bluuebunny/crossref_metadata_2025 huggingface dataset:
25-hydroxy vitamin D levels associated with cardiovascular risk factors among military personnel based on obesity status
Authors: Eghbalian Mostafa , Akbari Hesam , Moradalizadeh Saeideh , Norouzi Mojtaba , Nasab Habibeh , Karamali Mazyar , Imani Mousa , Zahiri Hossein , Raei Mehdi
Published: July 2024

Introduction
Vitamin D is an essential prohormone for body functions. Obesity and vitamin D deficiency both affect each other. Many obese individuals exhibit a combination of metabolic and cardiovascular risk factors.
Methods
The present study was conducted cross-sectional in 2023. The study population was considered to be 216 military personnel from Tehran. Blood samples were taken from the subjects to measure high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglyceride (TG), total cholesterol (TC), fasting blood sugar (FBS), and 25-hydroxy vitamin D. Height, weight, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse were also measured. Using a questionnaire, demographic information and information about the history of some diseases were collected from the study subjects.
Results
The mean age was 41.06 years, and the mean work experience was 8.02 years. A higher percentage of diabetic or pre-diabetic subjects were obese or overweight (p < 0.001). The average level of very low-density lipoprotein (VLDL) in subjects with normal weight was 27.23 times lower than other subjects. In normal-weight subjects in the study, there was a negative association between 25-hydroxyvitamin D and DBP (β= -0.061).
Conclusion
We discovered a significant link between 25-hydroxyvitamin D insufficiency in military personnel and heightened cardiovascular risk factors. Subsequent studies employing a longitudinal approach are necessary to validate our results and shed more light on the influence of vitamin D on cardiovascular risk.
A simplified approach of pumping rate optimization for production wells to mitigate saltwater intrusion: A case study in Vinh Hung district, Long An province, Vietnam
Authors: Pham Quy-Nhan , Tran Thanh-Le , Dang Tran-Trung , Pham Minh-Hoang
Published: July 2024

In the investigation of optimal groundwater extraction in coastal regions, conventional assumptions typically revolve around unconfined aquifers with specified boundary conditions. In such cases, intricate solutions for groundwater management have been documented. However, within extensive delta plains, the extraction wells are frequently drilled in confined aquifers with a not much variable density flow. This circumstance, characterized by paleo-saltwater intrusion, is further complicated by the placement of wells at a considerable distance from the coastal line. As a result, the design and implementation of groundwater supply systems in these areas necessitate strategic groundwater management to optimize groundwater utilization while mitigating the potential risk of saltwater intrusion. To address this challenge, analytical solutions and an optimization problem approach have been applied to solve the differential equations governing confined aquifers with salt-freshwater interfaces. These methodologies provide simplified yet dependable conditions tailored to the study area. A case study conducted in Vinh Hung district, Long An province, is focused on determining the optimal pumping rate for production wells to forestall saltwater intrusion during groundwater extraction. The findings contribute valuable insights into achieving an equilibrium between maximizing groundwater utilization and preventing saltwater intrusion in the aquifer systems by simplified approach.
Participation of children with obstructive sleep apnea in clinical trials: A systematic review of barriers and interventions
Authors: Wu Lan , Li Xin , Wang Xin , Du Zhuo , Zhang Xinli , Liu Zhijian
Published: July 2024

Background
Obstructive sleep apnea (OSA) in children is associated with negative effects on neurocognitive development and metabolic health. Despite this, children with OSA remain underrepresented in research, setting the standards for clinical diagnoses and treatments. Consequently, most evidence on treatment is based on adult data. This review focuses on studies assessing the barriers to clinical trial participation for children with OSA and discusses the intervention studies and efforts to overcome these barriers.
Method
We retrieved articles from Scopus, Embase, PubMed, and the Cochrane Library without date restrictions until May 2024. The language was restricted to English. The Joanna Briggs Institute (JBI) tool was used to assess methodological quality.
Results
From 25 studies included, 14 barrier subcategories were identified, which were classified as an objective factor, provider, and patient/caregiver.
Conclusions
Our findings emphasize the multifaceted barriers to enrolling children with OSA in clinical trials. Furthermore, we provide several underutilized solutions to facilitate children's participation in clinical trials. However, formal targeted strategies are still needed in the future because these recommendations alone might not solve the evidence gap in children.
Registration:
Registered in PROSPERO with ID: CRD42024533326.
Data-Driven City: An Innovative Approach to Urban Area Delineation
Authors: Fang Chenyu , Zhou Lin , Gu Xinyue , Liu Xing , Werner Martin
Published: July 2024

This study introduces a data-driven, bottom-up approach to urban delineation, integrating feature engineering with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, marking a significant shift from traditional methodologies reliant on simplistic OpenStreetMap (OSM) road node data aggregations. By employing a broad array of OSM categories and refining data selection through feature engineering, our research significantly enhances the precision and relevance of urban clustering. Using Bavaria, Germany, as a case study, we demonstrate that feature engineering effectively reduces noise and mitigates common DBSCAN clustering pitfalls by filtering out irrelevant and autocorrelated data. The method's robustness is validated through a comprehensive assessment involving accuracy metrics, optimal clustering selections based on entropy values, and empirical and theoretical confirmations using nighttime light data and Zipf’s Law, respectively. This study contributes to urban studies by providing a scalable, replicable model that incorporates advanced data processing techniques and multidimensional data sources, supporting improved urban planning and policy-making while effectively delineating urban boundaries in varied settings.
Standardization of Robot-Assisted Radical Nephroureterectomy via Intraperitoneal Approach: Insights from a High-Volume Japanese Center
Authors: Yajima Shugo , Nakanishi Yasukazu , Hirose Kohei , Kataoka Madoka , Masuda Hitoshi
Published: July 2024

We present the trial-and-error process of standardizing robot-assisted radical nephroureterectomy (RANU) at a high-volume center in Japan. Our urology team performed 53 RANU cases using the Da Vinci Xi system, undergoing five major evolutionary stages. We performed RANU via transperitoneal approach in all cases and lymph node dissection in selected cases. During the evolution, we adopted a lithotomy position and significantly modified port placement to facilitate lower ureter management. However, we ultimately arrived at a method that minimizes port and patient repositioning during lower ureter processing. By strategically placing ProGrasp™ forceps in the most caudal port, we effectively retracted the bladder and grasped the opened bladder wall during lower ureter manipulation. This approach also allowed us to perform pelvic, para-aortic, and renal portal lymph node dissection without major changes in patient positioning or port placement. Nevertheless, we acknowledge that some variations in positioning and techniques may be necessary depending on specific case requirements.
Optimizing AI models for predicting esophageal squamous cell carcinoma risk: incorporation of small datasets of soft palate images
Authors: Waki Kotaro , Gushima Ryosuke , Okubo Keishi , Kiyama Masato , Nagaoka Katsuya , Ohno Kento , Honda Munenori , Yamasaki Akira , Matsuno Kenshi , Furuta Yoki , Miyamoto Hideaki , Naoe Hideaki , Amagasaki Motoki , Tanaka Yasuhito
Published: July 2024

There is a currently an unmet need for non-invasive methods to predict the risk of esophageal squamous cell carcinoma (ESCC). Previously, we found that specific soft palate morphologies are strongly associated with increased ESCC risk. However, there is currently no artificial intelligence (AI) system that utilizes oral images for ESCC risk assessment. Here, we evaluated three AI models and three fine-tuning approaches with regard to their ESCC predictive power. Our dataset contained 539 cases, which were subdivided into 221 high-risk cases (2491 images) and 318 non-high-risk cases (2524 images). We used 480 cases (4295 images) for the training dataset, and the rest for validation. The Bilinear convolutional neural network (CNN) model (especially when pre-trained on fractal images) demonstrated diagnostic precision that was comparable to or better than other models for distinguishing between high-risk and non-high-risk groups. In addition, when tested with a small number of images containing soft palate data, the model showed high precision: the best AUC model had 0.91 (sensitivity 0.86, specificity 0.79). This study presents a significant advance in the development of an AI-based non-invasive screening tool for the identification of high-risk ESCC patients. The approach may be particularly suitable for institutes with limited medical imaging resources.
A Multi-Scale Attention-Based Model for Image Enhancement and Classification
Authors: guo mingyu , Takubo Tomohito
Published: July 2024

This paper presents EnhancedDetailNet, an advanced convolutional neural network meticulously crafted to address the intricate challenges of detail preservation and holistic comprehension inherent in the realm of image processing. Through the judicious incorporation of an all-encompassing image attention mechanism and a global feature encoding stratum, the model adeptly captures and amplifies pivotal image attributes, obviating the prerequisite for explicit image partitioning. By seamlessly amalgamating self-attention and multi-scale attention modules, EnhancedDetailNet acquires a nuanced appreciation of inter-scale feature interdependencies, judiciously elevating the salience of critical local regions and attributes. Moreover, the model accrues substantial benefits from the augmentation of its convolutional strata and the introduction of residual connections, thereby augmenting its aptitude for feature articulation and the expeditious propagation of gradients. Experimental assessments conducted on image classification and object detection benchmarks, leveraging the PlantVillage dataset, resoundingly underscore EnhancedDetailNet's supremacy in capturing intricate image nuances while concurrently curtailing parameter dimensions and preserving overarching features and semantic contexts. In comparative terms, EnhancedDetailNet emerges as the preeminent choice, outpacing traditional convolutional counterparts in the realms of detail retention and performance enhancement. Significantly, even when tailored to the same model dimensions as conventional approaches, EnhancedDetailNet persists in manifesting substantial performance strides, thus underscoring its preeminence in plant pathology image recognition endeavors. https://github.com/guo-mingyu/EnhancedDetailNet.git
Research on Agricultural Disease Recognition Methods Based on Very Large Kernel Convolutional Network-RepLKNet
Authors: Pei Guoquan , Wu Wendou , Zhou Bing , Liu Zigao , Li Peiyao , Qian Xueying , Yang Hongxin
Published: July 2024

Agriculture diseases are unavoidable problems in agricultural production. With the extensive adoption of deep learning in agricultural diseases, the impact of diseases on crops has been effectively reduced. Many existing disease recognition models use multi-level small kernel convolutional structures, but small kernel convolution is not conducive to extracting global information and cannot capture the relationship between distant pixels. To solve this problem, this paper adopts the large kernel convolutional network RepLKNet to identify the Plant Diseases Training Dataset, and the large kernel convolutional network can effectively increase the receptive field and extract long-term dependencies. Improving model accuracy and training speed using transfer learning techniques. In this paper, experiments were conducted at Plant Diseases Training Dataset and the overall accuracy of the model was 93.6%, with an average accuracy of 91.9% and a Kappa coefficient of 93.3%, which proves its validity and reliability in the identification of agricultural diseases.
Modeling and Mechanical Performance Analysis of Porous Gradient Bone Scaffolds
Authors: Lin Kaixin , Yu Kaihuan , Li Ning
Published: July 2024

Currently, personalized adaptive technology using 3D printing scaffold is widely employed in bone tissue repair engineering to aid in healing and restoring large segmental bone defects. Although the scaffold matches the external appearance, its internal tissue structure differs significantly from that of natural bone tissue, and issues of mechanical matching and stress shielding persist after transplantation, impacting patient recovery. Drawing on the tissue structure of natural bone, this study develops a bionic bone scaffold model that replicates the microstructure of natural bone using a noise topology approach. Model samples are created with pore sizes and porosity corresponding to the bone density of various ages. The mechanical properties of bone structures with varying pore sizes and porosities are assessed using finite element simulation analysis software. Mechanical simulation results for bionic bone scaffolds across different ages indicate that the modeling method can adjust the pore size and porosity of the bionic bone structure through voxel parameters, aligning the mechanical characteristics with those of natural bone. This alignment effectively mitigates the stress mismatch between the implant structure and natural tissue post-bone grafting. The hierarchical gradient porous structure and mechanical model of bionic bone scaffold offer valuable insights for the structural design and clinical selection of bone implants.
The metabolic profiles of phenolic acids and aromatic amino acids in the Orobanche crenata parasite and its host faba bean at different infestation stages
Authors: El-Mergawi Ragab , El-Dabaa Mahmoud , El-Khawaga Fathia
Published: July 2024

Orobanche crenata is a root holoparasite that depends on its host for nutritional requirements. The shikimate pathway that metabolizes about 30% of the assimilated carbon in photosynthetic plants plays a role in host-parasite relationships. Aromatic amino acids (AAAs) and phenolic acids are derived from the shikimate pathway and serve as precursors of a wide number of primary and secondary metabolites. The effects of the O. crenata parasite on the phenolic acid and AAA profiles of two faba bean varieties and the differences of these compounds between the host organs and attached parasite at infestation stages were studied. Hosts and attached parasites were collected and divided, depending on the stage of parasite development, into four stages. The results revealed that phenolic acids and free AAAs tended to increase in parasitized roots compared to those of healthy roots, and the greatest increase in phenolic contents occurred at the first infestation stage. Syringic acid was observed to be unique to the parasite. Profiles of phenolic acids and AAAs were changed during the developmental stages of the parasite and differed from those of its host. Caffeic in the parasite reached more than 100 times that in Nubaria 4 host roots at the fourth infestation stage. Free phenylalanine in the parasite ranged between 2.2 and 5.5 times its level in host roots at all infestation stages. This study provides much evidence that indicates O. crenata is able to self-regulate its phenolic and AAA metabolites during its developmental stages, which differ from those of its host.
Reference Values for the Adolescent Post Version of the Postconcussion Symptom Inventory from the German General Population
Authors: Zeldovich Marina , Krol Leonie , Timmermann Dagmar , Gioia Gerard , Cunitz Katrin , Buchheim Anna , von Steinbuechel Nicole
Published: July 2024

Purpose
The present study aims at providing reference values from the general pediatric population for the German version of the 21-item self-report post version of the Postconcussion Symptom Inventory for adolescents aged 13–17 years (PCSI-SR13) following pediatric traumatic brain injury (pTBI).
Methods
A total of N = 950 adolescents completed an adapted version of the PCSI-SR13. Prior to establishing reference values using percentiles, psychometric properties (i.e., reliability and factorial validity) and regression analyses were examined to identify factors contributing to PCSI-SR13 scores. In addition, construct assessment in the general population sample was compared to that in the pTBI sample (N = 234) using measurement invariance analyses and direct comparisons at the score levels.
Results
The results indicate good reliability (Cronbach’s α and McDonald’s ω of 0.97 each). The four-factor structure covering physical, emotional, cognitive, and fatigue symptom groups could be replicated with χ2(183) = 995.96, p < 0.001, χ2/df = 5.44, CFI = 0.99, TLI = 0.98, RMSEA[90% CI] = 0.068[0.064, 0.073], SRMR = 0.03. With minor restrictions, assessment of symptoms was comparable between the general population and the TBI samples. Participants in the pTBI sample reported a significantly higher symptom burden than those in the general population sample. Reference values were provided using the total sample without further stratification.
Conclusions
For the German post version of the PCSI-SR13, reference values are now available for direct score comparisons and for drawing conclusions about the clinical relevance of the reported symptoms, while considering the prevalence in a comparable general population without a history of pTBI.
Trial registration
The study is retrospectively registered in German Clinical Trials Register and in International Clinical Trials Registry Platform (ID DRKS00032854).
Accelerated Ensemble Optimization using Momentum Methods
Authors: Nilsen Mathias M. , Stordal Andreas S. , Lorentzen Rolf J. , Raanes Patrick N. , Eikrem Kjersti S.
Published: July 2024

In this paper, we investigate the use of various momentum methods combined with an ensemble approximation of gradients, for accelerated optimization. Although momentum gradient descent methods are popular in machine learning, it is unclear how they perform when applied to time-consuming dynamic problems such as production optimization for petroleum reservoir management. Four different momentum methods are extensively tested on a reservoir test case in one deterministic and one robust setting. The numerical experiments show that momentum strategies yield, on average, a higher net present value with fewer simulations needed.
Molecular characterisation of human rabies in East Africa - a case series report and phylogenetic investigation.
Authors: Jaswant Gurdeep , Campbell Kathryn , Czupryna Anna , Mwatondo Athman , Ogoti Brian , Embregts Carmen W.E. , Kayuki Charles , Kuchaka Davis , Wambura Gati , Oigo James , Changalucha Joel , Oyugi Julius O. , Lushasi Kennedy , Sikana Lwitiko , Zwetselaar Marco van , Dekker Marieke C.J. , Muturi Mathew , Maritim Marybeth , Mutunga Mumbua , Durrant Rowan , Abala Tom , Chuchu Veronicah , Brunker Kirstyn , Thumbi S.M. , Hampson Katie
Published: July 2024

Background: Rabies remains a major public health problem in low- and middle-income countries. However, human rabies deaths are rarely laboratory-confirmed, especially in Africa. Here, we use rabies virus sequence data to enhance investigations for a series of 5 human rabies deaths in East Africa and discuss the implications of these at individual, healthcare, and societal-level.
Case presentation: The epidemiological context and care of these cases is contrasting: three of the bite victims did not receive any post-exposure vaccinations to prevent the fatal onset of disease, despite one attending a health facility on the day of exposure; one received only their first post-exposure vaccination; while the last received timely post-exposure vaccination but following a vaccination timeline that is not recommended by WHO. These differences raise concerns about health-seeking behaviour, competency of healthcare professionals in handling rabies exposures and accessibility and effectiveness of post-exposure prophylaxis as it is administered in these settings.
Result and conclusion: Our investigation confirms dog-mediated rabies as the cause of each of these deaths. The viral genomic data highlight the transboundary circulation of rabies within domestic dog populations across the region. We conclude that urgent action is needed to improve awareness around the need for emergency post-exposure prophylaxis that should be accessible in local communities and administered appropriately, as well as investment in coordinated dog vaccination to control dog-mediated rabies, the underlying cause of these deaths.
Epidemiology of boxwood blight in hotspots of western North Carolina and Virginia and validation of the boxwood blight infection risk model
Authors: Khaliq Ihsanul , Avenot Herve F. , Baudoin Anton , Coop Leonard , Hong Chuanxue
Published: July 2024

Boxwood blight is a highly invasive emerging disease. Since the first US report in North Carolina and Connecticut in 2011, boxwood blight has spread to over 30 US states, risking more than 90% of boxwood production. A boxwood blight infection risk model was developed from limited studies in controlled environments. Our study investigated the disease field epidemiology and validated the model’s prediction, using leaf wetness estimated by leaf wetness sensor or algorithms, by analysing weekly blight monitoring data collected on detector plants exposed to the prevailing environmental conditions from spring through fall of 2014 to 2017. Boxwood blight was recorded in 61 of 86 weeks, with the highest infected leaf count recorded in late summer or early fall. Rainfall, high relative humidity outside rainy periods and optimal temperatures during prolonged leaf wetness had a significant positive effect on boxwood blight development. Classification analyses showed that disease predictions from the model using leaf wetness estimated by leaf wetness sensor were more closely aligned with observations from the field than predictions based on algorithms. This study improved our understanding of disease field epidemiology, provided leads to improve the existing model, and generated essential knowledge for formulating effective strategies for blight mitigation.
Neuroprotective Effects of VGLUT1 Inhibition in HT22 Cells Overexpressing VGLUT1 Under Oxygen Glucose Deprivation Conditions
Authors: Pomierny Bartosz , Krzyżanowska Weronika , Skórkowska Alicja , Budziszewska Bogusława , Pera Joanna
Published: July 2024

Glutamate (Glu) is a major excitatory neurotransmitter in the brain, essential for synaptic plasticity, neuronal activity, and memory formation. However, its dysregulation leads to excitotoxicity, implicated in neurodegenerative diseases and brain ischemia. Vesicular glutamate transporters (VGLUTs) regulate Glu loading into synaptic vesicles, crucial for maintaining optimal extracellular Glu levels. This study investigates the neuroprotective effects of VGLUT1 inhibition in HT22 cells overexpressing VGLUT1 under oxygen glucose deprivation (OGD) conditions. HT22 cells, a hippocampal neuron model, were transduced with lentiviral vectors to overexpress VGLUT1. Cells were subjected to OGD, with pre-incubation of Chicago Sky Blue 6B (CSB6B), an unspecific VGLUT inhibitor. Cell viability, lactate dehydrogenase (LDH) release, mitochondrial membrane potential, and hypoxia-related protein markers (PARP1, AIF, NLRP3) were assessed. Results indicated that VGLUT1 overexpression increased vulnerability to OGD, evidenced by higher LDH release and reduced cell viability. CSB6B treatment improved cell viability and reduced LDH release in OGD conditions, particularly at 0.1 µM and 1.0 µM concentrations. Moreover, CSB6B preserved mitochondrial membrane potential and decreased levels of PARP1, AIF, and NLRP3 proteins, suggesting neuroprotective effects through mitigating excitotoxicity. This study demonstrates that VGLUT1 inhibition could be a promising therapeutic strategy for ischemic brain injury, warranting further investigation into selective VGLUT1 inhibitors.
Object Detection Algorithm Based on Improved YOLOv8 for Drill Pipe on Coal Mines
Authors: Li Xiaojun , Zhao Mingyang , Li Miao
Published: July 2024

Gas extraction is an important measure for coal mine gas disaster control. Its effect is closely correlated to the drilling depth. The existing methods usually determine the drilling depth by manually counting the number of drill pipes, and the number of drill pipes can be automatically counted by object detection and real-time tracking algorithms. An improved object detection model DSD-YOLOv8 was proposed for the problem of the poor performance of the object detection algorithm due to such interference factors as bright light, low illuminance and heavy dust fog in coal mines. In order to solve the problem of leak detection caused by the irregular shape that appears due to the interference of bright light, the deformable convolution DCNv2 module was used in the C2f module to make the sampling points of the convolution kernel diffuse irregularly, so as to fully extract the shape features of the drill pipe and then improve the detection rate of the model. For the problem of too low confidence of the model in detecting drill pipes due to uneven illumination, the attention paid by the model to the features of the drill pipe could be improved by embedding the SimAM non-parametric attention mechanism module in the backbone network, which can further improve the confidence of the drill pipe. For the problem of low average category detection accuracy caused by the changeable environment of the underground drilling site, the dynamic head was used to improve the ability of the model to extract the features of the drill pipe in scale, space, and channel, and improve the average category detection accuracy of the drill pipe. Finally, the improved detection algorithm was verified with the homemade drill pipe verification data set. The experimental results showed that: the improved DSD-YOLOv8 model effectively alleviated the problem of partial leak detection of the original network for scenes such as heavy dust fog and uneven illumination; the recall rate increased by 4.1%; the mean average accuracy was improved by 2.9%. At the same time, it maintains a high real-time performance (the FPS is 125), providing the basis of the drill pipe detection model for the application of real-time tracking of the number of drill pipes.
Quantum Machine Learning Techniques Integrated in Quantum Software Testing
Authors: Gutam Bala Gangadhara , Malchi Sunil Kumar
Published: July 2024

Quantum computing (QC) is the era of the mechanisms that quantum solves complex problems faster than classical computers (CCs). Today, there are more opportunities for the public and private sectors to use high-value quantum solutions. QC relies on quantum phenomena to calculate incredible speeds and set for significant expansion. Quantum computers, QC-based Internet of Things (IoT), and communication devices to create, process, and transmit quantum states and entanglement are anticipated to enhance society’s quantum software. In this context, this paper introduces the Quantum Software Testing (QST) approach with Machine Learning (ML) techniques integrated into the Quantum Software Testing Life Cycle (QSTLC). The proposed Quantum Machine Learning Testing (QMLT) exploits ML techniques at critical stages of the QST to enhance testing efficiency, accuracy, and adaptability. Finally, the proposed automated test report generation, summarizing, and knowledge extraction technique facilitates comprehensive documentation and knowledge transfer.
Solvothermal Synthesis of Quaternary Cu-Fe-Bi-S Nanostructure as a Promising Electrode Material for OER and Supercapacitor Performance
Authors: Umer Muhammad , Awais Muhammad , Aslam Sidra , Bilal Anas , . Javaria , Abbas Waseem , Mirza Misbah , Safdar Muhammad
Published: July 2024

The rational development and synthesis of renewable nanostructured materials hold significant importance within the realm of energy storage applications. In this study, the quaternary chalcogenide of Cu2FeBiS4 was fabricated using a facile solvothermal method and evaluated for its performance in Oxygen Evolution Reaction (OER) and Supercapacitor applications. The material underwent characterization employing various analytical techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDX), X-ray photoelectron spectroscopy (XPS), and Raman analysis. Cu2FeBiS4 demonstrates an improved super-capacitive performance, exhibiting an outstanding specific capacity (Cs) of 1530 F g–1 at 2 A g–1. The admirable electrochemical activity is mainly due to the synergistic effect of mixed metal sulfide nanostructure that can escalate the rapid diffusion of ions and electrons, which is beneficial to the electrode/electrolyte contact area, thereby boosting the rate of charge transfer. Additionally, Cu2FeBiS4 exhibits a commendable energy density of 43.17 Wh kg− 1 at a power density of 327.87 W kg− 1, accompanied by a retention rate of 96.2% after 1000 cycles. As a result, the Cu2FeBiS4 electrode developed in this study is evinced to be a promising electrode material for high-performance energy storage devices. Moreover, Cu2FeBiS4 demonstrates proficient electrocatalytic properties for the oxygen evolution reaction, displaying a minimal overpotential of 202 mV versus the reversible hydrogen electrode (RHE) at 10 mA cm− 2 and exhibiting operational stability for up to 10 hours.
Is severe maternal morbidity a risk factor for postpartum hospitalization with mental health or substance use disorder diagnoses? Findings from a retrospective cohort study in Maryland: 2016-2019
Authors: WOLFSON Carrie L , ANGELSON Jessica Tsipe , CREANGA Andreea A
Published: July 2024

BACKGROUND
Perinatal mental health conditions and substance use are leading causes, often co-occurring, of pregnancy-related and pregnancy-associated deaths in the United States. This study compares odds of hospitalization with a mental health condition or substance use disorder or both during the first year postpartum between patients with and without severe maternal morbidity (SMM) during delivery hospitalization.
Methods
Data are from the Maryland’s State Inpatient Database and include patients with a delivery hospitalization during 2016–2018 (n = 197,749). We compare rate of hospitalization with a mental health condition or substance use disorder or both at 42 days and 42 days to 1 year postpartum by occurrence of SMM during the delivery hospitalization. We use multivariable logistic regression to derive the odds of hospitalization with each outcome for patients by SMM status, adjusted for patient sociodemographic characteristics, presence of mental health condition or substance use disorder diagnoses during the delivery hospitalization, and delivery outcome. SMM, mental health conditions, and substance use disorders are identified using ICD-10 diagnosis and procedure codes.
RESULTS
Overall, 5,793 patients (2.9%) who delivered during 2016–2018 experienced hospitalization in the year following delivery. Among these patients, 24.3% (n = 1,410) had a mental health condition diagnosis, 10.6% (n = 619) had a substance use disorder diagnosis, and 9.8% (n = 570) had co-occurring mental health condition and substance use disorder diagnoses. Patients with SMM had 3.7 times the odds (95% CI 2.7, 5.2) of hospitalization with a mental health condition diagnosis, 2.7 times the odds (95% CI 1.6, 4.4) of a hospitalization with substance use disorder diagnosis, and 3.0 times the odds (95% CI 1.8, 4.8) of hospitalization with co-occurring mental health condition and substance use disorder diagnoses during the first-year postpartum adjusting for covariates.
CONCLUSION
Patients who experience SMM during their delivery hospitalization had higher odds of hospitalization with a mental health condition, substance use disorder, and co-occurring mental health condition and substance use disorder in the one-year postpartum period. Treatment and support resources for mental health and substance use providers --including enhanced screening and warm handoffs -- should be made available to patients with SMM upon discharge after delivery, and evidence-based interventions to improve mental health and reduce substance use should be prioritized in these patients.
Electrocatalytic radical degradation of 2-aminoethanol by nickel, palladium and platinum complexes bearing non-innocent diarylamido/bis(phosphine) pincer ligand
Authors: Mikhailov Ilya K. , Gafurov Zufar N. , Kagilev Alexey A. , Sakhapov Il’yas F. , Morozov Vladimir I. , Ganeev Gumar R. , Khayarov Khasan R. , Kulikova Vasilisa A. , Kirkina Vladislava A. , Gutsul Evgenii I. , Shubina Elena S. , Belkova Natalia V. , Sinyashin Oleg G. , Yakhvarov Dmitry G.
Published: July 2024

The electrocatalytic activity of PNP pincer complexes with general formula (PNP)MCl (PNP is bis(2-diisopropylphosphino-4-methylphenyl)amide; M = nickel, palladium and platinum) in the oxidative 2-aminoethanol (MEA, a recalcitrant pollutant) degradation process was studied using cyclic voltammetry method. Complexes fulfil the requirements typically expected of a redox mediator or catalyst. Their electrochemical oxidation yields the aminyl radical species which unpaired electron is localized on the ligand. The mechanism of interaction of these aminyl radical complexes with MEA was studied using ESR- and NMR-spectroscopy methods. The addition of MEA to the solution of model platinum derivative leads to the disappearance of ESR signal responsible for the aminyl radical complex, whereas the 31P{1H} NMR spectrum revealed the regeneration of neutral (PNP)MCl. Palladium derivative was found to be the most effective catalyst of the series with an Icat/Ip value of 6.7. Controlled potential electrolysis has allowed us to identify the main products of the catalytic process and to propose the mechanism of the overall process.
A single-plasmid-based, easily curable CRISPR/Cas9 system for rapid, iterative genome editing in Pseudomonas putida KT2440
Authors: Wen Qifeng , Chen Jinjin , Li Jin , Dharmasiddhi Ida Putu Wiweka , Yang Maohua , Xing Jianmin , Liu Yilan
Published: July 2024

Background
Pseudomonas putida KT2440, a non-pathogenic soil bacterium, is a key platform strain in synthetic biology and industrial applications due to its robustness and metabolic versatility. Various systems have been developed for genome editing in P. putida, including transposon modules, integrative plasmids, recombineering systems, and CRISPR/Cas systems. However, rapid iterative genome editing is limited by the complex and lengthy processes.
Results
We discovered that the pBBR1MCS2 plasmid carrying the CRISPR/Cas9 module could be easily cured in P. putida KT2440 at 30 oC. We then developed an all-in-one CRISPR/Cas9 system for yqhD and ech-vdh-fcs deletions, respectively, and further optimized the editing efficiency by varying homology arm lengths and target sites. Sequential gene deletions of vdh and vanAB was carried out rapidly using single-round processing and easy plasmid curing. This system's user-friendliness was validated by novice users in two labs for various gene deletions, substitutions, and insertion. Finally, iterative genome editing was used to engineering P. putida for valencene biosynthesis, achieving a 10-fold increase in yield.
Conclusions
We developed and applied a rapid all-in-one plasmid CRISPR/Cas9 system for genome editing in P. putida. This system requires lest than 1.5 days for one edit due to simplified plasmid construction, electroporation and curing processes, thus accelerating the cycle of genome editing. To our knowledge, this is the fastest iterative genome editing system for P. putida. Using this system, we rapidly engineered P. putida for valencene biosynthesis for the first time, showcasing the system's potential for expanding biotechnological applications.
Diffraction imaging method using the pattern operator in migrated dip-angle gathers
Authors: Sheng Tongjie , Zhao Jingtao , Chen Zongnan
Published: July 2024

Diffracted wavefield carries high-resolution information about subwavelength geological structural elements crucial for imaging small-scale subsurface discontinuities. However, the presence of strong reflection often obscures weak diffraction, limiting the effective use of diffraction in depicting detailed small-scale structures. To obtain a high-resolution diffraction image, we present a novel two-phase diffraction separation method that combines pattern-based method and energy attenuation function of stationary reflection in migrated dip angle gathers. In the first phase, the pattern-based method utilizes pattern operator to characterize non-stationary features of reflection in dip angle gathers and effectively eliminates the reflection outside the stationary point. In the second phase, the energy attenuation function based on dip angle is used to further attenuate residual reflection near the stationary point. The conflicting dip angles often reduces the performance of dip-based energy attenuation methods. We introduce the covariance rate criterion to adaptively adjust the conflicting dip angle, so that the energy attenuation function can more accurately attenuate the residual reflection. The numerical experiment conducted on classic Sigsbee2B model validates the efficacy of the proposed method in suppressing strong high-slope reflection while preserving details of small-scale faults and scatterers. The real data application demonstrates the performance of the method to highlight deep fractures, presenting additional potential reservoir-related structural insights.
Advanced Brain Tumor Classification Utilizing MaskR-CNN with Fuzzy C-Means Clustering
Authors: K Muthulakshmi , M Jayalakshmi
Published: July 2024

Brain tumors are a major concern in medical diagnostics, necessitating precise and prompt detectionfor optimal treatment. While radiologists may diagnose them using medical imaging, automating theprocess has several advantages, including increased efficiency and accessibility. Magnetic resonanceimaging (MRI) is the most effective way to detect these tumors. An automated system that accuratelyidentifies and localizes them in MRI scans can greatly benefit patients lacking immediate access toa radiologist. Our study proposes a method combining Mask R-CNN and Fuzzy C-Means (FCM)clustering to boost the accuracy of brain tumor detection in MRI images. This approach identifiestumors and segments abnormal brain tissues but also gauges the likelihood of tumor presence, offering acomprehensive diagnostic tool. While Mask R-CNN excels in segmentation accuracy, integrating FCMclustering further refines the detection process. FCM, which allows data to belong to multiple clusters,is proven in medical images where tumor boundaries can be ambiguous. Our experiment utilizes theBraTS 2018 and 2020 datasets, employing five-fold cross-validation to demonstrate the effectiveness andvalidate the clinical application of our proposed method.
Perturbing the Gradient for alleviating Meta Overfitting
Authors: Gogoi Manas , Tiwari Sambhavi , Verma Shekhar
Published: July 2024

The reason for Meta Overfitting can be attributed to two factors: Mutual Non-exclusivity and the Lack of diversity, consequent to which a single global function can fit the support set data of all the meta-training tasks and fail to generalize to new unseen tasks. This issue is evidenced by low error rates on the meta-training tasks, but high error rates on new tasks. However, there can be a number of novel solutions to this problem keeping in mind any of the two objectives to be attained, i.e. to increase diversity in the tasks and to reduce the confidence of the model for some of the tasks. Inlight of the above, this paper proposes a number of solutions to tackle meta-overfitting on few-shot learning settings, such as few-shot sinusoid regression and few shot classification. Our proposed approaches demonstrate improved generalization performance compared to state-of-the-art baselines for learning in a non-mutually exclusive task setting. Overall, this paper aims to provide insights into tackling overfitting in meta-learning and to advance the field towards more robust and generalizable models.
Comparison of Models Predicting Efficacy of Radioiodine Therapy in Patients with Differentiated Thyroid Cancer
Authors: Borowczyk Martyna , Jodłowska-Siewert Elżbieta , Filipowicz Dorota , Verburg Frederik A. , Ziemnicka Katarzyna , Szczepanek-Parulska Ewelina , Ruchała Marek
Published: July 2024

Purpose
Differentiated thyroid cancer (DTC) is the most common type of endocrine malignancy, with its incidence on the rise over recent decades. Despite a favorable prognosis, DTC management remains complex, often involving thyroidectomy followed by radioactive iodine (RAI) therapy. While RAI is crucial for patient outcomes, its efficacy varies, necessitating the identification of predictors for treatment response. New guidelines underscore the need for personalized follow-up plans, prompting research into predictive models to refine prognostic accuracy.
Methods
We conducted a retrospective analysis of 744 DTC patients treated at a single center, focusing on clinicopathological factors and thyroid biomarkers. Multivariate logistic regression models were constructed to evaluate the predictive value of different DTC biomarkers, adjusting for covariates such as age, sex, and disease stage. Cut-off values for these biomarkers were determined to predict RAI efficacy.
Results
Analysis revealed no significant difference in predictive performance among models incorporating various DTC biomarkers. Stimulated thyroglobulin (sTg) emerged as a reliable predictor, with a mean cut-off value of 7.22 ng/mL. Additionally, chronic lymphocytic thyroiditis (CLT) status tended to enhance predictive accuracy, although not significantly.
Conclusions
Our study underscores the utility of sTg as a single parameter for predicting RAI efficacy in DTC patients, with a defined cut-off value facilitating clinical decision-making. The inclusion of CLT status may further enhance predictive models, warranting consideration in future analyses. Overall, our findings contribute to the advancement of personalized management approaches for DTC patients undergoing RAI therapy.
Crocin effects on the anticancer properties of Lactobacillus lactis probiotics against colorectal adenocarcinoma cells
Authors: Gholipour Faranak , Entezar Mehdi , Amini Mohammad , Vandghanooni Somayeh , Baradaran Behzad , Eskandani Morteza , Mokhtarzadeh Amir Ali
Published: July 2024

There are numerous potential mechanisms that can account for the propensity of probiotics to prevent the onset of colorectal cancer. These effects include but are not limited to deactivation of cancer-causing agents, competition with harmful bacteria, boosting the immune system, and reducing cell proliferation by controlling apoptosis and cell differentiation. Other benefits include fermenting undigested food and limiting the activity of certain signaling pathways. Research has shown that the gut microbiota is impacted by nutrition, which subsequently affects the metabolomics, thereby establishing a connection between diet, microbiota, and overall health. The objective of this study was to assess the resistance of Lactobacillus lactis to Crocin. The study involved extracting cell-free supernatants from untreated bacteria and bacteria treated with crocin, and then examining their ability to prevent the growth of HCT-116 colon cancer cells. It was proven that L. lactic, when treated with crocin, can effectively fight against various types of harmful bacteria and can survive in acidic conditions. After 48 hours, both CFS and cro-CFS showed a dose-dependent inhibition of HCT-116 cell growth. The half-maximal inhibitory concentration was 97.41 µL/mL for CFS and 72.07 µL/mL for cro-CFS. The results of flow cytometry tests confirmed the MTT assay findings, showing that cro-CFS-treated cells had a higher rate of apoptosis at 46.8% compared to CFS-treated cells at 25.42%. The results were confirmed by qPCR, which showed that the Caspase 9 and BAX genes were upregulated and the BCL-2 expression level was reduced in cells treated with cro-CFS and CFS. Crocin could alter the metabolomics of probiotics present in the gut, potentially impacting their ability to fight cancer.
Microenvironment engineering by targeted delivery of activated Ag NPs for boosting electrocatalytic CO2 reduction reaction
Authors: Wang Shun , Xu Ting , Yang Hao , Lu Tianrui , Zhong Rui , Lv Jing-Jing , Zhu Shaojun , Zhang Mingming , Wang Zheng-Jun , Yuan Yifei , Li Jun , Wang Jichang , Jin Huile , Pan Shuang , Wang Xin , Cheng Tao
Published: July 2024

To boost the performance of electrocatalytic CO2 reduction reaction (eCO2RR), a unique synthetic method that deploys the in situ reduction of precoated precursors was developed to produce activated Ag nanoparticles (NPs) within the gas diffusion layer (GDL), where the thus-obtained Ag NPs-Skeleton could block direct contact between the active Ag sites and electrolyte. Specifically, compared to the conventional surface loading mode in the acidic media, our freestanding and binder free electrode could achieve obvious higher CO selectivity of 94%, CO production rate of 23.3 mol g-1 h-1, single-pass CO2 conversion of 58.6%, and enhanced long-term stability of 8 hours. Our study shows that delivering catalysts within the GDL does not only gain the desired physical protection from GDL skeleton to achieve a superior local microenvironment for more efficient pH-universal eCO2RR, but also manifests the pore structures to effectively address gas accumulation and flood issues, thereby stabilizing the catalysts.
Calibrating global behaviour of equation of state by combining nuclear and astrophysics inputs in a machine learning approach
Authors: I Sk Md Adil , Saxena Prafulla , Malik Tuhin , Patra N.K. , Agrawal B. K.
Published: July 2024

We implemented symbolic regression techniques to identify suitable analytical functions that map various properties of neutron stars (NSs), obtained by solving the Tolman-Oppenheimer-Volkoff (TOV) equations, to a few key parameters of the equation of state (EoS). These symbolic regression models (SRMs) are then employed to perform Bayesian inference with a comprehensive dataset from nuclear physics experiments and astrophysical observations. The posterior distributions of EoS parameters obtained from Bayesian inference using SRMs closely match those obtained directly from the solutions of TOV equations. Our SRM-based approach is approximately 100 times faster, enabling efficient Bayesian analyses across different combinations of data to explore their sensitivity to various EoS parameters within a reasonably short time.