bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficiency Analysis of Die Attach Machines Using Overall Equipment Effectiveness Metrics and Failure Mode and Effects Analysis with an Ishikawa Diagram
Authors: Guste Rex Revian A. , Mariñas Klint Allen A. , Ong Ardvin Kester S.
Published: July 2024

The semiconductor manufacturing sector has contributed to the advancement of technical development in the sphere of industrial applications, but one crucial factor that cannot be overlooked is the evaluation of a machine’s state. Despite the presence of advanced equipment, data on their performances are not properly reviewed, resulting in a variety of concerns such as high rejection rates, lower production output, manufacturing overhead cost issues, and customer complaints. This study’s goal is to evaluate the performance of die attach machines made by a prominent subcontractor semiconductor manufacturing business in the Philippines; our findings will provide other organizations with important insights into the appropriate diagnosis of productivity difficulties via productivity metrics analyses. The study focuses on a specific type of die attach machine, with machine 10 showing to be the most troublesome, with an overall equipment effectiveness (OEE) rating of 43.57%. The Failure Mode and Effects Analysis (FMEA) identified that the primary reasons for the issue were idling, small stoppages, and breakdown loss resulting from loosened screws in the work holder. The risk priority number (RPN) was calculated to be 392, with a severity level of 7, an occurrence level of 7, and a detection level of 8. The findings provide new insight into the methods that should be included in the production process to boost efficiency and better suit the expectations of customers in a highly competitive market.
Detection of Demagnetization Faults in Electric Motors by Analyzing Inverter Based Current Data Using Machine Learning Techniques
Authors: Walch Daniel , Blechinger Christoph , Schellenberger Martin , Hofmann Maximilian , Eckardt Bernd , Lorentz Vincent R.H.
Published: July 2024

Demagnetization of the rotor magnets is a significant failure mode that can occur in permanent magnet synchronous machines (PMSMs). Early detection of demagnetization faults can help change system parameters to reduce power output or ensure safety. In this paper, the effects of demagnetization faults were analyzed both in simulation and experiments using the example of drone motors. An approach was investigated to detect even minor demagnetization faults that does not require any additional sensing effort. Machine learning (ML) techniques are used to analyze the phase current data directly received from the inverter to enable anomaly detection. For this purpose, the phase current is transformed by the Fast Fourier Transform (FFT), the spectral data is then reduced in dimensionality, followed by an anomaly detection algorithm using a one-class support vector machine (OC-SVM). To ensure simplified initialization of the ML model without the need for training sets of damaged drives, only data from magnetically undamaged motors was used to train the models for anomaly detection. Different selections of considered harmonics and different metrics were investigated using the experimental data, achieving a precision of up to 99%, a specificity of up to 98%, and an accuracy of up to 90%.
Novel Intelligent Traffic Light Controller Design
Authors: Zahwa Firas , Cheng Chi-Tsun , Simic Milan
Published: July 2024

Efficient traffic flow management at intersections is vital for optimizing urban transportation networks. This paper presents a comprehensive approach to refining traffic flow by analyzing the capacity of roads and integrating fuzzy logic-based traffic light control systems. We examined the capacity of roads connecting intersections, considering factors such as road vehicle capacity, vehicle speed, and traffic flow volume, through detailed mathematical modeling and analysis. Control is determined by the maximum capacity of each road segment, providing valuable insights into traffic flow dynamics. Building upon this capacity and flow analysis, the research introduces a novel intelligent traffic light controller (ITLC) system based on fuzzy logic principles. By incorporating real-time traffic data and leveraging fuzzy logic algorithms, our ITLC system dynamically adjusts traffic light timings to optimize vehicle flow at two intersections. The paper discusses the design and implementation of the ITLC system, highlighting its adaptive capabilities in response to changing traffic conditions. Simulation results demonstrate the effectiveness of the ITLC system in improving traffic flow and reducing congestion at intersections. Furthermore, this research provides an analysis of the mathematical models used to calculate road capacity, offering insights into the underlying principles of traffic flow optimization. Through the simulation, we have validated the accuracy and reliability of our controller.
Driver Analysis and Integrated Prediction of Carbon Emissions in China Using Machine Learning Models and Empirical Mode Decomposition
Authors: Suo Ruixia , Wang Qi , Han Qiutong
Published: July 2024

Accurately predicting the trajectory of carbon emissions is vital for achieving a sustainable shift toward a green and low-carbon future. Hence, this paper created a novel model to examine the driver analysis and integrated prediction for Chinese carbon emission, a large carbon-emitting country. The logarithmic mean divisia index (LMDI) approach initially served to decompose the drivers of carbon emissions, analyzing the annual and staged contributions of these factors. Given the non-stationarity and non-linear characteristics in the data sequence of carbon emissions, a decomposition–integration prediction model was proposed. The model employed the empirical mode decomposition (EMD) model to decompose each set of data into a series of components. The various carbon emission components were anticipated using the long short-term memory (LSTM) model based on the deconstructed impacting factors. The aggregate of these predicted components constituted the overall forecast for carbon emissions. The result indicates that the EMD-LSTM model greatly decreased prediction errors over the other comparable models. This paper makes up for the gap in existing research by providing further analysis based on the LMDI method. Additionally, it innovatively incorporates the EMD method into the carbon emission study, and the proposed EMD-LSTM prediction model effectively addresses the volatility characteristics of carbon emissions and demonstrates excellent predictive performance in carbon emission prediction.
Intuitionistic Type-2 Fuzzy Normed Linear Space and Some of Its Basic Properties
Authors: Biswas Amit , Chiney Moumita , Samanta Syamal Kumar
Published: July 2024

An intuitionistic fuzzy set is a more generalised tool than a fuzzy set for handling unpredictability as, in an intuitionistic fuzzy set, there is scope for considering a grade of non-membership, independent of the grade of membership, only satisfying the condition that their sum is less or equal to 1. The motivation of this paper is to introduce the notion of intuitionistic type-2 fuzzy normed linear space (IT2FNLS). Here, to each vector x, we assign two fuzzy real number valued grades, one for its norm and the other for the negation of its norm. A theorem of the decomposition of the intuitionistic type-2 fuzzy norm into a family of pairs of Felbin-type fuzzy norms is established. Also, we deal with Cauchyness and convergence of sequences in the IT2FNLS. Later on, in the finite-dimensional IT2FNLS, the completeness property and compactness property are explored. Finally, we define two types of intuitionistic type-2 fuzzy continuity and examine the relations between them.
Differential Transform Method and Neural Network for Solving Variational Calculus Problems
Authors: Brociek Rafał , Pleszczyński Mariusz
Published: July 2024

The history of variational calculus dates back to the late 17th century when Johann Bernoulli presented his famous problem concerning the brachistochrone curve. Since then, variational calculus has developed intensively as many problems in physics and engineering are described by equations from this branch of mathematical analysis. This paper presents two non-classical, distinct methods for solving such problems. The first method is based on the differential transform method (DTM), which seeks an analytical solution in the form of a certain functional series. The second method, on the other hand, is based on the physics-informed neural network (PINN), where artificial intelligence in the form of a neural network is used to solve the differential equation. In addition to describing both methods, this paper also presents numerical examples along with a comparison of the obtained results.Comparingthe two methods, DTM produced marginally more accurate results than PINNs. While PINNs exhibited slightly higher errors, their performance remained commendable. The key strengths of neural networks are their adaptability and ease of implementation. Both approaches discussed in the article are effective for addressing the examined problems.
Marshall–Olkin Bivariate Weibull Model with Modified Singularity (MOBW-μ): A Study of Its Properties and Correlation Structure
Authors: Brango Hugo , Guerrero Angie , Llinás Humberto
Published: July 2024

We propose the “Marshall–Olkin Bivariate Weibull Model with Modified Singularity MOBW-μ”, which focuses on bivariate distributions essential for reliability and survival analyses. Distributions such as the Marshall–Olkin bivariate exponential (MOBE) and the Marshall–Olkin bivariate Weibull (MOBW) are discussed. The MOBW-μ model is introduced, which incorporates a lag parameter μ in the singular part, and probabilistic properties such as the joint survival function, marginal density functions, and the bivariate hazard rate function are explored. In addition, aspects such as the correlation structure and survival copulation are addressed and we show that the correlation of the MOBW-μ is always lower than that of its copula, regardless of the parameters. The latter result implies that the MOBW-μ does not have the Lancaster’s phenomenon that explains that any nonlinear transformation of variables decreases the correlation in absolute value. The article concludes by presenting a robust theoretical framework applicable to various disciplines.
Endoscopic Advances in the Diagnosis and Management of Gastroesophageal Reflux Disease
Authors: Loganathan Priyadarshini , Gajendran Mahesh , Perisetti Abhilash , Goyal Hemant , Mann Rupinder , Wright Randy , Saligram Shreyas , Thosani Nirav , Umapathy Chandraprakash
Published: July 2024

Gastroesophageal reflux disease (GERD) is one of the most common diseases that occurs secondary to failure of the antireflux barrier system, resulting in the frequent and abnormal reflux of gastric contents to the esophagus. GERD is diagnosed in routine clinical practice based on the classic symptoms of heartburn and regurgitation. However, a subset of patients with atypical symptoms can pose challenges in diagnosing GERD. An esophagogastroduodenoscopy (EGD) is the most common initial diagnostic test used in the assessment for GERD, although half of these patients will not have any positive endoscopic findings suggestive of GERD. The advanced endoscopic techniques have improved the diagnostic yield of GERD diagnosis and its complications, such as Barrett’s esophagus and early esophageal adenocarcinoma. These newer endoscopic tools can better detect subtle irregularities in the mucosa and vascular structures. The management options for GERD include lifestyle modifications, pharmacological therapy, and endoscopic and surgical interventions. The latest addition to the armamentarium is the minimally invasive endoscopic interventions in carefully selected patients, including the electrical stimulation of the LES, Antireflux mucosectomy, Radiofrequency therapy, Transoral Incisionless Fundoplication, Endoscopic Full-Thickness plication (GERDx™), and suturing devices. With the emergence of these advanced endoscopic techniques, it is crucial to understand their selection criteria, advantages, and disadvantages.
Performance and Safety of Amino-Acid- and Hydroxyapatite Enriched-Hyaluronic Acid Intradermal Gel in Facial Skin Defects
Authors: Todde Salvatore , Svolacchia Fabiano , Svolacchia Lorenzo , Giuzio Federica , Panda Sameer Kumar , Ferraro Giuseppe A.
Published: July 2024

Background and Objectives: The facial skin defects associated with aging are common concerns in the aging population. Hyaluronic-acid-based intradermal gels have established themselves as safe and effective treatments for addressing these concerns. Recently developed enriched products aim to enhance the efficacy of these gels, yet their effectiveness lacks thorough validation in the existing literature. Materials and Methods: In this retrospective analysis, we investigated the outcomes of intradermal gel treatments in 103 patients with soft tissue defects. This study included three groups: 35 patients received amino-acid-enriched hyaluronic acid gel, another 35 were treated with hydroxyapatite-enriched hyaluronic acid gel, and the remaining 33 underwent hyaluronic acid treatment only. The efficacy of the treatments was assessed using the Global Aesthetic Improvement Scale (GAIS) score, while patient satisfaction was gauged through a detailed questionnaire. Any adverse event was monitored. Results: The treatments demonstrated remarkable efficacy, as evidenced by mean GAIS scores of 1.714 points for those treated with amino acid-enriched hyaluronic acid gel, 1.886 points for individuals receiving hydroxyapatite-enriched hyaluronic acid gel, and 1.697 for those treated with hyaluronic acid alone, all showing statistical significance (p < 0.0001). Patient satisfaction was very high. Significantly, there were no recorded instances of major adverse events. Conclusions: Hyaluronic gels, particularly those enriched with amino acids and hydroxyapatite, are effective and safe interventions for addressing facial skin aging defects. They serve as valuable tools in mitigating age-related blemishes and contribute to the overall improvement of skin aesthetics.
Challenges of Spatially Resolved Metabolism in Cancer Research
Authors: Lane Andrew N. , Higashi Richard M. , Fan Teresa W-M.
Published: July 2024

Stable isotope-resolved metabolomics comprises a critical set of technologies that can be applied to a wide variety of systems, from isolated cells to whole organisms, to define metabolic pathway usage and responses to perturbations such as drugs or mutations, as well as providing the basis for flux analysis. As the diversity of stable isotope-enriched compounds is very high, and with newer approaches to multiplexing, the coverage of metabolism is now very extensive. However, as the complexity of the model increases, including more kinds of interacting cell types and interorgan communication, the analytical complexity also increases. Further, as studies move further into spatially resolved biology, new technical problems have to be overcome owing to the small number of analytes present in the confines of a single cell or cell compartment. Here, we review the overall goals and solutions made possible by stable isotope tracing and their applications to models of increasing complexity. Finally, we discuss progress and outstanding difficulties in high-resolution spatially resolved tracer-based metabolic studies.
Non-Alcoholic Fatty Liver Disease Induced by Feeding Medium-Chain Fatty Acids Upregulates Cholesterol and Lipid Homeostatic Genes in Skeletal Muscle of Neonatal Pigs
Authors: Gerrard Samuel D. , Biase Fernando H. , Yonke Joseph A. , Yadav Ravi , Shafron Anthony J. , Sunny Nishanth E. , Gerrard David E. , El-Kadi Samer W.
Published: July 2024

Non-alcoholic fatty liver disease (NAFLD) is a range of disorders characterized by lipid accumulation in hepatocytes. Although this spectrum of disorders is associated with adult obesity, recent evidence suggests that this condition could also occur independently of obesity, even in children. Previously, we reported that pigs fed a formula containing medium-chain fatty acids (MCFAs) developed hepatic steatosis and weighed less than those fed an isocaloric formula containing long-chain fatty acids (LCFAs). Our objective was to determine the association between NAFLD and the skeletal muscle transcriptome in response to energy and lipid intake. Neonatal pigs were fed one of three formulas: a control formula (CONT, n = 6) or one of two isocaloric high-energy formulas containing either long (LCFA, n = 6) or medium (MCFA, n = 6) chain fatty acids. Pigs were fed for 22 d, and tissues were collected. Body weight at 20 and 22 d was greater for LCFA-fed pigs than their CONT or MCFA counterparts (p < 0.005). Longissimus dorsi weight was greater for LCFA compared with MCFA, while CONT was intermediate (p < 0.05). Lean gain and protein deposition were greater for LCFA than for CONT and MCFA groups (p < 0.01). Transcriptomic analysis revealed 36 differentially expressed genes (DEGs) between MCFA and LCFA, 53 DEGs between MCFA and CONT, and 52 DEGs between LCFA and CONT (FDR < 0.2). Feeding formula high in MCFAs resulted in lower body and muscle weights. Transcriptomics data suggest that the reduction in growth was associated with a disruption in cholesterol metabolism in skeletal muscles.
Fermented Purslane (Portulaca oleracea L.) Supplementation Enhances Growth and Immune Function Parallel to the Regulation of Gut Microbial Butyrate Production in Weaned Piglets
Authors: Xu Lei , Gao Ge , Zhou Zian , Wei Zixi , Sun Wenjuan , Li Yanpin , Jiang Xianren , Gu Jingang , Li Xilong , Pi Yu
Published: July 2024

Weaning is a challenging period for piglets, characterized by stress-related growth checks, compromised immunity, and gut dysbiosis. Purslane (Portulaca oleracea L.), known for its rich content of antioxidants, has potential as a functional feed ingredient. This study investigates the effects of feeding fermented purslane (FP) on the growth performance, immune function, intestinal microbiota, and metabolic profiles of weaned piglets. Forty-eight weaned piglets were randomly divided into two groups, with eight pens in each group and three pigs in each pen: a control diet (CON group) and a diet supplemented with 0.20% FP (FP group). The experiment lasted 28 days. The results show that FP supplementation did not affect the average daily feed intake (ADFI) but significantly increased the average daily gain (ADG) during the initial 14 days post-weaning. FP supplementation decreased diarrhea occurrence, with a pronounced reduction from days 10 to 13 (p < 0.05). Immunologically, the FP group had a trend towards reduced serum IgA levels on day 14 (p < 0.10). Importantly, the serum concentrations of the pro-inflammatory cytokine IL-6 were significantly reduced on both days 14 and 28 post-weaning. The antioxidative analysis showed increased serum superoxide dismutase (SOD) and decreased catalase (CAT) activities on day 14 (p < 0.05). In addition, FP supplementation significantly decreased serum diamine oxidase (DAO) activity and D-lactate levels by day 28, indicating a potential improvement in gut integrity. Fecal microbiota assessment demonstrated a distinctive clustering of microbial communities between the FP and CON groups, with an increase in the abundance of Clostridium_sensu_stricto_1, Tyzzerella, and Prevotellaceae_NK3B31_group and a decrease in Lactobacillus, Bacillus, and Subdoligranulum in the FP group (p < 0.05). Functional predictions suggested that the relative abundance of microbial butyrate synthesis enzymes (EC 2.7.2.7 and EC 2.3.1.19) was significantly enhanced by FP treatment. This modulation was further corroborated by elevated fecal butyrate levels (p < 0.05). In summary, dietary supplementation with FP promotes early-growth performance and has beneficial effects on immune function and intestinal health in weaned piglets. The enhancements may be attributed to distinct microbiota compositional changes and targeted modulation of microbial butyrate metabolism, which are crucial for piglet post-weaning adaptation and overall health.
Modulation of Photocatalytic CO2 Reduction by n–p Codoping Engineering of Single-Atom Catalysts
Authors: Yin Guowei , Zhang Chunxiao , Liu Yundan , Sun Yuping , Qi Xiang
Published: July 2024

Transition metal (TM) single-atom catalysts (SACs) have been widely applied in photocatalytic CO2 reduction. In this work, n–p codoping engineering is introduced to account for the modulation of photocatalytic CO2 reduction on a two-dimensional (2D) bismuth-oxyhalide-based cathode by using first-principles calculation. n–p codoping is established via the Coulomb interactions between the negatively charged TM SACs and the positively charged Cl vacancy (VCl) in the dopant–defect pairs. Based on the formation energy of charged defects, neutral dopant–defect pairs for the Fe, Co, and Ni SACs (PTM0) and the −1e charge state of the Cu SAC-based pair (PCu−1) are stable. The electrostatic attraction of the n–p codoping strengthens the stability and solubility of TM SACs by neutralizing the oppositely charged VCl defect and TM dopant. The n–p codoping stabilizes the electron accumulation around the TM SACs. Accumulated electrons modify the d-orbital alignment and shift the d-band center toward the Fermi level, enhancing the reducing capacity of TM SACs based on the d-band theory. Besides the electrostatic attraction of the n–p codoping, the PCu−1 also accumulates additional electrons surrounding Cu SACs and forms a half-occupied dx2−y2 state, which further upshifts the d-band center and improves photocatalytic CO2 reduction. The metastability of Cl multivacancies limits the concentration of the n–p pairs with Cl multivacancies (PTM@nCl (n > 1)). Positively charged centers around the PTM@nCl (n > 1) hinders the CO2 reduction by shielding the charge transfer to the CO2 molecule.
Antioxidant Lipid Supplement on Cardiovascular Risk Factors: A Systematic Review and Meta-Analysis
Authors: Wan Sitong , Wu Wenbin , Zhang Yan , He Jian , Wang Xiaoping , An Peng , Luo Junjie , Zhu Yinhua , Luo Yongting
Published: July 2024

The efficacy of functional lipids with antioxidant properties in reducing cardiovascular risk has not been consistent. Randomized controlled trials (RCTs) reporting estimates for the effects of antioxidant functional lipid supplementations on cardiometabolic risk factors were searched up to 1 May 2024. Overall, antioxidant lipid supplementations, compared with placebo, had favorable effects on systolic blood pressure (lycopene: −1.95 [−3.54, −0.36] mmHg), low-density lipoprotein cholesterol (n6 fatty acid: −0.39 [−0.71, −0.06] mmol/L; astaxanthin: −0.11 [−0.21, −0.01] mmol/L), high-density lipoprotein cholesterol (n3 fatty acid: 0.20 [0.13, 0.27] mmol/L; n6 fatty acid: 0.08 [0.01, 0.14] mmol/L; astaxanthin: 0.13 [0.05, 0.21] mmol/L), total cholesterol (n6 fatty acid: −0.24 [−0.37, −0.11] mmol/L; astaxanthin: −0.22 [−0.32, −0.12] mmol/L; beta-carotene: −0.13 [−0.23, −0.04] mmol/L), triglyceride (n3 fatty acid: −0.37 [−0.47, −0.28] mmol/L; astaxanthin: −0.46 [−0.83, −0.10] mmol/L), and fasting blood insulin (astaxanthin: −2.66 [−3.98, −1.34] pmol/L). The benefits of antioxidant lipid supplementations appeared to be most evident in blood pressure and blood lipids in participants with different cardiometabolic health statuses. Notably, n9 fatty acid increased triglyceride and hemoglobin A1C in the total population, which increases CVD risk. Antioxidant lipid supplementations ameliorate cardiometabolic risk factors, while their effect may depend on type and cardiometabolic health status. Long-term RCTs are needed to corroborate risk–benefit ratios across different antioxidant functional lipid supplementation settings.
Beneficial Effects of Manilkara zapota-Derived Bioactive Compounds in the Epigenetic Program of Neurodevelopment
Authors: Russo Cristina , Valle Maria Stella , D’Angeli Floriana , Surdo Sofia , Giunta Salvatore , Barbera Antonio Carlo , Malaguarnera Lucia
Published: July 2024

Gestational diet has a long-dated effect not only on the disease risk in offspring but also on the occurrence of future neurological diseases. During ontogeny, changes in the epigenetic state that shape morphological and functional differentiation of several brain areas can affect embryonic fetal development. Many epigenetic mechanisms such as DNA methylation and hydroxymethylation, histone modifications, chromatin remodeling, and non-coding RNAs control brain gene expression, both in the course of neurodevelopment and in adult brain cognitive functions. Epigenetic alterations have been linked to neuro-evolutionary disorders with intellectual disability, plasticity, and memory and synaptic learning disorders. Epigenetic processes act specifically, affecting different regions based on the accessibility of chromatin and cell-specific states, facilitating the establishment of lost balance. Recent insights have underscored the interplay between epigenetic enzymes active during embryonic development and the presence of bioactive compounds, such as vitamins and polyphenols. The fruit of Manilkara zapota contains a rich array of these bioactive compounds, which are renowned for their beneficial properties for health. In this review, we delve into the action of each bioactive micronutrient found in Manilkara zapota, elucidating their roles in those epigenetic mechanisms crucial for neuronal development and programming. Through a comprehensive understanding of these interactions, we aim to shed light on potential avenues for harnessing dietary interventions to promote optimal neurodevelopment and mitigate the risk of neurological disorders.
Microscopic Colitis and Celiac Disease: Sharing More than a Diagnostic Overlap
Authors: González-Castro Ana María , Fernández-Bañares Fernando , Zabana Yamile , Farago-Pérez Georgina , Ortega-Barrionuevo Jonathan , Expósito Elba , Guagnozzi Danila
Published: July 2024

Microscopic colitis (MC) is an emergent group of chronic inflammatory diseases of the colon, and celiac disease (CD) is a chronic gluten-induced immune-mediated enteropathy affecting the small bowel. We performed a narrative review to provide an overview regarding the relationship between both disorders, analyzing the most recent studies published at the epidemiological, clinical and pathophysiological levels. In fact, MC and CD are concomitantly prevalent in approximately 6% of the cases, mainly in the subset of refractory patients. Thus, physicians should screen refractory patients with CD against MC and vice versa. Both disorders share more than a simple epidemiological association, being multifactorial diseases involving innate and adaptive immune responses to known or unknown luminal factors based on a rather common genetic ground. Moreover, autoimmunity is a shared characteristic between the patients with MC and those with CD, with autoimmunity in the latter being quite well-established. Furthermore, CD and MC share some common clinical symptoms and risk factors and overlap with other gastrointestinal diseases, but some differences exist between both disorders. More studies are therefore needed to better understand the complex mechanisms involving the common pathogenetic ground contributing to the CD and MC epidemiological association.
Nutritional Prehabilitation in Patients Undergoing Abdominal Surgery—A Narrative Review
Authors: Wobith Maria , Hill Aileen , Fischer Martin , Weimann Arved
Published: July 2024

Malnutrition plays a crucial role as a risk factor in patients undergoing major abdominal surgery. To mitigate the risk of complications, nutritional prehabilitation has been recommended for malnourished patients and those at severe metabolic risk. Various approaches have been devised, ranging from traditional short-term conditioning lasting 7–14 days to longer periods integrated into a comprehensive multimodal prehabilitation program. However, a significant challenge is the considerable heterogeneity of nutritional interventions, leading to a lack of clear, synthesizable evidence for specific dietary recommendations. This narrative review aims to outline the concept of nutritional prehabilitation, offers practical recommendations for clinical implementation, and also highlights the barriers and facilitators involved.
Native Biocrust Cyanobacteria Strains Showing Antagonism against Three Soilborne Pathogenic Fungi
Authors: Águila-Carricondo Pilar , Román Raúl , Marín-Guirao José Ignacio , Cantón Yolanda , de Cara Miguel
Published: July 2024

The biocontrol potential of three native soil cyanobacteria from biological soil crusts (Nostoc commune, Scytonema hyalinum, and Tolypothrix distorta) was tested by means of in vitro mycelial growth inhibition assays for eighteen cyanobacteria-based products against three phytopathogenic soilborne fungi (Phytophthora capsici, Pythium aphanidermatum, and Fusarium oxysporum f. sp. radicis-cucumerinum). Three cyanobacteria-based production factors were considered: (i) cyanobacterium strain, (ii) cyanobacterial culture growth phase, and (iii) different post-harvest treatments: raw cultures, cyanobacterial filtrates, and cyanobacterial extracts. Results showed that any of the factors considered are key points for successfully inhibiting fungal growth. N. commune showed the highest growth inhibition rates for the three phytopathogens; stationary phase treatments produced higher inhibition percentages than logarithmic ones; and all the post-harvest treatments of N. commune at the stationary phase inhibited the growth of P. capsici, up to 77.7%. Thus, N. commune products were tested in planta against P. capsici, but none of the products showed efficacy in delaying the onset nor reducing the damage due to P. capsici, demonstrating the complexity of the in planta assay’s success and encouraging further research to design an appropriate scaling up methodology.
Somatic Mutation Accumulations in Micropropagated Cannabis Are Proportional to the Number of Subcultures
Authors: Adamek Kristian , Jones Andrew Maxwell Phineas , Torkamaneh Davoud
Published: July 2024

Advancements in micropropagation techniques have made it easier to produce large numbers of cannabis clones, but these methods may also introduce genetic instability over successive generations. This instability often manifests as somaclonal variation, characterized by the progressive accumulation of genetic mutations or epigenetic alterations with each subculture. In this study, we examined how mutations accumulate in cannabis clones subjected to 6–11 subcultures. Using genotyping-by-sequencing, we identified 9405 polymorphic variants across 70 clones. The analysis revealed a correlation between the number of subcultures and the frequency of these mutations, revealing that genetic changes accumulate over successive subcultures despite clones sharing the same chronological age. Furthermore, we evaluated the functional impacts of accumulated mutations, with particular attention to implications on gene function and overall plant health. While rare, 14 high-impact variants were identified in genes that are important for plant development. Notably, six variants were also found in genes related to cannabinoid and terpene synthesis pathways, potentially affecting the plant’s biochemical composition. These findings highlight the need for genetic assessments in micropropagation protocols, impacting plant breeding and conservation. Understanding genetic variations in clonally propagated plants optimizes practices for stability. Crucial for cannabis and horticultural plants, it emphasizes techniques to prevent genetic decay and ensure viability.
Integrated Transcriptomic and Proteomic Analysis Reveals Molecular Mechanisms of the Cold Stress Response during the Overwintering Period in Blueberries (Vaccinium spp.)
Authors: Wei Xin , Wang Hongguang , Guo Dan , Wang Baisong , Zhang Xiao , Wang Jian , Liu Youchun , Wang Xingdong , Liu Cheng , Dong Wenxuan
Published: July 2024

In China, the Liaodong Peninsula is an important growing area for blueberries because of the high organic matter content in the soil, the abundance of light, and the large temperature difference between day and night. However, the low temperature and relative humidity of the air during the winter and early spring in the Liaodong Peninsula are the main reasons for the damage to blueberry plants. Here, we documented the transcriptome and proteome dynamics in response to cold stress in three blueberry cultivars (‘Northland’, ‘Bluecrop’, and ‘Berkeley’). Functional enrichment analysis indicated that many differentially expressed genes (DEGs) and differentially abundant proteins (DAPs) were mainly involved in the pathways of protein processing in the endoplasmic reticulum, the glutathione metabolism pathway, and ribosomes. We identified 12,747 transcription factors (TFs) distributed in 20 families. Based on our findings, we speculated that cold tolerance development was caused by the expression of calcium-related genes (CDPKs and CMLs), glutathione proteins, and TFs (NAC, WRKY, and ERF). Our investigation found that three cultivars experienced cold damage when exposed to temperatures between −9 °C and −15 °C in the field. Therefore, the cold resistance of blueberries during overwintering should not only resist the influence of low temperatures but also complex environmental factors such as strong winds and low relative humidity in the air. The order of cold resistance strength in the three blueberry cultivars was ‘Berkeley’, ‘Bluecrop’, and ‘Northland’. These results provide a comprehensive profile of the response to cold stress, which has the potential to be used as a selection marker for programs to improve cold tolerance in blueberries.
Coffee Breeding and Stress Biology
Authors: Partelli Fábio Luiz , Vieira Henrique Duarte
Published: July 2024

Currently, 130 species of the genus Coffea have been identified [...]
Proline Metabolism Genes in Transgenic Plants: Meta-Analysis under Drought and Salt Stress
Authors: Renzetti Marco , Bertolini Elisa , Trovato Maurizio
Published: July 2024

The amino acid proline accumulates in plants during abiotic stresses such as drought and salinity and is considered a reliable marker of environmental stress. While its accumulation is well established, its precise role in stress tolerance and its underlying molecular mechanism remain less clear. To address these issues, we performed a meta-analysis—a robust statistical technique that synthesizes results from multiple independent studies while accounting for experimental differences. We focused on 16 physiological and morphological parameters affected by drought and salt stress in transgenic plants expressing proline metabolic genes. For each parameter, we calculated the effect size as the response ratio (RR), which represents the logarithm of the mean value in the transgenic group over the mean value of the control group (lnRR). Under stress, most parameters exhibited significantly higher response ratios in the transgenic group, confirming the beneficial effects of proline during drought and salt stress. Surprisingly, under non-stressed conditions, most stress markers showed no significant differences between transgenic and non-transgenic plants, despite elevated proline levels in the former. These results suggest that the benefits of proline may be related to proline catabolism or may only become apparent during stress, possibly due to interactions with reactive oxygen species (ROS), which accumulate predominantly under stress conditions.
Exploring the Water–Soil–Crop Dynamic Process and Water Use Efficiency of Typical Irrigation Units in the Agro-Pastoral Ecotone of Northern China
Authors: Wang Guoshuai , Miao Xiangyang , Xu Bing , Tian Delong , Ren Jie , Li Zekun , Li Ruiping , Zheng Hexiang , Wang Jun , Tang Pengcheng , Feng Yayang , Zhou Jie , Xu Zhiwei
Published: July 2024

Groundwater resources serve as the primary source of water in the agro-pastoral ecotone of northern China, where scarcity of water resources constrains the development of agriculture and animal husbandry. As a typical rainfed agricultural area, the agro-pastoral ecotone in Inner Mongolia is entirely dependent on groundwater for agricultural irrigation. Due to the substantial groundwater consumption of irrigated farmland, groundwater levels have been progressively declining. To obtain a sustainable irrigation pattern that significantly conserves water, this study faces the challenge of unclear water transport relationships among water, soil, and crops, undefined water cycle mechanism in typical irrigation units, and water use efficiency, which was not assessed. Therefore, this paper, based on in situ experimental observations and daily meteorological data in 2022–2023, utilized the DSSAT model to explore the growth processes of potato, oat, alfalfa, and sunflower, the soil water dynamics, the water balance, and water use efficiency, analyzed over a typical irrigation area. The results indicated that the simulation accuracy of the DSSAT model was ARE < 10%, nRMSE/% < 10%, and R2 ≥ 0.85. The consumption of the soil moisture during the rapid growth stage for the potatoes, oats, alfalfa, and sunflower was 7–13% more than that during the other periods, and the yield was 67,170, 3345, 6529, and 4020 kg/ha, respectively. The soil evaporation of oat, potato, alfalfa, and sunflower accounted for 18–22%, 78–82%; 57–68%, and 32–43%, and transpiration accounted for 40–44%, 56–60%, 45–47%, and 53–55% of ETa (333.8 mm–369.2 mm, 375.2 mm–414.2 mm, 415.7 mm–453.7 mm, and 355.0 mm–385.6 mm), respectively. It was advised that irrigation water could be appropriately reduced to decrease ineffective water consumption. The water use efficiency and irrigation water use efficiency for potatoes was at the maximum amount, ranging from 16.22 to 16.62 kg/m3 and 8.61 to 10.81 kg/m3, respectively, followed by alfalfa, sunflowers, and oats. For the perspective of water productivity, it was recommended that potatoes could be extensively cultivated, alfalfa planted appropriately, and oats and sunflowers planted less. The findings of this study provided a theoretical basis for efficient water resource use in the agro-pastoral ecotone of Northern China.
Soil and Foliar Zinc Biofortification of Triticale (x Triticosecale) under Mediterranean Conditions: Effects on Forage Yield and Quality
Authors: García-Latorre Carlos , Reynolds-Marzal María Dolores , De la Peña-Lastra Saúl , Pinheiro Nuno , Poblaciones María José
Published: July 2024

Zinc (Zn) deficiency represents a significant global concern, affecting both plant and human health, particularly in regions with Zn-depleted soils. Agronomic biofortification strategies, such as the application of Zn fertilizers, offer a cost-effective approach to increase Zn levels in crops. This study aimed to assess the efficacy of soil and foliar Zn biofortification, applied as an aqueous solution of 0.5% zinc sulphate (ZnSO4·7H2O), on triticale (x Triticosecale) grown under Mediterranean conditions. The study was conducted over two growing seasons (2017/18 and 2018/19) in southern Spain, evaluating the effects on biomass yield; forage quality, including crude protein, Van Soest detergent fiber, organic matter digestibility, and relative forage value; and nutrient accumulation. Soil treatment consisted in the application of 50 kg of ZnSO4·7H2O ha−1 solely at the beginning of the first campaign to assess the residual effect on the second year. In contrast, the foliar treatment consisted of two applications of 4 kg of ZnSO4·7H2O ha−1 per campaign, one at the beginning of tillering and the other at the appearance of the first node. The foliar application increased the Zn content of the forage to adequate levels, while the soil application resulted in a 33% increase in biomass production, which is particularly beneficial for farmers. Overall quality was favored by the combined soil + foliar application, and no adverse antagonistic effects on other nutrients were detected. Instead, a synergistic interaction between Se and Zn was observed, which improved the efficacy of this important micronutrient for livestock and human wellbeing.
Enhancing Water-Deficient Potato Plant Identification: Assessing Realistic Performance of Attention-Based Deep Neural Networks and Hyperspectral Imaging for Agricultural Applications
Authors: Lapajne Janez , Vojnović Ana , Vončina Andrej , Žibrat Uroš
Published: July 2024

Hyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants. In this context, the integration of attention-based deep learning models presents a promising avenue for enhancing the efficiency of stress detection, by enabling the identification of meaningful spectral channels. This study assesses the performance of deep learning models on two potato plant cultivars exposed to water-deficient conditions. It explores how various sampling strategies and biases impact the classification metrics by using a dual-sensor hyperspectral imaging systems (VNIR -Visible and Near-Infrared and SWIR—Short-Wave Infrared). Moreover, it focuses on pinpointing crucial wavelengths within the concatenated images indicative of water-deficient conditions. The proposed deep learning model yields encouraging results. In the context of binary classification, it achieved an area under the receiver operating characteristic curve (AUC-ROC—Area Under the Receiver Operating Characteristic Curve) of 0.74 (95% CI: 0.70, 0.78) and 0.64 (95% CI: 0.56, 0.69) for the KIS Krka and KIS Savinja varieties, respectively. Moreover, the corresponding F1 scores were 0.67 (95% CI: 0.64, 0.71) and 0.63 (95% CI: 0.56, 0.68). An evaluation of the performance of the datasets with deliberately introduced biases consistently demonstrated superior results in comparison to their non-biased equivalents. Notably, the ROC-AUC values exhibited significant improvements, registering a maximum increase of 10.8% for KIS Krka and 18.9% for KIS Savinja. The wavelengths of greatest significance were observed in the ranges of 475–580 nm, 660–730 nm, 940–970 nm, 1420–1510 nm, 1875–2040 nm, and 2350–2480 nm. These findings suggest that discerning between the two treatments is attainable, despite the absence of prominently manifested symptoms of drought stress in either cultivar through visual observation. The research outcomes carry significant implications for both precision agriculture and potato breeding. In precision agriculture, precise water monitoring enhances resource allocation, irrigation, yield, and loss prevention. Hyperspectral imaging holds potential to expedite drought-tolerant cultivar selection, thereby streamlining breeding for resilient potatoes adaptable to shifting climates.
Towards a Customizable, SLA 3D-Printed Biliary Stent: Optimizing a Commercially Available Resin and Predicting Stent Behavior with Accurate In Silico Testing
Authors: Cordista Victoria , Patel Sagar , Lawson Rebecca , Lee Gunhee , Verheyen Morgan , Westbrook Ainsley , Shelton Nathan , Sapkota Prakriti , Zabala Valencia Isabella , Gaddam Cynthia , Thomas Joanna
Published: July 2024

Inflammation of the bile ducts and surrounding tissues can impede bile flow from the liver into the intestines. If this occurs, a plastic or self-expanding metal (SEM) stent is placed to restore bile drainage. United States (US) Food and Drug Administration (FDA)-approved plastic biliary stents are less expensive than SEMs but have limited patency and can occlude bile flow if placed spanning a duct juncture. Recently, we investigated the effects of variations to post-processing and autoclaving on a commercially available stereolithography (SLA) resin in an effort to produce a suitable material for use in a biliary stent, an FDA Class II medical device. We tested six variations from the manufacturer’s recommended post-processing and found that tripling the isopropanol (IPA) wash time to 60 min and reducing the time and temperature of the UV cure to 10 min at 40 °C, followed by a 30 min gravity autoclave cycle, yielded a polymer that was flexible and non-cytotoxic. In turn, we designed and fabricated customizable, SLA 3D-printed polymeric biliary stents that permit bile flow at a duct juncture and can be deployed via catheter. Next, we generated an in silico stent 3-point bend test to predict displacements and peak stresses in the stent designs. We confirmed our simulation accuracy with experimental data from 3-point bend tests on SLA 3D-printed stents. Unfortunately, our 3-point bend test simulation indicates that, when bent to the degree needed for placement via catheter (~30°), the peak stress the stents are predicted to experience would exceed the yield stress of the polymer. Thus, the risk of permanent deformation or damage during placement via catheter to a stent printed and post-processed as we have described would be significant. Moving forward, we will test alternative resins and post-processing parameters that have increased elasticity but would still be compatible with use in a Class II medical device.
Characterization of Electrospinning Chitosan Nanofibers Used for Wound Dressing
Authors: Ali Shahla H. , Mahammed Manaf A. , Yasin Suhad A.
Published: July 2024

Wound dressings play a crucial role in promoting wound healing by providing a protective barrier against infections and facilitating tissue regeneration. Electrospun nanofibers have emerged as promising materials for wound dressing applications due to their high surface area, porosity, and resemblance to the extracellular matrix. In this study, chitosan, a biocompatible and biodegradable polymer, was electrospun into nanofibers for potential use in wound dressing. The chitosan nanofibers were characterized by using various analytical techniques to assess their morphology and biocompatibility. Scanning electron microscopy (SEM) revealed the formation of uniform and bead-free nanofibers with diameters ranging from tens to hundreds of nanometers. Structural analysis, including Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD), elucidated the chemical composition and crystalline structure of the nanofibers. Furthermore, in vitro studies evaluated the cytocompatibility of the chitosan nanofibers with human dermal fibroblasts, demonstrating cell viability and proliferation on the nanofibers. Additionally, antibacterial properties were assessed to evaluate the potential of chitosan nanofibers in preventing wound infections. Overall, the characterization results highlight the promising attributes of electrospun chitosan nanofibers as wound dressings, paving the way for further investigation and development in the field of advanced wound care. This study has been carried out for the first time in our region and has assessed the antibacterial properties of electrospun chitosan nanofiber material. The created mat has shown efficaciousness against bacteria that are both gram-positive and gram-negative.