bluuebunny/crossref_metadata_2025 huggingface dataset:
Early warning research on enterprise carbon emission reduction credit risk based on deep learning model under unbalanced data
Authors: Long Zhi , Chen Xiangzhou
Published: November 2023

To enhance the precision of predicting enterprise credit risk related to carbon emission reduction, this study focuses on publicly traded companies. It introduces a risk early warning model grounded in MLP deep learning. Primarily, this research employs the FA-TOPSIS fusion model to comprehensively assess the credit risk associated with carbon emission reduction in enterprises. Subsequently, it employs K-means clustering to compute enterprise similarities, which forms the basis for supervised learning in the MLP model to assign credit risk grade labels. Furthermore, the study tackles the challenge of imbalanced enterprise grade distribution using the ADASYN over-sampling algorithm. Ultimately, the effectiveness of the model proposed herein is confirmed through a series of multi-model comparison experiments. The results show that: First, carbon emission reduction indicators exhibit differing degrees of influence on enterprises at various credit risk levels. Notably, the most influential indicator is carbon emission intensity, while the development capacity indicator exerts the least influence. Second, the adoption of the XGBoost algorithm for screening carbon emission reduction indicators significantly enhances the prediction accuracy of the early warning model by 4.27%. Third, compared to other models, the MLP model achieves an impressive prediction accuracy of 99.48%, representing an average improvement of 15.24%. These results underscore the model’s feasibility and its potential to provide technical support for financial institutions and government entities in conducting credit ratings for enterprise carbon emission reduction.
Genomic annotation for vaccine target identification and immunoinformatics-guided multi-epitope-based vaccine design against Songling virus through screening its whole genome encoded proteins
Authors: Ali S. Luqman , Ali Awais , Alamri Abdulaziz , Baiduissenova Aliya , Dusmagambetov Marat , Abduldayeva Aigul
Published: November 2023

Songling virus (SGLV), a newly discovered tick-borne orthonairovirus, was recently identified in human spleen tissue. It exhibits cytopathic effects in human hepatoma cells and is associated with clinical symptoms including headache, fever, depression, fatigue, and dizziness, but no treatments or vaccines exist for this pathogenic virus. In the current study, immunoinformatics techniques were employed to identify potential vaccine targets within SGLV by comprehensively analyzing SGLV proteins. Four proteins were chosen based on specific thresholds to identify B-cell and T-cell epitopes, validated through IFN-γ epitopes. Six overlap MHC-I, MHC-II, and B cell epitopes were chosen to design a comprehensive vaccine candidate, ensuring 100% global coverage. These structures were paired with different adjuvants for broader protection against international strains. Vaccine constructions’ 3D models were high-quality and validated by structural analysis. After molecular docking, SGLV-V4 was selected for further research due to its lowest binding energy (-66.26 kcal/mol) and its suitable immunological and physiochemical properties. The vaccine gene is expressed significantly in E. coli bacteria through in silico cloning. Immunological research and MD simulations supported its molecular stability and robust immune response within the host cell. These findings can potentially be used in designing safer and more effective experimental SGLV-V4 vaccines.
Gut microbiota and immune mediation: a Mendelian randomization study on granulomatosis with polyangiitis
Authors: Chen Yizhen , Tang Shilin
Published: November 2023

Background

The gut microbiota plays a pivotal role in influencing various health outcomes, including immune-mediated conditions. Granulomatosis with Polyangiitis (GPA) is one such condition, and its potential associations with gut microbiota remain underexplored.

Method

Using a two-sample Mendelian randomization approach, we investigated the causal links between gut microbiota and GPA. We sourced our data from multiple cohorts and consortiums, including the MiBioGen consortium. Our study design incorporated both direct associations and mediation effects of immune traits on the relationship between gut microbiota and GPA.

Results

Our analysis revealed significant associations between 1 phylum, 1 family 9 genus microbiota taxa and GPA. Furthermore, we identified several immune cell traits that mediated the effects of gut microbiota on GPA. For instance, the family Defluviitaleaceae and genus Defluviitaleaceae UCG011 influenced GPA through CD11c in granulocytes. The mediation effect proportions further elucidated the complex dynamics between gut microbiota exposures, immune markers, and their combined influence on GPA.

Conclusion

Our findings underscore the intricate relationship between gut microbiota, immune markers, and GPA. The identified associations and mediation effects provide valuable insights into the potential therapeutic avenues targeting gut microbiota to manage GPA.
Assessing the effects of different management activities on the conservation of endangered Indo-Pacific humpback dolphin
Authors: Lu Yi , Zhuo Haojie , Xu Xinrong , Chen Bingyao , Yang Guang
Published: November 2023

Predictions of population viability can provide valid information for guiding the management and conservation of species. However, such studies are rarely conducted on cetaceans due to limited basic data. In this study, a detailed assessment of the population dynamics of Indo-Pacific humpback dolphins (Sousa chinensis) in Xiamen Bay, China was carried out using a Vortex model that was based on model parameters that were derived from monitoring data and published sources. Then, sensitivity analyses were conducted to test the relative importance of the uncertainty of the model parameters. Moreover, potential management models were evaluated to determine their effectiveness in assisting with species recovery. The baseline model projected a decline in the population abundance with a growth rate (r) of -0.031, and the probability of extinction was 58.7% over the next 100 years. The sensitivity analyses revealed that juvenile mortality and fertility were the most pivotal factors for the viability of this population. All the conservation measures, including habitat improvement, catastrophe control, and individual supplementation, contributed to population increases when compared with the baseline model. In particular, decreasing calf and juvenile mortality through habitat improvement, at high levels, was the only way to generate positive growth rates. The findings suggest that these practical management activities can reduce the risk of extinction for this species.
Bibliometric visual analysis of the poly(vinylidene fluoride–trifluoroethylene) piezoelectric nanomaterial: research history, hotspots, and developmental trend
Authors: Chen Zhiguang , He Wen , Zhang Wei
Published: November 2023

Introduction: Poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)] stands out as an organic polymer with remarkable piezoelectric properties, coupled with high chemically stability and biocompatibility, rendering it the most promising organic piezoelectric nanomaterial. Therefore, we aim to explore its developmental history, find current research hotspots, and identify emerging trends.

Methods: To comprehensively review the pertinent literature on poly(vinylidene fluoride-trifluoroethylene) or P(VDF-TrFE) in English from 1992 to 2022, we utilized the Web of Science Core Collection. Additionally, bibliometric tools such as CiteSpace, VOSviewer, Bibliometrics online analysis platforms (https://bibliometric.com), along with SCImage graphics, were employed to delineate historical features, track the evolution of active topics, and identify emerging trends on the P(VDF-TrFE) field.

Results: The annual publication and citation counts for articles on P(VDF-TrFE) have exhibited consistent growth since 1992, with Asia, notably China, contributing the highest number of publications on P(VDF-TrFE) materials. Interestingly, researchers in the United States demonstrated the most robust international collaborations across various countries. Keywords were categorized into nine clusters: 0# vinylidene fluoride, 1# thin-film transient, 2# energy harvesting, 3# dielectric property, 4# thin film, 5# film, 6# mobile modeling, 7# growth, and 8# pyroelectric sensor. The performance of constituent materials in electronic energy-storage applications and current and future research in nanofields, including nanofilms, nanofibers, and nanogenerator, are considered hotspots and developmental trends, respectively. Among the most cited articles, these articles are commonly related to PVDF and P(VDF-TrFE) ferroelectricity.

Conclusion: Therefore, we believe that the electromechanical coupling performance of P(VDF-TrFE) and the research in nano fields is research hotspot that will trend into the future.
Effect of astigmatism on visual outcomes after multifocal intraocular lens implantation: a systematic review and meta-analysis
Authors: Shen Wenqian , Zhuo Baoxian , Zhang Limei , Shen Jiying , Ma Dongmei , Yang Jin
Published: November 2023

Purpose

To investigate the effects of postoperative astigmatism on the visual outcomes following presbyopia-correcting surgery with multifocal intraocular lens implantation.

Methods

A comprehensive literature search was conducted using PubMed, Embase, and Web of Science for articles published until January 2023. Additionally, we included retrospective case series and prospective comparative studies. The combined mean difference (MD) with 95% confidence intervals (CI) and odds ratio (OR) with 95% CI were used to express continuous and categorical outcomes, respectively. All statistical analyses were performed using Review Manager (version 5.4.1).

Results

We included nine eligible studies that analyzed 3,088 eyes. The proportion of eyes with useful postoperative visual acuity (logMAR ≤ 0.20) and residual astigmatism significantly differed with respect to the magnitude of astigmatism and presence/absence of blurred vision (p < 0.001 for both). Additionally, the mean uncorrected distance visual acuity (MD, 0.14; 95% CI, 0.06 to 0.21; p = 0.0003) and uncorrected intermediate visual acuity (MD, 0.07; 95% CI, 0.00 to 0.13; p = 0.04), but not the uncorrected near visual acuity (MD, 0.02; 95%CI-0.01 to 0.05; p = 0.17), significantly differed according to the magnitude of astigmatism.

Conclusion

Astigmatism, even at low levels (≥ 0.5D), has a significant effect on visual outcomes, especially on UDVA and UIVA, following multifocal intraocular lens implantation. Accurate preoperative and postoperative evaluation of astigmatism is important.
Predicting the placement of biomolecular structures on AFM substrates based on electrostatic interactions
Authors: Amyot Romain , Nakamoto Kaho , Kodera Noriyuki , Flechsig Holger
Published: November 2023

Atomic force microscopy (AFM) and high-speed AFM allow direct observation of biomolecular structures and their functional dynamics. Based on scanning the molecular surface of a sample deposited on a supporting substrate by a probing tip, topographic images of its dynamic shape are obtained. Critical to successful AFM observations is a balance between immobilization of the sample while avoiding too strong perturbations of its functional conformational dynamics. Since the sample placement on the supporting substrate cannot be directly controlled in experiments, the relative orientation is a priori unknown, and, due to limitations in the spatial resolution of images, difficult to infer from a posteriori analysis, thus hampering the interpretation of measurements. We present a method to predict the macromolecular placement of samples based on electrostatic interactions with the AFM substrate and demonstrate applications to HS-AFM observations of the Cas9 endonuclease, an aptamer-protein complex, the Monalysin protein, and the ClpB molecular chaperone. The model also allows predictions of imaging stability taking into account buffer conditions. We implemented the developed method within the freely available BioAFMviewer software package. Predictions based on available structural data can therefore be made even prior to an actual experiment, and the method can be applied for post-experimental analysis of AFM imaging data.
Adherence to subcutaneous interferon beta-1a treatment among patients with relapsing multiple sclerosis: the MAIN-MS study
Authors: Al-Roughani Raed , Zakaria Magd , Cupler Edward J. , Taha Karim
Published: November 2023

Introduction and background

Adherence is a critical factor for optimal clinical outcomes in multiple sclerosis (MS) treatment. This study investigated the adherence and clinical outcomes of MS patients treated with subcutaneous (sc) interferon (IFN) (β)-1a, an established immunomodulatory treatment for relapsing MS. The benefits of a patient support programme (PSP) were also studied.

Methods

This phase-IV prospective, observational multicentre study enrolled patients with relapsing MS who were treated with sc IFN β-1a for 24 months was conducted at 53 centres across 17 countries. The primary endpoint was adherence to sc IFN β-1a treatment, as assessed using Morisky Green Levine Medication Adherence Scale (MGLS) scores at 24 months. The MGLS is a self-reported diagnostic tool to address medication non-adherence, with a score ranging from 0 to 4, with 0 representing high adherence, 1–2 representing medium adherence, and 3–4 representing low adherence. Other endpoints included time to study and treatment discontinuation over 24 months, the proportion of relapse-free patients, and Expanded Disability Status Scale (EDSS) progression (defined as ≥1.0 point increase sustained for 3 months) at 24 months. A subgroup analysis was performed for endpoints based on patients assigned to PSP (yes/no—PSP versus non-PSP subgroup).

Results

Of the 577 patients enrolled, 408 had evaluable MGLS scores at 24 months. A total of 336 (58.2%; 95% confidence interval [CI]: 54.1–62.3%) patients reported high adherence, 57 (9.9%; 95% CIs: 7.6–12.7%) reported medium adherence, and 15 (2.6%; 95% CI: 1.5–4.3%) reported low adherence at 24 months. The PSP subgroup reported higher adherence (n = 206; 65.8%) than the non-PSP subgroup (n = 130; 56.5%). By 24 months, 52.2% of the patients were relapse-free and 17.2% patients experienced ≥1 relapse. Expanded Disability Status Scale progression was observed in 12.3% of patients. Over the 24-month period, 30.8% of the patients discontinued treatment, and the most common reasons for treatment discontinuation were adverse events (AEs, 10.4%), being lost to followup (7.1%), and a lack of efficacy (5.5%). Overall, 39.6% patients experienced ≥1 AE, which ranged from mild to moderate.

Conclusion

The study demonstrated high adherence to sc IFN β-1a treatment with an added benefit of PSP participation. More than half of the patients remained relapse-free over a 24-month period. No new safety concerns to sc IFN β-1a treatment were observed.

Clinical trial registration

https://clinicaltrials.gov/study/NCT02921035, NCT02921035.
The Moralizing Effect: self-directed emotions and their impact on culpability attributions
Authors: Sirgiovanni Elisabetta , Smolenski Joanna , Abelson Ben , Webb Taylor
Published: November 2023

Introduction

A general trend in the psychological literature suggests that guilt contributes to morality more than shame does. Unlike shame-prone individuals, guilt-prone individuals internalize the causality of negative events, attribute responsibility in the first person, and engage in responsible behavior. However, it is not known how guilt- and shame-proneness interact with the attribution of responsibility to others.

Methods

In two Web-based experiments, participants reported their attributions of moral culpability (i.e., responsibility, causality, punishment and decision-making) about morally ambiguous acts of killing in different conditions. In Study 1 the vignettes were presented in the 1st person, while in Study 2 in the 3rd person. To test proneness to guilt and shame, we utilized the GASP scale, which differentiates between the affective and behavioral components of each emotion. Statistical analyses were performed in Matlab and R.

Results

We found that guilt- and shame-proneness were associated with the severity of attributions in both the first and the third person, but the effect was strong only in the guilt case (both subtypes) and shame-affect case, and not in the shame-behavior case. We call this the Moralizing Effect.

Discussion

We wonder whether our finding that guilt-prone people tend to attribute a higher degree of culpability to others is really consistent with the view that guilt motivates people to choose the “moral paths in life”. This echoes views about the harmful aspects of guilt, which have been expressed historically in philosophy, for example, by Friedrich Nietzsche.
Preliminary evaluation of the antiglycoxidant activity of verapamil using various in vitro and in silico biochemical/biophysical methods
Authors: Nesterowicz Miłosz , Lauko Kamil Klaudiusz , Dańkowska Karolina , Trocka Daria , Żendzian-Piotrowska Małgorzata , Ładny Jerzy Robert , Zalewska Anna , Maciejczyk Mateusz
Published: November 2023

Introduction: Glycoxidative stress is essential for linking glucose disturbances and cardiovascular diseases. Unfortunately, contemporary antidiabetic drugs do not have an antiglycative effect but only lower blood glucose levels. Therefore, there is an intense search for substances that could inhibit protein glycation and prevent diabetic complications. A potential antioxidant activity has been demonstrated with verapamil, a phenylalkylamine derivative belonging to selective calcium channel blockers. Verapamil has a well-established position in cardiology due to its wide range of indications and good safety profile. Nevertheless, the antidiabetic activity of verapamil is still unclear. We are the first to comprehensively evaluate the verapamil’s effect on protein glycoxidation using various in vitro and in silico models.

Methods: Bovine serum albumin (BSA) was used to assess the rate of glycoxidation inhibition by verapamil. As glycating factors, sugars (glucose, fructose, and ribose) and aldehyde (glyoxal) were used. Chloramine T was used as an oxidizing agent. Aminoguanidine (protein glycation inhibitor) and Trolox (antioxidant) were used as control substances. The biomarkers of oxidation (total thiols, protein carbonyls, advanced oxidation protein products), glycation (Amadori products, β-amyloid, advanced glycation end products [AGEs]), and glycoxidation (tryptophan, kynurenine, N-formylkynurenine, dityrosine) were evaluated using colorimetric and fluorimetric methods. The mechanism of antiglycative activity of verapamil was assessed using in silico docking to study its interaction with BSA, glycosidases, and seventeen AGE pathway proteins.

Results: In all in vitro models, biomarkers of protein glycation, oxidation, and glycoxidation were significantly ameliorated under the influence of verapamil. The glycoxidation inhibition rate by verapamil is comparable to that of potent antiglycating agents and antioxidants. The molecular docking simulations showed that verapamil bound preferentially to amino acids prone to glycoxidative damage out of an α-glucosidase’s active center. Among all AGE pathway proteins, verapamil was best docked with the Janus kinase 2 (JAK2) and nuclear factor-κB (NF-κB).

Discussion: The results of our study confirm the antiglycoxidant properties of verapamil. The drug’s action is comparable to recognized substances protecting against oxidative and glycation modifications. Verapamil may be particularly helpful in patients with cardiovascular disease and concomitant diabetes. Studies in animal models and humans are needed to confirm verapamil’s antiglycative/antidiabetic activity.
Modeling the influence of attitudes, trust, and beliefs on endoscopists’ acceptance of artificial intelligence applications in medical practice
Authors: Schulz Peter J. , Lwin May O. , Kee Kalya M. , Goh Wilson W. B. , Lam Thomas Y. T , Sung Joseph J. Y.
Published: November 2023

Introduction

The potential for deployment of Artificial Intelligence (AI) technologies in various fields of medicine is vast, yet acceptance of AI amongst clinicians has been patchy. This research therefore examines the role of antecedents, namely trust, attitude, and beliefs in driving AI acceptance in clinical practice.

Methods

We utilized online surveys to gather data from clinicians in the field of gastroenterology.

Results

A total of 164 participants responded to the survey. Participants had a mean age of 44.49 (SD = 9.65). Most participants were male (n = 116, 70.30%) and specialized in gastroenterology (n = 153, 92.73%). Based on the results collected, we proposed and tested a model of AI acceptance in medical practice. Our findings showed that while the proposed drivers had a positive impact on AI tools’ acceptance, not all effects were direct. Trust and belief were found to fully mediate the effects of attitude on AI acceptance by clinicians.

Discussion

The role of trust and beliefs as primary mediators of the acceptance of AI in medical practice suggest that these should be areas of focus in AI education, engagement and training. This has implications for how AI systems can gain greater clinician acceptance to engender greater trust and adoption amongst public health systems and professional networks which in turn would impact how populations interface with AI. Implications for policy and practice, as well as future research in this nascent field, are discussed.
Membership dropout rates and associated factors in a community-based health insurance scheme in southern Ethiopia: a mixed method study
Authors: Haile Yosef , Abdulkadir Hanan , Shewangizaw Misgun , Meskele Simeon , Temesgen Kidus , Haile Temesgen , Niguse Daniel , Hailegebreal Samuel , Biratu Getahun Gorfu
Published: November 2023

Background

Dropout from community-based health insurance (CBHI) membership is a common problem in low-income countries, even if its implementation leads to substantial improvement in the utilization of essential health services. Few studies have addressed the factors contributing to dropout rates in southern Ethiopia. Therefore, the purpose of this study was to determine the rate of CBHI dropout in southern Ethiopia as well as any contributing factors.

Methods

This mixed-method cross-sectional study was conducted among 460 randomly selected CBHI-enrolled households at the Arba Minch Health and Demography Surveillance System site from November 1, 2021, to April 30, 2022. The quantitative data were collected by an open data kit (ODK). using an interviewer-based structured questionnaire and analyzed using Statistical Package for the Social Sciences (SPSS) version 25.0. Multivariable logistic regression was applied to identify significant variables. The qualitative data were used to support the quantitative findings and were gathered through in-depth interviews (by the CBHI coordinator and three purposively selected health extension workers) and focus group discussions (in two randomly selected villages). The qualitative data were analyzed using thematic analysis. Finally, triangulation was used to present both the quantitative and qualitative findings.

Results

This study found that 92 (21.5%) people stopped their community-based health insurance membership. The presence of sick adults [AOR = 0.281, 95% CI (0.136–0.581)], trust of participants in the contracted health facilities [AOR = 0.227, 95% CI (0.121–0.436)], and poor knowledge of the participants [AOR = 5.518, 95% CI (1.526–19.950)] were significant predictors.

Conclusion

The magnitude of the dropout rate was high in this study when compared with the national target. The absence of a sick adult, the absence of trust among participants, and the poor knowledge status of the participants were significant predictors. We suggest that the health facility managers, the CBHI coordinating office, and the district health office give priority to implementing a wide range of knowledge improvement activities and a transparent system in public health facilities. Studies with longitudinal research designs are called for at a wide range of national levels to address the limitations of this study.
An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification
Authors: Ayub Nasir , Tayyaba  , Hussain Saddam , Ullah Syed Sajid , Iqbal Jawaid
Published: November 2023

Sentiment analysis holds great importance within the domain of natural language processing as it examines both the expressed and underlying emotions conveyed through review content. Furthermore, researchers have discovered that relying solely on the overall sentiment derived from the textual content is inadequate. Consequently, sentiment analysis was developed to extract nuanced expressions from textual information. One of the challenges in this field is effectively extracting emotional elements using multi-label data that covers various aspects. This article presents a novel approach called the Ensemble of DenseNet based on Aquila Optimizer (EDAO). EDAO is specifically designed to enhance the precision and diversity of multi-label learners. Unlike traditional multi-label methods, EDAO strongly emphasizes improving model diversity and accuracy in multi-label scenarios. To evaluate the effectiveness of our approach, we conducted experiments on seven distinct datasets, including emotions, hotels, movies, proteins, automobiles, medical, news, and birds. Our initial strategy involves establishing a preprocessing mechanism to obtain precise and refined data. Subsequently, we used the Vader tool with Bag of Words (BoW) for feature extraction. In the third stage, we created word associations using the word2vec method. The improved data were also used to train and test the DenseNet model, which was fine-tuned using the Aquila Optimizer (AO). On the news, emotion, auto, bird, movie, hotel, protein, and medical datasets, utilizing the aspect-based multi-labeling technique, we achieved accuracy rates of 95%, 97%, and 96%, respectively, with DenseNet-AO. Our proposed model demonstrates that EDAO outperforms other standard methods across various multi-label datasets with different dimensions. The implemented strategy has been rigorously validated through experimental results, showcasing its effectiveness compared to existing benchmark approaches.
Review on the Nonlinear Modeling of Hysteresis in Piezoelectric Ceramic Actuators
Authors: Dai Yingli , Li Dequan , Wang Dong
Published: November 2023

Piezoelectric ceramic actuators have the advantages of fast response speed and high positioning accuracy and are widely used in micro-machinery, aerospace, precision machining machinery, and other precision positioning fields. However, hysteretic nonlinearity has a great influence on the positioning accuracy of piezoelectric ceramic actuators, so it is necessary to establish a hysteretic model to solve this problem. In this paper, the principles of the Preisach model, the Prandtl Ishilinskii (PI) model, the Maxwell model, the Duhem model, the Bouc–Wen model, and the Hammerstein model and their application and development in piezoelectric hysteresis modeling are described in detail. At the same time, the classical model, the asymmetric model and the rate-dependent model of these models are described in detail, and the application of the inverse model corresponding to these models in the feedforward compensation is explained in detail. At the end of the paper, the methods of inverse model acquisition and control frequency of these models are compared. In addition, the future research trend of the hysteresis model is also prospected. The ideas and suggestions highlighted in this paper will guide the development of piezoelectric hysteresis models.
Organic Hydromulches in Young Olive Trees in Pots: Effects on Soil and Plant Parameters
Authors: Moreno Marta M. , González-Mora Sara , Villena Jaime , Moreno Carmen
Published: November 2023

Organic hydromulches (liquid spray-on mulches) have been used traditionally in land rehabilitation, mainly to mitigate post-fire runoff and erosion. However, in recent years, a new application of these materials as an eco-friendly alternative to the widely used polyethylene mulch, both in vegetable and woody crops, has been studyied. This work analyzes the effects of six hydromulches, based on organic by-products, on different soil parameters (water content, temperature, and CO2 flux), plant–water relations (stem water potential, leaf gas exchange, and leaf temperature), and the growth (trunk diameter) of young olive trees planted in large pots in the open field over a 2-year trial. The hydromulches tested were: rice husk (RH), rice husk with linen oil (RHL), mushroom substrate (MS), wheat straw (WS), pistachio (PW), and vineyard (VW) pruning wood chips, mixed with different additives (gypsum, recycled paper paste, and Kraft fiber). A non-mulched manual weeding control (NM) was included. The results indicated that hydromulches, in comparison with NM, resulted in increased volumetric soil water content (on average, 22.9% in hydromulches and 19.5% in NM), reduced soil temperature fluctuations (4.97 °C in hydromulches and 6.13 °C in NM), and increased soil CO2 fluxes (0.80 and 0.49 g CO2 m−2 h−1, respectively). Although the differences in the soil water content did not have an obvious effect on the plant–water status, crop growth was reduced in NM (≈23% lower than PW, MS, RHL, and WS), suggesting that vegetative growth, especially in young olive trees, is extremely sensitive to water deficit. The overall study leads to considering hydromulches as a good alternative to mulching in large pots, especially PW, which would be useful for nursery crops before their final establishment in the field.
Impacts of Multi-Strategy Nitrogen Fertilizer Management on Potato Yield and Economic Gains in Northeast China
Authors: He Yingbin , Luo Shanjun , Zha Yan , Jiang Xueqin , Wang Xiangyi , Chen Huicong , Han Shengnan
Published: November 2023

Differentiated nitrogen (N) application styles and dosages can directly affect crop yield and planting costs. To maximize the efficiency of N fertilizer utilization and solve the problem of increasing production without increasing income, the Decision Support Systems for Agrotechnology Transfer (DSSAT) model was used in this study to carry out experiments simulating different N application styles and dosages for potatoes at 15 meteorological observation stations in the northeast region of China during 2013–2017. The present study aims to analyze the relationship among yield, cost, and net gains under different N fertilizer management scenarios by combining the cost-benefit method. The main conclusions are as follows: (1) the staged and incremental application of N fertilizer during the potato growth period can promote a yield increase. The lowest yield was achieved by a one-time basal application of N fertilizer at sowing, and the highest yield was observed when N fertilizer was applied in a 1:1 ratio at sowing and the end of tuber formation, respectively. (2) The range for N fertilizer application to achieve the highest potato yield at each station is 275–330 kg·ha−1. (3) The highest potato yield under sufficient N application treatments did not correspond to the highest net gains. Potato net gains at all stations showed an increasing-then-decreasing trend with the increase in N application. The highest gains were obtained when 220 kg·ha−1, 220 kg·ha−1, and 275 kg·ha−1 of N fertilizer were applied in a 1:1 ratio as a basal fertilizer and supplementary fertilizer at sowing, and the end of tuber formation in Heilongjiang, Jilin, and Liaoning provinces, respectively. This study provides a reference program for potato planting with the highest benefits for regional governments, farmers, and enterprises and aims to improve the status quo of potato planting, increasing production without increasing income.
A Randomized Cross-Over Study Comparing Cooling Methods for Exercise-Induced Hyperthermia in Working Dogs in Training
Authors: Parnes Sara C. , Mallikarjun Amritha , Ramos Meghan T. , Stone Tesa A. , Otto Cynthia M.
Published: November 2023

Working dogs are at a high risk of canine non-pyrogenic hyperthermia, a life-threatening condition that can occur due to physical exertion or environmental factors that inhibit dogs’ ability to cool themselves. Two frequently recommended cooling methods to reduce body temperature are water immersion and the application of isopropyl alcohol to paw pads. This cross-over study compared the relative efficacy of these methods in 12 working-dogs-in-training with post-exertional heat stress. On each study day, dogs had a physical exam and performed a warm-up exercise followed by sequential recalls in which dogs ran approximately 25 m between two designated handlers for 10 min until they showed multiple signs of heat stress or their core temperature reached 105 °F (40.6 °C). Dogs’ temperature and heart rate were collected after each recall. Dogs completed three study days, and each day, randomly received one of three interventions: passive cooling (no intervention), partial water immersion, or isopropyl alcohol. Post-intervention dogs rested for 20 min. Partial water immersion and isopropyl alcohol both cooled dogs more than no intervention, and water immersion cooled dogs more efficiently than isopropyl alcohol. Additionally, the application of isopropyl alcohol raised dogs’ heart rates more than water immersion or no intervention, suggesting that the process of applying isopropyl alcohol is potentially stressful to dogs. Thus, partial water immersion is preferred to cool dogs post-exertion due to its more efficient cooling and better tolerance of use.
Effects of Dietary Folic Acid Supplementation on Sex Differences in Oriental River Prawn, Macrobrachium nipponense
Authors: Jiang Gang , Xue Yucai , Huang Xuxiong
Published: November 2023

To better understand the sex differences in the utilization of folic acid for male and female prawns, Macrobrachium nipponense, three experimental diets with graded folic acid contents (A: 1.22 mg/kg, B: 5.44 mg/kg, and C: 10.09 mg/kg) were provided to prawns for 8 weeks. The experimental study demonstrated that prawns fed diets containing 1.22 mg/kg showed the best values on body weight gain (BWG), specific growth rate (SGR), and individual weight for both males and females. Male prawns fed above 1.22 mg/kg of dietary folic acid significantly decreased the crude protein contents in their bodies (p < 0.05), while no significant differences were observed in females among all treatments (p > 0.05). The protease activity of the hepatopancreas in females was significantly (p < 0.05) improved by a dietary level of 10.09 mg/kg of folic acid. However, the opposite trend was observed in males, with the highest protease activity observed at a dietary level of 1.22 mg/kg folic acid. Increasing dietary folic acid levels did not suppress early maturation but led to an increase in the fecundity of females. Furthermore, prawns fed with 10.09 mg/kg of folic acid exhibited improved tolerance against thermal stress in both males and females. Transcriptome analysis revealed that during thermal stress, the “Oxidative phosphorylation” and “Pantothenate and CoA biosynthesis” signaling pathways were significantly enriched in females, and the “Fatty acid biosynthesis” signaling pathway was significantly enriched in males. The results of this study preliminarily evaluate the differences between male and female M. nipponense in response to different dietary folic acid levels and are helpful in promoting the health and growth of aquaculture production of this species.
miR-196a Promotes Proliferation of Mammary Epithelial Cells by Targeting CDKN1B
Authors: Chen Guanhe , Sun Wenqiang , Li Yuchao , Li Mengze , Jia Xianbo , Wang Jie , Lai Songjia
Published: November 2023

Heat stress (HS) has become one of the key challenges faced by the dairy industry due to global warming. Studies have reported that miR-196a may exert a role in the organism’s response to HS, enhancing cell proliferation and mitigating cellular stress. However, its specific role in bovine mammary epithelial cells (BMECs) remains to be elucidated. In this study, we aimed to investigate whether miR-196a could protect BMECs against proliferation arrest induced by HS and explore its potential underlying mechanism. In this research, we developed an HS model for BMECs and observed a significant suppression of cell proliferation as well as a significant decrease in miR-196a expression when BMECs were exposed to HS. Importantly, when miR-196a was overexpressed, it alleviated the inhibitory effect of HS on cell proliferation. We conducted RNA-seq and identified 105 differentially expressed genes (DEGs). Some of these DEGs were associated with pathways related to thermogenesis and proliferation. Through RT-qPCR, Western blotting, and dual-luciferase reporter assays, we identified CDKN1B as a target gene of miR-196a. In summary, our findings highlight that miR-196a may promote BMEC proliferation by inhibiting CDKN1B and suggest that the miR-196a/CDKN1B axis may be a potential pathway by which miR-196a alleviates heat-stress-induced proliferation arrest in BMECs.
Secretome Analysis of High- and Low-Virulent Bovine Pasteurella multocida Cultured in Different Media
Authors: Qiu Yangyang , Wang Jianan , He Fang , Wu Xiaoyan , Dan Ruitong , Hardwidge Philip R. , Li Nengzhang , Peng Yuanyi
Published: November 2023

Bovine Pasteurella multocida (P. multocida) serotype A is one of the major causes of bovine respiratory disease (BRD). We used data-independent acquisition (DIA) LC-MS/MS combined with bioinformatics analysis to identify proteins secreted by P. multocida. A total of 154 proteins were obtained from the supernatants of two isolates of bovine P. multocida serotype A (high virulent PmCQ2 and low virulent PmCQ6) cultured in Martin or BHI media, of which 50 were identified as putative secreted proteins. Further studies showed that Tuf, an elongation factor Tu, was highly expressed in P. multocida and secreted into infected tissues. Tuf stimulated strong innate immune responses of macrophages and had protective efficacy against P. multocida infection in a mouse model. The results provide insight into the secreted proteins of P. multocida and suggest new targets for vaccine development against P. multocida.
Predicting Beta-Lactam Target Non-Attainment in ICU Patients at Treatment Initiation: Development and External Validation of Three Novel (Machine Learning) Models
Authors: Wieringa André , Ewoldt Tim M. J. , Gangapersad Ravish N. , Gijsen Matthias , Parolya Nestor , Kats Chantal J. A. R. , Spriet Isabel , Endeman Henrik , Haringman Jasper J. , van Hest Reinier M. , Koch Birgit C. P. , Abdulla Alan
Published: November 2023

In the intensive care unit (ICU), infection-related mortality is high. Although adequate antibiotic treatment is essential in infections, beta-lactam target non-attainment occurs in up to 45% of ICU patients, which is associated with a lower likelihood of clinical success. To optimize antibiotic treatment, we aimed to develop beta-lactam target non-attainment prediction models in ICU patients. Patients from two multicenter studies were included, with intravenous intermittent beta-lactam antibiotics administered and blood samples drawn within 12–36 h after antibiotic initiation. Beta-lactam target non-attainment models were developed and validated using random forest (RF), logistic regression (LR), and naïve Bayes (NB) models from 376 patients. External validation was performed on 150 ICU patients. We assessed performance by measuring discrimination, calibration, and net benefit at the default threshold probability of 0.20. Age, sex, serum creatinine, and type of beta-lactam antibiotic were found to be predictive of beta-lactam target non-attainment. In the external validation, the RF, LR, and NB models confirmed good discrimination with an area under the curve of 0.79 [95% CI 0.72–0.86], 0.80 [95% CI 0.73–0.87], and 0.75 [95% CI 0.67–0.82], respectively, and net benefit in the RF and LR models. We developed prediction models for beta-lactam target non-attainment within 12–36 h after antibiotic initiation in ICU patients. These online-accessible models use readily available patient variables and help optimize antibiotic treatment. The RF and LR models showed the best performance among the three models tested.
Memory Recovery Effect of a New Bioactive Innovative Combination in Rats with Experimental Dementia
Authors: Tancheva Lyubka , Kalfin Reni , Minchev Borislav , Uzunova Diamara , Tasheva Krasimira , Tsvetanova Elina , Georgieva Almira , Alexandrova Albena , Stefanova Miroslava , Solak Ayten , Lazarova Maria , Hodzhev Yordan , Grigorova Valya , Yarkov Dobri , Petkova-Kirova Polina
Published: November 2023

Alzheimer’s disease manifests as a complex pathological condition, with neuroinflammation, oxidative stress and cholinergic dysfunction being a few of the many pathological changes. Due to the complexity of the disease, current therapeutic strategies aim at a multitargeted approach, often relying on a combination of substances with versatile and complementary effects. In the present study, a unique combination of α-lipoic acid, citicoline, extracts of leaves from olive tree and green tea, vitamin D3, selenium and an immune-supporting complex was tested in scopolamine-induced dementia in rats. Using behavioral and biochemical methods, we assessed the effects of the combination on learning and memory, and elucidated the mechanisms of these effects. Our results showed that, compared to its components, the experimental combination was most efficient in improving short- and long-term memory as assessed by the step-through method as well as spatial memory as assessed by T-maze and Barnes maze underlined by decreases in AChE activity (p < 0.05) and LPO (p < 0.001), increases in SOD activity in the cortex (p < 0.05) and increases in catalase (p < 0.05) and GPx (p < 0.01) activities and BDNF (p < 0.001) and pCREB (p < 0.05) levels in the hippocampus. No significant histopathological changes or blood parameter changes were detected, making the experimental combination an effective and safe candidate in a multitargeted treatment of AD.
Expression Recognition of Multiple Faces Using a Convolution Neural Network Combining the Haar Cascade Classifier
Authors: Yeh Jui-Feng , Lin Kuei-Mei , Chang Chia-Chen , Wang Ting-Hao
Published: November 2023

Facial expression serves as the primary means for humans to convey emotions and communicate social signals. In recent years, facial expression recognition has become a viable application within medical systems because of the rapid development of artificial intelligence and computer vision. However, traditional facial expression recognition faces several challenges. The approach is designed to investigate the processing of facial expressions in real-time systems involving multiple individuals. These factors impact the accuracy and robustness of the model. In this paper, we adopted the Haar cascade classifier to extract facial features and utilized convolutional neural networks (CNNs) as the backbone model to achieve an efficient system. The proposed approach achieved an accuracy of approximately 70% on the FER-2013 dataset in the experiment. This result represents an improvement of 7.83% compared to that of the baseline system. This significant enhancement improves the accuracy of facial expression recognition. Herein, the proposed approach also extended to multiple face expression recognition; the module was further experimented with and obtained promising results. The outcomes of this research will establish a solid foundation for real-time monitoring and prevention of conditions such as depression through an emotion alert system.
Microseismic Monitoring Signal Waveform Recognition and Classification: Review of Contemporary Techniques
Authors: Shu Hongmei , Dawod Ahmad Yahya
Published: November 2023

Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. This paper employs a literature review approach to summarize the research progress on microseismic signal identification methods and techniques over the past decade. The advantages and limitations of commonly used identification methods are systematically analyzed and summarized. Extensive discussions have been conducted on cutting-edge machine learning models, such as convolutional neural networks (CNNs), and their applications in waveform image processing. These models exhibit the ability to automatically extract relevant features and achieve precise event classification, surpassing traditional methods. Building upon existing research, a comprehensive analysis of the strengths, weaknesses, opportunities, and threats (SWOT) of deep learning in microseismic event analysis is presented. While emphasizing the potential of deep learning techniques in microseismic event waveform image recognition and classification, we also acknowledge the future challenges associated with data availability, resource requirements, and specialized knowledge. As machine learning continues to advance, the integration of deep learning with microseismic analysis holds promise for advancing the monitoring and early warning of geological engineering disasters.
Artificial Intelligence Applications and Innovations: Day-to-Day Life Impact
Authors: Rodrigues João M. F. , Cardoso Pedro J. S. , Chinnici Marta
Published: November 2023

The idea of an intelligent machine has fascinated humans for centuries [...]
Leveraging Prompt and Top-K Predictions with ChatGPT Data Augmentation for Improved Relation Extraction
Authors: Feng Ping , Wu Hang , Yang Ziqian , Wang Yunyi , Ouyang Dantong
Published: November 2023

Relation extraction tasks aim to predict the type of relationship between two entities from a given text. However, many existing methods fail to fully utilize the semantic information and the probability distribution of the output of pre-trained language models, and existing data augmentation approaches for natural language processing (NLP) may introduce errors. To address this issue, we propose a method that introduces prompt information and Top-K prediction sets and utilizes ChatGPT for data augmentation to improve relational classification model performance. First, we add prompt information before each sample and encode the modified samples by pre-training the language model RoBERTa and using these feature vectors to obtain the Top-K prediction set. We add a multi-attention mechanism to link the Top-K prediction set with the prompt information. We then reduce the possibility of introducing noise by bootstrapping ChatGPT so that it can better perform the data augmentation task and reduce subsequent unnecessary operations. Finally, we investigate the predefined relationship categories in the SemEval 2010 Task 8 dataset and the prediction results of the model and propose an entity location prediction task designed to assist the model in accurately determining the relative locations between entities. Experimental results indicate that our model achieves high results on the SemEval 2010 Task 8 dataset.