Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between changes in quality of life and genitourinary toxicity grade after brachytherapy with I-125 alone for localised prostate cancer

Background The relationship between the grading of toxicities based on toxicity criteria and longitudinal changes in quality of life (QOL) scores after permanent prostate brachytherapy (PPB) for localized prostate cancer remains unclear. This study aimed to evaluate these relationships. Materials and methods We assessed 107 patients treated with PPB using Iodine-125 alone from May 2007 to April 2010. Disease-specific QOL scores before PPB and at 1, 3, 6, 12, and 24 months after PPB were retrospectively evaluated with the Expanded Prostate Cancer Index Composite (EPIC), focusing on urinary domains. Toxicities were graded using the Radiation therapy oncology group and the European organization for research and treatment of cancer toxicity criteria. Results The median follow-up duration was 116 (range 18–148) months. Thirty-four patients (31.8%) developed grade ≥ 2 acute genitourinary (GU) toxicities; six (5.6%) developed grade ≥ 2 late GU toxicities. The general urinary domain score dropped significantly at 1 month (77.1 ± 14.1) post-PPB compared to the baseline score (92.2 ± 8.2), and then gradually returned to the baseline level by 12 months (93.7 ± 8.3) post-PPB. Reductions in the general urinary domain scores, including its subscale scores at 1, 3, and 6-months post-PPB were significantly greater among patients with grade ≥ 2 GU toxicity than among those with grade 0–1 GU toxicity. Changes in urinary domain scores demonstrated a close relationship with acute GU toxicity grades after PPB. Conclusions Longitudinal assessments of the EPIC QOL scores provided additional information regarding time-course changes in GU toxicities after PPB.
Evaluating Back-to-Back and Day-to-Day Reproducibility of Cortical GABA+ Measurements Using Proton Magnetic Resonance Spectroscopy (1H MRS)

γ-aminobutyric acid (GABA) is a major inhibitory neurotransmitter implicated in neuropsychiatric disorders. The best method for quantifying GABA is proton magnetic resonance spectroscopy (1H MRS). Considering that accurate measurements of GABA are affected by slight methodological alterations, demonstrating GABA reproducibility in healthy volunteers is essential before implementing the changes in vivo. Thus, our study aimed to evaluate the back-to-back (B2B) and day-to-day (D2D) reproducibility of GABA+ macromolecules (GABA+) using a 3 Tesla MRI scanner, the new 32-channel head coil (CHC), and Mescher–Garwood Point Resolved Spectroscopy (MEGA-PRESS) technique with the scan time (approximately 10 min), adequate for psychiatric patients. The dorsomedial pre-frontal cortex/anterior cingulate cortex (dmPFC/ACC) was scanned in 29 and the dorsolateral pre-frontal cortex (dlPFC) in 28 healthy volunteers on two separate days. Gannet 3.1 was used to quantify GABA+. The reproducibility was evaluated by Pearson’s r correlation, the interclass-correlation coefficient (ICC), and the coefficient of variation (CV%) (r/ICC/CV%). For Day 1, B2B reproducibility was 0.59/0.60/5.02% in the dmPFC/ACC and 0.74/0.73/5.15% for dlPFC. For Day 2, it was 0.60/0.59/6.26% for the dmPFC/ACC and 0.54/0.54/6.89 for dlPFC. D2D reproducibility of averaged GABA+ was 0.62/0.61/4.95% for the dmPFC/ACC and 0.58/0.58/5.85% for dlPFC. Our study found excellent GABA+ repeatability and reliability in the dmPFC/ACC and dlPFC.
A Hysteresis Model for Magneto-rheological Damper

In this paper, a bi-linear hysteresis model is proposed to describe the nonlinear response of an electromagnetic activated magneto-rheological fluid (MRF) damper. The model parameters have significant effects on MRF damper. An analytical solution for the primary resonance of an SDOF system with skyhook damping is obtained. There exists a specific condition for the primary resonance for discussed system.
The Benefits and Challenges of Antibiotics–Non-Steroidal Anti-Inflammatory Drugs Non-Covalent Reaction

Recently, non-covalent reactions have emerged as approaches to improve the physicochemical properties of active pharmaceutical ingredients (API), including antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs). This review aimed to present and discuss the non-covalent reaction products of antibiotics, including salt and neutral multi-component solid forms, by framing their substituents and molar ratios, manufacturing techniques, characterization methods, benefits, potency changes, and toxicity, and is completed with an analysis of the development of computational models used in this field. Based on the data, NSAIDs are the most-developed drugs in multi-component system preparations, followed by antibiotics, i.e., antituberculosis and fluoroquinolones. They have reacted with inorganic elements, excipients, nutraceuticals, natural products, and other drugs. However, in terms of treatments for common infections, fluoroquinolones are more frequently used. Generally, NSAIDs are acquired on an over-the-counter basis, causing inappropriate medication. In addition, the pKa differences between the two groups of medicine offer the potential for them to react non-covalently. Hence, this review highlights fluoroquinolone–NSAID multi-component solid systems, which offer some benefits. These systems can increase patient compliance and promote the appropriate monitoring of drug usage; the dual drug multi-component solids have been proven to improve the physicochemical properties of one or both components, especially in terms of solubility and stability. In addition, some reports show an enhancement of the antibiotic activity of the products. However, it is important to consider the possibility of activity changes, interaction, and toxicity when using drug combinations. Hence, these aspects also are discussed in this review. Finally, we present computational modeling, which has been utilized broadly to support multi-component system designs, including coformer screening, preparation methods, and structural modeling, as well as to predict physicochemical properties, potency, and toxicity. This integrated review is expected to be useful for further antibiotic–NSAID multi-component system development.
A Soft-Switching Bridgeless Buck-Boost Power Factor Correction Converter With Simple Auxiliary Circuit and Low Input Current THD

In this article, a new bridgeless (BL) buck-boost power factor correction (PFC) converter using pulsewidth modulation (PWM) control is presented. The introduced structure operates in a discontinuous conduction mode (DCM) to achieve the sinusoidal form of the input current inherently. The proposed converter utilizes a simple zero-voltage-transition (ZVT) circuit in which the auxiliary switch is turned on and off under zero current switching (ZCS) condition, and the main switches turn on and off under zero-voltage switching (ZVS). Moreover, all diodes are turned off under ZCS conditions, and the reverse recovery problem is solved. By reducing the number of semiconductor elements in the current flow path, conduction loss is minimized. Also, the proposed structure utilizes only one magnetic element. The above-mentioned advantages along with a low total harmonic distortion (THD) and high efficiency are the main benefits of the proposed structure compared to previous topologies. In addition, unlike the BL-buck converter, the input current dead angle is omitted without adding any extra components. Finally, a 500 W–100 kHz prototype at 90-265-Vrms input and 96-V output is implemented to verify the theoretical analysis and it illustrates the efficiency of 97.35% and the input current THD is about 2.7% at 220-Vrms input voltage.
High-Accuracy Patternless Calibration of Multiple 3-D LiDARs for Autonomous Vehicles

This article proposes a new method for estimating the extrinsic calibration parameters between any pair of multibeam LiDAR sensors on a vehicle. Unlike many state-of-the-art works, this method does not use any calibration pattern or reflective marks placed in the environment to perform the calibration; in addition, the sensors do not need to have overlapping fields of view. An iterative closest point (ICP)-based process is used to determine the values of the calibration parameters, resulting in better convergence and improved accuracy. Furthermore, a setup based on the car learning to act (CARLA) simulator is introduced to evaluate the approach, enabling quantitative assessment with ground-truth data. The results show an accuracy comparable with other approaches that require more complex procedures and have a more restricted range of applicable setups. This work also provides qualitative results on a real setup, where the alignment between the different point clouds can be visually checked. The open-source code is available at https://github.com/midemig/pcd_calib.
Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning

In this article, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL). The renewable energy is fully exploited under the uncertainty of renewable generation and load demand. The DRL agent learns an optimal policy from history renewable and load data of previous days, where the policy can generate real-time decisions based on observations of past renewable and load data of previous hours collected by connected sensors. The goal is to reduce operating cost on the premise of ensuring supply–demand balance. In specific, a novel finite-horizon partial observable Markov decision process (POMDP) model is conceived considering the spinning reserve. In order to overcome the challenge of discrete-continuous hybrid action space due to the binary DG switching decision and continuous energy dispatch (ED) decision, a DRL algorithm, namely, the hybrid action finite-horizon RDPG (HAFH-RDPG), is proposed. HAFH-RDPG seamlessly integrates two classical DRL algorithms, i.e., deep $Q$ -network (DQN) and recurrent deterministic policy gradient (RDPG), based on a finite-horizon dynamic programming (DP) framework. Extensive experiments are performed with real-world data in an IoT-driven MG to evaluate the capability of the proposed algorithm in handling the uncertainty due to interhour and interday power fluctuation and to compare its performance with those of the benchmark algorithms.
Deriving genomic breeding values for residual feed intake from covariance functions of random regression models

Random regression models were used to estimate covariance functions between cumulated feed intake (CFI) and BW in 8424 Danish Duroc pigs. Random regressions on second order Legendre polynomials of age were used to describe genetic and permanent environmental curves in BW and CFI. Based on covariance functions, residual feed intake (RFI) was derived as the conditional genetic variance in feed intake given mid-test breeding values for BW and rate of gain, leading to consistent definition of RFI. The heritability of RFI over the entire period was 0.36, but more interestingly, the genetic variance of RFI was only 6% of the genetic variance in feed intake, revealing that only a minor component of feed intake was genetically independent of maintenance and growth. The heritability of daily RFI ranged from 0.01 to 0.07. The breeding value for 70 test days RFI was defined based on the coefficients of the Legendre polynomials. The accuracy of the estimated breeding values for RFI was further increased by 7 % by adding SNP information in a single-step genomic BLUP model.
Feature level fine grained sentiment analysis using boosted long short-term memory with improvised local search whale optimization

Background In the modern era, Internet-based e-commerce world, consumers express their thoughts on the product or service through ranking and reviews. Sentiment analysis uncovers contextual inferences in user sentiment, assisting the commercial industry and end users in understanding the perception of the product or service. Variations in textual arrangement, complex logic, and sequence length are some of the challenges to accurately forecast the sentiment score of user reviews. Therefore, a novel improvised local search whale optimization improved long short-term memory (LSTM) for feature-level sentiment analysis of online product reviews is proposed in this study. Methods The proposed feature-level sentiment analysis method includes ‘data collection’, ‘pre-processing’, ‘feature extraction’, ‘feature selection’, and finally ‘sentiment classification’. First, the product reviews given from different customers are acquired, and then the retrieved data is pre-processed. These pre-processed data go through a feature extraction procedure using a modified inverse class frequency algorithm (LFMI) based on log term frequency. Then the feature is selected via levy flight-based mayfly optimization algorithm (LFMO). At last, the selected data is transformed to the improvised local search whale optimization boosted long short-term memory (ILW-LSTM) model, which categorizes the sentiment of the customer reviews as ‘positive’, ‘negative’, ‘very positive’, ‘very negative’, and ‘neutral’. The ‘Prompt Cloud dataset’ is used for the performance study of the suggested classifiers. Our suggested ILW-LSTM model is put to the test using standard performance evaluation. The primary metrics used to assess our suggested model are ‘accuracy’, ‘recall’, ’precision’, and ‘F1-score’. Results and Conclusion The proposed ILW-LSTM method provides an accuracy of 97%. In comparison to other leading algorithms, the outcome reveals that the ILW-LSTM model outperformed well in feature-level sentiment classification.
The Glutathione Peroxidase Gene Family in Nitraria sibirica: Genome-Wide Identification, Classification, and Gene Expression Analysis under Stress Conditions

Plant glutathione peroxidases (GPXs) are the main enzymes in the antioxidant defense system that sustain H2O2 homeostasis and normalize plant reaction to abiotic stress conditions. However, the genome-wide identification of the GPX gene family and its responses to environmental stresses, especially salt stress, in Nitraria sibirica, which is a shrub that can survive in saline environments, has not yet been reported. Here, we first report the genome-wide analysis of the GPX gene family in N. sibirica, leading to a total of seven NsGPX genes that are distributed on six of the twelve chromosomes. Phylogenetic analysis showed that NsGPX genes were grouped into four major groups (Group I-IV). Three types of cis-acting elements were identified in the NsGPX promoters, mainly related to hormones and stress response. The quantitative real-time PCR (qRT-PCR) analysis indicated that NsGPX1 and NsGPX3 were significantly up-regulated in stem and leaf, while NsGPX7 transcriptionally in root in response to salt stress. The current study identified a total seven NsGPX genes in N. sibirica via genome-wide analysis, and discovered that NsGPXs may play an important role in response to salt stress. Taken together, our findings provide a basis for further functional studies of NsGPX genes, especially in regarding to the resistance to salt stress of this halophyte plant N. sibirica, eventually aid in the discovery of new methods to restore overtly saline soil.
Identifying Aging and Alzheimer Disease–Associated Somatic Variations in Excitatory Neurons From the Human Frontal Cortex

Background and Objectives With age, somatic mutations accumulated in human brain cells can lead to various neurologic disorders and brain tumors. Because the incidence rate of Alzheimer disease (AD) increases exponentially with age, investigating the association between AD and the accumulation of somatic mutation can help understand the etiology of AD. Methods We designed a somatic mutation detection workflow by contrasting genotypes derived from whole-genome sequencing (WGS) data with genotypes derived from scRNA-seq data and applied this workflow to 76 participants from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP) cohort. We focused only on excitatory neurons, the dominant cell type in the scRNA-seq data. Results We identified 196 sites that harbored at least 1 individual with an excitatory neuron–specific somatic mutation (ENSM), and these 196 sites were mapped to 127 genes. The single base substitution (SBS) pattern of the putative ENSMs was best explained by signature SBS5 from the Catalogue of Somatic Mutations in Cancer (COSMIC) mutational signatures, a clock-like pattern correlating with the age of the individual. The count of ENSMs per individual also showed an increasing trend with age. Among the mutated sites, we found 2 sites tend to have more mutations in older individuals (16:6899517 [RBFOX1], p = 0.04; 4:21788463 [KCNIP4], p < 0.05). In addition, 2 sites were found to have a higher odds ratio to detect a somatic mutation in AD samples (6:73374221 [KCNQ5], p = 0.01 and 13:36667102 [DCLK1], p = 0.02). Thirty-two genes that harbor somatic mutations unique to AD and the KCNQ5 and DCLK1 genes were used for gene ontology (GO)–term enrichment analysis. We found the AD-specific ENSMs enriched in the GO-term “vocalization behavior” and “intraspecies interaction between organisms. ” Of interest we observed both age-specific and AD-specific ENSMs enriched in the K+ channel–associated genes. Discussion Our results show that combining scRNA-seq and WGS data can successfully detect putative somatic mutations. The putative somatic mutations detected from ROSMAP data set have provided new insights into the association of AD and aging with brain somatic mutagenesis.
A Pub/Sub-Based Mechanism for Inter-Component Exception Notification in Android Applications

Android developers can use both Java and Kotlin programming languages to create their own apps. Such programming languages provide exception handling facilities to improve apps’ robustness (providing means to implement fault-tolerant measures) and maintainability (separating the error-handling code from the regular one). The Android architecture imposes complexity on the way apps handle exceptions, once exceptions may be raised in parts that are not responsible for handling this abnormal situation. A straightforward solution is to send the Exception Notification (EN) to its concerned handler. However, the way developers are using Android’s inter-component communication mechanism to perform EN may lead to well-known exception handling drawbacks related to the usage of error/return code (e.g., decaying of code readability, programmability, and extensibility). To overcome this problem, we propose a pub/sub-based mechanism to perform EN between Android components called R-EventBus. We evaluate R-EventBus through a performance benchmark and by refactoring a set of apps from the F-Droid repository and comparing the original and refactored versions regarding behavior preservation and EN concern diffusion. Our results indicate that R-EventBus shows negligible performance issues, preserves the apps’ behavior, and considerably improves the modularization of EN concern in the refactored apps.
Female presence in local government and cost efficiency: the case of Italian municipalities

ABSTRACT This paper investigates how female presence in political institutions influences local governments’ cost efficiency, implementing a one-stage stochastic frontier model, based on a panel of Italian municipalities, with municipal fixed effects. The empirical results provide significant evidence for the presence of a gender effect in politics: higher efficiency is observed in municipalities with a female mayor and a greater presence of women in their councils. Further, the gender effect is confirmed taking into account the regional perspective, that is, the historic North–South Italian dualism, and performing several robustness checks. The empirical results support the usefulness of policies aimed at favouring female empowerment and at promoting gender equality in politics.
Effective Coverage of Emergency Obstetric and Newborn Care Services in Africa: A Scoping Review

Objective This scoping review aimed to map the evidence of effective coverage (EC) of EmONC (Emergency Obstetric and Neonatal Care) services and associated factors in Africa. Methodology The review used PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews) checklist to select, appraise, and report the findings. We searched four databases (PubMed, Web of Science, Google Scholar, and Scopus) and grey literature published between Jan 01, 2011 – Dec 31, 2020. The search terms included “emergency”, “obstetric”, “newborn”, “effective coverage”, and “quality” with Boolean terms, AND and OR. The review was conducted using title, abstract, and full-article screenings. The results were analyzed thematically using NVivo v12 qualitative research data analysis software. Results Of the 1811 searched studies, 32 met the eligibility criteria for review. The majority of the studies were from East (56.3%) and Western (28.1%) Africa. Most studies were cross-sectional, had targeted health facilities, and combined two or more data collection techniques. The thematic analysis yielded three themes: EmONC service utilization, quality of EmONC service, and factors associated with the quality of EmONC services. The review showed a scarcity of evidence and variations regarding the crude coverage, quality of care, and factors affecting the quality of EmONC services in Africa. Conclusion The review reported that the utilization of EmONC services was below the WHO-recommended 100% in all studies, though some reported improvements over time. Disparities in EmONC services quality were paramount across studies and contexts. However, the methodological and analytical incongruity across studies brought difficulties in tracing and comparing the progress made in EmONC services utilizations. Registration This scoping review protocol was first registered on the Open Science Framework (OSF) on Aug 27, 2021 (https://osf.io/khcte/).
Hepatitis B Virus Genotype D: An Overview of Molecular Epidemiology, Evolutionary History, and Clinical Characteristics

The hepatitis B virus (HBV) genotype D (HBV/D) is the most extensively distributed genotype worldwide with distinct molecular and epidemiological features. This report provides an up-to-date review on the history of HBV/D subgenotyping and misclassifications, along with large-scale analysis of over 1000 HBV/D complete genome sequences, with the aim of gaining a thorough understanding of the global prevalence and geographic distribution of HBV/D subgenotypes. We have additionally explored recent paleogenomic findings, which facilitated the detection of HBV/D genomes dating back to the late Iron Age and provided new perspectives on the origins of modern HBV/D strains. Finally, reports on distinct disease outcomes and responses to antiviral therapy among HBV/D subgenotypes are discussed, further highlighting the complexity of this genotype and the importance of HBV subgenotyping in the management and treatment of hepatitis B.