Miracle-dz/UniFAR-FTUs huggingface dataset:
Reflection efficiencies of AnaConDa-S and AnaConDa-100 for isoflurane under dry laboratory and simulated clinical conditions: a bench study using a test lung

ABSTRACT Background: Adequate sedation is important for the treatment of ICU patients. AnaConDa (Anesthetic-Conserving-Device; ACD; Sedana Medical, Sweden), connected between ventilator and the patient, retains isoflurane during expiration, and releases it back during inspiration. The reflection efficiency (RefEff) corresponds to the percentage of expired anesthetic molecules that are re-inspired. We compared RefEff of AnaConDa-S (ACD-50) and AnaConDa-100 (ACD-100) under laboratory (DRY) and simulated clinical conditions (CLIN) using a test lung. Methods: Measurements were made under DRY and CLIN, with different tidal volumes (TV: 300 mL & 500 mL) and infusion rates (0.5–10 mL·h−1). RefEff was calculated from the isoflurane concentration in the test-lung (CISO) and plotted against the anesthetic vapor volume exhaled in one breath (V-exh = CISO·TV). Results: DRY: RefEff of both devices was ≈90% over a wide range of V-exh, but decreased when V-exh exceeded 5–7 mL (ACD-50) or 10–15 mL (ACD-100). CLIN: RefEff of ACD-50 was 70–80% (ACD-100: 80–90%), decreasing gradually with increasing V-exh. For 1 Vol.% isoflurane at TV500, the infusion rate with ACD-50 was twofold higher compared to ACD-100 (4 versus 2 mL·h−1). Conclusion: Under DRY and concentrations <1.5 Vol.%, RefEff of both devices is around 90%. Under CLIN, ACD-100 performs better with RefEff between 80% and 90% (ACD-50:70–80%), decreasing with increased vapor volume exhaled in one breath.
Author-Reviewer Homophily in Peer Review

The fairness of scholarly peer review has been challenged by evidence of disparities in publication outcomes based on author demographic characteristics. To assess this, we conducted an exploratory analysis of peer review outcomes of 23,876 initial submissions and 7,192 full submissions that were submitted to the biosciences journal eLife between 2012 and 2017. Women and authors from nations outside of North America and Europe were underrepresented both as gatekeepers (editors and peer reviewers) and authors. We found evidence of a homophilic relationship between the demographics of the gatekeepers and authors and the outcome of peer review; that is, there were higher rates of acceptance in the case of gender and country homophily. The acceptance rate for manuscripts with male last authors was seven percent, or 3.5 percentage points, greater than for female last authors (95% CI = [0 . 5 , 6 . 4]); this gender inequity was greatest, at nine percent or about 4.8 percentage points (95% CI = [0 . 3 , 9 . 1]), when the team of reviewers was all male; this difference was smaller and not significantly different for mixed-gender reviewer teams. Homogeny between countries of the gatekeeper and the corresponding author was also associated with higher acceptance rates for many countries. To test for the persistence controlling for many potentially confounding factors. These results provide evidence affirming observations from the univariate analysis in Fig 6.
Fecal microbiota transplantation brings about bacterial strain displacement in patients with inflammatory bowel diseases

Fecal microbiota transplantation (FMT), which is thought to have the potential to correct dysbiosis of gut microbiota, has recently been used to treat inflammatory bowel disease (IBD). To elucidate the extent and principles of microbiota engraftment in IBD patients after FMT treatment, we conducted an interventional prospective cohort study. The cohort included two categories of patients: (1) patients with moderate to severe Crohn’s disease (CD) (Harvey-Bradshaw Index ≥ 7, n = 11, and (2) patients with ulcerative colitis (UC) (Montreal classification, S2 and S3, n = 4). All patients were treated with a single FMT (via mid-gut, from healthy donors) and follow-up visits were performed at baseline, 3 days, one week, and one month after FMT (missing time points included). At each follow-up time point, fecal samples of the participants were collected along with their clinical metadata. For comparative analysis, 10 fecal samples from 10 healthy people were included to represent the diversity level of normal gut microbiota. Additionally, the metagenomic data of 25 fecal samples from 5 individuals with metabolic syndrome who underwent autologous FMT treatment were downloaded from a previous published paper to represent natural microbiota shifts during FMT. All fecal samples underwent shotgun metagenomic sequencing.We found that 3 days after FMT, 11 out of 15 recipients were in remission (3 out of 4 UC recipients; 8 out of 11 CD recipients). Generally, bacterial colonization was observed to be lower in CD recipients than in UC recipients at both species and strain levels. Furthermore, across species, different strains displayed disease-specific displacement advantages under two-disease status. Finally, most post-FMT species (> 80%) could be properly predicted (AUC > 85%) using a random forest classification model, with the gut microbiota composition and clinical parameters of pre-FMT recipients acting as the most contributive factors for prediction accuracy.
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors

Machine learning (ML), especially deep learning (DL) techniques have been increasingly used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has shown to be extremely vulnerable to adversarial attacks, especially in such security-sensitive systems. Many adversarial attacks have been proposed to evaluate the robustness of ML-based NIDSs. Unfortunately, existing attacks mostly focused on feature-space and/or white-box attacks, which make impractical assumptions in real-world scenarios, leaving the study on practical gray/black-box attacks largely unexplored. To bridge this gap, we conduct the first systematic study of the gray/black-box traffic-space adversarial attacks to evaluate the robustness of ML-based NIDSs. Our work outperforms previous ones in the following aspects: (i) practical —the proposed attack can automatically mutate original traffic with extremely limited knowledge and affordable overhead while preserving its functionality; (ii) generic —the proposed attack is effective for evaluating the robustness of various NIDSs using diverse ML/DL models and non-payload-based features; (iii) explainable —we propose an explanation method for the fragile robustness of ML-based NIDSs. Based on this, we also propose a defense scheme against adversarial attacks to improve system robustness. We extensively evaluate the robustness of various NIDSs using diverse feature sets and ML/DL models. Experimental results show our attack is effective (e.g., >97% evasion rate in half cases for Kitsune, a state-of-the-art NIDS) with affordable execution cost and the proposed defense method can effectively mitigate such attacks (evasion rate is reduced by >50% in most cases).
Hypotheses for the Reasons behind Beer Consumer’s Willingness to Purchase Beer: An Expanded Theory from a Planned Behavior Perspective

Because beer is one of the most common alcoholic beverages consumed in the world, this research adopted an expanded theory of planned behavior (TPB) perspective to understand why beer consumers purchase beer. This study investigated the effects of injunctive norms, descriptive norms, attitude, and perceived behavioral control on alcohol identity and purchase intention. The possible mediating role of alcohol identity was also investigated. This study was conducted in Taiwan, and a total of 452 beer consumers participated in the survey. Structural equation modeling was used to examine the relationship among the study variables. The results revealed that alcohol identity positively influences purchase intention, and attitude positively affects alcohol identity and purchase intention. In addition, injunctive norms have a positive influence on alcohol identity, and descriptive norms positively affect purchase intention. In particular, perceived behavioral control has a negative influence on alcohol identity but has a positive influence on purchase intention. This study also found that alcohol identity mediates the attitude–purchase intention relationship. By examining the consumption behavior of beer consumers from the TPB perspective, this study contributed to an understanding of beer consumption behavior.
Blended Natural Support Materials—Collagen Based Hydrogels Used in Biomedicine

Due to their unique properties—the are biocompatible, easily accessible, and inexpensive with programmable properties—biopolymers are used in pharmaceutical and biomedical research, as well as in cosmetics and food. Collagen is one of the most-used biomaterials in biomedicine, being the most abundant protein in animals with a triple helices structure, biocompatible, biomimetic, biodegradable, and hemostatic. Its disadvantages are its poor mechanical and thermal properties and enzymatic degradation. In order to solve this problem and to use its benefits, collagen can be used blended with other biomaterials such as alginate, chitosan, and cellulose. The purpose of this review article is to offer a brief paper with updated information on blended collagen-based formulations and their potential application in biomedicine.
Non-Thermal Plasma—A New Green Priming Agent for Plants?

Since the earliest agricultural attempts, humankind has been trying to improve crop quality and yields, as well as protect them from adverse conditions. Strategies to meet these goals include breeding, the use of fertilisers, and the genetic manipulation of crops, but also an interesting phenomenon called priming or adaptive response. Priming is based on an application of mild stress to prime a plant for another, mostly stronger stress. There are many priming techniques, such as osmopriming, halopriming, or using physical agents. Non-thermal plasma (NTP) represents a physical agent that contains a mixture of charged, neutral, and radical (mostly reactive oxygen and nitrogen species) particles, and can cause oxidative stress or even the death of cells or organisms upon interaction. However, under certain conditions, NTP can have the opposite effect, which has been previously documented for many plant species. Seed surface sterilization and growth enhancement are the most-reported positive effects of NTP on plants. Moreover, some studies suggest the role of NTP as a promising priming agent. This review deals with the effects of NTP treatment on plants from interaction with seed and cell surface, influence on cellular molecular processes, up to the adaptive response caused by NTP.
Association between Exposure to Ambient Air Pollution and Age-Related Cataract: A Nationwide Population-Based Retrospective Cohort Study

This study aimed to investigate the association between ambient air pollutants and cataracts in the general population aged 50 years or older using data from the Korean National Insurance Service—National Sample Cohort. Cataract patients were defined as those diagnosed by a physician and having undergone cataract surgery. After matching the average concentrations of PM2.5, PM10, NO2, CO, SO2, and O3 in residential areas, the association between quartile level of air pollutants and incidence of cataract was analyzed using a multivariate Cox-proportional hazard risk model. Among the 115,728 participants, 16,814 (14.5%) were newly diagnosed with cataract and underwent related surgery between 1 January 2004, and 31 December 2015. Exposure to PM10, NO2, and SO2 was positively associated with cataract incidence, while O3 was negatively associated. The adjusted hazard ratio (HR) with 95% confidence interval was 1.069 (1.025–1.115) in PM10 and 1.080 (1.030–1.133) in NO2. However, the association between cataract and the quartile of PM2.5 measured during one year in 2015 was not clear. The HR of female participants aged 65 or older was significantly increased according to quartile of air pollutants. We identified exposure to PM10, NO2, SO2, and O3 associated with cataract development in Korean adults aged ≥ 50 years. This information may be helpful for policymaking to control air pollution as a risk factor for eye health.
Trends and Determinants of EBF among Adolescent Children Born to Adolescent Mothers in Rural Bangladesh

Exclusive breastfeeding (EBF) has proven benefits for both mothers and infants; however, adolescent mothers have poor EBF practices globally. In Bangladesh, the practice of EBF among adolescent mothers remains unexplored. The aim of our study was to understand the EBF practices among adolescent mothers and their determinants in both the Health and Demographic Surveillance (HDSS) system areas of the International Centre for Diarrheal Disease Research, Bangladesh (icddr,b) service area (ISA) and government service area (GSA) in rural Matlab, Bangladesh. For the purpose of our study, we collected relevant information from the database of the Health and Demographic Surveillance System (HDSS) of icddr,b and performed analysis to understand the trends and identify the determinants of EBF and identify the determinants of EBF among adolescent mothers living in two areas between 2007 and 2015. In total, 2947 children born to adolescent mothers were included in our final analysis. We used the Kaplan–Meier and the Cox-proportional hazards models to determine the differences in EBF practices in the two areas. We noted a lower trends of EBF in the ISA compared to the GSA in bivariate analysis. However, after adjusting for confounding variables, EBF status was 15% lower in the GSA than the ISA (HR: 0.85, 95% CI: 0.72–0.99). The father’s education was significantly different among the two populations. In both study areas, the coverage of EBF among adolescent mothers was lower than the national average (42% vs. 65%). Ensuring that adolescent mothers receive optimal care may improve EBF behavior.
Associations of Coarse Grain Intake with Undiagnosed Hypertension among Chinese Adults: Results from the China Kadoorie Biobank

Whole grain intake was associated with better blood pressure control, but evidence is lacking in non-Western populations with different grain intake patterns. We aimed to determine the associations between coarse grain intake, usually considered as the best proxy of whole grain intake for Chinese diets, with blood pressure and undiagnosed hypertension using baseline data from the China Kadoorie Biobank study. After excluding participants with clinically diagnosed hypertension or use of antihypertensive dugs, 435,907 participants were included in our analysis. A self-reported questionnaire was used to measure coarse grain intake frequency. Overall, 12.8% and 29.2% of the participants reported daily consumption and never consumption, respectively. With multivariable adjustments including BMI, outdoor temperature, and physical activity, higher frequency of coarse grain intake was associated with lower systolic and diastolic blood pressure in those older than 40 years, p trend < 0.05. Compared to never consumers, the odds ratio (95% CI) of hypertension was 0.78 (0.73–0.84), 0.84 (0.77–0.91), 0.91 (0.88–0.94), and 0.97 (0.95–0.99) for daily, 4–6 days/week, 1–3 days/week, and monthly groups, P trend < 0.001. Our cross-sectional study in a nationwide sample of Chinese adults suggests that higher coarse grain intake was associated with lower blood pressure and lower hypertension risk.
Reduced Pulsatile Trabecular Meshwork Motion in Eyes With Primary Open Angle Glaucoma Using Phase-Sensitive Optical Coherence Tomography

Purpose The purpose of this study was to investigate the difference in pulsatile trabecular meshwork (TM) motion between normal and eyes with POAG using phase-sensitive optical coherence tomography (PhS-OCT). Methods In this cross-sectional study, eight healthy subjects (16 eyes) and nine patients with POAG (18 eyes) were enrolled. A laboratory-based prototype PhS-OCT system was used to measure pulsatile TM motion. PhS-OCT images were analyzed to obtain parameters of pulsatile TM motion (i.e. maximum velocity [MV] and cumulative displacement [CDisp]). Outflow facility and ocular pulse amplitude were measured using pneumotonography. Detection sensitivity was compared among various parameters by calculating the area under the receiver operating characteristic curves (AUCs). Results A pulsatile TM motion waveform synchronous with digital pulse was observed using PhS-OCT in both healthy and POAG eyes. The mean MV in eyes with glaucoma was significantly lower than healthy eyes (P < 0.001). The mean CDisp in POAG eyes was also significantly lower than healthy eyes (P < 0.001). CDisp showed a significant correlation (r = 0.46; P = 0.0088) with ocular pulse amplitude in the study. Compared with the outflow facility, both the MV and CDisp were found to have a better discrimination of glaucoma (P < 0.001 and P = 0.0074, respectively). Conclusions Pulsatile TM motion was reduced in patients with POAG compared to healthy subjects. The underlying mechanism may be due to the altered tissue stiffness or other biomechanical properties of the TM in POAG eyes. Our evidence suggests that the measurement of pulsatile TM motion with PhS-OCT may help in characterizing outflow pathway abnormalities.
Predicting and Quantifying Antagonistic Effects of Natural Compounds Given with Chemotherapeutic Agents: Applications for High-Throughput Screening

Simple Summary Increasing numbers of cancer patients are turning to complementary and alternative medicines (CAM) to facilitate or replace their cancer treatments, or they are obtaining natural products in their diet. This is concerning, as there is evidence of molecular interactions between these bioactive compounds and cancer drugs that can impede or reverse their efficacy and prevent cancer regression. High-throughput drug screening and deep learning techniques have successfully been applied in the past to evaluate synergistic cancer drug and natural product combinations. However, these techniques should be applied more commonly in the context of drug antagonism to uncover potentially harmful interactions and drive safer recommendations for cancer patients. In this review, we evaluate the antagonistic interactions between natural products and chemotherapeutics and highlight how the application of high-throughput screening and deep learning techniques can strengthen this area of research. Abstract Natural products have been used for centuries to treat various human ailments. In recent decades, multi-drug combinations that utilize natural products to synergistically enhance the therapeutic effects of cancer drugs have been identified and have shown success in improving treatment outcomes. While drug synergy research is a burgeoning field, there are disagreements on the definitions and mathematical parameters that prevent the standardization and proper usage of the terms synergy, antagonism, and additivity. This contributes to the relatively small amount of data on the antagonistic effects of natural products on cancer drugs that can diminish their therapeutic efficacy and prevent cancer regression. The ability of natural products to potentially degrade or reverse the molecular activity of cancer therapeutics represents an important but highly under-emphasized area of research that is often overlooked in both pre-clinical and clinical studies. This review aims to evaluate the body of work surrounding the antagonistic interactions between natural products and cancer therapeutics and highlight applications for high-throughput screening (HTS) and deep learning techniques for the identification of natural products that antagonize cancer drug efficacy.
Comparison of Soil Total Nitrogen Content Prediction Models Based on Vis-NIR Spectroscopy

Visible-near-infrared spectrum (Vis-NIR) spectroscopy technology is one of the most important methods for non-destructive and rapid detection of soil total nitrogen (STN) content. In order to find a practical way to build STN content prediction model, three conventional machine learning methods and one deep learning approach are investigated and their predictive performances are compared and analyzed by using a public dataset called LUCAS Soil (19,019 samples). The three conventional machine learning methods include ordinary least square estimation (OLSE), random forest (RF), and extreme learning machine (ELM), while for the deep learning method, three different structures of convolutional neural network (CNN) incorporated Inception module are constructed and investigated. In order to clarify effectiveness of different pre-treatments on predicting STN content, the three conventional machine learning methods are combined with four pre-processing approaches (including baseline correction, smoothing, dimensional reduction, and feature selection) are investigated, compared, and analyzed. The results indicate that the baseline-corrected and smoothed ELM model reaches practical precision (coefficient of determination (R2) = 0.89, root mean square error of prediction (RMSEP) = 1.60 g/kg, and residual prediction deviation (RPD) = 2.34). While among three different structured CNN models, the one with more 1 × 1 convolutions preforms better (R2 = 0.93; RMSEP = 0.95 g/kg; and RPD = 3.85 in optimal case). In addition, in order to evaluate the influence of data set characteristics on the model, the LUCAS data set was divided into different data subsets according to dataset size, organic carbon (OC) content and countries, and the results show that the deep learning method is more effective and practical than conventional machine learning methods and, on the premise of enough data samples, it can be used to build a robust STN content prediction model with high accuracy for the same type of soil with similar agricultural treatment.
Molecular Insights into Determinants of Translational Readthrough and Implications for Nonsense Suppression Approaches

The fidelity of protein synthesis, a process shaped by several mechanisms involving specialized ribosome regions and external factors, ensures the precise reading of sense and stop codons. However, premature termination codons (PTCs) arising from mutations may, at low frequency, be misrecognized and result in PTC suppression, named ribosome readthrough, with production of full-length proteins through the insertion of a subset of amino acids. Since some drugs have been identified as readthrough inducers, this fidelity drawback has been explored as a therapeutic approach in several models of human diseases caused by nonsense mutations. Here, we focus on the mechanisms driving translation in normal and aberrant conditions, the potential fates of mRNA in the presence of a PTC, as well as on the results obtained in the research of efficient readthrough-inducing compounds. In particular, we describe the molecular determinants shaping the outcome of readthrough, namely the nucleotide and protein context, with the latter being pivotal to produce functional full-length proteins. Through the interpretation of experimental and mechanistic findings, mainly obtained in lysosomal and coagulation disorders, we also propose a scenario of potential readthrough-favorable features to achieve relevant rescue profiles, representing the main issue for the potential translatability of readthrough as a therapeutic strategy.