Strenuous Task Research Articles

Screening of Voice and Vocal Tract Changes in Professional Wind Instrument Players

Abstract Background Purpose Playing wind instruments is a strenuous task on the larynx, predisposing players to voice disorders. Aim of the Work This study aims to evaluate potential vocal symptoms and vocal tract alterations in professional wind instrumentalists. Patients and Methods Methods In this cross-sectional study, 26 male military subjects were interviewed, completed the voice handicap index (VHI)-10 questionnaire, and subjected to auditory-perceptual assessment, neck examination, rigid laryngostroboscopy and flexible nasofiberoscopy both before and during instrument playing. Results All participants had vocal fatigue symptoms, around one-quarter complained of voice change, one-quarter complained of shortness of breath while or after performing, and one-third complained of neck symptoms. The average score of VHI-10 was 16.2±6.5, and approximately three-quarters of participants scored above the cut-off point. There were no significant correlations between age, years of instrument playing, average hours of daily practice, and VHI-10. Participants with neck symptoms had significantly higher VHI-10 scores. Those (around one-fifth) with an external neck swelling during Valsalva maneuver had a significantly higher VHI-10 score. Dysphonia, mainly mild and of strained, leaky quality, was detected in almost one-third of participants. While the instrument was being played, the vocal folds were somewhat adducted, and the vocal tract became more compressed as the task became more demanding. The most frequent observations in the vocal tract examination were hyperemia of the vocal folds or all over the laryngeal and pharyngeal mucosa, excessive secretions over the vocal folds, signs of hyperadduction, arytenoid edema, and phonatory waste. Conclusion and Recommendations Wind instrumentalists frequently experience voice disorders, which necessitate further care and investigation.

Screening of voice and vocal tract changes in professional wind instrument players

PurposePlaying wind instruments is a strenuous task on the larynx, predisposing players to voice disorders. This study aims to evaluate potential vocal symptoms and vocal tract alterations in professional wind instrumentalists.MethodsIn this cross-sectional study, 26 male military subjects were interviewed, completed the voice handicap index (VHI) -10 questionnaire, and subjected to auditory-perceptual assessment, neck examination, rigid laryngostroboscopy and flexible nasofiberoscopy both before and during instrument playing.ResultsAll participants had vocal fatigue symptoms, around one-quarter complained of voice change, one-quarter complained of shortness of breath while or after performing, and one-third complained of neck symptoms. The average score of VHI-10 was 16.2 ± 6.5, and approximately three-quarters of participants scored above the cut-off point. There were no significant correlations between age, years of instrument playing, average hours of daily practice, and VHI-10. Participants with neck symptoms had significantly higher VHI-10 scores. Those (around one-fifth) with an external neck swelling during Valsalva maneuver had a significantly higher VHI-10 score. Dysphonia, mainly mild and of strained, leaky quality, was detected in almost one-third of participants. While the instrument was being played, the vocal folds were somewhat adducted, and the vocal tract became more compressed as the task became more demanding. The most frequent observations in the vocal tract examination were hyperemia of the vocal folds or all over the laryngeal and pharyngeal mucosa, excessive secretions over the vocal folds, signs of hyperadduction, arytenoid edema, and phonatory waste.ConclusionWind instrumentalists frequently experience voice disorders, which necessitate further care and investigation.

An enhanced jellyfish search optimizer for stochastic energy management of multi-microgrids with wind turbines, biomass and PV generation systems considering uncertainty

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.

Abstract LB386: Weakly-supervised prediction of tumor infiltrating lymphocytes per high power field from colorectal cancer histopathology slides using regression transformers

Abstract Tumor infiltrating lymphocytes (TILs)-based biomarkers have emerged as a robust method for predicting treatment efficacy and outcome in colorectal cancer (CRC). However, the quantification of TILs remains a strenuous task for pathologists and is susceptible to inter-pathologist variations. Deep learning (DL) can predict cancer biomarkers directly from routine hematoxylin and eosin (H&E) pathology slides, enabling the automated and consistent quantification of TILs for clinical decision-making. Our study focuses on the prediction of TILs per high-power field (HPF) on two large cohorts of CRC patients. We developed a weakly-supervised, transformer-based deep regression model to predict TILs per HPF from routine H&E-stained histology slides. We trained the model using 5-fold cross-validation on a large cohort of CRC patients (n=1,738), and validated its performance on an external cohort of CRC patients (n=223). The model was evaluated using the Pearson’s correlation coefficient r. Moreover, pathologist-determined TILs per HPF was categorized into low (<2) and high (≥2), enabling evaluation via the area under the receiver operating characteristic curve (AUROC). Our model achieved a significant correlation coefficient of 0.60 (p<0.0001) for the predicted TILs per HPF in the holdout test sets of the internal cohort, and a significant correlation coefficient of 0.57 (p<0.0001) on the external validation cohort. Using the thresholds for low and high TILs per HPF, we found that the model accurately predicted the presence of high TILs per HPF in both the internal and external test cohorts, yielding AUROCs of 0.76 and 0.81, respectively. These findings underscore the efficacy of our deep learning model in predicting TILs per HPF from routine pathology slides. This approach holds promise for cost-effective, efficient and uniform quantification of TILs per HPF in CRC, potentially across various cancer types. Citation Format: Omar S. El Nahhas, Joseph D. Bonner, Joel K. Greenson, Daniel B. Schmolze, Lawrence Shaktah, Jonathan Salazar, Lorena Reynaga, Sidney Lindsey, Jenny Lu, Victor Moreno, Stephanie L. Schmit, Ya-Yu Tsai, Stanley R. Hamilton, Gad Rennert, Jakob N. Kather, Stephen B. Gruber. Weakly-supervised prediction of tumor infiltrating lymphocytes per high power field from colorectal cancer histopathology slides using regression transformers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB386.

Amorphous and crystalline heterostructure MoSe2/NiFe-LDH through p-n junction formation for electrochemical water splitting-synergistic back bonding effect

Designing a long-lasting, highly effective, non-noble metal-free electrocatalyst for both hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) is a strenuous task for water electrolysis. Here, in our study, an ordered NiFe-layer double hydroxide (LDH)/MoSe2 as p-n junction material was designed using facile two-step hydrothermal process for the overall water splitting reaction. Additionally, synthesized NiFe-LDH/MoSe2, electrode exhibits long-term durability in both acidic and alkaline medium for HER and OER, respectively. Mott-Schottky experiments provides the insights of interfacial electron transfer, where the electron is transported from n-type NiFe-LDH to p-type MoSe2, resulting in the generation of the p-n junction in MoSe2/NiFe-LDH. As the formation LDH and transition dichalcogenides p-n junction leads to activation of the catalysts towards the OH− adsorption that may enable the water electrolysis with a low overpotential. The XPS analysis supports the electron transfer from Fe to Mo resulting in the formation synergistic back bonding between the p-n heterojunction. This strategy opens up an alternative path to a highly effective, relatively low-price catalyst for the global production of renewable energy fuels.

Effect of Academic-related and Teaching and Learning-Related Stressors on Eating Habits among Medical Students: A Cross-sectional Study

Introduction:: Stress occurs when people are faced with challenges and feel overwhelmed. Too much stress can cause negative effects on the body. Medical students especially feel a lot of stress due to their curriculum, which affects their eating habits and, in turn, their academic performance. Objective:: This study aimed to assess the effects of academic-related and teaching and learning-related stressors on the pattern of eating habits. Material and Methodology:: A cross-sectional study was carried out among students of private medical universities in UAE. MBBS and BDS students of and above the age of eighteen were included. A standardized questionnaire (Medical Student Stressor Questionnaire) was used to collect data. SPSS version 28 was used to analyze the data. Results:: We identified two main types of stressors: Academic Related Stressors (ARS) and Teaching and Learning Related Stressors (TLRS), resulting in high levels of stress causing them to eat more than usual or less than usual. Most students (82.6%) who ate less or much less than usual had experienced high or severe stress due to academic-related stressors of ‘strenuous task’ (P=0.007). Moreover, 81.5% of students who ate more than usual had experienced high or severe stress when they were nervous and stressed (P=0.004). When feeling “difficulties pile up” and during times when students “lacked confidence” due to teaching-related stressors, 71.7% and 66.3% of the participants experienced mild/moderate stress, respectively and ate less or much less than usual (P<0.05). Conclusion:: We were able to determine the effect of academic-related stressors (ARS) and teaching and learning-related stressors (TLRS) on eating habits. This has led us to discover the importance of well-developed and improved intervention programs by universities to reduce stress and promote healthy eating habits among medical students.

Media Multitasking in Younger and Older Adults: Associations with Cognitive Abilities and Biological Stress Responses

ABSTRACT Media multitasking is increasingly common in everyday life and can be a stressful experience, especially for older adults. The current study investigated the effects of media multi- vs. single-tasking on both task performance and biological as well as subjective stress responses in an experimental laboratory setting based on a scenario that closely replicated the demands of a multitasking situation, in which a strenuous and repetitive primary task was interrupted by relevant messages that require attention. We examined differences in media multitasking performance and associated stress between younger (18–35 years) and older (65–85 years) adults, and differences in cognitive abilities as a potential explanation for expected age-related differences in media multitasking. The findings suggested lower task performance in the multitasking condition in both age groups. The older adults showed even higher multitasking costs, but we found no evidence of lower cognitive abilities as an explanation of this effect. Media multitasking was not found to elicit greater stress responses (biological or subjective) than single-tasking. However, the results indicate that older adults have a greater biological stress response (salivary α-amylase) than younger adults when working on a computer under time pressure, regardless the task requirements (single- or multitasking).

TRI-POSE-Net: Adaptive 3D human pose estimation through selective kernel networks and self-supervision with trifocal tensors.

Accurate and flexible 3D pose estimation for virtual entities is a strenuous task in computer vision applications. Conventional methods struggle to capture realistic movements; thus, creative solutions that can handle the complexities of genuine avatar interactions in dynamic virtual environments are imperative. In order to tackle the problem of precise 3D pose estimation, this work introduces TRI-POSE-Net, a model intended for scenarios with limited supervision. The proposed technique, which is based on ResNet-50 and includes integrated Selective Kernel Network (SKNet) blocks, has proven to be efficient for feature extraction customised specifically to pose estimation scenarios. Furthermore, trifocal tensors and their trio-view geometry allow us to generate 3D ground truth poses from 2D poses, resulting in more refined triangulations. Through the proposed approach, the 3D poses can be estimated from a single 2D RGB image. Moreover, the proposed approach was evaluated on the HumanEva-I dataset yielding a Mean-Per-Joint-Position-Error (MPJPE) of 47.6 under self-supervision and an MPJPE of 29.9 under full supervision. In comparison with the other works, the proposed work has performed well in the self-supervision paradigm.

Sensory Co-laboring

Demining has not been an exclusively human affair. Mine detection dogs have been indispensable in the work of detection and in the slow but essential effort to regain trust in mine-suspected landscapes. Famously renowned for their extraordinary sensory perception, physical strength, and mental traits, they are part of human-nonhuman units training and working together to perceive explosives’ odors. This article considers the role of these units, known in Colombia as binomios caninos, in the strenuous task of mine clearance. Drawing on eighteen months of ethnographic engagement with global and local humanitarian demining efforts in Colombia, it examines detection choreographies and daily interactions, proposing to think of their joint work in terms of sensory co-laboring. Bringing anthropological work on collaboration between worlds, sensory labor, and animal work into dialogue, this composite term foregrounds detection as labor and as a result of human-nonhuman cooperation. It also highlights the asymmetrical field in which these collaborators converge and the divergent desires, affects, and attachments that mobilize their participation in demining. Mine detection is conceptualized as a sensory task through which dogs and humans intra-act, both together and apart. Recognizing this partial connection allows us to rethink how humans and other creatures are ontologically reconstituted and how overlapping histories of warfare and humanitarianism, legacies of animal behavioral practice, and instrumental-affective interactions shape these reconstitutions.

Trianglimine-Mediated Selective Sieving of Cis Isomer from the Mixture of Dihaloethenes: A Combined Molecular Dynamics and DFT Investigation.

The manufacture of alkenyl halides on a larger scale often results in the formation of a mixture of isomers, each having individual significant applications while their separation from each other is a strenuous task. Since most of the conventional distillation techniques are known to be intricate, energy consuming and expensive, the quest for an alternative separation strategies is still continuing. In this context, the recently reported trianglimine macrocycle - a new class of intrinsically porous material, is promising in discerning cis isomer from a mixture of cis and trans dichloroethene. Herein, an attempt has been made to apprehend the host-guest inclusion phenomenon accountable for the selectivity of cis over the trans isomers of 1,2-dihaloethene (F, Cl and Br) using molecular dynamics simulation and density functional calculations at ω-B97xd/6-311G+(d,p) level of theory. The average binding energy of selected snapshots has been calculated at different loadings, temperatures and pressures from molecular dynamics simulation. Our results show that trianglimine can stabilise the cis isomers of the dihaloethenes inside its cavity forming complexes with high interaction energies and the rationale behind the recyclability of the host molecule has been clarified. The outcomes of the calculations bring out the potential utility of this new host architecture to produce highly pure value added chemicals in industries.

Identifying the Question Similarity of Regulatory Documents in the Pharmaceutical Industry by Using the Recognizing Question Entailment System: Evaluation Study.

The regulatory affairs (RA) division in a pharmaceutical establishment is the point of contact between regulatory authorities and pharmaceutical companies. They are delegated the crucial and strenuous task of extracting and summarizing relevant information in the most meticulous manner from various search systems. An artificial intelligence (AI)-based intelligent search system that can significantly bring down the manual efforts in the existing processes of the RA department while maintaining and improving the quality of final outcomes is desirable. We proposed a "frequently asked questions" component and its utility in an AI-based intelligent search system in this paper. The scenario is further complicated by the lack of publicly available relevant data sets in the RA domain to train the machine learning models that can facilitate cognitive search systems for regulatory authorities. In this study, we aimed to use AI-based intelligent computational models to automatically recognize semantically similar question pairs in the RA domain and evaluate the Recognizing Question Entailment-based system. We used transfer learning techniques and experimented with transformer-based models pretrained on corpora collected from different resources, such as Bidirectional Encoder Representations from Transformers (BERT), Clinical BERT, BioBERT, and BlueBERT. We used a manually labeled data set that contained 150 question pairs in the pharmaceutical regulatory domain to evaluate the performance of our model. The Clinical BERT model performed better than other domain-specific BERT-based models in identifying question similarity from the RA domain. The BERT model had the best ability to learn domain-specific knowledge with transfer learning, which reached the best performance when fine-tuned with sufficient clinical domain question pairs. The top-performing model achieved an accuracy of 90.66% on the test set. This study demonstrates the possibility of using pretrained language models to recognize question similarity in the pharmaceutical regulatory domain. Transformer-based models that are pretrained on clinical notes perform better than models pretrained on biomedical text in recognizing the question's semantic similarity in this domain. We also discuss the challenges of using data augmentation techniques to address the lack of relevant data in this domain. The results of our experiment indicated that increasing the number of training samples using back translation and entity replacement did not enhance the model's performance. This lack of improvement may be attributed to the intricate and specialized nature of texts in the regulatory domain. Our work provides the foundation for further studies that apply state-of-the-art linguistic models to regulatory documents in the pharmaceutical industry.

Grid Cell Percolation.

Grid cells play a principal role in enabling cognitive representations of ambient environments. The key property of these cells-the regular arrangement of their firing fields-is commonly viewed as a means for establishing spatial scales or encoding specific locations. However, using grid cells' spiking outputs for deducing geometric orderliness proves to be a strenuous task due to fairly irregular activation patterns triggered by the animal's sporadic visits to the grid fields. This article addresses statistical mechanisms enabling emergent regularity of grid cell firing activity from the perspective of percolation theory. Using percolation phenomena for modeling the effect of the rat's moves through the lattices of firing fields sheds new light on the mechanisms of spatial information processing, spatial learning, path integration, and establishing spatial metrics. It is also shown that physiological parameters required for spiking percolation match the experimental range, including the characteristic 2/3 ratio between the grid fields' size and the grid spacing, pointing at a biological viability of the approach.

Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network

The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With numerical models, the choice of a spatio-temporal discretization scheme for the computational domain is a strenuous task that requires extensive calibration and potentially lab-based parameters and experimentation. The performance of GSI has high temporal dynamics due to natural, anthropogenic, and climatic processes that are not well represented by the traditional physics-based hydrologic models, which are calibrated against only a few historical observations and have a user-defined and constrained set of computational outcomes. Deep learning-based predictive models, such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity to quantify GSI performance, accounting for its highly dynamic and constantly evolving nature by leveraging advancements in observational data. A LSTM regression can overcome some of the limitations associated with traditional hydrological models to aid the development of a fully data-informed GSI performance predictor. To demonstrate the LSTM and traditional model outcomes, both methods were applied to a rain garden in Villanova, PA, USA. Specifically, an LSTM model was used to predict the recession of ponded water depth in the rain garden using five years of observed data. A set of eight predictors (i.e., precipitation, air temperature, soil temperature, soil moisture content at a depth of 10 cm, 35 cm, 65 cm and 91 cm, water depth) and a target variable (i.e., recession rate) were considered for training/testing the LSTM model. A comparative study with the USEPA Storm Water Management Model (SWMM) was performed to observe the performance of a physics-based model and a LSTM model for continuous recession rate time series and specific storms. The LSTM model had a performance score, Root Mean Square Error (RMSE), of 0.081 for the continuous time series, outperforming the SWMM performance with a score of 2.173 when compared to observed data. In the case of storm-specific prediction, LSTM also outperformed SWMM simulation for four storms with lower RMSE values when compared to the observed data. The application of the LSTM model in predicting GSI performance is a crucial stride towards efficient real-time forecasting.

Microbial Profile of Neonatal Septicemia and Antibiotic Susceptibility Pattern of the Isolates at A Tertiary Care Hospital, Western-India

Abstract Background: The emergence of multidrug-resistant microbial agents in hospitals is a strenuous task for clinicians to treat neonatal septicemia. It is one of the leading causes of mortality and morbidity in developing countries among neonates. Aims: To study the microbial profile of agents causing neonatal septicemia, their susceptibility pattern, risk factors, and review the antibiotic prophylaxis policy to limit the injudicious use of antimicrobial agents. Material &Methods: The present study included 2550 neonates less than 28 days with clinical manifestation of septicemia from neonatal intensive care unit at the Tertiary care teaching center in western India from December 2017 to May 2019. Aseptically collected blood inoculated into BacT/ALERT blood culture bottle. Further isolation, identification, susceptibility testing was done from the positive signal bottle and interpreted susceptibility according to the latest CLSI guidelines. Result: Microbiologically proven neonatal septicemia detected in 675(26.47%) patients. The predominant organisms isolated were Klebsiellapneumoniae 170 (25%). Pan-antibiotic resistance noted among 8(1.83%) gram-negative rods. Conclusion: Overall, increased emergence of resistance to the cephalosporin, penicillin group, and azole group of antibiotics. In the present study, fluoroquinolones, tetracycline and voriconazole are the better preferred. Keywords: Neonatal septicemia, injudicious use of antimicrobial agents, review the antibiotic prophylaxis policy

Disparities in modern health service utilization across socio-demographic and economic inequalities among households in Gida Ayana district, Oromia Regional state, Ethiopia: a community-based cross-sectional study

BackgroundHealth care disparities (HCD) occur across a broad range of dimensions and achieving equity in health care is a strenuous task. To overcome the disparities, countries worldwide have started implementing varies policies. HCD remains a challenge in the health care system of Ethiopia. Hence, the study aimed to estimate the disparities in health care utilization (HCU) among households.MethodsA community-based cross-sectional study was conducted from February 01 to April 30, 2022, among households in Gida Ayana District, Ethiopia. A single population proportion formula was used to determine the 393sample size, and participants were selected using systematic sampling. Data was entered into Epi-data 4.6 and exported to SPSS 25 for analysis. Descriptive analysis and binary and multivariable logistic regressions were performed.ResultsOf the 356 households that participated in the study, 321 (90.2%) of them reported at least one member of their family perceived morbidity in the last six months. The overall level of HCU determined was 207(64.5%), 95% confidence interval (CI),59.0-69.7%. Urban residents (AOR = 3.68, 95% CI = 1.94–6.97), attending secondary school and above (AOR = 2.79, CI = 1.27–5.98), rich (AOR = 2.47, CI = 1.03–5.92), small families (AOR = 2.83, CI = 1.26–6.55), and insured (AOR = 4.27, CI = 2.36–7.71) significantly contributed to HCD.ConclusionsHouseholds’ overall level of HCU for perceived morbidity was moderate. However, significant disparities were observed in HCU across place of residence, wealth status, level of education, family size, and health insurance. Hence, strengthening the strategy of financial protection by implementing health insurance that focuses on the socio-demographic and economic status of households is recommended to reduce the disparities.

A Methodical and Performance-based Investigation of Alzheimer Disease Detection on Magnetic Resonance and Multimodal Images.

In recent years, Alzheimer's Disease (AD) has received more attention in the field of medical imaging, which leads to cognitive disorders. Physicians mainly rely on MRI imaging to examine memory impairment, thinking skills, judge functional abilities, and detect behavioral abnormalities for diagnosing Alzheimer's disease. Early diagnosis of AD has become a challenging and strenuous task with conventional methods. The diagnostic procedure becomes complicated due to the structure and heterogeneous dimensions of the brain. This paper visualizes and analyzes the publications on AD and furnishes a detailed review based on the stages involved in the early detection of the disease. This paper also focuses on assorted stages of disease detection such as image preprocessing, segmentation, feature extraction, classification, and optimization techniques that have been used in the diagnosis of AD during the past five years. It also spotlights the deep learning models used in assorted stages of detection. This paper also highlights the benefits of each method for assorted modalities of images. AD has been analyzed with various computational methods on a few datasets, which leads to high computation time and loss of important features. Hybrid methods can perform better in every diagnosis stage of AD than others. Finally, the assorted datasets used for the diagnosis and investigation of Alzheimer's disease were analyzed and explored using a computerized system for future scope. From the review papers, we can conclude that DNN has greater accuracy in MR images and CNN +AEC has the best accuracy in the multimodal images.

Development of Online Exam System for the Institution

Abstract: Today online examination systems have become a fast-growing examination method because of its speed and accuracy. It is the simplest to use web-based applications for schools, colleges, universities, coaching classes, training centers, certification agencies, and recruitment firms to conduct timer-based, completely automated paperless examinations. The hassle of paper-based examination, physical presence of invigilators ensuring no cheating by the students or the strenuous task of checking each test paper by the examiner; this conservative practice is really tiring. Virtual Examination System is a web-based system, a modern standalone online examination platform for conducting virtual exams which has highlighting features like Full Screen constraint to eradicate malpractices by students, Instant results, and statistics right after completion of the test and many more.

Top-Down Genomic Surveillance Approach To Investigate the Genomic Epidemiology and Antibiotic Resistance Patterns of Enterococcus faecium Detected in Cancer Patients in Arkansas.

Control of hospital-associated Enterococcus faecium infection is a strenuous task due to the difficulty of identifying transmission routes and the persistence of this nosocomial pathogen despite the implementation of infection control measures that have been successful with other important nosocomial pathogens. This study provides a comprehensive analysis of over 100 E. faecium isolates collected from 66 cancer patients at the University of Arkansas for Medical Sciences (UAMS) between June 2018 and May 2019. In the top-down approach used in this study, we employed, in addition to the 106 E. faecium UAMS isolates, a filtered set of 2,167 E. faecium strains from the GenBank database to assess the current population structure of E. faecium species and, consequently, to identify the lineages associated with our clinical isolates. We then evaluated the antibiotic resistance and virulence profiles of hospital-associated strains from the species pool, focusing on antibiotics of last resort, to establish an updated classification of high-risk and multidrug-resistant nosocomial clones. Further investigation of the clinical isolates collected from UAMS patients using whole-genome sequencing analytical methodologies (core genome multilocus sequence typing [cgMLST], core single nucleotide polymorphism [coreSNP] analysis, and phylogenomics), with the addition of patient epidemiological data, revealed a polyclonal outbreak of three sequence types occurring simultaneously in different patient wards. The integration of genomic and epidemiological data collected from the patients increased our understanding of the relationships and transmission dynamics of the E. faecium isolates. Our study provides new insights into genomic surveillance of E. faecium to assist in monitoring and further limiting the spread of multidrug-resistant E. faecium. IMPORTANCE Enterococcus faecium is a member of the gastrointestinal microbiota. Although its virulence is low in healthy, immunocompetent individuals, E. faecium has become the third leading cause of health care-associated infections in the United States. This study provides a comprehensive analysis of over 100 E. faecium isolates collected from cancer patients at the University of Arkansas for Medical Sciences (UAMS). We employed a top-down analytical approach (from population genomics to molecular biology) to classify our clinical isolates into their genetic lineages and thoroughly evaluate their antibiotic resistance and virulence profiles. The addition of patient epidemiological data to the whole-genome sequencing analytical methodologies performed in the study allowed us to increase our understanding of the relationships and transmission dynamics of the E. faecium isolates. This study provides new insights into genomic surveillance of E. faecium to help monitor and further limit the spread of multidrug-resistant E. faecium.

Stock Index Return Volatility Forecast via Excitatory and Inhibitory Neuronal Synapse Unit with Modified MF-ADCCA

Financial prediction persists a strenuous task in Fintech research. This paper introduces a multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA)-based deep learning forecasting model to predict a succeeding day log return via excitatory and inhibitory neuronal synapse unit (EINS) using asymmetric Hurst exponent as input features, with return and volatility increment of Shanghai Stock Exchanges Composite Index (SSECI) from 2014 to 2020 as proxies for analysis. Experimental results revealed that multifractal elements by MF-ADCCA method as input features are applicable to time series forecasting in deep learning than multifractal detrended fluctuation analysis (MF-DFA) method. Further, the proposed biologically inspired EINS model achieved satisfactory performances in effectiveness and reliability in time series prediction compared with prevalent recurrent neural networks (RNNs) such as LSTM and GRU. The contributions of this paper are to (1) introduce a moving-window MF-ADCCA method to obtain asymmetric Hurst exponent sequences used directly as an input feature for deep learning prediction and (2) evaluate performances of various asymmetric multifractal approaches for deep learning time series forecasting.

Photovoltaic characteristics of ultrathin Cs2CuBiCl6 halide double perovskite based solar cell: theoretical studies

The strenuous task of managing overall stability and environmental hazards associated with regular halide single perovskites draws attention toward halide double perovskite (DP) as an alternative materials. These materials are relatively stable and potentially less-toxic and have been explored for various applications- optoelectronics, electrical, spintronics, etc. This article brings forth the photovoltaic promise of noble metal and pnictogen element based Cs2CuBiCl6 halide DP as an ultrathin absorber in solar cell configured with p-i-n geometry. The numerical simulation of Cs2CuBiCl6 based all-inorganic inverted solar cell configuration exhibits comparable power conversion efficiency of around 17.03% for a 100 nm ultrathin absorber in a single junction cell. We considered microscopic factors like radiative recombination coefficient, neutral and charged defects with their densities, intrinsic heat in the devices, and macroscopic quantity like thickness of constituent layers, including interfacial effect in the device. The simulated device shows better interfacial defect tolerance on either side of the absorber layer, good device tolerance to absorber defect density < 1016 cm−3 and tollerable 0.84% intrinsic heat (Peltier and Joule heat) fraction for the device. This article may provide a way forward for realizing Cs2CuBiCl6 halide DP absorber based solar cell with a predicted open-circuit voltage of 0.91 Volt and short-circuit density of 21.66 mA/cm2.