bluuebunny/crossref_metadata_2025_split huggingface dataset:
Posterior segment findings by spectral-domain optical coherence tomography and clinical associations in active toxoplasmic retinochoroiditis
Authors: Oliver Genevieve F. , Ferreira Lisia Barros , Vieira Barbara R. , Arruda Sigrid , Araújo Michelle , Carr Jillian M. , Smith Justine R. , Furtado João M. 
Published: January 2022

AbstractToxoplasmic retinochoroiditis is a common, potentially blinding parasitic infection. We sought to define the spectrum and frequency of signs of active toxoplasmic retinochoroiditis by spectral domain optical coherence tomography (SD-OCT), and to identify clinical associations. Ninety eyes of 90 individuals presenting consecutively to a tertiary referral uveitis service with active toxoplasmic retinochoroiditis and gradable SD-OCT scans were evaluated prospectively. SD-OCT features were collated, and associations with lesion location, primary versus recurrent episode, serological status, human immunodeficiency virus infection and best-corrected Snellen visual acuity were explored. Active toxoplasmic retinochoroiditis presented with thickened (65%) and hyperreflective (61%) retina, choroidal thickening (55%) and hyporeflectivity (61%), hyperreflective vitreous dots (80%) and deposits (36%), and posterior hyaloid thickening (35%) on SD-OCT. Most signs occurred with similar frequency across clinical groups. Retinal hyporeflectivity (17%) was significantly associated with a visual acuity of 20/200 or worse at resolution. Our observations demonstrate that active toxoplasmic retinochoroiditis has diverse SD-OCT signs and that none are universally present. Retinal hyporeflectivity—suggesting liquefactive necrosis—predicts poor visual outcome.
Context is key: normalization as a novel approach to sport specific preprocessing of KPI’s for match analysis in soccer
Authors: Phatak Ashwin A. , Mehta Saumya , Wieland Franz-Georg , Jamil Mikael , Connor Mark , Bassek Manuel , Memmert Daniel 
Published: January 2022

AbstractKey Performance Indicators (KPIs) have been investigated, validated and applied in multitude of sports for recruiting, coaching, opponent, self-analysis etc. Although a wide variety of in game performance indicators have been used as KPIs, they lack sports specific context. With the introduction of artificial intelligence and machine learning (AI/ML) in sports, the need for building intrinsic context into the independent variables is even greater as AI/ML models seem to perform better in terms of predictability but lack interpretability. The study proposes domain specific feature preprocessing method (normalization) that can be utilized across a wide range of sports and demonstrates its value through a specific data transformation by using team possession as a normalizing factor while analyzing defensive performance in soccer. The study performed two linear regressions and three gradient boosting machine models to demonstrate the value of normalization while predicting defensive performance. The results demonstrate that the direction of correlation of the relevant variables changes post normalization while predicting defensive performance of teams for the whole season. Both raw and normalized KPIs showing significant correlation with defensive performance (p < 0.001). The addition of the normalized variables contributes towards higher information gain, improved performance and increased interpretability of the models.
The impact of returning a pet to the shelter on future animal adoptions
Authors: Powell Lauren , Reinhard Chelsea L. , Satriale Donya , Morris Margaret , Serpell James , Watson Brittany 
Published: January 2022

AbstractUnsuccessful animal adoptions are stressful for many owners and may reduce their willingness to adopt again. The goal of this study was to determine the proportion of return owners who adopted post-return and investigate return characteristics that affected the likelihood of post-return adoption. We analyzed adoption records from a South Carolina animal shelter between 2015 and 2019 (n = 1999) using a logistic regression model including post-return adoption (binary) and return reason, species, animal sex and age. We found one in 10 individuals adopted from the shelter within 12 months of return, and post-return adoption was associated with return reason and species. Returns due to owner-related reasons, such as the owner’s health (OR 0.20, 95% CI 0.07, 0.57) or unrealistic expectations (OR 0.42, 95% CI 0.19, 0.94) were associated with significantly lower odds of post-return adoption. Owners who returned due to the animal’s health exhibited four times greater odds of post-return adoption compared with behavioral returns (OR 4.20, 95% CI 2.37, 7.45). Our findings highlight the value of ensuring adopters’ expectations are aligned with the reality of ownership and minimizing adopter-animal behavioral incompatibility as unsuccessful animal adoptions can reduce the owner’s willingness to adopt again and may affect the adopter’s relationship with the shelter.
Simulator acceleration and inverse design of fin field-effect transistors using machine learning
Authors: Kim Insoo , Park So Jeong , Jeong Changwook , Shim Munbo , Kim Dae Sin , Kim Gyu-Tae , Seok Junhee 
Published: January 2022

AbstractThe simulation and design of electronic devices such as transistors is vital for the semiconductor industry. Conventionally, a device is intuitively designed and simulated using model equations, which is a time-consuming and expensive process. However, recent machine learning approaches provide an unprecedented opportunity to improve these tasks by training the underlying relationships between the device design and the specifications derived from the extensively accumulated simulation data. This study implements various machine learning approaches for the simulation acceleration and inverse-design problems of fin field-effect transistors. In comparison to traditional simulators, the proposed neural network model demonstrated almost equivalent results (R2 = 0.99) and was more than 122,000 times faster in simulation. Moreover, the proposed inverse-design model successfully generated design parameters that satisfied the desired target specifications with high accuracies (R2 = 0.96). Overall, the results demonstrated that the proposed machine learning models aided in achieving efficient solutions for the simulation and design problems pertaining to electronic devices. Thus, the proposed approach can be further extended to more complex devices and other vital processes in the semiconductor industry.
An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus
Authors: Du Yuhan , Rafferty Anthony R. , McAuliffe Fionnuala M. , Wei Lan , Mooney Catherine 
Published: January 2022

AbstractGestational Diabetes Mellitus (GDM), a common pregnancy complication associated with many maternal and neonatal consequences, is increased in mothers with overweight and obesity. Interventions initiated early in pregnancy can reduce the rate of GDM in these women, however, untargeted interventions can be costly and time-consuming. We have developed an explainable machine learning-based clinical decision support system (CDSS) to identify at-risk women in need of targeted pregnancy intervention. Maternal characteristics and blood biomarkers at baseline from the PEARS study were used. After appropriate data preparation, synthetic minority oversampling technique and feature selection, five machine learning algorithms were applied with five-fold cross-validated grid search optimising the balanced accuracy. Our models were explained with Shapley additive explanations to increase the trustworthiness and acceptability of the system. We developed multiple models for different use cases: theoretical (AUC-PR 0.485, AUC-ROC 0.792), GDM screening during a normal antenatal visit (AUC-PR 0.208, AUC-ROC 0.659), and remote GDM risk assessment (AUC-PR 0.199, AUC-ROC 0.656). Our models have been implemented as a web server that is publicly available for academic use. Our explainable CDSS demonstrates the potential to assist clinicians in screening at risk patients who may benefit from early pregnancy GDM prevention strategies.
Heat sensitivity of first host and cercariae may restrict parasite transmission in a warming sea
Authors: Díaz-Morales Dakeishla M. , Bommarito Claudia , Vajedsamiei Jahangir , Grabner Daniel S. , Rilov Gil , Wahl Martin , Sures Bernd 
Published: January 2022

AbstractTo predict global warming impacts on parasitism, we should describe the thermal tolerance of all players in host–parasite systems. Complex life-cycle parasites such as trematodes are of particular interest since they can drive complex ecological changes. This study evaluates the net response to temperature of the infective larval stage of Himasthla elongata, a parasite inhabiting the southwestern Baltic Sea. The thermal sensitivity of (i) the infected and uninfected first intermediate host (Littorina littorea) and (ii) the cercarial emergence, survival, self-propelling, encystment, and infection capacity to the second intermediate host (Mytilus edulis sensu lato) were examined. We found that infection by the trematode rendered the gastropod more susceptible to elevated temperatures representing warm summer events in the region. At 22 °C, cercarial emergence and infectivity were at their optimum while cercarial survival was shortened, narrowing the time window for successful mussel infection. Faster out-of-host encystment occurred at increasing temperatures. After correcting the cercarial emergence and infectivity for the temperature-specific gastropod survival, we found that warming induces net adverse effects on the trematode transmission to the bivalve host. The findings suggest that gastropod and cercariae mortality, as a tradeoff for the emergence and infectivity, will hamper the possibility for trematodes to flourish in a warming ocean.
Matrix enrichment by black phosphorus improves ionization and reproducibility of mass spectrometry of intact cells, peptides, and amino acids
Authors: Mandal Govinda , Moráň Lukáš , Pečinka Lukáš , Vaňhara Petr , Havel Josef 
Published: January 2022

AbstractIntact (whole) cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS) is an established method for biotyping in clinical microbiology as well as for revealing phenotypic shifts in cultured eukaryotic cells. Intact cell MALDI-TOF MS has recently been introduced as a quality control tool for long-term cultures of pluripotent stem cells. Despite the potential this method holds for revealing minute changes in cells, there is still a need for improving the ionization efficiency or peak reproducibility. Here we report for the first time that supplementation by fine particles of black phosphorus to the standard MALDI matrices, such as sinapinic and α-cyano-4-hydroxycinnamic acids enhance intensities of mass spectra of particular amino acids and peptides, presumably by interactions with aromatic groups within the molecules. In addition, the particles of black phosphorus induce the formation of small and regularly dispersed crystals of sinapinic acid and α-cyano-4-hydroxycinnamic acid with the analyte on a steel MALDI target plate. Patterns of mass spectra recorded from intact cells using black phosphorus-enriched matrix were more reproducible and contained peaks of higher intensities when compared to matrix without black phosphorus supplementation. In summary, enrichment of common organic matrices by black phosphorus can improve discrimination data analysis by enhancing peak intensity and reproducibility of mass spectra acquired from intact cells.
Stability of hypothetical AgIICl2 polymorphs under high pressure, revisited: a computational study
Authors: Grzelak Adam , Grochala Wojciech 
Published: January 2022

AbstractA comparative computational study of stability of candidate structures for an as-yet unknown silver dichloride AgCl2 is presented. It is found that all considered candidates have a negative enthalpy of formation, but are unstable towards charge transfer and decomposition into silver(I) chloride and chlorine within the DFT and hybrid-DFT approaches in the entire studied pressure range. Within SCAN approach, several of the “true” AgIICl2 polymorphs (i.e. containing Ag(II) species) exhibit a region of stability below ca. 20 GPa. However, their stability with respect to aforementioned decomposition decreases with pressure by account of all three DFT methods, which suggests a limited possibility of high-pressure synthesis of AgCl2. Some common patterns in pressure-induced structural transitions observed in the studied systems also emerge, which further testify to an instability of hypothetical AgCl2 towards charge transfer and phase separation.
An autonomous TCR signal-sensing switch influences CD4/CD8 lineage choice in mice
Authors: Basu Jayati , Zha Jikun , Nicolas Emmanuelle , Coulton Michael , Czyzewicz Philip , Hua Xiang , Ge Lu , Kappes Dietmar J. 
Published: January 2022

AbstractHow multipotential cells initiate distinct gene expression programs in response to external cues to instruct cell fate choice remains a fundamental question in biology. Establishment of CD4 and CD8 T cell fates during thymocyte development is critically regulated by T cell receptor (TCR) signals, which in turn control expression of the CD4-determining transcription factor ThPOK. However, the mechanism whereby differential TCR signals are molecularly interpreted to promote or antagonize ThPOK expression, and thereby CD4 versus CD8 lineage fates remains unknown. Here we show, using reverse genetic and molecular approaches that an autonomous, position-independent TCR-sensing switch is embedded within the ThPOK locus. Further, using an in vivo mutagenesis approach, we demonstrate that differential TCR signals are interpreted during lineage commitment by relative binding of EGR, NFAT and Ebox factors to this bistable switch. Collectively our study reveals the central molecular mechanism whereby TCR signaling influences differential lineage choice. Ultimately, these findings may provide an important new tool for skewing T cell fate to treat cancer and autoimmune diseases.
Ensemble of coupling forms and networks among brain rhythms as function of states and cognition
Authors: Chen Bolun , Ciria Luis F. , Hu Congtai , Ivanov Plamen Ch. 
Published: January 2022

AbstractThe current paradigm in brain research focuses on individual brain rhythms, their spatiotemporal organization, and specific pairwise interactions in association with physiological states, cognitive functions, and pathological conditions. Here we propose a conceptually different approach to understanding physiologic function as emerging behavior from communications among distinct brain rhythms. We hypothesize that all brain rhythms coordinate as a network to generate states and facilitate functions. We analyze healthy subjects during rest, exercise, and cognitive tasks and show that synchronous modulation in the micro-architecture of brain rhythms mediates their cross-communications. We discover that brain rhythms interact through an ensemble of coupling forms, universally observed across cortical areas, uniquely defining each physiological state. We demonstrate that a dynamic network regulates the collective behavior of brain rhythms and that network topology and links strength hierarchically reorganize with transitions across states, indicating that brain-rhythm interactions play an essential role in generating physiological states and cognition.
An intelligent method for reducing the overhead of analysing big data flows in Openflow switch
Authors: Abbasi Mahdi , Maleki Shima , Jeon Gwanggil , Khosravi Mohammad R. , Abdoli Hatam 
Published: January 2022

AbstractSoftware‐defined networks have been developed to allow the entire network to be managed as a programmable entity. As a well‐known protocol in this field, OpenFlow installs new packet forwarding rules of the distinct packets of Big Data flows (known as flow entries) in the flow tables of network switches in order to implement the desired management policies. Despite the high speed, flow tables have limited capacity to store the information of Big Data flows. As a result of inefficient policy for replacing the entries of the flow table, lack of flow entries corresponding to the incoming packets in the flow table of the switch will increase the references to the controller for forwarding this packet as well as the amount of delay in packet forwarding. The underlying idea of the proposed method is to make use of the popularity of traffic flows in the table to select the intended flow for the replacement. For replacement of flow table entries, a novel and intelligent method is proposed in this research which uses a reference history of flows to assign an importance degree to each table entry. Comparison of the simulation results confirms the superiority of the method for reducing the controller's overflow.
Improving the performance of tasks offloading for internet of vehicles via deep reinforcement learning methods
Authors: Wang Ting , Luo Xiong , Zhao Wenbing 
Published: January 2022

AbstractWith the rapid development of communication technologies, the quality of our daily life has been improved with the applications of smart communications and networking, such as intelligent transportation and mobile service computing. However, high user demands for quality of service (QoS) are forcing intelligent transportation to continuously improve immediacy and reduce the tasks offloading delay for the internet of vehicles (IoV). To meet the low latency of vehicle tasks offloading, an offloading scheme combining mobile edge computing (MEC) and deep reinforcement learning (DRL), is proposed in this article. Firstly, a realistic map is simulated, while initializing the tasks queue and building a tasks offloading environment with multiple service nodes. Then, an algorithm that combines deep learning with reinforcement learning, that is, the deep Q‐learning network (DQN) algorithm, is developed to optimize the offloading scheme by reducing the offload latency. Finally, given that the complete information cannot be observed effectively in the environment, a long short‐term memory (LSTM) model is applied within the DQN to train its neural network to improve offloading efficiency. The simulation results show that the MEC‐based vehicle tasks offloading can effectively reduce the latency of vehicle offloading.
A coupling voltage based adaptive reclosing scheme for flexible DC grid
Authors: Wei Xiuyan , Zou Guibin , Zhang Shuo , Zhang Chengquan 
Published: January 2022

AbstractThe total electric field around the DC line is composed of both the ion flow field produced by coronae and the electrostatic field produced by the conductor charge. Under the effect of the total electric field, the disconnected pole will generate a coupling voltage. Therefore, based on the coupling voltage, this paper proposes an adaptive reclosing scheme for flexible DC grid to prevent power supply interruption caused by temporary fault. First, the disconnected pole coupling voltage (DPCV) under the total electric field is analysed when single pole to ground fault (SPGF) and pole to pole fault (PPF) occur respectively. Then, according to the DPCV difference between permanent fault and temporary fault, a fault property identification (FPI) criterion is constructed. The scheme is very simple and not affected by the transition resistance, fault position and wiring mode which has high reliability. Compared with the non‐selective reclosing strategy, the adaptive reclosing scheme can avoid the secondary impact on the system from the permanent fault. Extensive simulations and experiments verify the effectiveness of the proposed adaptive reclosing strategy.
Data‐driven look‐ahead voltage stability assessment of power system with correlated variables
Authors: Nejadfard‐jahromi Saeed , Mohammadi Mohammad 
Published: January 2022

AbstractProbabilistic voltage stability assessment (VSA) of distribution systems has recently gained significant research attention due to the proliferation of renewable energy sources (RESs). This study presents a nonparametric data‐driven VSA in a look‐ahead framework by considering correlated variables. First, an accurate but relatively fast correlation model is developed by considering stochastic interdependency with unknown dependency structure. Then, the probabilistic density function (PDF) of the voltage stability index is estimated for near‐future minutes. The proposed method employs Bernstein vine copula (BVC) for estimating the unknown underlying dependence structure among different variables. This non‐parametric method imposes no assumption on the dependence structure and can handle the problems where no information is available for judging about dependence structure and its belongingness to a specific type of copula function. Additionally, global sensitivity analysis (GSA), with an embedded surrogate model based on the extreme learning machine (ELM), is utilized to rank the loads and RESs according to their contribution to voltage stability margin. Furthermore, a parallel implementation of vine copula construction and dimension reduction is developed based on the GSA to speed up the proposed VSA. The efficiency of the proposed method is examined by simulating the results of modified IEEE 33‐node and 123‐node systems.
Improving the performance of automotive vision‐based applications under rainy conditions
Authors: Hamzeh Yazan , Mohammadi Alireza , Rawashdeh Samir A. 
Published: January 2022

AbstractInput images are the main source of information for vision‐based algorithms. The presence of raindrops in input images degrades their quality and, consequently, reduces the quality of the target vision‐based algorithm that consumes them. Many image restoration algorithms were proposed in the literature to remove rain presence in images to improve the input image quality. These algorithms, however, cannot remove all the raindrop presence and sometimes introduce undesirable side‐effects, such as the blurring rain‐occluded sections of the image and incorrectly de‐raining areas in the image that are clear. It is hypothesized that a comparable performance improvement can be achieved by decreasing the sensitivity of vision‐based algorithms to noisy input images, rather than denoising these images, through the process of de‐raining. To test this hypothesis, the performance of state‐of‐the‐art object detection and semantic segmentation models was evaluated, with de‐rained image datasets used as input, and compared it to that performance of the same models, retrained with rained image sets. Results showed that the performance of the retrained models was better than that of the baseline detector with de‐rained images used as input.