bluuebunny/crossref_metadata_2025 huggingface dataset:
Deaths in jail: a retrospective analysis of autopsies performed at the Legal Medicine Unit of Pavia (1999-2022)
Authors: Belli Giacomo , Romão Mateus E. , Lacquagni Riccardo , Bertoglio Barbara , Bertolotti Andrea , Tamellini Gaia , Morini Luca , Javan Gulnaz T. , Setti Ilaria , Visonà Silvia D.
Published: September 2024

Introduction

The high rate of incarceration, now exceeding 11.5 million people worldwide, has raised concerns about the conditions within penal institutions, such as the consequences of incarceration on the person. This retrospective study aims to investigate the issue of death in custody, exploring the relationship between incarceration, health vulnerabilities, and death from the forensic pathologist’s point of view.

Methods

We analyzed, from a forensic, clinical and toxicological perspective, 86 cases of deaths in detention facilities in North-Western Italy from 1999 to 2022.

Results

The analysis has shown that suicide, mainly committed by hanging, plastic bag suffocation and butane intoxication, represents the leading type of violent death (52%), followed by accidental deaths (16%). On the other hand, cardiovascular diseases are the leading cause of natural deaths (42%), followed by infectious diseases (especially HIV-related).

Discussion

The present study identifies the most frequent and critical situations and risk factors related to death in custody and the profile of the inmate who is at a higher risk of death, allowing to highlight the issues to be addressed from a public health point of view. On the whole, it calls for comprehensive reforms, aligned with international human rights standards, addressing mental and physical care gaps, improving correctional officers' education, and training, and focusing on rehabilitation with well-being and dignity.
A systematic review of suicide risk management strategies in primary care settings
Authors: Thangada Monika Sreeja , Kasoju Rahul
Published: September 2024

Introduction and Objective

Suicide is a major public health concern. Recently, suicide rates have increased among traditionally low-risk groups (e.g., white, middle-aged males). Suicide risk assessments and prevention strategies should be tailored to specific at-risk populations. This systematic review examines suicide risk detection and management in primary care, focusing on treatments to reduce suicide rates and improve prevention efforts.

Methodology

A systematic review was conducted following PRISMA guidelines. Literature was collected and analyzed using Boolean operators with relevant keywords in databases (e.g., PubMed, Google Scholar, PsycINFO) to identify randomized and non-randomized studies focusing on suicide risk factors and management strategies in primary care, published in the past 10 years. The risk of bias 2.0 and Newcastle Ottawa scale was used to assess risk of bias, and data from moderate-quality studies were synthesized.

Results

Thirteen moderate-quality studies were reviewed. Key findings include the need for assessing modifiable risk factors like substance use and mental health. General practitioner (GP) engagement post-suicide attempt (SA) improves outcomes and reduces repeat SAs. Effective strategies include comprehensive risk assessments, collaborative treatment, and enhanced GP support. Barriers to effective suicide prevention include insufficient information, judgmental communication, lack of positive therapeutic relationships, and inadequate holistic assessments. These findings highlight the need for tailored suicide prevention strategies in primary care. However, the evidence sample size is small with reduced statistical power that limits generalizability. The included studies were also regional examinations, which restrict their broader relevance.

Discussion

Significant risk factors, barriers, and effective strategies for suicide prevention were identified. For children aged 12 or younger, preexisting psychiatric, developmental, or behavioral disorders, impulsive behaviors, aggressiveness, and significant stressful life events within the family were critical. For adults, loneliness, gaps in depression treatment, and social factors are significant. Barriers to suicide prevention included insufficient information, judgmental communication, lack of positive therapeutic relationships, inadequate holistic risk assessments, lack of individualized care, insufficient tangible support and resources, inconsistent follow-up procedures, variability in risk assessment, poor communication, stigma, and negative attitudes. Effective methods include the Postvention Assisting Bereaved by Suicide training program, continued education, comprehensive clinical assessments, individualized care, and community-based interventions like the SUPRANET program.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42024550904.
Cross-cultural adaptation of the awareness and beliefs about cancer measure for Hispanics/Latinos living in the United States
Authors: Contreras Jennifer , Wang Chun , Castillo Wendy Camelo , Caicedo Juan , Guerrero Vázquez Monica , Robalino Tania , Hidalgo-Arroyo Aida , Villalonga-Olives Ester
Published: September 2024

Introduction

The purpose of this study is to culturally adapt the Awareness and Beliefs about Cancer (ABC) measure for use in the Hispanic/Latino population living in the United States (US).

Methods

In accordance with Patient Reported Outcomes (PRO) Consortium guidelines for cross-cultural adaptation of measures for content and linguistic validity, we conducted: two forward-translations, reconciliation, two back-translations, revision and harmonization, six cognitive interviews, revision, external expert review, and finalization of the version. We used a mixed methods approach, conducting cognitive interviews with Hispanic/Latino community members while also convening an expert panel of six clinicians, health professionals, and community representatives and including the in the entire process. After cross-culturally adapting the ABC measure, we assessed the psychometric properties of the instrument using item response theory analysis. Item parameters, discrimination and category thresholds, and standard errors were calculated. For each of the adapted subdomains, we used item information curves to report the graphical profile of item effectiveness.

Results

Twenty-two Hispanic/Latino community members were enrolled in cognitive interviews, and Hispanics/Latinos fluent in Spanish completed the measure to assess its psychometric properties. Cognitive interviews revealed opportunities to improve items. Key changes from the original measure include the inclusion of gender inclusive language and an inquiry into e-cigarette use on items related to smoking habits. Psychometric property analyses revealed that the anticipated delay in seeking medical help, general cancer beliefs, and cancer screening beliefs and behaviors subdomains had some slope parameters that were < 1; this implies that those items were not able to adequately discriminate the latent trait and had poor performance.

Discussion

The adapted ABC measure for US Hispanics/Latinos meets content and linguistic validity standards, with construct validity confirmed for cancer symptom recognition and barriers to symptomatic presentation subdomains, but revisions are necessary for others, highlighting the need for ongoing refinement to ensure the cultural appropriateness of instruments.
Effect of simulated hearing loss on automatic speech recognition for an android robot-patient
Authors: Röhl Jan Hendrik , Günther Ulf , Hein Andreas , Cauchi Benjamin
Published: September 2024

The importance of simulating patient behavior for medical assessment training has grown in recent decades due to the increasing variety of simulation tools, including standardized/simulated patients, humanoid and android robot-patients. Yet, there is still a need for improvement of current android robot-patients to accurately simulate patient behavior, among which taking into account their hearing loss is of particular importance. This paper is the first to consider hearing loss simulation in an android robot-patient and its results provide valuable insights for future developments. For this purpose, an open-source dataset of audio data and audiograms from human listeners was used to simulate the effect of hearing loss on an automatic speech recognition (ASR) system. The performance of the system was evaluated in terms of both word error rate (WER) and word information preserved (WIP). Comparing different ASR models commonly used in robotics, it appears that the model size alone is insufficient to predict ASR performance in presence of simulated hearing loss. However, though absolute values of WER and WIP do not predict the intelligibility for human listeners, they do highly correlate with it and thus could be used, for example, to compare the performance of hearing aid algorithms.
Family of 2:1 resonant quasi-periodic distant retrograde orbits in cislunar space
Authors: Wang Ming , Yang Chihang , Sun Yang , Zhang Hao
Published: September 2024

Given the current enthusiasm for lunar exploration, the 2:1 resonant distant retrograde orbit (DRO) in Earth-Moon space is of interest. To gain an in-depth understanding of the complex dynamic environment in cislunar space and provide more options for parking orbits, this paper investigates the existence of quasi-periodic orbits near the 2:1 resonant DRO in the circular restricted three-body problem (CR3BP). Firstly, the numerical computation approach, continuation strategy, and stability analysis method of quasi-periodic orbits are introduced. Then, addressing the primary challenges in the continuation progress, we have developed an adaptive continuation algorithm with automatic adjustment of the step size and the number of discrete points that characterize the invariant torus. Finally, two types of 2D quasi-DROs and their linear stability properties are explored. Using Poincaré sections, we investigated the boundaries of the maximum extent attainable by both 2D quasi-DRO families in the CR3BP at a specific Jacobi energy, confirming that both types of quasi-periodic families have reached their respective boundaries. The algorithm described in this paper is beneficial for facilitating the computation of quasi-periodic families and aids in discovering additional potential dynamical structures.
Characteristics of several soybean varieties (Glycine max L.) and weed management systems in an effort to increase productivity in low land rice
Authors: Fattah Abdul , Negara Abdi , Supriadi Khojin , Hannan Muhammad Fitrah Irawan , Ardjanhar Asni , Beding Petrus A. , Najamuddin Erwin , Pustika Arlyna Budi , Susilawati Susilawati , Nonci Nurnina , Latifah Evy , Arifin Zainal , Istiqomah Nurul , Udiarto Bagus K. , Dewayani Wanti
Published: September 2024

Soybean productivity in paddy fields is influenced by variety selection and grass management practices. This study aimed to assess several soybean varieties and evaluate the impact of soil processing and weed control on Summed Dominance Ratio (SDR), as well as growth and yield of soybean seeds. Conducted in Sungai Kakap, Kubu Raya, West Kalimantan, during 2021, the research employed a Randomized Block Design with 15 treatments and 3 replications. Treatments included various combinations of tillage methods, weed control techniques, and mulching. The study identified four soybean varieties Detap-1, Derap-1, Devon-1, and Dena-2 with large seed sizes and high yields. These varieties also exhibited resistance to common pests such as Etiella zinkenella, Riptortus linearis, and Spodoptera litura. Weed composition analysis revealed O. sativa and Ageratum conyzoides as dominant species. Weed dry weight was lowest in the perfect tillage + pre-emergence herbicide treatment and highest in the minimum tillage + weeds are not controlled treatment. The highest plant growth and seed yield were observed in the minimum tillage + pre-emergence herbicide and perfect tillage + pre-emergence herbicide treatments. Plant height, number of pods per plant, number of seeds per plant, and dry seed yield were significantly higher in these treatments compared to others. In conclusion, varieties Detap-1, Derap-1, Devon-1, and Dena-2 possess suitable physical characteristics for cultivation in Indonesia. The most effective grass management models identified were minimum tillage + pre-emergence herbicide and perfect tillage + pre-emergence herbicide. These findings contribute to optimizing soybean cultivation practices, emphasizing varietal selection and weed control strategies for improved crop performance.
Exploring the position of farmers within the European green transition: transformation for whom?
Authors: Stone Tiffanie Faye , Nichols Virginia , Thorsøe Martin Hvarregaard
Published: September 2024

Food systems have been framed as a “wicked problem” due to the complex socio-ecological impacts they foster, ranging from contributing nearly a quarter of anthropogenic greenhouse gas emissions to a myriad of social impacts (e.g., health, food safety, and food security). In the European green transition for food systems, multiple actors are involved. However, farmers play a unique and critical role as agricultural land managers and navigators of social, political, and environmental factors. Using cover cropping and intercropping as examples, we illustrate the complexities arising when decision-making and governance at multiple levels lead to tradeoffs and unexpected consequences at the farm scale. Amid complexity, we propose a conceptual model to address the question: how is an agricultural green transition best fostered? We find that changes are incremental, transformative or both depending on the level of analysis. Additionally, incoherence in agronomic recommendations across academic disciplines and policy agendas creates challenges at the farm scale that trickle up and can thwart sustainable agricultural land use. Although transdisciplinarity and knowledge production with farmers through co-creation are essential for food system transformation and can be part of the solution, it is crucial to examine the nature of change processes and to consider how knowledge and innovation are adopted. By balancing top-down and bottom-up approaches and distributing burden from the farm scale to governance and food systems, a more transformative green transition for European food systems with coherence across multiple agroecological objectives could be achieved.
Accessible Type 2 diabetes medication through stable expression of Exendin-4 in Saccharomyces cerevisiae
Authors: Balius Gia , Imani Kiana , Petroff Zoë , Beer Elizabeth , Feitosa Thiago Brasileiro , Mccall Nathan , Paule Lauren , Peng Neo Yixuan , Shen Joanne , Singh Vidhata , Strand Cambell , Zau Jonathan , Bernick D. L.
Published: September 2024

Diabetes mellitus affects roughly one in ten people globally and is the world’s ninth leading cause of death. However, a significant portion of chronic complications that contribute to mortality can be prevented with proper treatment and medication. Glucagon-like peptide 1 receptor agonists, such as Exendin-4, are one of the leading classes of Type 2 diabetes treatments but are prohibitively expensive. In this study, experimental models for recombinant Exendin-4 protein production were designed in both Escherichia coli and Saccharomyces cerevisiae. Protein expression in the chromosomally integrated S. cerevisiae strain was observed at the expected size of Exendin-4 and confirmed by immunoassay. This provides a foundation for the use of this Generally Regarded as Safe organism as an affordable treatment for Type 2 diabetes that can be propagated, prepared, and distributed locally.
Species-level characterization of the core microbiome in healthy dogs using full-length 16S rRNA gene sequencing
Authors: Rojas Connie A. , Park Brian , Scarsella Elisa , Jospin Guillaume , Entrolezo Zhandra , Jarett Jessica K. , Martin Alex , Ganz Holly H.
Published: September 2024

Despite considerable interest and research in the canine fecal microbiome, our understanding of its species-level composition remains incomplete, as the majority of studies have only provided genus-level resolution. Here, we used full-length 16S rRNA gene sequencing to characterize the fecal microbiomes of 286 presumed healthy dogs living in homes in North America who are devoid of clinical signs, physical conditions, medication use, and behavioral problems. We identified the bacterial species comprising the core microbiome and investigated whether a dog’s sex & neuter status, age, body weight, diet, and geographic region predicted microbiome variation. Our analysis revealed that 23 bacterial species comprised the core microbiome, among them Collinsella intestinalis, Megamonas funiformis, Peptacetobacter hiranonis, Prevotella copri, and Turicibacter sanguinis. The 23 taxa comprised 75% of the microbiome on average. Sterilized females, dogs of intermediate body sizes, and those exclusively fed kibble tended to harbor the most core taxa. Host diet category, geographic region, and body weight predicted microbiome beta-diversity, but the effect sizes were modest. Specifically, the fecal microbiomes of dogs fed kibble were enriched in several core taxa, including C. intestinalis, P. copri, and Holdemanella biformis, compared to those fed raw or cooked food. Conversely, dogs on a raw food diet exhibited higher abundances of Bacteroides vulgatus, Caballeronia sordicola, and Enterococcus faecium, among others. In summary, our study provides novel insights into the species-level composition and drivers of the fecal microbiome in healthy dogs living in homes; however, extrapolation of our findings to different dog populations will require further study.
Statistical Metamodel of Liner Acoustic Impedance Based on Neural Network and Probabilistic Learning for Small Datasets
Authors: Sinha Amritesh , Desceliers Christophe , Soize Christian , Cunha Guilherme
Published: September 2024

The main novelty of this paper consists of presenting a statistical artificial neural network (ANN)-based model for a robust prediction of the frequency-dependent aeroacoustic liner impedance using an aeroacoustic computational model (ACM) dataset of small size. The model, focusing on percentage of open area (POA) and sound pressure level (SPL) at a zero Mach number, takes into account uncertainties using a probabilistic formulation. The main difficulty in training an ANN-based model is the small size of the ACM dataset. The probabilistic learning carried out using the probabilistic learning on manifolds (PLoM) algorithm addresses this difficulty as it allows constructing a very large training dataset from learning the probabilistic model from a small dataset. A prior conditional probability model is presented for the PCA-based statistical reduced representation of the frequency-sampled vector of the log-resistance and reactance. It induces some statistical constraints that are not straightforwardly taken into account when training such an ANN-based model by classical optimizations methods under constraints. A second novelty of this paper consists of presenting an alternate solution that involves using conditional statistics estimated with learned realizations from PLoM. A numerical example is presented.
Effects of Storage Time and Temperature on the Fermentation Characteristics of Rumen Fluid from a High-Forage Diet
Authors: Fu Chuanpei , Qu Mingren , Ouyang Kehui , Qiu Qinghua
Published: September 2024

The objective of this experiment was to investigate the effects of storage temperature and preservation time on the fermentation characteristics of rumen fluid collected from six Hu sheep fed a high-forage diet. The storage temperatures were set at −80 °C and −20 °C, and the preservation times were labelled as follows: 0 d (fresh rumen fluid, D0), 7 d (D7), 14 d (D14), 30 d (D30), 60 d (D60), 120 d (D120), and 240 d (D240). A repeated-measures design was applied to analyze the fermentation characteristics of rumen fluid across each preservation time point and storage temperature. The results showed that storage temperature had no significant effects on pH value, ammonia nitrogen (NH3-N), microbial protein (MCP), and volatile fatty acid (VFA) concentration and proportion (p > 0.05). Specifically, the pH value increased on D7, D14, and D60 (p < 0.05), while the MCP concentration decreased on D7, D14, D30, D120, and D240 but increased on D60 (p < 0.05), and the concentration of NH3-N decreased on D14 (p < 0.05). The acetate concentration increased on D30, while the concentrations of propionate, butyrate, valerate, and total VFA increased on D60 (p < 0.05). The concentration of isovalerate decreased on D60, and isobutyrate and branched-chain VFA concentrations decreased on D120 (p < 0.05). The proportion of acetate increased on D30, while the butyrate and valerate proportions increased on D60, and the proportions of isovalerate, isobutyrate, and branched-chain VFA decreased on D60 (p < 0.05). For rumen fluid from a high-forage diet, the storage temperature (−80 °C and −20 °C) did not affect its fermentation characteristics, suggesting that rumen fluid could be preserved at −80 °C or −20 °C without altering its fermentation characteristics. The fermentation parameters of rumen fluid changed significantly after 7 days of preservation; hence, it is recommended to finish determining the fermentation parameters of rumen fluid within 7 days of collection. The concentrations and proportions of most VFA changed after 30 days of preservation; therefore, it is recommended that VFA determination should be completed within 30 days of rumen fluid collection.
Research on Silage Corn Forage Quality Grading Based on Hyperspectroscopy
Authors: Hao Min , Zhang Mengyu , Tian Haiqing , Sun Jianying
Published: September 2024

Corn silage is the main feed in the diet of dairy cows and other ruminant livestock. Silage corn feed is very susceptible to spoilage and corruption due to the influence of aerobic secondary fermentation during the silage process. At present, silage quality testing of corn feed mainly relies on the combination of sensory evaluation and laboratory measurement. The sensory review method is difficult to achieve precision and objectivity, while the laboratory determination method has problems such as cumbersome testing procedures, time-consuming, high cost, and damage to samples. In this study, the external sensory quality grading model for different qualities of silage corn feed was established using hyperspectral data. To explore the feasibility of using hyperspectral data for external sensory quality grading of corn silage, a hyperspectral system was used to collect spectral data of 200 corn silage samples in the 380–1004 nm band, and the samples were classified into four grades: excellent, fair, medium, and spoiled according to the German Agricultural Association (DLG) standard for sensory evaluation of silage samples. Three algorithms were used to preprocess the fodder hyperspectral data, including multiplicative scatter correction (MSC), standard normal variate (SNV), and S–G convolutional smoothing. To reduce the redundancy of the spectral data, variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS) were used for feature wavelength selection, and linear discriminant analysis (LDA) algorithm was used for data dimensionality reduction, constructing random forest classification (RFC), convolutional neural networks (CNN) and support vector machines (SVM) models. The best classification model was derived based on the comparison of the model results. The results show that SNV-LDA-SVM is the optimal algorithm combination, where the accuracy of the calibration set is 99.375% and the accuracy of the prediction set is 100%. In summary, combined with hyperspectral technology, the constructed model can realize the accurate discrimination of the external sensory quality of silage corn feed, which provides a reliable and effective new non-destructive testing method for silage corn feed quality detection.
Design and Experiment of Double-Nest Eye-Type Hole-Wheel Dense-Planting Wheat Dibbler
Authors: Fu Xuanhe , Yan Limin , Wang Long , Jiang Deli , Tian Xinliang , Wu Tao , Zhang Jinhao
Published: September 2024

To address the problems of the inaccurate seeding rate and uneven seeding in the process of dense planting of winter wheat in Xinjiang, according to the physical characteristics of the wheat seeds and the agronomic requirements of the high-yield cultivation techniques for the winter wheat “well” type, a double-hole wheel-type densely planted wheat hole sower was designed and produced. Through theoretical design and research, the structural design of the overall hole seeder and its key components was completed. The findings indicated that 5–7 wheat seeds could be planted in each hole at a 9.2 mm nest depth and 610 mm3 nest volume, which was consistent with the “well”-type high-yield dense-planting cultivation technology’s need for 400,000–500,000 basic seedlings per mu. The rotation speed and the quantity of the wave guide teeth were used as test factors and the qualifying index, replay index, and missed sowing index were used as test indicators to create the two-factor, three-level central composite design center combination test. It was possible to derive the mathematical model connecting the test factors and test indexes. The regression model underwent multi-objective optimization using the Design-Expert 13 program to determine the optimal parameters: the qualifying index was 91.24%, the replay index was 6.14%, and the missed seeding index was 2.62% when the wave guide rail had four teeth and the seed drill rotated at a speed of 40 revolutions per minute. The best parameter combinations were used for a bench verification test, and the test indicated that the qualified index was 90.25%, the replay index was 4.59%, and the missed broadcast index was 5.16%. The results demonstrated that the densely planted wheat hole seeder performs well, satisfies the requirements for winter wheat dense-planting and sowing operations, and serves as a model for the densely planted wheat hole seeders that will be optimized in the future.
Cotton-YOLO-Seg: An Enhanced YOLOV8 Model for Impurity Rate Detection in Machine-Picked Seed Cotton
Authors: Jiang Long , Chen Weitao , Shi Hongtai , Zhang Hongwen , Wang Lei
Published: September 2024

The detection of the impurity rate in machine-picked seed cotton is crucial for precision agriculture. This study proposes a novel Cotton-YOLO-Seg cotton-impurity instance segmentation algorithm based on the you only look once version 8 small segmentation model (Yolov8s-Seg). The algorithm achieves precise pixel-level segmentation of cotton and impurities in seed cotton images and establishes a detection model for the impurity rate, enabling accurate detection of the impurity rate in machine-picked cotton. The proposed algorithm removes the Pyramid 4 (P4) feature layer and incorporates Multi-Scale Convolutional Block Attention (MSCBCA) that integrates the Convolutional Block Attention Module (CBAM) and Multi-Scale Convolutional Attention (MSCA) into the Faster Implementation of Cross Stage Partial Bottleneck with 2 Convolutions (C2f) module of the feature extraction network, forming a novel C2f_MSCBCA module. The SlimNeck structure is introduced in the feature fusion network by replacing the P4 feature layer with the small-target detection layer Pyramid 2 (P2). Additionally, transfer learning is employed using the Common Objects in Context (COCO) instance segmentation dataset. The analysis of 100 groups of cotton image samples shows that the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) for impurity rate detection are 0.29%, 0.33%, and 3.70%, respectively, which are reduced by 52.46%, 48.44%, and 53.75% compared to the Yolov8s-seg model. The Precision (P), Recall (R), and mean Average Precision at an intersection over union of 0.5 (mAP@0.5) are 85.4%, 78.4%, and 80.8%, respectively, which are improved by 4.2%, 6.2%, and 6.4% compared to Yolov8s-seg model, significantly enhancing the segmentation performance of minor impurities. The Cotton-YOLO-Seg model demonstrates practical significance for precisely detecting the impurity rate in machine-picked seed cotton.
Cowpea: Prospecting for Heat-Tolerant Genotypes
Authors: Silva Jaqueline de Almeida , Barros Juliane Rafaele Alves , Silva Elioenai Gomes Freire , Rocha Maurisrael de Moura , Angelotti Francislene
Published: September 2024

Selecting genotypes tolerant to high temperatures is an important measure for agricultural maintenance and production in climate change scenarios. Thus, this study aimed to select cowpea genotypes tolerant to increased air temperature. A total of 20 cowpea genotypes were used, cultivated under temperature regimes of 20–26–33 °C and 24.8–30.8–37.8 °C in a completely randomized experimental design under a 2 × 20 factorial scheme (temperature regimes × genotypes). The BRS Inhuma, Bico-de-Ouro-17-45, BRS Guariba, and BRS Imponente genotypes did not show significant differences in the analyzed physiological responses to the increase in air temperature. The BRS Inhuma, Bico-de-Oouro-17-19, Bico-de-Ouro-17-44, Bico-de-Ouro-17-45, BRS Guariba, and BRS Imponente genotypes showed increased temperature tolerance as thermal stress did not affect production. The Pingo-de-Ouro-17-48, MNC00-595F-27, MNC06-895E-1, and MNC09-981B-2 genotypes reduced water efficiency by −26.85, −25.19, −40.04, and −60.37%, respectively, due to the increase in temperature. The results obtained in this work represent a pre-selection of genotypes that are tolerant to high temperatures, with the BRS Inhuma, Bico-de-Ouro-17-45, BRS Guariba, and BRS Imponente genotypes indicated as tolerant to increased temperatures based on the interaction of physiological and productive responses. There is an urgent need to select cowpea genotypes tolerant to increased temperature to maintain production in climate change scenarios and ensure agricultural systems’ sustainability and food security.
Responses of Methane Emission and Bacterial Community to Fertilizer Reduction Plus Organic Materials over the Course of an 85-Day Leaching Experiment
Authors: Gao Jiakai , Ma Zhenyi , Liu Ling , Shi Zhaoyong , Lv Jialong
Published: September 2024

Methane produced from paddy fields has a negative impact on global climate change. However, the role of soil bacterial community composition in mediating methane (CH4) emission from waterlogged paddy soil using the column experiment is poorly known. In the present study, various fertilization treatments were adopted to investigate the effects of fertilizer reduction combined with organic materials (CK: control; CF: conventional fertilization; RF: 20% fertilizer reduction; RFWS: RF plus wheat straw amendment; RFRS: RF plus rapeseed shell amendment; RFAS: RF plus astragalus smicus amendment) on CH4 emission and soil bacterial community during an 85-day leaching experiment. We hypothesized that the fertilizer reduction plus the organic materials could enrich the bacterial communities and increase CH4 emission. The average CH4 flux varied from 0.03 μg m−2 h−1 to 76.19 μg m−2 h−1 among all treatments in the nine sampling times, which may account for the experimental conditions such as air temperature, moisture, and anthropogenic factors. In addition, high-throughput sequencing was utilized to investigate the alteration of the soil bacterial community structure. It was revealed that the diversity and composition of the bacterial community in the topsoil amended with organic materials underwent significant shifts after the 85-day leaching experiment. Proteobacteria was identified as the dominant phylum of the soil bacteria, with an average proportion of 35.2%. For Firmicutes, the proportion of RFRS (11%) was higher than that in the CK (8%), RF (8%), RFWS (7%), RFAS (6%), and CF (5%) treatments. Additionally, Gammaproteobacteria and Alphaproteobateria were supposed to be the major class bacterial communities, with average proportions of 12.8% and 12.2%, respectively. For the RFWS treatment, the contribution of Alphaproteobateria was the highest among all the bacterial relative abundance. According to the correlation heatmap analysis, the top ten bacterial communities were positively related to soil microbial biomass carbon (MBC) and ammonia nitrogen (NH4+-N) (p < 0.01). The findings also indicated that the RFRS treatment was the favorable management to alleviate CH4 emission during an 85-day leaching experiment or possibly in paddy production. Collectively, these results predict that the impacts of different treatments on CH4 production are strongly driven by soil microbial communities and soil properties, with soil bacteria being more prone to the crop residue degradation stage and more sensitive to soil properties. The discoveries presented in this study will be useful for assessing the efficacy and mechanisms of organic material amendments on CH4 emissions in paddy soil.
The Effects of Bio-Fertilizer by Arbuscular Mycorrhizal Fungi and Phosphate Solubilizing Bacteria on the Growth and Productivity of Barley under Deficit of Water Irrigation Conditions
Authors: Alotaibi Mashael M. , Aljuaid Alya , Alharbi Maha Mohammed , Qumsani Alaa T. , Alzuaibr Fahad Mohammed , Alsubeie Moodi S. , Ismail Khadiga Ahmed , Gharib Hany S. , Awad-Allah Mamdouh M. A.
Published: September 2024

Bio-fertilizers are the most important and effective method used to reduce the quantities of chemical fertilizers consumed and reduce dependence on them in agricultural production to avoid their harmful effects on the environment and public health as well as reduce the cost of agricultural production in light of increasing pollution and under adverse conditions for production and climate change. A bio-fertilizer depends primarily on the use of beneficial microorganisms such as arbuscular mycorrhizal fungi (AMF) to improve the uptake of nutrients, improve plant growth, productivity, and grain yield. Crop production faces many challenges, and drought is one of the majority of the significant factors limiting crop production worldwide, especially in semi-arid regions. The objective of this study was to evaluate the effects of AMF and phosphate solubilizing bacteria (PSB), plus three rates of the recommended dose of phosphorus (RDP) fertilizer on yield, yield components, and nutrients uptake, in addition to evaluating the beneficial effects of these combinations to develop Phosphorus (P) management under three levels of irrigation water, i.e., three irrigations (normal or well-watered), two irrigations (moderate drought), and one irrigation (severe drought) on barley (Hordeum vulgare L.). The results showed that the treatment with AMF bio-fertilizer yielded the highest values of plant height, spike length, spike weight, number of grains/spike, 1000-grain weight, grain yield, straw yield, biological yield, and harvest index. Moreover, the grain and straw uptake of nitrogen (N), P, and potassium (K) (kg ha−1) in the two seasons under the three levels of irrigation, respectively, were superior followed by the inoculation by PSB. While the treatment without bio-fertilizer yielded the lowest values of these traits of barley, the treatment with bio-fertilizer yielded the increased percentage of the grain yield by 17.27%, 17.33% with applying AMF, and 10.31%, 10.40% with treatment by PSB. Treatment with AMF or PSB (Phosphorien), plus rates of phosphorus fertilizer under conditions of irrigation water shortage, whether irrigation was performed once or twice, led to an increase in grain yield and other characteristics compared to the same fertilization rates without inoculation. The results of this study showed that the use of bio-fertilizers led to an increase in plant tolerance to drought stress, and this was demonstrated by an increase in various traits with the use of treatments that include bio-fertilizers. Therefore, it is suggested to inoculate the seeds with AMF or PSB plus adding phosphate fertilizers at the recommended dose under drought conditions.
Assessing the Capabilities of UV-NIR Spectroscopy for Predicting Macronutrients in Hydroponic Solutions with Single-Task and Multi-Task Learning
Authors: Qi Haijun , Li Bin , Nie Jun , Luo Yizhi , Yuan Yu , Zhou Xingxing
Published: September 2024

Macronutrients, including nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S), are the most basic nutrient elements in the solution for the hydroponic system. However, the current management of hydroponic nutrient solutions usually depends on EC and pH sensors due to the lack of accurate specific macronutrient sensing equipment, which easily leads to nutritional imbalance for the cultivated plant. In this study, the UV-NIR absorption spectroscopy (200–1100 nm) was used to predict six macronutrients in hydroponic solutions; two kinds of single-task learning algorithms, including partial least squares (PLS) and least absolute shrinkage and selection operator (LASSO), and two kinds of multi-task learning algorithms, including dirty multi-task learning (DMTL) and robust multi-task learning (RMTL), were investigated to develop prediction models and assess capabilities of UV-NIR. The results showed that N and Ca could be quantitatively predicted by UV-NIR with the ratio of performance to deviation (RPD) more than 2, K could be qualitatively predicted (1.4 < RPD < 2), and P, Mg, and S could not be successfully predicted (RPD < 1.4); the RMTL algorithm outperformed others for predicting K and Ca benefit from the underlying task relationships with N; and predicting P, Mg, and S were identified as irrelevant (outlier) tasks. Our study provides a potential approach for predicting several macronutrients in hydroponic solutions with UV-NIR, especially using RMTL to improve model prediction ability.
Utilization of Machine Learning and Hyperspectral Imaging Technologies for Classifying Coated Maize Seed Vigor: A Case Study on the Assessment of Seed DNA Repair Capability
Authors: Wonggasem Kris , Wongchaisuwat Papis , Chakranon Pongsan , Onwimol Damrongvudhi
Published: September 2024

The conventional evaluation of maize seed vigor is a time-consuming and labor-intensive process. By contrast, this study introduces an automated, nondestructive framework for classifying maize seed vigor with different seed DNA repair capabilities using hyperspectral images. The selection of coated maize seeds for our case study also aligned well with practical applications. To ensure the accuracy and reliability of the results, rigorous data preprocessing steps were implemented to extract high-quality information from raw spectral data obtained from the hyperspectral images. In particular, commonly used pretreatment methods were explored. Instead of analyzing all the wavelengths of spectral data, a competitive adaptive reweighted sampling method was used to select more informative wavelengths, optimizing analysis efficiency. Furthermore, this study leveraged machine learning models, enriched through oversampling techniques to address data imbalance at the seed level. The results obtained using a support vector machine with enhanced techniques demonstrated promising results with 100% sensitivity, 96.91% specificity, and a 0.9807 Matthews correlation coefficient (MCC). Thus, this study highlighted the effectiveness of hyperspectral imaging and machine learning in modern seed assessment practices. By introducing a seed vigor classification system that can even accommodate coated seeds, this study offers a potential pathway for empowering seed producers in practical, real-world applications.
Multi-Omics Analysis Reveals the Molecular Mechanisms of the Glycolysis and TCA Cycle Pathways in Rhododendron chrysanthum Pall. under UV-B Stress
Authors: Yu Wang , Gong Fushuai , Cao Kun , Zhou Xiaofu , Xu Hongwei
Published: September 2024

UV-B radiation is becoming a bigger threat to plants as a result of the ozone layer’s depletion. As an alpine plant, Rhododendron chrysanthum Pall. (R. chrysanthum) may grow regularly under UV-B radiation throughout its lengthy acclimatization period, although the mechanism of acclimatization is still poorly understood. The current investigation uncovered a number of adaptation strategies that R. chrysanthum has developed in reaction to UV-B rays. UV-B radiation impeded photosynthesis and damaged the photosystem, according to OJIP testing. Through transcriptomics and proteomics analyses, this study found that the differential proteins and differential genes of R. chrysanthum were significantly enriched in glycolysis and tricarboxylic acid (TCA) cycle pathways after UV-B treatment. The metabolomics results showed that a total of eight differential metabolites were detected in the glycolytic and TCA cycle pathways, and the changes in the expression of these metabolites reflected the final outcome of gene regulation in the glycolytic and TCA cycle pathways. The combined experimental results demonstrated that R. chrysanthum’s photosynthetic system was impacted by UV-B stress and, concurrently, the plant activated an adaptation mechanism in response to the stress. To maintain its energy supply for growth, R. chrysanthum adapts to UV-B stress by adjusting the expression of the relevant proteins, genes, and metabolites in the glycolytic and TCA cycling pathways. This study provides a new perspective for understanding the changes in the carbon metabolism of R. chrysanthum under UV-B stress and its mechanisms for UV-B resistance, and provides an important theoretical basis for the study of enhancing plant resistance to stress.
Further Evidence That Science-Based Biosecurity Provides Sustainable Prevention of Porcine Reproductive and Respiratory Syndrome Virus Infection and Improved Productivity in Swine Breeding Herds
Authors: Dee Scott , Brands Lisa , Edler Roy , Schelkopf Adam , Nerem Joel , Spronk Gordon , Kikuti Mariana , Corzo Cesar A.
Published: September 2024

Porcine reproductive and respiratory syndrome virus (PRRSV) is a globally significant pathogen of pigs. Preventing the entry of PRRSV into swine breeding herds enhances animal health and welfare. A recently published retrospective cohort study reported significant differences in PRRSV incidence risk between breeding herds that practiced Next Generation Biosecurity (NGB) COMPLETE, versus herds that practiced a partial approach (NGB INCOMPLETE) over a 2-year period. This follow-up communication builds on this previous publication and brings new information regarding statistical differences in key performance indicators (KPIs) from 43 NGB COMPLETE herds and 19 NGB INCOMPLETE herds during disease years 1 and 2. Statistically significant differences included higher total born/farrow and pigs weaned/female along with a reduced pre-weaning mortality and wean to 1st service interval, as well as a 0.91 increase in the number of pigs weaned/mated female/year. In addition, this communication reports that PRRSV incidence risk throughout disease years 1–3 was 8.0%, and that the association of NGB status (COMPLETE vs. INCOMPLETE) and disease burden for the cumulative 3-year period was statistically significant (p < 0.0001). These findings support previously published data that NGB, while not perfect, provides sustainable prevention of PRRSV, and may help improve herd productivity.
Freshwater Mussel Viromes Increase Rapidly in Diversity and Abundance When Hosts Are Released from Captivity into the Wild
Authors: Richard Jordan C. , Lane Tim W. , Agbalog Rose E. , Colletti Sarah L. , Leach Tiffany C. , Dunn Christopher D. , Bollig Nathan , Plate Addison R. , Munoz Joseph T. , Leis Eric M. , Knowles Susan , Standish Isaac F. , Waller Diane L. , Goldberg Tony L.
Published: September 2024

Freshwater mussels (order: Unionida) are highly imperiled globally and are increasingly the focus of captive propagation efforts to protect and restore wild populations. The Upper Tennessee River Basin (UTRB) in Virginia is a freshwater biodiversity hotspot hosting at least 45 of North America’s ~300 species of freshwater mussels, including 21 threatened and endangered species listed under the U.S. Endangered Species Act. Recent studies have documented that viruses and other microbes have contributed to freshwater mussel population declines in the UTRB. We conducted a multi-year longitudinal study of captive-reared hatchery mussels released to restoration sites throughout the UTRB to evaluate their viromes and compare them to captive hatchery environments. We documented 681 viruses from 27 families. The hatchery mussels had significantly less viruses than those deployed to wild sites, with only 20 viruses unique to the hatchery mussels. After the hatchery mussels were released into the wild, their number of viruses initially spiked and then increased steadily over time, with 451 viruses in total unique to the mussels in the wild. We found Clinch densovirus 1 (CDNV-1), a virus previously associated with mass mortality events in the Clinch River, in all samples, but the wild site mussels consistently had significantly higher CDNV-1 levels than those held in the hatchery. Our data document substantial differences between the viruses in the mussels in the hatchery and wild environments and rapid virome shifts after the mussels are released to the wild sites. These findings indicate that mussel release programs might benefit from acclimatization periods or other measures to mitigate the potential negative effects of rapid exposure to infectious agents found in natural environments.
Environmental DNA-Based Identification of Non-Native Fish in Beijing: Diversity, Geographical Distribution, and Interactions with Native Taxa
Authors: Liu Bo , Wang Fuwen , Li Shiguo , Xiong Wei , Zhan Aibin
Published: September 2024

Rapid urbanization and its associated human activities have facilitated the colonization and spread of non-native species, rendering urban ecosystems, particularly in megacities such as Beijing, highly susceptible to biological invasions. This study employed environmental DNA (eDNA) metabarcoding to evaluate the biodiversity and geographical distribution of non-native fish, as well as their interactions with native fish species, across three river basins in Beijing pertaining to the Daqing River, the North Canal, and the Ji Canal. Across all the 67 sampling sites, we identified 60 fish taxa, representing 11 orders, 23 families, and 40 genera, with an average of 33.0 taxa per site. Of these, 40 taxa were native, accounting for only 47.1% of the historically recorded native fish species. Additionally, we detected 20 non-native fish taxa, spanning 11 orders, 13 families, and 17 genera. Native fish exhibited geographical homogenization across the basins, while non-native taxa displayed varied geographical distributions. Non-metric multidimensional scaling (NMDS) and analysis of similarities (ANOSIM) revealed no significant variation in the non-native communities across the river basins. Although most of the non-native taxa were widespread, some were restricted to specific sites or basins. The North Canal exhibited significantly lower non-native biodiversity compared with the Ji Canal across all alpha diversity indices. Simple linear regression analyses indicated positive correlations between the number of taxa and species richness for both native and non-native taxa. Interestingly, species co-occurrence analyses revealed predominantly positive interactions among both native and non-native species pairs, with only two negative relationships involving one native and two non-native taxa. This study provides insights into the biodiversity and geographical distribution of non-native fish in Beijing and establishes a baseline for future biomonitoring and conservation efforts. The findings underscore the need for further investigation into the mechanisms and dynamics of biological invasions within urban environments in Beijing.