Miracle-dz/UniFAR-FTUs huggingface dataset:
Dominance of Escherichia coli sequence types ST73, ST95, ST127 and ST131 in Australian urine isolates: a genomic analysis of antimicrobial resistance and virulence linked to F plasmids

Extraintestinal pathogenic Escherichia coli (ExPEC) are the most frequent cause of urinary tract infections (UTIs) globally. Most studies of clinical E. coli isolates are selected based on their antimicrobial resistance (AMR) phenotypes; however, this selection bias may not provide an accurate portrayal of which sequence types (STs) cause the most disease. Here, whole genome sequencing (WGS) was performed on 320 E. coli isolates from urine samples sourced from a regional hospital in Australia in 2006. Most isolates (91%) were sourced from patients with UTIs and were not selected based on any AMR phenotypes. No significant differences were observed in AMR and virulence genes profiles across age sex, and uro-clinical syndromes. While 88 STs were identified, ST73, ST95, ST127 and ST131 dominated. F virulence plasmids carrying senB-cjrABC (126/231; 55%) virulence genes were a feature of this collection. These senB-cjrABC+ plasmids were split into two categories: pUTI89-like (F29:A-:B10 and/or >95 % identity to pUTI89) (n=73) and non-pUTI89-like (n=53). Compared to all other plasmid replicons, isolates with pUTI89-like plasmids carried fewer antibiotic resistance genes (ARGs), whilst isolates with senB-cjrABC+/non-pUTI89 plasmids had a significantly higher load of ARGs and class 1 integrons. F plasmids were not detected in 89 genomes, predominantly ST73. Our phylogenomic analyses identified closely related isolates from the same patient associated with different pathologies and evidence of strain-sharing events involving isolates sourced from companion and wild animals.
2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines.

AIM The "2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease" provides an update to and consolidates new evidence since the "2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease" and the corresponding "2014 ACC/AHA/AATS/PCNA/SCAI/STS Focused Update of the Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease. " Methods A comprehensive literature search was conducted from September 2021 to May 2022. Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Structure This guideline provides an evidenced-based and patient-centered approach to management of patients with chronic coronary disease, considering social determinants of health and incorporating the principles of shared decision-making and team-based care. Relevant topics include general approaches to treatment decisions, guideline-directed management and therapy to reduce symptoms and future cardiovascular events, decision-making pertaining to revascularization in patients with chronic coronary disease, recommendations for management in special populations, patient follow-up and monitoring, evidence gaps, and areas in need of future research. Where applicable, and based on availability of cost-effectiveness data, cost-value recommendations are also provided for clinicians. Many recommendations from previously published guidelines have been updated with new evidence, and new recommendations have been created when supported by published data.
Structure of PD1 and its mechanism in the treatment of autoimmune diseases

PD‐1 and CTLA‐4 can play an important role in addressing the issue of autoimmune diseases. PD‐1 is a transmembrane glycoprotein expressed on T, B, and Dentric cells. This molecule functions as a checkpoint in T cell proliferation. Ligation of PD‐1 with its ligands inhibits the production of IL‐2, IL‐7, IL‐10, and IL‐12 as well as other cytokines by macrophages, natural killer (NK) cells, and T cells, which can suppress cell proliferation and inflammation. Today, scientists attempt to protect against autoimmune diseases by PD‐1 inhibitory signals. In this review, we discuss the structure, expression, and signaling pathway of PD‐1. In addition, we discuss the importance of PD‐1 in regulating several autoimmune diseases, reflecting how manipulating this molecule can be an effective method in the immunotherapy of some autoimmune diseases.
DeepTraSynergy: drug combinations using multimodal deep learning with transformers

Abstract Motivation Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. Results Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug–target interaction, protein–protein interaction, and cell–target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug–target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug–protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug–protein interaction significantly improves the prediction of synergistic drug combinations. Availability and implementation The source code and data are available at https://github.com/fatemeh-rafiei/DeepTraSynergy.
Optimal model averaging estimation for correlation structure in generalized estimating equations

ABSTRACT Longitudinal data analysis requires a proper estimation of the within-cluster correlation structure in order to achieve efficient estimates of the regression parameters. When applying likelihood-based methods one may select an optimal correlation structure by the AIC or BIC. However, such information criteria are not applicable for estimating equation based approaches. In this paper we develop a model averaging approach to estimate the correlation matrix by a weighted sum of a group of patterned correlation matrices under the GEE framework. The optimal weight is determined by minimizing the difference between the weighted sum and a consistent yet inefficient estimator of the correlation structure. The computation of our proposed approach only involves a standard quadratic programming on top of the standard GEE procedure and can be easily implemented in practice. We provide theoretical justifications and extensive numerical simulations to support the application of the proposed estimator. A couple of well-known longitudinal data sets are revisited where we implement and illustrate our methodology.
Engineering a Point-of-Care Paper-Microfluidic Electrochemical Device Applied to the Multiplexed Quantitative Detection of Biomarkers in Sputum

Health initiatives worldwide demand affordable point-of-care devices to aid in the reduction of morbidity and mortality rates of high-incidence infectious and noncommunicable diseases. However, the production of robust and reliable easy-to-use diagnostic platforms showing the ability to quantitatively measure several biomarkers in physiological fluids and that could in turn be decentralized to reach any relevant environment remains a challenge. Here, we show the particular combination of paper-microfluidic technology, electrochemical transduction, and magnetic nanoparticle-based immunoassay approaches to produce a unique, compact, and easily deployable multiplex device to simultaneously measure interleukin-8, tumor necrosis factor-α, and myeloperoxidase biomarkers in sputum, developed with the aim of facilitating the timely detection of acute exacerbations of chronic obstructive pulmonary disease. The device incorporates an on-chip electrochemical cell array and a multichannel paper component, engineered to be easily aligned into a polymeric cartridge and exchanged if necessary. Calibration curves at clinically relevant biomarker concentration ranges are produced in buffer and artificial sputum. The analysis of sputum samples of healthy individuals and acutely exacerbated patients produces statistically significant biomarker concentration differences between the two studied groups. The device can be mass-produced at a low cost, being an easily adaptable platform for measuring other disease-related target biomarkers.
Using Hybrid and Diversity-Based Adaptive Ensemble Method for Binary Classification

This paper proposes an adaptive and diverse hybrid-based ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and the application of adaptive selection of the most suitable model for each data instance. Ensemble method, an important machine learning technique uses multiple single models to construct a hybrid model. A hybrid model generally performs better compared to a single individual model. In a given dataset the application of diverse single models trained with different machine learning algorithms will have different capabilities in recognizing patterns in the given training sample. The proposed approach has been validated on Repeat Buyers Prediction dataset and Census Income Prediction dataset. The experiment results indicate up to 18.5% improvement on F1 score for the Repeat Buyers dataset compared to the best individual model. This improvement also indicates that the proposed ensemble method has an exceptional ability of dealing with imbalanced datasets. In addition, the proposed method outperforms two other commonly used ensemble methods (Averaging and Stacking) in terms of improved F1 score. Finally, our results produced a slightly higher AUC score of 0.718 compared to the previous result of AUC score of 0.712 in the Repeat Buyers competition. This roughly 1% increase AUC score in performance is significant considering a very big dataset such as Repeat Buyers.
On-Chip Narrowband Thermal Emitter for Mid-IR Optical Gas Sensing

Efficient light sources compatible to complementary metal oxide semiconductor (CMOS) technology are key components for low-cost, compact mid-infrared gas sensing systems. In this work we present an on-chip narrowband thermal light source for the mid-infrared wavelength range by combining microelectromechanical system (MEMS) heaters with metamaterial perfect emitter structures. Exhibiting a resonance quality factor of 15.7 at the center wavelength of 3.96 μm and an emissivity of 0.99, the demonstrated emitter is a spectrally narrow and efficient light source. We show temperature-stable (resonance wavelength shift 0.04 nm/°C) and angular-independent emission characteristics up to angles of 50° and provide an equivalent circuit model illustrating the structure’s resonance behavior. Owing to its spectrally tailored, nondispersive emission, additional filter elements in a free-space optical gas sensing setup become obsolete. In a proof-of-concept demonstration of such a filter-free gas sensing system with CO2 .
Extreme Value Theory Applied to the Statistical Distribution of the Largest Lengths of Fish

A method is presented which allows one to estimate, based on the maximum lengths of a series of length-frequency samples, and on the theory of extreme values, a single, expected largest value (Lm ) and its confidence interval. The method is applied to two sets of samples of chub mackerel, Rastrelliger brachysoma from the Central Philippines.
Linking Mathematics and Psychological Science

Abstract We describe a successful collaboration between mathematics and psychological science faculty members to create a learning community for our students that linked sections of introductory mathematical reasoning and psychological science courses. The students in our learning community were in their second or third semester. The learning community is designed so that, throughout the semester, students regularly move across the border between the two linked disciplines by completing common assignments, including a group project. We modified our existing course topics and frameworks to be intentional about building connections between the courses.
Assessing the influence of preconception diet on female fertility: a systematic scoping review of observational studies

Abstract BACKGROUND Preconception diet is a proposed modifiable risk factor for infertility. However, there is no official guidance for women in the preconception period as to which dietary approaches may improve fertility. OBJECTIVE AND RATIONALE A comprehensive synthesis of the relevant evidence is key to determine the potentially effective dietary patterns and components as well as evidence gaps, and to provide information for nutritional recommendations for couples planning a pregnancy. SEARCH METHODS In this systematic scoping review, four electronic databases (Medline and EMBASE via Ovid processing, CAB Direct, and CINAHL via EBSCO) were searched for observational studies (prospective and retrospective cohort, cross-sectional, and case–control studies) from inception to 27 September 2021. Eligible studies included women of reproductive age during the preconception period, and evaluated exposures related to preconception diet and outcomes related to fertility. Results were synthesized using a descriptive approach. OUTCOMES A total of 36 studies were eligible for inclusion (31 prospective, 3 cross-sectional, and 2 case–control studies) and were published between 2007 and 2022. Of the assessed dietary exposures, increased adherence to the Mediterranean diet displayed the strongest and most consistent association with improved clinical pregnancy rates. Reducing trans fatty acids (TFAs), saturated fatty acids, and discretionary food intake (fast food and sugar-sweetened beverages) were associated with improvements in live birth, clinical pregnancy rates, and related ART outcomes. The dietary components of seafood, dairy, and soy demonstrated inconsistent findings across the few included studies. WIDER IMPLICATIONS Due to heterogeneity and the limited available literature on most exposures, there is insufficient evidence to support any specific dietary approach for improving fertility. However, following some of the dietary approaches outlined in this review (anti-inflammatory diets, reducing TFA, and discretionary food intake) are consistent with broad healthy eating guidelines, have little to no associated risk, and offer a plausible set of possible benefits. This warrants further exploration in randomized controlled trials.
Green synthesis of Cu‐Mn co‐incorporated ZnO nanoparticles for antibacterial and photocatalytic applications

The synergistic effect of bimetallic co‐incorporated metal oxides have gained enormous attention due to their unique optoelectronic properties. Herein, we present the green synthesis of ZnO, Cu‐incorporated ZnO, Mn‐incorporated ZnO, and Cu‐Mn co‐incorporated nanoparticles (ZnO NPs, CuZnO NPs, MnZnO NPs, MnCuZnO NPs) for antimicrobial and photocatalytic reduction applications using corn silk extract and industrial metal wastes. The as‐synthesized NPs were characterized by using UV‐visible absorption spectroscopy (UV‐Vis), photoluminescence (PL) spectroscopy, Fourier‐transformed infrared spectroscopy (FT‐IR), powdered x‐ray diffraction (XRD), and scanning electron microscopy (SEM). CuZnO, MnZnO, and MnCuZnO NPs efficiently inhibited bacterial culture growth. The photocatalytic reduction activity of as‐synthesized NPs against the different concentrations of 4‐nitrophenol (4‐NP) in water was also investigated. CuZnO and MnCuZnO nanoparticles were to be efficient photocatalysts for reducing 4‐NP into 4‐aminophenol (4‐AP).
Regulation of the ATP‐binding cassette transporters ABCB1, ABCG2 and ABCC5 by nuclear receptors in porcine blood–brain barrier endothelial cells

Blood–brain barrier (BBB) ABCB1, ABCG2 and ABCC5 transporters influence central therapeutic drug distribution. Transporter expression is regulated by the NR3C1, NR1I3 and NR1I2 nuclear receptors, but their precise roles in brain are poorly understood. We investigated the effects of selective ligand‐based activation of NR3C1, NR1I3, NR1I2 and NR2B1 in porcine brain endothelial cells (PBECs).
User Retention and Engagement in the Digital-Based Diabetes Education and Self-Management for Ongoing and Newly Diagnosed (myDESMOND) Program: Descriptive Longitudinal Study

Background Digital health interventions have the potential to improve the physical and psychosocial health of people living with type 2 diabetes. However, research investigating the long-term (≥1 year) retention and engagement of users within these programs is limited. Objective The aim of this study was to evaluate long-term user retention and engagement in the digital-based Diabetes Education and Self-Management for Ongoing and Newly Diagnosed (myDESMOND) program, using real-world data. Methods Anonymized data from all myDESMOND users who registered with the program on or before November 16, 2020, were included in the analyses. User retention was defined as the period between the day a user registered with the myDESMOND program and their last day of access. The primary engagement outcome was defined as the total number of log-ins to the program per user. The associations between retention, engagement, and sociodemographic factors (age, sex, and ethnicity) were tested using Cox regression models and Wilcoxon rank sum tests. Results A total of 9522 myDESMOND users were included in this analysis. Of the 9522 users, 5360 (56.29%) remained on the program for at least a month, whereas 1676 (17.6%) remained on the program for at least 1 year. Retention was significantly higher among older users; the adjusted hazard ratio (representing the risk of users leaving the program within the first year) among users aged ≥50 years, compared with those aged <50 years, was 0.79 (95% CI 0.75-0.84; P<.001). The median number of myDESMOND log-ins per user was 8 (IQR 4-8); however, this was significantly lower among users aged <50 years (P<.001). Engagement metrics also differed according to sociodemographic characteristics; the estimated time spent per log-in was 5.35 (IQR 2.22-11.80) minutes among all users; however, this was significantly higher among female users (P<.001), users aged ≥50 years (P<.001), and users of White ethnicity (P=.02). Conclusions Although retention and engagement of users within myDESMOND were found to be high, these findings highlight the need for age- and culture-specific implementation strategies and content adaptations to improve retention and engagement among all users of self-management programs.
Phosphatidic acid regulates ammonium uptake by interacting with AMMONIUM TRANSPORTER 1; 1 in Arabidopsis.

Ammonium (NH4+) is a key inorganic nitrogen source in cellular amino acid biosynthesis. The coupling of transcriptional and posttranslational regulation of AMMONIUM TRANSPORTER (AMT) ensures that NH4+ acquisition by plant roots is properly balanced, which allows for rapid adaptation to a variety of nitrogen conditions. Here, we report that phospholipase D (PLD)-derived phosphatidic acid (PA) interacts with AMT1; 1 to mediate NH4+ uptake in Arabidopsis (Arabidopsis thaliana). We examined pldα1 pldδ-knockout mutants and found that a reduced PA level increased seedling growth under nitrogen deficiency and inhibited root growth upon NH4+ stress, which was consistent with the enhanced accumulation of cellular NH4+. PA directly bound to AMT1; 1 and inhibited its transport activity. Mutation of AMT1; 1 R487 to Gly (R487G) resulted in abolition of PA suppression and, subsequently, enhancement of ammonium transport activity in vitro and in vivo. Observations of AMT1; 1-GFP showed suppressed endocytosis under PLD deficiency or by mutation of the PA-binding site in AMT1; 1. Endocytosis was rescued by PA in the pldα1 pldδ mutant but not in the mutant AMT1; 1R487G-GFP line. Together, these findings demonstrated PA-based shutoff control of plant NH4+ transport and point to a broader paradigm of lipid-transporter function.
Within-person associations between alcohol use and memory lapses among middle-aged and older adults.

INTRODUCTION Alcohol use has been linked to impairment in both short- and long-term measures of objective memory. However, limited research has investigated the association between alcohol use and subjective memory in everyday life. The study purpose was to investigate within- and between-person associations between daily alcohol use and prospective (i.e., forgetting an intended task) and retrospective (i.e., forgetting something learned in the past) memory lapses among middle-aged and older adults. METHODS Participants (n = 925; Mage = 55.2) were non-abstaining adults from the Midlife in the United States (MIDUS) study or the MIDUS Refresher who participated in an 8-day telephone diary asking about their daily experiences. RESULTS Multilevel models revealed that within-individuals, heavier-than-usual alcohol use (i.e., having more drinks than one's daily average number of drinks) was associated with greater odds of reporting any memory lapses (odds ratio [OR] 1.06; 95% confidence interval [CI] 1.01, 1.12), while associations at the between-person level were nonsignificant (OR 1.07; 95% CI 0.99, 1.16). When assessing retrospective and prospective lapses separately, alcohol use was only associated with prospective lapses and only at the between-person level (OR 1.10; 95% CI 1.01, 1.19). Finally, alcohol use was unassociated with reported irritation or interference from memory lapses (p > 0.05). DISCUSSION AND CONCLUSIONS Heavier-than-usual alcohol use may have acute effects on daily memory functioning. Future studies should assess how alcohol use relates to an individual's ability to meet daily cognitive demands, as these findings may have critical implications for harm reduction efforts targeting daily functioning among older adults.