bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reducing Water, Nutrient, Pesticide, and Carbon Footprints and Costs for Drip-Irrigated Vine Crops with Compact Bed Plasticulture
Authors: Santikari Vijay , Shukla Sanjay , Sishodia Rajendra P. , Hansen Kira M. , Hendricks Gregory S. 
Published: April 2024

HighlightsNarrower and taller compact beds (CB) were designed to increase the system efficiency of drip-irrigated plasticulture.CB lowered water, fertilizer, plastic, pesticide, and fuel inputs while maintaining watermelon yield.CB reduced GHG emissions and input costs while increasing input productivity.CB are a synergistic and win-win climate-smart design to increase the sustainability of high-intensity plasticulture.Abstract. Raised bed plasticulture, used globally for producing high-value fruits and vegetables, is a high input and high intensity production system. We show that improved water and nutrient efficiency with reduced material inputs, costs, and carbon footprint is achieved with the use of taller and narrower plastic-mulched compact beds [61×30 (width × height in cm), 61×10, 46×30, and 41×30] compared to the wider and shorter conventional bed [76×20] used for growing watermelon. The primary motivation behind the compact bed design is to improve economic and environmental sustainability by reducing the bed width, to increase the fraction of the bed that is wetted by drip irrigation and to reduce the area of farmland covered under plastic. Compact beds were evaluated at two locations in South Florida with different hydrologic settings, using an ensemble of field data (inputs, plant health, yield, soil moisture, and nutrient levels) and a field-verified hydrologic model (HYDRUS). No reduction in yields was observed with compact beds, indicating no production risk for the growers. Compact beds conserve water and reduce deep percolation losses by 6 cm compared to conventional beds, thereby improving water productivity by 20%. Compact beds require less fertilizer (10%-50%) to maintain the same nutrient (N-P-K) concentrations as those in conventional beds because of smaller soil volumes. Reductions in water and fertilizer inputs provide the environmental benefit of reducing nutrient leaching to groundwater, while reduced impervious area reduces runoff losses. Compact beds reduced input (fertilizer, plastic mulch, pesticide, and diesel) costs of the system by US$720/ha and greenhouse gas emissions by 1630 kg CO2e/ha (9%) compared to conventional plasticulture. The annual cost savings create a win-win for farmers to increase profits and/or invest in conservation measures such as precision irrigation. Compact beds offer a climate change mitigation and adaptation strategy for the plasticulture system for watermelon and other similar cucurbit crops (e.g., cucumber, pumpkin, and zucchini). Keywords: Climate-Smart Agriculture, Economics, Greenhouse Gas Emissions, Nutrient Leaching, Sustainable Design, Water Conservation.
Development and Implementation of a Damage Model for Potato Tuber Blackspot in Discrete Element Method to Analyze Harvesting and Handling Processes
Authors: Poppa Lukas , Frerichs Ludger , Liu Jintian , Böl Markus 
Published: April 2024

HighlightsMethod for determining the discoloration of potato tissue samples induced by mechanical stress.Model of the discoloration susceptibility of specific potato tissue.Particle segmentation algorithm in the Discrete Element Method for contact localization in post-processing. Development of the "Discrete Element Method Blackspot Potato Tuber Model" capable of predicting internal blackspot damage in potato tubers.Abstract. Blackspot damage caused by mechanical load during the harvesting and processing stages of potato tubers remains difficult to mitigate. Field tests are inadequate as it takes around two days to develop blackspot discoloration, making it unclear when and where the initial damage occurs. Particle simulation, particularly with the Discrete Element Method (DEM), offers opportunities to improve machine performance and reduce crop losses. However, the DEM cannot predict local blackspot damage, making it unsuitable for reducing potato tuber damage. To address this, the “DEM blackspot potato tuber model” presented in this contribution integrates a discoloration function and a segmentation algorithm of DEM particles to predict and analyze blackspot damage during harvesting and processing. Based on mechanical experiments on tuber tissues with different loading conditions, a discoloration function was proposed dependent on strain and strain rate. The segmentation algorithm adapts to the varying potato tuber particle size by varying the number of segments to generate segments of average blackspot size. Two different validation tests are used to identify the single and multiple impact capabilities of the model. The preliminary results show that the discoloration induced by single impact events is well predicted in terms of the number of damaged tubers as well as the average damage per tuber. Damage of multiple impacts is less accurately predicted since the model does not take into account the accumulation of damage. Keywords: Blackspot, Damage model, Discrete Element Method, Potato handling, Simulation.
Graphene-enhanced decagonal patch antenna for terahertz frequency operation in breast cancer detection
Authors: Ashok Gandreddi Lakshmi Prasanna , Nath Ganimidi Veerendra , Neelapu Bala Chakravarthy 
Published: April 2024

This paper presents a decagonal patch antenna loaded with graphene designed for terahertz (THz) frequency applications, with a specific emphasis on its potential for early breast cancer detection. The proposed antenna features a hybrid structure, integrating both copper and graphene materials. A decagonal graphene strip is intricately incorporated into the copper patch, yielding significant improvements in reflection coefficient, bandwidth, and gain. The antenna, with dimensions of 155µm×130µm×13µm, is designed on a polyimide substrate, characterized by a dielectric constant of 3.5 and a loss tangent of 0.0027. To ensure relevance in medical contexts, the design is optimized to operate within the frequency range of 2.1 to 5.7 THz, a critical spectrum for medical applications. Simulation results validate the effectiveness of the proposed antenna, demonstrating S11<−10dB within the frequency band of 2.1 to 5.7 THz (92.3% fractional bandwidth). The antenna exhibits an impressive bandwidth of 3.6 THz and a gain of 7.87 dBi at 4 THz. These findings establish the graphene-loaded decagonal patch antenna as a highly promising solution for breast cancer detection applications, showcasing its potential in the realm of medical diagnostics.
Autofocusing in digital holography based on an adaptive genetic algorithm
Authors: Wang Zhongyang , Ma Hongwei , Chen Yuan , Liu Dengxue 
Published: April 2024

In digital holography (DH), determining the reconstruction distance is critical to the quality of the reconstructed image. However, traditional focal plane detection methods require considerable time investment to reconstruct and evaluate holograms at multiple distances. To address this inefficiency, this paper proposes a fast and accurate autofocusing method based on an adaptive genetic algorithm. This method only needs to find several reconstruction distances in the search area as an initial population, and then adaptively optimize the reconstruction distance through iteration to determine the optimal focal plane in the search area. In addition, an off-axis digital holographic optical system was used to capture the holograms of the USAF resolution test target and the coin. The simulation and experimental results indicated that, compared with the traditional autofocusing, the proposed method can reduce the computation time by about 70% and improve the focal plane accuracy by up to 0.5 mm.
Diagnosis of soil-transmitted helminth infections with digital mobile microscopy and artificial intelligence in a resource-limited setting
Authors: Lundin Johan , Suutala Antti , Holmström Oscar , Henriksson Samuel , Valkamo Severi , Kaingu Harrison , Kinyua Felix , Muinde Martin , Lundin Mikael , Diwan Vinod , Mårtensson Andreas , Linder Nina 
Published: April 2024

Background
Infections caused by soil-transmitted helminths (STHs) are the most prevalent neglected tropical diseases and result in a major disease burden in low- and middle-income countries, especially in school-aged children. Improved diagnostic methods, especially for light intensity infections, are needed for efficient, control and elimination of STHs as a public health problem, as well as STH management. Image-based artificial intelligence (AI) has shown promise for STH detection in digitized stool samples. However, the diagnostic accuracy of AI-based analysis of entire microscope slides, so called whole-slide images (WSI), has previously not been evaluated on a sample-level in primary healthcare settings in STH endemic countries.

Methodology/Principal findings
Stool samples (n = 1,335) were collected during 2020 from children attending primary schools in Kwale County, Kenya, prepared according to the Kato-Katz method at a local primary healthcare laboratory and digitized with a portable whole-slide microscopy scanner and uploaded via mobile networks to a cloud environment. The digital samples of adequate quality (n = 1,180) were split into a training (n = 388) and test set (n = 792) and a deep-learning system (DLS) developed for detection of STHs. The DLS findings were compared with expert manual microscopy and additional visual assessment of the digital samples in slides with discordant results between the methods. Manual microscopy detected 15 (1.9%) Ascaris lumbricoides, 172 (21.7%) Tricuris trichiura and 140 (17.7%) hookworm (Ancylostoma duodenale or Necator americanus) infections in the test set. Importantly, more than 90% of all STH positive cases represented light intensity infections. With manual microscopy as the reference standard, the sensitivity of the DLS as the index test for detection of A. lumbricoides, T. trichiura and hookworm was 80%, 92% and 76%, respectively. The corresponding specificity was 98%, 90% and 95%. Notably, in 79 samples (10%) classified as negative by manual microscopy for a specific species, STH eggs were detected by the DLS and confirmed correct by visual inspection of the digital samples.

Conclusions/Significance
Analysis of digitally scanned stool samples with the DLS provided high diagnostic accuracy for detection of STHs. Importantly, a substantial number of light intensity infections were missed by manual microscopy but detected by the DLS. Thus, analysis of WSIs with image-based AI may provide a future tool for improved detection of STHs in a primary healthcare setting, which in turn could facilitate monitoring and evaluation of control programs.
The high-density lipoprotein cholesterol (HDL-C)-concentration-dependent association between anti-inflammatory capacity and sepsis: A single-center cross-sectional study
Authors: Chen Kai-Lee , Chou Ruey-Hsing , Chang Chun-Chin , Kuo Chin-Sung , Wei Jih-Hua , Huang Po-Hsun , Lin Shing-Jong 
Published: April 2024

Introduction
Known to have pleiotropic functions, high-density lipoprotein (HDL) helps to regulate systemic inflammation during sepsis. As preserving HDL-C level is a promising therapeutic strategy for sepsis, the interaction between HDL and sepsis worth further investigation. This study aimed to determine the impact of sepsis on HDL’s anti-inflammatory capacity and explore its correlations with disease severity and laboratory parameters.

Methods and materials
We enrolled 80 septic subjects admitted to the intensive care unit and 50 controls admitted for scheduled coronary angiography in this cross-sectional study. We used apolipoprotein-B depleted (apoB-depleted) plasma to measure the anti-inflammatory capacity of HDL-C. ApoB-depleted plasma’s anti-inflammatory capacity is defined as its ability to suppress tumor necrosis factor-α–induced vascular cell adhesion molecule-1 (VCAM-1) expression in human umbilical-vein endothelial cells. A subgroup analysis was conducted to investigate in septic subjects according to disease severity.

Results
ApoB-depleted plasma’s anti-inflammatory capacity was reduced in septic subjects relative to controls (VCAM-1 mRNA fold change: 50.1% vs. 35.5%; p < 0.0001). The impairment was more pronounced in septic subjects with than in those without septic shock (55.8% vs. 45.3%, p = 0.0022). Both associations were rendered non-significant with the adjustment for the HDL-C level. In sepsis patients, VCAM-1 mRNA fold change correlated with the SOFA score (Spearman’s r = 0.231, p = 0.039), lactate level (r = 0.297, p = 0.0074), HDL-C level (r = –0.370, p = 0.0007), and inflammatory markers (C-reactive protein level: r = 0.441, p <0.0001; white blood cell: r = 0.353, p = 0.0013).

Conclusion
ApoB-depleted plasma’s anti-inflammatory capacity is reduced in sepsis patients and this association depends of HDL-C concentration. In sepsis patients, this capacity correlates with disease severity and inflammatory markers. These findings explain the prognostic role of the HDL-C level in sepsis and indirectly support the rationale for targeting HDL-C as sepsis treatment.
Rural and urban differences in the prevalence and determinants of Type-2 diabetes in Bangladesh
Authors: Talukder Ashis , Sara Sabiha Shirin , Hossain Md. Tanvir , Nath Chuton Deb , Rahman Riaz , Hussain Sadiq , Sarma Haribondhu , Huda Md. Nazmul 
Published: April 2024

ObjectiveTo estimate the prevalence of Type 2 Diabetes (T2D) in urban and rural settings and identify the specific risk factors for each location.MethodWe conducted this study using data from the 2017–18 Bangladesh Demographic and Health Survey (BDHS), sourced from the DHS website. The survey employed a stratified two-stage sampling method, which included 7,658 women and 7,048 men aged 18 and older who had their blood glucose levels measured. We utilized chi-square tests and ordinal logistic regression to analyze the association between various selected variables in both urban and rural settings and their relationship with diabetes and prediabetes.ResultsThe prevalence of T2D was 10.8% in urban areas and 7.4% in rural areas, while pre-diabetes affected 31.4% and 27% of the populations in these respective settings. The study found significant factors influencing diabetes in both urban and rural regions, particularly in the 55–64 age group (Urban: AOR = 1.88, 95% CI [1.46, 2.42]; Rural: AOR = 1.87, 95% CI [1.54, 2.27]). Highly educated individuals had lower odds of T2D, while wealthier and overweight participants had higher odds in both areas. In rural regions, T2D risk was higher among caffeinated drink consumers and those not engaged in occupation-related physical activity, while these factors did not show significant influence in urban areas. Furthermore, urban participants displayed a significant association between T2D and hypertension.ConclusionOur study outlines a comprehensive strategy to combat the increasing prevalence of T2D in both urban and rural areas. It includes promoting healthier diets to control BMI level, encouraging regular physical activity, early detection through health check-ups, tailored awareness campaigns, improving healthcare access in rural regions, stress management in urban areas, community involvement, healthcare professional training, policy advocacy like sugary drink taxation, research, and monitoring interventions. These measures collectively address the T2D challenge while accommodating the distinct features of urban and rural settings.
Serum Nostrin—A risk factor of death, kidney replacement therapy and acute kidney disease in acute kidney injury
Authors: Erfurt Stefan , Lauxmann Martin , Asmus Katharina , Oess Stefanie , Patschan Daniel , Hoffmeister Meike 
Published: April 2024

Background
The prediction of Acute Kidney Injury (AKI)-related outcomes remains challenging. Persistent kidney excretory dysfunction for longer than 7 days has been defined as Acute Kidney Disease (AKD). In this study, we prospectively quantified serum Nostrin, an essential regulator of endothelial NO metabolism, in hospitalized patients with AKI.

Design, setting, participants, & measurements
In-hospital subjects with AKI of various etiology were identified through the in-hospital AKI alert system of the Brandenburg University Hospital. Serum Nostrin, and serum NGAL and KIM-1 were measured within a maximum of 48 hours from the timepoint of initial diagnosis of AKI. The following endpoints were defined: in-hospital death, need of kidney replacement therapy (KRT), recovery of kidney function (ROKF) until discharge.

Results
AKI patients had significantly higher serum Nostrin levels compared to Controls. The level of serum Nostrin increased significantly with the severity of AKI. Within the group of AKI patients (n = 150) the in-hospital mortality was 16.7%, KRT was performed in 39.3%, no ROKF occurred in 28%. Patients who required KRT had significantly higher levels of serum Nostrin compared to patients who did not require KRT. Significantly higher levels of serum Nostrin were also detected in AKI patients without ROKF compared to patients with ROKF. In addition, low serum Nostrin levels at the timepoint of AKI diagnosis were predictive of in-hospital survival. For comparison, the serum concentrations of NGAL and KIM-1 were determined in parallel to the Nostrin concentrations and the results confirm the prognostic properties of serum Nostrin in AKI.

Conclusions
The current study suggests serum Nostrin as novel biomarker of AKI-associated mortality, KRT and Acute Kidney Disease.
Machine learning for prediction of acute kidney injury in patients diagnosed with sepsis in critical care
Authors: Shi Jianshan , Han Huirui , Chen Song , Liu Wei , Li Yanfen 
Published: April 2024

Background and objective
Acute Kidney Injury (AKI) is a common and severe complication in patients diagnosed with sepsis. It is associated with higher mortality rates, prolonged hospital stays, increased utilization of medical resources, and financial burden on patients’ families. This study aimed to establish and validate predictive models using machine learning algorithms to accurately predict the occurrence of AKI in patients diagnosed with sepsis.

Methods
This retrospective study utilized real observational data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. It included patients aged 18 to 90 years diagnosed with sepsis who were admitted to the ICU for the first time and had hospital stays exceeding 48 hours. Predictive models, employing various machine learning algorithms including Light Gradient Boosting Machine (LightGBM), EXtreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression (LR), were developed. The dataset was randomly divided into training and test sets at a ratio of 4:1.

Results
A total of 10,575 sepsis patients were included in the analysis, of whom 8,575 (81.1%) developed AKI during hospitalization. A selection of 47 variables was utilized for model construction. The models derived from LightGBM, XGBoost, RF, DT, ANN, SVM, and LR achieved AUCs of 0.801, 0.773, 0.772, 0.737, 0.720, 0.765, and 0.776, respectively. Among these models, LightGBM demonstrated the most superior predictive performance.

Conclusions
These machine learning models offer valuable predictive capabilities for identifying AKI in patients diagnosed with sepsis. The LightGBM model, with its superior predictive capability, could aid clinicians in early identification of high-risk patients.
MiR-132-3p suppresses peritoneal fibrosis induced by peritoneal dialysis via targeting TGF-β1/Smad2/3 signaling pathway
Authors: Yin Yangyang , Yang Yuqi , Zhang Yongqiang , Shang Yu , Li Qian , Yuan Jing 
Published: April 2024

Background
Peritoneal fibrosis (PF) is the main complication of peritoneal dialysis (PD) and the most common cause of cessation from PD. There is still no effective therapeutic approach to reserve PF. We aimed to investigate the role of miR-132-3p and underlying potential mechanisms in PF.

Methods
A total of 18 Sprague-Dawley (SD) rats were divided randomly into three groups (n = 6): (i)Control group (ii)PF group (iii)PF+Losartan group; Rats in the PF group and PF+Losartan group received daily intraperitoneal injections of 3 mg/kg chlorhexidine for 14 days, and rats in the PF+Losartan group simultaneously received daily intraperitoneal injections of 2 mg/kg losartan for 14 days. The control group was injected with saline in the same volume. Met-5A cells were treated for 24h with TGF-β1 dissolved in recombinant buffered saline at a concentration of 10 ng/ml, meanwhile, PBS solution as a negative control. The human peritoneal solution was collected for the detection of miR-132-3p.

Results
In vivo, SD rats were infused with chlorhexidine to establish PF model, and we found that miR-132-3p significantly decreased and the expressions of transforming growth factor-β1 (TGF-β1), and Smad2/3 were up-regulated in PF. In vitro, miR-132-3p mimics suppressed TGF-β1/Smad2/3 activity, whereas miR-132-3p inhibition activated the pathway. In human peritoneal solution, we found that the expression of miR-132-3p decreased in a time-dependent model and its effect became more pronounced with longer PD duration.

Conclusion
MiR-132-3p ameliorated PF by suppressing TGF-β1/Smad2/3 activity, suggesting that miR-132-3p represented a potential therapeutic approach for PF.
Identifying a group of factors predicting cognitive impairment among older adults
Authors: Zhao Longgang , Wang Yuan , Bawa Eric Mishio , Meng Zichun , Wei Jingkai , Newman-Norlund Sarah , Trivedi Tushar , Hasturk Hatice , Newman-Norlund Roger D. , Fridriksson Julius , Merchant Anwar T. 
Published: April 2024

Background
Cognitive impairment has multiple risk factors spanning several domains, but few studies have evaluated risk factor clusters. We aimed to identify naturally occurring clusters of risk factors of poor cognition among middle-aged and older adults and evaluate associations between measures of cognition and these risk factor clusters.

Methods
We used data from the National Health and Nutrition Examination Survey (NHANES) III (training dataset, n = 4074) and the NHANES 2011–2014 (validation dataset, n = 2510). Risk factors were selected based on the literature. We used both traditional logistic models and support vector machine methods to construct a composite score of risk factor clusters. We evaluated associations between the risk score and cognitive performance using the logistic model by estimating odds ratios (OR) and 95% confidence intervals (CI).

Results
Using the training dataset, we developed a composite risk score that predicted undiagnosed cognitive decline based on ten selected predictive risk factors including age, waist circumference, healthy eating index, race, education, income, physical activity, diabetes, hypercholesterolemia, and annual visit to dentist. The risk score was significantly associated with poor cognitive performance both in the training dataset (OR Tertile 3 verse tertile 1 = 8.15, 95% CI: 5.36–12.4) and validation dataset (OR Tertile 3 verse tertile 1 = 4.31, 95% CI: 2.62–7.08). The area under the receiver operating characteristics curve for the predictive model was 0.74 and 0.77 for crude model and model adjusted for age, sex, and race.

Conclusion
The model based on selected risk factors may be used to identify high risk individuals with cognitive impairment.