Custom Scripts Research Articles

Towards a container-based architecture for CMS data acquisition

The CMS data acquisition (DAQ) is implemented as a service-oriented architecture where DAQ applications, as well as general applications such as monitoring and error reporting, are run as self-contained services. The task of deployment and operation of services is achieved by using several heterogeneous facilities, custom configuration data and scripts in several languages. In this work, we restructure the existing system into a homogeneous, scalable cloud architecture adopting a uniform paradigm, where all applications are orchestrated in a uniform environment with standardized facilities. In this new paradigm DAQ applications are organized as groups of containers and the required software is packaged into container images. Automation of all aspects of coordinating and managing containers is provided by the Kubernetes environment, where a set of physical and virtual machines is unified in a single pool of compute resources. We demonstrate that a container-based cloud architecture provides an acrossthe-board solution that can be applied for DAQ in CMS. We show strengths and advantages of running DAQ applications in a container infrastructure as compared to a traditional application model.

A comparative analysis of the Flutter and React Native frameworks

The article describes a comparative analysis of the resource usage performance of mobile applications developed using Flutter and React Native frameworks. The purpose of this study was to see which technology is more efficient. The study consisted in creating two mobile apps with the same functionalities and then comparing usage of the following resources: Virtual Memory (VIRT), Shared Memory (SHR), Central Processing Unit (CPU), Resident Set Size (RES) and Memory (MEM). The functionality of the application consisted of actions on the GUI. The test was conducted on a Huawei P20 Lite smartphone, using the Android Debug Bridge (ADB) tool and custom scripts in the Bash shell language. The results do not clearly indicate which technology is more efficient.

IgMAT: immunoglobulin sequence multi-species annotation tool for any species including those with incomplete antibody annotation or unusual characteristics

BackgroundThe advent and continual improvement of high-throughput sequencing technologies has made immunoglobulin repertoire sequencing accessible and informative regardless of study species. However, to fully map dynamic changes in polyclonal responses precise framework and complementarity determining region annotation of rearranging genes is pivotal. Most sequence annotation tools are designed primarily for use with human and mouse antibody sequences which use databases with fixed species lists, applying very specific assumptions which select against unique structural characteristics. For this reason, data agnostic tools able to learn from presented data can be very useful with new species or with novel datasets.ResultsWe have developed IgMAT, which utilises a reduced amino acid alphabet, that incorporates multiple HMM alignments into a single consensus to automatically annotate immunoglobulin sequences from most organisms. Additionally, the software allows the incorporation of user defined databases to better represent the species and/or antibody class of interest. To demonstrate the accuracy and utility of IgMAT, we present analysis of sequences extracted from structural data and immunoglobulin sequence datasets from several different species.ConclusionsIgMAT is fully open-sourced and freely available on GitHub (https://github.com/TPI-Immunogenetics/igmat) for download under GPLv3 license. It can be used as a CLI application or as a python module to be integrated in custom scripts.

Isoform characterisation & splicing signatures of AD‐risk genes using long‐read sequencing

AbstractBackgroundAn increasing number of studies implicate a role for alternative splicing in development and neuropathology of Alzheimer’s disease (AD). However, it has been historically challenging to characterise splicing events, due to the inherent limitations of traditional RNA‐sequencing (RNA‐Seq) to capture full‐length transcripts critical for transcriptome assembly. In this study, we use two complementary targeted long‐read sequencing approaches, Pacific Biosciences (PacBio) isoform sequencing (Iso‐Seq) and Oxford Nanopore Technologies (ONT) nanopore cDNA sequencing, to profile the cortex of a mouse model of tau pathology (rTg4510) with ultra‐deep sequencing of a panel of 20 genes robustly implicated in AD.MethodTargeted PacBio Iso‐Seq and targeted ONT profiling were performed on RNA isolated from 24 female rTg4510 transgenic and wild‐type mice, aged 2, 4, 6 and 8 months. Following successful enrichment for target genes using custom‐designed probes (IDT), library preparation were performed for subsequent sequencing on Sequel and MinION. The panel of genes selected for enrichment included genes (i.e. App, Mapt, Snca) and dementia risk genes identified from GWAS analyses. Raw sequence data was processed using a customised bioinformatics, and full‐length reads were merged and annotated with our developed custom scripts (FICLE).ResultWe identify thousands of novel transcripts across the panel of 20 AD‐ and dementia‐associated genes. We reveal unprecedented diversity of alternatively‐spliced isoforms with widespread usage of alternative novel 5’ and 3’ splice sites. We further robust transcriptional and splicing differences in these AD‐risk genes associated with the development of tau pathology. Among these changes, we observed global up‐regulation of Trem2‐associated isoforms and isoform switches in Bin1, further supporting a role for the dysregulation of the immune response in the development of AD pathology.ConclusionThis represents the first study to comprehensively assess variable AS associated with the development of tau pathology using both PacBio Iso‐Seq and ONT nanopore sequencing. Our analyses confirm the importance of alternative RNA splicing in AD and identify evidence of differential transcript usage, even in the absence of gene‐level expression alterations. Further work will be undertaken to validate and functionally characterise these novel isoforms, and to extend these analyses to human post‐mortem AD brain samples.

Performance of a Liquid Biopsy Diagnostic Prediction Model for EBV-Positive Burkitt Lymphoma in Sub-Saharan Africa

Burkitt Lymphoma (BL) is classified into EBV-positive BL (EBL) and EBV-negative BL (sporadic BL), the former being more prevalent in SSA (Al-Khreisat et al 2023). Conventional diagnosis of BL depends on the morphological assessment of invasive tissue biopsy followed by immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH) to reveal the pathognomonic t(8;14), or t(8;22) translocations. This is associated with significant diagnostic delay in regions with limited pathology services (Mawalla et al 2023), often necessitating initiation of treatment based on clinical or histopathological findings without immunohistochemistry (Ogwang et al 2011), with ensuing mismanagement contributing to poor survival outcomes observed in SSA compared to High-Income Countries (HIC) (Sung et al 2021). Here, we sought to explore whether liquid biopsy could be used as an initial diagnostic aid for EBL in settings without easy access to pathology using different EBV DNA parameters and the MYC-Immunoglobulin ( MYC-Ig) gene rearrangement. This prospective study collected whole blood samples prospectively from 221 participants (90 BL and 131 non-BL) enrolled in the Aggressive Infection-Related East Africa Lymphoma (AI-REAL) project (Legason et al 2022) across 5 sites in Tanzania and Uganda. The revised BL diagnostic algorithm (Naresh et al 2021) was used as a gold-standard diagnosis. CfDNA was extracted using the QIAamp Circulating Nucleic Acid Kit (Qiagen, USA), according to the manufacturer's instructions. DNA libraries were prepared with the Thru PLEX Tag-Seq HV kit (Takara Bio Inc. Japan), and hybridization capture was performed using a custom, next-generation sequencing (NGS) panel targeting genes commonly mutated in BL and three EBV genes: EBER1, EBER2, and EBNA2. All sequencing was performed in-country (MiSeq, Illumina, CA, USA) and analyzed in a custom pipeline. Using custom scripts, EBV DNA copies per cell and EBV DNA fragment size ratio were calculated as the proportion of EBV fragments of length 180 - 200 base pairs (bp) divided by the proportion of autosomal fragments within the same size window. The diversity in the fragment size distribution of the EBV and autosomal DNA was calculated and presented as EBV entropy and autosomal entropy. Translocations were called using IgCaller (Nadeu et al 2020) and Genomic Rearrangement Identification Software Suite (GRIDSS) (Cameron et al 2017). Univariate analysis was performed to assess the association between predictor variables (clinical parameters, EBV parameters, and MYC translocation) and a diagnosis of EBL. Ten-fold cross-validation was performed on a liquid biopsy model consisting of all the liquid biopsy variables with the clinical variables included in the model as covariates. Performance metrics for the liquid biopsy model as a diagnostic aid were calculated, and variable importance was determined to assess each variable's relative influence on the model. In our cohort, EBL diagnosis was negatively associated with tumor site (neck) and not significantly associated with tumor site (jaw). A shorter duration of symptoms was associated with a diagnosis of EBL. Presence of MYC translocation t(8;14), higher EBV fragment size ratio, proportion, entropy, and copies per cell for EBER1, EBER2, and EBNA2 were all associated with a diagnosis of EBL.The combined liquid biopsy diagnostic prediction model had an estimated AUC of 90%, with sensitivity between 85%-90%, specificity between 75% - 80%, and positive and negative predictive values of 85% (Figure 1). MYC translocation t(8;14) was the most important diagnostic variable, followed by EBV entropy (Figure 2). This study demonstrates the feasibility of liquid biopsy as a non-invasive diagnostic aid for EBL in settings without easy access to pathology. To strengthen the application of this technology into clinical practice, further analysis is being conducted for weighting and assigning of scores for each predictor variable. This would assist clinicians in making a precise diagnosis, complementing existing diagnostic modalities for EBL, and reducing the rate of misdiagnosis arising from diagnosis based on clinical suspicion and tissue morphology without immunohistochemistry.

Higher Levels of SARS-CoV-2 Genetic Variation in Immunocompromised Patients: A Retrospective Case-Control Study.

A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection lasts longer in immunocompromised hosts than in immunocompetent patients. Prolonged infection is associated with a higher probability of selection for novel SARS-CoV-2 mutations, particularly in the spike protein, a critical target for vaccines and therapeutics. From December 2020 to September 2022, respiratory samples from 444 immunocompromised patients and 234 health care workers positive for SARS-CoV-2, diagnosed at 2 hospitals in Paris, France, were analyzed using whole-genome sequencing using Nanopore technology. Custom scripts were developed to assess the SARS-CoV-2 genetic diversity between the 2 groups and within the host. Most infections were SARS-CoV-2 Delta or Omicron lineages. Viral genetic diversity was significantly higher in infections of immunocompromised patients than those of controls. Minor mutations were identified in viruses sequenced from immunocompromised individuals, which became signature mutations for newer SARS-CoV-2 variants as the epidemic progressed. Two patients were coinfected with Delta and Omicron variants. The follow-up of immunocompromised patients revealed that the SARS-CoV-2 genome evolution differed in the upper and lower respiratory tracts. This study found that SARS-CoV-2 infection in immunocompromised patients is associated with higher genetic diversity, which could lead to the emergence of new SARS-CoV-2 variants with possible immune evasion or different virulence characteristics.

Identifying the Early Post-Mortem VOC Profile from Cadavers in a Morgue Environment Using Comprehensive Two-Dimensional Gas Chromatography

Understanding the VOC profile released during the early post-mortem period is essential for applications in training human remains detection dogs and urban search and rescue operations (USAR) to rapidly locate living and deceased victims. Human cadavers were sampled at the UQTR morgue within a 0–72 h post-mortem interval. VOC samples were collected from the headspace above the cadavers, using Tenax TA/Carbograph 5TD dual sorbent tubes, and analyzed using GC×GC-TOFMS. Multiple data processing steps, including peak table alignment and filtering, were undertaken using LECO ChromaToF and custom scripts in R programming language. This study identified 104 prevalent VOCs, some of which are linked to human decomposition, while others are connected to the persistence of living scent. Principal Component Analysis (PCA) further highlighted that VOC profiles can change dynamically over time, even in a controlled setting. The findings underscore the complexity and variability in VOC profiles during the early post-mortem period. This variability is influenced by multiple factors including the individual’s biological and physiological conditions. Despite the challenges in characterizing these profiles, the identified VOCs could potentially serve as markers in forensic applications. The study also highlights the need for additional research to build a dataset of VOCs for more robust forensic applications.

Spatial Dissection of the Bone Marrow Microenvironment in Multiple Myeloma By High Dimensional Multiplex Tissue Imaging

Introduction Multiple myeloma (MM) is shaped by interactions between immune, stromal and tumor cells in the bone marrow (BM) microenvironment (BMME). Our understanding of these processes is based on high parametric flow cytometry and single cell transcriptomics, which lack spatial resolution. Thus, crucial aspects of the in situ tumor ecosystem, including cell-cell interactions, can only be predicted. Novel high dimensional, multiplex tissue imaging methods are able to capture similar granularity while also preserving spatial organization. By optimizing antibody selection, as well as staining, imaging and data processing protocols we have established a BMME-targeted CODEX workflow based on a 60 marker panel. Here, we conducted the first comprehensive multiplex protein imaging survey of the BMME across the spectrum of MM and precursor stages and leveraged paired FISH and whole genome sequencing (WGS) to elucidate the interplay of tumor intrinsic features and the BMME. Methods We imaged 489 BM trephine biopsies in tissue microarray format on the CODEX (Akoya Biosciences) platform. After quality control (≥750 cells per core, <25% unclassifiable cells) we retained data from 12 non-malignant controls, 13 MGUS, 20 smoldering MM, 349 uniformly treated newly diagnosed MM (NDMM) patients from a phase III clinical trial, as well as 22 relapsed MM samples with a median of 3 (range 1-6) tissue cores per sample, comprising a total of 3.1 million single cells. CODEX data was processed and analyzed using custom scripts. Cellular neighborhoods were computed by clustering the cell type composition of sliding windows (n=10 nearest neighboring cells) across the tissue. WGS (n=212) and FISH (n=309) were performed on CD138-purified BM mononuclear cells and analyzed using in-house pipelines. Results We identified 40 cell types, encompassing the relevant lineages (myeloid, lymphoid, stromal, endothelial, tumor), subtypes (e.g. T, B, NK cells) and functional states (e.g GRZB, Ki67, and PD1) of the BMME. The degree of plasma cell infiltration determined by CODEX correlated with the clinical pathologist's quantification based on classical immunohistology (r=0.77, p<0.0001), and the kappa/lambda status of MM cells matched the reported light chain restriction, supporting the validity of our data. At the compositional level, we observed shifts in relative cell type abundance from precursor to MM stages. Major changes in the BMME included enriched stromal, endothelial and CD8+ T cells as well as depleted polymorphonuclear (PMN), erythropoietic and CD4+ T cells in advanced stages. The highest proportion of monocytoid dendritic cells, monocytes, mast cells and cytotoxic CD8+ T cells was seen in relapsed MM. Leveraging the spatial resolution of the CODEX data, we identified multiple distinct cellular neighborhoods (CNs) defined by recurrent local cell type composition. CNs enriched for plasma cells in combination with different immune cell subsets including exhausted T cells increased towards the MM stage, while CNs dominated by PMNs, erythropoietic cells and CD206 negative macrophages decreased. To investigate links between MM subtypes and BMME patterns, we correlated CODEX data with MM-initiating and driver events. While patients with t(4;14) showed a significant increase in eosinophils, patients with gain1q had higher levels of endothelial and stromal subsets colocalizing in the same CN. Distinct types of stromal cells were also enriched in patients with double hits (≥2 FISH high risk features). RAS mutations and biallelic inactivation of tumor suppressor genes were associated with altered T cell subset composition. Further corroborating the importance of BMME architecture, a CN with colocalization of plasma and exhausted CD8 T cells was associated with unfavorable prognosis in NDMM patients. Conclusions High dimensional, multiplex tissue imaging of the BMME enables interrogation of cellular interactions and tissue architecture in situ in clinical samples at single cell resolution. To our knowledge this is the first highly multiplexed spatial study of the human BMME in a large clinical cohort and the first such study in multiple myeloma. We track characteristic shifts in tissue composition associated with disease progression, reveal genotype-phenotype associations in the BMME as a correlate of tumor-microenvironment co-evolution and detect prognostically relevant tissue architectural features.

Interpretable Artificial Intelligence (AI) Differentiates Prefibrotic Primary Myelofibrosis (prePMF) from Essential Thrombocythemia (ET): A Multi-Center Study of a New Clinical Decision Support Tool

Introduction Overlapping clinical, molecular, and histopathological characteristics pose challenges in differentiating prePMF from ET. The median overall survival, however, significantly differs between prePMF and ET (11.9 vs 22.2 years, Jeryczynski, 2017). The difference in survival highlights the need to distinguish between these two myeloproliferative neoplasms (MPNs) to select disease-specific therapeutic options. This area of unmet need often requires expert assessment at high-volume academic institutions to render a definitive diagnosis. Our aim in this study is to develop and validate a biologically-motivated AI algorithm to rapidly, accurately, and inexpensively diagnose prePMF and ET directly from diagnostic bone marrow (BM) biopsy digital whole-slide images (WSI). Methods Patients with a clinical/histopathological diagnosis of prePMF or ET as determined by the International Consensus Classification of Myeloid Neoplasms were identified at the University of Florence, Italy (Florence) between 06/2007 and 05/2023 and Moffitt Cancer Center, Tampa, FL (Moffitt) between 01/2013 and 01/2022. Diagnostic H&E-stained BM biopsy slides were digitized using Aperio AT2 slide scanners (Leica Biosystems, Deer Park, IL) at each institution . The training cohort comprised of 200 (100 prePMF / 100 ET) patients from Florence, and the external test cohort entailed 26 (6 prePMF / 20 ET) patients from Moffitt. In total, the resultant model was trained on 32,226 patient-derived WSI. Our chosen pretrained neural network, RetCCL, was previously trained on 32,000 diagnostic WSIs to potentially represent a histologically-informed model (Wang, 2023). BM WSI were tessellated into representative image tiles extracted at 10x magnification (302 microns per image dimensions) for model training. Finally, a prediction upon each patient's WSI was calculated by attention-based multiple instance learning, which is a method that automatically assigns a numeric weight to an image portion representing its relative importance to the classification task. Model performance was assessed utilizing the area under the receiver operator curve (AUC). The cutoff threshold for diagnosis classification was determined by maximizing Youden's Index. For qualitative assessment, attention scores were plotted as a heatmap across the BM WSI and reviewed for morphological features by an expert hematopathologist. Custom scripts were written using our open-source AI framework, Slideflow (Dolezal, 2021). Model development was performed on the Minerva High Performance Computer at Mount Sinai Hospital. Evaluation time upon a single WSI was estimated using a consumer-grade computer with an NVIDIA RTX 3080 graphics processing unit. Results Within the training cohort, 5-fold cross validation resulted in a mean AUC of 0.90 and standard deviation of 0.04. A final locked model re-trained on the entire training cohort resulted in an AUC of 0.90 upon evaluation of the test cohort ( Figure 1). We optimized the classification threshold to balance sensitivity and specificity; the final diagnostic classification accuracy on the test cohort was 92.3% with a sensitivity and specificity for prePMF diagnosis of 66.6% and 100%, respectively. Upon review of the slides with highest prediction value per class, attention heatmaps highlighted the model's reliance on areas of cellular marrow without reliance on image artifacts or background ( Figure 2). Using affordable consumer-grade hardware, evaluation upon a previously unseen WSI was completed in approximately 6.1 seconds (4.9 for preprocessing and 1.2 for evaluation). Conclusion We developed a novel AI model with high accuracy for distinguishing between prePMF and ET in distinct clinical cohorts. To our knowledge, this study represents the largest image-based AI study within MPNs with external validation. Our proposed model may assist clinicians in appropriately identifying patient cohorts who would benefit from disease-specific therapies or enrollment onto clinical trials. We imagine that a potential high-speed, low-cost algorithm may reliably distinguish prePMF from ET patients with high specificity which can be democratized to the MPN clinical community in routine practice and drive clinical trial accrual for biologically rational novel therapeutics.

Immersive VR for investigating threat avoidance: The VRthreat toolkit for Unity.

All animals have to respond to immediate threats in order to survive. In non-human animals, a diversity of sophisticated behaviours has been observed, but research in humans is hampered by ethical considerations. Here, we present a novel immersive VR toolkit for the Unity engine that allows assessing threat-related behaviour in single, semi-interactive, and semi-realistic threat encounters. The toolkit contains a suite of fully modelled naturalistic environments, interactive objects, animated threats, and scripted systems. These are arranged together by the researcher as a means of creating an experimental manipulation, to form a series of independent "episodes" in immersive VR. Several specifically designed tools aid the design of these episodes, including a system to allow for pre-sequencing the movement plans of animal threats. Episodes can be built with the assets included in the toolkit, but also easily extended with custom scripts, threats, and environments if required. During the experiments, the software stores behavioural, movement, and eye tracking data. With this software, we aim to facilitate the use of immersive VR in human threat avoidance research and thus to close a gap in the understanding of human behaviour under threat.

A-202 Establishing of Lipid-Apolipoprotein Connectivity Map in Hyperglycemia

Abstract Background Hyperglycemia, a condition characterized by a significantly increased glucose level in the blood, can lead to insulin deficiency and pre-diabetes. Lack of insulin production also interferes with lipid metabolism and increases the risk for cardiovascular disease. Thus, diagnosis and treatment of hyperglycemic patients can benefit from advanced proteomics and lipidomics testing approaches. Methods Disease-focused serum samples from 50 specimens were collected and evaluated, including 29 specimens with a glucose level>180 mg/dL and 21 normal lipidemic specimens. The sample proteomics analysis was conducted with a Perfinity IDP workstation (Shimadzu Scientific, Columbia, MD, USA) using on-line protein digestion with an immobilized enzyme reactor (IMER) directly coupled to a HALO-C18 column (Advanced Materials Technology, Wilmington, DE, USA). Lipidomics data were collected with the Lipidyzer platform (AB SCIEX, Framingham, MA, USA). Statistical analysis was conducted using JMP software (SAS Institute, USA) and custom scripts. Results Using targeted lipidomics and proteomics platforms we identified 20 proteins and 575 lipid species across 10 lipid subclasses. We identified specific lipids and lipoproteins dysregulated in hyperglycemic specimens as compared to the normolipidemic samples and linked them to major metabolic pathways. Lipoprotein particles related-proteins apos A1/B/C2/C3/E and lecithin–cholesterol acyltransferase (LCAT) were significantly dysregulated and associated with changes in HDL-mediated lipid transport, very-low-density lipoprotein particle remodeling, acylglycerol homeostasis, and triglyceride homeostasis. Conclusion The conducted integrated analysis of lipid and protein concentrations in hyperglycemia specimens to provide new insights into molecular changes associated with hyperglycemia that may inform new diagnostic and therapeutic strategies. The network analysis can allow improved categorization of patients that can lead to more effective and individualized therapeutic intervention approaches. This study provides new experimental and informatics approaches to better assess underlying metabolic conditions and related diseases, including cardiovascular disease, diabetes, and metabolic syndrome. Disclaimer The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Use of trade names is for identification only and does not imply endorsement by the CDC.

Atypical language organization following perinatal infarctions of the left hemisphere is associated with structural changes in right-hemispheric grey matter.

To assess how atypical language organization after early left-hemispheric brain lesions affects grey matter in the contralesional hemisphere. This was a cross-sectional study with between-group comparisons of 14 patients (six female, 8-26 years) with perinatal left-hemispheric brain lesions (two arterial ischemic strokes, 11 periventricular haemorrhagic infarctions, one without classification) and 14 typically developing age-matched controls (TDC) with functional magnetic resonance imaging (fMRI) documented left-hemispheric language organization (six female, 8-28 years). MRI data were analysed with SPM12, CAT12, and custom scripts. Language lateralization indices were determined by fMRI within a prefrontal mask and right-hemispheric grey matter group differences by voxel-based morphometry (VBM). FMRI revealed left-dominance in seven patients with typical language organization (TYP) and right-dominance in seven patients with atypical language organization (ATYP) of 14 patients. VBM analysis of all patients versus controls showed grey matter reductions in the middle temporal gyrus of patients. A comparison between the two patient subgroups revealed an increase of grey matter in the middle frontal gyrus in the ATYP group. Voxel-based regression analysis confirmed that grey matter increases in the middle frontal gyrus were correlated with atypical language organization. Compatible with a non-specific lesion effect, we found areas of grey matter reduction in patients as compared to TDC. The grey matter increase in the middle frontal gyrus seems to reflect a specific compensatory effect in patients with atypical language organization. Perinatal stroke leads to decreased grey matter in the contralesional hemisphere. Atypical language organization is associated with grey matter increases in contralesional language areas.

Adaptations in Gait Mechanics When Walking in an Ankle Exoskeleton

Exoskeletons are robotic, brace-like devices that can apply assistive torques to joints, supplementing biological torques. For example, ankle exoskeletons are often designed to assist during the push-off phase of walking to reduce energy expenditure. However, the mechanism by which these exoskeletons reduce metabolic costs is often unclear due to complex human-machine interactions. Here, our purpose was to determine what lower-limb gait kinematic, kinetic, and muscle activity changes underly metabolic cost reductions during ankle exoskeleton-assisted walking. We had healthy participants (n=6) perform two, 12-minute walking trials, one without assistive torques applied (Exo Off) and one with assistive torques applied (Exo On). Participants walked on a force plate instrumented treadmill (Bertec) and were instrumented with optical motion capture (Qualisys) to measure lower-limb gait kinematics and kinetics using Opensim, electromyography (Delsys) to measure the muscle activity of nine lower-limb muscles of the dominant limb, and indirect calorimetry (Cosmed) to measure metabolic energy expenditure (Figure 1A). We aggregated the motion capture, electromyography, and metabolic data for a final analysis using custom scripts (Matlab). We found that metabolic energy expenditure was reduced by 9.52  1.25% (p=0.011; Figure 1B). This, however, did not coincide with any changes in biological ankle angle, torque, or power. However, there was a trending reduction in peak soleus activity (p=0.052). Surprisingly, we did find a reduction in knee power (p=7.3x10-3; Figure 1C) and an accompanying decrease in semitendinosus activity (p=5.9x10-3). Despite the exoskeleton acting at the ankle joint, we found the clearest benefits at the knee joint—highlighting that complex, multi-joint adaptations may underly cost savings. We are continuing data collection to increase our statistical power and better understand the mechanisms that underly exoskeleton assistance.

Shaping success: clinical implementation of a 3D-printed electron cutout program in external beam radiation therapy.

The integration of 3D-printing technology into radiation therapy (RT) has allowed for a novel method to develop personalized electron field-shaping blocks with improved accuracy. By obviating the need for handling highly toxic Cerrobend molds, the clinical workflow is significantly streamlined. This study aims to expound upon the clinical workflow of 3D-printed electron cutouts in RT and furnish one year of in-vivo dosimetry data. 3D-printed electron cutouts for 6x6 cm, 10x10 cm, and 15x15 cm electron applicators were designed and implemented into the clinical workflow after dosimetric commissioning to ensure congruence with the Cerrobend cutouts. The clinical workflow consisted of four parts: i) the cutout aperture was extracted from the treatment planning system (TPS). A 3D printable cutout was then generated automatically through custom scripts; ii) the cutout was 3D-printed with PLA filament, filled with tungsten ball bearings, and underwent quality assurance (QA) to verify density and dosimetry; iii) in-vivo dosimetry was performed with optically stimulated luminescence dosimeters (OSLDs) for a patient's first treatment and compared to the calculated dose in the TPS; iv) after treatment completion, the 3D-printed cutout was recycled. QA and in-vivo OSLD measurements were conducted (n=40). The electron cutouts produced were 6x6 cm (n=3), 10x10 cm (n=30), and 15x15 cm (n=7). The expected weight of the cutouts differed from the measured weight by 0.4 + 1.1%. The skin dose measured with the OSLDs was compared to the skin dose in the TPS on the central axis. The difference between the measured and TPS doses was 4.0 + 5.2%. The successful clinical implementation of 3D-printed cutouts reduced labor, costs, and removed the use of toxic materials in the workplace while meeting clinical dosimetric standards.

CDK12 loss inhibits cell proliferation by regulating TBK1 in non-small cell lung cancer cells

Lung cancer is one of the most common malignant tumors and has a poor prognosis and a low survival rate. Traditional treatments, such as radiotherapy and chemotherapy, still face some challenges because of high drug resistance and toxicity. Therefore, it is necessary to discover a new kind of targeted drug with low toxicity and high efficiency. CDK12 is a cell cycle-dependent kinase whose main function is to activate RNA polymerase II (RNAPII) and promote the transcriptional extension of RNA. However, the role and molecular mechanism of CDK12 in lung cancer are still unclear.In this study, the mutation and RNA-Seq data of CDK12 in lung adenocarcinoma and squamous cell carcinoma were downloaded from The Cancer Genome Atlas (TCGA) database and analyzed with the custom scripts. Cell proliferation was evaluated by Cell Counting Kit-8 (CCK-8) and cell colony formation assays. A subcutaneous tumor experiment in nude mice was used to examine the effects of CDK12 knockdown on the in vivo tumor growth of NSCLC cells. The cell cycle distribution and the apoptosis rate of lung cancer cells were assessed by flow cytometry. Regulation of TANK-binding kinase 1 (TBK1) by CDK12 was evaluated by quantitative PCR, immunoprecipitation and Western blot analysis.In this study we have analyzed the mutation and expression data of The Cancer Genome Atlas (TCGA) database and found that CDK12 is highly expressed in lung cancer tissues. Clinical correlation analysis showed that high expression of CDK12 in NSCLC reduces patient survival, but its high expression is only related to early tumor progression and has no significant correlation with late tumor progression and metastasis. Furthermore, we present evidence that CDK12 depletion in lung cancer cell lines not only leads to the inhibition of cell growth and induces apoptosis but also inhibits tumor growth of NSCLC cells in vivo. CDK12 positively regulates the expression of the oncogene TBK1 in lung cancer cells. These results revealed that CDK12 affects the progression of non-small cell lung cancer through positive regulation of TBK1 expression, suggesting that CDK12 might be a potential molecular target for the treatment of non-small cell lung cancer.

Natural Language Processing Increases Accuracy of Kick, Lost-Circulation Detection

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 206340, “Natural Language Processing Applied to Reduction of False and Missed Alarms in Kick and Lost-Circulation Detection,” by Michael Yi, Pradeepkumar Ashok, SPE, and Dawson Ramos, Intellicess, et al. The paper has not been peer reviewed. _ Kick and lost-circulation events are major contributors to nonproductive time. In the absence of good flow-in and flow-out sensors, pit-volume trends offer the best possibility for influx and loss detection, but errors occur because external mud addition and removal to the pits is not monitored or sensed. In the complete paper, the authors introduce a method that uses a Bayesian network to aggregate trends detected in time-series data with events identified by natural language processing of driller memos, thereby improving significantly the accuracy and robustness of kick and lost-circulation detection. Methodology The objective of this project was to develop a model to detect and quantify kick and lost-circulation events using readily available real-time surface signals. This model used drilling memos to supplement real-time surface signals. Fig. 1 shows the general work flow to create a lost-circulation- and kick-detection model using drilling-memo comments and Bayesian networks. Consumption of Drilling Memos. While real-time channels provide insight into potential kick and lost-circulation events, drilling memos provide additional details regarding events that could affect these real-time channels. By consuming the drilling memos and providing the rig a relationship and direct communication with the kick and lost-circulation models, the algorithm can use both surface channels and textual information from the memos to identify such events. Note that these drilling memos are also consumed in real-time. The drilling-memo data used included two channels, Date/Time and Drilling Memo. In total, 494,726 drilling memos from 271 wells were used. The data set first needed to be labeled into memo types. Given the size and many varieties of the drilling memos, a semisupervised method was used to develop a classifier to categorize the drilling memos. First, the memos were preprocessed to improve the model’s ability to cluster similar memos. This involved the following changes: - Conversion of similar words to assist with clustering - Removal of special characters - Removal of numbers - Removal of punctuation - Stemming (the process of reducing inflected or derived words to the base form) - Removal of stop words - Removal of extra white space and tabs - Lower-casing of all words The data was then clustered using a K-means clustering algorithm with 100 clusters. Many clusters were selected in order to create a large number of differentiating groups because of the variety of drilling memos provided in the data. Once clusters were generated, each cluster was grouped into classified memo types, which, in turn, provided labels for the drilling memos that fell into each cluster. For the memo data set, the 100 clusters were categorized into 34 different memo types. Drilling memos that contained text indicating they should be categorized into a certain memo type were processed separately through custom scripts.

MAPPING COVID-19 EPIDEMIC DATA USING FOSS

Abstract. The recognition of spatial and temporal patterns in the distribution of a pandemic plays a pivotal role in guiding policy approaches to its management, containment and elimination. For this purpose, a database has been built for the COVID-19 pandemic in the Trentino Province, in the eastern Italian alps, near the border between Italy and Austria. The database management system and the WebGIS mapping these data is based on Free and Open Source Software. The Data Base Management System (DBMS) runs on MySQL, available under the GNU General Public License, storing and processing geographic data. A custom procedure has been created to update the dataset, with the capability to import data from suitably formatted spreadsheets by an authenticated administrator. To ensure flexibility and responsiveness on desktop and mobile devices, the WebGIS has been created with a client-side approach, using the Leaflet and Bootstrap JavaScript language libraries, available with Open Source Licenses. These libraries, with additional custom scripts, create the user interface and render geographic data into maps. The exchange of data between the DBMS server and the client is performed using geojson tables. To protect the privacy of the patients, WebGIS users cannot access the source data even though maps and graphs can be downloaded as pictures. Geo-statistical analysis aimed at the detection of spatial and temporal patters is underway.

Development of a method for molecular subtyping <i>Bacillus anthracis</i> using HRM PCR

Introduction. Bacillus anthracis is the causative agent of anthrax, a pathogen characterized by high genetic monomorphism that complicates differentiation of strains. Thus, molecular methods for pathogen typing require the improvement.
 The aim of the study. To select marker SNPs for new genetic groups of B. anthracis and to develop a method for their laboratory identification using HRM PCR.
 Materials and methods. The core genome of 222 strains of B. anthracis from the GenBank database and 66 strains from the collection of pathogenic microorganisms of the Stavropol Anti-Plague Institute was aligned using the parsnp software. A dendrogram based on 7242 core genome SNPs was built in MEGA X software. The strains for validation of the HRM method included representatives of various genetic groups. The HRM PCR reaction was performed using the "Type-it HRM PCR Kit" and "KAPA HRM FAST qPCR Kit" and a Rotor Gene DNA thermocycler with HRM function. Data analysis and visualization were performed using custom scripts in the Python and R development environments.
 Results and discussion. Marker SNPs for 6 genetic groups have been identified, which make it possible to determine whether strains belong to one of 7 new subclusters. Pairs of primers were selected for the loci containing them, HRM PCR parameters were optimized for discrimination of different alleles of SNP loci, and an analysis scheme was developed.
 Conclusion. Thus, marker SNPs were selected to determine the genetic subclusters A.Br.CEA, A.Br.STI, A.Br.Tsiankovskii, B.Br.Europe, B.Br.Siberia, B.Br.Asia, B.Br.018, and a new laboratory method was developed for molecular subtyping of B. anthracis using HRM PCR.

Identification of pathogenic variants in the Brazilian cohort with Familial hypercholesterolemia using exon-targeted gene sequencing

Familial hypercholesterolemia (FH) is a monogenic disease characterized by high plasma low-density lipoprotein cholesterol (LDL-c) levels and increased risk of premature atherosclerotic cardiovascular disease. Mutations in FH‐related genes account for 40% of FH cases worldwide. In this study, we aimed to assess the pathogenic variants in FH-related genes in the Brazilian FH cohort FHBGEP using exon-targeted gene sequencing (ETGS) strategy. FH patients (n = 210) were enrolled at five clinical sites and peripheral blood samples were obtained for laboratory testing and genomic DNA extraction. ETGS was performed using MiSeq platform (Illumina). To identify deleterious variants in LDLR, APOB, PCSK9, and LDLRAP1, the long-reads were subjected to Burrows-Wheeler Aligner (BWA) for alignment and mapping, followed by variant calling using Genome Analysis Toolkit (GATK) and ANNOVAR for variant annotation. The variants were further filtered using in-house custom scripts and classified according to the American College Medical Genetics and Genomics (ACMG) guidelines. A total of 174 variants were identified including 85 missense, 3 stop-gain, 9 splice-site, 6 InDel, and 71 in regulatory regions (3′UTR and 5′UTR). Fifty-two patients (24.7%) had 30 known pathogenic or likely pathogenic variants in FH-related genes according to the American College Medical and Genetics and Genomics guidelines. Fifty-three known variants were classified as benign, or likely benign and 87 known variants have shown uncertain significance. Four novel variants were discovered and classified as such due to their absence in existing databases. In conclusion, ETGS and in silico prediction studies are useful tools for screening deleterious variants and identification of novel variants in FH-related genes, they also contribute to the molecular diagnosis in the FHBGEP cohort.

Teaching Python programming for bioinformatics with Jupyter notebook in the Post-COVID-19 era.

The COVID-19 pandemic has forced the Bioinformatics course to switch from on-site teaching to remote learning. This shift has prompted a change in teaching methods and laboratory activities. Students need to have a basic understanding of DNA sequences and how to analyze them using custom scripts. To facilitate learning, we have modified the course to use Jupyter Notebook, which offers an alternative approach to writing custom scripts for basic DNA sequence analysis. This approach allows students to acquire the necessary skills while working remotely. It is a versatile and user-friendly platform that can be used to combine explanations, code, and results in a single document. This feature enables students to interact with the code and results, making the learning process more engaging and effective. Jupyter Notebook provides a hybrid approach to learning basic Python scripting and genomics that is effective for remote teaching and learning during the COVID-19 pandemic.