Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the needs of family caregivers of older adults dying with dementia

Abstract Objectives: A challenge in understanding the needs of dementia family caregivers (DFC) within the purview of dementia as a terminal illness rests on the fact that literature in this area is dispersed across disciplines and not specifically grounded within the realm of palliative care. The objective of this paper is to describe the domains of DFC needs and their impact on the delivery of palliative care services. Methods: A literature search pertaining to dementia family caregivers and palliative/end-of-life care was conducted using the databases Medline, CINHAL, Ageline, PsychInfo, and Scopus for articles published in the English language between 1997 and 2011. Results: Supporting family caregivers of individuals with dementia throughout the disease trajectory requires consideration of caregivers : (1) physical, emotional, and psychological needs; (2) information and decisional support needs; and (3) instrumental support needs. The unique nature and prolonged duration of these needs directly influences the palliative care services and supports required by these family caregivers. Significance of results: Understanding the scope of DFC needs help further our understanding of how these needs may impact the delivery of palliative care services, and assists in developing a model of care for those dying from dementia and for their family caregivers.
"You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling

Labeling corpora constitutes a bottleneck to create models for new tasks or domains. Large language models mitigate the issue with automatic corpus labeling methods, particularly for categorical annotations. Some NLP tasks such as emotion intensity prediction, however, require text regression, but there is no work on automating annotations for continuous label assignments. Regression is considered more challenging than classification: The fact that humans perform worse when tasked to choose values from a rating scale lead to comparative annotation methods, including best-worst scaling. This raises the question if large language model-based annotation methods show similar patterns, namely that they perform worse on rating scale annotation tasks than on comparative annotation tasks. To study this, we automate emotion intensity predictions and compare direct rating scale predictions, pairwise comparisons and best-worst scaling. We find that the latter shows the highest reliability. A transformer regressor fine-tuned on these data performs nearly on par with a model trained on the original manual annotations.
Exploring LLMs as a Source of Targeted Synthetic Textual Data to Minimize High Confidence Misclassifications

Natural Language Processing (NLP) models optimized for predictive performance often make high confidence errors and suffer from vulnerability to adversarial and out-of-distribution data. Existing work has mainly focused on mitigation of such errors using either humans or an automated approach. In this study, we explore the usage of large language models (LLMs) for data augmentation as a potential solution to the issue of NLP models making wrong predictions with high confidence during classification tasks. We compare the effectiveness of synthetic data generated by LLMs with that of human data obtained via the same procedure. For mitigation, humans or LLMs provide natural language characterizations of high confidence misclassifications to generate synthetic data, which are then used to extend the training set. We conduct an extensive evaluation of our approach on three classification tasks and demonstrate its effectiveness in reducing the number of high confidence misclassifications present in the model, all while maintaining the same level of accuracy. Moreover, we find that the cost gap between humans and LLMs surpasses an order of magnitude, as LLMs attain human-like performance while being more scalable.
The Unreasonable Ineffectiveness of the Deeper Layers

We empirically study a simple layer-pruning strategy for popular families of open-weight pretrained LLMs, finding minimal degradation of performance on different question-answering benchmarks until after a large fraction (up to half) of the layers are removed. To prune these models, we identify the optimal block of layers to prune by considering similarity across layers; then, to"heal"the damage, we perform a small amount of finetuning. In particular, we use parameter-efficient finetuning (PEFT) methods, specifically quantization and Low Rank Adapters (QLoRA), such that each of our experiments can be performed on a single A100 GPU. From a practical perspective, these results suggest that layer pruning methods can complement other PEFT strategies to further reduce computational resources of finetuning on the one hand, and can improve the memory and latency of inference on the other hand. From a scientific perspective, the robustness of these LLMs to the deletion of layers implies either that current pretraining methods are not properly leveraging the parameters in the deeper layers of the network or that the shallow layers play a critical role in storing knowledge.
Causal Attributions for Failure and the Effect of Gender among Moroccan EFL University Learners

This paper reports a study that sought to investigate Moroccan university learners’ perceptions of failure. 333 subjects studying English at university ranked their perceptions of failure in a Causal Attribution Scale of University Failure (CASUF). The results show that Moroccan learners attribute their failure to teachers’ attitude, effort, interest and pressure. Factors like social problems, lack of teachers’ help and ability were not important. The study also investigated the link between gender and causal attributions of failure. Overall, male and female students reported similar causes. Still, some gender differences were detected.
Environmental exposure as an independent risk factor of chronic bronchitis in northwest Russia

Background . In some parts of the northwest Russia, Murmansk region, high exposures to heavy mining and refining industrial air pollution, especially sulphur dioxide, have been documented. Objective . Our aim was to evaluate whether living in the mining area would be an independent risk factor of the respiratory symptoms. Design . A cross-sectional survey of 200 Murmansk region adult citizens was performed. The main outcome variable was prolonged cough with sputum production that fulfilled the criteria of chronic bronchitis. Results . Of the 200 participants, 53 (26.5%) stated that they had experienced chronic cough with phlegm during the last 2 years. The prevalence was higher among those subjects living in the mining area with its high pollution compared to those living outside this region (35% vs. 18%). Multivariable regression model confirmed that the risk for the chronic cough with sputum production was elevated in a statistical significant manner in the mining and refining area (adjusted OR 2.16, 95% CI 1.07–4.35) after adjustment for smoking status, age and sex. Conclusions . The increased level of sulphur dioxide emitted during nickel mining and refining may explain these adverse health effects. This information is important for medical authorities when they make recommendations and issue guidelines regarding the relationship between environmental pollution and health outcomes. †Deceased November 17th 2012.
LRP1 integrates murine macrophage cholesterol homeostasis and inflammatory responses in atherosclerosis

Low-density lipoprotein receptor-related protein 1 (LRP1) is a multifunctional cell surface receptor with diverse physiological roles, ranging from cellular uptake of lipoproteins and other cargo by endocytosis to sensor of the extracellular environment and integrator of a wide range of signaling mechanisms. As a chylomicron remnant receptor, LRP1 controls systemic lipid metabolism in concert with the LDL receptor in the liver, whereas in smooth muscle cells (SMC) LRP1 functions as a co-receptor for TGFβ and PDGFRβ in reverse cholesterol transport and the maintenance of vascular wall integrity. Here we used a knockin mouse model to uncover a novel atheroprotective role for LRP1 in macrophages where tyrosine phosphorylation of an NPxY motif in its intracellular domain initiates a signaling cascade along an LRP1/SHC1/PI3K/AKT/PPARγ/LXR axis to regulate and integrate cellular cholesterol homeostasis through the expression of the major cholesterol exporter ABCA1 with apoptotic cell removal and inflammatory responses.
The difficult-to-control spread of carbapenemase producers among Enterobacteriaceae worldwide.

The spread of carbapenemase producers in Enterobacteriaceae has now been identified worldwide. Three main carbapenemases have been reported; they belong to three classes of β-lactamases, which are KPC, NDM, and OXA-48. The main reservoirs of KPC are Klebsiella pneumoniae in the USA, Israel, Greece, and Italy, those of NDM are K. pneumoniae and Escherichia coli in the Indian subcontinent, and those of OXA-48 are K. pneumoniae and Escherichia coli in North Africa and Turkey. KPC producers have been mostly identified among nosocomial isolates, whereas NDM and OXA-48 producers are both nosocomial and community-acquired pathogens. Control of their spread is still possible in hospital settings, and relies on the use of rapid diagnostic techniques and the strict implemention of hygiene measures.
Blue appendages and temperature acclimation increase survival during acute heat stress in the upside-down jellyfish, Cassiopea xamachana

Upside-down jellyfish (Cassiopea sp.) are highly tolerant to multiple abiotic stressors, including fluctuating temperatures associated with shallow marine habitats. This resilience may underlie the ability of Cassiopea sp. to inhabit a wide variety of tropical habitats across the globe. Additionally, Cassiopea sp. are marked by a conspicuous array of appendage coloration; individual medusae vary in the hue and number of oral appendages, which are often strikingly blue. The function of this coloration is not understood. We aimed to understand how extrinsic and intrinsic factors may shape thermal tolerance. Adult Cassiopea xamachana were collected from two sites that vary in daily temperature range within the Florida Keys and were subjected to acute lethal heat stress experiments. To quantify a whole-organism response to heat, we measured changes in bell pulsation, which likely plays a role in feeding, oxygen exchange, and symbiont uptake. Results show that C. xamachana from the two collection sites do not exhibit different responses to heat, suggesting that temperature fluctuations do not prime individuals for higher thermal tolerance. Additionally, C. xamachana with blue appendages survived significantly higher temperatures and exhibited less change in bell pulsation rates compared to non-blue individuals. Finally, color morphs were acclimated at either ambient (26 °C) or elevated (33 °C) temperatures. We found that acclimation at 33 °C, as well as appendage color in each treatment, led to higher survival under acute heat stress. Together, these findings highlight the importance of phenotypic plasticity and coloration in Cassiopea resilience during heat stress.
Sensory integration of food availability and population density during the diapause exit decision involves insulin-like signaling in Caenorhabditis elegans

Decisions made over long time scales, such as life cycle decisions, require coordinated interplay between sensory perception and sustained gene expression. The Caenorhabditis elegans dauer (or diapause) exit developmental decision requires sensory integration of population density and food availability to induce an all-or-nothing organismal-wide response, but the mechanism by which this occurs remains unknown. Here, we demonstrate how the ASJ chemosensory neurons, known to be critical for dauer exit, perform sensory integration at both the levels of gene expression and calcium activity. In response to favorable conditions, dauers rapidly produce and secrete the dauer exit-promoting insulin-like peptide INS-6. Expression of ins-6 in the ASJ neurons integrate population density and food level and can reflect decision commitment since dauers committed to exiting have higher ins-6 expression levels than those of non-committed dauers. Calcium imaging in dauers reveals that the ASJ neurons are activated by food, and this activity is suppressed by pheromone, indicating that sensory integration also occurs at the level of calcium transients. We find that ins-6 expression in the ASJ neurons depends on neuronal activity in the ASJs, cGMP signaling, a CaM-kinase pathway, and the pheromone components ascr#8 and ascr#2. We propose a model in which decision commitment to exit the dauer state involves an autoregulatory feedback loop in the ASJ neurons that promotes high INS-6 production and secretion. These results collectively demonstrate how insulin-like peptide signaling helps animals compute long-term decisions by bridging sensory perception to decision execution. Summary/Significance Statement Animals must respond appropriately to multiple sensory stimuli to make informed decisions. It remains unclear how the nervous system is able to integrate different sensory cues and propagate that information towards making decisions over longer timescales. We use the nematode Caenorhabditis elegans to investigate how sensory integration occurs during the decision to exit diapause, a stress-resistant developmentally arrested state triggered by multiple sensory inputs including food availability and population density. We show how expression of an insulin-like peptide critical to dauer exit reflects the sensory integration process and decision commitment, and we dissect the regulation of this insulin-like peptide’s expression. Our study explicitly analyzes the relationship between neuronal activity and neuropeptide expression during a complex decision with diverse sensory inputs.
Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform “reference-informed prediction” by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted ∼15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders, including 19 genes with recurrent splicing-altering mutations. Among the new candidate disease risk genes, MFN1 is involved in mitochondria fusion, which is frequently disrupted in autism patients. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.
Unveiling the control of N and P on DOM fate in a Mediterranean coastal environment

Dissolved organic matter (DOM) and heterotrophic prokaryotes (HP) are key players in the oceanic carbon cycle. Although several biotic and abiotic factors controlling DOM fates are known, the hierarchy of their respective influences is still debated. Two contrasting Mediterranean coastal sites were sampled: a harbour under strong continental and anthropogenic influence (T) and an open coastal area (G). Interestingly, similar dissolved organic carbon (DOC) concentrations were observed in both samples. However, they showed marked differences in dissolved inorganic nitrogen and organic phosphorus concentrations (60-fold and 80% higher value in T), as well as in DOM optical properties and molecular composition. Incubation experiments were performed to expose the HP communities of each site to dissolved substances from T and G for three weeks. DOC removal was similar (−10 %) regardless HP origin and dissolved substances characteristics. HP growth and their maximal abundance were higher (+ 300 %) with dissolved substances from T, regardless HP origins. This indicates different fates of DOC processed by microbial communities as a function of abiotic determinants. Higher HP growth was associated to elevated initial content and higher consumption of inorganic nitrogen, organic phosphorus, three fluorescent DOM components, nitrogen-containing molecules and carbohydrates. These results provide insights into the main drivers of marine DOM fate: at similar DOC concentrations and low inorganic P concentrations. We evience the preferential consumption of lignin-like compounds where theoretically more labile molecules were available, thus reinforcing the need of in depth molecular studies for a better understanding of DOM-microbes interactions in the ocean.
Bacterial defenses and their trade-off with growth are not ubiquitous but depend on ecological contexts

Bacteriophages, also known as bacterial viruses, exert significant influence on microbial ecosystems due to their widespread presence. In response to the threat of phage predation, bacteria have developed an extensive array of antiviral defense mechanisms. This study investigates the intricate relationship between bacterial defense mechanisms and growth rates across various ecological contexts. First, we found that bacteria lacking defense systems show prolonged doubling times. By integrating phylogenetic eigenvectors into the ecological feature matrix, we employed a phylogenetic random forest model to determine the most influential ecological features on the presence and abundance of bacterial defenses. Further phylogenetic regressions unveiled nuanced dependencies of bacterial defenses on specific environmental factors. Contrary to conventional assumptions, our findings challenge the idea of a universal distribution of bacterial defense systems, instead highlighting their reliance on subtle ecological characteristics. Particularly noteworthy are observations that symbiotic and endosymbiotic bacteria often exhibit reduced defense systems and negative correlations between minimal doubling time and defense system abundance. Conversely, as per conventional assumptions, free-living and motile bacteria displayed significant positive correlations between minimal doubling time and defense system abundance. Overall, our results suggest that the trade-off between growth and defense strategies is not universally applicable but rather contingent on context. Moreover, our study illuminates the critical role of ecological variables such as habitat specificity, motility, and nutrient availability in shaping bacterial growth rates and defense mechanisms. These findings underscore the complexity of microbial interactions and stress the importance of considering the ecological context in understanding bacterial defense strategies.
Inhibition of Brain Mitogen-Activated Protein Kinase Signaling Reduces Central Endoplasmic Reticulum Stress and Inflammation and Sympathetic Nerve Activity in Heart Failure Rats

Mitogen-activated protein kinase (MAPK) signaling and endoplasmic reticulum (ER) stress in the brain have been implicated in the pathophysiology of hypertension. This study determined whether ER stress occurs in subfornical organ and hypothalamic paraventricular nucleus in heart failure (HF) and how MAPK signaling interacts with ER stress and other inflammatory mediators. HF rats had significantly higher levels of the ER stress biomarkers (glucose-regulated protein 78, activating transcription factor 6, activating transcription factor 4, X-box binding protein 1, P58IPK, and C/EBP homologous protein) in subfornical organ and paraventricular nucleus, which were attenuated by a 4-week intracerebroventricular infusion of inhibitors selective for p44/42 MAPK (PD98059), p38 MAPK (SB203580), or c-Jun N-terminal kinase (SP600125). HF rats also had higher mRNA levels of tumor necrosis factor-&agr;, interleukin-1&bgr;, cyclooxygenase-2, and nuclear factor-&kgr;B p65, and a lower mRNA level of I&kgr;B-&agr;, in subfornical organ and paraventricular nucleus, compared with SHAM rats, and these indicators of increased inflammation were attenuated in the HF rats treated with the MAPK inhibitors. Plasma norepinephrine level was higher in HF rats than in SHAM rats but was reduced in the HF rats treated with PD98059 and SB203580. A 4-week intracerebroventricular infusion of PD98059 also improved some hemodynamic and anatomic indicators of left ventricular function in HF rats. These data demonstrate that ER stress increases in the subfornical organ and paraventricular nucleus of rats with ischemia-induced HF and that inhibition of brain MAPK signaling reduces brain ER stress and inflammation and decreases sympathetic excitation in HF. An interaction between MAPK signaling and ER stress in cardiovascular regions of the brain may contribute to the development of HF.
Profiling trace organic chemical biotransformation genes, enzymes and associated bacteria in microbial model communities

Microbial biotransformation of trace organic chemicals (TOrCs) is an essential process in wastewater treatment for eliminating environmental pollution. Understanding of TOrC biotransformation mechanisms, especially at their original concentrations, is important to optimize treatment performance, whereas our current knowledge is limited. Here we investigated the biotransformation of seven TOrCs by 24 model communities. The genome-centric analyses unraveled the biotransformation drivers concerning functional genes and enzymes and responsible bacteria. We obtained efficient model communities for complete removal on ibuprofen, caffeine and atenolol, and the transformation efficiencies for sulfamethoxazole, carbamazepine, trimethoprim and gabapentin were 0-45%. Biotransformation performance was not fully reflected by the presence of known biotransformation genes and enzymes. However, functional similar homologs to existing biotransformation genes and enzymes (e.g., long-chain-fatty-acid-CoA ligase encoded by fadD and fadD13 gene, acyl-CoA dehydrogenase encoded by fadE12 gene) could play critical roles in TOrC metabolism. Finally, we identified previously undescribed degrading strains, e.g., Rhodococcus qingshengii for caffeine, carbamazepine, sulfamethoxazole and ibuprofen biotransformation, and potential transformation enzymes, e.g., SDR family oxidoreductase targeting sulfamethoxazole and putative hypothetical proteins for caffeine, atenolol and gabapentin biotransformation.