bluuebunny/crossref_metadata_2025_split huggingface dataset:
In silico,neuroprotective effect of phenyl-methanol based benzylidenehydrazineyl derivatives by scavenging reactive oxygen species via sirtuin 3 in Parkinson's disease
Authors: Chand Jagdish , Subramanian Gomathy , baba Mohammad Zubair , Wahedi Umair 
Published: November 2022

Abstract
The authors have requested that this preprint be removed from Research Square.
Innovative Design of Environmental Visual Art Based on Edge Cloud Computing Technology under the Background of Artificial Intelligence
Authors: Han Lei , Yao Jia 
Published: November 2022

Abstract
In the traditional artificial city planning, the greenway research generally takes the greenway layout or environmental effects as a single evaluation criterion. This work aims to solve the problem of weak commonality and difference of greenway landscape vision in the construction of urban style features. First, an image super-resolution reconstruction technology is proposed based on the Convolutional Neural Network to achieve precise processing of the urban greenway layout at a high level. Secondly, a one-time batch processing technology of images based on edge-cloud collaborative deep learning semantic segmentation of edge-cloud computing technology is proposed, which can quickly find out the problematic areas in greenway planning. Finally, the data set obtained by image processing is expressed by mapping software to analyze the commonness and difference of urban greenway landscape visual elements characteristics and composition diversity. The results show the spatial distribution sequence characteristics of different types of greenways and evaluate the relationship between the same type of greenway landscape visual services and their accessibility. The experimental results show that: (1) the CNN-based image super-resolution reconstruction technology designed is superior to the traditional technology in image processing and can more accurately identify the information in the image; (2) The image batch processing technology of edge-cloud collaborative deep learning semantic segmentation based on edge cloud computing technology can greatly improve the efficiency of image information processing, and has broader application prospects in image batch processing. This research aims to provide technical support for the upgrading and transformation of artificial intelligence technology in technology enterprises and reduce the adverse impact on urban construction caused by blind and one-sided urban environmental planning. Some method innovation and reference are provided for environmental visual art design innovation.
Identification of Rboh genes from Neoporphyra haitanensis and their expression profiling under stress conditions
Authors: Gao Da-Hai , Gui Tian-Yu , Huang Lin-Bin , Yan Xing-Hong 
Published: November 2022

Abstract
Respiratory burst oxidase homolog (Rbohs) is key enzymes in the reactive oxygen species (ROS) metabolic network. Various studies had shown that plant Rboh genes were involved in plant growth and developmental processes as well as in biotic and abiotic stress responses, but little is known about its functional role in red algae. In the present study, 10 putative NhRboh genes were identified from the genome of Neoporphyra haitanensis. It predicted that most of NhRbohs encode acidic hydrophobic proteins, locating in plasma membrane and chloroplast. Genome-wide syntenic analysis indicated 6 of 10 NhRboh genes were in conserved collinear blocks between N. haitanensis and N. yezoensis, and many cis-acting elements related to stress response were predicted in the up-stream regions of NhRboh genes. Additionally, expression profiles of 8 NhRboh genes were analyzed under stress conditions of dehydration, heat stress and co-stresses of heat and dehydration. Together with transcriptomic and proteomic results, it showed that the expression patterns of these genes were divergent among different conditions, suggesting some genes may be involved in the process of stress tolerance in N. haitanensis. This study provides valuable information to help with further functional studies of Rboh genes in N. haitanensis.
Construction of an Ecological Model of Sambucus javanica Blume in China under Different Climate Scenarios Based on Maxent model
Authors: Liao Jianfeng , Yang Chuqun , Shao Qi , Sun Qian , Han Yulai 
Published: November 2022

Abstract
Sambucus javanica Blume. is a Chinese native medicinal plant with high medicinal value. In this study, the MaxEnt model was used to explore the relationship between the geographical distribution of S. javanica and environmental factors, and to construct the distribution pattern of S. javanica under different climate scenarios. The results showed that the environmental conditions suitable for the distribution of S. javanica were as follows: precipitation in June ranged from 156.36 mm to 383.25 mm; solar radiation in December ranged from 6750.00 kJ·m-2·day-1 to 10521.00 kJ·m-2·day-1; isothermality ranged from 24.06 to 35.50; precipitation of warmest quarter ranged from 447.92 mm to 825.00 mm. Among them, precipitation and temperature were the key environmental factors affecting the distribution patterns of S. javanica. This plant could grow well mainly in two regions in China, covering a total area of 2.73 × 106 km2. The first region mainly consists of Guizhou, western Hubei, southeastern Chongqing, southwestern Hunan, northern Guangxi, and a small part of eastern Yunnan. The second region mainly consists of Zhejiang, southern Anhui, and northern Fujian. Under the future SSP126 and SSP585 scenarios, potentially suitable habitats in the eastern part of the potential distribution of S. javanica (Jiangxi, Fujian, Zhejiang, and Anhui) might be at risk of habitat fragmentation. Based on the result of this study, Real-time monitoring of wild groups of S. javanica is now recommended to protect its genetic diversity. These findings are supposed to promote the effective conservation and utilization of S. javanica in the future.
PET-PCR reveals low parasitaemia and submicroscopic malarial infections in Honduran Moskitia
Authors: Escobar Denis , Pinto Alejandra , Serrano Delmy , Ksandrová Eliška , Grimaldi Nicole , Juárez-Fontecha Gabriel , Moncada Marcela , Valdivia Hugo O. , Fontecha Gustavo , Matamoros Gabriela 
Published: November 2022

Abstract

Background:Malaria remains the main parasitic disease of humans. Although the largest number of cases is reported in the African region, there are still endemic foci in the Americas. Central America reported 36,000 malaria cases in 2020, which represents 5.5% of cases in the Americas and 0.015% of cases globally. Most malaria infections in Central America are reported in La Moskitia, shared by Honduras and Nicaragua. In the Honduran Moskitia, less than 800 cases were registered in 2020, considering it an area of ​​low endemicity. In low endemicity settings, the number of submicroscopic and asymptomatic infections tends to increase, leaving many cases undetected and untreated. These reservoirs challenge national malaria elimination programs. This study aimed to compare and assess the diagnostic performance of Light Microscopy (LM) with photoinduced electron transfer polymerase chain reaction (PET-PCR) as the gold standard in a population of febrile patients from La Moskitia.Methods:A total of 309 febrile participants were recruited using a passive surveillance approach at the Puerto Lempira hospital. Blood samples were analyzed by LM, nested PCR, and PET-PCR. Diagnostic performance including sensitivity, specificity, negative and positive predictive values, kappa index, accuracy, and ROC analysis was evaluated. The parasitaemia of the positive samples was quantified by both LM and PET-PCR.Results:The overall prevalence of malaria was 19.1% by LM, 27.8% by nPCR, and 31.1% by PET-PCR. The sensitivity of LM and nPCR was 59.6% and 80.8%, respectively. LM showed a kappa index of 0.67, with a moderate level of agreement. Forty positive cases by PET-PCR were not detected by LM.Conclusions:This study demonstrated that LM is unable to detect parasitaemia at low levels and that there is a high degree of submicroscopic infections in the Honduran Moskitia.
Human impacts, habitat quantity and quality affect the dimensions of diversity and carbon stocks in subtropical forests: a landscape-based approach
Authors: Bastos Júlio Ricardo , Capellesso Elivane Salete , Vibrans Alexander Christian , Marques Márcia Cristina Mendes 
Published: November 2022

Abstract

Context
Fragmentation is recognized as one of the main factors affecting species and functionality losses in tropical landscapes.
Objectives
In this study, we assess how landscape quality and quantity affect taxonomic and functional diversities and carbon stocks in the Atlantic Forest.
Methods
We used a large dataset, which comprises 92,754 adult trees of 668 species, distributed over an area of ​​95,733 km² in the state of Santa Catarina, southern Brazil. In each plot, we quantified the taxonomic diversity (species richness), the functional diversity (functional richness), and the aboveground carbon stock and related it to different landscape metrics (fragment area and total area, number of fragments, total edge area, index of the largest fragment, effective network size and aggregation index) and anthropogenic impacts in three surrounding landscape buffers (radius 1000, 3000 and 5000 m). We built multiple regression models, selecting the best models (Akaike's criterion), to assess the influence of the landscape and anthropogenic index on diversities and carbon stocks.
Results
Our study shows that the landscape quantity and quality, and the anthropic effects are factors that negatively affect the functioning of ecosystems, reinforcing that small-scale exploration, within the fragment itself, is an important factor in reducing diversity and carbon stock.
Conclusions
The importance of considering local exploitation has important implications for conservation, and these results bring important insights for conservation, especially for forest fragments in anthropized landscapes, where exploration within the fragments are factors that interfere in the conservation and maintenance of biodiversity and ecosystem functioning.
Identification of serum peptide biomarkers for predicting the efficacy of first-line chemotherapy in advanced epithelial ovarian cancer by MALDI-TOF-MS with magnetic bead technology.
Authors: Xiu Lin , Li Ning , Wang Wenpeng , Chen Feng , Yuan Guangwen , Cui Wei , Wu Lingying 
Published: November 2022

Abstract
Objective This study explored serum protein biomarkers for predicting the efficacy of first-line chemotherapy in advanced epithelial ovarian cancer using MALDI-TOF-MS combined with magnetic bead technology.
Methods A total of 182 epithelial ovarian cancer patients were admitted to the Department of Gynaecologic Oncology, Cancer Hospital of the Chinese Academy of Medical Sciences between March 2018 and September 2018. Sixty-four of these patients had stage III/IV disease and received neoadjuvant chemotherapy followed by interval debulking surgery (NACT-IDS) and adjuvant chemotherapy (mucinous and low-grade serous carcinoma were excluded). Serum samples were collected before treatment. After a median follow-up time of 16 months, 25 chemosensitive patients with progression-free survival (PFS) >12 months and 10 chemoresistant patients <6 months after adjuvant chemotherapy were selected as the research subjects. Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) was used for peptide profiling of the serum samples to compare significantly differentially expressed peptides between the chemosensitive group and chemoresistant group (score >5), and peptide peaks with AUC ≥ 0.8 were identified. Liquid chromatography–mass spectrometry (LC‒MS/MS) was further used to determine the composition of the differentially expressed peptides.
Results In the range of 1000-10000 m/z, 71 differential peptide peaks between the chemoresistant and chemosensitive groups were initially detected. ROC curve analysis showed that among the 71 differential protein peaks, there were 22 differential polypeptide peaks with an AUC ≥0.8, of which 10 were highly expressed in the chemoresistant group and 12 were highly expressed in the chemosensitive group. LC‒MS/MS analysis identified the protein components of 4 polypeptide peaks among the 10 differentially expressed polypeptide peaks in the chemoresistant group: 4634.22 m/z as fragments of serum amyloid A-1, 1287.56 m/zas fragments of prothrombin, and 1099.56 m/z and 2018.39 m/z as fragments of complement C3; the sensitivity and specificity of these three proteins for predicting first-line chemotherapeutic efficacy were 100%, 100%, and 96% and 80%, 100%, and 100%, respectively. No protein component was identified among the 12 differential protein peaks in the chemosensitive group.
Conclusion Serum amyloid A-1, prothrombin and complement C3 were highly expressed in chemoresistant patients; thus, they might be related to the poor efficacy of paclitaxel/carboplatin first-line chemotherapy in advanced ovarian cancer. It is necessary to further explore their predictive value.
A survey on Artificial Intelligent based solutions using Augmentative and Alternative Communication for Speech Disabled
Authors: Evangeline Betsybha , - Anitha 
Published: November 2022

Abstract
This paper aims to analyse how innovative Artificial Intelligence (AI) systems for non-standard speech recognition may revolutionize Augmentative Alternative Communication (AAC) technology for people with severe speech impairments. The AI-based system is personalized for each person's unique speech production and offers a real step forward in improving the efficiency of AAC. With impressive enhancements in recognizing non-standard natural language supported by Machine Learning and Deep Learning algorithms, AI is offering a turning point for personalized and customized Augmentative Alternative Communication (AAC). However, there is a need of understanding the contextual needs of the user which enhances the experience while using AAC. To address this, a systematic review has been done to identify existing applications and the technologies behind them. Also, challenges are explored that can lead to the future directions of the research work. This paper presents the results of the systematic review, after filtering 62 journal articles that are more relevant to the context from a pool of 1088 papers, in the perspective of the existing AI technologies in speech generation, speech reconstruction and speech generation. AI based AAC applications are comprehended in term of input/output, cost, infrastructure and user background. The paper also highlights the future research directions in this domain by identifying the research gaps.
Modification of wood with organic-inorganic hybrid silica sol
Authors: Yang Shoulu , Liu Zhu , Wang Zhongwei , Wu Yiqiang 
Published: November 2022

Abstract
As a potential building material, fast-growing wood (such as poplar) is prone to easy combustion and has poor mechanical properties, which limit its application range. In order to improve the reinforcing effect of SiO2 on poplar wood, we propose a method of immersing silica sol into wood smoothly. In this method, the sol and a non-ionic surfactant are first treated via organic-inorganic hybridization. This surfactant has a strong penetration effect, and the sol is introduced into the wood through hybridization. By impregnating the wood, the flexural strength and flexural modulus of elasticity increased by 79.7% and 89.5%, and the compressive strength along the grain increased by 105.1%. The total loss of fatty alcohol polyoxyethylene ether (JFC)-silica sol hybrid material treated wood was 78.95% lower and the time of the maximum heat release peak of the curve is 215 s, which is 50 s later than that of the control group. This method incorporates silica sol in wood via physical filling and chemical bonding, thereby improving the mechanical and fire resistance properties of wood as an outdoor building material.
Rethinking the Value of Local Feature Fusion in Convolutional Neural Networks
Authors: Lou Zhenyu , Ye Xin , Zhang Luoming , Wu Weijia , He Yefei , Zhou Hong 
Published: November 2022

Abstract
Traditional CNN head for classification tasks typically consists of a global average pooling layer before the last fully-connected classifier. However, such a simple and light-weighted head lacks the ability of feature fusion, and can’t give full play to the strong feature extraction ability of the network body. In the present work, we analyze the Basic Block and Bottleneck structure in ResNet in-depth and reveal the importance of performing feature fusion inside local patches via 1 × 1 convolution. We propose a new head structure consisting of three stages with a series of 1 × 1 convolution to replace global average pooling. With little additional FLOPs and inference speed drop, our new head improves the accuracy for ResNet18by 3.6% on ImageNet, and for ResNet56 by 5.0% on CIFAR-100.
Security Performance of Advanced Block Cipher Technique in Cloud Computing
Authors: Agusthiyar R 
Published: November 2022

Abstract
Cloud security intends to give security among the common information in a public cloud among customer and worker. Cloud empowers all sort of online business to dispatch in simple way simultaneously security hazard at installment mode additionally increments on the grounds that the subtleties kept over the cloud can be hack in a public cloud. To keep away from this danger factor square and code and stream figure is utilized to lessen the programmers in the distributed storage, cloud planned with the assistance of Application Program Interface (API) which contains firewalls and Denial of Services to work on the security. Different encryption procedures are accessible in cloud to give security in that square code per based calculation and stream based calculation assumes a significant part to build the security level. This exploration paper, dissect both the calculations to track down the best encryption procedures to build the dependability among the distributed storage.
Effects of CoFlow-Jet Active Flow Control on Airfoil Stall
Authors: Yazdani Shima , Salimipour Erfan , Salimipour Ayoob , Sheremet Mikhail , Ghalambaz Mohammad 
Published: November 2022

Abstract
Originality/value – Furthermore, the current work intends to analyze the CoFlow-Jet performance by varying the jet momentum coefficient and comparing all states to the baseline airfoil, which has not been studied in prior research investigations.
Purpose – Active flow control on the NACA 0024 airfoil defined as suction-injection jet at the chord-based Reynolds number of 1.5×105 is studied.
Design/methodology/approach – The three-dimensional incompressible unsteady Reynolds-averaged Navier-Stokes equations with the SST k-ω turbulence model are utilized to study the effects of CoFlow-Jet on the dynamic and static stall phenomena. CoFlow-Jet implementation is conducted with several momentum coefficients to investigate their turnover. Furthermore, the current work intends to analyze the CoFlow-Jet performance by varying the Reynolds number and jet momentum coefficient and comparing all states to the baseline airfoil, which has not been studied in prior research investigations.
Findings – It is observed that at the momentum coefficient (Cμ) of 0.06, the lift coefficients at low attack angles (up to α = 15°) dramatically increase. Furthermore, the dynamic stall at the given Reynolds number and with the lowered frequency of 0.15 is explored. In the instance of Cμ = 0.07, the lift coefficient curve does not show a noticeable stall feature compared to Cμ = 0.05, suggesting that a more powerful stronger jet can entirely control the dynamic stall.
Model for prediction of the optimal dose of Jatropha curcas in FS dewatering
Authors: Benjamin Doglas Mmasi , Kimwaga Richard 
Published: November 2022

Abstract
Jatropha curcas (JC) is a highly effective conditioner in dewatering fecal sludge (FS); however, there are limited studies on the model predicting its optimal dose. This study presents the results of the developed model for predicting JC optimal doses. The developed model assessed the FS parameters and JC stock solution. We analyzed the FS samples from a mixture of a pit latrine and septic tank at the water quality laboratory of the University of Dar es Salaam. The multiple linear regression model was used to establish a relationship between JC optimal dose as a function of FS characteristics (pH, electrical conductivity, total solids, total suspended solids and concentration of the JC stock solution). The results indicated that 90.4% of the JC optimum dosage was determined and contributed by FS characteristics and JC stock solution concentrations. Also, the main explanatory factors determining the JC optimal dose were the JC stock solution concentration, followed by the pH of FS. The model results showed a good agreement between the predicted and observed JC optimal dose with a coefficient of determination of R2 = 0.904 and 0.7879 for calibration and validation, respectively. Therefore, the model can be adapted to determine the JC optimal dose without running the jar test experiment.
Synthesis of Aryl/Heteroaryl Selenides Using Transition Metals Catalyzed Cross Coupling and C─H Activation
Authors: Kundu Debasish , Mahata Atanu , Roy Totan 
Published: November 2022

Abstract:Aryl and heteroaryl selenides are an important class of organic compounds and their synthesis has been widely studied all over the world in the last two decades. Transition metals catalysed cross-coupling and directed C─H activation in unactivated arenes in the presence of diselenides/aryl selenols are found to be the most important tools for their synthesis. In recent years different transition metal catalysts were found to be effective to perform C─Se crosscoupling in both aryl and heteroaryl rings. The present review article covers all the recent advances made in the last ten years in the field of the synthesis of aryl and heteroaryl selenides through homogeneous and heterogeneous transition metals catalyzed cross-coupling reactions and directed selenylation via C─H bond activations.
The Role of Graphene Oxide in the Inhibition of Migration and Invasion of
Cancer Cells by Destroying Actin Cytoskeleton and via Inhibiting the
Activities of ETC Complexes
Authors: Hyseni Besmir , Mazreku Ilir , Bislimi Kemajl 
Published: November 2022

Abstract:
Cancer is one of the most important threats to public health. Cancer is characterized by cell proliferation that has eluded central endogenous control mechanisms. Cervical cancer is the third most common cancer among women, followed by skin cancer and breast cancer, the first and second most common causes, respectively. In developing countries, cervical cancer is usually the most common cancer in women and may account for 25% of all female cancers.
Over the years, the diagnosis and treatment of cervical cancer have made rapid progress, resulting in a decline in the mortality and morbidity of the disease. Unfortunately, although surgery and radiotherapy have effective treatment options for early cervical cancer, poor prognosis is still a challenge in the treatment of metastatic cervical cancer. Therefore, it is very important to reveal the mechanism of cervical cancer and explore new therapies against tumor invasiveness.
At present, it is reported that nanomaterials are used in the detection and treatment of various types of malignant tumors due to their different targeting effects in many fields, such as imaging, immune detection, chemotherapy, radiotherapy and immunotherapy.
The cytotoxicity and inhibitory effects of graphene oxide (GO) on tumor invasion and metastasis were studied in recent studies using the human cervical cancer Hela cell line, as well as the probable mechanisms and signaling pathways involved.
Here we collect the last reports, with focus on the role of GO in the inhibition of migration and invasion of cancer cells with the goal of exposing possible potential pathways to provide new insights for specific treatment of cancer.