Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and initial validation of a family activation measure for acute care

Background Activation of a family member refers to their desire, knowledge, confidence, and skills that can inform engagement in healthcare. Family activation combined with opportunity can lead to engagement in care. No tool currently exists to measure family activation in acute care. Therefore, we aimed to develop and validate a tool to measure family activation in acute care. Methods An interdisciplinary team of content experts developed the FAMily Activation Measure (FAM-Activate) through an iterative process. The FAM-Activate tool is a 4-item questionnaire with 5 Likert-type response options (ranging from strongly agree to strongly disagree). Scale scores are converted to a 0–100 point scoring range so that higher FAM-Activate scores indicate increased family activation. An overall FAM-Activate score (range 0–100) is calculated by adding the scores for each item and dividing by 4. We conducted reliability and predictive validity assessments to validate the instrument by administering the FAM-Activate tool to family members of patients in an acute cardiac unit at a tertiary care hospital. We obtained preliminary estimates of family engagement and satisfaction with care. Results We surveyed 124 family participants (age 54.1±14.4; 73% women; 34% non-white). Participants were predominantly the adult child (38%) or spouse/partner (36%) of patients. The mean FAM-Activate score during hospitalization was 84.1±16.1. FAM-Activate had acceptable internal consistency (Cronbach’s a = 0.74) and showed test-retest responsiveness. FAM-Activate was moderately correlated with engagement behavior (Pearson’s correlation r = 0.47, P <0.0001). The FAM-Activate score was an independent predictor of family satisfaction, after adjusting for age, gender, relationship, and living status. Conclusion The FAM-Activate tool was reliable and had predictive validity in the acute cardiac population. Further research is needed to explore whether improving family activation can lead to improved family engagement in care.
A novel Multi-Level Refined (MLR) knowledge graph design and chatbot system for healthcare applications

Imagine having a knowledge graph that can extract medical health knowledge related to patient diagnosis solutions and treatments from thousands of research papers, distilled using machine learning techniques in healthcare applications. Medical doctors can quickly determine treatments and medications for urgent patients, while researchers can discover innovative treatments for existing and unknown diseases. This would be incredible! Our approach serves as an all-in-one solution, enabling users to employ a unified design methodology for creating their own knowledge graphs. Our rigorous validation process involves multiple stages of refinement, ensuring that the resulting answers are of the utmost professionalism and solidity, surpassing the capabilities of other solutions. However, building a high-quality knowledge graph from scratch, with complete triplets consisting of subject entities, relations, and object entities, is a complex and important task that requires a systematic approach. To address this, we have developed a comprehensive design flow for knowledge graph development and a high-quality entities database. We also developed knowledge distillation schemes that allow you to input a keyword (entity) and display all related entities and relations. Our proprietary methodology, multiple levels refinement (MLR), is a novel approach to constructing knowledge graphs and refining entities level-by-level. This ensures the generation of high-quality triplets and a readable knowledge graph through keyword searching. We have generated multiple knowledge graphs and developed a scheme to find the corresponding inputs and outputs of entity linking. Entities with multiple inputs and outputs are referred to as joints, and we have created a joint-version knowledge graph based on this. Additionally, we developed an interactive knowledge graph, providing a user-friendly environment for medical professionals to explore entities related to existing or unknown treatments/diseases. Finally, we have advanced knowledge distillation techniques.
Effects of Cyclic High Ambient Temperature on Muscle Imidazole Dipeptide Content in Broiler Chickens

ABSTRACT Imidazole dipeptides possess important bioregulatory properties in animals. This study aimed to evaluate the effect of high ambient temperature on muscle imidazole dipeptides (carnosine, anserine, and balenine) in broiler chickens. Sixteen 14-day-old male broiler chickens were divided into two groups, which were reared under thermoneutral (25 ± 1 °C) or cyclic high ambient temperature (35 ± 1 °C for 8 h/day) for 4 weeks. Chickens exposed to cyclic high ambient temperatures displayed lower skeletal muscle anserine and carnosine content than control chickens. Balenine could not be detected in the pectoral muscle of either group. The pectoral muscles of broiler chickens kept under cyclic high-temperature exhibited significantly lower mRNA expression of carnosine synthase 1, which synthesizes carnosine and anserine; but a significantly higher mRNA expression of carnosinase 2, which degrades carnosine and anserine. Our results suggest that heat exposure decreases pectoral imidazole dipeptide content in broiler chickens. This may be attributed to a lower expression of imidazole dipeptide-synthesizing genes, but higher levels of genes involved in their degradation.
A test of balanced fitness limitations theory: Pollen limitation in plants

Abstract When reproductive success is determined by the relative availabilities of a series of essential, non‐substitutable resources, the theory of balanced fitness limitations predicts that the cost of harvesting a particular resource shapes the likelihood that a shortfall of that resource will constrain realized fitness. Plant reproduction through female function offers a special opportunity to test this theory; essential resources in this context include, first, the pollen received from pollinators or abiotic vectors that is used to fertilize ovules, and, second, the resources needed to provision the developing seeds and fruit. For many plants realized reproductive success through female function can be readily quantified in the field, and one key potential constraint on fitness, pollen limitation, can be assessed experimentally by manually supplementing pollen receipt. We assembled a comparative dataset of pollen limitation using only studies that supplement pollen to all flowers produced over the plant's reproductive lifespan. Pre‐ and post‐pollination costs were estimated using the weight of flowers and fruits and estimates of fruit set. Consistent with expectations, we find self‐incompatible plants make greater pre‐pollination investments and experience greater pollen limitation. However, contrary to theoretical expectations, when variation due to self‐compatibility is accounted for by including self‐compatibility in the statistical model as a covariate, we find no support for the prediction that plants that invest more heavily in pre‐pollination costs are subject to greater pollen limitation. Strong within‐species, between‐population variation in the expression of pollen limitation makes the quantification of mean pollen limitation difficult. We urge plant ecologists to conduct more studies of pollen limitation using whole‐plant pollen supplementation to produce a richer comparative dataset that would support a more robust test of the balanced limitations hypothesis.
Immobilization of C. elegans with different concentrations of an anesthetic for time-lapse imaging of dynamic protein trafficking in neurons

Here we compare the percentage of anterograde and retrograde trafficking events as well as the average velocity of these events in worms immobilized with microbeads or 0.5-7.5 mM tetramisole. Our results show that the percentage and average velocity of TIR-1 ::GFP moving events in the C. elegans AWC axons are not significantly different between worms immobilized with 7.5 mM tetramisole and other conditions. Our results suggest that 7.5 mM tetramisole, compared to 0.5 mM, 1 mM, and 2 mM tetramisole, does not have a significant effect on the axonal transport of TIR-1 ::GFP along the AWC axons.
Genome-wide Analysis Identifies Novel Loci Associated with Ovarian Cancer Outcomes: Findings from the Ovarian Cancer Association Consortium

Purpose— Chemotherapy resistance remains a major challenge in the treatment of ovarian cancer. We hypothesize that germline polymorphisms might be associated with clinical outcome. The grant for Cancer funding are follows- Multi-State Application Numbers 191, 211 and 182. The Bavarian study (BAV) was supported by ELAN the University of Erlangen-Nuremberg. The Belgian study (BEL) was funded by Nationaal Kankerplan. The Diseases of the Ovary and their Evaluation (DOV) study was by National Institutes of Health R01-CA112523 and R01-CA87538. The German Ovarian Cancer Study (GER) was supported by the German Federal Ministry of Education and Research of Germany, Programme of Clinical Biomedical Research (01 GB 9401) and the German Cancer Research Center (DKFZ). The Hawaii Ovarian Cancer Study (HAW) was supported by R01 CA 058598. The Hormones and Ovarian Cancer Prediction study (HOP) Experimental Design— We analyzed ~2.8 million genotyped and imputed SNPs from the iCOGS experiment for progression-free survival (PFS) and overall survival (OS) in 2,901 European epithelial ovarian cancer (EOC) patients who underwent firstline treatment of cytoreductive surgery and chemotherapy regardless of regimen, and in a subset of 1,098 patients treated with ≥4 cycles of paclitaxel and carboplatin at standard doses. We evaluated the top SNPs in 4,434 EOC patients including patients from The Cancer Genome Atlas. Additionally we conducted pathway analysis of all intragenic SNPs and tested their association with PFS and OS using gene set enrichment analysis. Results— Five SNPs were significantly associated (p≤1.0x10 −5 ) with poorer outcomes in at least one of the four analyses, three of which, rs4910232 (11p15.3), rs2549714 (16q23) and rs6674079 (1q22) were located in long non-coding RNAs (lncRNAs) RP11–179A10.1, RP11–314O13.1 and RP11–284F21.8 respectively (p≤7.1x10 −6 ). ENCODE ChIP-seq data at 1q22 for normal ovary shows evidence of histone modification around RP11–284F21.8, and rs6674079 is perfectly correlated with another SNP within the super-enhancer MEF2D, expression levels of which were reportedly associated with prognosis in another solid tumor. YAP1- and WWTR1 (TAZ)-stimulated gene expression, and HDL-mediated lipid transport pathways were associated with PFS and OS, respectively, in the cohort who had standard chemotherapy (p GSEA ≤6x10 −3 ). Conclusion— We have identified SNPs in three lncRNAs that might be important targets for novel EOC therapies.
The terrestrial end-Permian mass extinction in the paleotropics postdates the marine extinction

The end-Permian mass extinction was the most severe ecological event during the Phanerozoic and has long been presumed contemporaneous across terrestrial and marine realms with global environmental deterioration triggered by the Siberian Traps Large Igneous Province. We present high-precision zircon U-Pb geochronology by the chemical abrasion–isotope dilution–thermal ionization mass spectrometry technique on tuffs from terrestrial to transitional coastal settings in Southwest China, which reveals a protracted collapse of the Cathaysian rainforest beginning after the onset of the end-Permian marine extinction. Integrated with high-resolution geochronology from coeval successions, our results suggest that the terrestrial extinction occurred diachronously with latitude, beginning at high latitudes during the late Changhsingian and progressing to the tropics by the early Induan, spanning a duration of nearly 1 million years. This latitudinal age gradient may have been related to variations in surface warming with more degraded environmental conditions at higher latitudes contributing to higher extinction rates.
Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communication networks (CCNs) decoded from the patient’s bulk tumor transcriptome. The model could (i) predict ICI efficacy for patients across four cancer types (median AUROC: 0.79) and (ii) identify key communication pathways with crucial players responsible for patient response or resistance to ICIs by analyzing more than 700 ICI-treated patient samples from 11 cohorts. The model prioritized chemotaxis communication of immune-related cells and growth factor communication of structural cells as the key biological processes underlying response and resistance to ICIs, respectively. We confirmed the key communication pathways and players at the single-cell level in patients with melanoma. Our network-based ML approach can be used to expand ICIs’ clinical benefits in cancer patients.
Unveiling the Complex World of Extracellular Vesicles: Novel Characterization Techniques and Manufacturing Considerations

Extracellular vesicles (EVs) function as potent mediators of intercellular communication for many in vivo processes, contributing to both health and disease related conditions. Given their biological origins and diverse functionality from correspondingly unique “cargo” compositions, both endogenous and modified EVs are garnering attention as promising therapeutic modalities and vehicles for targeted therapeutic delivery applications. Their diversity in composition, however, has revealed a significant need for more comprehensive analytical-based characterization methods, and manufacturing processes that are consistent and scalable. In this review, we explore the dynamic landscape of EV research and development efforts, ranging from novel isolation approaches, to their analytical assessment through novel characterization techniques, and to their production by industrial-scale manufacturing process considerations. Expanding the horizon of these topics to EVs for in-human applications, we underscore the need for stringent development and adherence to Good Manufacturing Practice (GMP) guidelines. Wherein, the intricate interplay of raw materials, production in bioreactors, and isolation practices, along with analytical assessments compliant with the Minimal Information for Studies of Extracellular Vesicles (MISEV) guidelines, in conjunction with reference standard materials, collectively pave the way for standardized and consistent GMP production processes.
Time-resolved chemical monitoring of whole plant roots with printed electrochemical sensors and machine learning

Traditional single-point measurements fail to capture dynamic chemical responses of plants, which are complex, nonequilibrium biological systems. We report TETRIS (time-resolved electrochemical technology for plant root environment in situ chemical sensing), a real-time chemical phenotyping system for continuously monitoring chemical signals in the often-neglected plant root environment. TETRIS consisted of low-cost, highly scalable screen-printed electrochemical sensors for monitoring concentrations of salt, pH, and H2O2 in the root environment of whole plants, where multiplexing allowed for parallel sensing operation. TETRIS was used to measure ion uptake in tomato, kale, and rice and detected differences between nutrient and heavy metal ion uptake. Modulation of ion uptake with ion channel blocker LaCl3 was monitored by TETRIS and machine learning used to predict ion uptake. TETRIS has the potential to overcome the urgent “bottleneck” in high-throughput screening in producing high-yielding plant varieties with improved resistance against stress.
An anti-occlusion optimization algorithm for multiple pedestrian tracking

Frequent occlusion of tracking targets leads to poor performance of tracking algorithms. A common practice in multi-target tracking algorithms is to re-identify the occluded tracking targets, which increases the number of identity switching occurrences. This paper focuses on online multi-object tracking and designs an anti-occlusion, robust association strategy, and feature extraction model. Specifically, the least squares algorithm and the Kalman filter are used to predict the trajectory of the tracking target, while the two-way self-attention mechanism is employed to extract the features of the tracking target, as well as positive and negative samples. After the tracking target is occluded, the association strategy is used to assign the identity information from before the occlusion. The experimental results demonstrate that the algorithm proposed in this paper has achieved excellent tracking performance on the MOT dataset.
Stem Cell Repor ts

Deep learning is a signiﬁcant step forward for developing autonomous tasks. One of its branches, computer vision, allows image recog-nitionwith highaccuracythanksto the useof convolutional neuralnetworks(CNNs). Ourgoal wasto train a CNN with transmitted light microscopy images to distinguish pluripotent stem cells from early differentiating cells. We induced differentiation of mouse embryonic stemcells to epiblast-like cells and took images at severaltime points from the initial stimulus. We foundthat the networkscan be trained to recognize undifferentiated cells from differentiating cells with an accuracy higher than 99%. Successful prediction started just 20 min after the onset of differentiation. Furthermore, CNNs displayed great performance in several similar pluripotent stem cell (PSC) settings, including mesoderm differentiation in human induced PSCs. Accurate cellular morphology recognition in a simple microscopic set up may have a signiﬁcant impact on how cell assays are performed in the near future.
New optical and radiocarbon dates from Ngarrabullgan Cave, a Pleistocene archaeological site in Australia: implications for the comparability of time clocks and for the human colonization of Australia

The human settlement of Australia falls into that period where dating is hard because it is near or beyond the reliable limit of radiocarbon study; instead a range of luminescence methods are being turned to (such as thermoluminescence at Jinmium: December 1996 ANTIQUITY). Ngarrabullgan Cave, a rock-shelter in Queensland, now offers a good suite of radiocarbon determinations which match well a pair of optically stimulated luminescence (OSL) dates — encouraging sign that OSL determinations can be relied on.
Impact Assessment of a Trap-Neuter-Return Program on Selected Features of Auburn, Alabama Feral Cat Colonies

60 INTRODUCTION . 61 STUDY AREA . 65 METHODS . 66 Pre-TNR (year 1) laboratory protocol.66 Anesthesia . 67 xii Data Collection . 67 Radio Transmitters and Microchips.68 Anesthetic Reversal and Release . 68 Post-TNR initiation (year2) laboratory protocol.69 Data collection . 69 Radio Tracking.70 DATA ANALYSIS.71 RESULTS . 72 DISCUSSION.74 MANAGEMENT IMPLICATIONS . 76 LITERATURE CITED . 77 IV. CONCLUSIONS.94 APPENDIX A. 97 APPENDIX B. 111 APPENDIX C. 114
Variant reclassification and clinical implications.

Genomic technologies have transformed clinical genetic testing, underlining the importance of accurate molecular genetic diagnoses. Variant classification, ranging from benign to pathogenic, is fundamental to these tests. However, variant reclassification, the process of reassigning the pathogenicity of variants over time, poses challenges to diagnostic legitimacy. This review explores the medical and scientific literature available on variant reclassification, focusing on its clinical implications.Variant reclassification is driven by accruing evidence from diverse sources, leading to variant reclassification frequency ranging from 3.6% to 58.8%. Recent studies have shown that significant changes can occur when reviewing variant classifications within 1 year after initial classification, illustrating the importance of early, accurate variant assignation for clinical care.Variants of uncertain significance (VUS) are particularly problematic. They lack clear categorisation but have influenced patient treatment despite recommendations against it. Addressing VUS reclassification is essential to enhance the credibility of genetic testing and the clinical impact. Factors affecting reclassification include standardised guidelines, clinical phenotype-genotype correlations through deep phenotyping and ancestry studies, large-scale databases and bioinformatics tools. As genomic databases grow and knowledge advances, reclassification rates are expected to change, reducing discordance in future classifications.Variant reclassification affects patient diagnosis, precision therapy and family screening. The exact patient impact is yet unknown. Understanding influencing factors and adopting standardised guidelines are vital for precise molecular genetic diagnoses, ensuring optimal patient care and minimising clinical risk.
Couplage d'une pile à combustible avec des supercapacités pour étudier les gains en termes de rendement et de durée de vie

Les piles a combustible sont des convertisseurs electrochimiques. Elles convertissent l’energie chimique en une energie electrique directement disponible grâce a une reaction d’oxydoreduction. Dans notre cas, nous nous interessons aux piles a membrane protonique plus communement appelees PEMFC (Proton Exchange Membrane Fuel Cell) qui fonctionne a l’hydrogene. Ce type de pile domine le marche puisqu’elles conviennent a de nombreuses applications portables et stationnaires. Si l’on s’interesse aux PAC pour des utilisations stationnaires, des durees de vie superieures a 10 000 h peuvent etre atteintes. Cependant, celles-ci se reduisent significativement pour des applications de type transport avec de frequents arrets et demarrages ainsi que d’importantes variations de puissance : dans ce cas, 3000 h de fonctionnement semblent constituer une limite difficile a depasser. En regime instationnaire, les fluctuations de puissance impactent les conditions operatoires de la pile et generent des phenomenes locaux accelerant son vieillissement. L’interet de l’hybridation est d’elargir le champ d’application des systemes pile a combustible en les rendant capables de repondre aux exigences des applications de type transport. Dans notre cas, il s’agit d’une structure parallele, avec comme source d’energie principale, une pile a combustible assistee d’un moyen de stockage, les supercapacites. A travers nos travaux, nous avons recherche a determiner les avantages que peut offrir l’hybridation en termes de simplification de systeme : identifier les composants ou sous-systemes qui limitent la dynamique de la pile a combustible, comprendre les mecanismes mis en jeu et etudier les solutions que peut offrir l’association des SC a la PAC. L’ajout de supercapacites, en tant que sources secondaires, permet d’obtenir une densite de puissance plus elevee et ainsi d’adapter le systeme PAC a des applications instationnaires. Nous avons egalement observe qu’il est possible de concevoir des systemes fiables en limitant le vieillissement des piles. Les travaux ont permis de tester des supercapacites planes elaborees par l’IMN. L’interet est de pouvoir produire des supercapacites planes performantes a bas cout et non nefaste pour l’environnement