bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative study of broiler chicken performance in closed house and conventional system in North Sumatera
Authors: Farida T E , Hanafi N D , Tafsin M 
Published: July 2022

Abstract
The influence of a comfortable environment provides good performance results on the quality of chicken meat for sustainability to improve the health quality of broiler chickens. This research was conducted from July 2021 to September 2021 in Deli Serdang, Serdang Bedagai and Simalungun districts. This research aims to look at the effects of maintaining closed house and conventional systems. The parameters observed are temperature, humidity, FCR, depletion, Performance Index. As primary data was conducted direct observation to the cage closed house JS population 30,000 tails and conventional cage RS population 4500 tails and interviewed as many as 104 samples (31 respondents closed house, 73 respondents conventional)with the sample method intentionally (purposive sample) and uses the formula Slovin. The appropriate temperature and humidity influence will result in a good performance of broiler chickens. Temperature and humidity detection using the HTC-2 thermohydrometer tool. Data processing results with statistical tests describe FCR results at closed house 1,516 and conventional 1,554; depletion closed house 3% and conventional 4.14% and IP on closed house389 and conventional 358. Display of t-test calculation results on FCR P (sig)<0.05; depletion P(sig)<0.05 and IP P(sig)<0.05 (real difference).
The effect of gamma ray iradiation on the growth, production and quality of Indigofera zollingeriana to support the development of forage crops
Authors: Hutasoit R , Romjali E , Tarigan A , Sirait J , Ginting S P , Harahap M K 
Published: July 2022

Abstract

Indigofera zollingeriana plant has high production and quality as ruminant feed. Its development is constrained by the availability of land. The alternative is to expand the agricultural area towards marginal lands. This study aims to evaluate the genetic diversity of Indigofera plants through gamma ray irradiation. Indigofera seeds were irradiated at doses: 0, 100, 200, 300, 400, 500, 600, 700, 800, 900, and 1000 grays. Based on the LD50 test, plants with radiation doses of 0 Gy, 100 Gy, 200 Gy, and 300 Gy are the best. The study was continued with a field growth test, in a randomized block design, four radiation dose treatments with 20 replications (plants). The parameters observed were plant growth characteristics, dry matter production and chemical composition. The results showed that the 300 Gy dose was higher and significantly different from other treatments in the parameters of plant height (268 cm), canopy width (195.5 cm) and fiber content (14.81%). The 0 Gy treatment was superior on the number of leaves (2,972), number of flowers 311, and calcium content of 1.76%. It was concluded that the dose of 0 and 300 Gy had advantages on the growth and production of Indigofera plants.
Fecundity profiles and physiological traits of Iraqi Awassi ewes supplemented mulberry leaves powder extract
Authors: Khafaji S S 
Published: July 2022

Abstract
The fecundity efficacy of ewes depends on reproductive hormones, ovarian activity so the current research was designed to improve the fecundity of Iraqi anestrous ewes by mulberry leaves (Morus alba) powder extract supplementation. 30th healthy non-pregnant ewes aged 2-3.5 year outbreeding season were divided into equal three groups, 1st group represent a (control) had fed the standard diet, second and third group represent T1 and T2 supplemented 15 and 30 mg/kg mulberry powder extract, respectively, for 40 day before sponge insertion. At day 28th of beginning supplementation the intra-vaginal sponge were inserted for 12 days. After sponges eliminated, estrous onset reported and blood collected before 20 and after 23 & 40 hrs of VSPR to estimate FSH, LH, progesterone and Estradiol concentration. Also, the fecundity, lambing and prolificacy rates had manifested. The results recorded a significant elevation in FSH, estradiol and LH while progesterone registered significant decreased in T1 &T2 comparing with C at 23 & 40 hr after VSPR. Also the fecundity, lambing rates were significantly higher in T1 &T2 in comparing with C. In concluded, that the mulberry leaves powder extract can augment the fecundity in anestrus Iraqi ewes by enhancing the fecundity profile and prolificacy rate.
Quality of critical care clinical practice guidelines involving pharmacotherapy recommendations
Authors: Edwards Christopher , Lam Jonathan , Gardiner Jordan , Erstad Brian L 
Published: July 2022

Abstract

Purpose
To assess the quality of critical care clinical practice guidelines (CPGs) involving pharmacotherapy recommendations.

Methods
A systematic electronic search was performed using PubMed, MEDLINE, and Embase for critical care CPGs published between 2012 and 2022 and involving pharmacotherapy recommendations. The Appraisal of Guidelines for Research & Evaluation II (AGREE II) instrument was employed to appraise CPG quality through independent assessment by 2 appraisers.

Results
Twenty-one CPGs were evaluated. The number of recommendations in each guideline ranged from 2 to 250, with a total of 1,604 recommendations. The number of strong (vs weak) recommendations in each guideline ranged from 0 to 31, with a total of 116 strong recommendations, or 7.23% of the total number of recommendations. There was at least 1 pharmacist author for 9 (43%) of the guidelines. The AGREE II domains for which mean quality scores of evaluated guidelines were highest were scope and purpose (0.88; 95% CI, 0.85-0.92), rigor of development (0.80; 95% CI, 0.77-0.83), clarity of presentation (0.84; 95% CI, 0.81-0.87), and editorial independence (0.86; 95% CI, 0.79-0.94), while those for which mean scores were lowest were stakeholder involvement (0.69; 95% CI, 0.63-0.75) and applicability (0.49; 95% CI, 0.43-0.55). Involvement of a pharmacist in CPG development was associated with significantly higher scoring for stakeholder involvement (P = 0.0356).

Conclusion
Strong recommendations accounted for less than 10% of the recommendations in the evaluated CPGs. Moreover, there are concerns related to guideline applicability (ie, advice or tools for putting recommendations into practice) and stakeholder involvement (ie, inclusion of individuals from all relevant groups). It is important to involve pharmacists in CPGs with pharmacotherapy recommendations.
Multivariate Base Rates of Low Neuropsychological Test Scores in Cognitively Intact Older Adults with Subjective Cognitive Decline from a Specialist Memory Clinic
Authors: Romero Kristoffer , Coleman Astrid , Heir Arjan , Leach Larry , Proulx Guy B 
Published: July 2022

Abstract

Objective
To avoid misdiagnosing mild cognitive impairment (MCI), knowledge of the multivariate base rates (MVBRs) of low scores on neuropsychological tests is crucial. Base rates have typically been determined from normative population samples, which may differ from clinically referred samples. The current study addresses this limitation by calculating the MVBR of low or high cognitive scores in older adults who presented to a memory clinic experiencing subjective cognitive decline but were not diagnosed with MCI.

Method
We determined the MVBRs on the Kaplan–Baycrest Neurocognitive Assessment for 107 cognitively healthy older adults (M age = 75.81), by calculating the frequency of patients producing n scores below or above different cut-off values (i.e., 1, 1.5, 2.0, 2.5 SD from the mean), stratifying by education and gender.

Results
Performing below or above cut-off was common, with more stringent cut-offs leading to lower base rates (≥1 low scores occurred in 84.1% of older adults at −1 SD, 55.1% at −1.5 SD, and 39.3% at −2 SD below the mean; ≥1 high scores occurred in 80.4% of older adults at +1 SD, 35.5% at +1.5 SD, and 16.8% at +2 SD above the mean). Higher education was associated with varying base rates. Overall, the MVBR of obtaining a low cognitive test score was higher in this clinic sample, compared with prior studies of normative samples.

Conclusions
MVBRs for clinically referred older adults experiencing memory complaints provide a diagnostic benefit, helping to prevent attributing normal variability to cognitive impairment and limiting false positive diagnoses.
Best practice and reproducible science are required to advance artificial intelligence in real-world applications
Authors: Liu Zhichao , Li Ting , Connor Skylar , Thakkar Shraddha , Roberts Ruth , Tong Weida 
Published: July 2022

Abstract
Drug-induced liver injury (DILI) is one of the most significant concerns in medical practice but yet it still cannot be fully recapitulated with existing in vivo, in vitro and in silico approaches. To address this challenge, Chen et al. [ 1] developed a deep learning-based DILI prediction model based on chemical structure information alone. The reported model yielded an outstanding prediction performance (i.e. 0.958, 0.976, 0.935, 0.947, 0.926 and 0.913 for AUC, accuracy, recall, precision, F1-score and specificity, respectively, on a test set), far outperforming all publicly available and similar in silico DILI models. This extraordinary model performance is counter-intuitive to what we know about the underlying biology of DILI and the principles and hypothesis behind this type of in silico approach. In this Letter to the Editor, we raise awareness of several issues concerning data curation, model validation and comparison practices, and data and model reproducibility.
AI for predicting chemical-effect associations at the chemical universe level—deepFPlearn
Authors: Schor Jana , Scheibe Patrick , Bernt Matthias , Busch Wibke , Lai Chih , Hackermüller Jörg 
Published: July 2022

AbstractMany chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods—even if high throughput—are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful—experimentally verified—associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.
Predicting miRNA-disease associations based on graph attention networks and dual Laplacian regularized least squares
Authors: Wang Wengang , Chen Hailin 
Published: July 2022

AbstractIncreasing biomedical evidence has proved that the dysregulation of miRNAs is associated with human complex diseases. Identification of disease-related miRNAs is of great importance for disease prevention, diagnosis and remedy. To reduce the time and cost of biomedical experiments, there is a strong incentive to develop efficient computational methods to infer potential miRNA-disease associations. Although many computational approaches have been proposed to address this issue, the prediction accuracy needs to be further improved. In this study, we present a computational framework MKGAT to predict possible associations between miRNAs and diseases through graph attention networks (GATs) using dual Laplacian regularized least squares. We use GATs to learn embeddings of miRNAs and diseases on each layer from initial input features of known miRNA-disease associations, intra-miRNA similarities and intra-disease similarities. We then calculate kernel matrices of miRNAs and diseases based on Gaussian interaction profile (GIP) with the learned embeddings. We further fuse the kernel matrices of each layer and initial similarities with attention mechanism. Dual Laplacian regularized least squares are finally applied for new miRNA-disease association predictions with the fused miRNA and disease kernels. Compared with six state-of-the-art methods by 5-fold cross-validations, our method MKGAT receives the highest AUROC value of 0.9627 and AUPR value of 0.7372. We use MKGAT to predict related miRNAs for three cancers and discover that all the top 50 predicted results in the three diseases are confirmed by existing databases. The excellent performance indicates that MKGAT would be a useful computational tool for revealing disease-related miRNAs.
m6 Aexpress-BHM: predicting m6A regulation of gene expression in multiple-groups context by a Bayesian hierarchical mixture model
Authors: Zhang Teng , Zhang Shao-wu , Feng Jian , Zhang Bei 
Published: July 2022

AbstractAs the most abundant RNA modification, N6-methyladenosine (m6A) plays an important role in various RNA activities including gene expression and translation. With the rapid application of MeRIP-seq technology, samples of multiple groups, such as the involved multiple viral/ bacterial infection or distinct cell differentiation stages, are extracted from same experimental unit. However, our current knowledge about how the dynamic m6A regulating gene expression and the role in certain biological processes (e.g. immune response in this complex context) is largely elusive due to lack of effective tools. To address this issue, we proposed a Bayesian hierarchical mixture model (called m6Aexpress-BHM) to predict m6A regulation of gene expression (m6A-reg-exp) in multiple groups of MeRIP-seq experiment with limited samples. Comprehensive evaluations of m6Aexpress-BHM on the simulated data demonstrate its high predicting precision and robustness. Applying m6Aexpress-BHM on three real-world datasets (i.e. Flaviviridae infection, infected time-points of bacteria and differentiation stages of dendritic cells), we predicted more m6A-reg-exp genes with positive regulatory mode that significantly participate in innate immune or adaptive immune pathways, revealing the underlying mechanism of the regulatory function of m6A during immune response. In addition, we also found that m6A may influence the expression of PD-1/PD-L1 via regulating its interacted genes. These results demonstrate the power of m6Aexpress-BHM, helping us understand the m6A regulatory function in immune system.
Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug–drug interactions prediction
Authors: Zhang Xudong , Wang Gan , Meng Xiangyu , Wang Shuang , Zhang Ying , Rodriguez-Paton Alfonso , Wang Jianmin , Wang Xun 
Published: July 2022

Abstract
Multi-drug combinations for the treatment of complex diseases are gradually becoming an important treatment, and this type of treatment can take advantage of the synergistic effects among drugs. However, drug–drug interactions (DDIs) are not just all beneficial. Accurate and rapid identifications of the DDIs are essential to enhance the effectiveness of combination therapy and avoid unintended side effects. Traditional DDIs prediction methods use only drug sequence information or drug graph information, which ignores information about the position of atoms and edges in the spatial structure. In this paper, we propose Molormer, a method based on a lightweight attention mechanism for DDIs prediction. Molormer takes the two-dimension (2D) structures of drugs as input and encodes the molecular graph with spatial information. Besides, Molormer uses lightweight-based attention mechanism and self-attention distilling to process spatially the encoded molecular graph, which not only retains the multi-headed attention mechanism but also reduces the computational and storage costs. Finally, we use the Siamese network architecture to serve as the architecture of Molormer, which can make full use of the limited data to train the model for better performance and also limit the differences to some extent between networks dealing with drug features. Experiments show that our proposed method outperforms state-of-the-art methods in Accuracy, Precision, Recall and F1 on multi-label DDIs dataset. In the case study section, we used Molormer to make predictions of new interactions for the drugs Aliskiren, Selexipag and Vorapaxar and validated parts of the predictions. Code and models are available at https://github.com/IsXudongZhang/Molormer.
Lakes in Hot Water: The Impacts of a Changing Climate on Aquatic Ecosystems
Authors: Woolway R Iestyn , Sharma Sapna , Smol John P 
Published: July 2022

AbstractOur planet is being subjected to unprecedented climate change, with far-reaching social and ecological repercussions. Below the waterline, aquatic ecosystems are being affected by multiple climate-related and anthropogenic stressors, the combined effects of which are poorly understood and rarely appreciated at the global stage. A striking consequence of climate change on aquatic ecosystems is that many are experiencing shorter periods of ice cover, as well as earlier and longer summer stratified seasons, which often result in a cascade of ecological and environmental consequences, such as warmer summer water temperatures, alterations in lake mixing and water levels, declines in dissolved oxygen, increased likelihood of cyanobacterial algal blooms, and the loss of habitat for native cold-water fisheries. The repercussions of a changing climate include impacts on freshwater supplies, water quality, biodiversity, and the ecosystem benefits that they provide to society.
A controlled effects approach to assessing immune correlates of protection
Authors: Gilbert Peter B , Fong Youyi , Kenny Avi , Carone Marco 
Published: July 2022

Summary
An immune correlate of risk (CoR) is an immunologic biomarker in vaccine recipients associated with an infectious disease clinical endpoint. An immune correlate of protection (CoP) is a CoR that can be used to reliably predict vaccine efficacy (VE) against the clinical endpoint and hence is accepted as a surrogate endpoint that can be used for accelerated approval or guide use of vaccines. In randomized, placebo-controlled trials, CoR analysis is limited by not assessing a causal vaccine effect. To address this limitation, we construct the controlled risk curve of a biomarker, which provides the causal risk of an endpoint if all participants are assigned vaccine and the biomarker is set to different levels. Furthermore, we propose a causal CoP analysis based on controlled effects, where for the important special case that the biomarker is constant in the placebo arm, we study the controlled vaccine efficacy curve that contrasts the controlled risk curve with placebo arm risk. We provide identification conditions and formulae that account for right censoring of the clinical endpoint and two-phase sampling of the biomarker, and consider G-computation estimation and inference under a semiparametric model such as the Cox model. We add modular approaches to sensitivity analysis that quantify robustness of CoP evidence to unmeasured confounding. We provide an application to two phase 3 trials of a dengue vaccine indicating that controlled risk of dengue strongly varies with 50$\%$ neutralizing antibody titer. Our work introduces controlled effects causal mediation analysis to immune CoP evaluation.
Interictal epileptiform discharges show distinct spatiotemporal and morphological patterns across wake and sleep
Authors: Fouad Amal , Azizollahi Hamed , Le Douget Jean-Eudes , Lejeune François-Xavier , Valderrama Mario , Mayor Liliana , Navarro Vincent , Le Van Quyen Michel 
Published: July 2022

Abstract
Presurgical evaluation of mesial temporal and neocortical focal pharmacoresistant epilepsy patients using intracranial EEG recordings has led to the generation of extensive data on interictal epileptiform discharges, located within or remotely from seizure onset zones. In this study, we used this data to investigate how interictal epileptiform discharges are modulated and how their spatial distribution changes during wake and sleep and analysed the relationship between these discharge events and seizure onset zones. Preoperative evaluation data from 11 adult patients with focal pharmacoresistant epilepsy were extracted from the Epilepsiae database. Interictal epileptiform discharges were automatically detected during wakefulness and over several hours of continuous seizure-free sleep (total duration of EEG recordings:106.7 h; mean per patient: 9.7 h), and analysed across four brain areas (mesial temporal, lateral neocortical, basal cortical and the temporal pole). Sleep stages were classified manually from scalp EEG. Discharge events were characterized according to their rate and morphology (amplitude, sharpness and duration). Eight patients had a seizure onset zone over mesial areas and three patients over lateral neocortical areas. Overall, discharge rates varied across brain areas during wakefulness and sleep [wake/sleep stages × brain areas interaction; Wald χ2(df = 6) = 31.1, P &lt; 0.0001]. N2–N3 non-rapid eye movement sleep increased interictal epileptiform discharges in mesial areas compared with wakefulness and rapid eye movement sleep (P &lt; 0.0001), and to other areas (P &lt; 0.0001 for all comparisons). This mesial pattern was observed both within and outside of seizure onset zones. During wakefulness, the rate of interictal epileptiform discharges was significantly higher than during N2–N3 non-rapid eye movement sleep (P = 0.04), and rapid eye movement sleep (P = 0.01) in lateral neocortical areas (referred to as lateral neocortical pattern), a finding that was more pronounced in seizures onset zones (P = 0.004). The morphological characteristics of the discharge events were modulated during wakefulness and sleep stages across brain areas. The effect of seizure onset zones on discharge morphology was conditioned by brain area and was particularly marked in temporal pole areas. Our analysis of discharge patterns in relation to cerebral localization, vigilance state and the anatomical affiliation of seizure onset zones revealed the global and local aspects of the complex relationship between interictal discharges, sleep and seizure onset zones. This novel approach may lead to a better understanding of cognitive decline and responses to therapy, as well as to adaptation of surgical interventions for epileptic patients.