bluuebunny/crossref_metadata_2025 huggingface dataset:
Risk of post-fracture pneumonia and its association with cardiovascular events and mortality in adults with intellectual disabilities
Authors: Whitney Daniel G. , Erickson Steven R. , Berri Maryam
Published: November 2023

Objective

Fragility fractures are associated with an increased risk of pneumonia, which is a leading cause of death in adults with intellectual disabilities; however, the timing and complications of post-fracture pneumonia are underinvestigated. The objectives of this study were to determine the 30-day pneumonia rate post-fracture and the association of post-fracture pneumonia with mortality and cardiovascular events among adults with intellectual disabilities.

Methods

This retrospective cohort study was conducted using Medicare and commercial claims from 01 January 2011 to 31 December 2016. Incidence of pneumonia 30 days after a fragility fracture among adults ≥18 years old with intellectual disabilities (Fx cohort) was compared to the incidence among matched adults with intellectual disabilities without fractures (w/oFx cohort) and the general population of patients with an incident fragility fracture (GP+Fx). For the Fx cohort, Cox regression was used to examine the adjusted association of time-varying pneumonia (within 30 days post-fracture) with mortality and incidence of cardiovascular events 0–30, 31–365, and 366–730 days post-fracture.

Results

There was a high–early rate of pneumonia within 30 days post-fracture for young, middle-aged, and elderly adults with intellectual disabilities (n = 6,183); this rate was 2.2- to 6.1-fold higher than the rate among the w/oFx (n = 12,366) and GP+Fx (n = 363,995) cohorts (all P < 0.05). For the Fx cohort, post-fracture 30-day incidence of pneumonia was associated with an increased 30-day rate of mortality (adjusted HR [aHR] = 5.19; 95% confidence interval [CI] = 3.68–7.32), heart failure (aHR = 2.96; 95% CI = 1.92–4.56), and cerebrovascular disease (aHF = 1.48; 95% CI = 0.93–2.35; P = 0.098), with sustained effects to 1 year for heart failure (aHR = 1.61; 95% CI = 1.19–2.17) and 2 years for mortality (aHR = 1.39; 95% CI = 1.06–1.83), and without evidence of effect modification by age.

Discussion

Adults with intellectual disabilities are vulnerable to post-fracture pneumonia within 30 days, and complications arising from this, across the adult lifespan, and not only during the elderly years.
Equine-assisted interventions for veterans with posttraumatic stress disorder: a systematic review
Authors: Li Jiaxin , Sánchez-García Raúl
Published: November 2023

Equine-assisted intervention therapy has a nearly 60-year history and has been shown to have a significant positive impact on various types of psychotherapy patients. Due to an increase in research on EAT, the number of existing methods of equine-assisted intervention therapy has gradually increased. Based on existing literature on the application of equine-assisted intervention therapy on veterans with post-traumatic stress disorder (PTSD), this study examines the characteristics of several types of equine interventions and includes a systematic review of peer-reviewed literature on equine-assisted interventions for veterans with PTSD published over the past 5 years, from 2018 to the present. Ten articles met the review criteria and served as the primary data for analysis. Several types of equine-assisted interventions were shown to have a beneficial psychological impact on veterans. However, some limitations were also found in the studies, such as that the majority of experiments were constrained by small sample sizes. Equine-assisted intervention therapy has been shown to be effective, but further research is merited, in order to focus on the specific details and theories involved in equine-assisted interventions, and on the welfare of the horses involved in the therapy.
Risky business: human-related data is lacking from Lyme disease risk models
Authors: Fellin Erica , Varin Mathieu , Millien Virginie
Published: November 2023

Used as a communicative tool for risk management, risk maps provide a service to the public, conveying information that can raise risk awareness and encourage mitigation. Several studies have utilized risk maps to determine risks associated with the distribution ofBorrelia burgdorferi, the causal agent of Lyme disease in North America and Europe, as this zoonotic disease can lead to severe symptoms. This literature review focused on the use of risk maps to model distributions ofB. burgdorferiand its vector, the blacklegged tick (Ixodes scapularis), in North America to compare variables used to predict these spatial models. Data were compiled from the existing literature to determine which ecological, environmental, and anthropic (i.e., human focused) variables past research has considered influential to the risk level for Lyme disease. The frequency of these variables was examined and analyzed via a non-metric multidimensional scaling analysis to compare different map elements that may categorize the risk models performed. Environmental variables were found to be the most frequently used in risk spatial models, particularly temperature. It was found that there was a significantly dissimilar distribution of variables used within map elements across studies: Map Type, Map Distributions, and Map Scale. Within these map elements, few anthropic variables were considered, particularly in studies that modeled future risk, despite the objective of these models directly or indirectly focusing on public health intervention. Without including human-related factors considering these variables within risk map models, it is difficult to determine how reliable these risk maps truly are. Future researchers may be persuaded to improve disease risk models by taking this into consideration.
Machine learning algorithms improve MODIS GPP estimates in United States croplands
Authors: Menefee Dorothy , Lee Trey O. , Flynn K. Colton , Chen Jiquan , Abraha Michael , Baker John , Suyker Andy
Published: November 2023

Introduction: Machine learning methods combined with satellite imagery have the potential to improve estimates of carbon uptake of terrestrial ecosystems, including croplands. Studying carbon uptake patterns across the U.S. using research networks, like the Long-Term Agroecosystem Research (LTAR) network, can allow for the study of broader trends in crop productivity and sustainability.

Methods: In this study, gross primary productivity (GPP) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) for three LTAR cropland sites were integrated for use in a machine learning modeling effort. They are Kellogg Biological Station (KBS, 2 towers and 20 site-years), Upper Mississippi River Basin (UMRB - Rosemount, 1 tower and 12 site-years), and Platte River High Plains Aquifer (PRHPA, 3 towers and 52 site-years). All sites were planted to maize (Zea mays L.) and soybean (Glycine max L.). The MODIS GPP product was initially compared to in-situ measurements from Eddy Covariance (EC) instruments at each site and then to all sites combined. Next, machine learning algorithms were used to create refined GPP estimates using air temperature, precipitation, crop type (maize or soybean), agroecosystem, and the MODIS GPP product as inputs. The AutoML program in the h2o package tested a variety of individual and combined algorithms, including Gradient Boosting Machines (GBM), eXtreme Gradient Boosting Models (XGBoost), and Stacked Ensemble.

Results and discussion: The coefficient of determination (r2) of the raw comparison (MODIS GPP to EC GPP) was 0.38, prior to machine learning model incorporation. The optimal model for simulating GPP across all sites was a Stacked Ensemble type with a validated r2 value of 0.87, RMSE of 2.62 units, and MAE of 1.59. The machine learning methodology was able to successfully simulate GPP across three agroecosystems and two crops.
β-hydroxybutyrate administration improves liver injury and metabolic abnormality in postnatal growth retardation piglets
Authors: Wang Chengming , Wang Nan , Deng Yuankun , Zha Andong , Li Junyao , Tan Bie , Qi Ming , Wang Jing , Yin Yulong
Published: November 2023

Abnormal hepatic energy metabolism limits the growth and development of piglets. We hypothesized that β-hydroxybutyrate (BHB) might improve the growth performance of piglets by maintaining hepatic caloric homeostasis. A total of 30 litters of newborn piglets were tracked, and 30 postnatal growth retardation (PGR) piglets and 40 healthy piglets were selected to treat with normal saline with or without BHB (25 mg/kg/days) at 7-d-old. At the age of 42 days, 8 piglets in each group were sacrificed, and serum and liver were collected. Compared with the healthy-control group piglets, PGR piglets showed lower body weight (BW) and liver weight (p < 0.05), and exhibited liver injury and higher inflammatory response. The contents of serum and hepatic BHB were lower (p < 0.05), and gene expression related to hepatic ketone body production were down-regulated in PGR piglets (p < 0.05). While BHB treatment increased BW and serum BHB levels, but decreased hepatic BHB levels in PGR piglets (p < 0.05). BHB alleviated the liver injury by inhibiting the apoptosis and inflammation in liver of PGR piglets (p < 0.05). Compared with the healthy-control group piglets, liver glycogen content and serum triglyceride level of PGR piglets were increased (p < 0.05), liver gluconeogenesis gene and lipogenesis gene expression were increased (p < 0.05), and liver NAD+level was decreased (p < 0.05). BHB supplementation increased the ATP levels in serum and liver (p < 0.05), whereas decreased the serum glucose, cholesterol, triglyceride and high-density lipoprotein cholesterol levels and glucose and lipid metabolism in liver of PGR piglets (p < 0.05). Therefore, BHB treatment might alleviate the liver injury and inflammation, and improve hepatic energy metabolism by regulating glucose and lipid metabolism, thereby improving the growth performance of PGR piglets.
Lignocellulosic Composition Not Associated with Stem Borer Resistance in Select Louisiana Sugarcane Cultivars
Authors: Penn Hannah J. , Johnson Richard M. , Richard Katie A. , Richard Randy T. , White William H.
Published: November 2023

The two most economically damaging insect pests of sugarcane in Louisiana are the sugarcane borer (SCB) and the newly invasive Mexican rice borer (MRB), both of which can be managed in part with cultivar resistance. High stalk fiber levels is a well-documented aspect of stem borer resistance but is inversely correlated with recoverable sugar levels. However, lignocellulosic components such as hemicellulose, cellulose, and lignin are associated with resistance to other borer species in poaceous crops, potentially indicating mechanisms that may provide resistance without substantial trade-offs in yield. The goal of this study was to determine whether lignocellulosic composition varied among four cultivars—HoCP 85-845, HoCP 04-838, Ho 07-613, and HoCP 00-950—selected based on known variation in SCB and MRB resistance and total fiber content. We estimated lignocellulosic composition as well as Brix throughout the growing season and the total stalk fiber and recoverable sugar content at harvest for both plant cane and first ratoon crop years. We found that the Brix content throughout the growing season, as well as total fiber and sugar content at harvest, were significantly associated with the cultivar, aligning with previously documented trends in borer resistance (i.e., higher Brix and lower total fiber indicate a more susceptible cultivar). While lignocellulosic composition during the growing season was not associated with cultivar or resistance to either borer species, it was significantly impacted by sampling month and crop year. These data indicate the potential influence of alternative resistance mechanisms and interactions with abiotic conditions.
Sustenance Trial to Analyze the Effects of Black Soldier Fly Larvae Meal on the Reproductive Efficiency of Sows and the Hematological Properties of Suckling and Weaning Piglets
Authors: Kawasaki Kiyonori , Zhao Junliang , Takao Natsu , Sato Masaki , Ban Takuma , Tamamaki Kaoru , Kagami Masanori , Yano Kiminobu
Published: November 2023

The escalating demand for meat, driven by global population growth, necessitates sustainable solutions for animal feed production. This study investigated the effects of substituting conventional protein resources in sow and piglet dietary regimens with black soldier fly (BSF; Hermetia illucens) meal on reproductive efficiency, blood profile, piglet growth, and intestinal tissue morphology. The results indicate that substituting animal-derived and soy proteins with BSF meal does not compromise sow reproductive performance. Although no notable disparities were observed in piglet growth, the feed conversion ratio from the 28- to 35-day age marks were lower in the BSF-fed groups. This suggests that the animal protein-BSF substitution rate may require optimization, potentially involving chitin removal from BSF meal to enhance digestibility. Minor variations in the hematological composition and properties in piglets, with elevated high-density lipoprotein cholesterol levels in the high BSF group at the 28-day mark, were potentially attributable to the unique fatty acid composition of BSF meal. Moreover, this study potentiates future exploration into the efficacy of complete animal protein substitution with BSF meals on piglet nutrition and physiology, particularly in fattening pigs. The practical implementation of BSF meals in animal feed production holds promise for enhancing the sustainability of the swine industry.
Evaluation of Two Chemoenzymatic Glycan Remodeling Approaches to Generate Site-Specific Antibody–Drug Conjugates
Authors: Yang Qiang , Chen He , Ou Chong , Zheng Zhihao , Zhang Xiao , Liu Yunpeng , Zong Guanghui , Wang Lai-Xi
Published: November 2023

Fc-glycosite-specific antibody–drug conjugation represents a promising direction for the preparation of site-specific antibody–drug conjugates (ADCs). In the present research, we conducted a systemic evaluation of two endoglycosidase-catalyzed chemoenzymatic glycoengineering technologies to prepare glycosite-specific ADCs. In the first two-step approach, the antibody was deglycosylated and then reglycosylated with a modified intact N-glycan oxazoline. In the second one-pot approach, antibodies were deglycosylated and simultaneously glycosylated with a functionalized disaccharide oxazoline. For the comprehensive evaluation, we first optimized and scaled-up the preparation of azido glycan oxazolines. Afterwards, we proved that the one-pot glycan-remodeling approach was efficient for all IgG subclasses. Subsequently, we assembled respective ADCS using two technology routes, with two different linker-payloads combinations, and performed systemic in vitro and in vivo evaluations. All the prepared ADCs achieved high homogeneity and illustrated excellent stability in buffers with minimum aggregates, and exceptional stability in rat serum. All ADCs displayed a potent killing of BT-474 breast cancer cells. Moving to the mouse study, the ADCs prepared from two technology routes displayed potent and similar efficacy in a BT-474 xenograft model, which was comparable to an FDA-approved ADC generated from random conjugation. These ADCs also demonstrated excellent safety and did not cause body weight loss at the tested dosages.
Evaluation of Antibiotic Resistance Mechanisms in Gram-Negative Bacteria
Authors: Gauba Anusha , Rahman Khondaker Miraz
Published: November 2023

Multidrug-resistant Gram-negative bacterial infections are exponentially increasing, posing one of the most urgent global healthcare and economic threats. Due to the lack of new therapies, the World Health Organization classified these bacterial species as priority pathogens in 2017, known as ESKAPE pathogens. This classification emphasizes the need for urgent research and development of novel targeted therapies. The majority of these priority pathogens are Gram-negative species, which possess a structurally dynamic cell envelope enabling them to resist multiple antibiotics, thereby leading to increased mortality rates. Despite 6 years having passed since the WHO classification, the progress in generating new treatment ideas has not been sufficient, and antimicrobial resistance continues to escalate, acting as a global ticking time bomb. Numerous efforts and strategies have been employed to combat the rising levels of antibiotic resistance by targeting specific resistance mechanisms. These mechanisms include antibiotic inactivating/modifying enzymes, outer membrane porin remodelling, enhanced efflux pump action, and alteration of antibiotic target sites. Some strategies have demonstrated clinical promise, such as the utilization of beta-lactamase inhibitors as antibiotic adjuvants, as well as recent advancements in machine-based learning employing artificial intelligence to facilitate the production of novel narrow-spectrum antibiotics. However, further research into an enhanced understanding of the precise mechanisms by which antibiotic resistance occurs, specifically tailored to each bacterial species, could pave the way for exploring narrow-spectrum targeted therapies. This review aims to introduce the key features of Gram-negative bacteria and their current treatment approaches, summarizing the major antibiotic resistance mechanisms with a focus on Escherichia coli, Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae. Additionally, potential directions for alternative therapies will be discussed, along with their relative modes of action, providing a future perspective and insight into the discipline of antimicrobial resistance.
Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
Authors: Odeh Ammar , Abu Taleb Anas
Published: November 2023

Cybersecurity finds widespread applications across diverse domains, encompassing intelligent industrial systems, residential environments, personal gadgets, and automobiles. This has spurred groundbreaking advancements while concurrently posing persistent challenges in addressing security concerns tied to IoT devices. IoT intrusion detection involves using sophisticated techniques, including deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and anomaly detection algorithms, to identify unauthorized or malicious activities within IoT ecosystems. These systems continuously monitor and analyze network traffic and device behavior, seeking patterns that deviate from established norms. When anomalies are detected, security measures are triggered to thwart potential threats. IoT intrusion detection is vital for safeguarding data integrity, ensuring users’ privacy, and maintaining critical systems’ reliability and safety. As the IoT landscape evolves, effective intrusion detection mechanisms become increasingly essential to mitigate the ever-growing spectrum of cyber threats. Practical security approaches, notably deep learning-based intrusion detection, have been introduced to tackle these issues. This study utilizes deep learning models, including convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs), while introducing an ensemble deep learning architectural framework that integrates a voting policy within the model’s structure, thereby facilitating the computation and learning of hierarchical patterns. In our analysis, we compared the performance of ensemble deep learning classifiers with traditional deep learning techniques. The standout models were CNN-LSTM and CNN-GRU, achieving impressive accuracies of 99.7% and 99.6%, along with exceptional F1-scores of 0.998 and 0.997, respectively.
Towards a Method to Enable the Selection of Physical Models within the Systems Engineering Process: A Case Study with Simulink Models
Authors: Cibrián Eduardo , Álvarez-Rodríguez Jose María , Mendieta Roy , Llorens Juan
Published: November 2023

The use of different techniques and tools is a common practice to cover all stages in the development life-cycle of systems generating a significant number of work products. These artefacts are frequently encoded using diverse formats, and often require access through non-standard protocols and formats. In this context, Model-Based Systems Engineering (MBSE) emerges as a methodology to shift the paradigm of Systems Engineering practice from a document-oriented environment to a model-intensive environment. To achieve this major goal, a formalised application of modelling is employed throughout the life-cycle of systems to generate various system artefacts represented as models, such as requirements, logical models, and multi-physics models. However, the mere use of models does not guarantee one of the main challenges in the Systems Engineering discipline, namely, the reuse of system artefacts. Considering the fact that models are becoming the main type of system artefact, it is necessary to provide the capability to properly and efficiently represent and retrieve the generated models. In light of this, traditional information retrieval techniques have been widely studied to match existing software assets according to a set of capabilities or restrictions. However, there is much more at stake than the simple retrieval of models or even any piece of knowledge. An environment for model reuse must provide the proper mechanisms to (1) represent any piece of data, information, or knowledge under a common and shared data model, and (2) provide advanced retrieval mechanisms to elevate the meaning of information resources from text-based descriptions to concept-based ones. This need has led to novel methods using word embeddings and vector-based representations to semantically encode information. Such methods are applied to encode the information of physical models while preserving their underlying semantics. In this study, a text corpus from MATLAB Simulink models was preprocessed using Natural Language Processing (NLP) techniques and trained to generate word vector representations. Then, the presented method was validated using a testbed of MATLAB Simulink physical models in which verbalisations of models are transformed into vectors. The effectiveness of the proposed solution was assessed through a use case study. Evaluation of the results demonstrates a precision value of 0.925, a recall value of 0.865, and an F1 score of 0.884.
Seamless Industry 4.0 Integration: A Multilayered Cyber-Security Framework for Resilient SCADA Deployments in CPPS
Authors: Wai Eric , Lee C. K. M.
Published: November 2023

The increased connectivity and automation capabilities of Industry 4.0 cyber-physical production systems (CPPS) create significant cyber-security vulnerabilities in supervisory control and data acquisition (SCADA) environments if robust protections are not properly implemented. Legacy industrial control systems and new IP-enabled sensors, instruments, controllers, and appliances often lack basic safeguards like encryption, rigorous access controls, and endpoint security. This exposes manufacturers to substantial risks of cyberattacks that could manipulate, disrupt, or disable critical physical assets and processes related to their production lines and facilities. This study presents a multilayered cybersecurity framework to address these challenges and harden SCADA environments by implementing granular access controls, network micro-segmentation, anomaly detection, encrypted communications, and legacy system upgrades. The multilayered defense-in-depth (DID) approach combines policies, processes, and technologies to counter emerging vulnerabilities. The methodology was implemented in an electronics manufacturing facility across access control, zoning, monitoring, and encryption scenarios. Results show security improvements, including 57.4% fewer unauthorized access events, 41.2% faster threat containment, and 79.2% fewer hacking attempts. The quantified metrics highlight the CPPS resilience and threat mitigation capabilities enabled by the securely designed SCADA architecture, which allows manufacturers to confidently pursue Industry 4.0 integration and digital transformation with minimized disruption.
Bridge Construction Quality Evaluation Based on Combination Weighting Method- Technique for Order Preference by Similarity to an Ideal Solution Theory
Authors: Jin Ruibao , Wang Lei , Zhang Tianjing , Li Qingfu
Published: November 2023

The process of bridge construction is accompanied by many uncertainties. These uncertainties can have an impact on the quality of bridge construction and are thus directly related to the safe operation of the bridge. Therefore, it is very important to conduct bridge construction quality control evaluations for safe bridge construction and operation. In this paper, a three-tier bridge construction quality control evaluation system is established. This study uses a combination of subjective and objective assignment methods and TOPSIS theory to carry out an evaluation calculation of bridge construction quality. The CWM-TOPSIS theory was applied to the actual engineering calculation by taking the construction process of a mega bridge across the Yellow River as an example. After a series of calculations, the bridge construction quality evaluation results were obtained as level IV. This showed that a construction quality control method was needed for this bridge as soon as possible, and inspection and protection of the bridge should be started to avoid the emergence of bigger quality problems. Finally, the accuracy and applicability of the method proposed in this paper were proved by comparing and analyzing the evaluation results with the standard element theory.
Machine Learning Methods in Weather and Climate Applications: A Survey
Authors: Chen Liuyi , Han Bocheng , Wang Xuesong , Zhao Jiazhen , Yang Wenke , Yang Zhengyi
Published: November 2023

With the rapid development of artificial intelligence, machine learning is gradually becoming popular for predictions in all walks of life. In meteorology, it is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors, including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modeling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.
Collaborative Search Model for Lost-Link Borrowers Information Based on Multi-Agent Q-Learning
Authors: You Ge , Guo Hao , Dagestani Abd Alwahed , Alnafrah Ibrahim
Published: November 2023

To reduce the economic losses caused by debt evasion amongst lost-link borrowers (LBs) and improve the efficiency of finding information on LBs, this paper focuses on the cross-platform information collaborative search optimization problem for LBs. Given the limitations of platform/system heterogeneity, data type diversity, and the complexity of collaborative control in cross-platform information search for LBs, a collaborative search model for LBs’ information based on multi-agent technology is proposed. Additionally, a multi-agent Q-learning algorithm for the collaborative scheduling of multi-search subtasks is designed. We use the Q-learning algorithm based on function approximation to update the description model of the LBs. The multi-agent collaborative search problem is transformed into a reinforcement learning problem by defining search states, search actions, and reward functions. The results indicate that: (i) this model greatly improves the comprehensiveness and accuracy of the search for key information of LBs compared with traditional search engines; (ii) during searching for the information of LBs, the agent is more inclined to search on platforms and data types with larger environmental rewards, and the multi-agent Q-learning algorithm has a stronger ability to acquire information value than the transition probability matrix algorithm and the probability statistical algorithm for the same number of searches; (iii) the optimal search times of the multi-agent Q-learning algorithm are between 14 and 100. Users can flexibly set the number of searches within this range. It is significant for improving the efficiency of finding key information related to LBs.
Structural and Functional Insights into the Stealth Protein CpsY of Mycobacterium tuberculosis
Authors: Liu Dafeng , Yuan Cai , Guo Chenyun , Huang Mingdong , Lin Donghai
Published: November 2023

Mycobacterium tuberculosis (Mtb) is an important and harmful intracellular pathogen that is responsible for the cause of tuberculosis (TB). Mtb capsular polysaccharides can misdirect the host’s immune response pathways, resulting in additional challenges in TB treatment. These capsule polysaccharides are biosynthesized by stealth proteins, including CpsY. The structure and functional mechanism of Mtb CpsY are not completely delineated. Here, we reported the crystal structure of CpsY201−520 at 1.64 Å. CpsY201−520 comprises three β-sheets with five α-helices on one side and three on the other. Four conserved regions (CR1–CR4) are located near and at the base of its catalytic cavity, and three spacer segments (S1–S3) surround the catalytic cavity. Site-directed mutagenesis demonstrated the strict conservation of R419 at CR3 and S1–S3 in regulating the phosphotransferase activity of CpsY201−520. In addition, deletion of S2 or S3 (∆S2 or ∆S3) dramatically increased the activity compared to the wild-type (WT) CpsY201−520. Results from molecular dynamics (MD) simulations showed that S2 and S3 are highly flexible. Our study provides new insights for the development of new vaccines and targeted immunotherapy against Mtb.
Seismic Response Compression of Various MSE Walls Based on 3D Modeling
Authors: Akbar Muhammad , Pan Huali , Ou Guoqiang , Nikitas Georgios , Ahmed Bilal
Published: November 2023

This study evaluates the earthquake-induced movement of mechanically stabilized earth (MSE) walls. A thorough investigation was conducted on an MSE wall model, utilizing a comprehensive finite element (FE) analysis. This research focuses on investigating and designing MSE walls made of reinforcement concrete and hollow precast concrete panels. It also involves comparative studies such as on the vertical pressure of the wall, horizontal pressure of the wall, lateral pressure of the wall, settlement of the wall, settlement of the backfill reinforcement, vertical pressure of the backfill, horizontal pressure of the backfill, lateral pressure of the backfill, vertical settlement of the foundation, and settlements of soil layers across the height of the MSE walls. The FE simulations used a three-dimensional (3D) nonlinear dynamic FE model of full-scale MSE walls. The seismic performance of MSE walls has also been examined in terms of wall height. It was found that the seismic motion significantly impacts the height of the walls. In addition, the validity of the proposed study model was assessed by comparing it to the reinforcement concrete wall and ASSHTO guidelines using finite element (FE) simulation results. Based on the findings, the hollow prefabricated MSE wall was the most practical alternative due to its lower displacement and settlement. The specifics of the modeling approach used in this study and the lessons learned serve as benchmarks for future comparable lines of inquiry and practitioners, especially as the computational power of desktop computers continues to rise.
Investigating the Adsorption Kinetics of Dimethoate, Malathion and Chlorpyrifos on Cellulose-Derived Activated Carbons: Understanding the Influence of Physicochemical Properties
Authors: Lazarević-Pašti Tamara , Jocić Ana , Milanković Vedran , Tasić Tamara , Batalović Katarina , Breitenbach Stefan , Unterweger Christoph , Fürst Christian , Pašti Igor A.
Published: November 2023

In light of the escalating environmental concerns regarding pesticide accumulation, it is imperative to devise efficient strategies for their removal. Among the various options, activated carbons have emerged as promising candidates for adsorptive pesticide removal due to their many advantages, such as large surface area, well-developed porosity, and cost-effectiveness. However, the intricate relationship between the properties of these materials and their performance in pesticide adsorption remains largely unexplored. This study primarily focuses on examining the adsorption kinetics of three organophosphate pesticides: dimethoate, malathion (aliphatic), and chlorpyrifos (aromatic), using a range of cellulose-based activated carbon fibers with diverse specific surface areas, pore size distributions, and elemental compositions. By employing sophisticated data analysis tools, principal component analysis, and semi-empirical quantum chemical calculations, this study uncovers the importance of these distinct properties in efficiently removing structurally diverse pesticides. The results of the adsorption experiments suggested that these processes can be described using a pseudo-second-order kinetic model, which is confirmed via multiple linear regression. The obtained data suggest that the most effective carbon material for pesticide removal should have a pore diameter of approximately 4 nm, low oxygen content, a unimodal pore size distribution, and a high presence of sp2 domains. The insights from this research have the potential to guide the development of improved adsorbents and facilitate the rational selection of adsorbents tailored to specific pollutants based on their physicochemical properties and the pollutants’ chemical structure. By shedding light on the vital connection between adsorbent properties and performance, our findings significantly advance sustainable and effective pesticide removal, thereby fostering a cleaner and healthier environment.
Anticancer Effect of E26 Transformation-Specific Homologous Factor through the Induction of Senescence and the Inhibition of Epithelial–Mesenchymal Transition in Triple-Negative Breast Cancer Cells
Authors: Lim Soyoung , Lim Jihyun , Lee Aram , Kim Keun-Il , Lim Jong-Seok
Published: November 2023

The aim of the present study was to evaluate the effect of ETS homologous factor (EHF) in malignant breast cancer cells. The overexpression and knockdown of the EHF gene in human and mouse breast cancer cells were performed, and the TCGA dataset and Q-omics were analyzed. We found that the tumor suppressor NDRG2 is correlated with EHF gene expression in triple-negative breast cancer cells, that EHF overexpression results in reduced cell proliferation and that apoptosis is promoted by the chemotherapeutic reagent treatment of EHF-overexpressing cells. By EHF overexpression, senescence-associated β-galactosidase activity and p21WAF1/CIP1 expression were increased, suggesting that EHF may induce cellular senescence. In addition, the overexpression of EHF reduced the migratory ability and inhibited epithelial–mesenchymal transition (EMT). Furthermore, EHF inhibited the phosphorylation of STAT3. The overexpression of EHF also reduced the tumor size, and lung metastasis in vivo. At the tumor site, β-galactosidase activity was increased by EHF. Finally, the Kaplan–Meier-plotter analysis showed that TNBC patients with a high expression of EHF had a longer relapse-free survival rate. Our findings demonstrated that EHF inhibits breast tumor progression by inducing senescence and regulating EMT in TNBC cells.
Enhancing the Catalytic Performance of Platinum-Doped Lanthanum Hexaaluminate through Reduction Method Variation
Authors: Bhosale Vikas Khandu , Yoon Wonjae , Yoon Hosung
Published: November 2023

The development of a catalyst for the ammonium dinitramide (ADN)-based monopropellant system has been of growing interest in space research. In this study, we investigated the development methodology for the platinum hexaaluminate (Pt–LHA) catalyst. The reduction of Platinum was performed in muffle and tubular furnaces by considering the different heating rates, with or without hydrogen gas flow conditions. The dispersion of Platinum on LHA was confirmed by using SEM analysis; the particle size of Platinum in Pt–LHA-1 and Pt–LHA-11 was 5.1–9.1 μm and <100 nm, respectively. Notably, agglomeration of Platinum was observed when the catalyst was calcinated in a muffle furnace without air or H2 gas flow. Interestingly, the even dispersion of Platinum was revealed when the catalyst was calcinated in a tubular furnace at 4% H2 in N2 gas flow. As a result, Pt–LHA-7 to Pt–LHA-11 exhibited higher catalytic activity than Pt–LHA-1 to Pt–LHA-6 under H2O2 reactor conditions. The catalyst Pt–LHA-9 was further tested in a 1 N ADN thruster, demonstrating its capability to decompose the ADN-based monopropellant in an operation thruster.
Recent Innovative Progress of Metal Oxide Quantum-Dot-Integrated g-C3N4 (0D-2D) Synergistic Nanocomposites for Photocatalytic Applications
Authors: Salunkhe Tejaswi Tanaji , Gurugubelli Thirumala Rao , Babu Bathula , Yoo Kisoo
Published: November 2023

Modern industrialization has unleashed unprecedented environmental challenges, primarily in the form of pollution. In response to these pressing issues, the quest for innovative and sustainable solutions has intensified. Photocatalysis, with its unique capabilities, has emerged as a potent technology to combat the adverse effects of industrialization on the environment. This review highlights recent advances in harnessing photocatalysis to address environmental pollution. Photocatalysis offers a multifaceted approach, utilizing solar energy for catalytic reactions and enabling efficient pollutant removal. Quantum dots and graphitic carbon nitride (g-C3N4) are essential elements in this science. In contrast to quantum dots, which have enormous potential due to their size-dependent bandgap tunability and effective charge carrier production, g-C3N4 has properties like chemical stability and a configurable bandgap that make it a versatile material for photocatalysis. In this review, we explore recent achievements in integrating metal oxide quantum dots with g-C3N4, forming nanocomposites with superior photocatalytic activity. These nanocomposites exhibit extended light absorption ranges and enhanced charge separation efficiency, positioning them at the forefront of diverse photocatalytic applications. In conclusion, this comprehensive review underscores the critical role of photocatalysis as a potent tool to counteract the adverse environmental effects of modern industrialization. By emphasizing recent advancements in g-C3N4 and quantum dots and highlighting the advantages of metal oxide quantum dots decorated/integrated with g-C3N4 nanocomposites, this work contributes to the evolving landscape of sustainable solutions for environmental remediation and pollution control. These innovations hold promise for a cleaner and more sustainable future.
The Adaptive Role of Carotenoids and Anthocyanins in Solanum lycopersicum Pigment Mutants under High Irradiance
Authors: Ashikhmin Aleksandr , Bolshakov Maksim , Pashkovskiy Pavel , Vereshchagin Mikhail , Khudyakova Alexandra , Shirshikova Galina , Kozhevnikova Anna , Kosobryukhov Anatoliy , Kreslavski Vladimir , Kuznetsov Vladimir , Allakhverdiev Suleyman I.
Published: November 2023

The effects of high-intensity light on the pigment content, photosynthetic rate, and fluorescence parameters of photosystem II in high-pigment tomato mutants (hp 3005) and low-pigment mutants (lp 3617) were investigated. This study also evaluated the dry weight percentage of low molecular weight antioxidant capacity, expression patterns of some photoreceptor-regulated genes, and structural aspects of leaf mesophyll cells. The 3005 mutant displayed increased levels of photosynthetic pigments and anthocyanins, whereas the 3617 mutant demonstrated a heightened content of ultraviolet-absorbing pigments. The photosynthetic rate, photosystem II activity, antioxidant capacity, and carotenoid content were most pronounced in the high-pigment mutant after 72 h exposure to intense light. This mutant also exhibited an increase in leaf thickness and water content when exposed to high-intensity light, suggesting superior physiological adaptability and reduced photoinhibition. Our findings indicate that the enhanced adaptability of the high-pigment mutant might be attributed to increased flavonoid and carotenoid contents, leading to augmented expression of key genes associated with pigment synthesis and light regulation.
Comparing Public Participation in Coastal and Marine Planning in the Arctic: Lessons from Iceland and Norway
Authors: Wilke Maria
Published: November 2023

Amid a changing global climate, Northern coastal communities face a variety of challenges to their livelihoods, which are dependent on marine resources. Marine spatial planning (MSP) provides opportunities for cooperation between authorities, stakeholders, and the public to ensure sustainable marine management. Public participation is a crucial element of coastal and marine planning for its long-term democratic legitimacy and sustainability. However, the process of MSP is often wrought with conflict and challenges of involving stakeholders and the public in decisions concerning an often-contested marine space. Whereas coastal zone planning (CZP) is well established in Norway and a reiteration of previous CZP was conducted 2020–23, MSP is new to Iceland, and has only recently been launched with its first pilot plans in 2019. This study investigates how participation in coastal and marine planning processes compare between Iceland and Norway and what lessons can be shared between them. Data were collected from two case studies in the Tromsø region in Norway and the Westfjords of Iceland through analysis of planning documentation, literature review, as well as participant observation in the Westfjords and 11 semi-structured interviews across both case studies. The results show that public participation is formally integral to both processes but, in practice, varies considerably. Both planning processes are driven by the expansion of the aquaculture industry, and a variety of issues faced during the planning process are similar. In Norway, public participation is politically desired and guided by a participation strategy emphasising synergies between expert and local knowledge. In the Tromsø region, meaningful public participation varied across municipalities and issues regarding Indigenous participation remain. In Iceland, there is little evident political expectation of public engagement, and the process is characterised by a passive approach to participation that aims to inform the public but does not include wider sharing of decision-making power. The findings do not only make clear that a revision of current public participation processes is needed in both case studies but also point towards wider issues in marine governance that have consequences for blue justice, such as the exclusion of groups in decision-making, lack of public discussion of marine issues and top–down governance supporting established power hierarchies.
Comparison of Mini-Tablets and Pellets as Multiparticulate Drug Delivery Systems for Controlled Drug Release
Authors: Priese Florian , Wiegel Dimitri , Funaro Caterina , Mondelli Giusi , Wolf Bertram
Published: November 2023

Mini-tablets made into hard capsules or administered using special dosing units, as well as pellets in hard capsules or compressed into tablets, offer the advantages of multiparticulate drug delivery systems and are suitable for controlled drug release using polymer coatings. Four different kinds of solid drug preparations were manufactured and investigated concerning drug release. Inert pellets were coated with the model drug sodium benzoate and, in a second step, with the insoluble polymer ethylcellulose. The coated pellets were compressed into mini-tablets and into normal tablets. Another kind of mini-tablet was compressed from a sodium benzoate compression mixture and finally coated with ethylcellulose. The coating of the tablets was performed using fluidized bed technology. The sodium benzoate release plots of the coated pellets show a lag time and retarded release according first-order kinetics. The mini-tablets and normal tablets compressed from pellets release sodium benzoate according to first-order kinetics as well, but without the lag time due to distinct ethylcellulose layer destruction during tableting. The release is retarded with increasing ethylcellulose layer thickness on directly compressed mini-tablets. The different formulations of coated pellets, mini-tablets, and normal tablets offer a broad choice for variable drug release kinetics depending on the biopharmaceutical and pharmacological requirements.
Structural and Functional Changes in Non-Paraneoplastic Autoimmune Retinopathy
Authors: Akhavanrezayat Amir , Khatri Anadi , Onghanseng Neil Gregory L. , Halim Muhammad Sohail , Or Christopher , Sredar Nripun , Razeen Moataz , Hasanreisoglu Murat , Regenold Jonathan , Thng Zheng Xian , Mohammadi S. Saeed , Jain Tanya , Yavari Negin , Bazojoo Vahid , Gupta Ankur Sudhir , Mobasserian Azadeh , Yasar Cigdem , Than Ngoc Trong Tuong , Uludag Kirimli Gunay , Karaca Irmak , Shin Yong-Un , Yoo Woong-Sun , Ghoraba Hashem , Do Diana V. , Dubra Alfredo , Nguyen Quan Dong
Published: November 2023

Background: To describe longitudinal changes in patients with non-paraneoplastic autoimmune retinopathy (npAIR) by utilizing different diagnostic modalities/tests. Methods: The index study is a retrospective longitudinal review of sixteen eyes of eight patients from a tertiary care eye hospital diagnosed with npAIR. Multiple diagnostic modalities such as wide-angle fundus photography (WAFP), WA fundus autofluorescence (WAFAF), spectral-domain optical coherence tomography (SD-OCT), Goldmann visual field (GVF) perimetry, microperimetry (MP), electrophysiologic testing, and adaptive optics scanning laser ophthalmoscopy (AOSLO) were reviewed and analyzed. Results: At the baseline visits, anomalies were detected by multimodal diagnostic tests on all patients. Subjects were followed up for a median duration of 11.5 [3.0–18.7] months. Structural changes at the baseline were detected in 14 of 16 (87.5%) eyes on WAFP and WAFAF and 13 of 16 (81.2%) eyes on SD-OCT. Eight of the ten (80%) eyes that underwent AOSLO imaging depicted structural changes. Functional changes were detected in 14 of 16 (87.5%) eyes on GVF, 15 of 16 (93.7%) eyes on MP, and 11 of 16 (68.7%) eyes on full-field electroretinogram (ff-ERG). Multifocal electroretinogram (mf-ERG) and visual evoked potential (VEP) tests were performed in 14 eyes, of which 12 (85.7%) and 14 (100%) of the eyes demonstrated functional abnormalities, respectively, at baseline. Compared to all the other structural diagnostic tools, AOSLO had a better ability to demonstrate deterioration in retinal microstructures occurring at follow-ups. Functional deterioration at follow-up was detected on GVF in 8 of 10 (80%) eyes, mf-ERG in 4 of 8 (50%) eyes, and MP in 7 of 16 (43.7%) eyes. The ff-ERG and VEP were stable in the majority of cases at follow-up. Conclusions: The utilization of multimodal imaging/tests in the diagnosing and monitoring of npAIR patients can aid in identifying anomalous changes over time. Analysis of both the anatomical and functional aspects by these devices can be supportive of detecting the changes early in such patients. AOSLO shows promise as it enables the capture of high-resolution images demonstrating quantifiable changes to retinal microstructure.