bluuebunny/crossref_metadata_2025_split huggingface dataset:
Urease-powered nanobots for radionuclide bladder cancer therapy
Authors: Simó Cristina , Serra-Casablancas Meritxell , Hortelao Ana C. , Di Carlo Valerio , Guallar-Garrido Sandra , Plaza-García Sandra , Rabanal Rosa Maria , Ramos-Cabrer Pedro , Yagüe Balbino , Aguado Laura , Bardia Lídia , Tosi Sébastien , Gómez-Vallejo Vanessa , Martín Abraham , Patiño Tania , Julián Esther , Colombelli Julien , Llop Jordi , Sánchez Samuel 
Published: January 2024

AbstractBladder cancer treatment via intravesical drug administration achieves reasonable survival rates but suffers from low therapeutic efficacy. To address the latter, self-propelled nanoparticles or nanobots have been proposed, taking advantage of their enhanced diffusion and mixing capabilities in urine when compared with conventional drugs or passive nanoparticles. However, the translational capabilities of nanobots in treating bladder cancer are underexplored. Here, we tested radiolabelled mesoporous silica-based urease-powered nanobots in an orthotopic mouse model of bladder cancer. In vivo and ex vivo results demonstrated enhanced nanobot accumulation at the tumour site, with an eightfold increase revealed by positron emission tomography in vivo. Label-free optical contrast based on polarization-dependent scattered light-sheet microscopy of cleared bladders confirmed tumour penetration by nanobots ex vivo. Treating tumour-bearing mice with intravesically administered radio-iodinated nanobots for radionuclide therapy resulted in a tumour size reduction of about 90%, positioning nanobots as efficient delivery nanosystems for bladder cancer therapy.
Liquid-metal-based three-dimensional microelectrode arrays integrated with implantable ultrathin retinal prosthesis for vision restoration
Authors: Chung Won Gi , Jang Jiuk , Cui Gang , Lee Sanghoon , Jeong Han , Kang Haisu , Seo Hunkyu , Kim Sumin , Kim Enji , Lee Junwon , Lee Seung Geol , Byeon Suk Ho , Park Jang-Ung 
Published: January 2024

AbstractElectronic retinal prostheses for stimulating retinal neurons are promising for vision restoration. However, the rigid electrodes of conventional retinal implants can inflict damage on the soft retina tissue. They also have limited selectivity due to their poor proximity to target cells in the degenerative retina. Here we present a soft artificial retina (thickness, 10 μm) where flexible ultrathin photosensitive transistors are integrated with three-dimensional stimulation electrodes of eutectic gallium–indium alloy. Platinum nanoclusters locally coated only on the tip of these three-dimensional liquid-metal electrodes show advantages in reducing the impedance of the stimulation electrodes. These microelectrodes can enhance the proximity to the target retinal ganglion cells and provide effective charge injections (72.84 mC cm−2) to elicit neural responses in the retina. Their low Young’s modulus (234 kPa), owing to their liquid form, can minimize damage to the retina. Furthermore, we used an unsupervised machine learning approach to effectively identify the evoked spikes to grade neural activities within the retinal ganglion cells. Results from in vivo experiments on a retinal degeneration mouse model reveal that the spatiotemporal distribution of neural responses on their retina can be mapped under selective localized illumination areas of light, suggesting the restoration of their vision.
Generating free-space structured light with programmable integrated photonics
Authors: Bütow Johannes , Eismann Jörg S. , Sharma Varun , Brandmüller Dorian , Banzer Peter 
Published: January 2024

AbstractStructured light is a key component of many modern applications, ranging from super-resolution microscopy to imaging, sensing and quantum information processing. As the utilization of these powerful tools continues to spread, the demand for technologies that enable the spatial manipulation of fundamental properties of light, such as amplitude, phase and polarization grows further. The rapidly advancing field of reconfigurable integrated photonics allows entirely new routes towards beam shaping that not only outperform existing devices in terms of speed but also have substantial potential with respect to their footprints, robustness and conversion efficiencies. In this study, we demonstrate how a multipurpose programmable integrated photonic processor can generate and control a wide range of higher-order free-space structured light beams, all starting from only a single injection waveguide. Our method controls the distribution of light’s amplitude and phase within sub-milliseconds, and it is fully reconfigurable and has no moving parts. These advancements broaden the spectrum of potential methods, applications and devices that utilize spatially tailored light by providing a pathway to combine the strengths and versatility of integrated photonics and free-space structured light.
The Pelagic Species Trait Database, an open data resource to support trait-based ocean research
Authors: Gleiber Miram R. , Hardy Natasha A. , Roote Zachary , Krug-MacLeod Alana M. , Morganson Caitlin J. , Tandy Zackary , George Iris , Matuch Cindy , Brookson Cole B. , Daly Elizabeth A. , Portner Elan J. , Choy C. Anela , Crowder Larry B. , Green Stephanie J. 
Published: January 2024

AbstractTrait-based frameworks are increasingly used for predicting how ecological communities respond to ongoing global change. As species range shifts result in novel encounters between predators and prey, identifying prey ‘guilds’, based on a suite of shared traits, can distill complex species interactions, and aid in predicting food web dynamics. To support advances in trait-based research in open-ocean systems, we present the Pelagic Species Trait Database, an extensive resource documenting functional traits of 529 pelagic fish and invertebrate species in a single, open-source repository. We synthesized literature sources and online resources, conducted morphometric analysis of species images, as well as laboratory analyses of trawl-captured specimens to collate traits describing 1) habitat use and behavior, 2) morphology, 3) nutritional quality, and 4) population status information. Species in the dataset primarily inhabit the California Current system and broader NE Pacific Ocean, but also includes pelagic species known to be consumed by top ocean predators from other ocean basins. The aim of this dataset is to enhance the use of trait-based approaches in marine ecosystems and for predator populations worldwide.
Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment
Authors: Robinson Celine , Bradbury Kyle , Borsuk Mark E. 
Published: January 2024

AbstractRemotely sensed imagery has increased dramatically in quantity and public availability. However, automated, large-scale analysis of such imagery is hindered by a lack of the annotations necessary to train and test machine learning algorithms. In this study, we address this shortcoming with respect to above-ground storage tanks (ASTs) that are used in a wide variety of industries. We annotated available high-resolution, remotely sensed imagery to develop an original, publicly available multi-class dataset of ASTs. This dataset includes geospatial coordinates, border vertices, diameters, and orthorectified imagery for over 130,000 ASTs from five labeled classes (external floating roof tanks, closed roof tanks, spherical pressure tanks, sedimentation tanks, and water towers) across the contiguous United States. This dataset can be used directly or to train machine learning algorithms for large-scale risk and hazard assessment, production and capacity estimation, and infrastructure evaluation.
Addition of nocturnal pollinators modifies the structure of pollination networks
Authors: García Yedra , Giménez-Benavides Luis , Iriondo José M. , Lara-Romero Carlos , Méndez Marcos , Morente-López Javier , Santamaría Silvia 
Published: January 2024

AbstractAlthough the ecological network approach has substantially contributed to the study of plant-pollinator interactions, current understanding of their functional structure is biased towards diurnal pollinators. Nocturnal pollinators have been systematically ignored despite the publication of several studies that have tried to alleviate this diurnal bias. Here, we explored whether adding this neglected group of pollinators had a relevant effect on the overall architecture of three high mountain plant-pollinator networks. Including nocturnal moth pollinators modified network properties by decreasing total connectivity, connectance, nestedness and robustness to plant extinction; and increasing web asymmetry and modularity. Nocturnal moths were not preferentially connected to the most linked plants of the networks, and they were grouped into a specific “night” module in only one of the three networks. Our results indicate that ignoring the nocturnal component of plant-pollinator networks may cause changes in network properties different from those expected from random undersampling of diurnal pollinators. Consequently, the neglect of nocturnal interactions may provide a distorted view of the structure of plant-pollinator networks with relevant implications for conservation assessments.
Explicit discrimination and ingroup favoritism, but no implicit biases in hypothetical triage decisions during COVID-19
Authors: Gradwohl Nico , Neth Hansjörg , Giese Helge , Gaissmaier Wolfgang 
Published: January 2024

AbstractDisturbingly realistic triage scenarios during the COVID-19 pandemic provide an opportunity for studying discrimination in moral reasoning. Biases and favoritism do not need to be explicit and overt, but can remain implicit and covert. In addition to assessing laypeople’s propensity for engaging in overt discrimination, the present study examines whether they reveal implicit biases through seemingly fair random allocations. We present a cross-sectional online study comprising 6 timepoints and a total of 2296 participants. Each individual evaluated 19 hypothetical scenarios that provide an allocation dilemma between two patients who are in need of ventilation and differ only in one focal feature. Participants could either allocate the last ventilator to a patient, or opt for random allocation to express impartiality. Overall, participants exhibited clear biases for the patient who was expected to be favored based on health factors, previous ethical or caretaking behaviors, and in-group favoritism. If one patient had been pre-allocated care, a higher probability of keeping the ventilator for the favored patient indicates persistent favoritism. Surprisingly, the absence of an asymmetry in random allocations indicates the absence of covert discrimination. Our results demonstrate that laypeople’s hypothetical triage decisions discriminate overtly and show explicit biases.
Novel rapid method for identifying and quantifying pathogenic bacteria within four hours of blood collection
Authors: Miyakoshi Akio , Niimi Hideki , Ueno Tomohiro , Wakasugi Masahiro , Higashi Yoshitsugu , Miyajima Yuki , Mori Masashi , Tabata Homare , Minami Hiroshi , Takaoka Akinori , Hayashi Atsushi , Yamamoto Yoshihiro , Kitajima Isao 
Published: January 2024

AbstractSepsis is life-threatening organ dysfunction and is considered a major cause of health loss. However, since the current biomarkers of sepsis reflect the host’s immune response to microorganisms, they would inevitably cause a time-lag. This means that there is still no truly reliable biomarker of sepsis. In the present study, we developed a novel method for identifying and quantifying unknown pathogenic bacteria within four hours of sample collection. The most important point of this study is that the novel method can be used to determine the number of bacteria in a sample as a novel biomarker of infectious diseases. Indeed, based on the number of bacteria, we were able to accurately estimate the severity of microbial infection. Furthermore, using the time-dependent changes in the number of bacteria, we were able to monitor the therapeutic effect accurately. The rapid identification and quantification of bacteria may change our approach to medical care.
A radiomics nomogram based on MSCT and clinical factors can stratify fibrosis in inflammatory bowel disease
Authors: Zeng Xu , Jiang Huijie , Dai Yanmei , Zhang Jin , Zhao Sheng , Wu Qiong 
Published: January 2024

AbstractIntestinal fibrosis is one of the major complications of inflammatory bowel disease (IBD) and a pathological process that significantly impacts patient prognosis and treatment selection. Although current imaging assessment and clinical markers are widely used for the diagnosis and stratification of fibrosis, these methods suffer from subjectivity and limitations. In this study, we aim to develop a radiomics diagnostic model based on multi-slice computed tomography (MSCT) and clinical factors. MSCT images and relevant clinical data were collected from 218 IBD patients, and a large number of quantitative image features were extracted. Using these features, we constructed a radiomics model and transformed it into a user-friendly diagnostic nomogram. A nomogram was developed to predict fibrosis in IBD by integrating multiple factors. The nomogram exhibited favorable discriminative ability, with an AUC of 0.865 in the validation sets, surpassing both the logistic regression (LR) model (AUC = 0.821) and the clinical model (AUC = 0.602) in the test set. In the train set, the LR model achieved an AUC of 0.975, while the clinical model had an AUC of 0.735. The nomogram demonstrated superior performance with an AUC of 0.971, suggesting its potential as a valuable tool for predicting fibrosis in IBD and improving clinical decision-making. The radiomics nomogram, incorporating MSCT and clinical factors, demonstrates promise in stratifying fibrosis in IBD. The nomogram outperforms traditional clinical models and offers personalized risk assessment. However, further validation and addressing identified limitations are necessary to enhance its applicability.
Analysis of maintaining human maximal voluntary contraction control strategies through the power grip task in isometric contraction
Authors: Yoo Jinyeol , Choi Woong , Kim Jaehyo 
Published: January 2024

AbstractPower grip force is used as a representative indicator of the ability of the human neuromuscular system. However, people maintain the power grip force via different control strategies depending on the visual feedback that shows the magnitude of the force, the magnitude of the target grip force, and external disturbance. In this study, we investigated the control strategy of maintaining the power grip force in an isometric contraction depending on these conditions by expressing the power grip force as a person’s Maximal Voluntary Contraction (MVC). The participants were asked to maintain the MVC for each condition. Experimental results showed that humans typically control their MVC constant abilities based on proprioception, and maintaining the target MVC becomes relatively difficult as the magnitude of the target MVC increases. In addition, through interactions between the external disturbance and the target MVC, the MVC error increases when the target MVC increases and an external disturbance is applied. When the MVC error reaches a certain level, the offset effect is expressed through visual feedback, helping to reduce the MVC error and maintain it smoothly, revealing a person’s MVC maintenance control strategy for each condition.
Influence of larval growth and habitat shading on retreatment frequencies of biolarvicides against malaria vectors
Authors: Msugupakulya Betwel J. , Ngajuma Swedi K. , Ngayambwa Athuman N. , Kidwanga Baraka E. , Mpasuka Ibrahim R. , Selvaraj Prashanth , Wilson Anne L. , Okumu Fredros O. 
Published: January 2024

AbstractEffective larviciding for malaria control requires detailed studies of larvicide efficacies, aquatic habitat characteristics, and life history traits of target vectors. Mosquitoes with brief larval phases present narrower timeframes for biolarvicidal effects than mosquitoes with extended periods. We evaluated two biolarvicides, VectoBac (Bacillus thuringiensis israelensis (Bti)) and VectoMax (Bti and Bacillus sphaericus) against Anopheles funestus and Anopheles arabiensis in shaded and unshaded habitats; and explored how larval development might influence retreatment intervals. These tests were done in semi-natural habitats using field-collected larvae, with untreated habitats as controls. Additionally, larval development was assessed in semi-natural and natural habitats in rural Tanzania, by sampling daily and recording larval developmental stages. Both biolarvicides reduced larval densities of both species by >98% within 72 h. Efficacy lasted one week in sun-exposed habitats but remained >50% for two weeks in shaded habitats. An. funestus spent up to two weeks before pupating (13.2(10.4–16.0) days in semi-natural; 10.0(6.6–13.5) in natural habitats), while An. arabiensis required slightly over one week (8.2 (5.8–10.6) days in semi-natural; 8.3 (5.0–11.6) in natural habitats). The findings suggest that weekly larviciding, which is essential for An.arabiensis might be more effective for An. funestus whose prolonged aquatic growth allows for repeated exposures. Additionally, the longer residual effect of biolarvicides in shaded habitats indicates they may require less frequent treatments compared to sun-exposed areas.
Synthesis, structural characterizations, in vitro biological evaluation and computational investigations of pyrazole derivatives as potential antidiabetic and antioxidant agents
Authors: Mortada Salma , Karrouchi Khalid , Hamza El Hadki , Oulmidi Afaf , Bhat Mashooq Ahamd , Mamad Hassane , Aalilou Youssra , Radi Smaail , Ansar M’hammed , Masrar Azlarab , Faouzi My El Abbes 
Published: January 2024

AbstractIn this study, a two pyrazole derivatives; 2-(5-methyl-1H-pyrazole-3-carbonyl)-N-phenylhydrazine-1-carboxamide (Pyz-1) and 4-amino-5-(5-methyl-1H-pyrazol-3-yl)-4H-1,2,4-triazole-3-thiol (Pyz-2) were synthesized and characterized by 13C-NMR, 1H-NMR, FT-IR, and mass spectrometry. A complete molecular structures optimization, electronic and thermodynamic properties of Pyz-1 and Pyz-2 in gas phase and aqueous solution were predicted by using hybrid B3LYP method with the 6-311++G** basis sets. Pyz-1 and Pyz-2 were evaluated in vitro for their anti-diabetic, antioxidant and xanthine oxidase inhibition activities. For anti-diabetic activity, Pyz-1 and Pyz-2 showed a potent α-glucosidase and α-amylase inhibition with IC50 values of 75.62 ± 0.56, 95.85 ± 0.92 and 119.3 ± 0.75, 120.2 ± 0.68 µM, respectively, compared to Acarbose (IC50(α-glucosidase) = 72.58 ± 0.68 µM, IC50(α-amylase) = 115.6 ± 0.574 µM). In xanthine oxidase assay, Pyz-1 and Pyz-2 exhibited remarkable inhibitory ability with IC50 values 24.32 ± 0.78 and 10.75 ± 0.54 µM, respectively. The result of antioxidant activities showed that the title compounds have considerable antioxidant and radical scavenger abilities. In addition, molecular docking simulation was used to determine the binding modes and energies between the title compounds and α-glucosidase and α-amylase enzymes.
Twitter social mobility data reveal demographic variations in social distancing practices during the COVID-19 pandemic
Authors: Xu Paiheng , Broniatowski David A. , Dredze Mark 
Published: January 2024

AbstractThe COVID-19 pandemic demonstrated the importance of social distancing practices to stem the spread of the virus. However, compliance with public health guidelines was mixed. Understanding what factors are associated with differences in compliance can improve public health messaging since messages could be targeted and tailored to different population segments. We utilize Twitter data on social mobility during COVID-19 to reveal which populations practiced social distancing and what factors correlated with this practice. We analyze correlations between demographic and political affiliation with reductions in physical mobility measured by public geolocation tweets. We find significant differences in mobility reduction between these groups in the United States. We observe that males, Asian and Latinx individuals, older individuals, Democrats, and people from higher population density states exhibited larger reductions in movement. Furthermore, our study also unveils meaningful insights into the interactions between different groups. We hope these findings will provide evidence to support public health policy-making.
Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait
Authors: Alkhamis Moh A. , Al Jarallah Mohammad , Attur Sreeja , Zubaid Mohammad 
Published: January 2024

AbstractThe relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.
A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives
Authors: Taber Christopher B. , Sharma Srishti , Raval Mehul S. , Senbel Samah , Keefe Allison , Shah Jui , Patterson Emma , Nolan Julie , Sertac Artan N. , Kaya Tolga 
Published: January 2024

AbstractPredictive sports data analytics can be revolutionary for sports performance. Existing literature discusses players' or teams' performance, independently or in tandem. Using Machine Learning (ML), this paper aims to holistically evaluate player-, team-, and conference (season)-level performances in Division-1 Women's basketball. The players were monitored and tested through a full competitive year. The performance was quantified at the player level using the reactive strength index modified (RSImod), at the team level by the game score (GS) metric, and finally at the conference level through Player Efficiency Rating (PER). The data includes parameters from training, subjective stress, sleep, and recovery (WHOOP straps), in-game statistics (Polar monitors), and countermovement jumps. We used data balancing techniques and an Extreme Gradient Boosting (XGB) classifier to predict RSI and GS with greater than 90% accuracy and a 0.9 F1 score. The XGB regressor predicted PER with an MSE of 0.026 and an R2 of 0.680. Ensemble of Random Forest, XGB, and correlation finds feature importance at all levels. We used Partial Dependence Plots to understand the impact of each feature on the target variable. Quantifying and predicting performance at all levels will allow coaches to monitor athlete readiness and help improve training.