bluuebunny/crossref_metadata_2025 huggingface dataset:
Feeding on an exotic host plant enhances plasma levels of phenoloxidase by modulating feeding efficiency in a specialist insect herbivore
Authors: Mo Carmen , Smilanich Angela M.
Published: February 2023

Background: Exotic plant species represent a novel resource for invertebrates and many herbivorous insects have incorporated exotic plants into their diet. Using a new host plant can have physiological repercussions for these herbivores that may be beneficial or detrimental. In this study, we compared how using an exotic versus native host plant affected the immune system response and feeding efficiency of a specialist lepidopteran, the common buckeye (Junonia coenia: Nymphalidae, Hübner 1822).

Materials and Methods: In a lab experiment, larvae were reared on either the exotic host plant, Plantago lanceolata (Plantaginaceae), or the native host plant, Mimulus guttatus (Phrymaceae). Beginning at second instar feeding efficiency data were collected every 2 days until fifth instar when immune assays were performed. Immune assays consisted of standing phenoloxidase activity, total phenoloxidase activity, and melanization.

Results: Interestingly, we found that all three immune system parameters were higher on the exotic host plant compared to the native host plant. The exotic host plant also supported higher pupal weights, faster development time, greater consumption, and more efficient approximate digestibility. In contrast, the native host plant supported higher efficiency of conversion of ingested and digested food. The relationship between immunity and feeding efficiency was more complex but showed a large positive effect of greater host plant consumption on all immune parameters, particularly for the exotic host plant. While not as strong, the efficiency of conversion of digested food tended to show a negative effect on the three immune parameters.

Conclusion: Overall, the exotic host plant proved to be beneficial for this specialist insect with regard to immunity and many of the feeding efficiency parameters and continued use of this host plant is predicted for populations already using it.
Distinguishing the mechanisms driving multifaceted plant diversity in subtropical reservoir riparian zones
Authors: Zheng Jie , Arif Muhammad , He Xinrui , Liu Xiaolin , Li Changxiao
Published: February 2023

Understanding the multifaceted plant diversity and its maintenance mechanisms is crucial for biodiversity conservation. Dam-induced water level fluctuations dramatically alter various aspects of riparian diversity, such as taxonomic (TD), phylogenetic (PD), or functional (FD) diversity. However, few studies simultaneously evaluated plant TD, FD, and PD, especially in the subtropical reservoir riparian zone. Here we sampled plant diversity and environmental drivers along inundation gradients of the Three Gorges Reservoir Region in China. We integrated multifaceted plant diversity to assess how distinct ecological processes affect the plant community assembly and how they respond to inundation gradients, spatial variability, climate, and soils in dam-regulated riparian zones. We found that alpha TD, PD, and FD diversity exhibited decreasing trends with increasing inundation gradients and significant positive correlations with soil organic matter. The number of clustering plant communities increases along the inundation gradients. Beta TD and PD diversity were mainly dominated by species turnover with fewer contributions from nestedness, while beta FD diversity was mainly dominated by nestedness with fewer contributions from species turnover. The explainable rates of different dimensions of beta diversity, turnover, and nestedness ranged from 11% to 61%, with spatial factors explaining the highest beta diversity in different dimensions, followed by inundation gradients, soil properties, and climate variables. Our results suggest dispersal limitations are more important for species turnover in dam-regulated riparian zones at regional scales, while inundation gradients and soil fertility are more critical in shaping plant community assemblages at the local scale. This study emphasizes that environmental and spatial gradients are critical for understanding the assembly mechanisms driving multifaceted plant communities at local and regional scales and reinforces the importance of protecting seed sources and dispersal pathways and maintaining river connectivity when implementing restoration projects.
The predictive effect of subjective well-being and stress on foreign language enjoyment: The mediating effect of positive language education
Authors: Proietti Ergün Anna Lia , Ersöz Demirdağ Hande
Published: February 2023

The present study is the first to investigate the extent to which positive language education can improve foreign language enjoyment in the same learners. At the same time, it explores the relation between life quality variables, subjective well-being (SWB), and stresses that have emerged as important variables to explain foreign language enjoyment (FLE). Participants were 50 native speakers of Turkish, university students, 24 having a high proficiency in one language (English) and 26 having a high proficiency in two languages (French and English). Quantitative data were collected before and after the intervention (“3 Good Things” and “Use your Strength and Virtues in a Creative Way”) and qualitative data were collected at the end of the course. The results of pre-test and post-test analysis were not significant FLE (t(49) = −1.3, p > 0.05), SWB (t(49) = −0.58, p > 0.05), and stress (t(49) = −0.7, p > 0.05). Manova with a level of multilingualism as a fixed factor revealed that there was a significant difference in the pre-test and post-test F(3, 46) = 3.49, p < 0.023, Wilk’s Λ = 0.81, partial η2 = 0.023. The Between Subjects’ Test reveals a significant difference in the level of SWB (F(1, 48) = 7.04, p < 0.01) and FLE (F(1, 48) = 8.5, p < 0.005), but not for the level of stress (F(1, 48) = 0.29, p < 0.59). A subsequent set of regressions revealed that in pre-test there is not a significant effect of the factors on the dependent variable (R2 = 0.20, R2 adjusted = 0.140). The analysis after the intervention shows a significant effect of the SWB on FLE R2 = 0.35, R2 adjusted = 0.31, Beta = 0.25, p < 0.002. The analysis of the quantitative data supports the statistical analysis as students report that the intervention has helped to improve the atmosphere in the classroom as well as their general attitude in life and they have learned valuable knowledge about themselves as an added value to the acquisition of the new language. We conclude that positive language education can increase the level of subjective well-being in students and that, in turn, improves the level of positive emotions in the language classroom.
Positive effects of online games on the growth of college students: A qualitative study from China
Authors: Li Feiyue , Zhang Di , Wu Suowei , Zhou Rui , Dong Chaoqun , Zhang Jingjing
Published: February 2023

Objectives

This study aimed to explore the positive effects of online games on college students’ psychological demands and individual growth.

Methods

A qualitative study design was carried out in September 2021. Semi-structured, in-depth, and individual interviews were conducted with a purposive sample of 20 undergraduates who played the online game “Glory of Kings” from six universities. Thematic analysis was employed to explore the positive features caused by “Glory of Kings”.

Results

College students reported three positive effects of online games, namely, satisfying the need for personal growth, meeting the requirement of social life and promoting academic performance.

Conclusion

College educators and families should take advantage of the positive effect of online games to guide college students to use online games reasonably.
On the relationship between the social brain, social connectedness, and wellbeing
Authors: Kim M. Justin , Sul Sunhae
Published: February 2023

The emergence of social neuroscience in the past two decades has offered a useful neurocognitive framework for understanding human social behavior. Of importance, social neuroscience research aimed to provide mechanistic explanations for the established link between wellbeing and social behavioral phenomena–particularly those reflective of social connectedness. Here, we provide an overview of the relevant literature focusing on recent work using functional magnetic resonance imaging (fMRI). In general, fMRI research demonstrated that aspects of social connectedness that are known to either positively (e.g., social acceptance) or negatively (e.g., social isolation) impact wellbeing also modulated the activity of subcortical reward system accordingly. Similar modulatory influence was found for the activity of other brain regions such as the medial prefrontal cortex, which are typically regarded as components of the “social brain” that support a wide range of functions related to social cognition and behavior. Elucidating such individual differences in brain activity may shed light onto the neural underpinnings of the link between social connectedness and wellbeing.
A randomized controlled trial to test the effectiveness of two technology-enhanced diabetes prevention programs in primary care: The DiaBEAT-it study
Authors: Almeida Fabio A. , You Wen , Brito Fabiana A. , Alves Thais F. , Goessl Cody , Wall Sarah S. , Seidel Richard W. , Davy Brenda M. , Greenawald Mark H. , Hill Jennie L. , Estabrooks Paul A.
Published: February 2023

Objective

To evaluate the effectiveness of two technology-enhanced interventions for diabetes prevention among adults at risk for developing diabetes in a primary care setting.

Methods

The DiaBEAT-it study employed a hybrid 2-group preference (Choice) and 3-group randomized controlled (RCT) design. This paper presents weight related primary outcomes of the RCT arm. Patients from Southwest Virginia were identified through the Carilion Clinic electronic health records. Eligible participants (18 and older, BMI ≥ 25, no Type 2 Diabetes) were randomized to either Choice (n = 264) or RCT (n = 334). RCT individuals were further randomized to one of three groups: (1) a 2-h small group class to help patients develop a personal action plan to prevent diabetes (SC, n = 117); (2) a 2-h small group class plus automated telephone calls using an interactive voice response system (IVR) to help participants initiate weight loss through a healthful diet and regular physical activity (Class/IVR, n = 110); or (3) a DVD with same content as the class plus the same IVR calls over a period of 12 months (DVD/IVR, n = 107).

Results

Of the 334 participants that were randomized, 232 (69%) had study measured weights at 6 months, 221 (66%) at 12 months, and 208 (62%) at 18 months. Class/IVR participants were less likely to complete weight measures than SC or DVD/IVR. Intention to treat analyses, controlling for gender, race, age and baseline BMI, showed that DVD/IVR and Class/IVR led to reductions in BMI at 6 (DVD/IVR −0.94, p < 0.001; Class/IVR −0.70, p < 0.01), 12 (DVD/IVR −0.88, p < 0.001; Class/IVR-0.82, p < 0.001) and 18 (DVD/IVR −0.78, p < 0.001; Class/IVR −0.58, p < 0.01) months. All three groups showed a significant number of participants losing at least 5% of their body weight at 12 months (DVD/IVR 26.87%; Class/IVR 21.62%; SC 16.85%). When comparing groups, DVD/IVR were significantly more likely to decrease BMI at 6 months (p < 0.05) and maintain the reduction at 18 months (p < 0.05) when compared to SC. There were no differences between the other groups.

Conclusions

The DiaBEAT-it interventions show promise in responding to the need for scalable, effective methods to manage obesity and prevent diabetes in primary care settings that do not over burden primary care clinics and providers.

Registration

https://clinicaltrials.gov/ct2/show/NCT02162901, identifier: NCT02162901.
Attitudes of the general public toward community pharmacy services in Saudi Arabia: A cross-sectional study
Authors: Almanasef Mona
Published: February 2023

Aim

This research was conducted to evaluate the attitudes of the general public toward community pharmacy services in Saudi Arabia.

Methods

The current study followed a cross-sectional design using an online anonymous self-administered questionnaire. The study was conducted in Saudi Arabia between February and April 2022. The study participants were selected and recruited using a convenience sampling strategy. The questionnaire was adapted from previous work and involved four sections: demographic information, use of community pharmacy, perception and willingness to use community pharmacist services, and barriers to approaching a community pharmacist for advice.

Results and conclusions

A total of 449 participants agreed to take part in this study and completed the questionnaire. Just above half (55%) of the participants indicated that they had visited a community pharmacy at least once a month over the last 12 months. The most frequently reported reason for visiting community pharmacies was to buy over-the-counter (OTC) medications. The majority (n = 318) of the participants were satisfied or highly satisfied with community pharmacy services in Saudi Arabia. A willingness to use community pharmacy services was expressed mostly for the essential community pharmacist roles, involving selecting over-the-counter products, management of minor ailments, selecting non-pharmaceuticals and receiving counseling on using medications. However, an unwillingness was noted to approach a community pharmacist for counseling on alcohol dependence and drug misuse. Lack of privacy in the community pharmacy was found to be the most frequently reported barrier that would hinder individuals from approaching community pharmacists to get help or advice. Policymakers in Saudi Arabia could considerably advance the community pharmacy practice and improve the public utilization of various community pharmacist services beyond their limited essential roles such as dispensing and counseling.
Cell and gene therapy regulatory, pricing, and reimbursement framework: With a focus on South Korea and the EU
Authors: Lee SungKyung , Lee Jong Hyuk
Published: February 2023

Ever since relevant bioengineering technologies have sufficiently matured to the platformizable commercialization stage, a slew of money has flocked to the cell and gene therapy market over the last few years, resulting in an abundance of clinical studies in the field. Newer modalities have brought up a string of regulatory and legislative tasks, such as developing guidelines and legislative rules to systematically regulate newer pharmaceutical products. Accordingly, another layer of legislation and guidelines tailored for cell and gene therapies has been introduced and is expected to evolve on par with technological progress. Furthermore, authorities have shifted to pricing and reimbursement policies that can share risks for cost and outcome among stakeholders altogether, such as developers and the government, while expanding the accessibility of patients to innovative cell and gene therapies. This review attempts to capture the salient regulatory features of the cell and gene therapy market in the context of South Korea and the European Union and points out where two sovereign entities currently stand on each policy element and how each tackles regulatory challenges. We can observe the converging trend where regulatory, pricing and reimbursement rules of adjoining countries in the supranational union or member countries of a consortium are getting more aligned. Evidently, concerted efforts to share regulatory science knowledge and embrace reference pricing have played their parts. The authors argue that policy priorities should be placed on initiatives to harmonize with other medical authorities to better the rights of patients and clear out the uncertainties of developers, ultimately to share and advance regulatory science and layout forward-looking policies at opportune times.
Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning
Authors: Taheri-Shirazi Maryam , Namdar Khashayar , Ling Kelvin , Karmali Karima , McCradden Melissa D. , Lee Wayne , Khalvati Farzad
Published: February 2023

In this work, we examine magnetic resonance imaging (MRI) and ultrasound (US) appointments at the Diagnostic Imaging (DI) department of a pediatric hospital to discover possible relationships between selected patient features and no-show or long waiting room time endpoints. The chosen features include age, sex, income, distance from the hospital, percentage of non-English speakers in a postal code, percentage of single caregivers in a postal code, appointment time slot (morning, afternoon, evening), and day of the week (Monday to Sunday). We trained univariate Logistic Regression (LR) models using the training sets and identified predictive (significant) features that remained significant in the test sets. We also implemented multivariate Random Forest (RF) models to predict the endpoints. We achieved Area Under the Receiver Operating Characteristic Curve (AUC) of 0.82 and 0.73 for predicting no-show and long waiting room time endpoints, respectively. The univariate LR analysis on DI appointments uncovered the effect of the time of appointment during the day/week, and patients' demographics such as income and the number of caregivers on the no-shows and long waiting room time endpoints. For predicting no-show, we found age, time slot, and percentage of single caregiver to be the most critical contributors. Age, distance, and percentage of non-English speakers were the most important features for our long waiting room time prediction models. We found no sex discrimination among the scheduled pediatric DI appointments. Nonetheless, inequities based on patient features such as low income and language barrier did exist.
Allocation of emergency medical resources for epidemic diseases considering the heterogeneity of epidemic areas
Authors: Hu Bin , Jiang Guanhua , Yao Xinyi , Chen Wei , Yue Tingyu , Zhao Qitong , Wen Zongliang
Published: February 2023

Background

The resources available to fight an epidemic are typically limited, and the time and effort required to control it grow as the start date of the containment effort are delayed. When the population is afflicted in various regions, scheduling a fair and acceptable distribution of limited available resources stored in multiple emergency resource centers to each epidemic area has become a serious problem that requires immediate resolution.

Methods

This study presents an emergency medical logistics model for rapid response to public health emergencies. The proposed methodology consists of two recursive mechanisms: (1) time-varying forecasting of medical resources and (2) emergency medical resource allocation. Considering the epidemic's features and the heterogeneity of existing medical treatment capabilities in different epidemic areas, we provide the modified susceptible-exposed-infected-recovered (SEIR) model to predict the early stage emergency medical resource demand for epidemics. Then we define emergency indicators for each epidemic area based on this. By maximizing the weighted demand satisfaction rate and minimizing the total vehicle travel distance, we develop a bi-objective optimization model to determine the optimal medical resource allocation plan.

Results

Decision-makers should assign appropriate values to parameters at various stages of the emergency process based on the actual situation, to ensure that the results obtained are feasible and effective. It is necessary to set up an appropriate number of supply points in the epidemic emergency medical logistics supply to effectively reduce rescue costs and improve the level of emergency services.

Conclusions

Overall, this work provides managerial insights to improve decisions made on medical distribution as per demand forecasting for quick response to public health emergencies.
DeepHeartCT: A fully automatic artificial intelligence hybrid framework based on convolutional neural network and multi-atlas segmentation for multi-structure cardiac computed tomography angiography image segmentation
Authors: Bui Vy , Hsu Li-Yueh , Chang Lin-Ching , Sun An-Yu , Tran Loc , Shanbhag Sujata M. , Zhou Wunan , Mehta Nehal N. , Chen Marcus Y.
Published: February 2023

Cardiac computed tomography angiography (CTA) is an emerging imaging modality for assessing coronary artery as well as various cardiovascular structures. Recently, deep learning (DL) methods have been successfully applied to many applications of medical image analysis including cardiac CTA structure segmentation. However, DL requires a large amounts of data and high-quality labels for training which can be burdensome to obtain due to its labor-intensive nature. In this study, we aim to develop a fully automatic artificial intelligence (AI) system, named DeepHeartCT, for accurate and rapid cardiac CTA segmentation based on DL. The proposed system was trained using a large clinical dataset with computer-generated labels to segment various cardiovascular structures including left and right ventricles (LV, RV), left and right atria (LA, RA), and LV myocardium (LVM). This new system was trained directly using high-quality computer labels generated from our previously developed multi-atlas based AI system. In addition, a reverse ranking strategy was proposed to assess the segmentation quality in the absence of manual reference labels. This strategy allowed the new framework to assemble optimal computer-generated labels from a large dataset for effective training of a deep convolutional neural network (CNN). A large clinical cardiac CTA studies (n = 1,064) were used to train and validate our framework. The trained model was then tested on another independent dataset with manual labels (n = 60). The Dice score, Hausdorff distance and mean surface distance were used to quantify the segmentation accuracy. The proposed DeepHeartCT framework yields a high median Dice score of 0.90 [interquartile range (IQR), 0.90–0.91], a low median Hausdorff distance of 7 mm (IQR, 4–15 mm) and a low mean surface distance of 0.80 mm (IQR, 0.57–1.29 mm) across all segmented structures. An additional experiment was conducted to evaluate the proposed DL-based AI framework trained with a small vs. large dataset. The results show our framework also performed well when trained on a small optimal training dataset (n = 110) with a significantly reduced training time. These results demonstrated that the proposed DeepHeartCT framework provides accurate and rapid cardiac CTA segmentation that can be readily generalized for handling large-scale medical imaging applications.
What clinical metabolomics will bring to the medicine of tomorrow
Authors: Le Gouellec Audrey , Plazy Caroline , Toussaint Bertrand
Published: February 2023

The purpose of this review is to explore how clinical metabolomics could help physicians in the future. The recent advent of medical genomics brings new and interesting technological tools to measure genetic predispositions to a disease. But metabolomics will allow us to go even further by linking the patient’s pathological phenotype with gene expression defects and metabolic disorders. It is in this context that the clinical chemist must adapt and be a force of proposal to meet these health challenges. He must help the clinician by mastering these new innovative tools, in order to participate in the implementation of clinical studies for the discovery of biomarkers, but also to propose the assays of biomarkers called “signatures,” which can be composite biomarkers or fingerprints, which will ultimately guide the clinician. He will have to propose them as clinical chemistry tests. In the first part, we will look at some concrete examples of the use of clinical metabolomics in clinical research projects that have led to the identification of a new biomarker. We will use the example of trimethylamine N-oxide (or TMAO) and review the clinical studies that have proposed TMAO as a biomarker for cardiovascular diseases. In a second part, we will see through bibliographic studies, how the metabolomic fingerprint can be useful to build a supervised model for patient stratification. In conclusion, we will discuss the limitations currently under debate.
Disrupted water governance in the shadows: Revealing the role of hidden actors in the Upper Cauca River Basin in Colombia
Authors: Figueroa-Benitez Alejandro , Nagheeby Mohsen , Figueroa Apolinar , Amezaga Jaime
Published: February 2023

Colombia's policy in the Upper Cauca River Basin is diminished by a lack of legitimacy in local areas respecting the control of territory and water. Such illegitimate interference provides a hiding place and fertile ground for the illegal activities of “hidden” actors. This paper aims to scrutinize the potential power of such hidden actors to influence water governance. We engage with critical discussions of water governance to reveal the role of these actors in controlling territory and water in the Upper Cauca River Basin. Extensive fieldwork was carried out, including workshops, interviews, and informal talks. Despite the Colombian government not recognizing hidden actors and their part in influencing water-related policies, the information gathered revealed their active agency in the basin. The paper shows how these actors play a determining role in territorial development and water resource management, disrupting the functioning of the State's water governance. Hidden actors, to promote their own, mostly illegal and illegitimate businesses, seize upon the current lack of clarity in the national normative standards, the deficiencies created by an inconsiderate implementation of national water policy, and the State's lack of legitimacy at the local level. This paper concludes that the awareness of hidden actors and their invisible power over water governance provide a better sense of the reality on the ground for policymakers in Colombia.
The development of machine learning in bariatric surgery
Authors: Enodien Bassey , Taha-Mehlitz Stephanie , Saad Baraa , Nasser Maya , Frey Daniel M. , Taha Anas
Published: February 2023

Background

Machine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the medical domain. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery.

Methods

The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR). A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. Eligible studies included journals published from 2016 to the current date. The PRESS checklist was used to evaluate the consistency demonstrated during the process.

Results

A total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML algorithms in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation.

Conclusions

This study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. The evidence suggests that bariatric surgeons can benefit from ML algorithms since they will facilitate the prediction and evaluation of patient outcomes. Also, ML approaches to enhance work processes by making data categorization and analysis easier. However, further large multicenter studies are required to validate results internally and externally as well as explore and address limitations of ML application in bariatric surgery.
Prognostic nomogram for estimating survival in patients with resected muscle-invasive bladder cancer receiving chemotherapy
Authors: Hu Bing , Chen Ru , Chen Guoxian , Zheng Ping , Fu Bin
Published: February 2023

Background

Chemotherapy has been proven to bring survival benefit in patients with resected muscle-invasive bladder cancer (MIBC), which is increasingly recommended. Our objective was to establish an effective model for estimating the overall survival (OS) and cancer-specific survival (CSS) in these patients.

Methods

2,030 patients diagnosed with resected MIBC receiving chemotherapy were acquired from the Surveillance, Epidemiology, and End Result (SEER) database, which were randomized 7:3 into a primary set (1,421 patients) and an internal validation set (609 patients). Significant predictors for OS and CSS were identified by Cox regression models, which were then utilized to develop prognostic nomogram. The performance of the model was assessed by utilizing calibration, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).

Results

Six independent prognostic factors, including age, race, histology, T stage, N stage and regional nodes examined, made up the nomogram. The AUCs of the primary cohort was 0.751 and 0.753 for 3- and 5- year OS and 0.751 and 0.754 for 3-and 5- year CSS, respectively. The calibration plots proved the nomograms' satisfactory discrimination. The results of DCA manifested that our models had an excellent clinical applicability. In addition, a risk stratification system was established according to the nomogram' risk score. Obvious difference was found in different groups (P < 0.001).

Conclusion

The established prediction nomogram provides a simple-to-use tool for estimating the survival probability of resected MIBC patients treated with chemotherapy, which can assist clinicians make individualized treatment plans.
Early exposure to wildfire smoke can lead to birth defects
Authors: Lasley Bill L.
Published: February 2023

The results of two previously published reports of the events and impacts of the Campfire wildfire smoke exposure that occurred in California in 2018 are amplified from the point of view of the potential toxic mechanism involved. The Campfire wildfire led to the exposure of a breeding colony of macaque monkeys (Macaca mulatta) during the peak of their breeding season in 2018–2019. Considering the timing, adverse effects, and endocrine implications reported, the cumulative evidence points to an early toxic sensitive period that can lead to birth defects in higher primates and human pregnancies. This deeper inspection of the published observations provides important caveats and useful guidance for future investigators. The unique higher primate placental–adrenal–brain axis may limit the use of many traditional toxicologic approaches. Retrospective neurological evaluations of human fetuses exposed to air pollutants during organogenesis and subsequent retrospective characterization of air samples using in vitro and animal models may be the best procedures to follow.
An epithelial–mesenchymal transition-related mRNA signature associated with the prognosis, immune infiltration and therapeutic response of colon adenocarcinoma
Authors: Zhang Yu , Li Yan , Zuo Zan , Li Ting , An Ying , Zhang Wenjing
Published: February 2023

Background: Epithelial-mesenchymal transition (EMT) is closely associated with cancer cell metastasis. Colon adenocarcinoma (COAD) is one of the most common malignancies in the world, and its metastasis leading to poor prognosis remains a challenge for clinicians. The purpose of this study was to explore the prognostic value of EMT-related genes (EMTRGs) by bioinformatics analysis and to develop a new EMTRGs prognostic signature for COAD.

Methods: The TCGA-COAD dataset was downloaded from the TCGA portal as the training cohort, and the GSE17538 and GSE29621 datasets were obtained from the GEO database as the validation cohort. The best EMTRGs prognostic signature was constructed by differential expression analysis, Cox, and LASSO regression analysis. Gene set enrichment analysis (GSEA) is used to reveal pathways that are enriched in high-risk and low-risk groups. Differences in tumor immune cell levels were analyzed using microenvironmental cell population counter and single sample gene set enrichment analysis. Subclass mapping analysis and Genomics of Drug Sensitivity in Cancer were applied for prediction of immunotherapy response and chemotherapy response, respectively.

Results: A total of 77 differentially expressed EMTRGs were identified in the TCGA-COAD cohort, and they were significantly associated with functions and pathways related to cancer cell metastasis, proliferation, and apoptosis. We constructed EMTRGs prognostic signature with COMP, MYL9, PCOLCE2, SCG2, and TIMP1 as new COAD prognostic biomarkers. The high-risk group had a poorer prognosis with enhanced immune cell infiltration. The GSEA demonstrated that the high-risk group was involved in “ECM Receptor Interaction,” “WNT Signaling Pathway” and “Colorectal Cancer.” Furthermore, patients with high risk scores may respond to anti-CTLA4 therapy and may be more resistant to targeted therapy agents BI 2536 and ABT-888.

Conclusion: Together, we developed a new EMTRGs prognostic signature that can be an independent prognostic factor for COAD. This study has guiding implications for individualized counseling and treatment of COAD patients.
Analysis Method Development of Hybrid Linear Motor Considering Cogging Force Effect
Authors: Jiang Zhixiong , Park Jihun , Xu Danping , Hwang Sangmoon
Published: February 2023

This study examines the distributions of the cogging and current forces in a hybrid linear motor and their impact on its performance. Subsequently, using the Maxwell stress tensor to analyze the force acting on the motor, a method has been proposed for calculating the cogging force effect on the mechanical system stiffness. Based on the force profile, it was found that the cogging force was a noncurrent force that could reduce the spring stiffness of a hybrid linear motor when it is in operation. An electromagnetic–mechanical coupling analysis was conducted to calculate the displacement and acceleration of the hybrid linear motor on a dummy jig. The experimental and analytical results were in good agreement, indicating that the proposed method is accurate and reliable.
Aero-Engine Modeling and Control Method with Model-Based Deep Reinforcement Learning
Authors: Gao Wenbo , Pan Muxuan , Zhou Wenxiang , Lu Feng , Huang Jin-Quan
Published: February 2023

Due to the strong representation ability and capability of learning from data measurements, deep reinforcement learning has emerged as a powerful control method, especially for nonlinear systems, such as the aero-engine control system. In this paper, a novel application of deep reinforcement learning (DRL) is presented for aero-engine control. In addition, transition dynamic characteristic information of the aero-engine is extracted from the replay buffer of deep reinforcement learning to train a neural-network dynamic prediction model for the aero-engine. In turn, the dynamic prediction model is used to improve the learning efficiency of reinforcement learning. The practical applicability of the proposed control system is demonstrated by the numerical simulations. Compared with the traditional control system, this novel aero-engine control system has faster response speed, stronger self-learning ability, and avoids the complicated manual parameter adjustment without sacrificing the control performance. Moreover, the dynamic prediction model has satisfactory prediction accuracy, and the model-based method can achieve higher learning efficiency than the model-free method.
A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities
Authors: Albahar Marwan
Published: February 2023

The objective of this study was to provide a comprehensive overview of the recent advancements in the use of deep learning (DL) in the agricultural sector. The author conducted a review of studies published between 2016 and 2022 to highlight the various applications of DL in agriculture, which include counting fruits, managing water, crop management, soil management, weed detection, seed classification, yield prediction, disease detection, and harvesting. The author found that DL’s ability to learn from large datasets has great promise for the transformation of the agriculture industry, but there are challenges, such as the difficulty of compiling datasets, the cost of computational power, and the shortage of DL experts. The author aimed to address these challenges by presenting his survey as a resource for future research and development regarding the use of DL in agriculture.
Evidence of the Contribution of the Technological Progress on Aquaculture Production for Economic Development in China—Research Based on the Transcendental Logarithmic Production Function Method
Authors: Jiang Qijun , Wu Mengmeng , Zhang Dongyong
Published: February 2023

Technology includes hard technology and soft technology. Material technology embodied in production conditions and working conditions such as machinery, equipment and infrastructure is called hard technology, referring to the technology directly used in the development and production of means of production and means of subsistence, such as product design technology, equipment manufacturing technology, etc. Non-material technology, which embodies the experience, skills and management ability of process management, decision support and information technology, is called soft technology. Hard technologies make things easier and faster, while soft technologies promote flexibility and creativity. However, hard technologies take time to produce and have negative environmental impacts, while soft technologies are simple to produce but hard to use. Hence, finding the right balance between hard and soft technology investment is important for the sustainable increase of productivity. In recent years, China has continuously increased its investment in science and technology in aquaculture industry. However, the majority of the investment has gone to hard technology, which has hampered the long-term development of the industry. This paper aims to look at the status quo of the scientific and technological progress of the aquaculture industry in China and explore how the advancement of hard and soft technologies play a role in the economic growth of the aquaculture industry. Transcendental logarithmic production function is employed to calculate the contribution rate of technological progress on China’s aquaculture industry, and the contribution rates of hard technologies and soft technologies are examined separately. The results indicate that, from 2012 to 2020, the contribution rate of overall technical progress on aquaculture in China was 80.159%, of which 71.720% came from the progress of hard technologies, while only 8.439% came from the progress of soft technologies. Based on this conclusion, the paper calls for a balance of hard and soft technologies in the aquaculture industry in China to ensure a healthy and sustainable aquaculture industry. Policy suggestions are also put forward.
Assessment of Earthworm Viability and Soil Health after Two Years of Raw and Composted De-Inking Paper Sludge Amendment
Authors: Zoghlami Rahma Inès , Toukabri Wael , Boudabbous Khaoula , Hechmi Sarra , Barbouchi Meriem , Oueriemmi Houda , Moussa Mohammed , Bahri Haithem
Published: February 2023

The improvement of soil fertility properties is a priority for meeting sustainable development goals and world food security. One potential benefit of using paper sludge in agriculture is the reduction of waste and associated environmental impacts. By using paper sludge as a soil amendment, it is possible to divert away this material from landfills and instead use it to improve soil fertility and support the growth of crops. However, it is important to note that paper sludge may contain contaminants harmful to plants and soil health, of which earthworm viability serves as a key indicator. The present investigation aimed to evaluate changes in soil properties after the application of raw and composted de-inking paper sludge for two years. Accordingly, a field study was conducted in Manouba, a semi-arid region of Tunisia with a clay loam soil. The raw de-inking sludge (DS) and composted de-inking paper sludge (DSC) were applied at 30 and 60 t ha−1 and 20 and 40 t ha−1, respectively. Soil treatments were compared to unamended soils (C), to determine the optimal sludge treatment and rate for increasing the soil quality. Soil chemical (soil organic matter SOM, total carbon TC, and nitrogen TN, nutrient soil contents organic matter fractioned), physical (porosity and structural stability), and biological parameters (earthworms viability) were assessed. The results showed an increase of soil OM in the DS and DSC amended soils with the lowest rates (30 and 20 t ha−1). The humic fraction was found to be the dominant form. TC and TN were improved in the DS and DSC amended soils with the highest rates: 60 (DS2) and 40 t ha−1 (DSC2). Phosphorus and potassium were also increased in a dose-dependent manner. However, the soil porosity decreased in all treatments. The composted de-inking sludge was toxic for epigeic species, which could be explained by the use of litter while composting. Overall, the application of DS and DSC at low rates (30 and 20 t ha−1, respectively) might be a promising alternative for improving soil quality and at the same time ensuring the proper management of these wastes.
Lidar-Based 3D Obstacle Detection Using Focal Voxel R-CNN for Farmland Environment
Authors: Qin Jia , Sun Ruizhi , Zhou Kun , Xu Yuanyuan , Lin Banghao , Yang Lili , Chen Zhibo , Wen Long , Wu Caicong
Published: February 2023

With advances in precision agriculture, autonomous agricultural machines can reduce human labor, optimize workflow, and increase productivity. Accurate and reliable obstacle-detection and avoidance systems are essential for ensuring the safety of automated agricultural machines. Existing LiDAR-based obstacle detection methods for the farmland environment process the point clouds via manually designed features, which is time-consuming, labor-intensive, and weak in terms of generalization. In contrast, deep learning has a powerful ability to learn features autonomously. In this study, we attempted to apply deep learning in LiDAR-based 3D obstacle detection for the farmland environment. In terms of perception hardware, we established a data acquisition platform including LiDAR, a camera, and a GNSS/INS on the agricultural machine. In terms of perception method, considering the different agricultural conditions, we used our datasets to train an effective 3D obstacle detector, known as Focal Voxel R-CNN. We used focal sparse convolution to replace the original 3D sparse convolution because of its adaptable ability to extract effective features from sparse point cloud data. Specifically, a branch of submanifold sparse convolution was added to the upstream of the backbone convolution network; this adds weight to the foreground point and retains more valuable information. In comparison with Voxel R-CNN, the proposed Focal Voxel R-CNN significantly improves the detection performance for small objects, and the AP in the pedestrian class increased from 89.04% to 92.89%. The results show that our model obtains an mAP of 91.43%, which is 3.36% higher than the base model. The detection speed is 28.57 FPS, which is 4.18 FPS faster than the base model. The experiments show the effectiveness of our model, which can provide a more reliable obstacle detection model for autonomous agricultural machines.
Prediction of Soil Moisture Content from Sentinel-2 Images Using Convolutional Neural Network (CNN)
Authors: Hegazi Ehab H. , Samak Abdellateif A. , Yang Lingbo , Huang Ran , Huang Jingfeng
Published: February 2023

Agriculture is closely associated with food and water. Agriculture is the first source of food but the biggest consumer of freshwater. The population is constantly increasing. Smart agriculture is one of the means of achieving food and water security. Smart agriculture can help improve water management and increase agricultural production, thus counteracting rapid population growth requirements. Soil moisture estimation is a critical step in agricultural water management. Soil moisture measurement techniques in situ are point measurements, labor-intensive, time-consuming, tedious, and expensive. We propose, in this research, a new approach to predict soil moisture over vegetation-covered areas from Sentinel-2 images based on a convolutional neural network (CNN). CNN architecture (3) consisting of six convolutional layers, one pooling layer, and two fully connected layers has achieved the highest prediction accuracy. Three well-known criteria including coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) are utilized to measure the accuracy of the proposed algorithm. The Red Edge 3, NIR, and SWIR 1 are the most appropriate Sentinel-2 bands for retrieving soil moisture in vegetation-covered areas. Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) are the best indicators. The use of the indicator is more proper than the use of the single Sentinel-2 band as input data for the proposed CNN architecture for predicting soil moisture. However, using combinations “that consist of some number of Sentinel-2 bands” as input data for CNN architecture is better than using each indicator separately or all of them as a group. The best values of the performance metrics were achieved using the sixth combination (R2=0.7094, MAE=0.0277, RMSE=0.0418) composed of the Red, Red Edge 1, Red Edge 2, Red Edge 3, NIR, and Red Edge 4 bands as input data to the CNN architecture (3), as well as by using the fifth combination (R2=0.7015, MAE=0.0287, RMSE=0.0424) composed of the Red Edge 3, NIR, Red Edge 4, and SWIR 1 bands.
Antimicrobial Activity and Cytotoxicity of Nonsteroidal Anti-Inflammatory Drugs against Endodontic Biofilms
Authors: Ferrer-Luque Carmen María , Solana Carmen , Aguado Beatriz , Ruiz-Linares Matilde
Published: February 2023

Persistent infections have become a challenge in dentistry because of growing antibiotic resistance. Nonsteroidal anti-inflammatory drugs (NSAIDs) appear to be a therapeutic alternative to control biofilm infection. The objective of this work is to evaluate the antimicrobial activity and cytotoxicity of sodium diclofenac (DCS), ibuprofen (IBP) and ibuprofen arginine (IBP-arginine) solutions against endodontic polymicrobial biofilms. Sterile radicular dentin blocks of 4 mm × 4 mm × 0.7 mm were used as substrate to grow biofilm. The dentin blocks were submerged into solutions for 5 min. The antimicrobial activity was evaluated by means of the adenosine triphosphate (ATP) assay and confocal laser scanning microscopy (CLSM). Fibroblasts 3T3-L1 (ECACC 86052701) were used to test the cytotoxicity of irrigating solutions. The antibiofilm effects determined by the ATP assay showed that 4% IBP-arginine solution exerted the highest antibiofilm activity, followed by 4% DCS and 4% IBP, with statistical differences among groups (p < 0.001). As for CLSM, 4% DCS and 4% IBP-arginine solutions gave the lowest viable cell percentages, without significant differences between them. Cytotoxicity results at 1/10 dilution were similar for all solutions. At 1/100 dilution, a 4% DCS solution obtained the lowest cell viability for both time periods assayed, 1 h and 24 h. The IBP-arginine group showed the highest cell viability at 24 h. In this preliminary study, in terms of antibiofilm activity and cytotoxicity, a mixed 4% IBP-arginine solution gave the most promising results. NSAID solutions could be recommendable drugs for endodontic disinfection procedures.