bluuebunny/crossref_metadata_2025 huggingface dataset:
Association between illness perception and medication adherence in patients with diabetes mellitus in North Shoa, Zone: cross-sectional study
Authors: Eshete Akine , Getye Birhan , Aynaddis Getachew , Tilaye Bantalem , Mekonnen Elda , Taye Bethlehem , Zeleke Dereje , Deresse Tilahun , Kifleyohans Tewodros , Assefa Yibeltal
Published: December 2023

Background

Although the impact of illness perception on medication adherence is well-established, its specific influence on medication adherence in Ethiopia remains unclear. Consequently, the objective of this study was to examine the association between illness perception and medication adherence among patients with diabetes mellitus in the North Shoa Zone.

Methods

An institution-based cross-sectional study was conducted from 24 May to 25 June 2022 in the North Shoa zone. The study included a random sample of 552 individuals with diabetes from four public hospitals. Data was collected and entered into Epi Data V.3.1, and analysis was performed using SPSS version 22. Descriptive statistics were used to summarize continuous variables as means with standard deviations, while categorical variables were presented as percentages. The study variables were analyzed using binary logistic regression models to assess the associations between illness perception and medication adherence. In the bivariable analysis, variables with p-values less than 0.20 were entered into a multivariable logistic regression model. Associations with a p-value ≤0.05 and an odds ratio with a 95% confidence interval were considered statistically significant.

Results

The study results revealed that medication adherence was 64.4% (95% CI: 60.1, 67.9), while illness perception was 54.7% (95% CI, 41.2, 49.4). There was a significant and strong association between illness perception and medication adherence (p < 0.0001). In the adjusted model, the illness perception components of consequence showed a significant association with medication adherence (AOR = 3.10, 95% CI: 2.11, 4.55). Similarly, personal control (AOR = 1.77, 95% CI: 1.20, 2.61) and emotional representation of diabetes (AOR = 2.26, 95% CI: 1.54, 3.32) were also significantly associated with medication adherence in patients with diabetes.

Conclusion

The findings of this study indicate a positive association between higher illness perception and increased medication adherence and practice. Therefore, when engaging in discussions about diabetic self-management, diabetes educators should employ psychoeducational approaches that take into account the illness perceptions of patients.
Developing the theoretical model of Chinese physical education teachers’ health communication competence: based on grounded theory
Authors: Chen Lilin , Xu Yue , Li Fangfei , Sun Mingzhu , Yin Zhihua , Guo Zhen , Liu Bo
Published: December 2023

Background

Physical education teachers’ health communication competence is a key factor in health promotion. Although health communication is a multidisciplinary field, medical practitioners are the primary focus of health communication research, whereas physical education teachers are marginalized. Therefore, this study proposes a theoretical model of health communication competence for physical education teachers.

Methods

This qualitative research utilized interviews as the primary data collection method. Purposeful sampling was employed to select participants, including university teachers, primary and secondary school teachers, and health education professionals from diverse regions of China. A total of 31 participants were interviewed through two focus groups (N = 15) and individual semi-structured interviews (N = 16). Grounded theory was used to analyze and code the collected interview materials.

Results

The health communication competence of physical education teachers consisted of three main categories, 10 subcategories, 30 concepts, and 240 statement labels. The three main categories were as follows: (i) foundations of health communication knowledge and skills (this category encompassed three subcategories, namely sport and health knowledge reserve, health beliefs, and health behaviors); (ii) health communication perception competence (this category included two subcategories, namely health risk and crisis perception competence and communication audience perception competence); and (iii) practical competence of health communication (this category consisted of five subcategories, namely language expression competence, organizational and design competence, utilization of new media tools competence, communication content selection and processing competence, and professional skills).

Conclusion

The theoretical model of health communication competence in this study provides a foundation for the involvement of physical education teachers in health communication work. It can serve as a reference for the development of both pre-service health education courses and in-service training systems for physical education teachers. Future research can expand the sample size and geographic coverage to further validate the applicability of the findings. Additionally, investigating the factors influencing the formation of the identified competencies is recommended.
The combined impact of social networks and connectedness on anxiety, stress, and depression during COVID-19 quarantine: a retrospective observational study
Authors: Luo Huiting , Luo Dan , Tang Qiao , Niu Zhiang , Xu Jiajun , Li Jing
Published: December 2023

Introduction

The COVID-19 pandemic and associated quarantine measures have precipitated a surge in mental health disorders, particularly depression and anxiety. Government policies and restrictions on physical activity have contributed to this phenomenon, as well as diminished subjective social connectedness and exacerbated objective social isolation. As two dimensions of social isolation, it is worth noting that subjectively perceived social connectedness serves as a protective factor for mental health, whereas the decline in the size of objectively evaluated social networks poses a significant risk. However, research investigating the combined influence of these two dimensions remains limited.

Methods

This study used an online survey to collect data to investigate the effects of objective social connectedness and objective social networks on anxiety, stress, and depression during COVID-19 quarantine. A total of 485 participants were analyzed using statistical methods, including paired t-test, Pearson correlation analysis, linear regression, cluster analysis, ANOVA, and moderated mediated.

Results

The study found that anxiety and depression scores increased during the quarantine, with age, education, and social connectedness scores associated with the increase. Pre-quarantine anxiety and depression levels were strongly correlated with mental health status during quarantine. Cluster analysis, respectively, revealed three clusters for those without increasing anxiety and depression scores. The study also found that objective social network influences the impact of subjective social connectedness on pre-quarantine mental health, which in turn affects anxiety and depression levels during quarantine.

Conclusion

The study identified that quarantine increased anxiety and depression, with age being protective, and education and subjective social connectedness as risk factors. The study also emphasizes the comprehensive impact of objective and subjective social isolation. Although individuals perceive the same degree of social connectedness, those with smaller social networks are more prone to developing symptoms of anxiety and depression, which are also more likely to worsen during quarantine.
Using normalization process theory to inform practice: evaluation of a virtual autism training for clinicians
Authors: O’Hagan Belinda , Augustyn Marilyn , Amgott Rachel , White Julie , Hardesty Ilana , Bangham Candice , Ursitti Amy , Foster Sarah , Chandler Alana , Greece Jacey
Published: December 2023

Background

There is growing demand for developmental and behavioral pediatric services including autism evaluation and care management. Clinician trainings have been found to result in an increase of knowledge and attitudes. This study utilizes Normalization Process theory (NPT) to evaluate a clinician training program and its effects on practice.

Methods

The year-long virtual training program about autism screening and care management included didactic portions and case presentations. Focus groups and interviews were conducted with primary care clinicians (n = 10) from community health centers (n = 6) across an urban area five months post-training. Transcripts were deductively coded using NPT to uncover barriers to implementation of autism screening and care, benefits of the training program, and areas for future training.

Results

Participants were motivated by the benefits of expanding and improving support for autistic patients but noted this effort requires effective collaboration within a complex network of care providers including clinicians, insurance agencies, and therapy providers. Although there were support that participants could provide to families there were still barriers including availability of behavior therapy and insufficient staffing. Overall, participants positively viewed the training and reported implementing new strategies into practice.

Conclusion

Despite the small sample size, application of NPT allowed for assessment of both training delivery and implementation of strategies, and identification of recommendations for future training and practice sustainability. Follow-up focus groups explored participants' practice five months post-program. Variations in participants' baseline experience and context at follow-up to enable application of skills should be considered when using NPT to evaluate clinician trainings.
Smoothed particle magnetohydrodynamics
Authors: Tricco Terrence S.
Published: December 2023

Smoothed particle magnetohydrodynamics has reached a level of maturity that enables the study of a wide range of astrophysical problems. In this review, the numerical details of the modern SPMHD method are described. The three fundamental components of SPMHD are methods to evolve the magnetic field in time, calculate accelerations from the magnetic field, and maintain the divergence-free constraint on the magnetic field (no monopoles). The connection between these three requirements in SPMHD will be highlighted throughout. The focus of this review is on the methods that work well in practice, with discussion on why they work well and other approaches do not. Numerical instabilities will be discussed, as well as strategies to overcome them. The inclusion of non-ideal MHD effects will be presented. A prospective outlook on possible avenues for further improvements will be discussed.
Characterizing stress during animal interaction: a focus on the human endocrine response during equine-assisted services
Authors: Rigby Brandon R.
Published: December 2023

Repeated stresses applied to the rider may contribute to the documented physical and psychosocial outcomes from equine-assisted services. In this brief review, a summary of neuroendocrine markers of stress, including immunoglobulin A, serotonin, cortisol, progesterone, and oxytocin, is presented within the context of the physiology of stress modulation. Results are mixed with regard to the effects of these hormones on rider physiology before, during, and after equine-assisted services. However, some results from existing studies are promising with regard to the attenuation of stress. Future research should include a cross-disciplinary approach when conducting well-controlled studies with proper treatment and experimental fidelity, while also considering exogenous and endogenous factors that influence rider physiology.
Morphologic and Molecular Features of Antibody-Mediated Transplant Rejection: Pivotal Role of Molecular Injury as an Independent Predictor of Renal Allograft Functional Decline
Authors: Herz Carsten T. , Diebold Matthias , Kainz Alexander , Mayer Katharina A. , Doberer Konstantin , Kozakowski Nicolas , Halloran Philip F. , Böhmig Georg A.
Published: December 2023

Current knowledge about the factors correlating with functional decline and subsequent failure of kidney allografts in antibody-mediated rejection (ABMR) is limited. We conducted a cohort study involving 75 renal allograft recipients diagnosed with late ABMR occurring at least 6 months after transplantation. The study aimed to examine the correlation of molecular and histologic features with estimated glomerular filtration rate (eGFR) trajectories and death-censored graft survival. We focused on sum scores reflecting histologic ABMR activity versus chronicity and molecular scores of ABMR probability (ABMRProb), injury-repair response (IRRAT) and fibrosis (ciprob). In multivariable Cox analysis, a Banff lesion-based chronicity index (ci+ct+cg[x2]; hazard ratio per interquartile range [IQR]: 1.97 [95% confidence interval: 0.97 to 3.99]) and IRRAT (1.93 [0.96 to 3.89]) showed the strongest associations with graft failure. Among biopsy variables, IRRAT exhibited the highest relative variable importance and emerged as the sole independent predictor of eGFR slope (change per IQR: −4.2 [−7.8 to −0.6] mL/min/1.73 m2/year). In contrast, morphologic chronicity associated with baseline eGFR only. We conclude that the extent of molecular injury is a robust predictor of renal function decline. Transcriptome analysis has the potential to improve outcome prediction and possibly identify modifiable injury, guiding targeted therapeutic interventions.
Fixed-Wing UAV Formation Path Planning Based on Formation Control: Theory and Application
Authors: Liu Chenglou , Xie Fangfang , Ji Tingwei
Published: December 2023

Formation path planning is a significant cornerstone for unmanned aerial vehicle (UAV) swarm intelligence. Previous methods were not suitable for large-scale UAV formation, which suffered from poor formation maintenance and low planning efficiency. To this end, this paper proposes a novel millisecond-level path planning method appropriate for large-scale fixed-wing UAV formation, which consists of two parts. Instead of directly planning paths independently for each UAV in the formation, the proposed method first introduces a formation control strategy. It controls the chaotic UAV swarm to move as a single rigid body, so that only one planning can obtain the feasible path of the entire formation. Then, a computationally lightweight Dubins path generation method with a closed-form expression is employed to plan feasible paths for the formation. During flight, the aforementioned formation control strategy maintains the geometric features of the formation and avoids internal collisions within the formation. Finally, the effectiveness of the proposed framework is exemplified through several simulations. The results show that the proposed method can not only achieve millisecond-level path planning for the entire formation but also excellently maintain formation during the flight. Furthermore, simple formation obstacle avoidance in a special case also highlights the application potential of the proposed method.
Superhydrophobic Coatings for Corrosion Protection of Stainless Steel
Authors: Piscitelli Filomena , Volpe Annalisa
Published: December 2023

Corrosion is a persistent challenge in the aviation industry, affecting the safety, performance, and maintenance costs of aircraft. While composite materials have gained widespread use due to their lightweight properties and corrosion resistance, certain critical parts, such as the wing and empennage leading edges and the engine inlet, demand alternative solutions. Aluminum, titanium, and stainless steel emerge as mandatory materials for such components, given their exceptional strength and durability. However, protecting these metallic components from corrosion remains crucial. In this paper, we present a study aimed at evaluating the corrosion resistance of stainless steel, employed as an erosion shielding panel for a composite vehicle’s wing, layered with a superhydrophobic coating. The samples with and without coating have been characterized by contact angle measurements, microscopy (optical and electronic), and visual inspection after immersion in two solutions, NaCl and NaOH, respectively. The application of the superhydrophobic coating demonstrated a significant reduction in corrosion extent, especially in the demanding NaCl environment. This was evidenced by diminished formation of ripples and surface roughness, decreased iron oxide formation from oxidative processes, and a lower Surface Free Energy value in both liquid environments. Notably, the surface maintained its superhydrophobic properties even following an 8-day immersion in NaCl and NaOH solutions, demonstrating the reliability of the superhydrophobic coating offering as a potential solution to enhance the longevity and reliability of aircraft structures.
Single-Season Rice Area Mapping by Combining Multi-Temporal Polarization Decomposition Components and the Two-Stage Segmentation Method
Authors: Jiang Jingling , Zhang Hong , Ge Ji , Xu Lu , Song Mingyang , Sun Chunling , Wang Chao
Published: December 2023

Recently, Synthetic Aperture Radar (SAR) data, especially Sentinel-1 data, have been increasingly used in rice mapping research. However, current studies usually use long time series data as the data source to represent the differences between rice and other ground objects, especially other crops, which results in complex models and large computational complexity during classification. To address this problem, a novel method for single season rice mapping is proposed, based on the principle that the scattering mechanism of rice paddies in the early flooding period is strongly influenced by water bodies, causing the volume scattering to be lower than that of other crops. Thus, a feature combination that can effectively and stably extract rice planting areas was constructed by combining multi-temporal volume scattering in the early flooding period of rice using dual-polarization SAR data, so that a simple semantic segmentation model could realize high-precision rice mapping tasks. A two-stage segmentation structure was introduced to further improve the mapping result with the Omni-dimensional Dynamic Convolution Residual Segmentation model (ODCRS model) as the bone model. In the experiment, Suihua City, Heilongjiang Province was selected as the study site, and the VH/VV polarized data of Sentinel-1 satellite in 2022 was used as the data source. The mapping accuracy of the ODCRS model was 88.70%, and the user accuracy was 84.19% on the field survey data. Furthermore, experiments with different years and regions also proved the effectiveness and stability of the proposed method.
Exploring the Robustness of Causal Structures in Omics Data: A Sweet Cherry Proteogenomic Perspective
Authors: Ganopoulou Maria , Xanthopoulou Aliki , Michailidis Michail , Angelis Lefteris , Ganopoulos Ioannis , Moysiadis Theodoros
Published: December 2023

Causal discovery is a highly promising tool with a broad perspective in the field of biology. In this study, a causal structure robustness assessment algorithm is proposed and employed on the causal structures obtained, based on transcriptomic, proteomic, and the combined datasets, emerging from a quantitative proteogenomic atlas of 15 sweet cherry (Prunus avium L.) cv. ‘Tragana Edessis’ tissues. The algorithm assesses the impact of intervening in the datasets of the causal structures, using various criteria. The results showed that specific tissues exhibited an intense impact on the causal structures that were considered. In addition, the proteogenomic case demonstrated that biologically related tissues that referred to the same organ induced a similar impact on the causal structures considered, as was biologically expected. However, this result was subtler in both the transcriptomic and the proteomic cases. Furthermore, the causal structures based on a single omic analysis were found to be impacted to a larger extent, compared to the proteogenomic case, probably due to the distinctive biological features related to the proteome or the transcriptome. This study showcases the significance and perspective of assessing the causal structure robustness based on omic databases, in conjunction with causal discovery, and reveals advantages when employing a multiomics (proteogenomic) analysis compared to a single-omic (transcriptomic, proteomic) analysis.
Body Condition of Reproductive and Non-Reproductive Broad-Snouted Caiman Females
Authors: Viotto Evangelina V. , Leiva Pamela M. L. , Pierini Sofía E. , Simoncini Melina S. , Navarro Joaquín L. , Piña Carlos I.
Published: December 2023

In this work, we calculated the body condition indices, K-Fulton and scaled mass index (SMI), of reproductive and non-reproductive Caiman latirostris adult females as an indication of stored energy. We considered 87 adult females captured from 2001 to 2018, both reproductive and non-reproductive. The body condition was calculated considering two scenarios: (a) only the weight of the female, and (b) the sum of the weight of the female and the average dry weight of her nest. We tested the difference in body condition between reproductive and non-reproductive females. We also evaluated the minimal body condition required to guarantee that females above it are reproductive by drawing a line that separated the body condition of strictly reproductive individuals from those that may or may not be reproductive. Reproductive females had better body condition than non-reproductive ones. Our SMI.S line separated almost 70% of the reproductive females. Based on our results, we can guarantee that a female whose body condition is above the line will reproduce, although not all those females below the line are non-reproductive, as a few of those under the line will nest. With this information, we have one more biological indicator to take into account when making management and conservation decisions.
Virulence of Pseudomonas aeruginosa in Cystic Fibrosis: Relationships between Normoxia and Anoxia Lifestyle
Authors: Papa Rosanna , Imperlini Esther , Trecca Marika , Paris Irene , Vrenna Gianluca , Artini Marco , Selan Laura
Published: December 2023

The airways of cystic fibrosis (CF) patients are colonized by many pathogens and the most common is Pseudomonas aeruginosa, an environmental pathogen that is able to infect immunocompromised patients thanks to its ability to develop resistance to conventional antibiotics. Over 12% of all patients colonized by P. aeruginosa harbour multi-drug resistant species. During airway infection in CF, P. aeruginosa adopts various mechanisms to survive in a hostile ecological niche characterized by low oxygen concentration, nutrient limitation and high osmotic pressure. To this end, P. aeruginosa uses a variety of virulence factors including pigment production, biofilm formation, motility and the secretion of toxins and proteases. This study represents the first report that systematically analyzes the differences in virulence features, in normoxia and anoxia, of clinical P. aeruginosa isolated from CF patients, characterized by multi- or pan-drug antibiotic resistance compared to antibiotic sensitive strains. The virulence features, such as biofilm formation, protease secretion and motility, are highly diversified in anaerobiosis, which reflects the condition of chronic CF infection. These findings may contribute to the understanding of the real-world lifestyle of pathogens isolated during disease progression in each particular patient and to assist in the design of therapeutic protocols for personalized medicine.
Comparative Pharmacokinetics and Bioequivalence of Pour-On Ivermectin Formulations in Korean Hanwoo Cattle
Authors: Kim Suyoung , Chae HyunYoung , Lee Eon-Bee , Lee Gayeong , Park Seung-Chun , Kang Jeongwoo
Published: December 2023

This study aimed to conduct a bioequivalence study of applying three pour-on ivermectin formulations at a dose of 1 mg/kg on the back of Korean native beef cattle (Hanwoo). To conduct bioequivalence testing, the pharmacokinetics of three groups (control Innovator, test Generic A, and test Generic B) of five clinically healthy Korean Hanwoo cattle (average weight 500 kg) were studied. After topical application to the skin, blood samples were drawn at the indicated times. These blood samples were analyzed using liquid chromatography–tandem mass spectrometry (LC-MS/MS). The time required to reach the maximum concentration (Tmax), the maximum concentration (Cmax), and the area under the curve (AUClast) of each pharmacokinetic parameter were compared for bioequivalence. The results showed that the control had a Tmax of 41 ± 1.24 h, a Cmax of 0.11 ± 0.01 μg/mL, and an AUClast of 9.33 ± 0 h*μg/mL). The comparator Generic A had a Tmax of 40 ± 1.14 h, a Cmax of 0.10 ± 0.01 (μg/mL, and an AUClast of 9.41 ± 0.57 h*μg/mL, while Generic B had a Tmax of 40 ± 2.21 h, a Cmax of 0.10 ± 0.01 μg/mL, and an AUClast of 9 h*μg/mL. The values of the bioequivalence indicators Cmax, Tmax, and AUC were all within the range of 80% to 120%, confirming that all three tested formulations were bioequivalent. In conclusion, the study showed that the two generic products were bioequivalent to the original product in Hanwoo cattle.
A Review of the Clinical Utilization of Oral Antibacterial Therapy in the Treatment of Bone Infections in Adults
Authors: Haddad Nicholas , Ajaz Jibran , Mansour Lina , Kasemodel Robert , Jarvis Jennifer , Jarad John , Gorski Haley , Carr Maddie
Published: December 2023

Chronic osteomyelitis in adults is managed with prolonged courses of intravenous antibiotics in conjunction with surgical debridement of necrotic bone. Over the past 40 years, there has been no paradigm shift in this approach, as randomized controlled trials of this standard of care compared to alternatives such as prolonged oral antibiotics are scarce. However, there have been many small trials, case reports, and review papers evaluating the effectiveness of oral treatment for chronic osteomyelitis. The oral route for infections requiring prolonged treatment is intuitively and practically more favorable due to several advantages, the most important of which is the avoidance of long-term IV antimicrobial therapy with its complications, inconvenience, and cost. In this paper, we review the literature evaluating oral antibiotic therapy in the management of chronic bone infections since 1975. The majority of osteomyelitis infections are caused by Staphylococcus aureus, hence we focus on its treatment using oral antibiotics; however, we also emphasize subpopulations of patients with diabetes, implanted hardware, and with less common bacterial organisms. The primary objective of this review is to promulgate clinical recommendations on the use of oral antibiotics in bone infections in the context of initial therapy, transition from intravenous therapy, and the role of chronic suppression. The secondary objective is to summarize current knowledge of the specific oral antimicrobial agents that are commonly utilized, together with a synopsis of the available literature pertaining to their pharmacokinetic/pharmacodynamic properties and duration of therapy in bone infection.
Progressive Design of a Ranatuerin-2 Peptide from Amolops wuyiensis: Enhancement of Bioactivity and In Vivo Efficacy
Authors: Yao Aifang , Liu Tianxing , Cai Yuhai , Zhou Siqi , Chen Xiaoling , Zhou Mei , Ma Chengbang , Chen Tianbao , Shaw Chris , Wang Lei
Published: December 2023

Antimicrobial peptides (AMPs) that exert multiple functions are considered promising candidates to combat the bacterial drug resistance crisis. Nowadays, targeted peptide modification has been widely recognised to improve biological activity and make up for deficiencies in clinical applications such as toxicity. In this study, a helix-loop peptide was isolated and identified from the skin secretion of the Wuyi torrent frog Amolops wuyiensis, namely, ranatuerin-2-AW (R2AW) (GFMDTAKNVAKNVAATLLDKLKCKITGGC). Target modifications were made to R2AW to study the structure–activity relationships and to optimise its bioactivities. Five analogues were progressively designed via residue substitution and truncation and the antibacterial and anticancer activities were evaluated. We found that the serine-substitution and cyclic-domain-deletion products showed similar antibacterial activity to the natural peptide R2AW, implying that the disulphide bridge and Rana box were dispensable for the antibacterial activity of ranatuerin-2 peptides. Notably, the cationicity- and hydrophobicity-enhanced variant, [Lys4,19, Leu20]R2AW(1-22)-NH2, exhibited significantly optimised antibacterial and anticancer activities. Additionally, it killed bacteria by membrane disruption at a highly efficient rate. Moreover, [Lys4,19, Leu20]R2AW(1-22)-NH2 exerted potential in vivo efficacy in a methicillin-resistant Staphylococcus aureus (MRSA)-infected waxworm model. Overall, this study demonstrated some rational design ideas for optimising the dual antibacterial and anticancer activities of ranatuerin-2 peptides and it proposes [Lys4,19, Leu20]R2AW(1-22)-NH2 as an appealing candidate for therapeutic development.
Predicting River Discharge in the Niger River Basin: A Deep Learning Approach
Authors: Ogunjo Samuel , Olusola Adeyemi , Olusegun Christiana
Published: December 2023

Across West Africa, the River Niger is a major source of freshwater. In addition, the river system also provides services such as aquaculture, transportation, and hydropower. The river network plays a critical role in the hydropolitics and hydroeconomics of the region. Therefore, River Niger is integral to the development of West Africa, hence, there is a need to ensure that the river’s ecosystem is a healthy one. In light of the changing climate and its associated threats such as droughts and floods, constant monitoring and measurements of the the river’s flow system cannot be overemphasized. This study investigates temporal variations in annual river discharge characteristics at eight stations (Koulikoro, Dioila, Kirango, Douna, Mopti, Dire, Ansongo, and Niamey) in the Niger River basin, presenting detailed quantitative findings. Analyzing discharge data of River Niger from 1950 to 1990, the minimum discharge measures (minimum and 10th percentile) exhibit a consistent decreasing trend post-1960, persisting into the 1990s at several stations. Central tendency measures (mean and 50th percentile) also consistently reduced since 1950, with near-zero median values observed in Diola and Douna. Recovery in mean discharge is evident in Ansongo after 1980. Extreme values (maximum and 90th percentile) show decreasing trends across all stations, with some locations exhibiting a slight recovery after 1980. The decreasing trend in annual minimum, mean, and maximum values has implications for water resources, affecting hydroelectric generation, fish farming, and dry season irrigation. Machine learning algorithms (MLAs) are deployed to predict the prediction of monthly river discharge, with LSTM identified as the best-performing model overall. However, model performance varies across locations, with TCN excelling in Diola but underperforming in Koulikoro. This study emphasizes the chaotic nature of time series data and external drivers limiting the long-term predictive capabilities of MLAs. Quantitative evaluation of MLA performance reveals specific strengths and weaknesses at each station. This study underscores the importance of predicting the 10th percentile of annual river discharge for water resource planning. Models exhibit diverse performance across basins, emphasizing the need for tailored approaches. Further analysis considers measures of central tendencies, predicting the 50th percentile (Q50) and mean discharge values. TCN emerges as the best model for Q50 prediction, showcasing superior performance over other models. Additionally, the study delves into predicting high and low extreme discharges, crucial for understanding potential flood events and preparing for meteorological and hydrological droughts. This study concludes by emphasizing the necessity for location-specific studies in the River Niger basin to facilitate an enhanced integrated river management system.
Modeling the Optimal Maintenance Strategy for Bridge Elements Based on Agent Sequential Decision Making
Authors: Xin Gongfeng , Liang Zhiqiang , Hu Yerong , Long Guanxu , Zhang Yang , Liang Peng
Published: December 2023

In addressing the issues of low efficiency in bridge maintenance decision making, the inaccurate estimation of maintenance costs, and the lack of specificity in decision making regarding maintenance measures for specific defects, this study utilizes data from regular bridge inspections. It employs a two-parameter Weibull distribution to model the duration variables of the states of bridge elements, thereby enabling the prediction of the duration time of bridge elements in various states. Referring to existing bridge maintenance and repair regulations, the estimation process of maintenance costs is streamlined. Taking into account the specific types and development state of bridge defects, as well as considering the adequacy of maintenance and the restorative effects of maintenance measures, an intelligent agent sequential decision-making model for bridge maintenance decisions is established. The model utilizes dynamic programming algorithms to determine the optimal maintenance and repair measures for elements in various states. The decision results are precise, all the way down to the specific bridge elements and maintenance measures for individual defects. In using the case of the regular inspection data of 222 bridges along a highway loop, this study further validates the effectiveness of the proposed research methods. By constructing an intelligent agent sequential decision-making model for bridge element maintenance, the optimal maintenance measures for 21 bridge elements in different states are obtained, thereby significantly enhancing the efficiency of actual bridge maintenance and the practicality of decision results.
Path Planning of a Mobile Robot Based on the Improved RRT Algorithm
Authors: Li Xiangjie , Tong Yala
Published: December 2023

RRT (rapidly exploring random tree) is a sampling-based planning algorithm that has been widely used due to its simple structure and fast speed. However, the RRT algorithm has several issues such as low planning efficiency, high randomness, and poor path quality. To address these issues, this paper proposes a novel method, the adjustable probability and sampling area and the Dijkstra optimization-based RRT algorithm (APSD-RRT), which consists of the following two modules: an APS-RRT planner and an optimizer. The APS-RRT planner can reach a feasible path quickly using the proposed adjustable probability and sampling area strategies, while the optimizer applies the Dijkstra algorithm to prune and improve the initial path generated by the APS-RRT planner and smooths-out sharp nodes based on the interpolation method. A series of experiments are conducted to demonstrate that our method can perform much better in terms of the balance between the computing cost and performance.
Distributed Bayesian Inference for Large-Scale IoT Systems
Authors: Vlachou Eleni , Karras Aristeidis , Karras Christos , Theodorakopoulos Leonidas , Halkiopoulos Constantinos , Sioutas Spyros
Published: December 2023

In this work, we present a Distributed Bayesian Inference Classifier for Large-Scale Systems, where we assess its performance and scalability on distributed environments such as PySpark. The presented classifier consistently showcases efficient inference time, irrespective of the variations in the size of the test set, implying a robust ability to handle escalating data sizes without a proportional increase in computational demands. Notably, throughout the experiments, there is an observed increase in memory usage with growing test set sizes, this increment is sublinear, demonstrating the proficiency of the classifier in memory resource management. This behavior is consistent with the typical tendencies of PySpark tasks, which witness increasing memory consumption due to data partitioning and various data operations as datasets expand. CPU resource utilization, which is another crucial factor, also remains stable, emphasizing the capability of the classifier to manage larger computational workloads without significant resource strain. From a classification perspective, the Bayesian Logistic Regression Spark Classifier consistently achieves reliable performance metrics, with a particular focus on high specificity, indicating its aptness for applications where pinpointing true negatives is crucial. In summary, based on all experiments conducted under various data sizes, our classifier emerges as a top contender for scalability-driven applications in IoT systems, highlighting its dependable performance, adept resource management, and consistent prediction accuracy.
Chronic Pelvic Pain, Vulvar Pain Disorders, and Proteomics Profiles: New Discoveries, New Hopes
Authors: Di Tucci Chiara , Muzii Ludovico
Published: December 2023

Chronic pelvic pain (CPP) is generally defined as non-cyclic pain perceived in the pelvic area that has persisted from three to six months or longer and is unrelated to pregnancy. The etiology of CPP is complex, multifactorial, with heterogeneous presentation, and includes several diseases such as endometriosis, adenomyosis, and interstitial cystitis/bladder pain syndrome. It may also be associated with sexual dysfunction, musculoskeletal disorders, and comorbid psychiatric symptoms. Vulvar pain disorders (VPDs) are typically categorized separately from chronic pelvic pain; among all VPDs, vulvodynia is a chronic vulvar pain of unknown etiology, lasting at least 3 months and that might be associated with other potentially linked factors. Proteomics represents a useful approach to study the proteome profiles of clinical samples. In this review, we have considered a selection of articles that have analyzed the protein abundance and novel protein species from various biological samples, including eutopic/ectopic endometrium, urine, serum, follicular, peritoneal fluid, and cervical mucus, potentially involved in the pathogenesis and progression of CPP and VPDs. These findings could represent valuable targets for paving the way for the differential diagnosis and therapeutic management of CPP and VDPs, thereby optimizing both the prevention and treatment of these conditions.
Oxidative Stress and the Pathogenesis of Aortic Aneurysms
Authors: Kazaleh Matthew , Gioscia-Ryan Rachel , Ailawadi Gorav , Salmon Morgan
Published: December 2023

Aortic aneurysms are responsible for significant morbidity and mortality. Despite their clinical significance, there remain critical knowledge gaps in the pathogenesis of aneurysm disease and the mechanisms involved in aortic rupture. Recent studies have drawn attention to the role of reactive oxygen species (ROS) and their down-stream effectors in chronic cardiovascular diseases and specifically in the pathogenesis of aortic aneurysm formation. This review will discuss current mechanisms of ROS in mediating aortic aneurysms, the failure of endogenous antioxidant systems in chronic vascular diseases, and their relation to the development of aortic aneurysms.
Interaction of Tau with Kinesin-1: Effect of Kinesin-1 Heavy Chain Elimination on Autophagy-Mediated Mutant Tau Degradation
Authors: Selvarasu Karthikeyan , Singh Abhay Kumar , Dakshinamoorthy Avinash , Sreenivasmurthy Sravan Gopalkrishnashetty , Iyaswamy Ashok , Radhakrishnan Moorthi , Patnaik Supriti , Huang Jian-Dong , Williams Leonard L. , Senapati Sanjib , Durairajan Siva Sundara Kumar
Published: December 2023

Natively unfolded tau has a low propensity to form aggregates, but in tauopathies, such as Alzheimer’s disease (AD), tau aggregates into paired helical filaments (PHFs) and neurofibrillary tangles (NFTs). Multiple intracellular transport pathways utilize kinesin-1, a plus-end-directed microtubule-based motor. Kinesin-1 is crucial in various neurodegenerative diseases as it transports multiple cargoes along the microtubules (MT). Kinesin-1 proteins cannot progress along MTs due to an accumulation of tau on their surfaces. Although kinesin-1-mediated neuronal transport dysfunction is well-documented in other neurodegenerative diseases, its role in AD has received less attention. Very recently, we have shown that knocking down and knocking out of kinesin-1 heavy chain (KIF5B KO) expression significantly reduced the level and stability of tau in cells and tau transgenic mice, respectively. Here, we report that tau interacts with the motor domain of KIF5B in vivo and in vitro, possibly through its microtubule-binding repeat domain. This interaction leads to the inhibition of the ATPase activity of the motor domain. In addition, the KIF5B KO results in autophagy initiation, which subsequently assists in tau degradation. The mechanisms behind KIF5B KO-mediated tau degradation seem to involve its interaction with tau, promoting the trafficking of tau through retrograde transport into autophagosomes for subsequent lysosomal degradation of tau. Our results suggest how KIF5B removal facilitates the movement of autophagosomes toward lysosomes for efficient tau degradation. This mechanism can be enabled through the downregulation of kinesin-1 or the disruption of the association between kinesin-1 and tau, particularly in cases when neurons perceive disturbances in intercellular axonal transport.
The Synergistic Effects of Hyaluronic Acid and Platelet-Rich Plasma for Patellar Chondropathy
Authors: Costa Fábio Ramos , Santos Márcia da Silva , Martins Rubens Andrade , Costa Cláudia Bruno , Hamdan Paulo César , Da Silva Marcos Britto , Azzini Gabriel Ohana Marques , Pires Luyddy , Menegassi Zartur , Santos Gabriel Silva , Lana José Fábio
Published: December 2023

Musculoskeletal disorders are increasingly prevalent worldwide, causing significant socioeconomic burdens and diminished quality of life. Notably, patellar chondropathy (PC) is among the most widespread conditions affecting joint structures, resulting in profound pain and disability. Hyaluronic acid (HA) and platelet-rich plasma (PRP) have emerged as reliable, effective, and minimally invasive alternatives. Continuous research spanning from laboratory settings to clinical applications demonstrates the numerous advantages of both products. These encompass lubrication, anti-inflammation, and stimulation of cellular behaviors linked to proliferation, differentiation, migration, and the release of essential growth factors. Cumulatively, these benefits support the rejuvenation of bone and cartilaginous tissues, which are otherwise compromised due to the prevailing degenerative and inflammatory responses characteristic of tissue damage. While existing literature delves into the physical, mechanical, and biological facets of these products, as well as their commercial variants and distinct clinical uses, there is limited discussion on their interconnected roles. We explore basic science concepts, product variations, and clinical strategies. This comprehensive examination provides physicians with an alternative insight into the pathophysiology of PC as well as biological mechanisms stimulated by both HA and PRP that contribute to tissue restoration.
Archimedes Optimization Algorithm-Based Feature Selection with Hybrid Deep-Learning-Based Churn Prediction in Telecom Industries
Authors: Mengash Hanan Abdullah , Alruwais Nuha , Kouki Fadoua , Singla Chinu , Abd Elhameed Elmouez Samir , Mahmud Ahmed
Published: December 2023

Customer churn prediction (CCP) implies the deployment of data analytics and machine learning (ML) tools to forecast the churning customers, i.e., probable customers who may remove their subscriptions, thus allowing the companies to apply targeted customer retention approaches and reduce the customer attrition rate. This predictive methodology improves active customer management and provides enriched satisfaction to the customers and also continuous business profits. By recognizing and prioritizing the relevant features, such as usage patterns and customer collaborations, and also by leveraging the capability of deep learning (DL) algorithms, the telecom companies can develop highly robust predictive models that can efficiently anticipate and mitigate customer churn by boosting retention approaches. In this background, the current study presents the Archimedes optimization algorithm-based feature selection with a hybrid deep-learning-based churn prediction (AOAFS-HDLCP) technique for telecom companies. In order to mitigate high-dimensionality problems, the AOAFS-HDLCP technique involves the AOAFS approach to optimally choose a set of features. In addition to this, the convolutional neural network with autoencoder (CNN-AE) model is also involved for the churn prediction process. Finally, the thermal equilibrium optimization (TEO) technique is employed for hyperparameter selection of the CNN-AE algorithm, which, in turn, helps in achieving improved classification performance. A widespread experimental analysis was conducted to illustrate the enhanced performance of the AOAFS-HDLCP algorithm. The experimental outcomes portray the high efficiency of the AOAFS-HDLCP approach over other techniques, with a maximum accuracy of 94.65%.
The Protective Role of Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Protein Sigma-1 Receptor in Regulating Endothelial Inflammation and Permeability Associated with Acute Lung Injury
Authors: Mahamed Zahra , Shadab Mohammad , Najar Rauf Ahmad , Millar Michelle Warren , Bal Jashandeep , Pressley Traci , Fazal Fabeha
Published: December 2023

Earlier studies from our lab identified endoplasmic reticulum (ER) chaperone BiP/GRP78, an important component of MAM, to be a novel determinant of endothelial cell (EC) dysfunction associated with acute lung injury (ALI). Sigma1R (Sig1R) is another unique ER receptor chaperone that has been identified to associate with BiP/GRP78 at the MAM and is known to be a pluripotent modulator of cellular homeostasis. However, it is unclear if Sig1R also plays a role in regulating the EC inflammation and permeability associated with ALI. Our data using human pulmonary artery endothelial cells (HPAECs) showed that siRNA-mediated knockdown of Sig1R potentiated LPS-induced the expression of proinflammatory molecules ICAM-1, VCAM-1 and IL-8. Consistent with this, Sig1R agonist, PRE-084, known to activate Sig1R by inducing its dissociation from BiP/GRP78, blunted the above response. Notably, PRE-084 failed to blunt LPS-induced inflammatory responses in Sig1R-depleted cells, confirming that the effect of PRE-084 is driven by Sig1R. Furthermore, Sig1R antagonist, NE-100, known to inactivate Sig1R by blocking its dissociation from BiP/GRP78, failed to block LPS-induced inflammatory responses, establishing that dissociation from BiP/GRP78 is required for Sig1R to exert its anti-inflammatory action. Unlike Sig1R, the siRNA-mediated knockdown or Subtilase AB-mediated inactivation of BiP/GRP78 protected against LPS-induced EC inflammation. Interestingly, the protective effect of BiP/GRP78 knockdown or inactivation was abolished in cells that were depleted of Sig1R, confirming that BiP/GRP78 knockdown/inactivation-mediated suppression of EC inflammation is mediated via Sig1R. In view of these findings, we determined the in vivo relevance of Sig1R in a mouse model of sepsis-induced ALI. The intraperitoneal injection of PRE-084 mitigated sepsis-induced ALI, as evidenced by a decrease in ICAM-1, IL-6 levels, lung PMN infiltration, and lung vascular leakage. Together, these data evidence a protective role of Sig1R against endothelial dysfunction associated with ALI and identify it as a viable target in terms of controlling ALI in sepsis.