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1---2tags:3- sentence-transformers4- sentence-similarity5- feature-extraction6- dense7- generated_from_trainer8- dataset_size:1299719- loss:MultipleNegativesRankingLoss10base_model: thenlper/gte-small11widget:12- source_sentence: >-13    Integrated health care for infectious diseases and non-communicable diseases14    in low-and middle-income countries15  sentences:16  - >-17    The purposes of this study were to create a new flow-chart of prehospital18    electrocardiography (ECG)-transmission, evaluate its predictive ability for19    ST-elevation myocardial infarction (STEMI) and shorten door-to-balloon time20    (DTBT). The new transmission flow-chart was created using symptoms from21    previous medical records of STEMI patients. A total of 4090 consecutive22    patients transferred emergently to our hospital were divided into two23    groups: those in ambulances with an ECG-transmission device with the new24    flow-chart (ECGT-FC) and those transferred without an ECG-transmission25    device (non-ECGT) groups. A STEMI group comprising walk-in patients during26    the same period was used as a control group. The predictive ability of STEMI27    and the effectiveness of shortening the DTBT by the new flow-chart of28    ECG-transmission was evaluated. In the ECGT-FC group, the prevalence of29    STEMI in the ECG-transmission by the new flow-chart were significantly30    higher than in the non-ECG-transmission patients (6.71% vs. 0.19%; p<0.001).31    The sensitivity and specificity of the new ECG-transmission flow-chart were32    83.3% and 88.1%, respectively. The median DTBT was significantly shortened33    (p=0.045) and the prevalence of DTBT<90min was significantly higher in the34    ECGT-FC group (p=0.018) than the other groups. The sensitivity and35    specificity of the new flow-chart for ECG-transmission were high. The new36    flow-chart combined with an ECG-transmission device could detect STEMI37    efficiently and shorten DTBT.38  - >-39    Multiple strains of the SARS-CoV-2 have arisen and jointly influence the40    trajectory of the coronavirus disease (COVID-19) pandemic. However, current41    models rarely account for this multi-strain dynamics and their different42    transmission rate and response to vaccines. We propose a new mathematical43    model that accounts for two virus variants and the deployment of a44    vaccination program. To demonstrate utility, we applied the model to45    determine the control reproduction number 46  - >-47    The co-occurrence of infectious diseases (ID) and non-communicable diseases48    (NCD) is widespread, presenting health service delivery challenges49    especially in low-and middle-income countries (LMICs). Integrated health50    care is a possible solution but may require a paradigm shift to be51    successfully implemented. This literature review identifies integrated care52    examples among selected ID and NCD dyads. We searched PubMed, PsycINFO,53    Cochrane Library, CINAHL, Web of Science, EMBASE, Global Health Database,54    and selected clinical trials registries. Eligible studies were published55    between 2010 and December 2022, available in English, and report health56    service delivery programs or policies for the selected disease dyads in57    LMICs. We identified 111 studies that met the inclusion criteria, including58    56 on tuberculosis and diabetes integration, 46 on health system adaptations59    to treat COVID-19 and cardiometabolic diseases, and 9 on COVID-19, diabetes,60    and tuberculosis screening. Prior to the COVID-19 pandemic, most studies on61    diabetes-tuberculosis integration focused on clinical service delivery62    screening. By far the most reported health system outcomes across all63    studies related to health service delivery (n = 72), and 19 addressed health64    workforce. Outcomes related to health information systems (n = 5),65    leadership and governance (n = 3), health financing (n = 2), and essential66    medicines (n = 4)) were sparse. Telemedicine service delivery was the most67    common adaptation described in studies on COVID-19 and either68    cardiometabolic diseases or diabetes and tuberculosis. ID-NCD integration is69    being explored by health systems to deal with increasingly complex health70    needs, including comorbidities. High excess mortality from COVID-1971    associated with NCD-related comorbidity prompted calls for more integrated72    ID-NCD surveillance and solutions. Evidence of clinical integration of73    health service delivery and workforce has grown-especially for HIV and74    NCDs-but other health system building blocks, particularly access to75    essential medicines, health financing, and leadership and governance, remain76    in disease silos.77- source_sentence: >-78    Foot-and-mouth disease virus 3C(pro) inhibits interferon-/ response and79    expression of IFN-stimulated genes80  sentences:81  - >-82    Repeated bottleneck passages of RNA viruses result in accumulation of83    mutations and fitness decrease. Here, we show that clones of foot-and-mouth84    disease virus (FMDV) subjected to bottleneck passages, in the form of85    plaque-to-plaque transfers in BHK-21 cells, increased the thermosensitivity86    of the viral clones. By constructing infectious FMDV clones, we have87    identified the amino acid substitution M54I in capsid protein VP1 as one of88    the lesions associated with thermosensitivity. M54I affects processing of89    precursor P1, as evidenced by decreased production of VP1 and accumulation90    of VP1 precursor proteins. The defect is enhanced at high temperatures.91    Residue M54 of VP1 is exposed on the virion surface, and it is close to the92    B-C loop where an antigenic site of FMDV is located. M54 is not in direct93    contact with the VP1-VP3 cleavage site, according to the three-dimensional94    structure of FMDV particles. Models to account for the effect of M54 in95    processing of the FMDV polyprotein are proposed. In addition to revealing a96    distance effect in polyprotein processing, these results underline the97    importance of pursuing at the biochemical level the biological defects that98    arise when viruses are subjected to multiple bottleneck events.99  - >-100    To improve the delivery of liposomes to tumors using P-selectin glycoprotein101    ligand 1 (PSGL1) mediated binding to selectin molecules, which are102    upregulated on tumorassociated endothelium.103  - >-104    Foot-and-mouth disease is a highly contagious viral illness of wild and105    domestic cloven-hoofed animals. The causative agent, foot-and-mouth disease106    virus (FMDV), replicates rapidly, efficiently disseminating within the107    infected host and being passed on to susceptible animals via direct contact108    or the aerosol route. To survive in the host, FMDV has evolved to block the109    host interferon (IFN) response. Previously, we and others demonstrated that110    the leader proteinase (L(pro)) of FMDV is an IFN antagonist. Here, we report111    that another FMDV-encoded proteinase, 3C(pro), also inhibits IFN-α/β112    response and the expression of IFN-stimulated genes. Acting in a proteasome-113    and caspase-independent manner, the 3C(pro) of FMDV proteolytically cleaved114    nuclear transcription factor kappa B (NF-κB) essential modulator (NEMO), a115    bridging adaptor protein essential for activating both NF-κB and116    interferon-regulatory factor signaling pathways. 3C(pro) specifically117    targeted NEMO at the Gln 383 residue, cleaving off the C-terminal zinc118    finger domain from the protein. This cleavage impaired the ability of NEMO119    to activate downstream IFN production and to act as a signaling adaptor of120    the RIG-I/MDA5 pathway. Mutations specifically disrupting the cysteine121    protease activity of 3C(pro) abrogated NEMO cleavage and the inhibition of122    IFN induction. Collectively, our data identify NEMO as a substrate for FMDV123    3C(pro) and reveal a novel mechanism evolved by a picornavirus to counteract124    innate immune signaling.125- source_sentence: Measuring flourishing among adolescent and adult populations126  sentences:127  - >-128    Flourishing is an evolving wellbeing construct and outcome of interest129    across the social and biological sciences. Despite some conceptual130    advancements, there remains limited consensus on how to measure flourishing,131    as well as how to distinguish it from closely related wellbeing constructs,132    such as thriving and life satisfaction. This paper aims to provide an133    overview and comparison of the diverse scales that have been developed to134    measure flourishing among adolescent and adult populations to provide135    recommendations for future studies seeking to use flourishing as an outcome136    in social and biological research.137  - >-138    Although well-being at work is important for occupational health,139    multi-dimensional workplace well-being measures do not exist for Japanese140    workers. The purpose of this study was to investigate the validity of the141    Japanese version of the Workplace PERMA-Profiler. Japanese workers completed142    online surveys at baseline (N = 310) and 1 month later (N = 100). The143    Workplace PERMA-Profiler was translated according to international144    guidelines. Job and life satisfaction, work engagement, psychological145    distress, work-related psychosocial factors, and work performance were146    measured as comparisons for convergent validity. Cronbach's alphas,147    Intra-class Correlation Coefficients (ICCs), and measurement errors were148    calculated for the reliability, and the validity of the measure was tested149    by correlational analyses and confirmatory factor analysis. A total of 310150    (baseline) and 86 (follow-up) workers responded and were included in the151    analyses. Cronbach's alphas and ICCs of the Japanese Workplace152    PERMA-Profiler ranged from 0.75 to 0.96. Confirmatory factor analysis153    indicated that the 5-factor model demonstrated a marginally acceptable fit154    (χ2 (80) = 351.30, CFI = 0.892, TLI = 0.858, RMSEA = 0.105, SRMR = 0.051).155    Overall well-being and the five PERMA domains had moderate-to-strong156    correlations with job satisfaction, psychological distress (inversely), and157    work-related factors. The Japanese version of the Workplace PERMA-Profiler158    demonstrated adequate reliability and validity. This measure could be useful159    to assess well-being at work, promote well-being research among Japanese160    workers, and address the problem of definition for well-being in further161    studies.162  - >-163    We experience countless pieces of new information each day, but remembering164    them later depends on firmly instilling memory storage in the brain.165    Numerous studies have implicated non-rapid eye movement (NREM) sleep in166    consolidating memories via interactions between hippocampus and cortex.167    However, the temporal dynamics of this hippocampal-cortical communication168    and the concomitant neural oscillations during memory reactivations remains169    unclear. To address this issue, the present study used the procedure of170    targeted memory reactivation (TMR) following learning of object-location171    associations to selectively reactivate memories during human NREM sleep.172    Cortical pattern reactivation and hippocampal-cortical coupling were173    measured with intracranial EEG recordings in patients with epilepsy. We174    found that TMR produced variable amounts of memory enhancement across a set175    of object-location associations. Successful TMR increased hippocampal176    ripples and cortical spindles, apparent during two discrete sweeps of177    reactivation. The first reactivation sweep was accompanied by increased178    hippocampal-cortical communication and hippocampal ripple events coupled to179    local cortical activity (cortical ripples and high-frequency broadband180    activity). In contrast, hippocampal-cortical coupling decreased during the181    second sweep, while increased cortical spindle activity indicated continued182    cortical processing to achieve long-term storage. Taken together, our183    findings show how dynamic patterns of item-level reactivation and184    hippocampal-cortical communication support memory enhancement during NREM185    sleep.186- source_sentence: >-187    Agrobacterium tumefaciens Hfq binds to sRNA AbcR1 and its target mRNA188    atu2422189  sentences:190  - >-191    Amyloid β (Aβ) assemblies exist not only in the central nervous system, but192    can circulate within the bloodstream to trigger and exacerbate peripheral,193    cerebrovascular, and neurodegenerative disorders. Eliminating excess194    peripheral Aβ fibrils, therefore, holds promise to improve the management of195    amyloid-related diseases. Here, we present nanoemulsion-mediated ultrasonic196    ablation of circulating Aβ fibrils to both destroy established plaques and197    prevent the re-growth of ablated fragments back into toxic species. This198    approach is made possible using a de novo designed peptide emulsifier that199    contains the self-associating sequence from the amyloid precursor protein.200    Emulsification of the peptide surfactant with fluorous nanodroplets produces201    contrast agents that rapidly adsorb Aβ assemblies and allows their202    ultrasound-controlled destruction via acoustic cavitation. Vessel-mimetic203    flow experiments demonstrate that nanoemulsion-assisted Aβ disruption can be204    achieved in circulation using clinical diagnostic ultrasound transducers.205    Additional cell-based assays confirm the ablated fragments are less toxic to206    neuronal and glial cells compared to mature fibrils, and can be rapidly207    phagocytosed by both peripheral and brain macrophages. These results208    highlight the potential of nanoemulsion contrast agents to deliver new209    imaging enabled strategies for non-invasive management of Aβ-related210    diseases using traditional diagnostic ultrasound modalities.211  - >-212    The Hfq protein mediates gene regulation by small RNAs (sRNAs) in about 50%213    of all bacteria. Depending on the species, phenotypic defects of an hfq214    mutant range from mild to severe. Here, we document that the purified Hfq215    protein of the plant pathogen and natural genetic engineer Agrobacterium216    tumefaciens binds to the previously described sRNA AbcR1 and its target mRNA217    atu2422, which codes for the substrate binding protein of an ABC transporter218    taking up proline and γ-aminobutyric acid (GABA). Several other ABC219    transporter components were overproduced in an hfq mutant compared to their220    levels in the parental strain, suggesting that Hfq plays a major role in221    controlling the uptake systems and metabolic versatility of A. tumefaciens.222    The hfq mutant showed delayed growth, altered cell morphology, and reduced223    motility. Although the DNA-transferring type IV secretion system was224    produced, tumor formation by the mutant strain was attenuated, demonstrating225    an important contribution of Hfq to plant transformation by A. tumefaciens.226  - >-227    Hfq is an RNA-binding protein that functions in post-transcriptional gene228    regulation by mediating interactions between mRNAs and small regulatory RNAs229    (sRNAs). Two proteins encoded by BAB1_1794 and BAB2_0612 are highly230    over-produced in a Brucella abortus hfq mutant compared with the parental231    strain, and recently, expression of orthologues of these proteins in232    Agrobacterium tumefaciens was shown to be regulated by two sRNAs, called233    AbcR1 and AbcR2. Orthologous sRNAs (likewise designated AbcR1 and AbcR2)234    have been identified in B. abortus 2308. In Brucella, abcR1 and abcR2 single235    mutants are not defective in their ability to survive in cultured murine236    macrophages, but an abcR1 abcR2 double mutant exhibits significant237    attenuation in macrophages. Additionally, the abcR1 abcR2 double mutant238    displays significant attenuation in a mouse model of chronic Brucella239    infection. Quantitative proteomics and microarray analyses revealed that the240    AbcR sRNAs predominantly regulate genes predicted to be involved in amino241    acid and polyamine transport and metabolism, and Northern blot analyses242    indicate that the AbcR sRNAs accelerate the degradation of the target mRNAs.243    In an Escherichia coli two-plasmid reporter system, overexpression of either244    AbcR1 or AbcR2 was sufficient for regulation of target mRNAs, indicating245    that the AbcR sRNAs from B. abortus 2308 perform redundant regulatory246    functions.247- source_sentence: >-248    Neural correlates of advice evaluation and integration in the judge-advisor249    paradigm250  sentences:251  - >-252    Considering advice from others is a pervasive element of human social life.253    We used the judge-advisor paradigm to investigate the neural correlates of254    advice evaluation and advice integration by means of functional magnetic255    resonance imaging. Our results demonstrate that evaluating advice recruits256    the "mentalizing network," brain regions activated when people think about257    others' mental states. Important activation differences exist, however,258    depending upon the perceived competence of the advisor. Consistently,259    additional analyses demonstrate that integrating others' advice, i.e., how260    much participants actually adjust their initial estimate, correlates with261    neural activity in the centromedial amygdala in the case of a competent and262    with activity in visual cortex in the case of an incompetent advisor. Taken263    together, our findings, therefore, demonstrate that advice evaluation and264    integration rely on dissociable neural mechanisms and that significant265    differences exist depending upon the advisor's reputation, which suggests266    different modes of processing advice depending upon the perceived competence267    of the advisor.268  - >-269    The role of antibodies in kidney transplant (KT) has evolved significantly270    over the past few decades. This role of antibodies in KT is multifaceted,271    encompassing both the challenges they pose in terms of antibody-mediated272    rejection (AMR) and the opportunities for improving transplant outcomes273    through better detection, prevention, and treatment strategies. As our274    understanding of the immunological mechanisms continues to evolve, so too275    will the approaches to managing and harnessing the power of antibodies in276    KT, ultimately leading to improved patient and graft survival. This277    narrative review explores the multifaceted roles of antibodies in KT,278    including their involvement in rejection mechanisms, advancements in279    desensitization protocols, AMR treatments, and their potential role in280    monitoring and improving graft survival.281  - >-282    Humans regulate intergroup conflict through parochial altruism; they283    self-sacrifice to contribute to in-group welfare and to aggress against284    competing out-groups. Parochial altruism has distinct survival functions,285    and the brain may have evolved to sustain and promote in-group cohesion and286    effectiveness and to ward off threatening out-groups. Here, we have linked287    oxytocin, a neuropeptide produced in the hypothalamus, to the regulation of288    intergroup conflict. In three experiments using double-blind289    placebo-controlled designs, male participants self-administered oxytocin or290    placebo and made decisions with financial consequences to themselves, their291    in-group, and a competing out-group. Results showed that oxytocin drives a292    "tend and defend" response in that it promoted in-group trust and293    cooperation, and defensive, but not offensive, aggression toward competing294    out-groups.295pipeline_tag: sentence-similarity296library_name: sentence-transformers297language:298- en299---300 301# SentenceTransformer based on thenlper/gte-small302 303This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-small](https://huggingface.co/thenlper/gte-small). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.304 305## Model Details306 307### Model Description308- **Model Type:** Sentence Transformer309- **Base model:** [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) <!-- at revision 17e1f347d17fe144873b1201da91788898c639cd -->310- **Maximum Sequence Length:** 512 tokens311- **Output Dimensionality:** 384 dimensions312- **Similarity Function:** Cosine Similarity313<!-- - **Training Dataset:** Unknown -->314<!-- - **Language:** Unknown -->315<!-- - **License:** Unknown -->316 317### Model Sources318 319- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)320- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)321- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)322 323### Full Model Architecture324 325```326SentenceTransformer(327  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})328  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})329  (2): Normalize()330)331```332 333## Usage334 335### Direct Usage (Sentence Transformers)336 337First install the Sentence Transformers library:338 339```bash340pip install -U sentence-transformers341```342 343Then you can load this model and run inference.344```python345from sentence_transformers import SentenceTransformer346 347# Download from the 🤗 Hub348model = SentenceTransformer("sentence_transformers_model_id")349# Run inference350sentences = [351    'Neural correlates of advice evaluation and integration in the judge-advisor paradigm',352    'Considering advice from others is a pervasive element of human social life. We used the judge-advisor paradigm to investigate the neural correlates of advice evaluation and advice integration by means of functional magnetic resonance imaging. Our results demonstrate that evaluating advice recruits the "mentalizing network," brain regions activated when people think about others\' mental states. Important activation differences exist, however, depending upon the perceived competence of the advisor. Consistently, additional analyses demonstrate that integrating others\' advice, i.e., how much participants actually adjust their initial estimate, correlates with neural activity in the centromedial amygdala in the case of a competent and with activity in visual cortex in the case of an incompetent advisor. Taken together, our findings, therefore, demonstrate that advice evaluation and integration rely on dissociable neural mechanisms and that significant differences exist depending upon the advisor\'s reputation, which suggests different modes of processing advice depending upon the perceived competence of the advisor.',353    'Humans regulate intergroup conflict through parochial altruism; they self-sacrifice to contribute to in-group welfare and to aggress against competing out-groups. Parochial altruism has distinct survival functions, and the brain may have evolved to sustain and promote in-group cohesion and effectiveness and to ward off threatening out-groups. Here, we have linked oxytocin, a neuropeptide produced in the hypothalamus, to the regulation of intergroup conflict. In three experiments using double-blind placebo-controlled designs, male participants self-administered oxytocin or placebo and made decisions with financial consequences to themselves, their in-group, and a competing out-group. Results showed that oxytocin drives a "tend and defend" response in that it promoted in-group trust and cooperation, and defensive, but not offensive, aggression toward competing out-groups.',354]355embeddings = model.encode(sentences)356print(embeddings.shape)357# [3, 384]358 359# Get the similarity scores for the embeddings360similarities = model.similarity(embeddings, embeddings)361print(similarities)362# tensor([[1.0000, 0.9575, 0.8147],363#         [0.9575, 1.0000, 0.8303],364#         [0.8147, 0.8303, 1.0000]])365```366 367<!--368### Direct Usage (Transformers)369 370<details><summary>Click to see the direct usage in Transformers</summary>371 372</details>373-->374 375<!--376### Downstream Usage (Sentence Transformers)377 378You can finetune this model on your own dataset.379 380<details><summary>Click to expand</summary>381 382</details>383-->384 385<!--386### Out-of-Scope Use387 388*List how the model may foreseeably be misused and address what users ought not to do with the model.*389-->390 391<!--392## Bias, Risks and Limitations393 394*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*395-->396 397<!--398### Recommendations399 400*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*401-->402 403## Training Details404 405### Training Dataset406 407#### Unnamed Dataset408 409* Size: 129,971 training samples410* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>411* Approximate statistics based on the first 1000 samples:412  |         | sentence_0                                                                        | sentence_1                                                                         | sentence_2                                                                           |413  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|414  | type    | string                                                                            | string                                                                             | string                                                                               |415  | details | <ul><li>min: 6 tokens</li><li>mean: 19.55 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 210.7 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 312.31 tokens</li><li>max: 512 tokens</li></ul> |416* Samples:417  | sentence_0                                                                          | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | sentence_2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |418  |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|419  | <code>Microbiology and immunomics in male infertility</code>                        | <code>Up to 50% of infertility is caused by the male side. Varicocele, orchitis, prostatitis, oligospermia, asthenospermia, and azoospermia are common causes of impaired male reproductive function and male infertility. In recent years, more and more studies have shown that microorganisms play an increasingly important role in the occurrence of these diseases. This review will discuss the microbiological changes associated with male infertility from the perspective of etiology, and how microorganisms affect the normal function of the male reproductive system through immune mechanisms. Linking male infertility with microbiome and immunomics can help us recognize the immune response under different disease states, providing more targeted immune target therapy for these diseases, and even the possibility of combined immunotherapy and microbial therapy for male infertility.</code> | <code>There are currently no sensitive and specific assays for activin B that could be utilized to study human biological fluids. The aim of this project was to develop and validate a 'total' activin B ELISA for use with human biological fluids and establish concentrations of activin B in the circulation and fluids from the reproductive organs. The new ELISA was validated and then used to measure activin B levels in the circulation of healthy participants, IVF patients, pregnant women and in ovarian follicular fluid and seminal plasma. Healthy adult subjects (n = 143), subjects from an IVF clinic (n = 27) and pregnancy groups (n = 29) were sampled. The sensitivity of the assay was 0.019 ng/ml. Validation of the activin B ELISA showed good recovery (90.7 +/- 9.8%) and linearity in biological fluid and cell culture media and low cross-reactivity with related analytes (inhibin B = 0.077% and activin A = 0.0034%). There was a negative correlation between activin B concentration (r = -0.281, P < ...</code> |420  | <code>Biomarkers of heterogeneity in type 1 diabetes</code>                         | <code>The 'Biomarkers of heterogeneity in type 1 diabetes' study cohort was set up to identify genetic, physiological and psychosocial factors explaining the observed heterogeneity in disease progression and the development of complications in people with long-standing type 1 diabetes (T1D).</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | <code>In patients with type 1 diabetes, there has been concern about the effects of recurrent hypoglycaemia and chronic hyperglycaemia on cognitive function. Because other biomedical factors may also increase the risk of cognitive decline, this study examined whether macrovascular risk factors (hypertension, smoking, hypercholesterolaemia, obesity), sub-clinical macrovascular disease (carotid intima-media thickening, coronary calcification) and microvascular complications (retinopathy, nephropathy) were associated with decrements in cognitive function over an extended time period. Type 1 diabetes patients (n = 1,144) who had completed a comprehensive cognitive test battery at entry into the Diabetes Control and Complications Trial were re-assessed at a mean of 18.5 (range: 15-23) years later. Univariate and multivariable models examined the relationship between cognitive change and the presence of micro- and macrovascular complications and risk factors. Univariate modelling showed that smoki...</code> |421  | <code>Role of Molecular Profiling and Subgroups in Pediatric Medulloblastoma</code> | <code>As advances in the molecular and genetic profiling of pediatric medulloblastoma evolve, associations with prognosis and treatment are found (prognostic and predictive biomarkers) and research is directed at molecular therapies. Medulloblastoma typically affects young patients, where the implications of any treatment on the developing brain must be carefully considered. The aim of this article is to provide a clear comprehensible update on the role molecular profiling and subgroups in pediatric medulloblastoma as it is likely to contribute significantly toward prognostication. Knowledge of this classification is of particular interest because there are new molecular therapies targeting the Shh subgroup of medulloblastomas. </code>                                                                                                                                                | <code>The Wnt/beta-catenin pathway plays important roles during embryonic development and growth control. The B56 regulatory subunit of protein phosphatase 2A (PP2A) has been implicated as a regulator of this pathway. However, this has not been investigated by loss-of-function analyses. Here we report loss-of-function analysis of PP2A:B56epsilon during early Xenopus embryogenesis. We provide direct evidence that PP2A:B56epsilon is required for Wnt/beta-catenin signaling upstream of Dishevelled and downstream of the Wnt ligand. We show that maternal PP2A:B56epsilon function is required for dorsal development, and PP2A:B56epsilon function is required later for the expression of the Wnt target gene engrailed, for subsequent midbrain-hindbrain boundary formation, and for closure of the neural tube. These data demonstrate a positive role for PP2A:B56epsilon in the Wnt pathway.</code>                                                                                                                              |422* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:423  ```json424  {425      "scale": 20.0,426      "similarity_fct": "cos_sim"427  }428  ```429 430### Training Hyperparameters431#### Non-Default Hyperparameters432 433- `per_device_train_batch_size`: 32434- `per_device_eval_batch_size`: 32435- `num_train_epochs`: 1436- `max_steps`: 20437- `multi_dataset_batch_sampler`: round_robin438 439#### All Hyperparameters440<details><summary>Click to expand</summary>441 442- `overwrite_output_dir`: False443- `do_predict`: False444- `eval_strategy`: no445- `prediction_loss_only`: True446- `per_device_train_batch_size`: 32447- `per_device_eval_batch_size`: 32448- `per_gpu_train_batch_size`: None449- `per_gpu_eval_batch_size`: None450- `gradient_accumulation_steps`: 1451- `eval_accumulation_steps`: None452- `torch_empty_cache_steps`: None453- `learning_rate`: 5e-05454- `weight_decay`: 0.0455- `adam_beta1`: 0.9456- `adam_beta2`: 0.999457- `adam_epsilon`: 1e-08458- `max_grad_norm`: 1459- `num_train_epochs`: 1460- `max_steps`: 20461- `lr_scheduler_type`: linear462- `lr_scheduler_kwargs`: {}463- `warmup_ratio`: 0.0464- `warmup_steps`: 0465- `log_level`: passive466- `log_level_replica`: warning467- `log_on_each_node`: True468- `logging_nan_inf_filter`: True469- `save_safetensors`: True470- `save_on_each_node`: False471- `save_only_model`: False472- `restore_callback_states_from_checkpoint`: False473- `no_cuda`: False474- `use_cpu`: False475- `use_mps_device`: False476- `seed`: 42477- `data_seed`: None478- `jit_mode_eval`: False479- `use_ipex`: False480- `bf16`: False481- `fp16`: False482- `fp16_opt_level`: O1483- `half_precision_backend`: auto484- `bf16_full_eval`: False485- `fp16_full_eval`: False486- `tf32`: None487- `local_rank`: 0488- `ddp_backend`: None489- `tpu_num_cores`: None490- `tpu_metrics_debug`: False491- `debug`: []492- `dataloader_drop_last`: False493- `dataloader_num_workers`: 0494- `dataloader_prefetch_factor`: None495- `past_index`: -1496- `disable_tqdm`: False497- `remove_unused_columns`: True498- `label_names`: None499- `load_best_model_at_end`: False500- `ignore_data_skip`: False501- `fsdp`: []502- `fsdp_min_num_params`: 0503- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}504- `fsdp_transformer_layer_cls_to_wrap`: None505- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}506- `deepspeed`: None507- `label_smoothing_factor`: 0.0508- `optim`: adamw_torch509- `optim_args`: None510- `adafactor`: False511- `group_by_length`: False512- `length_column_name`: length513- `ddp_find_unused_parameters`: None514- `ddp_bucket_cap_mb`: None515- `ddp_broadcast_buffers`: False516- `dataloader_pin_memory`: True517- `dataloader_persistent_workers`: False518- `skip_memory_metrics`: True519- `use_legacy_prediction_loop`: False520- `push_to_hub`: False521- `resume_from_checkpoint`: None522- `hub_model_id`: None523- `hub_strategy`: every_save524- `hub_private_repo`: None525- `hub_always_push`: False526- `hub_revision`: None527- `gradient_checkpointing`: False528- `gradient_checkpointing_kwargs`: None529- `include_inputs_for_metrics`: False530- `include_for_metrics`: []531- `eval_do_concat_batches`: True532- `fp16_backend`: auto533- `push_to_hub_model_id`: None534- `push_to_hub_organization`: None535- `mp_parameters`: 536- `auto_find_batch_size`: False537- `full_determinism`: False538- `torchdynamo`: None539- `ray_scope`: last540- `ddp_timeout`: 1800541- `torch_compile`: False542- `torch_compile_backend`: None543- `torch_compile_mode`: None544- `include_tokens_per_second`: False545- `include_num_input_tokens_seen`: False546- `neftune_noise_alpha`: None547- `optim_target_modules`: None548- `batch_eval_metrics`: False549- `eval_on_start`: False550- `use_liger_kernel`: False551- `liger_kernel_config`: None552- `eval_use_gather_object`: False553- `average_tokens_across_devices`: False554- `prompts`: None555- `batch_sampler`: batch_sampler556- `multi_dataset_batch_sampler`: round_robin557- `router_mapping`: {}558- `learning_rate_mapping`: {}559 560</details>561 562### Framework Versions563- Python: 3.10.14564- Sentence Transformers: 5.0.0565- Transformers: 4.53.2566- PyTorch: 2.6.0+cu124567- Accelerate: 1.6.0568- Datasets: 3.6.0569- Tokenizers: 0.21.1570 571## Citation572 573### BibTeX574 575#### Sentence Transformers576```bibtex577@inproceedings{reimers-2019-sentence-bert,578    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",579    author = "Reimers, Nils and Gurevych, Iryna",580    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",581    month = "11",582    year = "2019",583    publisher = "Association for Computational Linguistics",584    url = "https://arxiv.org/abs/1908.10084",585}586```587 588#### MultipleNegativesRankingLoss589```bibtex590@misc{henderson2017efficient,591    title={Efficient Natural Language Response Suggestion for Smart Reply},592    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},593    year={2017},594    eprint={1705.00652},595    archivePrefix={arXiv},596    primaryClass={cs.CL}597}598```599 600#### If our work was helpful consider citing us ☺️601```bibtext602@misc{sinha2025bicaeffectivebiomedicaldense,603      title={BiCA: Effective Biomedical Dense Retrieval with Citation-Aware Hard Negatives}, 604      author={Aarush Sinha and Pavan Kumar S and Roshan Balaji and Nirav Pravinbhai Bhatt},605      year={2025},606      eprint={2511.08029},607      archivePrefix={arXiv},608      primaryClass={cs.IR},609      url={https://arxiv.org/abs/2511.08029}, 610}611```612 613<!--614## Glossary615 616*Clearly define terms in order to be accessible across audiences.*617-->618 619<!--620## Model Card Authors621 622*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*623-->624 625<!--626## Model Card Contact627 628*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*629-->