bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing the Complexity of Higher-Order Moment Inclusion in Portfolio Management with the Non-Dominated Sorting Genetic Algorithm III
Authors: MUTEBA MWAMBA John Weirstrass , MBUCICI Leon Mishindo
Published: September 2024

This paper explores the effectiveness of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and traditional Mean-Variance optimization in financial portfolio management. Using a dataset comprising global financial assets, we applied both methodologies to optimize portfolios based on multiple objectives, including risk, return, skewness, and kurtosis. The findings reveal that NSGA-III outperforms the Mean-Variance method in achieving a more diverse set of Pareto-optimal portfolios. NSGA-III portfolios exhibited superior performance in balancing risk and return, demonstrated by higher Sharpe ratios, more favorable skewness, and lower kurtosis. Additionally, NSGA-III's ability to simultaneously optimize across multiple conflicting objectives highlights its robustness in navigating complex financial landscapes, offering enhanced portfolio resilience. In contrast, the Mean-Variance approach, while effective in achieving balanced risk and return, was limited in addressing higher-order moments of the return distribution. These results underscore NSGA-III's potential as a powerful tool for portfolio optimization, providing a comprehensive alternative to traditional methods in modern financial markets where multiple objectives must be considered.
Foot-Floor Contact Sequences: A Metric for Gait Assessment in Parkinson’s Disease after Deep Brain Stimulation
Authors: Ghislieri Marco , Agostini Valentina , Rizzi Laura , Fronda Chiara , Knaflitz Marco , Lanotte Michele
Published: September 2024

Digital gait monitoring is increasingly used to assess locomotion and fall risk. The aim of this work is analyzing the changes in the foot-floor contact sequences of Parkinson’s Disease (PD) patients in the year following the implant for deep brain stimulation (DBS). During their best-ON condition, 30 PD patients underwent gait analysis at baseline (T0), at 3 months (T1), and 12 months (T2) after the DBS neurosurgery for bilateral high-frequency subthalamic nucleus stimulation. Thirty age-matched controls underwent gait analysis once. Each subject was equipped with bilateral foot-switches and a 5-minute walk was recorded, including both straight-line and turnings. The walking speed, turning time, stride time variability, percentage of atypical gait cycles, stance, swing, and double support duration were estimated. Overall, the gait performance of PD patients improved after DBS, as also confirmed by the decrease in the UPDRS-III score from 19.4±1.8 to 10.2±1.0 points (T0 vs. T2) (p < 0.001). In particular, the percentage change of atypical gait cycles of PD more affected side decreased at T1 (Straigh-line: -72%; Turnings: -43%) and at T2 (Straigh-line: -54%; Turnings: -20%). The percentage of atypical gait cycles proved an informative digital biomarker for quantifying PD gait changes after DBS, both in straight-line paths and turnings.
Toward systemic beneficial use of dredged sediments in San Pablo Bay : demonstration of a proposed framework for matching sediment needs with dredging requirements
Authors: Russ Emily , Kurth Margaret , Runion Kyle , Piercy Candice
Published: September 2024

Coastal wetlands provide a suite of valuable ecosystem services, but they are rapidly disappearing due to reductions in sediment supply and rising sea levels, making them ideal candidates for restoration through beneficial use of dredged sediment. Because sediment dredged from navigation channels is a limited resource relative to the number of degraded wetlands, a framework has been developed to align coastal restoration sediment needs with dredging requirements to maximize social, environmental, and flood risk reduction benefits while also completing the navigation mission. The framework is comprised of four key steps: (1) geographic scoping and suitability considerations, (2) quantification of the dredged sediment available and restoration project sediment needs, (3) definition of cost and benefit objectives, and (4) optimization of costs and benefits to determine the most efficient solutions. This report is a demonstration of this framework on a subset of wetland sites and local federal navigation channels in San Pablo Bay, California.
Lineamiento – Apoyo para participación en congresos y eventos científicos a profesores, investigadores y estudiantes de la Universidad
Authors:   Universidad Científica del Sur
Published: September 2024

Lineamiento – Apoyo para participación en congresos y eventos científicos a profesores, investigadores y estudiantes de la Universidad. Versión 1.0 (2018).
Normas y procedimientos para el registro, aprobación y seguimiento de proyectos de investigación
Authors:   Universidad Científica del Sur
Published: September 2024

Normas y procedimientos para el registro, aprobación y seguimiento de proyectos de investigación. Versión 1.0 (2020)
Reglamento del comité del Fondo editorial
Authors:   Universidad Científica del Sur
Published: September 2024

Reglamento del comité editorial del fondo editorial, 2022.
Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures
Authors: Yang Yuedong , Song Yidong , Yuan Qianmu , Chen Sheng , Zeng Yuansong , Zhao Huiying
Published: September 2024

Enzymes are crucial in numerous biological processes, with the Enzyme Commission (EC) number being a commonly used method for defining enzyme function. However, current EC number prediction technologies have not fully recognized the importance of enzyme active sites and structural characteristics. Here, we propose GraphEC, a geometric graph learning-based EC number predictor using the ESMFold-predicted structures and a pre-trained protein language model. Specifically, we first construct a model to predict the enzyme active sites, which is utilized to predict the EC number. The prediction is further improved through a label diffusion algorithm by incorporating homology information. In parallel, the optimum pH of enzymes is predicted to reflect the enzyme-catalyzed reactions. Experiments demonstrate the superior performance of our model in predicting active sites, EC numbers, and optimum pH compared to other state-of-the-art methods. Additional analysis reveals that GraphEC is capable of extracting functional information from protein structures, emphasizing the effectiveness of geometric graph learning. This technology can be used to identify unannotated enzyme functions, as well as to predict their active sites and optimum pH, with the potential to advance research in synthetic biology, genomics, and other fields.
Wavelet-Based Dual-Stream Network for Inpainting of Dunhuang Murals
Authors: Li Jiacheng , Shi Yuqing , Lian Jing , Du Shiqiang , Zhang Yang
Published: September 2024

The preservation and restoration of the Dunhuang murals, a paramount element of China's ancient artistic heritage, are confronted with significant challenges due to their extensive degradation over centuries. Traditional inpainting methods, which are mainly focused on the spatial domain, frequently fall short in authentically restoring these murals, especially considering their distinct artistic style. In response to these challenges, our study introduces a novel wavelet-based dual-stream network (WDSN). This innovative approach bifurcates the restoration process into distinct structure and texture pathways, employing wavelet decomposition to dissect the damaged image into low-frequency (structural) and high-frequency (textural) subbands. A key feature of our method is the incorporation of an Efficient Transformer Block (ETB) within the structure branch, alongside a Residual in Residual Dense Block (RRDB) in the texture branch. These elements synergistically enhance the fidelity of global structural reconstruction and local texture refinement. Furthermore, the integration of the CIEDE2000 color difference formula plays a pivotal role in ensuring the color authenticity of the restored murals, aligning more closely with human visual perception. Our comprehensive tests indicate that this methodology substantially outperforms existing algorithms in mural inpainting, offering a groundbreaking solution for the restoration and preservation of these invaluable cultural artifacts, thereby contributing significantly to the conservation of global art heritage.
TMEM100 acts as a TAK1 receptor that prevents pathological cardiac hypertrophy progression
Authors: Zhang Bin-Bin , Zhao Yi-Lin , Shen Ji-Hong , Li Hui-Yong , Zhang Han-Xue , Yu Xiao-Yue , Zhang Wen-Cai , Li Gang , Han Zhan-Ying , Guo Sen , Zhang Xu-Tao , Lu Yan-Yu
Published: September 2024

Pathological cardiac hypertrophy is the primary cause of heart failure, yet its underlying mechanisms remain incompletely understood. Transmembrane protein 100 (TMEM100) plays a role in various disorders, such as nervous system disease, pain and tumorigenesis, but its function in pathological cardiac hypertrophy is still unknown. In this study, we observed that TMEM100 is upregulated in cardiac hypertrophy. Functional investigations have shown that adeno-associated virus 9 (AAV9) mediated-TMEM100 overexpression mice attenuates transverse aortic constriction (TAC)-induced cardiac hypertrophy, including cardiomyocyte enlargement, cardiac fibrosis, and impaired heart structure and function. We subsequently demonstrated that adenoviral TMEM100 (AdTMEM100) mitigates phenylephrine (PE)-induced cardiomyocyte hypertrophy and downregulates the expression of cardiac hypertrophic markers in vitro, whereas TMEM100 knockdown exacerbates cardiomyocyte hypertrophy. The RNA sequences of the AdTMEM100 group and control group revealed that TMEM100 was involved in oxidative stress and the MAPK signaling pathway after PE stimulation. Mechanistically, we revealed that the transmembrane domain of TMEM100 (amino acids 53–75 and 85–107) directly interacts with the C-terminal region of TAK1 (amino acids 1-300) and inhibits the phosphorylation of TAK1 and its downstream molecules JNK and p38. TAK1-binding-defective TMEM100 failed to inhibit the activation of the TAK1-JNK/p38 pathway. Finally, the application of a TAK1 inhibitor (iTAK1) revealed that TAK1 is necessary for TMEM100-mediated cardiac hypertrophy. In summary, TMEM100 protects against pathological cardiac hypertrophy through the TAK1-JNK/p38 pathway and may serve as a promising target for the treatment of cardiac hypertrophy.
Developing An Automatic Road Safety Model for Accident Identification, Detection, And Prevention Using Deep Learning Algorithms
Authors: Devi Chokkakula , S Gowri
Published: September 2024

Road traffic monitoring systems are one of the leading real-time applications that use the Internet of Things to monitor and identify traffic scenarios on the road. IoT devices are integrated with sensors to sense and capture data within a specific distance range and transmit it to other sensors within a coverage range. Communication is possible, and data will be passed from one device to another only if they are located within the sensing and coverage region. Thus, multiple IoT devices are interconnected logically and communicate with one another within a coverage region. This paper focused on creating an efficient IoT network to monitor and broadcast accidental information immediately to the other vehicles on the road at a defined distance. Some exciting works included installing CCTV cameras, IoT devices, and other sensors only on road junctions and signals, where they can monitor only at particular locations, and they are not efficient in accident detection over urban city roads. This paper has focused on deploying more IoT devices within an urban city and creating an IoT network for accident detection and prevention. The IoT data are analyzed using a robust and efficient deep learning model, Convolution Neural Network work, that can quickly predict accidents from the IoT data analytics and intimate to the admin to broadcast the message to all the vehicles and the users on the road to take prevention actions. The IoT data is analyzed using the CNN algorithm implemented in Python, and the results are verified. The performance of the proposed CNN model is evaluated by comparing its output with the other state-of-the-art methods and proving that CNN outperforms the others.
The context-dependent epigenetic and organogenesis programs determine 3D vs. 2D cellular fitness of MYC-driven murine liver cancer cells
Authors: Yang Jun , Fang Jie , Singh Shivendra , Wells Brennan , Wu Qiong , Jin Hongjian , Janke Laura , Wan Shibiao , Steele Jacob , Connelly Jon , Murphy Andrew , Wang Ruoning , Davidoff Andrew , Ashcroft Margaret , Pruett-Miller Shondra
Published: September 2024

3D cellular-specific epigenetic and transcriptomic reprogramming is critical to organogenesis and tumorigenesis. Here we dissect the distinct cell fitness in 2D (normoxia vs. chronic hypoxia) vs 3D (normoxia) culture conditions for a MYC-driven murine liver cancer model. We identify over 600 shared essential genes and additional context-specific fitness genes and pathways. Knockout of the VHL-HIF1 pathway results in incompatible fitness defects under normoxia vs. 1% oxygen or 3D culture conditions. Moreover, deletion of each of the mitochondrial respiratory electron transport chain complex has distinct fitness outcomes. Notably, multicellular organogenesis signaling pathways including TGFb-SMAD specifically constrict the uncontrolled cell proliferation in 3D while inactivation of epigenetic modifiers (Bcor, Kmt2d, Mettl3 and Mettl14) has opposite outcomes in 2D vs. 3D. We further identify a 3D-dependent synthetic lethality with partial loss of Prmt5 due to a reduction of Mtap expression resulting from 3D-specific epigenetic reprogramming. Our study highlights unique epigenetic, metabolic and organogenesis signaling dependencies under different cellular settings.
Bulk adhesion of ice to concrete – strength
Authors: Barker Anne , Bruneau Stephen , Colbourne Bruce , Bugden Austin
Published: September 2024

This paper presents the results of a laboratory test program designed to investigate the adhesive effects of large-scale (bulk) ice on concrete. Medium-strength concrete cylinders were sawn into discs, and attached to a sample table. Freshwater ice samples, frozen using smaller, standard-sized concrete cylinders, were adhered to the concrete with both varying bond times and added weight during bonding. Shear strength tests were conducted at a set displacement rate, under a number of temperatures. The effect of these variables on the adhesive strength of ice to concrete was examined, as well as whether there was any noticeable removal of concrete cement paste or aggregate during testing. The tests indicate that the adhesive strength is negligible when the method of adhesion is “dry” (no liquid layer at the onset of adhesion). Tests with “wet” adhesion indicated a significantly higher strength. The nominal versus the apparent contact area had significant implications for the determination of the adhesive strength of the bond between the ice and the concrete. Removal of cement paste was evident in a number of tests, however the amount was not significant. The results have relevance for design of structures in a marine environment, such as revetement dams or rubblemound breakwaters, as well as for the standardization of adhesion tests with ice and concrete.
Effectiveness of long-short term memory network in financial fraud detection
Authors: Qiao YongSheng
Published: September 2024

To limit the impact of the financial frauds in capital market, it is essential to create a rigorous and effective financial fraud identification model. Our paper discusses the effectiveness of long-term and short-term memory (LSTM) network in detecting financial fraud. In total, 660 Shanghai and Shenzhen listed companies from 1994 to 2018 have been selected as our research samples. Among them, 165 entities were with records of false financial information disclosure. Different types of research approaches are adopted during two stages of the study. The first stage is data preprocessing, during which Artificial neural networks (ANN) are used to screen important variables. In order to accomplish a high-precision financial fraud detection model, both financial and non-financial variables are included. The second stage is the performance evaluation and classifiers comparison. Various classifiers are used to execute and compare the performance of the model: support vector machine, K-Nearest Neighbor (KNN), random forest, multilayer perceptron and LSTM neural network. The results show that the variables screened by ANN and processed by LSTM neural network have high accuracy in identifying financial statement fraud.
Production capacity prediction of fractured oil wells based on improved Convolutional Neural Network
Authors: Wang Jianbo , Gao Bingkun , Guo Zhengyang , Xue Yuxiang
Published: September 2024

The significance of predicting the productivity of fractured oil wells for optimizing production strategies and enhancing efficiency is widely acknowledged. However, traditional algorithms often fall short in accurately predicting productivity, especially when dealing with fractured oil wells with limited production data, significant data fluctuations, and missing values. An algorithm for predicting fractured oil well productivity based on improved convolutional neural networks(CNN) is proposed in this study. This algorithm not only enables adaptive feature extraction but also enhances prediction performance on small sample data. Experimental results demonstrate that compared to classic CNN and MLP models, the proposed algorithm achieves superior performance. The average absolute errors of initial oil production and incremental oil production are 0.023t and 15.04t, respectively, with corresponding root mean square errors of 0.0011t and 23.74t. The prediction accuracy rates stand at 95.62% and 86.68%, respectively. These performance indicators outperform those of classic models, showcasing promising predictive capabilities. This research holds practical significance in evaluating fractured oil well productivity, devising optimization strategies, and guiding oilfield development endeavors to improve resource utilization efficiency and overall production outcomes.
In-silico study of E169G and F242K double mutations in Leucine-Rich Repeats (LRR) PolyGalacturonase Inhibiting Protein (PGIP) of Gossypium barbadense and associated defense mechanism against plant pathogens
Authors: Murmu Sneha , Rashmi Mayank , Nagrale Dipak T , Kour Tejasman , Singh Mahender Kumar , Chaurasia Anurag , Behera Santosh Kumar , Shankar Raja , Ranjan Rajiv , Jha Girish Kumar , Gawande Shailesh P. , Hiremani Neelakanth S. , Prasad Y. G. , Kumar Sunil
Published: September 2024

PolyGalacturonase Inhibiting Proteins (PGIPs) play a pivotal role in plant defense against pathogens by inhibiting PolyGalacturonase (PG), an enzyme produced by pathogens to degrade plant cell wall pectin. PGIPs, also known as leucine-rich repeat pathogenesis-related (PR) proteins, activate the host's defense response upon interaction with PG, thereby fortifying the plant against pathogenic attacks. In cotton (Gossypium barbadense), the interaction between PGIP and PG is a crucial step in the defense mechanism against major pathogens such as Xanthomonas citri pv. malvacearum and Alternaria macrospora, responsible for bacterial leaf blight and leaf spot diseases, respectively. To unravel the molecular mechanisms underlying these pathogenesis-related proteins, we conducted a comprehensive study involving molecular modeling, protein-protein docking, site-specific double mutation (E169G and F242K), and molecular dynamics simulations. Both wild-type and mutated cotton PGIP were examined in interaction with the PG enzyme from pathogens. Our findings revealed that changes in conformations of double-mutated residues in the active site of PGIP lead to the inhibition of PG binding. The molecular dynamics simulations provide insights into the dynamic behavior and stability of the PGIP-PG complexes, shedding light on the intricate details of the inhibitory and exhibitory mechanism against the fungal and bacterial pathogens respectively. This study not only enhances our understanding of the molecular interactions between PG and PGIP but also presents a potential strategy for developing disease-resistant cotton varieties. By variations in the binding affinities of PG through specific mutations in PGIP, this research offers a promising avenue for the development of cotton plants with enhanced resistance to bacterial leaf blight and leaf spot diseases.
The Supplier Selection of Prefabricated Component Production Line through a Lean- based AHP-Improved VIKOR framework
Authors: Dang Pei , Geng Linna , Niu Zhanwen , Gao Hui , Sun Chao
Published: September 2024

Over the past decades, prefabrication has emerged as a sustainable construction practice, with its performance significantly affected by the efficiency of production lines for the prefabricated components (PCs). However, insights from the industry reveal that selecting suppliers for PCs production lines has become increasingly challenging due to evolving, uncertain demands and complexity. Such issue negatively impacts PCs production performance, which in turn, affects overall efficiency of prefabrication. Despite previous research focusing on various PCs supplier selection criteria, there is limited research concerning its improvement in aligning with current industrial demands. Hence, this study developed an integrated AHP-improved VIKOR decision making framework with the advanced Lean principles incorporated, to enhance the efficiency of selected PCs production line suppliers. To achieve this, a Lean-based AHP-Improved VIKOR evaluation framework, including general criteria and specific criteria, was designed via an extensive literature review and expert interviews. Lastly, the developed framework was validated via a real-world case with Yizhong Construction Co., Ltd. in China. It demonstrates that this developed evaluation framework can be a valuable tool for selecting PCs production line suppliers, enhancing its efficiency through the lens of lean, and advancing prefabrication in developing countries.
Spatial Association Network Structure of Agricultural Carbon Emission Efficiency in Chinese Cities and Its Driving Factors
Authors: Wei Xu , Chen Bohong
Published: September 2024

In light of the Chinese government's dual carbon goals, achieving cleaner production activities has become a central focus, with regional environmental collaborative governance, including the management of agricultural carbon reduction, emerging as a mainstream approach. This study examines 268 prefecture-level cities in China, measuring the carbon emission efficiency of city agriculture from 2000 to 2021. By integrating social network analysis and a modified gravity model, the study reveals the characteristics of the spatial association network of city agricultural carbon emission efficiency in China. Additionally, the quadratic assignment procedure is employed to investigate the driving factors. The findings indicate that: (1) The carbon emission efficiency of cities agriculture in China displays substantial spatiotemporal heterogeneity, characterized by marked regional clustering. Central cities generally exhibit higher efficiency levels, while the surrounding cities tend to have lower efficiency. (2) The carbon emission efficiency of city agriculture in China displays multidimensional, complex, and organic characteristics, with potential for enhanced network stability. (3) Agricultural regions in central and southeastern China dominate the spatial network, while regions with weaker agricultural sectors, like Beijing, Shanghai, and Ningxia, occupy peripheral positions. (4) Within the spatial association network of city agricultural carbon emission efficiency in China, Intra block spatial correlations are low, while interblock spatial correlations are strong, exhibiting significant spillover effects. (5)Variations in agricultural development levels and mechanization significantly enhance the formation of networks related to city agricultural carbon emission efficiency in China. Conversely, differences in industrial structure and fertilizer application levels exert a substantial negative influence on these networks.
Identification of potential SARS-CoV-2 genetic markers resulting from host domestication
Authors: Chen Heng-Chang , Wiśniewski Janusz
Published: September 2024

We developed a k-mer-based pipeline, namely the Pathogen Origin Recognition Tool using Enriched K-mers (PORT-EK) to identify genomic regions enriched in the respective hosts after the comparison of metagenomes of isolates between two host species. Using it we identified thousands of k-mers enriched in US white-tailed deer and betacoronaviruses in bat reservoirs while comparing them with human isolates. We demonstrated different coverage landscapes of k-mers enriched in deer and bats and unraveled 148 mutations in enriched k-mers yielded from the comparison of viral metagenomes between bat and human isolates. We observed that the third position within a genetic codon is prone to mutations, resulting in a high frequency of synonymous mutations of amino acids harboring the same physicochemical properties as unaltered amino acids. Finally, we classified and predicted the likelihood of host species based on the enriched k-mer counts. Altogether, PORT-EK showcased its feasibility for identifying enriched viral genomic regions, illuminating the different intrinsic tropisms of coronavirus after host domestication.
Text2Protein: A Generative Model for Designated Protein Design on Given Description
Authors: Hosseini Ramtin , Zhang Siyang , Xie Pengtao
Published: September 2024

Designing protein structures from text is challenging in computational biology. We propose Text2Protein, a pipeline combining large language models (LLMs) with diffusion models to generate full-atomic protein structures from text. Using a conditional diffusion model and the Vicuna-7B language model, we learn data distributions of 6D interresidue coordinates, refined into full-atomic structures with PyRosetta. Trained on a curated RCSB-PDB dataset, Text2Protein focuses on single-chain proteins with 40-256 residues. Our extensive experiments validate Text2Protein’s effectiveness by generating high-fidelity protein structures similar to ground truth proteins using raw texts. We evaluate Text2Protein using multiple metrics, including Mean Square Error (MSE) of 6D coordinates, Rosetta Energy Units (REU), and TM-score. Our results show that 5% of the generated proteins have a TM-score greater than 0.5, indicating similar folds in SCOP/CATH. Additionally, 16% of pairs have a TM-score greater than 0.4, 89% have a TM-score greater than 0.3, and none have a TM-score less than 0.17, below the threshold for unrelated proteins. Text2Protein presents a promising framework for automated protein design, potentially accelerating novel protein discovery. This work opens new avenues for integrating natural language understanding with protein structure generation, with implications in drug discovery, enzyme engineering, and material science.
Effect of Processing Methods on Mineral Bioaccessibility in Common Beans (Phaseolus Vulgaris L.)
Authors: Nagessa Wabi Bajo , Chambal Borges , Macuamule Custodia
Published: September 2024

Mineral bioavailability is a concern in legume based foods due to presence of Antinutritional factors. Zinc and iron deficiency is widespread in low-income countries because of the low consumption of animal products due to their unaffordable costs. The objective of this study was to evaluate the effects of processing conditions on the in vitro mineral bioaccessibility of small black common beans cultivated in Mozambique. The samples were collected from ‘Instituto de Investigação Agrária de Moçambique’ (IIAM), Maputo, Mozambique. Soaking (in water and sodium bicarbonate), germination (for 24 and 48 hours at 25oC), and cooking (ordinary and pressure) were applied. The oven-dried and finely ground samples of processed beans were in vitro digested and analyzed for mineral bioaccessibility.
The in vitro gastrointestinal simulation assay was performed and the dialyzable part of the samples was used for iron, zinc, and copper bioaccessibility determination. Though there is a slight difference, all the processing methods investigated in this study; soaking, germination, and cooking treatments affected the mineral bioaccessibility of beans. The in vitro iron, zinc, and copper bioaccessibility was enhanced by the respective processing methods as compared to the control sample. The iron bioaccessibility was observed to be 2.22% for water soaking and 2.59% for sodium carbonate soaking, 2.75% for 24-hour germination 4.27% for 48-hours germination, and 3.56% for ordinary cooking and 7.79% for pressure cooking. The in vitro iron bioaccessibility is relatively low as compared to that of zinc. In vitro, Zinc bioaccessibility was found to be the same for water and sodium bicarbonate soaking which was 6.94%.
Germination for 24 and 48 hours resulted in zinc bioaccessibility of 7.58% and 10.08% respectively while zinc bioaccessibility of 6.52% for ordinary cooking and 8.41% for pressure cooking was achieved. The respective in vitro copper bioaccessibility obtained for soaking in water and sodium bicarbonate was found to be 6.04% and 6.78% which are almost similar. Similarly, the germination for 24 and 48 hours showed copper bioaccessibility of 7.01% and 7.63% in respective order whereas it was observed that copper bioaccessibility of 5.79% for ordinary cooking and 8.50% for pressure cooking was achieved in this study. This shows that it is expected that the solubility of pressure-cooked beans in the intestine and the release of minerals from its matrix is high. The result illustrates that the pressure cooking of presoaked beans had the greater value for all mineral's bioaccessibility except for zinc, and it can be concluded that the processing techniques could help improve minerals' bioaccessibility in beans which in turn helps to combat malnutrition and ensure food security in developing countries.
Hcy accelerates the biological dysfunction of ANA-1 macrophages and vascular injure via Rap1A/MAPK signal
Authors: Huang Xiaobo , Yang Yali , Wang Lexin , Liu Huijuan , Yang Ziyao , Li Xinyi , Luo Huanyu , Li Zhen , Wang Yangjia , Xu Xi , Chi Hao , Yang Guanhu , Yang Xiaoling
Published: September 2024

Hyperhomocysteinemia (HHcy) is a kind of risk factor for various vascular diseases. The dysfunction of macrophages plays a crucial role in the development of HHcy-aggravated vascular injury. However, macrophages injure is still unclear. In this study, we propose the hypothesis that the Rap1A/ERK1/2 signal pathway is responsible for mediating the dysfunction of macrophages in HHcy-induced vascular diseases. We found that Cell proliferation, migration and invasion ability were increased after Hcy treatment. In addition, the mRNA levels of CD80 and CD86, and protein expression of Rap1A, iNOS were also increased in Hcy group. Interference of Rap1A reversed Hcy-induced cell proliferation, migration and invasion, and also reduced macrophage inflammatory response and M1 polarization. Phosphorylation of ERK1/2 was inhibited after Rap1A knockdown. Suppression of ERK1/2 alleviated Hcy-induced macrophage dysfunction. Together, Rap1A mediates Hcy-induced macrophage dysfunction via ERK1/2 signaling, which highlights a new mechanism in the pathogenesis and development of HHcy-aggravated vascular diseases.
Mechanisms of Tropical Indian Ocean circulation biases in CESM historical experiments
Authors: Mohan Soumya S , Srini Gangiredla
Published: September 2024

The study examines the mechanisms of Tropical Indian Ocean (TIO) circulation biases in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) historical simulations across four variants of the Community Earth System Model (CESM): CESM2, CESM2-FV2, CESM2-WACCM, and CESM2-WACCM-FV2. The dominant equatorial flow, known as the Wyrtki Jets (WJ), is considerably underestimated due to the predominance of easterly wind bias, with the fall WJs showing the least skill, particularly in CESM2-WACCM-FV2, due to the underestimated westerlies. However, the eastward Equatorial Undercurrent (EUC) is strongly overestimated in all models, with maximum amplitudes observed in CESM2-WACCM-FV2, followed by CESM2-FV2, due to wind-induced westward thermocline tilt through the positive Bjerknes feedback mechanism. The northward intensification and deepening of south equatorial currents are attributed to strong easterly wind stress in the southern TIO in CESM models. The equatorial easterly wind bias in CESM models originates from a southeasterly wind bias in their Atmospheric Model Intercomparison Project (AMIP) counterparts during June-August, with air-sea coupling driving the westerly wind bias over the equatorial region. Overly strong easterlies and the intensification of midlatitude westerlies in AMIP models contribute to the intensification and poleward shift of the subtropical gyre in CESM models. This, in turn, weakens the Agulhas leakage (AL) transport from the south Indian Ocean to the Atlantic, which is partially due to the underestimated Indonesian Throughflow (ITF) in CESM models.
Optimizing Maxillomandibular Position in Orthognathic Surgery: Introducing the T Concept in Treatment Planning
Authors: Alyahya Abdulmalik , Jasser Saud Bin
Published: September 2024

Purpose:
Orthognathic surgery aims to align the jaws with the facial skeleton and correct dental occlusion. This study introduces the T concept, incorporating the maxillary central incisor, chin, and occlusal plane as a novel predictor for properly positioning the maxillomandibular complex (MMC).
Methods:
The background, hypothesis, and rationale of the new T concept are explained. A case of a 28-year-old male with skeletal class III malocclusion and an open bite was used to illustrate the application of the T concept in step-by-step surgical planning. The planning encompasses four phases: phase one involves correcting frontal deformity and various asymmetries, phase two involves correcting chin anterior-posterior deformity, phase three involves correcting anterior-posterior and vertical MMC position, and phase four involves correcting MMC rotation.
Results:
The T concept provided a structured approach to MMC positioning, considering the vertical alignment of the maxillary central incisor, occlusal plane angulation, and chin positioning. The resulting postoperative outcomes showed an improved facial profile without the need for unnecessary genioplasty.
Conclusion:
A reliable MMC positioning in orthognathic surgery is offered by the T concept, addressing both functional and aesthetic concerns. By incorporating the chin as a key landmark, surgeons can achieve better alignment and avoid compensatory procedures. This method serves as a checkpoint where the MMC position is verified.
Insight into the Structural and Dynamic Properties of Novel HSP90 Inhibitors through DFT Calculations and Molecular Dynamics Simulations
Authors: Saouli Ibtissam , Abrane Rahma , Bidjou-Haiour Chahra , Boudiba Sameh
Published: September 2024

Context:
Heat-shock proteins (HSPs), particularly HSP90, are critical molecular chaperones that maintain protein stability, especially in cancer cells. Elevated HSP90 levels in tumors aid in oncogenic protein stabilization. This study focuses on developing potent, selective HSP90 inhibitors to disrupt its chaperone function, targeting cancer cell survival. Using a de novo hybridization approach, we designed novel inhibitors by integrating structural fragments from a known HSP90-binding drug, leading to the creation of hybrid compounds C1, C2, and C3. A 300 ns molecular dynamics simulation of each system revealed that C1, C2, and C3 formed more stable complexes with HSP90 compared to the reference compound, MEY. RMSD, RMSF, Rg, SASA, and MM-PBSA metrics supported these findings. DCCM and FEL analyses confirmed that the inhibitors did not alter HSP90's initial configuration. Further DFT calculations with the B3LYP/6-311++(d,p) basis set assessed frontier molecular orbitals, MEP surfaces, ELF, LOL maps, TDOS and PDOS, affirming their potential as new anti-cancer therapies.
Methods
Maestro 11.8, Discovery Studio Visualizer, Gromacs-2023, Gaussian 16, and online platforms like SwissADME and ProTox-II were utilized. Fragments generated from eight known HSP90-binding drugs were subjected to SP-docking, leading to 170 fragments. The highest-scoring fragments were merged using the breed panel to create new HSP90 inhibitors. XP-docking and ADMET analyses identified C1, C2, and C3 as the most promising candidates. These compounds were selected for a 300 ns dynamic simulation and subsequent DFT calculations.
Deep Learning for Effective Electronic Waste Management and Environmental Health
Authors: Oise Godfrey Perfectson , Susan Konyeha
Published: September 2024

Creating an advanced deep learning methodology for efficient management of electronic waste (e-waste) and preservation of environmental health is the aim of this research. The research tackles the growing problem of e-waste by compiling and preprocessing various datasets of e-waste images using a Sequential Neural Network (SNN) with TensorFlow and Keras. To improve the model's performance, this all-inclusive approach uses image augmentation techniques. In this work, efforts have been made to enhance the performance and computational efficiency of deep learning classifiers using hyperparameters. The study utilized e-waste data obtained from standard online repositories. The hyperparameter tuned modified CNN-based Sequential Neural Network model achieved an accuracy of 87%, precision of 87%, recall of 86% and f1_score of 86%. This model's strong performance highlights its real-time application potential and ease of integration into current e-waste management workflows. The suggested system is ready for widespread implementation and offers substantial advantages for environmental sustainability and resource conservation. This deep learning system helps reduce health risks associated with improper e-waste disposal, while also supporting ecological preservation by enabling the efficient sorting and classification of e-waste. The innovation resides in its capacity to streamline and automate the management of e-waste, offering a viable resolution to one of the most urgent environmental problems. This study is an excellent example of how cutting-edge artificial intelligence technologies can be integrated to improve waste management systems, giving global environmental health initiatives a scalable and useful tool.
MS-NET-v2: Modular Selective Network Optimized by Systematic Generation of Expert Modules
Authors: Chowdhury Md Intisar , Zhao Qiangfu
Published: September 2024

Modular architectures enhance Deep Neural Networks (DNNs) by reducing error rates, enabling uncertainty estimation, and increasing inference efficiency through selective module execution. While modular ensemble methods like bagging, boosting, and stacking improve DNNs performance, they are often computationally intensive. Adaptive inference strategies, such as the Modular Selective Network (MS-NET), address these issues by using independent router and expert modules, allowing parallel training and selective inference. In this study, we introduce MS-NET-v2, an optimized version of MS-NET, which enhances the construction of subsets and the expert training methodology. First, we introduce a cut-off variable, O, which systematically limits the sampling of binary class pairs from the Inter-Class Correlation (ICC) matrix. Next, we propose a subset merging algorithm that generates multi-class subsets from these binary subsets. This algorithm creates subsets that encode coarse concepts, in contrast to the fine-grained concepts represented by binary subsets. Based on loss landscape theory and to exploit the non-convexity of DNNs, we train these experts from scratch on multi-class subsets. This approach enhances diversity by covering several unique local minima , resulting in improved collective accuracy. We conduct extensive empirical studies with MS-NET-v2 on the CIFAR-10, CIFAR-100, Tiny-ImageNet, and Pets datasets. To verify the enhanced diversity of the expert networks, we perform function and weight space analyses on MS-NET-v2 experts. These studies demonstrate that MS-NET-v2 significantly improves collective accuracy, expert networks' diversity, and parameter efficiency compared to its predecessor. Additionally, MS-NET-v2 outperforms heavier ensemble methods in both single and multi expert settings.
Incidence and influencing factors of preterm birth, low birth weight and small for gestational age neonates with congenital heart disease in Changsha City, China, 2022-2023
Authors: Fu Jin , Zhang Bei , Liao Wenbo , Xiao Yaling , Wang Jinlian , Chen Huan , Fang Jie
Published: September 2024

Objective To analyze the incidence and influencing factors of preterm birth (PTB), low birth weight (LBW) and small for gestational age (SGA) neonates with congenital heart disease (CHD) in Changsha City, to provide a scientific basis for the development of target preventive measures. Methods Neonates with CHD reported by all obstetrics institutions in Changsha City from 2022 to 2023, elucidating the incidence of PTB, LBW and SGA. Statistical methods such as Chi-square tests and binary logistic regression were utilized to determine the associations of influencing factors with PTB, LBW and SGA neonates with CHD. Results A total of 1040 neonates with CHD were eligible for analysis, and the incidence of PTB, LBW and SGA were 15.00% (156/1040), 13.27% (138/1040) and 12.12% (126/1040), respectively. Binary logistic regression analysis showed that both taking folic acid during pregnancy (aOR = 0.386, 95% CI: 0.217–0.687), and parity 1 (aOR = 0.400, 95% CI: 0.243–0.659) or 2 (aOR = 0.278, 95% CI: 0.160–0.482) were protective factors for PTB While cesarean delivery (aOR = 2.517, 95% CI: 1.725–3.671) was a risk factor for PTB. Cesarean delivery (aOR = 2.130, 95% CI: 1.447–3.134), female infants (aOR = 1.589, 95% CI: 1.100–2.297), midwifery institutions of municipal level or above (aOR = 2.379, 95% CI: 1.334–4.245) were risk factors for LBW. Parity 1 (aOR = 5.083, 95% CI: 1.824–14.164) was a risk factor for SGA, while assisted reproduction(aOR = 0.332, 95% CI: 0.131–0.838) was a protective factor. Conclusions The incidence of PTB, LBW and SGA neonates with CHD in Changsha City is not in high level, and it was affected by maternal folic acid consumption, parity, mode of delivery, level of midwifery institutions, mode of conception, as well as neonatal gender.
MicroRNA-130a-3p regulates osimertinib resistance by targeting runt-related transcription factor 3 in lung adenocarcinoma
Authors: Shintani Takuya , Shun Yu-Ting , Toyozumi Yuji , Ikemura Kenji , Takeda Yoshito , Shiroyama Takayuki , Nagatomo Izumi , Kumanogoh Atsushi , Okuda Masahiro , Jingushi kentaro
Published: September 2024

Overcoming resistance to epidermal growth factor receptor tyrosine kinase inhibitors, including osimertinib, is urgent to improve lung cancer treatment outcomes. Extracellular vesicle (EV)-derived microRNAs (EV-miRNAs) play important roles in drug resistance and serve as promising biomarkers. In this study, we aimed to identify EV-miRNAs associated with osimertinib resistance and investigate their clinical relevance. The release of excess EVs was confirmed in an osimertinib-resistant lung adenocarcinoma cell line (PC9OR). The exposure of EVs and EV-miRNAs derived from PC9OR cells to PC9 cells increased cell viability after osimertinib treatment. Microarray analysis revealed that miR-130a-3p was upregulated in EVs derived from PC9OR cells and another osimertinib-resistant cell line (H1975OR). Transfection with miR-130a-3p attenuated osimertinib-induced cytotoxicity and apoptosis in both PC9 and H1975 cells, whereas osimertinib resistance in PC9OR cells was reversed after miR-130a-3p inhibition. Bioinformatics analysis revealed that runt-related transcription factor 3 is a target gene of miR-130a-3p, and it induced osimertinib resistance in PC9 cells. Finally, patients with lower baseline serum miR-130a-3p concentrations had longer progression-free survival. miR-130a-3p is a potential therapeutic target and a predictive biomarker of osimertinib resistance in adenocarcinoma.
A novel mechanism by which c-MYC is aberrantly activated by epigenetic silencing in cancer cells
Authors: Hu Xuming , wei ye , Zhang Meiying , Dou Chunfeng , Wang Liping , Zaib Gul , Wu Huixian , Guo Wang , Wang Xiaoyuan , Chen Shihao , Xu Qi , Guo Mingzhou , Cui Hengmi
Published: September 2024

Proto-oncogenes are abnormally activated in nearly all types of tumors. However, the epigenetic mechanism of proto-oncogene activation has not yet been well elucidated. Here, we show that a subset of proto-oncogenes, including c-MYC, possess antisense RNAs. Upregulation of c-MYC in cancer tissues was attributed to the silencing of its antisense RNA MYC-AS1 via DNA hypermethylation. MYC-AS1 RNA markedly inhibited the proliferation of cancer cells in vitro and impeded tumor growth in nude mice in vivo by repressing the expression of c-MYC via an RNAi mechanism. MYC-AS1 RNA bound directly to the HuR protein in the cytoplasm, enhancing the RNA stability of MYC-AS1. Furthermore, MYC-AS1 inhibited c-MYC-targeted gene LDHA expression. Our work provides a novel mechanism by which c-MYC is activated in cancer cells by epigenetic silencing of its antisense RNA, which functions as a tumor suppressor.
Device-Aware Decomposition and Mapping of Multiple-Control Toffoli Circuits in Quantum Computing Systems
Authors: Jiang Yibo , Cheng Xueyun , Zhu Pengcheng , Gu Hui , Guan Zhijin
Published: September 2024

With the rapid development of quantum computing, quantum computing devices have entered the Noisy Intermediate-Scale Quantum(NISQ)era. However, current NISQ quantum computing devices still face the challenge of physical limitations. To enable quantum algorithms to be directly executed on quantum computing devices, high-level quantum gates (such as MCT gates) in quantum circuits need to be decomposed and mapped onto quantum computing devices. This paper proposes a quantum device-aware method for decomposing high-level quantum gates. This method selects an optimal sub-topology of the quantum device as the target based on the circuit scale and the goal of reducing error rates. According to the proposed MCT gate decomposition rules, an associated gate-pairs generation algorithm is designed to facilitate subsequent decomposition. The control bits of the MCT gates are partitioned based on the associated gate-pair to better match the selected sub-topology. After eliminating redundant gates through the inverse decomposition of associated gate-pairs, the mapping is performed. Experimental results show that the proposed decomposition method reduces the number of basic quantum gates in quantum circuits, with an average optimization rate of 14.6%. For benchmark circuits decomposed using the method, applying the routing algorithm for mapping requires fewer additional gates, with an average optimization rate of 20.6%. The fidelity of the circuits improved by an average of 14.1% after mapping.