria Teresa Landi, Tongwu Zhang. Sherlock-Genome: a R shiny app for genomic analysis and visualization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2087.

The Allele Catalog Tool: a web-based interactive tool for allele discovery and analysis

BackgroundThe advancement of sequencing technologies today has made a plethora of whole-genome re-sequenced (WGRS) data publicly available. However, research utilizing the WGRS data without further configuration is nearly impossible. To solve this problem, our research group has developed an interactive Allele Catalog Tool to enable researchers to explore the coding region allelic variation present in over 1,000 re-sequenced accessions each for soybean, Arabidopsis, and maize.ResultsThe Allele Catalog Tool was designed originally with soybean genomic data and resources. The Allele Catalog datasets were generated using our variant calling pipeline (SnakyVC) and the Allele Catalog pipeline (AlleleCatalog). The variant calling pipeline is developed to parallelly process raw sequencing reads to generate the Variant Call Format (VCF) files, and the Allele Catalog pipeline takes VCF files to perform imputations, functional effect predictions, and assemble alleles for each gene to generate curated Allele Catalog datasets. Both pipelines were utilized to generate the data panels (VCF files and Allele Catalog files) in which the accessions of the WGRS datasets were collected from various sources, currently representing over 1,000 diverse accessions for soybean, Arabidopsis, and maize individually. The main features of the Allele Catalog Tool include data query, visualization of results, categorical filtering, and download functions. Queries are performed from user input, and results are a tabular format of summary results by categorical description and genotype results of the alleles for each gene. The categorical information is specific to each species; additionally, available detailed meta-information is provided in modal popups. The genotypic information contains the variant positions, reference or alternate genotypes, the functional effect classes, and the amino-acid changes of each accession. Besides that, the results can also be downloaded for other research purposes.ConclusionsThe Allele Catalog Tool is a web-based tool that currently supports three species: soybean, Arabidopsis, and maize. The Soybean Allele Catalog Tool is hosted on the SoyKB website (https://soykb.org/SoybeanAlleleCatalogTool/), while the Allele Catalog Tool for Arabidopsis and maize is hosted on the KBCommons website (https://kbcommons.org/system/tools/AlleleCatalogTool/Zmays and https://kbcommons.org/system/tools/AlleleCatalogTool/Athaliana). Researchers can use this tool to connect variant alleles of genes with meta-information of species.

MMR Deficiency Defines Distinct Molecular Subtype of Breast Cancer with Histone Proteomic Networks.

Mismatch repair (MMR) alterations are important prognostic and predictive biomarkers in a variety of cancer subtypes, including colorectal and endometrial. However, in breast cancer (BC), the distinction and clinical significance of MMR are largely unknown. This may be due in part to the fact that genetic alterations in MMR genes are rare and only seen to occur in around 3% of BCs. In the present study, we analyzed TCGA data using a multi-sample protein-protein interaction (PPI) analysis tool, Proteinarium, and showed a distinct separation between specific MMR-deficient and -intact networks in a cohort of 994 BC patients. In the PPI networks specific to MMR deficiency, highly connected clusters of histone genes were identified. We also found the distribution of MMR-deficient BC to be more prevalent in HER2-enriched and triple-negative (TN) BC subtypes compared to luminal BCs. We recommend defining MMR-deficient BC by next-generation sequencing (NGS) when any somatic mutation is detected in one of the seven MMR genes.

Screening and identification of hub-gene associated with brain metastasis in breast cancer.

The presence of breast cancer in the brain, also known as brain metastasis (BMS), is the primary reason for a bad prognosis in cases of breast cancer. Breast cancer is the most prevalent malignant tumor seen in women in developing nations. At present, there is no effective method to inhibit brain metastasis of breast cancer. Therefore, it is necessary to conduct a systematic study on BMS of breast cancer, which will not provide ideas and sites for follow-up studies on the treatment and inhibition of BMS. In this study, data set GSE43837 was screened from gene expression omnibus database, and then R language tool was used for differential analysis of its expression spectrum, The gene ontology functional enrichment and Kyoto encyclopedia of genes and genomes signal pathway enrichment analyses, as well as the interactive gene retrieval tool for hub-gene analysis, were performed. According to the findings, the primary genes linked to breast cancer brain metastases are those that involve interactions between cytokines and their respective receptors and between neuroactive ligands and their respective receptors. The majority of the gene ontology enrichment took place in the extracellular structural tissues, the extracellular matrix tissues, and the second message-mediated signaling. We were able to identify 8 genes that are linked to breast cancer spreading to the brain. The gene score for matrix metallopeptidase1 (MMP-1) was the highest among them, and the genes MMP10, tumor necrosis factor alpha-inducible protein 8, collagen type I alpha 2 chain, vascular cell adhesion molecule 1, and TNF superfamily member 11 were all connected to 1 another in an interaction way. There is a possibility that the 8 key genes that were identified in this research are connected to the progression of BMS in breast cancer. Among them, MMP1 is 1 that has the potential to have a role in the diagnosis and treatment of BMS in breast cancer.

Business Analytics in Healthcare: Past, Present, and Future Trends

Problem definition: Business analytics (BA) in healthcare research offers numerous valuable insights that can enhance patient care and hospital performance. Consequently, there has been a rapid surge of research in this area. Academic/practical relevance: The objective of this study is to provide a data-driven summary of the extant BA in healthcare literature and a guide for future research. Methodology: Leveraging a topic modeling technique and network analysis, we provide insight into how BA topics change over time. Results: We provide an in-depth analysis of 320 articles from the University of Texas at Dallas journal list and a basic topic model and network analyses for an additional 6,515 relevant articles from PubMed published in top-tier journals across 69 medical subcategories. Our study bridges research in operations management, information systems, healthcare, and analytics by providing a definition of BA in healthcare and a road map for future research. Managerial implications: Our study provides a single source of information into operations- and analytics-related issues, such as wait times, admissions, hospital performance, etc., that scholars and administrators might use to rethink how specific processes are handled in hospitals. In addition, this work highlights how operations management research has addressed clinically important issues such as patient satisfaction, doctor ratings, readmission rates, mortality, efficiency, cost of care, and compliance with protocols of care, as all are represented in our sample. Another key contribution of our study is that we provide an interactive article analysis tool as a web application for scholars in hopes of facilitating research in this area. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1192 .

Gd3+-Trityl-Nitroxide Triple Labeling and Distance Measurements in the Heterooligomeric Cobalamin Transport Complex in the Native Lipid Bilayers.

Increased efforts are being made for observing proteins in their native environments. Pulsed electron-electron double resonance spectroscopy (PELDOR, also known as DEER) is a powerful tool for this purpose. Conventionally, PELDOR employs an identical spin pair, which limits the output to a single distance for monomeric samples. Here, we show that the Gd3+-trityl-nitroxide (NO) three-spin system is a versatile tool to study heterooligomeric membrane protein complexes, even within their native membrane. This allowed for an independent determination of four different distances (Gd3+-trityl, Gd3+-NO, trityl-NO, and Gd3+-Gd3+) within the same sample. We demonstrate the feasibility of this approach by observing sequential ligand binding and the dynamics of complex formation in the cobalamin transport system involving four components (cobalamin, BtuB, TonB, and BtuF). Our results reveal that TonB binding alone is sufficient to release cobalamin from BtuB in the native asymmetric bilayers. This approach provides a potential tool for the structural and quantitative analysis of dynamic protein-protein interactions in oligomeric complexes, even within their native surroundings.

Analysis of continuous enzyme kinetic data using ICEKAT.

ICEKAT (Interactive Continuous Enzyme Analysis Tool) is an interactive web-based program for calculating initial rates and kinetic parameters (e.g., Vmax, kcat, KM, EC50, IC50) from continuous enzyme kinetic assay data that satisfy Michaelis-Menten and steady-state kinetic assumptions. ICEKAT is valuable in educational and research settings to consistently and accurately calculate initial rates and kinetic parameters, increasing assay veracity and reproducibility. Provided freely online to the scientific community, ICEKAT has been cited in at least 26 publications, and the initial journal article has been accessed nearly 9000 times since its debut in 2020 (Olp et al., 2020). Here, we provide in-depth instructions for software use, offer vital considerations for data analysis, and highlight updated software features for new and existing users. Through ICEKAT, we aim for the analysis of data from continuous enzyme kinetic studies worldwide to become more rapid, reliable, and repeatable. ICEKAT remains free of charge and available to all scientists at https://icekat.herokuapp.com/icekat; the source code for local use is found at https://github.com/SmithLabMCW/icekat.

Framework and Use Case for a Web-Based Interactive Analysis Tool to Investigate Urban Expansion and Sustainable Development Goal Indicators

Land cover changes have been mapped for decades to investigate urban expansion patterns. Under the UN Sustainable Development Goals (SDGs), several indices are employed to interpret urban growth trends quantitatively and comprehensively. However, landowners and the interested public usually have limited insights into these types of information as access to data and software is limited. Static maps and the inability to access special file formats increase the difficulty of viewing and investigating the data. This contribution presents a dedicated, interactive, web-based analysis tool for integrating land cover and land use maps as well as urban expansion indices. The tool’s concept, development and functionality are presented, and its general design is reviewed based on an actual implementation case. The setup allows integrating land use and land cover (LULC) change data alongside SDG indicators. The tool’s design aims to enhance user accessibility to information on urban expansion indices and LULC. We demonstrate that such a tool can be used to help disseminate results and to improve communication with the public in the context of other use cases.

Use of Lt Systems in Large Class Lab Delivery.

The attainment of laboratory-based skills is essential for the development of all students who study the Life Sciences. When the COVID-19 pandemic hit and Universities worldwide closed campuses, there was great uncertainty around how long people would have to work from home, leading to a real risk that students could miss out on obtaining laboratory practice for one or perhaps 2 years. It, therefore, became critical that processes were put in place that would allow students the opportunity to gain insight into what the laboratory environment is like, and to also gain experience in data collection, interpretation, and analysis. The adoption of Lt systems (a cloud-based learning platform for the Life Sciences) to create laboratory-based teaching allowed for these issues to be addressed. Detailed planning, teamwork and production of online lab sessions allowed for the creation of bespoke lessons that replicated as best as possible the 'face-to-face' experience. This was achieved by taking aspects of pre-existing labs, filming new material and utilising interactive data analysis tools in order to create online Lt labs that gave students a feel of what it would be like to be in a laboratory on campus. Lt systems also doubled as a revision tool to enhance student learning and ultimately allowed intended learning outcomes to be successfully met. The labs were well received by students and the online material meant that they could access content at a time convenient to them. This 'online anytime' possibility was crucial for such a large class (n=378) who were working from home in many different countries worldwide during a pandemic. Going forward, the online lessons built during the pandemic can be integrated with future face-to-face sessions to create a more enhanced learning experience for the student.

Epithelial Membrane Protein 1 is a Potential Prognostic Biomarker for Ovarian Cancer

Background: Epithelial membrane protein 1 (EMP1) is a cell junction protein located in the plasma membrane. The function of EMP1 in cancer cells varies in different tumour types. In the present study we investigated the expression of EMP1 and its clinical significance in ovarian cancer. Methods: Sequencing data from the Cancer Genome Atlas (TCGA) and from several other public databases was used to study the expression of EMP1 and its gene regulation networks in ovarian cancer patients. Oncomine and Gene Expression Profiling Interactive Analysis tools (GEPIA) were used to evaluate EMP1 expression. EMP1 alterations were identified using cBioPortal and the prognostic value of EMP1 was assessed using Kaplan-Meier analysis and OncoLnc online tools. Genes that were co-expressed with EMP1 were identified using LinkedOmics and the Link-Interpreter module was used to analyse pathway enrichment and the possible functions of co-expressed genes. Results: EMP1 was highly expressed in ovarian cancer and was associated with advanced tumour stage and poor overall survival (OS). Amplification of the EMP1 gene was also common in ovarian cancer. EMP1 participates mainly in the biological processes of epidermal development, cell adhesion, peptidyl tyrosine modification and angiogenesis. Functional network analysis suggests that EMP1 regulates tumorigenesis and progression of ovarian cancer through different signalling pathways that include several kinases, microRNAs (miRNAs) and transcription factors related to tumorigenesis. Conclusions: This study confirmed the expression of EMP1 in ovarian cancer and elucidated its regulatory networks, thus providing a theoretical basis for further studies into its functions. EMP1 could potentially serve as a diagnostic and prognostic biomarker in ovarian cancer patients. However, owing to the limited sample size and range of experimental work, further validation studies are needed to confirm the role of EMP1 in ovarian cancer.

Broadband infrared absorption spectroscopy of low-frequency inter-molecular vibrations in crystalline poly(L-lactide)

The broadband vibrations of poly l-lactide (PLLA) were investigated by terahertz (THz) spectroscopy in the range of 2–16 THz, infrared (IR) spectroscopy, density functional theory (DFT), and classical molecular dynamics (MD) simulations. Characteristic functional group peaks were observed in the IR spectra, which were independent of the crystallinity. A simple model analysis utilizing DFT calculations with an isolated l-lactide monomer and MD simulations for nine polymer chains in a unit cell were performed to investigate the origin of intra-molecular vibrations between molecular chains. Experimentally observed peaks in the THz region were assigned to the inter- and intra-molecular vibration modes. Results showed that classical MD calculations are a particularly powerful tool for the vibrational-mode analysis of inter-molecular interactions.

RNF26 Promotes Pancreatic Cancer Proliferation by Enhancing RBM38 Degradation.

RING finger protein 26 (RNF26) plays an essential role in determining malignant tumor growth, whereas the role of which in pancreatic cancer (PC) has not been reported. This study aimed to investigate the role of RNF26 in PC cells. The Gene Expression Profiling Interactive Analysis was applied to study the role of RNF26 in malignant tumors. The in vitro or in vivo cell proliferation assays were used to investigate the role of RNF26 on the PC. The protein-protein interaction network analysis was used to search the binding partner of RNF26. The Western blot was used to reveal whether RNF26 promoted RNA binding motif protein-38 (RBM38) degradation in PC cells. The Gene Expression Profiling Interactive Analysis tool showed that RNF26 was overexpressed in PC. Repressing RNF26 expression decreased PC cells growth, but overexpression of RNF26 increased PC proliferation. Furthermore, we demonstrated RNF26 degraded RBM38 to promote PC cell proliferation. RNF26 was abnormally increased in PC, and upregulated RNF26 was correlated with a poor prognosis. RNF26 enhanced PC proliferation by inducing RBM38 degradation. We identified a novel RNF26-RBM28 axis involved in the progression of PC.

Computational Approach for Mining Simple Sequence Repeats in Expressed Sequence Tags

Expressed sequence tags, the short sequences of cDNA are mined for identifying and characterizing simple sequence repeats for studying genetic variations. Web-based tools due to lack of server maintenance, become unusable; also few available stand-alone tools lack processing adequateness. Therefore with the intent to process multiple expressed sequence tag files without size limitations, proper validations, and the ability to retrieve more genome-related features; a simple to use, speed efficient portable standalone tool has been developed. The algorithm is implemented in Java using microsatellite search algorithm, with dictionary-based approach MISA – Perl script, called via command line for data mining. Another parallel module retrieves additional information from GenBank files. In the pipeline primer 3 was invoked for designing batch primers. This algorithm with an extended interface in Java Net Beans provides naïve users with a simple interactive tool for mining microsatellites, statistical analysis, and primer designing on one platform in the form of a stand-alone application. The number of repeats/ interruptions parameters can be reset through the graphical interface. This tool has interactive modules with proper validations; batch processing and cost-effective analysis of tandem repeats as compared to peers, the source code can be upgraded in the future.

Identification of high-wind features within extratropical cyclones using a probabilistic random forest – Part 1: Method and case studies

Abstract. Strong winds associated with extratropical cyclones are one of the most dangerous natural hazards in Europe. These high winds are mostly associated with five mesoscale dynamical features: the warm (conveyor belt) jet (WJ); the cold (conveyor belt) jet (CJ); cold frontal convection (CFC); strong cold-sector winds (CS); and, at least in some storms, the sting jet (SJ). The timing within the cyclone's life cycle, the location relative to the cyclone core and some further characteristics differ between these features and, hence, likely also the associated forecast errors. Here, we present a novel objective identification approach for these high-wind features using a probabilistic random forest (RF) based on each feature’s most important characteristics in near-surface wind, rainfall, pressure and temperature evolution. As the CJ and SJ are difficult to distinguish in near-surface observations alone, these two features are considered together here. A strength of the identification method is that it works flexibly and is independent of local characteristics and horizontal gradients; thus, it can be applied to irregularly spaced surface observations and to gridded analyses and forecasts of different resolution in a consistent way. As a reference for the RF, we subjectively identify the four storm features (WJ, CS, CFC, and CJ and SJ) in 12 winter storm cases between 2015 and 2020 in both hourly surface observations and high-resolution reanalyses of the German Consortium for Small-scale Modeling (COSMO) model over Europe, using an interactive data analysis and visualisation tool. The RF is then trained on station observations only. The RF learns physically consistent relations and reveals the mean sea level pressure (tendency), potential temperature, precipitation amount and wind direction to be most important for the distinction between the features. From the RF, we get probabilities of each feature occurring at the single stations, which can be interpolated into areal information using Kriging. The results show a reliable identification for all features, especially for the WJ and CFC. We find difficulties in the distinction of the CJ and CS in extreme cases, as the features have rather similar meteorological characteristics. Mostly consistent results in observations and reanalysis data suggest that the novel approach can be applied to other data sets without the need for adaptation. Our new software RAMEFI (RAndom-forest-based MEsoscale wind Feature Identification) is made publicly available for straightforward use by the atmospheric community and enables a wide range of applications, such as working towards a climatology of these features for multi-decadal time periods (see Part 2 of this paper; Eisenstein et al., 2022d), analysing forecast errors in high-resolution COSMO ensemble forecasts and developing feature-dependent post-processing procedures.

IN SILICO SCREENING OF MAJOR CANCER DRUG TARGETS (GROWTH FACTOR RECEPTORS) FOR NATURE DERIVED PHYTOCHEMICALS

Cancer i s a group of abn ormal cells. The unregulated gro wth factor recepto r t yrosin e kinase ( GFR- TK) proteins are implicated in the proliferation o f mo re than 60 % o f all can cer t ypes. Screenin g o f ph ytoch emicals for their anti - angiogenic potential has b een a gro wing area o f research in the mod ern d ecad e. There i s a well - kno wn principle that natural co mpounds are active against several diseases, includin g variou s t yp es of can cer. Th e present research work fo cuses on kno wn gro wth factor receptors ( GFRs) as an important target fo r co mputation al s tudies. In this stud y, 96 curated anti - can cer co mpounds were virtu ally screened against the EGFR, FGFR, IGFR, and HGFR usin g molecular dockin g so ftware. For each GFR, we h ave considered ten top most results as potential hits. Among them, co mmon f i ve results are: Spiro solan e, Ginkgetin, Fangchinoline, Theaflavin and Ursolic acid. These co mpounds have b een reported to show antican cer activities in the l i t erature. With the help of different interaction analysis tools, the protein - l i gand inter action patterns between th e functional groups o f these co mpounds were analyzed. Hydro gen bonding and h ydrophobic forces are th e main co mponents o f th e interactions o f th ese hits, similar to those exp erimental fo r th e kno wn inhibitors. Fro m the maximu m nu mb ers o f hits, i t could be indicated that co mpounds Spirosolan e, Ginkgetin, Fan gchinoline, Th eaflavin and Ursolic acid are pro miscuous l ead s in the drug disco very process.

A multi-omics approach to visualize early neuronal differentiation from hESCs in 4D

SummaryNeuronal differentiation of pluripotent stem cells is an established method to study physiology, disease, and medication safety. However, the sequence of events in human neuronal differentiation and the ability of in vitro models to recapitulate early brain development are poorly understood. We developed a protocol optimized for the study of early human brain development and neuropharmacological applications. We comprehensively characterized gene expression and epigenetic profiles at four timepoints, because the cells differentiate from embryonic stem cells towards a heterogeneous population of progenitors, immature and mature neurons bearing telencephalic signatures. A multi-omics roadmap of neuronal differentiation, combined with searchable interactive gene analysis tools, allows for extensive exploration of early neuronal development and the effect of medications.

STUDI EKSPLORATIF KINERJA GURU MATEMATIKA SELAMA PANDEMIC COVID-19 PADA SEKOLAH MENENGAH KEJURUAN (SMK) DI PERBAUNGAN

The purpose of this study was to find out how the performance of mathematics teachers in vocational secondary schools during the Covid-19 Pandemic. The sampling technique used in this study is purposive sampling. The research method used is qualitative data method. Data Collection techniques used in this study were Observation, Interviews and Documentation of five schools. The determine the teacher’s performance, testing was carried out through an interactive analysis tool consisting of three types of activities that occurred simultaneously, namely Data Reduction, Data Presentation and Conclusing Drawing. Based on the results of the discussion obtained from this research 1) The teacher’s obstacle in the online learning process during the Covid-19 pandemic at the Perbaungan City Vocational High School, the main factor that is more important is the lack of facilities owned by students when studying online because not all students have a laptops or smartphones as online learning media.besides that another factor is internet quota which can not be reached by all students. Efforts are being made to minimize the obstacles experienced by teachers and student during the Covid-19 pandemic, namely conducting offline learning (outside the network) or offline with students and making innovations in the online learning process during the Covid-19 pandemic. Keywords: Teacher Performance,Online Learning, Covid-19 Pandemic.