bluuebunny/crossref_metadata_2025_split huggingface dataset:
Title：Gγ-protein GS3 function in tight genetic relation with OsmiR396/GS2 to regulate grain size in rice
Authors: Zhu Lin , Shen Yanjie , Dai Zhengyan , Miao Xuexia , Shi Zhenying 
Published: June 2024

Abstract

Manipulating grain size demonstrates great potential for yield promotion in cereals since it is tightly associated with grain weight. Genetically, grain size is under complicated molecular and genetic modulation of different signaling and metabolic pathways. Several pathways modulating grain size have been elaborated in rice, but the possible crosstalk between the ingredients are rarely studied, together with the underlying mechanism. OsmiR396 negatively regulates grain size through targeting OsGRF4 and OsGRF8, and proves to be multi-functioning. G proteins function extensively in rice to regulate grain size. Here we showed that expression of GS3 gene that encodes Gγ-protein was down-regulated in the young embryos of MIM396, GRF8OE and GS2OE plants. Multiple biochemical assays proved possible transcriptional regulation of GRF4 and GRF8 on GS3 gene. Further genetic relation analysis revealed tight genetic association between not only OsmiR396 and GS3 gene, but also GS2 and GS3 gene. Thus, we revealed that the miR396 pathway and the G protein pathway could cross talk in the regulation of grain size.
Lone and Combine Effect of Accelerated Irradiation and Thermal Aging Stressors on Silica Reinforced Ethylenepropylenediene Monomer Rubber
Authors: Naz Sumaira , Waqas Hassan , Ahmed Muhammad Bin , Danish Muhammad 
Published: June 2024

Abstract

The aim of this study was to determine the affect of irradiation and thermal accelerated aging (i.e. equivalent to 8years) on Ethylenepropylenediene Monomer (EPDM) rubber contained silica as a filler and also grafted in polymeric matrix. For this purpose, accelerated thermal aging was performed at T = 123°C for 15 days. Whereas, accelerated i.e. short term irradiation aging was done separately under thermal neutron flux in research reactor for 3.6 hrs. The combine affect of thermal aging before and after irradiation was also studied. The results revealed that irradiation promoted crosslinking in EPDM rubber, while thermal aging preferably induced secissioning in silica grafted network chain structure. The further study revealed that irradiation after thermal aging have a propensity to cure the adverse effects of temperature by increase of crosslink density and elimination of pores which made the polymeric matrix more dense as observed by structural (XRD, pole density, FTIR) and microscopic (SEM) Analyses. However, in converse order i.e. thermal aging after irradiation aggravated detrimental effects on EPDM rubber due to prevailing scissioning over crosslinking in polymeric chain structure.
Molecular Identification of stink bug (Pentatomidae sp. BD39) and pod sucking bug (Mirperus sp.) associated with crops under rotation
Authors: Sanda Nafiu Bala , Hussaini Muhammad Auwal , Wudil Baba Sani , Lado Abdulrahman , Adnan Adnan Aminu , Abdulrahman Bassam Lawal , Garba Ismail Ibrahim , Kamara Alpha Yaya , Buhari Fatima Zahrah , Aliyu Ma’amun Da’u , Yakubu Bashir Hussaini , Abdullahi Abuhuraira 
Published: June 2024

Abstract

One of the problems facing agricultural production in Nigeria is the pests and diseases, which farmers depend heavily on the use of chemicals for their control. However, in time of outbreak of invasive species, difficulties amount on the proper identifications of these species for proper record and management strategies due to reliance on morphological methods of insect identification. The use of morphological methods of insect identification, not only slow but it sometimes leads to variations between experts. The use of Molecular techniques identifies insect species using molecular markers; which minimize false positive results by targeting the specific molecules of interests. This study was conducted to identify major insect pests of crops grown under rotation experiment. Cytochrome oxidase I (CO1) gene was used for the molecular identification of the insect pests of crops under rotation. The expected amplicons were sequenced and the results were analyzed using the nucleotide BLAST at the NCBI database for identification. The COI gene sequences were identified as Pentatomidae sp. BD39 and Mirperus sp. with 97.47% and 99.75% similarities, respectively. The former is a Stink bug and is a pest of cereal grains while the latter is a Pod sucker and is a major pest of cowpea and soybean in sub-Saharan Africa. Taken together, this knowledge is useful in developing different sustainable cropping options in order to monitor different types of insect pests and their population dynamics.
A Common Architecture-based Smart Home Hardware Forensics for Scalable Investigations
Authors: Kim Sungbum , Lee Gwangsik , Song Jian , Lee Insoo , Shon Taeshik 
Published: June 2024

Abstract

The smart home platform communicates with internet of things(IoT) devices, smartphones, and cloud servers to provide convenient services, storing user information and device operation and user behavior-related data. This data is crucial for criminal investigations, highlighting the importance of smart home forensics. Currently, advanced software and hardware technologies continue to be developed in the smart home market, and based on this, companies are releasing new services and devices. Therefore, scalable platform-oriented forensic research is needed for efficient digital investigation. This study identifies the components and structures of smart homes to derive a common architecture representing various environments. It proposes a three-stage smart home forensics framework: analyzing application functions to infer data, extracting and analyzing data from devices, and identifying data useful for criminal investigations. Its applicability is demonstrated with Samsung SmartThings and Xiaomi Mi Home platform testbeds.
The Role of TNF-R1-RIPK1/RIPK3 Signaling Pathway in Lead-Induced Necroptosis in the Hippocampus of Mice
Authors: Li Huishuai , Li Zhenning , Yang Chun , Wei Ruokun , Wei Peiqi , Yuan Haiyan , Aschner Michael , Ou Shiyan , Peng Dongjie , Li Shaojun 
Published: June 2024

Abstract

Lead (Pb), a dense, soft, blue-gray metal, is widely used in metallurgy, cables, storage batteries, pigments, and other industrial applications. Pb has been shown to cause hippocampal neurodegeneration. Necroptosis, a newly described cell death modality, is closely associated with neurodegenerative diseases. Whether Pb can cause programmed neuronal cell death and its mechanisms has yet to be characterized. Here, we explored the role of the TNF-R1-RIPK1/RIPK3 signaling pathway in necroptosis induced by Pb. Our results showed that Pb exposure elevated lead levels in murine whole blood and hippocampal tissue in a dose-response relationship. Protein expression levels of PARP, c-PARP, RIPK1, p-RIPK1, RIPK3, MLKL, and p-MLKL in the hippocampal tissues were elevated, while the protein expression of caspase-8 was decreased. Furthermore, Pb exposure reduced the survival rates in HT-22 cells and primary mouse hippocampal neurons, while increasing the protein expressions of RIPK1 and p-MLKL. Collectively, these novel findings suggest that the TNF-R1/RIPK1/RIPK3 signaling pathway mediates Pb-induced necroptosis in hippocampal neurons in mice.
Fake Review Detection and Text Analysis by Using Clustering Techniques on LSTM
Authors: Dai Tianle , Wang Xiaodong 
Published: June 2024

Abstract

With the growth of the internet, the issue of fake reviews has become increasingly prominent. Traditional models for fake review detection have underperformed on balanced datasets, leading to difficulties in differentiating between the genuine and the fake reviews. This paper introduces a model based on k-Means text clustering neural networks. Firstly, the genuine review texts are vectorized after stop-word removal using TF-IDF to determine the weights of each word and map them into a high-dimensional space. Then, the k-Means clustering algorithm is employed to cluster these reviews in the space. From each cluster center, the genuine reviews equal in number to the fake reviews are extracted. By analyzing keywords from different clusters, the model learns the linguistic behaviors distinguishing the genuine reviewers from the fake ones. These reviews are then processed by a Long Short-Term Memory (LSTM) network to extract and analyze their features. Finally, prediction outcomes are produced. Experimental results on the Yelp dataset demonstrate that the text clustering neural network model outperforms existing models, showcasing enhanced performance.
CGProNet: Continual Graph-based Prototypical Networks for Few-Shot Skin Disease Classification
Authors: Noman Abdulrahman , Beiji Zou , Zhu Chengzhang , Alhabib Mohammed , Al-Sabri Raeed 
Published: June 2024

Abstract

The prototypical network effectively classifies skin diseases in few-shot learning but faces challenges with prototype accuracy due to limited data and insufficient long-term knowledge retention, affecting generalization to new classes. However, the prototypical network faces challenges such as inaccurate prototype estimations due to limited data and insufficient long-term knowledge retention, affecting its generalization to new classes. This study introduces CGProNet (Continual Graph-based Prototypical Networks for Few-Shot Skin Disease Classification) to address these issues and improve the accuracy of uncommon skin disease identification. CGProNet employs prototype network-based support samples and considers their interdependencies while transferring knowledge across tasks. It uses Convolutional CNNs to generate initial features, graph-based methods to capture relational information, and gated recurrent units (GRUs) to preserve graph-aggregated features. CGProNet achieves 80.5\% accuracy on the ISIC-2018, 86.03\% on the Derm7pt, and 92.51\% on the SD-198 datasets in 5-shot learning scenarios, proving effective for skin disease classification with limited data.
Diffusion Model for Multi-scale Ship Object Detection and Recognition in Remote Sensing Images
Authors: Chen Lei , Wang Bin , Liu Ying , Zhao Shuang , Guan Qinghe , Li Guandian 
Published: June 2024

Abstract

Ship object detection and recognition in remote sensing images (RSIs) is a challenging task due to the multi-scale and complex background characteristics of ship objects. Currently, convolution-based methods cannot adequately solve these problems. Firstly, this article first applies the diffusion model to the task of ship object detection and recognition in RSIs, and proposes a new diffusion model for multi-scale ship object detection and recognition in remote sensing images (MSDiffDet). Secondly, in order to reduce the loss of multi-scale information in the feature extraction process, this article proposes the Channel Fusion FPN (CF-FPN) based on FPN and constructs the Large-Scale Feature Enhancement Module (LSFEM), which further enhances the algorithm's ability to extract large-scale ship object features and improves the detection accuracy of ship objects in RSIs. Finally, this article prunes and reconstructs MobileNetV2 to obtain the Sparse MobileNetV2, which is used as the backbone network of the image encoder, which enhances detection accuracy while reducing the overall parameter count of the algorithm. The experimental results demonstrate that the MSDiffDet algorithm is effective in detecting and recognizing four types of remote sensing ship objects: aircraft carriers, warships, commercial ships, and submarines. The mAP0.5 achieved a notable 89.8\%. A significant improvement of 5.8\% in mAP0.5 is observed compared to the DiffusionDet algorithm, indicating the potential of the MSDiffDet algorithm for applications in remote sensing ship object detection and recognition.
SWM: Enhancing Spatiotemporal Awareness in Remote Photoplethysmography for Non-Contact Heart Rate Estimation
Authors: Zhao Chaoyu , Sun Peng , Chen Zhencheng 
Published: June 2024

Abstract

Remote Photoplethysmography (rPPG) is a method for non-contact estimation of heart rate (HR) using facial video. In comparison to traditional electrocardiography (ECG), which requires physical contact and may cause discomfort to the subjects, rPPG technology offers a more comfortable and convenient solution. This is particularly applicable to patients such as newborns who find it challenging to wear monitoring devices, thereby reducing the risk of infections associated with contact devices.In recent years, Remote Photoplethysmography (rPPG) technology based on deep learning methods has advanced rapidly. Currently, most models use Convolutional Neural Networks (CNN) as feature extractors. However, the spatiotemporal perception capacity of CNN has certain limitations, potentially leading to the oversight of crucial information.Therefore, we propose a network with enhanced spatiotemporal awareness, named SWM. SWM employs Swin Transformer blocks as feature extractors, and after differencing and normalizing the video sequence, it enters Wrapping Temporal Shift Modules (WTSM).We proposed a subsampling method named Patch WTSM Mask, which facilitates the interaction of contextual information and weights facial features. SWM demonstrates excellent performance on the UBFC-rPPG dataset, with MAE=0.49, RMSE=1.21, MAPE=0.54, Pearson=0.99. The robustness of SWM is further validated across datasets, specifically on the UBFC-Phys dataset, where MAE=6.10, RMSE=10.82, MAPE=7.22, Pearson=0.70.In the end, we conducted ablation experiments and compared SWM with advanced methods, confirming its outstanding performance. SWM no longer relies on CNN, potentially heralding a new direction in the research field of rPPG.The data and code used in this study can be found at \url{https://github.com/tannixi/SWM}, and each record has a corresponding DOI: [10.5281/zenodo.11487732].
Distribution characteristics of pCO2 at the air-sea in the East China Sea and changes over the last 20 years
Authors: Ye Linan , Lin Chongyang , Ren Ming , Xu Guofeng , Zhang Haibo , Wei Yongjie , Jing Yudi 
Published: June 2024

Abstract

According to the carbon dioxide (pCO2), temperature (T), salinity (S) and pH of the surface seawater obtained from a survey of carbon dioxide (CO2) in the East China Sea(ECS) region from 2004 to 2023, the seasonal distribution characteristics of pCO2 in the ECS were analyzed and discussed. The results show that the overall trend of pCO2 in the ECS region indicated a slow increase followed by a decrease over the past 20 years, with the highest peak reached in 2016. The overall carbon sequestration capacity of the ECS shows a trend of first decreasing and then increasing. This is most likely related to China's adoption of more effective policies and measures in achieving carbon peak and carbon neutrality. In terms of seasonal changes, the distribution of pCO2 in the East Sea area roughly shows the differences among the offshore area. The annual seasonal changes are almost similar betweenthe continental shelf area and the outer sea area. In terms of the average distribution of pCO2 in surface seawater of the investigated area, pCO2 in autumn and summer is higher than that in winter and spring, while atmospheric pCO2 levelin spring and autumn is higher than that in summer and winter, and the seasons are all represented by the exchange of atmospheric CO2. The water system in the East Sea area is complicated, and the distribution of pCO2 in surface seawater is mainly affected by tfour major water systems: the Yangtze River, the continental shelf water, the Kuroshio water, and the coastal current of the Yellow Sea. It is generally affected by vertical mixing of water in winter and late autumn and biological activities in summer, late spring, and early autumn.
Machine Learning Regions of Reliability based on Sampling Distance Evaluation with Feature Decorrelation for Tabular Time Datasets
Authors: Askanazi Evan , Grinberg Ilya 
Published: June 2024

Abstract

Despite successful use in a wide variety of disciplines for data analysis and prediction, machine learning (ML) methods suffer from a lack of understanding of the reliability of predictions due to the lack of transparency and black-box nature of ML models. In materials science and other fields, typical ML model results include a significant number of low-quality predictions. This problem is known to be particularly acute for target systems which differ significantly from the data used for ML model training. However, to date, a general method for uncertainty quantification (UQ) of ML predictions has not been available. Focusing on the intuitive and computationally efficient similarity-based UQ, we show that a simple metric based on Euclidean feature space distance and sampling density together with the decorrelation of the features using Gram-Schmidt orthogonalization allows effective separation of the accurately predicted data points from data points with poor prediction accuracy. To demonstrate the generality of the method, we apply it to light GBM machine learning using a set of time series tabular data sets. We also show that this metric is a more effective UQ tool than the standard approach of using the average distance of k nearest neighbors (k = 1–10) in features space for similarity evaluation. The computational simplicity of this dataset combined with its applicability to time series datasets allows it to be readily used in numerous real world problems.
An evaluation of spirooxindoles as blocking agents of SARS-CoV-2 spike/ACE2 fusion and M pro inhibitory agents: Synthesis, biological evaluation and computational analysis
Authors: Ehinak Albert Enama , Lobe Maloba M. M. , Simoben Conrad V. , Tietjen Ian , Eni Donatus B. , Cassel Joel , Salvino Joseph M. , Montaner Luis J. , Sippl Wolfgang , Efange Simon M. N. , Ntie-Kang Fidele 
Published: June 2024

Abstract

Both tetrahydroisoquinolines (THIQs) and oxindoles (OXs) display a broad range of biological activities, including antiviral activity. They are, therefore, recognized as privileged scaffolds in drug discovery. Here, we describe the synthesis of spirofused tetrahydroisoquinoline–oxindole hybrids (spirooxindoles) and their evaluation as potential blocking agents of both SARS-CoV-2 spike/ACE fusion and inhibitors of the main protease (Mpro). The most active synthesized compound showed a 50% inhibitory concentration (IC50) of 3.6 µM against SARS-CoV-2 spike/ACE fusion. None of the tested compounds was shown to be active against Mpro. The most active compound possesses a bulky naphthyl group, which addresses voluminous hydrophobic regions of the ACE2 binding site and interacts with the hydrophobic residues of the target; this finding agrees with previous studies revealing that bulky compounds block spike/ACE2 fusion, e.g., the natural product hopeaphenol. Therefore, spirooxindoles may provide useful leads in the search for SARS-CoV-2 spike/ACE fusion blocking agents.
Listening to the women’s experience of perinatal domestic violence during the COVID-19 pandemic: a qualitative content analysis
Authors: Babazadeh Raheleh , Barez Malikeh Amel 
Published: June 2024

Abstract

Background
Pregnant women and their fetuses would be high risk population and more susceptible to COVID-19 infection. Since the outbreak of COVID-19, quarantine, social distancing and isolation, and the consequences were likely to dramatically increase the risk of domestic violence. The aim of this study was to explore women’s experience of perinatal domestic violence in COVID-19 pandemic.
Methods
This qualitative study was carried out from September 2020 to December 2022 in Mashhad, Iran. Data were collected through remote individual semi structured interviews with 9 women who experienced perinatal domestic violence, and 7 key informant with relevant specialist until the data saturation was achieved. The data was analyzed using conventional content analysis adopted by Graneheim & Lundman.
Findings:
The 4 main themes that emerged from the data analysis were "aggravators of perinatal violence", "enhanced vulnerability", "strategies for dealing with perinatal violence" and "neglected needs".
Conclusion
The high prevalence and the adverse consequences of perinatal domestic violence during COVID-19 crisis provide a golden opportunity for performing appropriate interventions. Perinatal period is a window of opportunity to identify and support abused pregnant who may have lost contact with other supportive services during COVID-19 pandemic. Psychological and physical support and social connectedness are important strategies during the crisis isolation for those who are at risk of domestic violence during COVID-19 pandemic.
Changing Precipitation Patterns and Extremes in Southern Thailand under Warmer Climate
Authors: Kuinkel Dipesh , Promchote Parichart , Upreti Khem R. , Wang S.-Y. Simon , Dahal Ngamindra , Pokharel Binod 
Published: June 2024

Abstract

Southern Thailand has experienced significant shifts in precipitation patterns in recent years, exerting substantial impacts on regional water resources and infrastructure systems. This study aims to elucidate these changes and underlying factors based on daily precipitation observations from Nakhon Si Thammarat Province spanning 1980 to 2022. Additionally, data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) is utilized to investigate projected changes in precipitation for 2015-2100 relative to the historical period (1980-2014), employing a comprehensive analysis considering two emissions scenarios (SSP245 and SSP585) across six models. Various precipitation indices are selected to assess trends and statistical significance using the Mann-Kendall test.
Both observed and climate model data indicate an increasing precipitation trend in Southern Thailand, with a reduced association with the El Niño-Southern Oscillation (ENSO) under warming conditions. Extreme precipitation indices also exhibit an increasing trend, with total precipitation and the 95th percentile of daily precipitation (R95p) revealing very wet conditions in recent years, projected to continue increasing. Contrastingly, the number of dry days is also mounting, suggesting that both dry and wet extremes will impact Southern Thailand under a warmer climate. The findings from this study provide an early indication of future precipitation and extreme event scenarios, which can inform the development of measures to mitigate climate change-related hazards in the region.