bluuebunny/crossref_metadata_2025 huggingface dataset:
Introduction to Large Language Models (LLMs) for dementia care and research
Authors: Treder Matthias S. , Lee Sojin , Tsvetanov Kamen A.
Published: May 2024

Introduction

Dementia is a progressive neurodegenerative disorder that affects cognitive abilities including memory, reasoning, and communication skills, leading to gradual decline in daily activities and social engagement. In light of the recent advent of Large Language Models (LLMs) such as ChatGPT, this paper aims to thoroughly analyse their potential applications and usefulness in dementia care and research.

Method

To this end, we offer an introduction into LLMs, outlining the key features, capabilities, limitations, potential risks, and practical considerations for deployment as easy-to-use software (e.g., smartphone apps). We then explore various domains related to dementia, identifying opportunities for LLMs to enhance understanding, diagnostics, and treatment, with a broader emphasis on improving patient care. For each domain, the specific contributions of LLMs are examined, such as their ability to engage users in meaningful conversations, deliver personalized support, and offer cognitive enrichment. Potential benefits encompass improved social interaction, enhanced cognitive functioning, increased emotional well-being, and reduced caregiver burden. The deployment of LLMs in caregiving frameworks also raises a number of concerns and considerations. These include privacy and safety concerns, the need for empirical validation, user-centered design, adaptation to the user's unique needs, and the integration of multimodal inputs to create more immersive and personalized experiences. Additionally, ethical guidelines and privacy protocols must be established to ensure responsible and ethical deployment of LLMs.

Results

We report the results on a questionnaire filled in by people with dementia (PwD) and their supporters wherein we surveyed the usefulness of different application scenarios of LLMs as well as the features that LLM-powered apps should have. Both PwD and supporters were largely positive regarding the prospect of LLMs in care, although concerns were raised regarding bias, data privacy and transparency.

Discussion

Overall, this review corroborates the promising utilization of LLMs to positively impact dementia care by boosting cognitive abilities, enriching social interaction, and supporting caregivers. The findings underscore the importance of further research and development in this field to fully harness the benefits of LLMs and maximize their potential for improving the lives of individuals living with dementia.
Adult psychiatric inpatient admissions and length of stay before and during the COVID-19 pandemic in a large urban hospital setting in Vancouver, British Columbia
Authors: Russolillo Angela , Carter Michelle , Guan Mejiao , Singh Pulkit , Kealy David , Raudzus Julia
Published: May 2024

Introduction

During the COVID-19 pandemic individuals with mental illnesses faced challenges accessing psychiatric care. Our study aimed to describe patient characteristics and compare admissions and length of stay (LOS) for psychiatric-related hospitalizations before and during the COVID-19 pandemic.

Methods

We conducted a retrospective analysis using health administrative data comparing individuals with an acute psychiatric admission between two time periods: 1st March 2019 to 31st December 2019 (pre-COVID) and 1st March 2020 to 31st December 2020 (during-COVID). Multivariable negative binomial regression was used to model the association between most responsible diagnosis type and the two-time periods to hospital LOS, reporting the Rate Ratio (RR) as the measure of effect.

Results

The cohort comprised 939 individuals who were predominately male (60.3%) with a severe mental illness (schizophrenia or mood-affective disorder) (72.7%) and a median age of 38 (IQR: 28.0, 52.0) years. In the multivariable analysis, anxiety disorders (RR: 0.63, CI: 0.4, 0.99) and personality disorders (RR: 0.52, CI: 0.32, 0.85) were significantly associated with a shorter LOS when compared to individuals without those disorders. Additionally, when compared to hospital admissions for non-substance related disorders the LOS for patients with substance-related disorders were significantly shorter during the COVID period (RR: 0.45, CI: 0.30, 0.67) and pre-COVID period (RR: 0.31, CI: 0.21, 0.46).

Conclusions

We observed a significant difference in the type and length of admissions for various psychiatric disorders during the COVID-19 period. These findings can support systems of care in adapting to utilization changes during pandemics or other global health events.
Tuning of polymeric membranes to mitigate fouling and removal of dissolved compounds for wastewater treatment: a Review
Authors: Tarun S. S. , Dakshesh S. K. , Arthanareeswaran G.
Published: May 2024

Wastewater, referred to as sewage, has been a byproduct of human settlement since ancient times. An increase in human activities leads to more wastewater effluents, resulting in higher concentrations of organic compounds, which are harmful to all forms of living organisms and drinking water purposes. Traditional methods cannot satisfy this issue for higher concentrations. The advanced membrane process is an alternative to this conventional method for removing organic compounds and various effluents due to its high permeate quality and less toxicity. Moreover, the modification of polymeric membranes by increasing its content led to a higher flux thereby enhancing the fouling property for effective wastewater treatment. The processes, UF, RO, NF, and FO, ion exchange, MD, and pervaporation, were developed for more robust methods to improve the quality of the environment and lead to higher salt rejection. This review provides an overview of the fabrications, methods and modifications of substrates utilized in different processes with varying modules to achieve a higher flux rate, lowering the fouling. We discuss the materials used for various membrane modules in ceramic membranes under different operating circumstances and the methods to enhance the performance of membrane fouling. This review also aims to track the ongoing research works to broaden different process combinations for further research purposes, showcasing better antifouling performance and maximizing water quality in the future.
ARCnet: A Multi-Feature-Based Auto Radio Check Model
Authors: Pan Weijun , Wang Yidi , Zhang Yumei , Han Boyuan
Published: May 2024

Radio checks serve as the foundation for ground-to-air communication. To integrate machine learning for automated and reliable radio checks, this study introduces an Auto Radio Check network (ARCnet), a novel algorithm for non-intrusive speech quality assessment in civil aviation, addressing the crucial need for dependable ground-to-air communication. By employing a multi-scale feature fusion approach, including the consideration of audio’s frequency domain, comprehensibility, and temporal information within the radio check scoring network, ARCnet integrates manually designed features with self-supervised features and utilizes a transformer network to enhance speech segment analysis. Utilizing the NISQA open-source dataset and the proprietary RadioCheckSpeech dataset, ARCnet demonstrates superior performance in predicting speech quality, showing a 12% improvement in both the Pearson correlation coefficient and root mean square error (RMSE) compared to existing models. This research not only highlights the significance of applying multi-scale attributes and deep neural network parameters in speech quality assessment but also emphasizes the crucial role of the temporal network in capturing the nuances of voice data. Through a comprehensive comparison of the ARCnet approach to traditional methods, this study underscores its innovative contribution to enhancing communication efficiency and safety in civil aviation.
Modeling Positions and Orientations of Cantaloupe Flowers for Automatic Pollination
Authors: Duc Tai Nguyen , Minh Trieu Nguyen , Truong Thinh Nguyen
Published: May 2024

An automatic system for cantaloupe flower pollination in greenhouses is proposed to meet the requirements of automatic pollination. The system consists of a mobile platform, robotic manipulator, and camera that reaches the flowers to detect and recognise their external features. The main task of the vision system is to detect the position and orientation of the flower in Cartesian coordinates, allowing the manipulator to reach the pose and perform pollination. A comprehensive method to ensure the accuracy of the pollination process is proposed that accurately determines the position and orientation of cantaloupe flowers in real environments. The vision system is used to capture images, detect the flower, and recognise its state according to its external features, such as size, colour, and shape, thereby providing appropriate nozzle access during pollination. The proposed approach begins with a segmentation method designed to precisely locate and segment the target cantaloupe flowers. Subsequently, a mathematical model is used to determine the key points that are important for establishing the growth orientation of each flower. Finally, an inverse-projection method is employed to convert the position of the flower from a two-dimensional (2D) image into a three-dimensional (3D) space, providing the necessary position for the pollination robot. The experimental process is conducted in a laboratory and proves the efficacy of the cantaloupe flower segmentation method, yielding precision, recall, and F1 scores of 87.91%, 90.76%, and 89.31%, respectively. Furthermore, the accuracy of the growth-orientation prediction method reaches approximately 86.7%. Notably, positional errors in 3D space predominantly fall within the allowable range, resulting in a successful pollination rate of up to 83.1%.
Design and Optimization of Power Shift Tractor Starting Control Strategy Based on PSO-ELM Algorithm
Authors: Qian Yu , Wang Lin , Lu Zhixiong
Published: May 2024

Power shift tractors have been widely used in agricultural tractors in recent years because of their advantages of uninterrupted power during shifting, high transmission efficiency and high stability. As one of the indispensable driving states of the power shift tractor, the starting process requires a small impact and a starting speed that meets the driver’s requirements. In this paper, aiming at such contradictory requirements, the starting control strategy of a power shift tractor is formulated with the goal of starting quality and the driver’s intention. Firstly, the identification characteristics of the driver under three starting intentions are obtained by a real vehicle test. An extreme learning machine with fast identification speed and short training time is used to establish the basic driver’s intention identification model. For the instability of the identification results of the Extreme Learning Machine (ELM), the particle swarm optimization algorithm (PSO) is used to optimize the ELM. The optimized extreme learning machine model has an accuracy of 96.891% for driver’s intention identification. The wet clutch is an important part of the power shift gearbox. In this paper, the starting control strategy knowledge base of the starting clutch is established by a combination of bench tests and simulation tests. Through the fuzzy algorithm, the driver’s intention is combined with the starting control strategy. Different drivers’ intentions will affect the comprehensive evaluation model of the clutch (the single evaluation index of the clutch is: the maximum sliding power, the sliding power, the speed stability time, the impact degree), thus affecting the final choice of the starting clutch control strategy considering the driver’s intention. On this basis, this paper studies and establishes the MPC starting controller for the power shift gearbox. Compared with the linear control strategy, the PSO-ELM-fuzzy weight starting strategy proposed in this paper can reduce the maximum sliding friction power by 45%, the sliding friction power by 69.45%, and the speed stabilization time by 0.11 s. The effectiveness of the starting control strategy considering the driver’s intention proposed in this paper to improve the starting quality of the power shift tractor is verified.
Examining the Percent Canopy Cover and Health of Winter Wheat in No-Till and Conventional Tillage Plots Using a Drone
Authors: Akumu Clement E. , Oppong Judith N. , Dennis Sam
Published: May 2024

The percent canopy cover and health of winter wheat are important crop performance indicators. Thus, understanding how tillage management practices affect these indicators is beneficial for improving crop performance and consequently yield. The availability of high-resolution drone data with spectral characteristics provides an opportunity to examine the percent canopy cover and health of winter wheat in different tillage systems. This is because the use of drones provides real-time high spatial resolution and temporal images to effectively monitor winter wheat conditions throughout the growing season. Nonetheless, very limited studies have utilized drone data for assessing the percent canopy cover and health conditions of winter wheat for different tillage practices. This study aimed to examine the percent canopy cover and health of winter wheat in no-till and conventional tillage plots using a drone. We used the mean Normalized Difference Vegetation Index (NDVI) ± Standard Deviation (SD) (0.89 ± 0.04) of winter wheat for the growth stages of tillering, jointing, and boot/heading to generate the percent wheat canopy cover. The Normalized Difference Red-Edge (NDRE) produced for winter wheat at the middle and late growth stages was used as a proxy for wheat health condition. We found that the mean percentage canopy cover of winter wheat was about 4% higher in no-till compared to conventional tillage plots in most of the growing season. The mean NDRE ± standard error (SE) of winter wheat was about 0.44 ± 0.01 and 0.43 ± 0.01 for no-till and conventional tillage plots, respectively, during the mid- and late growth stages. There was no significant difference in either the percent canopy cover or health of winter wheat between no-till and conventional tillage plots. The results generated in this study could be used to support farmers’ decision-making process regarding tillage practices and wheat crop performance.
Carbapenem-Resistant NDM and OXA-48-like Producing K. pneumoniae: From Menacing Superbug to a Mundane Bacteria; A Retrospective Study in a Romanian Tertiary Hospital
Authors: Lazar Dragos Stefan , Nica Maria , Dascalu Amalia , Oprisan Corina , Albu Oana , Codreanu Daniel Romeo , Kosa Alma Gabriela , Popescu Corneliu Petru , Florescu Simin Aysel
Published: May 2024

Background: Carbapenem-resistant Klebsiella pneumoniae (Cr-Kpn) is becoming a growing public health problem through the failure of adequate treatment. This study’s objectives are to describe the sources of Cr-Kpn in our hospital over 22 months, associating factors with the outcome of Cr-Kpn-positive patients, especially those with NDM+OXA-48-like (New Delhi Metallo-β-Lactamase and oxacillinase-48), and the effectiveness of the treatments used. Methods: A retrospective observational cohort study including all hospitalized patients with Cr-Kpn isolates. We reported data as percentages and identified independent predictors for mortality over hospital time through multivariate analysis. Results: The main type of carbapenemases identified were NDM+OXA-48-like (49.4%). The statistical analysis identified that diabetes and co-infections with the Gram-negative, non-urinary sites of infection were factors of unfavorable evolution. The Cox regression model identified factors associated with a poor outcome: ICU admission (HR of 2.38), previous medical wards transition (HR of 4.69), and carbapenemase type NDM (HR of 5.98). We did not find the superiority of an antibiotic regimen, especially in the case of NDM+OXA-48-like. Conclusions: The increase in the incidence of Cr-Kpn infections, especially with NDM+OXA-48-like pathogens, requires a paradigm shift in both the treatment of infected patients and the control of the spread of these pathogens, which calls for a change in public health policy regarding the use of antibiotics and the pursuit of a One Health approach.
A Comparison of Currently Available and Investigational Fecal Microbiota Transplant Products for Recurrent Clostridioides difficile Infection
Authors: Wang Yifan , Hunt Aaron , Danziger Larry , Drwiega Emily N.
Published: May 2024

Clostridioides difficile infection (CDI) is an intestinal infection that causes morbidity and mortality and places significant burden and cost on the healthcare system, especially in recurrent cases. Antibiotic overuse is well recognized as the leading cause of CDI in high-risk patients, and studies have demonstrated that even short-term antibiotic exposure can cause a large and persistent disturbance to human colonic microbiota. The recovery and sustainability of the gut microbiome after dysbiosis have been associated with fewer CDI recurrences. Fecal microbiota transplantation (FMT) refers to the procedure in which human donor stool is processed and transplanted to a patient with CDI. It has been historically used in patients with pseudomembranous colitis even before the discovery of Clostridioides difficile. More recent research supports the use of FMT as part of the standard therapy of recurrent CDI. This article will be an in-depth review of five microbiome therapeutic products that are either under investigation or currently commercially available: Rebyota (fecal microbiota, live-jslm, formerly RBX2660), Vowst (fecal microbiota spores, live-brpk, formerly SER109), VE303, CP101, and RBX7455. Included in this review is a comparison of the products’ composition and dosage forms, available safety and efficacy data, and investigational status.
Novel Siderophore Cephalosporin and Combinations of Cephalosporins with β-Lactamase Inhibitors as an Advancement in Treatment of Ventilator-Associated Pneumonia
Authors: Viscardi Szymon , Topola Ewa , Sobieraj Jakub , Duda-Madej Anna
Published: May 2024

In an era of increasing antibiotic resistance among pathogens, the treatment options for infectious diseases are diminishing. One of the clinical groups especially vulnerable to this threat are patients who are hospitalized in intensive care units due to ventilator-associated pneumonia caused by multidrug-resistant/extensively drug-resistant Gram-negative bacteria. In order to prevent the exhaustion of therapeutic options for this life-threatening condition, there is an urgent need for new pharmaceuticals. Novel β-lactam antibiotics, including combinations of cephalosporins with β-lactamase inhibitors, are proposed as a solution to this escalating problem. The unique mechanism of action, distinctive to this new group of siderophore cephalosporins, can overcome multidrug resistance, which is raising high expectations. In this review, we present the summarized results of clinical trials, in vitro studies, and case studies on the therapeutic efficacy of cefoperazone-sulbactam, ceftolozane-tazobactam, ceftazidime-avibactam, and cefiderocol in the treatment of ventilator-associated pneumonia. We demonstrate that treatment strategies based on siderophore cephalosporins and combinations of β-lactams with β-lactamases inhibitors show comparable or higher clinical efficacy than those used with classic pharmaceuticals, like carbapenems, colistin, or tigecycline, and are often associated with a lower risk of adverse events.
Listeria monocytogenes from Food Products and Food Associated Environments: Antimicrobial Resistance, Genetic Clustering and Biofilm Insights
Authors: Silva Adriana , Silva Vanessa , Gomes João Paulo , Coelho Anabela , Batista Rita , Saraiva Cristina , Esteves Alexandra , Martins Ângela , Contente Diogo , Diaz-Formoso Lara , Cintas Luis M. , Igrejas Gilberto , Borges Vítor , Poeta Patrícia
Published: May 2024

Listeria monocytogenes, a foodborne pathogen, exhibits high adaptability to adverse environmental conditions and is common in the food industry, especially in ready-to-eat foods. L. monocytogenes strains pose food safety challenges due to their ability to form biofilms, increased resistance to disinfectants, and long-term persistence in the environment. The aim of this study was to evaluate the presence and genetic diversity of L. monocytogenes in food and related environmental products collected from 2014 to 2022 and assess antibiotic susceptibility and biofilm formation abilities. L. monocytogenes was identified in 13 out of the 227 (6%) of samples, 7 from food products (meat preparation, cheeses, and raw milk) and 6 from food-processing environments (slaughterhouse-floor and catering establishments). All isolates exhibited high biofilm-forming capacity and antibiotic susceptibility testing showed resistance to several classes of antibiotics, especially trimethoprim-sulfamethoxazole and erythromycin. Genotyping and core-genome clustering identified eight sequence types and a cluster of three very closely related ST3 isolates (all from food), suggesting a common contamination source. Whole-genome sequencing (WGS) analysis revealed resistance genes conferring resistance to fosfomycin (fosX), lincosamides (lin), fluoroquinolones (norB), and tetracycline (tetM). In addition, the qacJ gene was also detected, conferring resistance to disinfecting agents and antiseptics. Virulence gene profiling revealed the presence of 92 associated genes associated with pathogenicity, adherence, and persistence. These findings underscore the presence of L. monocytogenes strains in food products and food-associated environments, demonstrating a high virulence of these strains associated with resistance genes to antibiotics, but also to disinfectants and antiseptics. Moreover, they emphasize the need for continuous surveillance, effective risk assessment, and rigorous control measures to minimize the public health risks associated to severe infections, particularly listeriosis outbreaks. A better understanding of the complex dynamics of pathogens in food products and their associated environments can help improve overall food safety and develop more effective strategies to prevent severe health consequences and economic losses.
The Effect of Lemon Juice (Citrus limon L.) Treated with Melatonin on the Health Status and Treatment of K14HPV16 Mice
Authors: Badiche-El Hilali Fátima , Medeiros-Fonseca Beatriz , Silva Jéssica , Silvestre-Ferreira Ana C. , Pires Maria João , Gil da Costa Rui M. , Peixoto Francisco , Oliveira Paula A. , Valero Daniel
Published: May 2024

Lemon is a fruit rich in antioxidant properties and has several health benefits, namely the reduction of skin edema and anticarcinogenic properties, which are due to its high content of bioactive compounds. Melatonin can improve and preserve the properties of lemon for longer and also has health benefits. The aim of this study was to evaluate the effects of oral administration of lemon juice after melatonin treatment on murinometric parameters of wild-type (WT) mice and transgenic mice carrying human papillomavirus (HPV). Two trials were performed for oral administration of the lemon extract compound: in drinking water and in diet. First of all, lemons were treated by immersion with melatonin at 10 mM. Then, lemons were squeezed, and the juice obtained was freeze-dried and stored to be subsequently added to drinking water or diet, according to the assay. Thus, mice were divided into eight groups in the drink assay (each with n = 5): group 1 (G1, WT, control), group 2 (G2, WT, 1 mL lemon), group 3 (G3, WT, 1.5 mL lemon), group 4 (G4, WT, 2 mL lemon), group 5 (G5, HPV16, control), group 6 (G6, HPV16, 1 mL lemon) group 7 (G6, HPV16, 1.5 mL lemon) and group 8 (G6, HPV16, 2 mL lemon). The diet assay was divided into four groups: group 1 (G1, WT, control), group 2 (G2, WT, 4 mL lemon), group 3 (G3, HPV16, control) and group 4 (G4, HPV16, 4 mL lemon). In the drink assay, the highest concentration of melatonin (308 ng/100 mL) was for groups 4 and 8, while in the food assay, there was only one concentration of melatonin (9.96 ng/g) for groups 2 and 4. Both trials lasted 30 days. During this time, body weight, food and water were recorded. Afterward, they were sacrificed, and samples were collected for different analyses. At the concentrations used, the lemon juice with melatonin had no adverse effects on the animals’ health and showed a positive outcome in modifying weight gain and enhancing antioxidant activity in mice. Moreover, a reduction in the incidence of histological lesions was observed in treated animals. Further research is needed to better understand the effects of lemon extract on health and treatment outcomes in this animal model.
The Interplay between Perioperative Oxidative Stress and Hepatic Dysfunction after Human Liver Resection: A Prospective Observational Pilot Study
Authors: Primavesi Florian , Senoner Thomas , Schindler Sophie , Nikolajevic Aleksandar , Di Fazio Pietro , Csukovich Georg , Eller Silvia , Neumayer Bettina , Anliker Markus , Braunwarth Eva , Oberhuber Rupert , Resch Thomas , Maglione Manuel , Cardini Benno , Niederwieser Thomas , Gasteiger Silvia , Klieser Eckhard , Tilg Herbert , Schneeberger Stefan , Neureiter Daniel , Öfner Dietmar , Troppmair Jakob , Stättner Stefan
Published: May 2024

Post-hepatectomy liver failure (PHLF) remains the major contributor to death after liver resection. Oxidative stress is associated with postoperative complications, but its impact on liver function is unclear. This first in-human, prospective, single-center, observational pilot study evaluated perioperative oxidative stress and PHLF according to the ISGLS (International Study Group for Liver Surgery). Serum 8-isoprostane, 4-hydroxynonenal (4-HNE), total antioxidative capacity, vitamins A and E, and intraoperative, sequential hepatic tissue 4-HNE and UCP2 (uncoupling protein 2) immunohistochemistry (IHC) were assessed. The interaction with known risk factors for PHLF and the predictive potential of oxidative stress markers were analyzed. Overall, 52 patients were included (69.2% major liver resection). Thirteen patients (25%) experienced PHLF, a major factor for 90-day mortality (23% vs. 0%; p = 0.013). Post-resection, pro-oxidative 8-isoprostane significantly increased (p = 0.038), while 4-HNE declined immediately (p < 0.001). Antioxidative markers showed patterns of consumption starting post-resection (p < 0.001). Liver tissue oxidative stress increased stepwise from biopsies taken after laparotomy to post-resection in situ liver and resection specimens (all p < 0.001). Cholangiocarcinoma patients demonstrated significantly higher serum and tissue oxidative stress levels at various timepoints, with consistently higher preoperative values in advanced tumor stages. Combining intraoperative, post-resection 4-HNE serum levels and in situ IHC early predicted PHLF with an AUC of 0.855 (63.6% vs. 0%; p < 0.001). This was also associated with grade B/C PHLF (36.4% vs. 0%; p = 0.021) and 90-day mortality (18.2% vs. 0%; p = 0.036). In conclusion, distinct patterns of perioperative oxidative stress levels occur in patients with liver dysfunction. Combining intraoperative serum and liver tissue markers predicts subsequent PHLF. Cholangiocarcinoma patients demonstrated pronounced systemic and hepatic oxidative stress, with increasing levels in advanced tumor stages, thus representing a worthwhile target for future exploratory and therapeutic studies.
Expression of ChAT, Iba-1, and nNOS in the Central Nervous System following Facial Nerve Injury
Authors: Lee Jae Min , Yoo Myung Chul , Kim Yong Jun , Kim Sung Soo , Yeo Seung Geun
Published: May 2024

Facial nerve injury can cause significant functional impairment, impacting both the peripheral and central nervous systems. The present study evaluated changes in facial motor function, numbers of cholinergic neurons and microglia, and nNOS levels in the facial nucleus of the central nervous system (CNS) following peripheral facial nerve injury. Facial nerve function, as determined by eyeblink and whisker-movement reflexes, was evaluated at baseline and 1, 2, 3, 4, 8, and 12 weeks after inducing facial nerve injury through compression or axotomy. The expression of choline acetyltransferase (ChAT), ionized calcium-binding adaptor molecule 1 (Iba-1), and neuronal nitric oxide synthase (nNOS) in the facial nucleus of the CNS was analyzed 2, 4, and 12 weeks after peripheral facial nerve injury. Compression-induced facial nerve injury was found to lead to temporary facial motor impairment, whereas axotomy resulted in persistent impairment. Moreover, both compression and axotomy reduced ChAT expression and increased Iba-1 and nNOS expression in the facial nucleus, indicating upregulation of an inflammatory response and neurodegeneration. These results indicate that, compared with compression-induced injury, axotomy-induced facial nerve injury results in greater facial motor dysfunction and more persistent microglial and nitric oxide activation in the facial nucleus of the CNS.
Assessment of the Antioxidant and Hypolipidemic Properties of Salicornia europaea for the Prevention of TAFLD in Rats
Authors: Souid Aymen , Giambastiani Lucia , Castagna Antonella , Santin Marco , Vivarelli Fabio , Canistro Donatella , Morosini Camilla , Paolini Moreno , Franchi Paola , Lucarini Marco , Raffaelli Andrea , Giorgetti Lucia , Ranieri Annamaria , Longo Vincenzo , Pozzo Luisa , Vornoli Andrea
Published: May 2024

Halophyte species represent valuable reservoirs of natural antioxidants, and, among these, Salicornia europaea stands out as a promising edible plant. In this study, young and old S. europaea leaves were compared for the content of bioactive compounds and antioxidant activity to assess changes in different growth phases; then, the potential protective effects against low-dose CCl4-induced toxicant-associated fatty liver disease (TAFLD) were investigated by administering an aqueous suspension of young leaves to rats daily for two weeks. Quantification of total and individual phenolic compounds and in vitro antioxidant activity assays (DPPH, FRAP, and ORAC) showed the highest values in young leaves compared to mature ones. Salicornia treatment mitigated CCl4-induced hepatic oxidative stress, reducing lipid peroxidation and protein carbonyl levels, and preserving the decrease in glutathione levels. Electronic paramagnetic resonance (EPR) spectroscopy confirmed these results in the liver and evidenced free radicals increase prevention in the brain. Salicornia treatment also attenuated enzymatic disruptions in the liver’s drug metabolizing system and Nrf2-dependent antioxidant enzymes. Furthermore, histopathological examination revealed reduced hepatic lipid accumulation and inflammation. Overall, this study highlights Salicornia’s potential as a source of bioactive compounds with effective hepatoprotective properties capable to prevent TAFLD.
Cellular ROS and Antioxidants: Physiological and Pathological Role
Authors: Kozlov Andrey V. , Javadov Sabzali , Sommer Natascha
Published: May 2024

Reactive oxygen species (ROS) are highly reactive oxygen derivatives that include free radicals such as superoxide anion radical (O2•−) and hydroxyl radical (HO•), as well as non-radical molecules hydrogen peroxide (H2O2), peroxynitrite (ONOO−), and hypochlorous acid (HOCl) [...]
Phenolic and Antioxidant Characterization of Fruit By-Products for Their Nutraceuticals and Dietary Supplements Valorization under a Circular Bio-Economy Approach
Authors: Terenzi Cristina , Bermudez Gabriela , Medri Francesca , Davani Lara , Tumiatti Vincenzo , Andrisano Vincenza , Montanari Serena , De Simone Angela
Published: May 2024

Agri-food by-products, obtained as waste from the food industry, negatively impact the global economy and the environment. In order to valorize waste materials from fruit juices and tomato sauces as upcycled materials rich in health-promoting compounds, they were characterized in terms of polyphenolic and protein content. The results obtained were compared with those collected for their final products. The recovery of polyphenols was performed via ultrasound-assisted extraction (UAE). A high-performance liquid chromatography–diode array detector (HPLC-DAD) method was developed and validated to depict the quali-quantitative polyphenolic profile of both the by-products and the final products. The antioxidant capacity of the resulting extracts was characterized by UV-Vis spectrophotometric assays in terms of total phenolic content (TPC) and total antioxidant status (TAS). Moreover, the protein content was assessed with the Kjeldahl method too. The results highlighted a significant quantity of polyphenols remaining in peach, apricot, and apple by-products, which were able to exert an antioxidant activity (in the range of 4.95 ± 5.69 × 10−1 to 7.06 ± 7.96 × 10−1 mmol Trolox 100 g−1 of dry weight (DW) sample). Conversely, the tomato by-products were highly rich in proteins (11.0 ± 2.00 to 14.4 ± 2.60 g of proteins 100 g−1 DW). The results proved that all by-products may potentially be sustainable ingredients with nutritional and functional value in a circular bio-economy prospect.
Unavoidability and Functionality of Nervous System and Behavioral Randomness
Authors: Gómez Carlos M. , Rodríguez-Martínez Elena I. , Altahona-Medina María A.
Published: May 2024

The basic functioning of the central nervous system is based on the opening and closing of ionic channels in the membranes of neurons. The behavior of ionic channels is considered to be a random process with an exponential probability distribution function. The central limit theorem implies that the mean of the sum of random variables generates a distribution in which the new variable tends to be normally distributed. The theorem implicitly implies that randomness can be embedded in a certain probability distribution but does not disappear. The present report will explore the possible implications for the functioning of nervous system and behavior of the constituent neural randomness. The possible functionality of “noise” to increase the exploratory space of nervous and behavioral systems will be considered.
Innovative Design of Solid-State Hydrogen Storage and Proton Exchange Membrane Fuel Cell Coupling System with Enhanced Cold Start Control Strategy
Authors: Gao Jianhua , Zhou Su , Fan Lei , Zhang Gang , Jiang Yongyuan , Shen Wei , Zhai Shuang
Published: May 2024

This paper presents an innovative thermally coupled system architecture with a parallel coolant-heated metal hydride tank (MHT) designed to satisfy the hydrogen supply requirements of proton exchange membrane fuel cell s(PEMFCs). This design solves a problem by revolutionising the cold start capability of PEMFCs at low temperatures. During the design process, LaNi5 was selected as the hydrogen storage material, with thermodynamic and kinetic properties matching the PEMFC operating conditions. Afterwards, the MHT and thermal management subsystem were customised to integrate with the 70 kW PEMFC system to ensure optimal performance. Given the limitations of conventional high-pressure gaseous hydrogen storage for cold starting, this paper provides insights into the challenges faced by the PEMFC-MH system and proposes an innovative cold start methodology that combines internal self-heating and externally assisted preheating techniques, aiming to optimise cold start time, energy consumption, and hydrogen utilisation. The results show that the PEMFC-MH system utilises the heat generated during hydrogen absorption by the MHT to preheat the PEMFC stack, and the cold start time is only 101 s, which is 59.3% shorter compared to that of the conventional method. Meanwhile, the cold start energy consumption is reduced by 62.4%, achieving a significant improvement in energy efficiency. In conclusion, this paper presents a PEMFC-MH system design that achieves significant progress in terms of time saving, energy consumption, and hydrogen utilisation.
Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals
Authors: Wang Yang , Wang Min , Song Zishuo , Bian Weihao
Published: May 2024

Accurately extracting weak signals is extremely important for overall performance and application in optoelectronic imaging and optical communication systems. While weak signals are susceptible to noise, adaptive filtering is a commonly used noise removal method. Still, its convergence speed is slow, the steady-state error is large, and the anti-interference ability is weak. To solve the above problems, this paper proposes a new type of variable-step-length adaptive filtering algorithm (DSLMS) based on the minutiae function, which effectively reduces the noise component in error through its combination with the pair cancelation system, utilizing the low correlation property of the noise signal, to improve the anti-noise interference ability of the adaptive filter. Using FPGA and Matlab (2018b) for experimental verification, the results show that this algorithm shows significant advantages in noise suppression, accelerated algorithm convergence, and low steady-state error, and it has effectiveness and application potential for the optoelectronic detection of weak signal processing.
A Novel Nonlinear Magnetic Equivalent Circuit Model for Magnetic Flux Leakage System
Authors: Kara Okan , Çelik Hasan Hüseyin
Published: May 2024

To ensure efficient inspection using the magnetic flux leakage (MFL) method, generating a flux density near the saturation level within the tested material is essential. This requirement brings high flux density conditions in the system’s pole regions. Hence, leakage flux within the slot is excessively triggered, leading to distortion of the defect signal. In this context, the system dimensions stand out as one of the most significant factors affecting the mentioned flux distributions. Therefore, various alternative solutions with different system dimensions arise in the design process of the MFL system. This study proposes a magnetic equivalent circuit (MEC) model to achieve optimal system design. The proposed MEC model is designed considering the nonlinear behavior of the material, leakage flux, and fringing effects. Verification results demonstrate that the MEC model consistently tracks the finite element analysis (FEA) results in calculating the flux densities. Furthermore, the relative errors in the flux density calculations of the tested material are at a maximum level of 10.2% and an average of 5.2% compared to the FEA. These findings indicate that the proposed MEC model can be effectively utilized in rapid prototyping and optimization procedures of MFL system design by providing fast solutions with reasonable accuracy.
The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand
Authors: Yin Jiexiang , Zhao Feiyan , Tang Wenyun , Ma Jianxiao
Published: May 2024

While numerous studies have explored the correlation between the built environment and ride-hailing demand, few have assessed their nonlinear interplay. Utilizing ride-hailing order data and multi-source built environment data from Nanjing, China, this paper uses the machine learning method, eXtreme Gradient Boosting (XGBoost), combined with Shapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) to investigate the impact of built environment factors on ride-hailing travel demand, including their nonlinear and threshold effects. The findings reveal that dining facilities have the most significant impact, with a contribution rate of 30.75%, on predicting ride-hailing travel demand. Additionally, financial, corporate, and medical facilities also exert considerable influence. The built environment factors need to reach a certain threshold or within a certain range to maximize the impact of ride-hailing travel demand. Population density, land use mix, and distance to the subway station collectively influence ride-hailing demand. The results are helpful for TNCs to allocate network ride-hailing resources reasonably and effectively.
Using Dynamo for Automatic Reconstruction of BIM Elements from Point Clouds
Authors: Rocha Gustavo , Mateus Luís
Published: May 2024

The integration of 3D laser scanning and digital photogrammetry in the architecture, engineering, and construction (AEC) industry has facilitated high-quality architectural surveys. However, the processes remains constrained by significant costs, extensive manual labor, and accuracy issues associated with manual data processing. This article addresses these operational challenges by introducing automated Building Information Modeling (BIM) techniques that minimize manual input through the use of Dynamo for Autodesk Revit. We developed algorithms that efficiently convert point cloud data into accurate BIM models, enhancing productivity and reducing the potential for errors. The application of these algorithms is analyzed in a case study of the Old Lifeguard Station of Fuseta, showcasing notable reductions in modeling time and improvements in accuracy. The findings suggest that automated scan-to-BIM methods could provide a viable solution for enhancing BIM workflows across the industry, with the potential for wider adoption given their impact on efficiency and model quality.
Rehabilitation and Return to Play of an Ice Hockey Athlete after Lateral Meniscus Repair: A Case Report
Authors: Jeon Hyung Gyu , Lee Jong Hyeon , Kang Tae Kyu , Lee Dong Wook , Oh Kyoung Uk , Lee Sae Yong , Kim Byong Hun
Published: May 2024

This report presents the case of an ice hockey player diagnosed with a tear in the anterior horn of the lateral meniscus at 19 years of age. In addition, we suggest a rehabilitation exercise program that enabled an early return to play for an ice hockey athlete in a university sports league. The patient underwent arthroscopic meniscal repair and was discharged one day after surgery. A three-stage rehabilitation program was developed, as follows: Phase I, the protective phase (reduce inflammation symptoms and restore range of motion to enable weight-bearing); Phase II, the restorative phase (improve the strength of the quadriceps muscle and recover cardiovascular endurance); Phase III, the return to full functional activity (familiarize the patient with sport-specific skills and recover a symmetric functional level on both sides). After completing the 10-week rehabilitation program, the patient returned to play and took part in games 71 and 84 days after surgery. The patient had no meniscus reinjury in the 10 years after surgery while playing in university and professional ice hockey leagues. The functional performance-focused rehabilitation program presented in this case report resulted in an early return to sport and good long-term outcomes.
Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
Authors: Stańczyk Urszula , Zielosko Beata , Baron Grzegorz
Published: May 2024

Supervised discretisation is widely considered as far more advantageous than unsupervised transformation of attributes, because it helps to preserve the informative content of a variable, which is useful in classification. After discretisation, based on employed criteria, some attributes can be found irrelevant, and all their values can be represented in a discrete domain by a single interval. In consequence, such attributes are removed from considerations, and no knowledge is mined from them. The paper presents research focused on extended transformations of attribute values, thus combining supervised with unsupervised discretisation strategies. For all variables with single intervals returned from supervised algorithms, the ranges of values were transformed by unsupervised methods with varying numbers of bins. Resulting variants of the data were subjected to selected data mining techniques, and the performance of a group of classifiers was evaluated and compared. The experiments were performed on a stylometric task of authorship attribution.
A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding
Authors: Li Peng , Guo Shufang , Zhang Chenghao , Parvej Mosharaf Md , Zhang Jing
Published: May 2024

The rapid development of high-throughput technology has generated a large amount of protein–protein interaction (PPI) data, which provide a large amount of data support for constructing dynamic protein–protein interaction networks (PPINs). Constructing dynamic PPINs and applying them to recognize protein complexes has become a hot research topic. Most existing methods for complex recognition cannot fully mine the information of PPINs. To address this problem, we propose a construction method of dynamic weighted protein network by multi-level embedding (DWPNMLE). It can reflect the protein network’s dynamics and the protein network’s higher-order proximity. Firstly, the protein active period is calculated to divide the protein subnetworks at different time points. Then, the connection probability is used for the proteins possessing the same time points to judge whether there is an interaction relationship between them. Then, the corresponding protein subnetworks (multiple adjacency matrices) are constructed. Secondly, the multiple feature matrices are constructed using one-hot coding with the gene ontology (GO) information. Next, the first embedding is performed using variational graph auto-encoders (VGAEs) to aggregate features efficiently, followed by the second embedding using deep attributed network embedding (DANE) to strengthen the node representations learned in the first embedding and to maintain the first-order and higher-order proximity of the original network; finally, we compute the cosine similarity to obtain the final dynamic weighted PPIN. To evaluate the effectiveness of DWPNMLE, we apply four classical protein-complex-recognition algorithms on the DWPNMLE and compare them with two other dynamic protein network construction methods. The experimental results demonstrate that DWPNMLE significantly enhances the accuracy of complex recognition with high robustness, and the algorithms’ efficiency is also within a reasonable range.
Trusted Data Access Control Based on Logistics Business Collaboration Semantics
Authors: Zhang Xue , Jing Changqiang , Chen Yeh-Cheng , Wang Li , Xu Lianzheng , Fu Deqian
Published: May 2024

In the context of the digital evolution of the logistics industry, the interconnection of logistics information systems and associated data have become an obstacle of business collaboration among various stakeholders. A critical challenge in this domain is ensuring controllable access to logistics business data, given the industry’s current state characterized by independence, autonomy, disconnection, and heterogeneity, alongside the non-negotiable requirement for data privacy. We propose a novel model of trusted data access control based on of logistics business collaboration semantics. This approach incorporates semantic inference technologies into attribute-based access control mechanisms, thereby enabling the streamlined formulation of access control policies and facilitating unified authorization and control. Moreover, the method addresses the issue of access control policy management and maintenance at the semantic level. The proposed solution can pave the way for enhanced business collaboration between business entities, and further enable the building of a data exchange service platform within the logistics industry.