bluuebunny/crossref_metadata_2025 huggingface dataset:
Pathological Functions of Lysosomal Ion Channels in the Central Nervous System
Authors: Cen Jianke , Hu Nan , Shen Jiawen , Gao Yongjing , Lu Huanjun
Published: June 2024

Lysosomes are highly dynamic organelles that maintain cellular homeostasis and regulate fundamental cellular processes by integrating multiple metabolic pathways. Lysosomal ion channels such as TRPML1-3, TPC1/2, ClC6/7, CLN7, and TMEM175 mediate the flux of Ca2+, Cl−, Na+, H+, and K+ across lysosomal membranes in response to osmotic stimulus, nutrient-dependent signals, and cellular stresses. These ion channels serve as the crucial transducers of cell signals and are essential for the regulation of lysosomal biogenesis, motility, membrane contact site formation, and lysosomal homeostasis. In terms of pathophysiology, genetic variations in these channel genes have been associated with the development of lysosomal storage diseases, neurodegenerative diseases, inflammation, and cancer. This review aims to discuss the current understanding of the role of these ion channels in the central nervous system and to assess their potential as drug targets.
A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors
Authors: Gutiérrez-Mondragón Mario A. , Vellido Alfredo , König Caroline
Published: June 2024

G-protein coupled receptors (GPCRs) are transmembrane proteins that transmit signals from the extracellular environment to the inside of the cells. Their ability to adopt various conformational states, which influence their function, makes them crucial in pharmacoproteomic studies. While many drugs target specific GPCR states to exert their effects—thereby regulating the protein’s activity—unraveling the activation pathway remains challenging due to the multitude of intermediate transformations occurring throughout this process, and intrinsically influencing the dynamics of the receptors. In this context, computational modeling, particularly molecular dynamics (MD) simulations, may offer valuable insights into the dynamics and energetics of GPCR transformations, especially when combined with machine learning (ML) methods and techniques for achieving model interpretability for knowledge generation. The current study builds upon previous work in which the layer relevance propagation (LRP) technique was employed to interpret the predictions in a multi-class classification problem concerning the conformational states of the β2-adrenergic (β2AR) receptor from MD simulations. Here, we address the challenges posed by class imbalance and extend previous analyses by evaluating the robustness and stability of deep learning (DL)-based predictions under different imbalance mitigation techniques. By meticulously evaluating explainability and imbalance strategies, we aim to produce reliable and robust insights.
Characterising Biological and Physiological Drought Signals in Diverse Parents of a Wheat Mapping Population
Authors: Laskoś Kamila , Czyczyło-Mysza Ilona Mieczysława , Waligórski Piotr , Dziurka Kinga , Skrzypek Edyta , Warchoł Marzena , Juzoń-Sikora Katarzyna , Janowiak Franciszek , Dziurka Michał , Grzesiak Maciej T. , Grzesiak Stanisław , Quarrie Steve , Marcińska Izabela
Published: June 2024

Water deficit affects the growth as well as physiological and biochemical processes in plants. The aim of this study was to determine differences in physiological and biochemical responses to drought stress in two wheat cultivars—Chinese Spring (CS) and SQ1 (which are parents of a mapping population of doubled haploid lines)—and to relate these responses to final yield and agronomic traits. Drought stress was induced by withholding water for 14 days, after which plants were re-watered and maintained until harvest. Instantaneous gas exchange parameters were evaluated on the 3rd, 5th, 10th, and 14th days of seedling growth under drought. After 14 days, water content and levels of chlorophyll a+b, carotenoids, malondialdehyde, soluble carbohydrates, phenolics, salicylic acid, abscisic acid (ABA), and polyamines were measured. At final maturity, yield components (grain number and weight), biomass, straw weight, and harvest index were evaluated. Physiological and biochemical parameters of CS responded more than those of SQ1 to the 14-day drought, reflected in a greater reduction in final biomass and yield in CS. Marked biochemical differences between responses of CS and SQ1 to the drought were found for soluble carbohydrates and polyamines. These would be good candidates for testing in the mapping population for the coincidence of the genetic control of these traits and final biomass and yield.
Involvement of the GH38 Family Exoglycosidase α-Mannosidase in Strawberry Fruit Ripening
Authors: Méndez-Yáñez Angela , Sáez Darwin , Rodríguez-Arriaza Francisca , Letelier-Naritelli Claudio , Valenzuela-Riffo Felipe , Morales-Quintana Luis
Published: June 2024

Exoglycosidase enzymes hydrolyze the N-glycosylations of cell wall enzymes, releasing N-glycans that act as signal molecules and promote fruit ripening. Vesicular exoglycosidase α-mannosidase enzymes of the GH38 family (EC 3.2.1.24; α-man) hydrolyze N-glycans in non-reduced termini. Strawberry fruit (Fragaria × ananassa) is characterized by rapid softening as a result of cell wall modifications during the fruit ripening process. Enzymes acting on cell wall polysaccharides explain the changes in fruit firmness, but α-man has not yet been described in F. × ananassa, meaning that the indirect effects of N-glycan removal on its fruit ripening process are unknown. The present study identified 10 GH38 α-man sequences in the F. × ananassa genome with characteristic conserved domains and key residues. A phylogenetic tree built with the neighbor-joining method and three groups of α-man established, of which group I was classified into three subgroups and group III contained only Poaceae spp. sequences. The real-time qPCR results demonstrated that FaMAN genes decreased during fruit ripening, a trend mirrored by the total enzyme activity from the white to ripe stages. The analysis of the promoter regions of these FaMAN genes was enriched with ripening and phytohormone response elements, and contained cis-regulatory elements related to stress responses to low temperature, drought, defense, and salt stress. This study discusses the relevance of α-man in fruit ripening and how it can be a useful target to prolong fruit shelf life.
Fully Buried Pipeline Floatation in Poro-Elastoplastic Seabed under Combined Wave and Current Loadings
Authors: Leng Jian , Liu Libin , Liao Chencong , Ye Guanlin
Published: June 2024

The floatation capacity of seabed pipelines has long been considered a key risk element during design, especially with the combined loading of waves and currents. This paper presents a two-dimensional coupled approach with a poro-elastoplastic theory to study the floatation of pipelines with the combined loading of waves and currents. The findings suggest that the proposed method is able to capture the mechanical performance of pipeline floatation. Pipeline floatation occurs in two distinct phases. In the initial phases, the pipelines float slowly with the cyclic loadings. In the second stage, when the backfill soil in the middle position of the pipelines begins to liquefy, the floating displacement increases obviously. The boundary constraints provided by the pipelines strengthen the backfill soil as well as accelerate the release of excessive pore water pressure. Meanwhile, a nonliquefiable region is formed under the pipelines. The floating displacement of the pipelines increases as well as current velocity, wave height, and wave period, and reduces with increased backfill soil permeability. Increasing the permeability coefficient of backfill soil can obviously restrain the floatation of pipelines.
Is Staged Surgery Always Necessary for Schatzker Type IV–VI Tibial Plateau Fractures? A Comparison Study
Authors: Lin Kai-Cheng , Huang Fu-Ting , Chen Chun-Yu , Tarng Yih-Wen
Published: June 2024

Aims: This study aims to compare the outcomes of immediate (followed by closed-incision negative-pressure therapy use) versus delayed ORIF in patients with Schatzker type IV–VI TPFs. Patients and Methods: A prospective study of patients undergoing ORIF between January 2018 and December 2019 was performed. The inclusion criteria were patients (>18 years) with a closed fracture sent to the emergency room (ER) within 24 h of injury. All the patients underwent preoperative image evaluation. Two senior orthopedic trauma surgeons evaluated the soft tissue condition in the ER by 5P’s of the compartment syndrome, judging the timing of the operation of definitive ORIF. Group 1 (n = 16) received delayed ORIF. Group 2 (n = 16) received immediate ORIF and ciNPT use. Patient follow-up occurred after 2 and 6 weeks and 3, 6, and 12 months after surgery. The assessments included the time to definitive fixation, the length of hospital stay, the time to bone union, surgical site complications, and reoperation within 12 months. A universal goniometer was used to measure the postoperative 3 m, 6 m, and 12 m ROM. Results: The patient demographics were similar between the groups (p > 0.05). Group 2 displayed significantly a shorter time to definitive fixation (5.94 ± 2.02 vs. 0.61 ± 0.28, p < 0.0001) and hospital stay (14.90 ± 8/78 vs. 10.30 ± 6.78, p = 0.0016). No significant difference was observed in the time to bone union, surgical site complication incidence, and reoperation rates (p > 0.05). Flexion and flexion–extension knee ROM were demonstrated to be significantly improved in Group 2, 3, 6, and 12 months postoperatively (p < 0.0001). Conclusions: In this study, early ORIF and ciNPT use resulted in a shorter hospital length of stay, a reduced time to early active motion of the knee, and improved knee ROM. These results suggest that early ORIF with ciNPT for Schatzker type IV–VI TPFs is safe and effective in some patients. However, further research to confirm these findings across larger and more diverse populations is needed.
Temporary Ureter Occlusion with Simultaneous Urinary Diversion via a Single-Access Route Using a 4-French Balloon Catheter and a Pigtail Nephrostomy Drainage Catheter
Authors: Oh Chang Hoon , Cho Soo Buem , Choi Sang Lim , Kim Sungwon , Kwon Hyeyoung
Published: June 2024

Background and Objectives: This study evaluated the efficacy and safety of temporary ureteral occlusion combined with urinary diversion using a single-access route created by inserting a balloon catheter through a pigtail nephrostomy drainage catheter. With this approach, we aimed to offer an alternative for patients with ureteral leaks who are suboptimal surgical candidates. Materials and Methods: This retrospective study included nine patients (eight of which were bilateral cases and one was unilateral, totaling seventeen cases) who underwent the surgery between September 2023 and March 2024. The method involved gaining percutaneous access to the pelvicalyceal system, inserting a 4-French Fogarty balloon catheter through a pigtail nephrostomy catheter, and inflating the balloon at the proximal or mid-ureter. Results: All 17 cases achieved technical successful with no major complications. The procedure effectively relieved symptoms associated with urinary leakage in most patients. However, the significant deflation of the balloon catheter occurred in five cases (29.4%), with three (17.6%) experiencing complete deflation. In these five cases, the final balloon size was 5.81 mm (range: 0–8.9 mm), confirming a 25.0% decrease in size from pre- to post-procedure. Ureteral occlusion was 28.3 d long on average (range: 8–57 d). All patients experienced symptom relief during temporary ureteral occlusion. Except for two patients lost to follow-up, three patients showed symptom improvement with only PCN and four patients underwent surgical closure of the fistula tract before or after balloon catheter removal. Conclusions: This study confirms that this approach is safe and effective.
Culinary Comfort: Understanding the Connection between Food and Well-Being
Authors: Yasmeen Bushra , Iftikhar Ifra , Fischer Florian
Published: June 2024

This study investigated the complex interaction between individuals’ culinary tastes, at-home dining habits, and their broader impact on general well-being and relationships dynamics. An all-encompassing tool for assessing the impact of dietary choices on emotions related to coziness, social interaction, and general wellness, the multidimensional Culinary Comfort and Connection Index (CCCI) focuses on traditional home-cooked meals, in particular. We used an online-based survey to validate the CCCI. With a Cronbach alpha of 0.844, this scale is reliable and valid. It covers a wide range of aspects including self-care advocacy, traditional values, and a preference for handmade food. We performed descriptive and stratified analyses and tested correlations. The CCCI shows complicated patterns when analyzed with respect to gender, education level, and family income that demonstrate a myriad of factors impacting people’s views on food and its relationship to health. While some patterns emerged, the results imply that dietary choices do not necessarily correlate with overall health. The research highlights the complex interaction between cultural, societal, and personal elements in determining perspectives on nutrition and health by drawing on theoretical models like Bronfenbrenner’s ecological systems theory and the Theory of Planned Behavior. Future research should incorporate broader age ranges, longitudinal designs, different populations, objective measurements, and intervention trials to better understand the dynamic link between dietary preferences and health outcomes.
Can Diet Alter the Intestinal Barrier Permeability in Healthy People? A Systematic Review
Authors: Nascimento Daniele de Souza Marinho do , Mota Ana Carolina Costa Campos , Carvalho Maria Clara da Cruz , Andrade Eva Débora de Oliveira , Oliveira Érika Paula Silva Freitas de , Galvão Liana Letícia Paulino , Maciel Bruna Leal Lima
Published: June 2024

Dietary factors can modify the function of the intestinal barrier, causing permeability changes. This systematic review analyzed evidence on the link between diet or dietary interventions and changes in intestinal barrier permeability (IBP) in healthy individuals. A systematic search for primary studies was conducted using the virtual databases EMBASE, PubMed, Web of Science, CINAHL, and Scopus. This review adhered to PRISMA 2020 guidelines, assessing the methodological quality using the Newcastle–Ottawa scale for observational studies and ROB 2.0 for randomized clinical trials. Out of 3725 studies recovered, 12 were eligible for review. Chicory inulin and probiotics reduced IBP in adults with a moderate GRADE level of evidence. The opposite result was obtained with fructose, which increased IBP in adults, with a very low GRADE level of evidence. Only intervention studies with different dietary components were found, and few studies evaluated the effect of specific diets on the IBP. Thus, there was no strong evidence that diet or dietary interventions increase or decrease IBP in healthy individuals. Studies on this topic are necessary, with a low risk of bias and good quality of evidence generated, as there is still little knowledge on healthy populations.
Role of Folate in Liver Diseases
Authors: Yang Minlan , Wang Dingye , Wang Xiyuan , Mei Jie , Gong Quan
Published: June 2024

Folate is a water-soluble B vitamin involved in the synthesis of purines and pyrimidines and is one of the essential vitamins for human growth and reproduction. Folate deficiency due to low dietary intake, poor absorption of folate, and alterations in folate metabolism due to genetic defects or drug interactions significantly increases the risk of diseases such as neural tube defects, cardiovascular disease, cancer, and cognitive dysfunction. Recent studies have shown that folate deficiency can cause hyperhomocysteinemia, which increases the risk of hypertension and cardiovascular disease, and that high homocysteine levels are an independent risk factor for liver fibrosis and cirrhosis. In addition, folate deficiency results in increased secretion of pro-inflammatory factors and impaired lipid metabolism in the liver, leading to lipid accumulation in hepatocytes and fibrosis. There is substantial evidence that folate deficiency contributes to the development and progression of a variety of liver diseases, including non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), alcoholic liver disease (ALD), viral hepatitis, hepatic fibrosis, and liver cancer. Here we review key studies on the role of folate in the pathophysiology of liver diseases, summarize the current status of studies on folate in the treatment of liver diseases, and speculate that folate may be a potential therapeutic target for liver diseases.
Low Vitamin D Concentration Is Associated with Increased Depression Risk in Adults 20–44 Years Old, an NHANES 2007–2018 Data Analysis with a Focus on Perinatal and Breastfeeding Status
Authors: Hollinshead Victoria Rose Barri Benters , Piaskowski Julia L. , Chen Yimin
Published: June 2024

The objective was to investigate associations of serum vitamin D concentration with depressive symptoms and assess the impact that vitamin D concentration has on the occurrence of depressive symptoms in 20–44-year-old pregnant women, postpartum women, non-pp women (non-pregnant/postpartum women), and men, including a separate subgroup analysis of postpartum breastfeeding and non-breastfeeding women. The study populations were selected from the 2007–2018 NHANES public data. Subjective interview data and objective laboratory data including depressive symptoms, serum vitamin D concentration, nutrient intake, and demographic information were utilized. Two diet patterns were created using principal component analysis, and a Bayesian multinomial model was fit to predict the depression outcomes for each subpopulation. The estimates for the log vitamin D slope parameter were negative for all cohorts; as vitamin D increased, the probability of having no depression increased, while the probability of depression decreased. The pregnant cohort had the steepest vitamin D slope, followed by postpartum women, then non-pp women and men. Higher vitamin D concentration had more impact on decreasing depression risk in pregnant and postpartum women compared to non-pp women and men. Among postpartum women, higher vitamin D concentration had a greater influence on decreasing breastfeeding women’s depression risk than non-breastfeeding women.
Unveiling the Nutritional Veil of Sulforaphane: With a Major Focus on Glucose Homeostasis Modulation
Authors: Wang Yanan , He Xiaoyun , Cheng Nan , Huang Kunlun
Published: June 2024

Abnormal glucose homeostasis is associated with metabolic syndromes including cardiovascular diseases, hypertension, type 2 diabetes mellitus, and obesity, highlighting the significance of maintaining a balanced glucose level for optimal biological function. This highlights the importance of maintaining normal glucose levels for proper biological functioning. Sulforaphane (SFN), the primary bioactive compound in broccoli from the Cruciferae or Brassicaceae family, has been shown to enhance glucose homeostasis effectively while exhibiting low cytotoxicity. This paper assesses the impact of SFN on glucose homeostasis in vitro, in vivo, and human trials, as well as the molecular mechanisms that drive its regulatory effects. New strategies have been proposed to enhance the bioavailability and targeted delivery of SFN in order to overcome inherent instability. The manuscript also covers the safety evaluations of SFN that have been documented for its production and utilization. Hence, a deeper understanding of the favorable influence and mechanism of SFN on glucose homeostasis, coupled with the fact that SFN is abundant in the human daily diet, may ultimately offer theoretical evidence to support its potential use in the food and pharmaceutical industries.
How the Microbiota May Affect Celiac Disease and What We Can Do
Authors: Matera Mariarosaria , Guandalini Stefano
Published: June 2024

Celiac disease (CeD) is an autoimmune disease with a strong association with human leukocyte antigen (HLA), characterized by the production of specific autoantibodies and immune-mediated enterocyte killing. CeD is a unique autoimmune condition, as it is the only one in which the environmental trigger is known: gluten, a storage protein present in wheat, barley, and rye. How and when the loss of tolerance of the intestinal mucosa to gluten occurs is still unknown. This event, through the activation of adaptive immune responses, enhances epithelial cell death, increases the permeability of the epithelial barrier, and induces secretion of pro-inflammatory cytokines, resulting in the transition from genetic predisposition to the actual onset of the disease. While the role of gastrointestinal infections as a possible trigger has been considered on the basis of a possible mechanism of antigen mimicry, a more likely alternative mechanism appears to involve a complex disruption of the gastrointestinal microbiota ecosystem triggered by infections, rather than the specific effect of a single pathogen on intestinal mucosal homeostasis. Several lines of evidence show the existence of intestinal dysbiosis that precedes the onset of CeD in genetically at-risk subjects, characterized by the loss of protective bacterial elements that both epigenetically and functionally can influence the response of the intestinal epithelium leading to the loss of gluten tolerance. We have conducted a literature review in order to summarize the current knowledge about the complex and in part still unraveled dysbiosis that precedes and accompanies CeD and present some exciting new data on how this dysbiosis might be prevented and/or counteracted. The literature search was conducted on PubMed.gov in the time frame 2010 to March 2024 utilizing the terms “celiac disease and microbiota”, “celiac disease and microbiome”, and “celiac disease and probiotics” and restricting the search to the following article types: Clinical Trials, Meta-Analysis, Review, and Systematic Review. A total of 364 papers were identified and reviewed. The main conclusions of this review can be outlined as follows: (1) quantitative and qualitative changes in gut microbiota have been clearly documented in CeD patients; (2) intestinal microbiota’s extensive and variable interactions with enterocytes, viral and bacterial pathogens and even gluten combine to impact the inflammatory immune response to gluten and the loss of gluten tolerance, ultimately affecting the pathogenesis, progression, and clinical expression of CeD; (3) gluten-free diet fails to restore the eubiosis of the digestive tract in CeD patients, and also negatively affects microbial homeostasis; (4) new tools allowing targeted microbiota therapy, such as the use of probiotics (a good example being precision probiotics like the novel strain of B. vulgatus (20220303-A2) begin to show exciting potential applications.
Effects of a Dietary Supplement Composed of Baicalin, Bromelain and Escin for Venous Chronic Insufficiency Treatment: Insights from a Retrospective Observational Study
Authors: Drago Selene Francesca Anna , Rottura Michelangelo , Molonia Antonino , Gianguzzo Viviana Maria , Pallio Giovanni , Irrera Natasha , Orlando Luana , De Fazio Marianna Gigliotti , Isgrò Marilena , Zirilli Natalia , Arcoraci Vincenzo , Imbalzano Egidio
Published: June 2024

Chronic venous insufficiency (CVI) represents a risk factor for cardiovascular events. The first-line treatment includes the use of compression stockings and lifestyle changes. Natural products, such as flavonoids, could be used to improve the effects of compression therapy due to their anti-inflammatory and anti-oxidant properties. This study aims to evaluate the effects of a dietary supplement containing baicalin, bromeline and escin in CVI patients. A retrospective cohort study was performed by using the medical records of CVI affected outpatients. Patients treated with the dietary supplement were defined as “users”. A modified Venous Clinical Severity Score (VCSS) was calculated, including pain, inflammation, vessels induration and skin pigmentation. All clinical variables were evaluated at baseline (T0), after 30 (T1) and 90(T2) days in “users” and “non-users”. Out of 62 patients, 30 (48.4%) were “users”. No difference was observed between groups at baseline. A lower VCSS value was recorded in “users” than that observed in “non-users” at T2 (7.0 (4.0–9.0) vs. 9.0 (5.0–10.0); p = 0.025). Vessels’ induration and pain significantly reduced in 53.3% and 43.3% of “users” and in 18.8% and 9.4% of “non-users”. Only “users” (33.3%) showed a reduction of the inflammatory signs as well as a decrease in malleolar circumference, from 29.0 (26.5–30.0) to 27.5 (26.0–28.5) (p < 000.1). A reduction of C-reactive Protein levels was found in “users” compared to “non-users” at T2 (1.0 (0.9–1.2) vs. 1.3 (1.0–1.5); p = 0.006). These findings suggest that implementation of a dietary supplement could improve the clinical outcomes of CVI patients.
Jingzhi Guanxin Oral Liquids Attenuate Atherosclerotic Coronary Heart Disease via Modulating Lipid Metabolism and PPAR-Related Targets
Authors: Wang Xinning , Hu Tao , Jiang Yuliang , He Yan , Li Peibo , Peng Wei , Wang Yonggang , Su Weiwei
Published: June 2024

Jingzhi Guanxin Oral Liquids (JZGX), a traditional Chinese medicine formulation prepared from the decoction of five herbs, has been utilized to relieve chest pain with coronary artery disease (CAD). However, the chemical composition and therapeutic mechanisms of JZGX remain obscured. In this research, the potential targets and pathways of JZGX against CAD were anticipated through network pharmacology based on analyzing its chemical constituents using UPLC-Q-TOF-MS/MS. One hundred seven ingredients in JZGX were identified. The 39 active chemicals and 37 key targets were screened, and CAD-related signaling pathways were clustered, mainly associated with lipid metabolism. Subsequently, the atherosclerotic CAD animal model employing 24 weeks of high-fat diet (HFD) ApoE−/− mice was constructed to investigate the JZGX efficacy and underlying mechanisms validating network forecasts. The histological staining examination and cardiovascular biomarker tests confirmed that JZGX reduced plaque formation in the aorta and decreased blood lipids in vivo. It featured anti-inflammatory, anti-thrombotic, and myocardial protective effects. JZGX prevented excessive lipid deposits and inflammation within the liver and exhibited hepatoprotective properties. Serum untargeted metabolomics analysis indicated that JZGX ameliorated metabolic abnormalities in atherosclerotic CAD mice and prompted lipid metabolism, especially linoleic acid. The PPARs and attached critical targets (SREBP1, FASN, PTGS2, and CYP3A), filtered from the networks and connected with lipid metabolism, were dramatically modulated through JZGX administration, as revealed by western blotting. The molecular docking outcomes showed that all 39 active ingredients in JZGX had good binding activity with PPARα and PPARγ. These findings illustrate that JZGX alleviates atherosclerotic CAD progression by remodeling the lipid metabolism and regulating PPAR-related proteins.
Citrulline Malate Fails to Improve Repeated 300 m Swimming Times in Highly Trained Swimmers
Authors: Newbury Josh W. , Cole Matthew , Bailey Stephen J. , Kelly Adam L. , Gough Lewis A.
Published: June 2024

Citrulline malate (CM) has been touted as a nutritional ergogenic aid for sports performance, with purported mechanisms of increased muscle blood flow, ammonia clearance, and adenosine triphosphate resynthesis. Combined, these physiological benefits may be best applied to whole-body endurance exercises, such as swimming, though this postulate has not yet been explored. In a double-blind, randomised, and crossover design, 11 national-level swimmers (age: 17 ± 3 years, height: 1.71 ± 0.05 m, body mass: 60.6 ± 8.3 kg) from a high-performance swimming club ingested either 15 g CM or a placebo (PLA) 60 min before six × 300 m freestyle bouts (at 4.5 min intervals). Blood lactate, blood pressure, and ratings of perceived exertion were measured at baseline, 60 min post-ingestion, and immediately post-exercise. Neither mean 300 m swimming time (CM: 212.0 ± 9.6 vs. PLA: 212.8 ± 7.7 s, p = 0.683, g = 0.09) nor any individual swimming bouts (p = 0.679, Pŋ2 = 0.02) were improved with CM ingestion. Moreover, no differences in any physiological or subjective measures were identified between conditions (all p > 0.05). Whether the proposed CM mechanisms were active was unclear as more direct physiological measures (i.e., plasma NO, ammonia) may have been required. Resultantly, these observations do not support an ergogenic effect of acute CM ingestion in highly trained swimmers.
Mechanical Properties of Carbon Fiber-Reinforced Plastic with Two Types of Bolted Connections at Low Temperatures
Authors: Li Hua , Guo Feng , Han Chenglin , Su Wei , Wen Shuqi
Published: June 2024

Carbon fiber-reinforced plastic (CFRP) is frequently utilized as a bolted joint material in aircraft applications because of its high specific strength and specific modulus. Therefore, the performance of CFRP under −50° is significant. Here, we discuss the specimens of two bolted connections (single-nailed and double-nailed) used for static load tensile and tensile fatigue tests. We obtained the failure curves and fatigue life relationships of the specimens with two different connection methods at different tightening torques (2 N/m, 4 N/m, and 6 N/m) and low room temperatures. Our analysis reveals the effect of the bolt tightening torque and temperature on the structural mechanical properties of a CFRP bolted joint. It provides a data reference for researchers to design a composite bolted joint structure in an airplane flight environment.
Simulating the Net Primary Production of Even-Aged Forests by the Use of Remote Sensing and Ecosystem Modelling Techniques
Authors: Chiesi Marta , Fibbi Luca , Vanucci Silvana , Bottai Lorenzo , Chirici Gherardo , Maselli Fabio
Published: June 2024

A recently proposed modelling strategy predicts the net primary production (NPP) of forest ecosystems by combining the outputs of a NDVI-driven model, Modified C-Fix, and a bio-geochemical model, BIOME-BGC. This combination strategy takes into account the effects of forest disturbances but still assumes the presence of a mixture of differently aged trees. The application of this strategy to even-aged forests, therefore, requires a methodological advancement aimed at properly modifying the modelling of main ecosystem processes. In particular, the adaptation of the method to even-aged forests is based on the use of high-spatial-resolution airborne laser scanning (ALS) datasets, which yields green and woody biomass estimates that regulate the simulation of photosynthetic and respiratory processes, respectively. This approach was experimented in a Mediterranean study area, San Rossore Regional Park (Central Italy), which is covered by even-aged pine stands in different development phases. The modelling strategy is driven by MODIS NDVI images and meteorological data across five years (2011–2015), which are combined with estimates of forest canopy cover and height obtained from ALS data taken in 2015. This allows the production of stand NPP estimates, which, when converted into respective current annual increment (CAI) values, reasonably reproduce the age dependency of the available ground observations. The CAI estimates also show a highly significant correlation with these observations (r = 0.773) and moderate error levels (RMSE = 2.03 m3 ha−1 year−1, MBE = −0.45 m3 ha−1 year−1). These results confirm the potential of the modified simulation method to yield accurate high-spatial-resolution NPP estimates, which can offer valuable insights into C cycling and storage, in even-aged forests.
Agricultural Drought Monitoring Using an Enhanced Soil Water Deficit Index Derived from Remote Sensing and Model Data Merging
Authors: Wu Xiaotao , Xu Huating , He Hai , Wu Zhiyong , Lu Guihua , Liao Tingting
Published: June 2024

Droughts present substantial challenges to agriculture, food security, and water resources. Employing a drought index based on soil moisture dynamics is a common and effective approach for agricultural drought monitoring. However, the precision of a drought index heavily relies on accurate soil moisture and soil hydraulic parameters. This study leverages remote sensing soil moisture data from the Climate Change Initiative (CCI) series products and model-generated soil moisture data from the Variable Infiltration Capacity (VIC) model. The extended triple collocation (ETC) method was applied to merge these datasets from 1992 to 2018, resulting in enhanced accuracy by 28% and 15% compared to the CCI and VIC soil moisture, respectively. Furthermore, this research establishes field capacity and a wilting point map using multiple soil datasets and pedotransfer functions, facilitating the development of an enhanced Soil Water Deficit Index (SWDI) based on merged soil moisture, field capacity, and wilting points. The findings reveal that the proposed enhanced SWDI achieves a higher accuracy in detecting agricultural drought events (probability of detection = 0.98) and quantifying their severity (matching index = 0.33) compared to an SWDI based on other soil moisture products. Moreover, the enhanced SWDI exhibits superior performance in representing drought-affected crop areas (correlation coefficient = 0.88), outperforming traditional drought indexes such as the Standardized Precipitation Index (correlation coefficient = 0.51), the Soil Moisture Anomaly Percent Index (correlation coefficient = 0.81), and the Soil Moisture Index (correlation coefficient = 0.83). The enhanced SWDI effectively captures the spatiotemporal dynamics of a drought, supporting more accurate agricultural drought monitoring and management strategies.
Employing Machine Learning for Seismic Intensity Estimation Using a Single Station for Earthquake Early Warning
Authors: Abdalzaher Mohamed S. , Soliman M. Sami , Krichen Moez , Alamro Meznah A. , Fouda Mostafa M.
Published: June 2024

An earthquake early-warning system (EEWS) is an indispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively managing earthquake disasters and implementing successful risk-reduction strategies. In this regard, the utilization of an Internet of Things (IoT) network enables the real-time transmission of on-site intensity measurements. This paper introduces a novel approach based on machine-learning (ML) techniques to accurately and promptly determine earthquake intensity by analyzing the seismic activity 2 s after the onset of the p-wave. The proposed model, referred to as 2S1C1S, leverages data from a single station and a single component to evaluate earthquake intensity. The dataset employed in this study, named “INSTANCE,” comprises data from the Italian National Seismic Network (INSN) via hundreds of stations. The model has been trained on a substantial dataset of 50,000 instances, which corresponds to 150,000 seismic windows of 2 s each, encompassing 3C. By effectively capturing key features from the waveform traces, the proposed model provides a reliable estimation of earthquake intensity, achieving an impressive accuracy rate of 99.05% in forecasting based on any single component from the 3C. The 2S1C1S model can be seamlessly integrated into a centralized IoT system, enabling the swift transmission of alerts to the relevant authorities for prompt response and action. Additionally, a comprehensive comparison is conducted between the results obtained from the 2S1C1S method and those derived from the conventional manual solution method, which is considered the benchmark. The experimental results demonstrate that the proposed 2S1C1S model, employing extreme gradient boosting (XGB), surpasses several ML benchmarks in accurately determining earthquake intensity, thus highlighting the effectiveness of this methodology for earthquake early-warning systems (EEWSs).
Integrating Satellite Images and Machine Learning for Flood Prediction and Susceptibility Mapping for the Case of Amibara, Awash Basin, Ethiopia
Authors: Wedajo Gizachew Kabite , Lemma Tsegaye Demisis , Fufa Tesfaye , Gamba Paolo
Published: June 2024

Flood is one of the most destructive natural hazards affecting the environment and the socioeconomic system of the world. The effects are higher in the developing countries due to their higher vulnerability to disaster and limited coping capacity. The Awash basin is one of the flood-prone basins in Ethiopia where the frequency and severity of flooding has been increasing. Amibara district is one of the flood-affected areas in the Awash basin. To minimize the effects of flooding, reliable and up-to-date information on flooding is highly required. However, flood monitoring and forecasting systems are lacking in most basins of Ethiopia including the Awash basin. Therefore, this study aimed to (i) identify important flood causative factors, (ii) evaluate the performance of random forest (RF), linear regression, support vector machine (SVM), and long short-term memory (LSTM) machine learning models for flood prediction and susceptibility mapping in the Amibara area. For developing flood prediction and susceptibility modeling, nine causative factors were considered, namely elevation, slope, aspect, curvature, topographic wetness index, soil texture, rainfall, land use/land cover, and curve number. The Pearson correlation coefficient and information gain ratio (InGR) techniques were used to evaluate the relative importance of the factors. The machine learning models were trained and tested using 400 historic flood points collected from the 10 September 2020 Sentinel 2 image, during which a flood event occurred in the area. Multiple metrics, namely precession, recall, F1-score, accuracy, and receiver operating characteristics (area under curve), were used to evaluate the performance of the models. The results showed that all the factors considered in this study were important; elevation, rainfall, topographic wetness index, aspect, and slope were more important while land use/land cover, curve number, curvature, and soil texture were less important. Furthermore, the results showed that random forest outperformed in predicting and mapping flooding for the study area whereas the linear regression model showed the next best performance to RF. However, SVM performed poorly in flood prediction and susceptibility mapping. The integration of satellite and field datasets coupled with state-of-the-art-machine learning models are novel approaches and thus improved the accuracy of flood prediction and susceptibility mapping. Such methodology improves the state-of-the-art knowledge in this field and fills the gaps of traditional flood mapping techniques. Thus, the results of the study can provide crucial information for informed decision-making in the processes of designing flood control strategies and risk management.
Understanding Diffusion in a Single-Metal Organic Framework Crystal Used for Sensing Applications
Authors: Cheemalapati Surya , Konnaiyan Karthik , Chen Yao , Ma Shengqian , Pyayt Anna
Published: June 2024

Metal–organic frameworks (MOFs) stand out as remarkable materials renowned for their exceptionally high surface area and large number of pores, making them invaluable for diverse sensing applications including gas, biomedical, chemical, and optical sensing. Traditional methods of molecule infusion and release often involve a large number of crystals with varying shapes and sizes, leading to averaged outcomes across a heterogeneous crystal population. In this study, we present continuous monitoring of the infusion and release dynamics of model drug molecules, specifically vitamin B12, within individual Tb-mesoMOF crystals. Our findings underscore the critical influence of crystal size and shape on the infusion and diffusion processes and corresponding color change, underscoring the necessity to account for these factors in the design of large-scale systems. Leveraging optical microscopy, we employed a histogram-based algorithm for image processing, enabling automated tracking of diffusion phenomena. This investigation offers crucial insights into the dynamics of these processes, laying the groundwork for optimizing parameters in future sensing systems.
Real-Time Synthetic Aperture Radar Imaging with Random Sampling Employing Scattered Power Mapping
Authors: Kazemivala Romina , Nikolova Natalia K.
Published: June 2024

A novel image-reconstruction method is proposed for the processing of data acquired at random spatial positions. The images are reconstructed and updated in real time concurrently with the measurements to produce an evolving image, the quality of which is continuously improving and converging as the number of data points increases with the stream of additional measurements. It is shown that the images converge to those obtained with data acquired on a uniformly sampled surface, where the sampling density satisfies the Nyquist limit. The image reconstruction employs a new formulation of the method of scattered power mapping (SPM), which first maps the data into a three-dimensional (3D) preliminary image of the target on a uniform spatial grid, followed by fast Fourier space image deconvolution that provides the high-quality 3D image.
Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A Focused Review
Authors: Kang Mijeong , Kim Donghyeon , Kim Jihee , Kim Nakyung , Lee Seunghun
Published: June 2024

In this review, recent advances regarding the integration of machine learning into electrochemical analysis are overviewed, focusing on the strategies to increase the analytical context of electrochemical data for enhanced machine learning applications. While information-rich electrochemical data offer great potential for machine learning applications, limitations arise when sensors struggle to identify or quantitatively detect target substances in a complex matrix of non-target substances. Advanced machine learning techniques are crucial, but equally important is the development of methods to ensure that electrochemical systems can generate data with reasonable variations across different targets or the different concentrations of a single target. We discuss five strategies developed for building such electrochemical systems, employed in the steps of preparing sensing electrodes, recording signals, and analyzing data. In addition, we explore approaches for acquiring and augmenting the datasets used to train and validate machine learning models. Through these insights, we aim to inspire researchers to fully leverage the potential of machine learning in electroanalytical science.
Real-Time Object Detection from UAV Inspection Videos by Combining YOLOv5s and DeepStream
Authors: Xie Shidun , Deng Guanghong , Lin Baihao , Jing Wenlong , Li Yong , Zhao Xiaodan
Published: June 2024

The high-altitude real-time inspection of unmanned aerial vehicles (UAVs) has always been a very challenging task. Because high-altitude inspections are susceptible to interference from different weather conditions, interference from communication signals and a larger field of view result in a smaller object area to be identified. We adopted a method that combines a UAV system scheduling platform with artificial intelligence object detection to implement the UAV automatic inspection technology. We trained the YOLOv5s model on five different categories of vehicle data sets, in which mAP50 and mAP50-95 reached 93.2% and 71.7%, respectively. The YOLOv5s model size is only 13.76 MB, and the detection speed of a single inspection photo reaches 11.26 ms. It is a relatively lightweight model and is suitable for deployment on edge devices for real-time detection. In the original DeepStream framework, we set up the http communication protocol to start quickly to enable different users to call and use it at the same time. In addition, asynchronous sending of alarm frame interception function was added and the auxiliary services were set up to quickly resume video streaming after interruption. We deployed the trained YOLOv5s model on the improved DeepStream framework to implement automatic UAV inspection.
The Kinematic Models of the SINS and Its Errors on the SE(3) Group in the Earth-Centered Inertial Coordinate System
Authors: Fang Ke , Cai Tijing , Wang Bo
Published: June 2024

In this paper, the kinematic models of the Strapdown Inertial Navigation System (SINS) and its errors on the SE(3) group in the Earth-Centered Inertial frame (ECI) are established. On the one hand, with the ECI frame being regarded as the reference, based on the joint representation of attitude and velocity on the SE(3) group, the dynamic of the local geographic coordinate system (n-frame) and the body coordinate system (b-frame) evolve on the differentiable manifold, respectively, and the high-order expansion of the Baker–Campbell–Haussdorff equation compensates for the non-commutative motion errors stimulated by strong maneuverability. On the other hand, the kinematics of the left- and right-invariant errors of the n-frame and the b-frame on the SE(3) group are separately derived, where the errors of the b-frame completely depend on inertial sensor errors, while the errors of the n-frame rely on position errors and velocity errors. In this way, the errors brought by the inconsistency of the reference coordinate system are tackled, and a novel attitude error definition is introduced to separate and decouple the factors affecting the dynamic of the n-frame errors and the b-frame errors for better attitude estimation. Through a turntable experiment and a car-mounted field experiment, the effectiveness of the proposed kinematic models in estimating attitude has been verified, with a remarkable improvement in yaw angle accuracy in the case of large initial misalignment angles, and the models developed have better robustness compared to the traditional SE(3) group-based model.