Database Files Research Articles

Prospectively predicting violent and aggressive incidents in prison practice with the Risk Screener Violence (RS-V): Results from a multisite prison study.

Preventing in-prison violence and maintaining a safe environment is an important goal within prison settings. Screening for violence risk may provide a valuable addition to reach this goal. Within the Dutch prison system, the Risk Screener Violence (RS-V) has become an important new element in overall risk management. Prior research shows that RS-V scores are a sound predictor of institutional violence and aggression of incarcerated persons. Whereas previous studies predominantly analyzed retrospective file-based data, the present study includes RS-Vs rated by prison employees in real-life prison practice shortly after implementation. The RS-Vs of 956 detained individuals from 25 Dutch prisons were analyzed. Disciplinary reports were consulted to measure violent and aggressive incidents during 4 months after the screening. In addition, detention duration (length of stay in prison before the screening) was included as an additional variable. Results of the present prison practice study show good predictive values of the RS-V ratings for violent and aggressive incidents during prison stay. The predictive validity was similar for prisoner-to-prisoner assault as well as prisoner-to-staff assault. In addition, the predictive validity of the RS-V scores did not differ between individuals with a longer detention duration versus individuals with a shorter detention duration. This multisite prison practice study is an important step in the further validation of the RS-V and shows that the RS-V is a valuable tool in determining who is more likely to show institutional violence. Nonetheless, it remains important to continually monitor the implementation and correct application of the RS-V in prison practice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comprehensive Screening of Plasma-Facing Materials for Nuclear Fusion

Plasma-facing materials (PFMs) represent one of the most significant challenges for the design of future nuclear fusion reactors. Inside the reactor, the divertor will experience the harshest material environment: intense bombardment of neutrons and plasma particles coupled with extremely large and intermittent heat fluxes. The material designated to cover this role in ITER is tungsten. While no other materials have shown the potential to match the properties of tungsten, many drawbacks associated with its application remain, including cracking and erosion induced by a low recrystallization temperature combined with a high ductile-brittle transition temperature and neutron-initiated embrittlement; surface morphology changes (helium bubbles and fuzz layer) due to plasma-tungsten interaction with subsequent risk of spontaneous material melting and delamination; and low oxidation resistance in the case of air contamination. Exploring alternatives to tungsten requires the design of a multivariable optimization problem. This work aims to produce a structured and comprehensive material screening of PFM candidates based on known inorganic materials. The method applied in this study to identify the most promising PFM candidates combines peer-reviewed data present in the PAULING FILE database and first-principles density-function theory calculations focusing on two key PFM defects—namely, the sputtering of surface atoms and the incorporation of interstitial hydrogen, respectively characterized by the surface binding energy and the interstitial formation energy. The crystal structures and their related properties, extracted from the PAULING FILE, are ranked according to the heat-balance equation of a PFM subjected to the heat loads in the divertor region of an ITER-like tokamak. The materials satisfying the requirements are critically compared with the state-of-the-art in plasma-facing materials research and in studies of refractory materials exposed to high temperatures, plasma, and neutron bombardment. This comparison assesses their thermo-mechanical properties under such conditions, identifying a subset of promising materials for first-principles electronic structure calculations. Most previously known and extensively studied PFMs, such as tungsten, molybdenum, and carbon-based materials, are captured by this screening process, confirming its reliability. Additionally, less familiar refractory materials suggest performance that calls for further investigations. Published by the American Physical Society 2024

ProCogGraph: a graph-based mapping of cognate ligand domain interactions.

Mappings of domain-cognate ligand interactions can enhance our understanding of the core concepts of evolution and be used to aid docking and protein design. Since the last available cognate-ligand domain database was released, the PDB has grown significantly and new tools are available for measuring similarity and determining contacts. We present ProCogGraph, a graph database of cognate-ligand domain mappings in PDB structures. Building upon the work of the predecessor database, PROCOGNATE, we use data-driven approaches to develop thresholds and interaction modes. We explore new aspects of domain-cognate ligand interactions, including the chemical similarity of bound cognate ligands and how domain combinations influence cognate ligand binding. Finally, we use the graph to add specificity to partial EC IDs, showing that ProCogGraph can complete partial annotations systematically through assigned cognate ligands. The ProCogGraph pipeline, database and flat files are available at https://github.com/bashton-lab/ProCogGraph and https://doi.org/10.5281/zenodo.13165851.

Association of Intellectual and Developmental Disabilities With Worse Outcomes After Surgical Treatment of Cancer.

Patients with intellectual and developmental disabilities (IDD) face unique challenges resulting in disparities in their health care. We sought to define the effect that IDD had on achievement of a "textbook outcome" (TO) following a cancer operation among a nationally representative cohort of patients. Data on patients who underwent surgery for a malignant indication, including lung, breast, liver, biliary tract, pancreas, and colorectal, between 2014 and 2020 were extracted from the 100% Medicare Standard Analytical Files database. The association of IDD with TO (defined as the absence of postoperative complications, extended length of stay, 90-day readmission, and 90-day mortality), expenditures, and discharge status was assessed using multivariable logistic regression. Among 500,472 Medicare beneficiaries, 4,326 (0.9%) with IDD had a cancer diagnosis (breast, n=481; lung, n=419; hepatobiliary, n=194; pancreas, n=145; colorectal, n=3,087). Although overall incidence of TO was 50.5%, patients with IDD were less likely to achieve a TO than those without (37.1% vs 50.6%, respectively; odds ratio [OR], 0.50; 95% CI, 0.46-0.53; P<.001). On multivariable regression, patients with IDD had higher odds of a postoperative complication (OR, 1.53; 95% CI, 1.43-1.64), extended length of stay (OR, 2.06; 95% CI, 1.93-2.21), 90-day readmission (OR, 1.15; 95% CI, 1.07-1.24), 90-day mortality (OR, 1.90; 95% CI, 1.70-2.13), and discharge to a skilled nursing facility (OR, 4.28; 95% CI, 3.97-4.62) (all P<.001). Patients with IDD had a much lower chance of a postoperative TO, as well as discharge to a nonhome setting. The data highlight the need to improve the care of patients with IDD to assure equitable oncologic surgical care.

MIC: Memory analysis of IndexedDB data on Chromium-based applications

As Chromium-based applications continue to gain popularity, it is necessary for forensic investigators to obtain a comprehensive understanding of how they store and manage browsing artifacts from both filesystem and memory perspectives. In particular, the incognito mode developed in the current version of Chromium uses only physical memory to manage data related to active sessions. Therefore, handling physical memory is essential for tracking a user's browsing behaviour in incognito mode. This paper provides an in-depth examination of LevelDB, a lightweight key-value database utilized as Chromium's implementation for IndexedDB. In particular, we delve into the details of how IndexedDB data is managed through LevelDB, taking into account its low-level database file format. Furthermore, we thoroughly explore the possibility of residual data, both complete and incomplete, being retained as applications create and initialize IndexedDB-related data. Based on our research findings, we propose a systematic methodology for inspecting the internal structures of LevelDB-related C++ classes, carving these structures from binary streams, and interpreting the data for forensic analysis. In addition, we develop a proof-of-concept tool based on our approach and demonstrate its performance and effectiveness through case studies.

ЩОДО ОДНОГО ПІДХОДУ ДО ВИРІШЕННЯ ПИТАННЯ АВТОМАТИЗОВАНОГО БУДУВАННЯ СХЕМИ ПОСИЛАНЬ РЕЛЯЦІЙНИХ БАЗ ДАНИХ

Introduction. The current trend of continuous integration of various areas of scientific and practical human activity contributes to the constant increase in the complexity of problems, the successful solution of which becomes possible only through the use of large volumes of information, as a rule, geographically distributed across multiple database files. In this case, the need to know the locations and features of the interaction of its components becomes of fundamental importance when organizing the most convenient and efficient ways to access it. In some cases, to meet such needs, you can use the services of existing products (Microsoft Access, Microsoft SQL Server, MySQL, FileMaker Pro, Oracle Database, dBASE, etc.). Sometimes, using a specialized SQL language or its dialects, it is more convenient to create a “handmade” one that will satisfy the stated requirements. If the user desires or needs to know the location of the information used in the work, understand the logic of the mechanism for obtaining it, and evaluate the effectiveness of the ways of its delivery to constantly monitor its current and future nomenclature and quantity, its developments can help, which can provide more detailed information, than known means. As experience shows, obtaining an integral and effective solution to this set of closely interrelated issues becomes possible only with the availability of a purely specialized software tool for the automated construction of link diagrams, which is a multifunctional, operational, easy-to-learn, and easy-to-use user environment. Endowed with the ability at any time and with minimal user participation to visualize on the screen the current structure of the created database with the required degree of clarity, detail, and ease of perception, such an environment should become a means of operational analysis and assessment of ways of possible modification of the database itself in the direction of creating a more effective one in practical terms, regarding the variant of its structure. Purpose. Research and practical testing of the existing capabilities of approaches, methods, algorithms, and software modules that were developed during the creation of a universal information software technology for working with undirected graphs of the “mixed forest” type. To evaluate the efficiency and feasibility of using these approaches to improve a set of integrated software tools for the automated construction of relational database link diagrams with minimal user participation in this process. Methods. Database systems, apparatus of graph-tree theory. Result. A new approach is proposed, and the result of its software implementation is presented in the form of a “pilot” version of a set of software tools for the automated construction of relational database link schemes. The basis of the approach is techniques, methods, algorithms, etc. which were created and implemented programmatically in a new, universal information software technology for working with (temporarily) undirected graphs of the “sour cream forest” type. , Software package (SP) makes “formal” the user’s participation in the process of building link diagrams. By greatly reducing the time required for this and providing more than 90% savings in the technical resources of the computer used, SP makes it possible to take into account data tables, the total number of fields of which is limited only by the latter’s capabilities. A positive conclusion about the suitability of such an approach will become an incentive for its further improvement in the form of specialized information software technology as an effective means of solving the problems of constructing relational database link schemes.

Health care contact days in older adults enrolled in SWOG cancer clinical trials.

64 Background: Health care contact days—days with health care contact outside the home—are a measure of how much of a patient’s life is consumed by health care. This number of contact days are especially relevant for older adults with advanced cancer facing limited survival. One concern is that patients with different sociodemographic and clinical backgrounds may be at particular risk of increased contact days. Thus, there is a critical need to understand factors associated with contact days. Methods: We linked data from 6 SWOG trials in advanced cancer to Medicare claims data. These trials encompassed a variety of primary cancer types [prostate (S9916, S0421), lung (S0003, S0124, S0819), and pancreas (S0205)]. Health care contact days (days with ambulatory, emergency department, inpatient, or facility-based care) in the first 12 months were calculated using Medicare database files. A day with &gt;1 source of contact counted as 1. We examined sociodemographic (age, race, ethnicity, sex, insurance status, rurality, area deprivation index) and clinical (prognostic risk, cancer type, performance status) predictors of contact days, using negative binomial regression, with a log link and clustering by study ID, including an offset variable for duration of observation. We examined predictors in univariate and multivariable analysis. We report relative risk (RRs) estimates and 95% CI. Results: We included 1,429 patients (median age, 71 years, 7.6% Black, 2.7% Hispanic, 21.4% female, and 23.5% rural residence). Of these, 741 (51.9%) patients died in the first year. The median number of contact days was 48, out of a median of 350 days of observation. At a trial level, median percentage of contact days ranged from 14% (prostate) to 27% (lung). The most common sources of contact days were ambulatory clinician visits (median=17), tests (median=12), and treatments (median=11). In multivariable regression, factors associated with increased contact days included increasing age (per year, RR=1.02 [1.01-1.02], p&lt;.001), insurance status (Medicare alone or with Private or Medicaid insurance, vs Military/VA, none, or other insurance, RR=2.47 [2.16-2.83], p&lt;.001), higher prognostic risk score (RR=1.14 [1.04-1.25], p=.004), and type of cancer (pancreas, RR=1.69 [1.51-1.89] and lung, RR=1.69 [1.54-1.85] vs prostate, p&lt;.001 for both). Conclusions: This novel linkage of trial and claims data to evaluate contact days and associated factors found that older trial participants spent up to one in four days with health care contact. Ambulatory appointments were the most common source of contact days. Encouragingly, rural residence and neighborhood deprivation were not associated with contact days, increasing confidence that participants from diverse geographic areas may receive equitable care in trials. These data highlight the frequency of contact days for trial participants, while identifying factors to consider when benchmarking contact days.

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

BackgroundDerivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations.ResultsThe results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organism’s specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points.ConclusionsWhile we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.

Dynamic Modeling and Control Strategy Optimization of a Volkswagen Crafter Hybrid Electrified Powertrain

This article studies the transformation and assembly process of the Volkswagen (VW) Crafter from conventional to hybrid vehicle of the department of vehicles engineering, University of Debrecen, and uses a computer-aided simulation (CAS) to design the vehicle based on the real measurement data (hardware-in-the-loop, HIL method) obtained from an online CAN bus data measurement platform using MATLAB/Simulink/Simscape and LabVIEW software. The conventional vehicle powered by a 6-speed manual transmission and a 4-stroke, 2.0 Turbocharged Direct Injection Common Rail (TDI CR) Diesel engine and the transformed hybrid electrified powertrain are designed to compare performance. A novel methodology is introduced using Netcan plus 110 devices for the CAN bus analysis of the vehicle’s hybrid version. The acquired raw CAN data is analyzed using LabVIEW and decoded with the help of the database (DBC) file into physical values. A classical proportional integral derivative (PID) controller is utilized in the hybrid powertrain system to manage the vehicle consumption and CO2 emissions. However, the intricate nonlinearities and other external environments could make its performance unsatisfactory. This study develops the energy management strategies (EMSs) on the basis of enhanced proportional integral derivative-based genetic algorithm (GA-PID), and compares with proportional integral-based particle swarm optimization (PSO-PI) and fractional order proportional integral derivative (FOPID) controllers, regulating the vehicle speed, allocating optimal torque and speed to the motor and engine and reducing the fuel and energy consumption and the CO2 emissions. The integral time absolute error (ITAE) is proposed as a fitness function for the optimization. The GA-PID demonstrates superior performance, achieving energy efficiency of 90%, extending the battery pack range from 128.75 km to 185.3281 km and reducing the emissions to 74.79 gCO2/km. It outperforms the PSO-PI and FOPID strategies by consuming less battery and motor energy and achieving higher system efficiency.

Crystal Structure Determination from Powder Diffraction Patterns with Generative Machine Learning.

Powder X-ray diffraction (PXRD) is a cornerstone technique in materials characterization. However, complete structure determination from PXRD patterns alone remains time-consuming and is often intractable, especially for novel materials. Current machine learning (ML) approaches to PXRD analysis predict only a subset of the total information that comprises a crystal structure. We developed a pioneering generative ML model designed to solve crystal structures from real-world experimental PXRD data. In addition to strong performance on simulated diffraction patterns, we demonstrate full structure solutions over a large set of experimental diffraction patterns. Benchmarking our model, we predicted the structure for 134 experimental patterns from the RRUFF database and thousands of simulated patterns from the Materials Project on which our model achieves state-of-the-art 42 and 67% match rate, respectively. Further, we applied our model to determine the unreported structures of materials such as NaCu2P2, Ca2MnTeO6, ZrGe6Ni6, LuOF, and HoNdV2O8 from the Powder Diffraction File database. We extended this methodology to new materials created in our lab at high pressure with previously unsolved structures and found the new binary compounds Rh3Bi, RuBi2, and KBi3. We expect that our model will open avenues toward materials discovery under conditions which preclude single crystal growth and toward automated materials discovery pipelines, opening the door to new domains of chemistry.

Geographic and racial disparities in the quality of surgical care among patients with nonmetastatic uterine cancer

BackgroundWhile utilization of minimally invasive surgery and sentinel lymph node biopsy have increased considerably over time for surgical management of early-stage uterine cancer, practice varies significantly in the United States, with disparities among low-volume centers and patients of Black race. A significant number of counties in the US are without a gynecologic oncologist, and almost half of counties with the highest gynecologic cancer rates lack a local gynecologic oncologist. ObjectiveTo evaluate relationships of distance traveled and proximity to gynecologic oncologists with receipt of and racial disparities in the quality of surgical care in patients undergoing hysterectomy for nonmetastatic uterine cancer. Study designPatients who underwent hysterectomy for nonmetastatic uterine cancer in Kentucky, Maryland, Florida, and North Carolina were identified in 2012-2018 State Inpatient Database and State Ambulatory Surgery Services Database files. County-to-county distances were used for distances traveled and to nearest gynecologic oncologist. Factors associated with receipt of minimally invasive surgery and lymph node dissection were analyzed using multivariable logistic regression models including assessment for interactions of travel for surgery with patient race. ResultsAmong 21,837 cases, 45.5% lived in a county without a gynecologic oncologist; 55.5% overall traveled to another county for surgery, including 88% of those lacking a local gynecologic oncologist. Patients lacking local access to a gynecologic oncologist in their county who did not travel for surgery were more likely to receive open surgery and no lymph node dissection, and those in counties without access in any surrounding county were even more likely. Among patients in counties without a gynecologic oncologist, those who traveled for surgery had similar likelihood of minimally invasive surgery (71%) but greater likelihood of lymph node dissection (64.7% vs 57.2%) compared to non-travelers. Among counties without a gynecologic oncologist, longer distance traveled was associated with receipt of lymph node assessment. Compared to non-Black patients, Black patients were less likely to undergo minimally invasive surgery (57.0% vs 74.1%). In adjusted regression models controlling for a diagnosis of fibroids, Black race was an independent risk factor for receipt of open surgery. There was a significant interaction of Black race and travel for surgery, with Black patients who lived in counties without a gynecologic oncologist who did not travel facing incrementally lower likelihood of receiving minimally invasive surgery (OR=0.57 vs non-Black patients who traveled for surgery; OR=0.60 as interaction term; p<0.001 for both). Similar disparities in surgical quality by race were noted for Black patients who lived in counties with a gynecologic oncologist who traveled out of county for surgery. ConclusionsPatients, particularly those of Black race, who lack local access to gynecologic oncologist specialty care benefit from traveling to specialty centers to ensure access to high-quality surgery for nonmetastatic uterine cancer. Further work is needed to ensure equitable and universal access to high-quality care through patient travel or specialist outreach.

Empowering Research With the American Heart Association Get With The Guidelines Registries Through Integration of a Database and Research Tools.

The American Heart Association's Get With The Guidelines (GWTG) has emerged as a vital resource in advancing the standards and practices of inpatient care across stroke, heart failure, coronary artery disease, atrial fibrillation, and resuscitation focus areas. The GWTG registry data have also created new opportunities for secondary use of real-world clinical data in biomedical research. Our goal was to implement a scalable database with an integrated user interface (UI) to improve GWTG data management and accessibility. The curation of registry data begins by going through a data processing and quality control pipeline programmed in Python. This pipeline includes data cleaning and record exclusion, variable derivation and unit harmonization, limited data set preparation, and documentation generation of the registry data. The database was built using PostgreSQL, and integrations between the database and the UI were built using the Django Web Framework in Python. Smaller subsets of data were created using SQLite database files for distribution purposes. Use cases of these tools are provided in the article. We implemented an automated data curation pipeline, centralized database, and UI application for the American Heart Association GWTG registry data. The database and the UI are accessible through a Precision Medicine Platform workspace. As of March 2024, the database contains over 13.2 million cleaned GWTG patient records. The SQLite subsets benefit researchers by optimizing data extraction and manipulation using Structured Query Language. The UI improves accessibility for nontechnical researchers by presenting data in a user-friendly tabular format with intuitive filtering options. With the implementation of the GWTG database and UI application, we addressed data management and accessibility concerns despite its growing scale. We have launched tools to provide streamlined access and accessibility of GWTG registry data to all researchers, regardless of familiarity or experience in coding.

Predictors and risk factors of pulmonary embolism after total hip arthroplasty: an NSQIP study.

Pulmonary embolism (PE) is a well-known contributor to morbidity after total hip arthroplasty (THA). Considerable efforts have been invested in reducing PE occurrence through pharmacological and perioperative interventions. Nonetheless, the effectiveness of these strategies in reducing the incidence and overall mortality associated with pulmonary embolism events remains a matter of debate. Therefore, identifying risky patients has been gaining importance. We utilised data from the National Surgical Quality Improvement Program (NSQIP) participant usage file (PUF) database spanning the years 2016 to 2021. All preoperative parameters were analysed with chi-square afterwards, meaningful ones were run with logistic regression test. A study examined factors influencing pulmonary embolism (PE) prevalence in 235,393 total hip arthroplasty patients. Univariate analysis identified significant associations between PE and female gender, diabetes, smoking, dyspnea, CHF, COPD, hypertension (HT), bleeding disorders, disseminated cancer, steroid use, and functional health status. Multivariate analysis revealed male gender as protective, while COPD, hypertension, and disseminated cancer increased PE risk. Notably, smoking appeared protective. PE patients had higher return-to-operation rates (41.7% vs. 2.2%) but similar 30-day mortality (0.2% vs. 0.04%), though mortality's odds ratio was not significant. Our findings suggest that certain patient characteristics, such as COPD and metastatic malignancy, significantly influence the likelihood of PE development.

Characteristics of women on opioid substitution therapy in primary healthcare in Tshwane (South Africa): a retrospective observational study.

Women who use drugs face specific challenges compared to men, such as higher rates of HIV infection, unsafe injecting practices and intimate partner violence. However, this population's access to drug dependence treatment and gender-sensitive interventions remains limited, leading to unmet needs and increased vulnerability. To investigate the characteristics of and associations with retention in care among women on opioid substitution therapy (OST) in a community based primary care setting. A descriptive observational study within the Community Orientated Substance Use Programme in Tshwane, South Africa. Data from 199 women (>18 years) on OST was extracted from an electronic database and paper-based files. Data was analysed descriptively, and inferential analysis looked for association of variables with retention on OST for≥6 months. Majority of participants were unemployed, with 44.3% falling within the 20-29 years age range. During the initiation and course of OST, 39.2% of women experienced intimate partner violence, and 19.0% were pregnant. Retention on OST was significantly associated with increasing age at initiation (p=0.047), knowledge of HIV status (p=0.029), an increase in the ASSIST score (p=0.023), and methadone dose (p<0.001). Factors such as race, employment status, health system level, pregnancy, intimate partner using substances, intimate partner violence, route of administering opioids, and having tuberculosis and/or hepatitis C exposure did not show a significant relationship with retention on OST (p>0.05). This study reveals specific vulnerabilities in women receiving OST, emphasising the need for the integration of interventions to address reproductive health, violence mitigation, infectious disease and polydrug use into care.

MotifQuest: An Automated Pipeline for Motif Database Creation to Improve Peptidomics Database Searching Programs.

Endogenous peptides are an abundant and versatile class of biomolecules with vital roles pertinent to the functionality of the nervous, endocrine, and immune systems and others. Mass spectrometry stands as a premier technique for identifying endogenous peptides, yet the field still faces challenges due to the lack of optimized computational resources for reliable raw mass spectra analysis and interpretation. Current database searching programs can exhibit discrepancies due to the unique properties of endogenous peptides, which typically require specialized search considerations. Herein, we present a high throughput, novel scoring algorithm for the extraction and ranking of conserved amino acid sequence motifs within any endogenous peptide database. Motifs are conserved patterns across organisms, representing sequence moieties crucial for biological functions, including maintenance of homeostasis. MotifQuest, our novel motif database generation algorithm, is designed to work in partnership with EndoGenius, a program optimized for database searching of endogenous peptides and that is powered by a motif database to capitalize on biological context to produce identifications. MotifQuest aims to quickly develop motif databases without any prior knowledge, a laborious task not possible with traditional sequence alignment resources. In this work we illustrate the utility of MotifQuest to expand EndoGenius' identification utility to other endogenous peptides by showcasing its ability to identify antimicrobial peptides. Additionally, we discuss the potential utility of MotifQuest to parse out motifs from a FASTA database file that can be further validated as new peptide drug candidates.

Non-Hodgkin lymphoma presenting with spinal cord compression: A population-based analysis of the NHL-BFM study group.

Spinal cord compression is a rare presentation of non-Hodgkin lymphoma (NHL) in children. We aimed to describe the prevalence, histological subtypes, clinical presentation, therapy, and outcome of those children in a population-based cohort. The chemotherapy regimen remained comparable over time. We retrospectively identified all children and adolescents with paresis as initial manifestations of the NHL between January 1990 and December 2020 from the NHL-BFM database. Characteristics, therapy, and outcome data were gathered from the database and patient files. Fifty-seven of 4779 children (1.2%) presented with initial paresis due to spinal cord compression. The median age was 10.3 years (range, 3.1-18.0 years), and 33% were female. Initial symptoms were paresis/weakness (n=50, 88%), back pain (n=33, 58%), paresthesia (n=23, 40%), and bladder dysfunction and/or constipation (n=22, 39%), persisting for a median of 14 days before diagnosis. Subtype distribution was mature B-NHL (n=41, 72%), precursor B-lymphoblastic lymphoma (LBL) (n=12, 21%), anaplastic large cell lymphoma (ALCL) (n=3, 5%), and T-LBL (n=1, 2%). Initial emergency therapy included surgery (70%) and/or chemotherapy/steroids (63%). Five-year event-free survival and overall survival (80%±5% and 82%±5%, respectively) were comparable with all other NHL patients. Neurological symptoms persisted in approximately one-third of surviving patients at the last follow-up. 1.2% of pediatric NHL patients presented with paresis from spinal cord compression mainly due to B-cell lymphomas. Neurological sequelae were observed in one-third of surviving patients.

Comparison of maternal neonatal outcomes of normotensive and hypertensive placental abruptions: A novel approach.

To analyze maternal and neonatal effects of placental abruption (PA) through a novel classification in the presence of hypertension. Initial hemoglobin parameters were also compared to predict pregnancy outcomes in addition to hypertension. This retrospective cohort designed study was conducted on 115 pregnant women with PA. The main parameters scanned and recorded from the hospital database and patient medical files. Two groups were classified regarding of presence or absence of hypertension (53 hypertensive, 62 normotensive). Maternal demographical and clinical characteristics (abdominal pain, vaginal bleeding) were recorded. APGAR scores below 5 at 1st and 5th minute, fetal or neonatal death, admission and length of stay in Neonatal Intensive Care Unit were also investigated and compared between the groups. Stillborn to live-born ratio and lower APGAR scores < 5 at 5th minute were significantly higher in hypertensive group than normotensive group (P = .006 and 0.047, respectively). Poor maternal outcomes were detected in the hypertensive group than normotensive group regarding rate of blood transfusion (27/53, 50.9%; 18/62, 29%, respectively, P = .017). More abdominal pain and less vaginal bleeding were seen in PA with HT. Higher lymphocyte count, mean platelet volume, and platelet distribution width were reported in hypertensive group. Poorer maternal and neonatal outcomes of hypertensive patients with PA were detected. These patients should deserve greater attention to assess not only the possible risks associated with abruption but also the accompanying complications.

Improving Performance of Data Science Applications in Python

Objectives: The objective of this paper is to focus on the need of performance optimization of Data Science applications with an aim to enhance the speed, responsiveness, and effective resource utilization. Methods: This paper identifies the factors which effect the performance of an application and then classifies them into internal and external factors. This study mainly focuses on internal factors such as execution speed and space requirement. It uses code developed in Python as use cases to explore and examine features which improve the performance and effectiveness of Data Science (DS) applications. Findings: This paper provides comprehensive way of improving performance of applications by implementing features of Python like generator, vectorization, profiling, concurrency &amp; parallelism, and caching. It is observed that space requirement reduces with the use of generators in code, leading to better space management. The conventional way of writing code in Python required additional 5.2 MiB (Mebibyte) space, while there was no additional space requirement with the use of generators in the same code. It is further observed that vectorization feature of Python helps in reducing execution time in comparison to the code written without vectorization. This paper further uses parallelism in the code and gets approximately 68% improvement in execution time. The implementation of profiling feature helps in identifying the time and space requirement which can be improved using features like generator, vectorization, concurrency, and parallelism. Other features like handling I/O operations, use of caching and choice between flat file data and database are also deliberated to improve the performance of code. Novelty: This paper highlights the potential of improving performance of DS applications using generator, vectorization, concurrency and parallelism features of Python and examines the effectiveness of the claim through its implementation. Keywords: Data Science, Generator, Vectorization, Parallelism, Profiling

Impact of Trauma Center Type on Outcomes in Pediatric Population Following Severe Isolated Blunt Traumatic Brain Injuries: A National Analysis.

This study aims to evaluate the association between trauma center type, verification level, and clinical outcomes in pediatric trauma patients with moderate and severe isolated blunt traumatic brain injury (TBI). This is a retrospective cohort study utilizing the American College of Surgeons (ACS) Trauma Quality Program (TQP) Participant Use File (PUF) database from 2017 to 2021. Severely injured pediatric (<18years) trauma patients with isolated moderate and severe TBI (AIS head >2, all other body regions <3) were included. Outcomes included in-hospital mortality, discharge disposition, intensive care unit length-of-stay (ICU-LOS), and ventilator-free days (VFDs). Patients treated at a level-I combined adult and pediatric trauma centers (CTCs) had significantly lower odds of in-hospital mortality than those treated at adult trauma centers (ATCs) (OR .495, 95% CI 0.291-.841, P = .009). Patients treated at level-I pediatric trauma centers (PTCs) (OR 2.726, 95% CI 2.059-3.609, P < .001) and level-II PTCs (OR 6.18, 95% CI 3.402-11.239, P < .001) were significantly more likely to be discharged home than equivalent-level ATCs. Pediatric patients with isolated blunt moderate and severe TBI treated at level-I PTCs and CTCs had reduced odds of in-hospital mortality compared to level-I ATCs. Patients at level I and II PTCs had significantly higher odds of discharge home than those at equivalent-level CTCs and ATCs.

Analysis and Design of Responsive Web-Based Learning Media Equipped with Pomodoro Feature in the Course of Software Engineering

The development of information and communication technology has brought about changes in the field of education, one of which is the emergence of web-based learning. Based on preliminary observation data collected through questionnaires, students still expressed uncertainty about their understanding of software engineering (SE) materials. The current learning media for SE include PowerPoint (PPT), e-modules, and instructional videos, which students find less engaging and challenging to comprehend. Additionally, students face distractions like games and social media, leading to difficulty maintaining focus while studying. The objectives of this research are (1) to analyze user and system needs and (2) to design web-based responsive learning media for the software engineering course, equipped with a Pomodoro feature. The research method used is research and development (R&amp;D) with the 4D Thiagarajan development model, consisting of four stages: define, design, develop, and disseminate. However, only the definition and design stages were carried out due to research limitations. The presentation of data analysis results involves (1) needs analysis, which includes frontend analysis, learner analysis, task analysis, concept analysis, and specifying instructional objectives; (2) system design, which includes context diagrams and data flow diagrams; (3) database design, which includes Entity Relationship Diagram (ERD) and database file structure; (4) initial design in the form of a prototype; (5) technology design for development and implementation; and (6) instructional media testing design