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1_Pooling/config.json

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+{
+    "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
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:129971
+- loss:MultipleNegativesRankingLoss
+base_model: thenlper/gte-small
+widget:
+- source_sentence: Integrated health care for infectious diseases and non-communicable
+    diseases in low-and middle-income countries
+  sentences:
+  - 'The purposes of this study were to create a new flow-chart of prehospital electrocardiography
+    (ECG)-transmission, evaluate its predictive ability for ST-elevation myocardial
+    infarction (STEMI) and shorten door-to-balloon time (DTBT). The new transmission
+    flow-chart was created using symptoms from previous medical records of STEMI patients.
+    A total of 4090 consecutive patients transferred emergently to our hospital were
+    divided into two groups: those in ambulances with an ECG-transmission device with
+    the new flow-chart (ECGT-FC) and those transferred without an ECG-transmission
+    device (non-ECGT) groups. A STEMI group comprising walk-in patients during the
+    same period was used as a control group. The predictive ability of STEMI and the
+    effectiveness of shortening the DTBT by the new flow-chart of ECG-transmission
+    was evaluated. In the ECGT-FC group, the prevalence of STEMI in the ECG-transmission
+    by the new flow-chart were significantly higher than in the non-ECG-transmission
+    patients (6.71% vs. 0.19%; p<0.001). The sensitivity and specificity of the new
+    ECG-transmission flow-chart were 83.3% and 88.1%, respectively. The median DTBT
+    was significantly shortened (p=0.045) and the prevalence of DTBT<90min was significantly
+    higher in the ECGT-FC group (p=0.018) than the other groups. The sensitivity and
+    specificity of the new flow-chart for ECG-transmission were high. The new flow-chart
+    combined with an ECG-transmission device could detect STEMI efficiently and shorten
+    DTBT.'
+  - 'Multiple strains of the SARS-CoV-2 have arisen and jointly influence the trajectory
+    of the coronavirus disease (COVID-19) pandemic. However, current models rarely
+    account for this multi-strain dynamics and their different transmission rate and
+    response to vaccines. We propose a new mathematical model that accounts for two
+    virus variants and the deployment of a vaccination program. To demonstrate utility,
+    we applied the model to determine the control reproduction number '
+  - The co-occurrence of infectious diseases (ID) and non-communicable diseases (NCD)
+    is widespread, presenting health service delivery challenges especially in low-and
+    middle-income countries (LMICs). Integrated health care is a possible solution
+    but may require a paradigm shift to be successfully implemented. This literature
+    review identifies integrated care examples among selected ID and NCD dyads. We
+    searched PubMed, PsycINFO, Cochrane Library, CINAHL, Web of Science, EMBASE, Global
+    Health Database, and selected clinical trials registries. Eligible studies were
+    published between 2010 and December 2022, available in English, and report health
+    service delivery programs or policies for the selected disease dyads in LMICs.
+    We identified 111 studies that met the inclusion criteria, including 56 on tuberculosis
+    and diabetes integration, 46 on health system adaptations to treat COVID-19 and
+    cardiometabolic diseases, and 9 on COVID-19, diabetes, and tuberculosis screening.
+    Prior to the COVID-19 pandemic, most studies on diabetes-tuberculosis integration
+    focused on clinical service delivery screening. By far the most reported health
+    system outcomes across all studies related to health service delivery (n = 72),
+    and 19 addressed health workforce. Outcomes related to health information systems
+    (n = 5), leadership and governance (n = 3), health financing (n = 2), and essential
+    medicines (n = 4)) were sparse. Telemedicine service delivery was the most common
+    adaptation described in studies on COVID-19 and either cardiometabolic diseases
+    or diabetes and tuberculosis. ID-NCD integration is being explored by health systems
+    to deal with increasingly complex health needs, including comorbidities. High
+    excess mortality from COVID-19 associated with NCD-related comorbidity prompted
+    calls for more integrated ID-NCD surveillance and solutions. Evidence of clinical
+    integration of health service delivery and workforce has grown-especially for
+    HIV and NCDs-but other health system building blocks, particularly access to essential
+    medicines, health financing, and leadership and governance, remain in disease
+    silos.
+- source_sentence: Foot-and-mouth disease virus 3C(pro) inhibits interferon-/ response
+    and expression of IFN-stimulated genes
+  sentences:
+  - Repeated bottleneck passages of RNA viruses result in accumulation of mutations
+    and fitness decrease. Here, we show that clones of foot-and-mouth disease virus
+    (FMDV) subjected to bottleneck passages, in the form of plaque-to-plaque transfers
+    in BHK-21 cells, increased the thermosensitivity of the viral clones. By constructing
+    infectious FMDV clones, we have identified the amino acid substitution M54I in
+    capsid protein VP1 as one of the lesions associated with thermosensitivity. M54I
+    affects processing of precursor P1, as evidenced by decreased production of VP1
+    and accumulation of VP1 precursor proteins. The defect is enhanced at high temperatures.
+    Residue M54 of VP1 is exposed on the virion surface, and it is close to the B-C
+    loop where an antigenic site of FMDV is located. M54 is not in direct contact
+    with the VP1-VP3 cleavage site, according to the three-dimensional structure of
+    FMDV particles. Models to account for the effect of M54 in processing of the FMDV
+    polyprotein are proposed. In addition to revealing a distance effect in polyprotein
+    processing, these results underline the importance of pursuing at the biochemical
+    level the biological defects that arise when viruses are subjected to multiple
+    bottleneck events.
+  - To improve the delivery of liposomes to tumors using P-selectin glycoprotein ligand
+    1 (PSGL1) mediated binding to selectin molecules, which are upregulated on tumorassociated
+    endothelium.
+  - Foot-and-mouth disease is a highly contagious viral illness of wild and domestic
+    cloven-hoofed animals. The causative agent, foot-and-mouth disease virus (FMDV),
+    replicates rapidly, efficiently disseminating within the infected host and being
+    passed on to susceptible animals via direct contact or the aerosol route. To survive
+    in the host, FMDV has evolved to block the host interferon (IFN) response. Previously,
+    we and others demonstrated that the leader proteinase (L(pro)) of FMDV is an IFN
+    antagonist. Here, we report that another FMDV-encoded proteinase, 3C(pro), also
+    inhibits IFN-α/β response and the expression of IFN-stimulated genes. Acting in
+    a proteasome- and caspase-independent manner, the 3C(pro) of FMDV proteolytically
+    cleaved nuclear transcription factor kappa B (NF-κB) essential modulator (NEMO),
+    a bridging adaptor protein essential for activating both NF-κB and interferon-regulatory
+    factor signaling pathways. 3C(pro) specifically targeted NEMO at the Gln 383 residue,
+    cleaving off the C-terminal zinc finger domain from the protein. This cleavage
+    impaired the ability of NEMO to activate downstream IFN production and to act
+    as a signaling adaptor of the RIG-I/MDA5 pathway. Mutations specifically disrupting
+    the cysteine protease activity of 3C(pro) abrogated NEMO cleavage and the inhibition
+    of IFN induction. Collectively, our data identify NEMO as a substrate for FMDV
+    3C(pro) and reveal a novel mechanism evolved by a picornavirus to counteract innate
+    immune signaling.
+- source_sentence: Measuring flourishing among adolescent and adult populations
+  sentences:
+  - Flourishing is an evolving wellbeing construct and outcome of interest across
+    the social and biological sciences. Despite some conceptual advancements, there
+    remains limited consensus on how to measure flourishing, as well as how to distinguish
+    it from closely related wellbeing constructs, such as thriving and life satisfaction.
+    This paper aims to provide an overview and comparison of the diverse scales that
+    have been developed to measure flourishing among adolescent and adult populations
+    to provide recommendations for future studies seeking to use flourishing as an
+    outcome in social and biological research.
+  - Although well-being at work is important for occupational health, multi-dimensional
+    workplace well-being measures do not exist for Japanese workers. The purpose of
+    this study was to investigate the validity of the Japanese version of the Workplace
+    PERMA-Profiler. Japanese workers completed online surveys at baseline (N = 310)
+    and 1 month later (N = 100). The Workplace PERMA-Profiler was translated according
+    to international guidelines. Job and life satisfaction, work engagement, psychological
+    distress, work-related psychosocial factors, and work performance were measured
+    as comparisons for convergent validity. Cronbach's alphas, Intra-class Correlation
+    Coefficients (ICCs), and measurement errors were calculated for the reliability,
+    and the validity of the measure was tested by correlational analyses and confirmatory
+    factor analysis. A total of 310 (baseline) and 86 (follow-up) workers responded
+    and were included in the analyses. Cronbach's alphas and ICCs of the Japanese
+    Workplace PERMA-Profiler ranged from 0.75 to 0.96. Confirmatory factor analysis
+    indicated that the 5-factor model demonstrated a marginally acceptable fit (χ2
+    (80) = 351.30, CFI = 0.892, TLI = 0.858, RMSEA = 0.105, SRMR = 0.051). Overall
+    well-being and the five PERMA domains had moderate-to-strong correlations with
+    job satisfaction, psychological distress (inversely), and work-related factors.
+    The Japanese version of the Workplace PERMA-Profiler demonstrated adequate reliability
+    and validity. This measure could be useful to assess well-being at work, promote
+    well-being research among Japanese workers, and address the problem of definition
+    for well-being in further studies.
+  - We experience countless pieces of new information each day, but remembering them
+    later depends on firmly instilling memory storage in the brain. Numerous studies
+    have implicated non-rapid eye movement (NREM) sleep in consolidating memories
+    via interactions between hippocampus and cortex. However, the temporal dynamics
+    of this hippocampal-cortical communication and the concomitant neural oscillations
+    during memory reactivations remains unclear. To address this issue, the present
+    study used the procedure of targeted memory reactivation (TMR) following learning
+    of object-location associations to selectively reactivate memories during human
+    NREM sleep. Cortical pattern reactivation and hippocampal-cortical coupling were
+    measured with intracranial EEG recordings in patients with epilepsy. We found
+    that TMR produced variable amounts of memory enhancement across a set of object-location
+    associations. Successful TMR increased hippocampal ripples and cortical spindles,
+    apparent during two discrete sweeps of reactivation. The first reactivation sweep
+    was accompanied by increased hippocampal-cortical communication and hippocampal
+    ripple events coupled to local cortical activity (cortical ripples and high-frequency
+    broadband activity). In contrast, hippocampal-cortical coupling decreased during
+    the second sweep, while increased cortical spindle activity indicated continued
+    cortical processing to achieve long-term storage. Taken together, our findings
+    show how dynamic patterns of item-level reactivation and hippocampal-cortical
+    communication support memory enhancement during NREM sleep.
+- source_sentence: Agrobacterium tumefaciens Hfq binds to sRNA AbcR1 and its target
+    mRNA atu2422
+  sentences:
+  - Amyloid β (Aβ) assemblies exist not only in the central nervous system, but can
+    circulate within the bloodstream to trigger and exacerbate peripheral, cerebrovascular,
+    and neurodegenerative disorders. Eliminating excess peripheral Aβ fibrils, therefore,
+    holds promise to improve the management of amyloid-related diseases. Here, we
+    present nanoemulsion-mediated ultrasonic ablation of circulating Aβ fibrils to
+    both destroy established plaques and prevent the re-growth of ablated fragments
+    back into toxic species. This approach is made possible using a de novo designed
+    peptide emulsifier that contains the self-associating sequence from the amyloid
+    precursor protein. Emulsification of the peptide surfactant with fluorous nanodroplets
+    produces contrast agents that rapidly adsorb Aβ assemblies and allows their ultrasound-controlled
+    destruction via acoustic cavitation. Vessel-mimetic flow experiments demonstrate
+    that nanoemulsion-assisted Aβ disruption can be achieved in circulation using
+    clinical diagnostic ultrasound transducers. Additional cell-based assays confirm
+    the ablated fragments are less toxic to neuronal and glial cells compared to mature
+    fibrils, and can be rapidly phagocytosed by both peripheral and brain macrophages.
+    These results highlight the potential of nanoemulsion contrast agents to deliver
+    new imaging enabled strategies for non-invasive management of Aβ-related diseases
+    using traditional diagnostic ultrasound modalities.
+  - The Hfq protein mediates gene regulation by small RNAs (sRNAs) in about 50% of
+    all bacteria. Depending on the species, phenotypic defects of an hfq mutant range
+    from mild to severe. Here, we document that the purified Hfq protein of the plant
+    pathogen and natural genetic engineer Agrobacterium tumefaciens binds to the previously
+    described sRNA AbcR1 and its target mRNA atu2422, which codes for the substrate
+    binding protein of an ABC transporter taking up proline and γ-aminobutyric acid
+    (GABA). Several other ABC transporter components were overproduced in an hfq mutant
+    compared to their levels in the parental strain, suggesting that Hfq plays a major
+    role in controlling the uptake systems and metabolic versatility of A. tumefaciens.
+    The hfq mutant showed delayed growth, altered cell morphology, and reduced motility.
+    Although the DNA-transferring type IV secretion system was produced, tumor formation
+    by the mutant strain was attenuated, demonstrating an important contribution of
+    Hfq to plant transformation by A. tumefaciens.
+  - Hfq is an RNA-binding protein that functions in post-transcriptional gene regulation
+    by mediating interactions between mRNAs and small regulatory RNAs (sRNAs). Two
+    proteins encoded by BAB1_1794 and BAB2_0612 are highly over-produced in a Brucella
+    abortus hfq mutant compared with the parental strain, and recently, expression
+    of orthologues of these proteins in Agrobacterium tumefaciens was shown to be
+    regulated by two sRNAs, called AbcR1 and AbcR2. Orthologous sRNAs (likewise designated
+    AbcR1 and AbcR2) have been identified in B. abortus 2308. In Brucella, abcR1 and
+    abcR2 single mutants are not defective in their ability to survive in cultured
+    murine macrophages, but an abcR1 abcR2 double mutant exhibits significant attenuation
+    in macrophages. Additionally, the abcR1 abcR2 double mutant displays significant
+    attenuation in a mouse model of chronic Brucella infection. Quantitative proteomics
+    and microarray analyses revealed that the AbcR sRNAs predominantly regulate genes
+    predicted to be involved in amino acid and polyamine transport and metabolism,
+    and Northern blot analyses indicate that the AbcR sRNAs accelerate the degradation
+    of the target mRNAs. In an Escherichia coli two-plasmid reporter system, overexpression
+    of either AbcR1 or AbcR2 was sufficient for regulation of target mRNAs, indicating
+    that the AbcR sRNAs from B. abortus 2308 perform redundant regulatory functions.
+- source_sentence: Neural correlates of advice evaluation and integration in the judge-advisor
+    paradigm
+  sentences:
+  - 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.
+  - The role of antibodies in kidney transplant (KT) has evolved significantly over
+    the past few decades. This role of antibodies in KT is multifaceted, encompassing
+    both the challenges they pose in terms of antibody-mediated rejection (AMR) and
+    the opportunities for improving transplant outcomes through better detection,
+    prevention, and treatment strategies. As our understanding of the immunological
+    mechanisms continues to evolve, so too will the approaches to managing and harnessing
+    the power of antibodies in KT, ultimately leading to improved patient and graft
+    survival. This narrative review explores the multifaceted roles of antibodies
+    in KT, including their involvement in rejection mechanisms, advancements in desensitization
+    protocols, AMR treatments, and their potential role in monitoring and improving
+    graft survival.
+  - 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.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on thenlper/gte-small
+
+This 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) <!-- at revision 17e1f347d17fe144873b1201da91788898c639cd -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
+  (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})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Neural correlates of advice evaluation and integration in the judge-advisor paradigm',
+    '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.',
+    '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.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.9575, 0.8147],
+#         [0.9575, 1.0000, 0.8303],
+#         [0.8147, 0.8303, 1.0000]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 129,971 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                         | sentence_2                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | string                                                                               |
+  | 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> |
+* Samples:
+  | sentence_0                                                                          | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | sentence_2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+  |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <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> |
+  | <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> |
+  | <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>                                                                                                                              |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 1
+- `max_steps`: 20
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 1
+- `max_steps`: 20
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 5.0.0
+- Transformers: 4.53.2
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.6.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    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},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.53.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d18c55aee96d6d4ab34ae9a0a2ea1f0493fa2ef63b669142cfded5af95b08280
+size 133462128

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}