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Correction: Associations between diet quality, demographics, health conditions and spice and herb intake of adults with chronic kidney disease
Numerical analysis of flow configuration and channel design for thermoelectric OTEC systems
Abstract This research examines the optimized integration of Bi₂Te₃-based thermoelectric generators (TEGs) in Ocean Thermal Energy Conversion (OTEC) systems, evaluating their performance via detailed numerical analysis. We conducted finite element simulations using COMSOL Multiphysics to analyze thermoelectric generators (TEGs) placed between a warm surface and cold deep seawater channels under different operational conditions. The research examined parallel and counter flow configurations at Reynolds numbers between 3987 and 73,800, with channel heights varying from 0.002 to 0.072 m. Results indicate that Reynolds numbers above 12,000 ensure stable heat supply to TEGs, resulting in a consistent output power of 3.01 W. The optimal net power of 1.45 W was attained at a channel height of 0.002 m, attributed to reduced pump power consumption. A comparative analysis of Bi₂Te₃-based material combinations demonstrated that improved electrical and decreased thermal conductivity notably enhanced system performance. This study offers essential insights for improving the design and implementation of TEG-OTEC systems, especially in offshore contexts where operational efficiency and system durability are critical, thereby contributing to the advancement of sustainable ocean energy technologies.
Prevalence of HIV drug resistance, its correlates and common mutations among people living with HIV failing on ART in northern Uganda: A cross-sectional study
Background HIV drug resistance (HIVDR) poses a challenge to managing people living with HIV (PLHIV), particularly among those experiencing virological failure (VF). The West-Nile region of Uganda faces HIV treatment challenges and has a high virological failure rate. We estimated the prevalence of HIV drug resistance, described the HIV drug resistance mutations and evaluated the factors associated with HIVDR among PLHIV with virological failure in the West-Nile region of Uganda. Methods We conducted a retrospective cross-sectional analysis of HIVDR data in the West-Nile region of Uganda across the 161 health facilities that offer comprehensive Anti-retroviral therapy (ART) services. All PLHIV, regardless of age, who had been on ART for at least one-year, experienced virological failure and underwent an HIVDR test between 1st January 2021–30th December 2023 were included in the study. Demographic and clinical data were extracted from the National HIVDR database. HIVDR was defined as having at least one mutation with a penalty score of ≥15. PLHIV were characterized based on age, gender and clinical history. Logistic regression models determined factors associated with HIVDR with a p-value of <0.05 considered significant. Results A total 295 records were analyzed. Of these, majority were female (56.6%) and adults aged ≥20 years (49.2%). The median age was 19 (Inter quartile range [IQR]: 13–41) years, and median duration on ART was 8 (IQR: 5–10) years. Overall, 218 (73.9%) had HIVDR with 66% of subjects having Non-nucleoside reverse transcriptase (NNRTI) mutations. M184V/I (50%), K103N (34%) and TAMS (26%) were the commonest mutations. Resistance to Etravirine (27%) was higher than that of Dolutegravir (12%) and Darunavir (5%). After accounting for gender, age, Nucleoside reverse transcriptase inhibitor (NRTI) anchor drug, ART regimen type and World Health Organization (WHO) clinical stage of the participants; long duration on ART (aOR=; 1.15 95%CI 1.05–1.26 p = 0.003), adolescents failing on first line (aOR=; 3.80 95%CI 1.02–14.08 p = 0.046) and participants failing on 2nd line (aOR=; 3.64 95%CI 1.18–11.21 p = 0.024) as indications for the HIVDR test, and the year of HIVDR sample collection (aOR=; 0.21 95%CI 0.07–0.69 p = 0.010), were independently associated with HIVDR mutations. Conclusion The study found a high HIVDR prevalence strongly associated with long ART duration which is likely to lead to increased ART treatment failure rates. The high Etravirine resistance and increasing Dolutegravir resistance are likely to complicate future treatment options while low Darunavir resistance makes it a future third-line treatment option. Strengthening routine resistance surveillance, timely VL monitoring, and adherence support are critical to mitigating drug resistance and preserving ART effectiveness among PLHIV in the West-Nile region.
Resilient math inspired EDA optimized fuzzy adaptive exponent controller for LFC improvement of an EV integrated microgrid
Abstract This study aims to stabilize the frequency of an electric vehicle-integrated AC microgrid in different electrical uncertainties. The recommended microgrid is built by incorporating different distributed generation (DG) oriented power plants. The DG system includes a wind power plant, solar PV plant, diesel generator, fuel cell and geothermal plant. The microgrid frequency goes on oscillating under the action of few uncertainties like dynamics in applied load, fluctuation in wind power and variability in solar power intensity. Further, the charging of electric vehicles extremely disturbs the grid frequency and causes frequency instability issues in the microgrid. This proposed study has anticipated a Fuzzy adaptive exponent PID (Fuzzy PI-DÆ) controller to obtain stability in microgrid frequency under different disturbances. Further, the microgrid is associated with different energy-storing devices for improving overall power quality of the system. The Fuzzy PI-DÆ parameters are selected in optimum by incorporating an advanced Math inspired-Exponential distribution algorithm (Mi-EDA) in different operations. The potential of the optimal Fuzzy PI-DÆ controller is compared with fractional ordered fuzzy PID (FO-FPID), Fuzzy PID and PID controllers in concern to the microgrid’s frequency stabilization. The research findings conclude that, the anticipated Fuzzy PI-DÆ approach promptly advances the settling time of frequency by 72.72% and 136.32% and 345.46% to that of FO-FPID, Fuzzy PID and PID controllers respectively. The optimal property of the recommended Mi-EDA technique is compared with the typical sine cosine algorithm (SCA), GA and PSO techniques for validating the potential of the technique.
DPRM: DeBERTa-based potential relationship multi-headed self-attention joint extraction model
Traditional entity-relationship joint extraction models are typically designed to address generic domain data, which limits their effectiveness when applied to domain-specific applications such as manufacturing. This study presents the DeBERTa-based Potential Relationship Multi-Headed Self-Attention Joint Extraction Model (DPRM), which has been specifically designed to enhance the accuracy and efficiency of entity-relationship extraction in manufacturing knowledge graphs. The model is comprised of three core components: a semantic representation module, a relationship extraction and entity recognition module, and a global entity pairing module. In the semantic representation module, a DeBERTa encoder is employed to train the input sentences, thereby generating word embeddings. The capture of word dependencies is achieved through the utilization of Bi-GRU and Multi-Headed Self-Attention mechanisms, which serve to enhance the overall representation of the sentence. The relationship extraction and entity recognition module is responsible for identifying potential relationships within the sentences and integrating a relational gated mechanism to minimize the interference of irrelevant information during the entity recognition process. The global entity pairing module simplifies the model’s architecture by extracting potential relationships and constructing a matrix of global pairing entity pairs based on fault-specific data. The efficacy of the proposed model is validated through experiments conducted on fault datasets. The results demonstrate that the DPRM achieves superior performance, with an F1 score that surpasses that of existing models, thereby highlighting its effectiveness in the fault domain.
Modeling the impact of intercellular signaling on dose metrics and therapeutic outcomes in spatially fractionated radiation therapy (SFRT) for lung cancer
Cerebrospinal fluid haptoglobin levels and outcome after aneurysmal subarachnoid haemorrhage: Evidence from Mendelian randomization
Background Subarachnoid haemorrhage (SAH) poses a life-threatening risk, contributing to half of all haemorrhagic strokes, with aneurysmal SAH (aSAH) affecting approximately 6 individuals per 100,000 annually. Following aSAH, the influx of plasma haptoglobin into the cerebrospinal fluid (CSF) is insufficient to sequester the released free haemoglobin in the subarachnoid space and avoid its neurotoxic effects. Exogenous administration of haptoglobin could be a therapeutic strategy for improving outcomes after aSAH. Methods Using individual level data in the UK Biobank and genetic summary statistics from the largest relevant cohorts, Mendelian randomization analysis was conducted to explore the associations of genetically predicted CSF haptoglobin with the risk of catastrophic aSAH (i.e., fatal aSAH or non-fatal aSAH with at least one of: hemiparesis, aphasia, apraxia, or visual field defects within 7 days) and secondary traits, including stroke-related outcomes and imaging markers of brain swelling. Results Higher levels of genetically predicted CSF haptoglobin was associated with lower risk of catastrophic aSAH in multi-ancestry (OR: 0.79, 95% CI: 0.65 to 0.96, p = 0.019) and White British sample analyses (OR: 0.78, 95% CI: 0.63 to 0.95, p = 0.013), while the lack of association with related traits supported effects specific to outcomes after aSAH. A proof-of-concept analysis showing associations of genetically predicted haptoglobin with haemoglobin in plasma validated the proposed mechanism. Conclusions Using human genetic data, we provide evidence to support a causal role of higher CSF haptoglobin in improving outcomes after aSAH. However, there was no evidence for an effect on the risk of aSAH or related cerebrovascular events. These findings support haptoglobin as a potential treatment in aSAH to mitigate the neurotoxic effects of free haemoglobin and improve outcomes.
TCN-QRNN model for short term energy consumption forecasting with increased accuracy and optimized computational efficiency
Longitudinal tract-based spatial statistics analysis of white matter diffusivity changes and cognitive decline during the transition from MCI to Alzheimer’s disease
Longitudinal studies that analyze the changes in the axial diffusivity (AxD) and radial diffusivity (RD) values over time can elucidate the progression of white matter damage and its causal relationship with cognitive decline. This study aimed to investigate the longitudinal changes in white matter integrity based on AxD and RD and their association with cognitive decline in patients with mild cognitive impairment (MCI) that progressed to Alzheimer’s disease (AD). Eighteen participants diagnosed with MCI at baseline and AD at the follow-up examination were selected from the AD Neuroimaging Initiative and included in this 2-year study Tract-based spatial statistics (TBSS) was used to assess longitudinal changes in WM. Voxel-wise and region-of-interest (ROI) analyses were conducted, and statistical models controlled for age, sex, education, and APOE ε4 status. Correlation and multiple regression analyses were performed to examine the association between AxD/RD changes and changes in clinical dementia rating (CDR) scores. Significant increases in AxD and RD were observed over 2 years in widespread WM tracts, including the corpus callosum, internal capsule, corona radiata, cingulum, superior longitudinal fasciculus, and fornix. AxD changes, particularly in the left retrolenticular internal capsule, left posterior corona radiata, left fornix, and right superior longitudinal fasciculus, showed significant correlations with cognitive decline. In contrast, RD changes were not significantly associated with CDR changes in any region. Multivariate regression analysis identified AxD in the left retrolenticular internal capsule as a significant independent predictor of CDR changes. AxD was sensitive to microstructural alterations in WM associated with cognitive decline during the transition from MCI to AD and may serve as a valuable biomarker for early detection and monitoring of AD progression. Longitudinal DTI analyses provide critical insights into the temporal dynamics of WM degeneration and its role in clinical deterioration.
Integrated single-cell and transcriptomic analysis of bone marrow-derived metastatic neuroblastoma reveals molecular mechanisms of metabolic reprogramming
Leveraging deep learning for the detection of socially desirable tendencies in personnel selection: A proof-of-concept
We propose a deep learning-based method for detecting Socially Desirable Responding (SDR)—the tendency for individuals to distort questionnaire responses to present themselves in a favorable light. Our objective is to showcase that such novel methods can be leveraged to design instruments that have the potential to measure this construct in an effective way. Participants’ tendency to engage in SDR was initially modelled by specifying a latent variable model from Big Five personality scores, using data from 91 participants in a job application simulation (Big Five questionnaire and video introduction). Nonverbal visual cues (5,460 data points following data augmentation) were extracted from the participants’ video presentations in form of sequences of images for training a transfer learning model designated as Entrans. The objective of Entrans is to discern patterns within these cues in order to detect whether sample participants manifest higher or lower SDR tendency. We conducted a regression-based prediction task to train and evaluate Entrans, resulting in a promising performance (MSE = .07, RMSE = .27, ρ = .27). A further analysis was conducted using a classification-based prediction task, which corroborated the potential of Entrans as a tool for detecting SDR (AUC = .71). These results were further analyzed by a Grad-CAM method to elucidate the underlying model behaviors. Findings suggest that the middle and lower parts of the face were the regions relied upon by Entrans to identify individuals with higher tendency of SDR in the classification task. These tentative interpretations give rise to the suggestion that socially desirable responding in a questionnaire and impression management in a job interview might share a common underlying cause. While the detection of SDR during personnel selection presents a significant challenge for organizations, our proof-of-concept demonstrates how machine learning might be leveraged to develop practical solutions as well as addressing theoretical questions.
A study on the spatial distribution and driving factors of traditional villages–a case study of the Beijing-Tianjin-Hebei region in China
Correction: Examining the longitudinal associations between activity limitations, instrumental supports and social participation in osteoarthritis: A CLSA population-based study
Plasma activated PVDF-BaTiO3 composite nanofiber scaffolds loaded with vancomycin for enhancing biocompatibility and piezoelectric response
Establishing a non-viral transfection system for fibroblasts using branched polyethylenimine
Enhanced survival of spiral ganglion neurons (SGN) could improve hearing in patients with cochlear implants. Supplying these cells with growth factors like brain-derived neurotropic factor (BDNF) has been shown to improve cell survival and vitality. Direct applications of BDNF, e.g., via integrated drug-delivering cannula, elevate the surgical risks, as well as the probability of infections. Therefore, in vivo production of BDNF by on-site transfection of cells with plasmid DNA coding for BDNF might be an option. Polyethylenimine (PEI) was chosen as a non-viral transfection reagent, due to its comparatively low cytotoxicity, ease of preparation and use. NIH/3T3 fibroblasts were used as model cells for fibroblast transfection that could be transferred to cochlear implants. Branched 25 kDa PEI was diluted in PBS and mixed in different ratios with two different plasmids coding for BDNF and tdTomato simultaneously. Particle size and zeta potential were determined, and cell metabolic activity was measured using MTT. Transfection efficiency was determined by counting cells with and without transfection-induced fluorescence. Complexes of DNA and PEI were mostly larger than DNA and PEI molecules alone. In addition, complexation of DNA with PEI altered the surface charge of the particles. The cell metabolic activity test confirmed cytocompatibility for almost all tested complexes of DNA and PEI. Plasmid A, which was based on a lentiviral vector backbone, resulted in a very low transfection efficiency of 0.4%, whereas with plasmid B, which was based on pUC19, a transfection efficiency of about 9% was achieved. Size and zeta potential indicate the formation of complexes with both plasmids. Transfection efficacy appears to be dependent on the size of the DNA molecule used. With successful transformation of nearly 10% efficiency and a comparatively low cytotoxicity, the proposed transfection system using plasmid B may be used for further experiments.
Faster quantum subroutine for matrix chain multiplication via Chebyshev approximation
Correction: Carbon price prediction based on a scaled PCA approach
Comparative analysis of bevacizumab and sorafenib on the survival of retinal ganglion cells in the treatment of retinal diseases
Linguistic mechanisms of knowledge-exchange in a dark-web money laundering forum
Money laundering facilitates serious crime, enables the expansion of criminal operations, and destabilises economies. Extant scholarship is largely concerned with anti-money laundering approaches, with far less attention being paid to the language and behaviours of the individuals who engage in money laundering. ‘Dark-web’ discussion fora are prime loci for illicit knowledge exchange and key enablers of money laundering, yet, are underexplored as sites for understanding the online activities and behaviours of users. This paper reports on a corpus-assisted discourse analysis of one such forum, guided by research questions around the key topics and common linguistic strategies by which knowledge is exchanged within a large community of individuals interested in money laundering, and the ways in which this community serves its members. The analysis identifies the forum as an extremely efficient and productive site for knowledge-exchange and thus ‘criminal upskilling’, which is attributed to three core characteristics: a strict adherence to community rules, a highly knowledgeable user base, and a culture of friendliness and reciprocity.