Miracle-dz/UniFAR-FTUs huggingface dataset:
Fiber-Optic Distributed Strain Sensing Needle for Real-Time Guidance in Epidural Anesthesia

Epidural anesthesia is a pain relief treatment mainly used for pregnant women during delivery. The procedure is described as an injection of anesthetic fluid into an epidural space using a hemodynamic Tuohy needle. The correct identification of epidural space is crucial for successful anesthesia, but due to the blindness of the procedure, the existing methods can fail with potentially severe consequences for patients. Since the tissue preceding the epidural space has high density, the strain significantly changes when the needle reaches the proximity of the epidural space, and this change can be used as an indicator. Distributed fiber optic sensors, working with an optical backscatter reflectometer, can improve the scenario by measuring the strain along each section of the epidural needle. This work goes beyond the epidural space identification: distributed strain data detect strain events leading to a potential failure to reach the epidural space before the procedure is finalized. The experiments have shown that the penetrations along different paths (with different speeds, with misalignment, and with rotation) result in different strain patterns. The proposed method has been validated on phantoms reproducing the epidural space, in different insertion conditions.
Drug Target Commons 2.0: a community platform for systematic analysis of drug–target interaction profiles

Abstract Drug Target Commons (DTC) is a web platform (database with user interface) for community-driven bioactivity data integration and standardization for comprehensive mapping, reuse and analysis of compound–target interaction profiles. End users can search, upload, edit, annotate and export expert-curated bioactivity data for further analysis, using an application programmable interface, database dump or tab-delimited text download options. To guide chemical biology and drug-repurposing applications, DTC version 2.0 includes updated clinical development information for the compounds and target gene–disease associations, as well as cancer-type indications for mutant protein targets, which are critical for precision oncology developments.
Residuals and Diagnostics for Ordinal Regression Models: A Surrogate Approach

ABSTRACT Ordinal outcomes are common in scientific research and everyday practice, and we often rely on regression models to make inference. A long-standing problem with such regression analyses is the lack of effective diagnostic tools for validating model assumptions. The difficulty arises from the fact that an ordinal variable has discrete values that are labeled with, but not, numerical values. The values merely represent ordered categories. In this article, we propose a surrogate approach to defining residuals for an ordinal outcome Y. The idea is to define a continuous variable S as a “surrogate” of Y and then obtain residuals based on S. For the general class of cumulative link regression models, we study the residual’s theoretical and graphical properties. We show that the residual has null properties similar to those of the common residuals for continuous outcomes. Our numerical studies demonstrate that the residual has power to detect misspecification with respect to (1) mean structures; (2) link functions; (3) heteroscedasticity; (4) proportionality; and (5) mixed populations. The proposed residual also enables us to develop numeric measures for goodness of fit using classical distance notions. Our results suggest that compared to a previously defined residual, our residual can reveal deeper insights into model diagnostics. We stress that this work focuses on residual analysis, rather than hypothesis testing. The latter has limited utility as it only provides a single p-value, whereas our residual can reveal what components of the model are misspecified and advise how to make improvements. Supplementary materials for this article are available online.
Selective Classification via Curve Optimization

In practical applications of machine learning, it is often desirable to identify and abstain on examples where the model's predictions are likely to be incorrect. We consider the problem of selecting a budget-constrained subset of test examples to abstain on, with the goal of maximizing performance on the remaining examples. We develop a novel approach to this problem by analytically optimizing the expected marginal improvement in a desired performance metric, such as the area under the ROC curve or Precision-Recall curve. We compare our approach to other abstention techniques for deep learning models based on posterior probability and uncertainty estimates obtained using test-time dropout. On various tasks in computer vision, natural language processing, and bioinformatics, we demonstrate the consistent effectiveness of our approach over other techniques.
Role of Histamine in Modulating the Immune Response and Inflammation

Inflammatory mediators, including cytokines, histamine, bradykinin, prostaglandins, and leukotrienes, impact the immune system, usually as proinflammatory factors. Other mediators act as regulatory components to establish homeostasis after injury or prevent the inflammatory process. Histamine, a biogenic vasoactive amine, causes symptoms such as allergies and has a pleiotropic effect that is dependent on its interaction with its four histamine receptors. In this review, we discuss the dualistic effects of histamine: how histamine affects inflammation of the immune system through the activation of intracellular pathways that induce the production of inflammatory mediators and cytokines in different immune cells and how histamine exerts regulatory functions in innate and adaptive immune responses. We also evaluate the interactions between these effects.
Analyzing the Linguistic Structure of Question Texts to Characterize Answerability in Quora

Quora is one of the most popular community question & answer (Q&A) sites of recent times. However, with increasing question posts over time and the posts covering a wide range of topics (unlike focused Q&A sites like Stack Overflow), not all of them are getting answered. Measuring <italic>answerability</italic> (i.e., whether a question shall get answered or not) involves collecting expensive human judgment data that can differentiate the characteristics of an answered question from an unanswered (aka <italic>open</italic>) one. Factors to judge if a question would remain open include its subjectivity, openendedness, vagueness, ambiguity, and so on. It is difficult to collect such judgments for thousands of questions, requiring automatic framework to deal the issue of answerability of questions. In this paper, we quantify: 1) <italic>user-level</italic> and 2) <italic>question-level linguistic activities</italic>—that can <italic>nicely correspond to many of the judgment factors noted earlier</italic>, can be <italic>easily measured for each question post</italic> and that <italic>appropriately discriminates an answered question from an unanswered one</italic>. Our central finding is that <italic>the way users use language while writing the question text can be a very effective means to characterize answerability. </italic> This characterization further helps us to predict early if a question remaining unanswered for a specific time period <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> will eventually be answered or not and achieve an accuracy of 76.26% (<inline-formula> <tex-math notation="LaTeX">$t=1$ </tex-math></inline-formula> month) and 68.33% (<inline-formula> <tex-math notation="LaTeX">$t=3$ </tex-math></inline-formula> months). Notably, features representing the <italic>language use patterns</italic> of the users are most discriminative and alone account for an accuracy of 74.18%. We also compare our method with some of the similar works <xref ref-type="bibr" rid="ref1">[1]</xref>, <xref ref-type="bibr" rid="ref2">[2]</xref> achieving a maximum improvement of ~39% in terms of accuracy.
Statistical Analysis of the Slavonic Paraenesis by Ephrem the Syrian (on Three Electronic Copies of the 13–14th Centuries from the Manuscript Corpus)

The work presents an experience of applying statistical methods to discovering thematically valuable words in three Old Russian (Old East Slavic) copies of the Ephrem the Syrian’s Paraenesis. The quantitative data were obtained with the help of the search forms in the historical corpus Manuscript (manuscripts.ru), namely the multitext and N-Gram modules. The basic corpus for analysis of the 28 most frequent lemmas of content words from the Paraenesis (the collection volume exceeds 100 thousand word forms) comprised five corpus collections of different genres: copies of the Menaion for May, Service Menaions for other months of the year, Sticherarion (Book of stichera), Acts and Epistles of the Apostles, and Gospels (the total amount of word forms is more than 1 million). To evaluate the lemmas obtained with the help of the system automatic morphological analyser the statistic TF-ICTFʹ (version of the weighting scheme TF-IDF) and Log-Likelihood were used. The increase of the number of analysed lemmas from 10 to 28 allowed demonstration of the great statistical weight of lemmas that are used less often than the most frequent lemmas. To eliminate the discrepancies in the statistical evaluation of lemmas there were made a comparison of the lemmas’ ranks and corresponding diagrams. The analysis of the diagrams made it possible to find the core and periphery of the lists; identify the lemmas with the greatest averaged statistical weight – ПОМЫСЛЪ and СТРАХЪ and also the lemmas in the nearest periphery – ВЕКЪ, ЖИТИ, ХОТЕТИ, БРАТИЯ that represent the orientation of the Paraenesis texts to the spiritual search and motifs of punishment and apocalypse. The conclusion concerns efficiency and effectiveness of the statistical methods as regards the evaluation of linguistic data in the historical corpora that due to the objective causes considerably cede to the modern corpora by their volume. Moreover, the materials for analysis shall involve the data from the entire corpus of the Slavonic texts (Manuscript) and the entire list of word forms (lemmas) from the analysed manuscript (subcorpus).
Dielectrophoretic separation of microalgae cells in ballast water in a microfluidic chip

The composition of the ship's ballast water is complex and contains a large number of microalgae cells, bacteria, microplastics, and other microparticles. To increase the accuracy and efficiency of detection of the microalgae cells in ballast water, a new microfluidic chip for continuous separation of microalgae cells based on alternating current dielectrophoresis was proposed. In this microfluidic chip, one piece of 3‐dimensional electrode is embedded on one side and eight discrete electrodes are arranged on the other side of the microchannel. An insulated triangular structure between electrodes is designed for increasing the inhomogeneity of the electric field distribution and enhancing the dielectrophoresis (DEP) force. A sheath flow is designed to focus the microparticles near the electrode, so as to increase the suffered DEP force and improve separation efficiency. To demonstrate the performance of the microfluidic separation chip, we developed two species of microalgae cells (Platymonas and Closterium) and a kind of microplastics to be used as test samples. Analyses of the related parameters and separation experiments by our designed microfluidic chip were then conducted. The results show that the presented method can separate the microalgae cells from the mixture efficiently, and this is the first time to separate two or more species of microalgae cells in a microfluidic chip by using negative and positive DEP force simultaneously, and moreover it has some advantages including simple operation, high efficiency, low cost, and small size and has great potential in on‐site pretreatment of ballast water.
The Inflammation-Based Index Can Predict Response and Improve Patient Selection in NETs Treated With PRRT: A Pilot Study

Background Peptide receptor radionuclide therapy (PRRT) is an effective treatment of certain patients with metastatic neuroendocrine tumors (NETs). Tumor response is highly variable; no biomarker in clinical practice has been demonstrated to reliably predict outcome. The inflammation-based index (IBI), derived from serum C-reactive protein and albumin levels, predicts survival and response to treatment in patients in several cancer types and was therefore explored in this setting. Materials and Methods Clinico-pathological data from patients undergoing PRRT for metastatic NETs were collected at baseline and during treatment. The primary endpoint was progression-free survival (PFS) with a secondary endpoint of overall survival (OS). Cox regression analysis tested associations between baseline variables and their dynamic changes and PFS and OS. Decision curve analysis (DCA) was used to determine the net benefit associated with a treatment strategy determined by the baseline IBI and nonresponse to PRRT. Results Fifty-five patients were recruited. Baseline IBI > 0 was associated with inferior PFS (hazard ratio, 14.2; 95% CI, 5.25 to 38.5; P < 0.001) and OS (P < 0.001). Multivariate analysis confirmed an independent association between IBI and PFS (P = 0.001). DCA indicated a net clinical benefit at risk thresholds between 0.03 and 0.58. Conclusion Baseline IBI score and its dynamic change through treatment are associated with both PFS and OS. At a risk threshold equivalent to the currently accepted rate of nonresponse to therapy, implementation of this easily derived score could avoid a substantial number of futile treatments. These findings should be validated in additional independent cohorts.
Analysing the efficacy of the I-gel supraglottic airway device in the supine and lateral decubitus positions.

BACKGROUND The advantages of the I-gel supraglottic airway device include ease and speed of insertion, reduced trauma incidence, an integral bite block, gastric access, a non-inflatable cuff and superior seal pressure. The primary goal of this study was to compare airway leak pressures and the fibreoptic view in the supine and lateral positions. Our secondary aim was to analyse the effects of I-gel insertion on haemodynamic parameters. METHODS One hundred patients undergoing saturation biopsy due to prostatic hyperplasia were recruited to this prospective randomised study. An I-gel device was inserted in the supine position. Taking of measurements, patients were placed in the lateral decubitus position. Mean arterial pressure, heart rate, peripheral O₂ saturation and end-tidal CO₂ were recorded before and after insertion. We recorded the number of attempts and insertion time for the I-gel device. Oropharyngeal leak pressures and I-gel device positioning were scored in the lateral decubitus and supine positions. RESULTS It was possible to insert the I-gel device in 88 patients on the first attempt. The median time for insertion was 7.97 ± 2.18 sec. The mean arterial pressure and heart rate decreased 1 and 2 min after insertion. Oropharyngeal leak pressure was similar in the supine (27.45 ± 5.37 mm Hg) and lateral decubitus positions (26.04 ± 4.92 mm Hg) (P > 0.05). On fibreoptic examination through the I-gel device, the scores of patients were comparable in different positions (P = 0.542). CONCLUSION As there was no significant difference in oropharyngeal leak pressure and fibreoptic view, we concluded that the I-gel device may be used safely in both the supine and lateral positions.
Circulating tumor DNA as a marker of treatment response in BRAF V600E mutated non-melanoma solid tumors

Purpose We evaluated longitudinal tracking of BRAF V600E in circulating cell-free DNA (cfDNA) as a marker of treatment response to BRAF inhibitor (BRAFi) combination therapies in non-melanoma solid tumors included in the Copenhagen Prospective Personalized Oncology (CoPPO) program. Experimental design Patients with BRAF V600E-mutated tumors were treated with combination therapies including BRAFi. Quantification of mutant cfDNA from plasma was determined and correlated to clinical outcomes. Exome sequencing was performed to identify possible resistance mutations. Results Twenty-three patients had BRAF-mutated tumors out of 455 patients included in CoPPO and 17 started BRAFi combination (EGFRi/MEKi) therapy. Tumor responses were achieved in 8 out of 16 evaluable patients and the median overall- and progression-free survival (OS and PFS) was 15 and 4.8 months, respectively. Longitudinal measurements of BRAF V600E-mutant cfDNA indicated disease progression prior to radiological evaluation and a reduction in the mutant fraction of more than 50% after 4 and 12 weeks of therapy was associated with a significantly longer PFS (p=0.003 and p=0.029) and OS (p=0.029 and p=0.017). Furthermore, the baseline mutant fraction and total level of cfDNA positively correlated with tumor burden (p=0.026 and p=0.024). Finally, analysis of cfDNA at progression revealed novel mutations potentially affecting the MAPK pathway. Conclusion BRAFi combination therapies showed a response rate of 50% in BRAF V600E-mutated non-melanoma tumors. The fraction of BRAF-mutant cfDNA represent a sensitive indicator for clinical outcomes with plasma collected at week 4 and 12 as crucial time points for monitoring response and disease progression.
FOXA2 depletion leads to mucus hypersecretion in canine airways with respiratory diseases

Because of exposure to environmental pollutants, infectious agents, and genetic predisposition, companion animals develop respiratory illnesses similar to those in humans. Older dogs of smaller breeds develop canine infectious respiratory disease, chronic bronchitis, and chronic obstructive pulmonary disease, with chronic lung infection, airway goblet cell hyperplasia and metaplasia, and mucus hypersecretion. Excessive mucus clogs airways, reduces gas exchanges, disables the mucociliary clearance, and reduces drug penetration. The Forkhead box protein A2 (FOXA2) is a key transcriptional regulator that maintains airway mucus homeostasis. Prior studies have shown that FOXA2 expression is frequently depleted in diseased human airways. Unfortunately, FOXA2 depletion has not been examined in dogs. Our current study indicated that both single bacterial infection by Pseudomonas aeruginosa and Bordetella bronchiseptica and polymicrobial infection by viral/bacterial pathogens depleted FOXA2 in canine airways, resulting in goblet cell hyperplasia and metaplasia and excessive mucus production. Furthermore, P. aeruginosa virulence factor pyocyanin activated the antagonistic STAT6 and epidermal growth factor receptor signalling pathways to inhibit FOXA2. Unravelling the mechanism of FOXA2 inactivation will hasten the development of non‐antibiotic therapeutics to improve mucociliary clearance of pathogens in canine airway.
Naysaying and Negativity Promote Initial Power Establishment and Leadership Endorsement

Conventional wisdom holds that leaders should behave in a supportive and positive manner. Yet the past decade has also seen a rise in naysayers’ ascent to power. This research investigates the intriguing possibility that although we may want our leaders to be cheerleaders, we instead empower naysayers. Integrating theoretical perspectives from psychology, leadership, and organizational theory, I present the Naysaying-Agency-Power-Leadership Efficacy (NAPLE) model, which captures the causal link between naysaying and power, and examine leadership efficacy as a downstream implication. Eleven studies provide empirical support for the model. Ten experimental studies demonstrate that naysaying and power are causally linked through the perception of agency. An additional study analyzed 518 eligible voters’ assessments of actual statements from U.S. presidential debates between 1980 and 2008. Results reveal that voters perceived negative and critical presidential candidates as more powerful and, in turn, were more willing to vote for them; this finding was robust to controlling for age, gender, ethnicity, political orientation, and voting history. By systematically establishing that naysaying induces power at the onset, and why, these findings illuminate an unanticipated, yet formidable, determinant of power.
Consumption of vitamin and mineral supplements and its correlates among medical students in Eastern Province, Saudi Arabia

Background: Although vitamin and mineral supplements are popular in many countries, few studies have documented their use among college students. Moreover, there is not much national data on the use of supplements by Saudi medical students. The aim of this study was to determine the prevalence of the use of vitamin and mineral supplements by female medical students at Imam Abdulrahman Bin Faisal University (IAU) in Dammam, Saudi Arabia, and to identify characteristics associated with the use of supplements in this population. Materials and Methods: For this cross-sectional study, all female medical students at IAU in Dammam, Saudi Arabia, were approached during September to November 2016. Results were presented as frequency distribution. Chi-square test and logistic regression analysis were performed to determine factors associated with supplement use; P < 0.05 was considered statistically significant. Results: Completed questionnaires were returned by 474 students (response rate of 77%). The prevalence of the use of vitamin and mineral supplements was 44.6%; Vitamin D was the most commonly used supplement. Association of supplement use with a higher family income and the habit of regular exercise was statistically significant. However, this use of supplements was not significantly associated with smoking or marital status. Conclusion: The use of supplements, particularly Vitamin D, by female medical students was 44.6%. The highest percentage of users belonged to families with higher incomes and had the habit of exercising regularly.
Choosing to Rank

Ranking data arises in a wide variety of application areas but remains difficult to model, learn from, and predict. Datasets often exhibit multimodality, intransitivity, or incomplete rankings---particularly when generated by humans---yet popular probabilistic models are often too rigid to capture such complexities. In this work we leverage recent progress on similar challenges in discrete choice modeling to form flexible and tractable choice-based models for ranking data. We study choice representations, maps from rankings (complete or top-$k$) to collections of choices, as a way of forming ranking models from choice models. We focus on the repeated selection (RS) choice representation, first used to form the Plackett-Luce ranking model from the conditional multinomial logit choice model. We fully characterize, for a prime number of alternatives, the choice representations that admit ranking distributions with unit normalization, a desirably property that greatly simplifies maximum likelihood estimation. We further show that only specific minor variations on repeated selection exhibit this property. Our choice-based ranking models provide higher out-of-sample likelihood when compared to Plackett-Luce and Mallows models on a broad collection of ranking tasks including food preferences, ranked-choice elections, car racing, and search engine relevance tasks.