bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mocetinostat as a novel selective histone deacetylase (HDAC) inhibitor in the promotion of apoptosis in glioblastoma cell line C6 and T98G
Authors: khathayer Firas , Mikael Mohammed 
Published: April 2024

Abstract

Histon deacetylase (HDAC) enzyme is one of the enzymes involved in regulating gene expression and epigenetic alternation of cells by removing acetyl groups from lysine residue on a histone, allowing the histones to wrap the DNA more tightly and suppressing a tumor-suppressing gene. HDAC inhibitors play an important role in inhibiting the proliferation of tumor cells by restricting the mechanism of action of HDAC enzyme, leading to the addition of acetyl groups to lysine. Mocetinostat, also known by its chemical name (MGCD0103), is a novel isotype selective HDAC enzyme that explicitly targets HDAC isoforms inhibiting Class1(HDAC 1,2,3,8) and Class IV (HDAC11) enzymes. It was approved for treating the phase II trial of Hodgkin's lymphoma in 2010. Our study revealed that different doses of Mocetinostat inhibit the growth of glioblastoma cells, metastasis, and angiogenesis and induce the apoptosis and differentiation of glioblastoma cells C6 and T98G. Western blot has shown that MGCD0103 has many biological activities to control glioblastoma cancer cells. MGCD0103 can modulate the molecular mechanism for several pathways in cells, such as inhibition of the PI3K/AKT pathway and suppression of HDAC1 enzyme activity in charge of many biological processes in the initiation and progression of cancer. The high doses of Mocetinostat drug significantly induce apoptosis and suppress cancer cell proliferation through increased pro-apoptotic proteins (BAX) and a down level of anti-apoptotic proteins(Bid, Bcl2). Also, the mocetinostat upregulated the expression of the tumor suppressor gene and downregulated the gene expression of the E2f1 transcription factor. Additionally, MGCDO103-induced differentiation was facilitated by activating the differentiation marker GFAP and preventing the undifferentiation marker from expression (Id2, N-Myc). The MGCD0103 is a potent anticancer drug crucial in treating glioblastoma cells.
DCNN: A Novel Binary and Multi-Class Network Intrusion Detection Model via Deep Convolutional Neural Network
Authors: Shebl Ahmed , Elsedimy Sayed , Ismail Amr , Salama Ahmed , Herajy Mostafa 
Published: April 2024

Abstract

Network security has become imperative in the context of our interconnected networks and everyday communications. Recently, many deep learning models have been proposed to tackle the problem of predicting intrusions and malicious activities in interconnected systems. However, they solely focus on binary classification and lack reporting on individual class performance in case of multi-class classification. Therefore, the need for an efficient and accurate network intrusion detection system has reached a pivotal point. In this paper, we propose a novel intelligent detection system based on convolutional neural network, namely DCNN. The proposed model can be utilized to analyze and detect attacks and intrusions in intelligent network systems. The DCNN model is applied against two benchmark datasets and compared with state-of-the-art models. Experimental results show that the proposed model improved resilience to intrusions and malicious activities for binary as well as multi-class classification. Furthermore, our DCNN model outperforms similar intrusion detection systems in terms of positive predicted value, true positive rate, F1 measure, and accuracy. The scores obtained for binary and multi-class classifications on the CICIoT2023 dataset are 99.50% and 99.25%, respectively. Additionally, for the CICIDS-2017 dataset, DCNN attains a score of 99.96% for both binary and multi-class classifications.
Impact of Virtual reality training on executive functions and cognitive Dominance in children with developmental coordination disorder: A quasi-experimental study
Authors: hashemi ayoub , Zamani Mohammad Hossein , Shahrbanian Shahnaz , saadatian Abouzar 
Published: April 2024

Abstract
Executive functions are basic for essential for regulating, monitoring, and controlling behavior in daily life. This study aimed to investigate the effects of virtual reality training on executive functions and cognitive dominance in children with developmental coordination disorder. The show think about was a quasi-experimental pre-post intervention design. The sample population of the study included 40 boys aged 6 to 9 years with DCD and were randomly divided into experimental (n = 20) and control (n = 20) groups. Developmental Coordination Disorder Questionnaire (DCDQ7), Raven Intelligence Test, Conner's child behavior rating scale, and Wechsler Intelligence Test (WISC-IV) were used to diagnose DCD children and evaluated variables study. the experimental group performed virtual reality training (Xbox Kinect 360) for 8 weeks (2 sessions per week and 30 minutes per session). Analysis of covariance with a 95% confidence level was used to analyze the data. The comes about appeared that virtual reality training had a significant impact on executive functions (F = 110.73, P = 0.001, Eta = 0.92) and cognitive dominance (F = 222.89, P = 0.001, Eta = 0.89) Children with DCD. The results of the present study provide further support for the potential of incorporating virtual reality training into an application environment, in a way that children enjoy while enhancing cognitive abilities
Distance from x-ray beam affects magnification of single marker calibration in non-linear fashion
Authors: Akalonu Chibuzo , Montgomery Spencer 
Published: April 2024

Abstract
Background
Preoperative templating commonly employs the use of a pre-determined size of an external calibration marker used to estimate the size of the final implant. Due to positioning differences of the ball to the X-ray beam, the size of the final implant can be incorrect by several cups sizes. The purpose of this study was to quantify the changes in magnification of a 25mm ECM at several distances between the XR emitter and receiver.
Methods
We placed 25mm X-ray ball at 100mm distances from the X-ray beam emitter, from 112 to 612mm and measured the size of the external calibration marker on our Picture Archiving and Communication Systems (PACS).
Results
We found a non-linear size increase as the ECM was placed closer to the X-ray beam with the closest ECM at 612 mm from the plate 5 cup sizes larger than the ECM 112mm from the plate.
Conclusion
Position of the ECM from the X-ray plate increases the apparent size in a nonlinear fashion as it is moved farther form the x-ray detection plate and closer to the x-ray emitter.
Light-weight Frequency Re-calibration Network for Diabetic Retinopathy Multi-lesion Segmentation
Authors: Fu Yinghua , Liu Mangmang , Zhang Ge , Peng Jiansheng 
Published: April 2024

Abstract
Automated segmentation of Diabetic Retinopathy (DR) lesions is crucial for assessing DR severity and diagnosis. Most previous segmentation methods overlook the detrimental impact of texture information bias, resulting in suboptimal segmentation results. Additionally, the role of lesion shape is also not thoroughly considered. In this paper, we propose a light-weight frequency re-calibration network (LFRC-Net) for simultaneous multi-lesion DR segmentation, which integrates a Laplacian pyramid into the bottleneck layer of the encoder to analyze object features in different frequency domains. The high-frequency component handles texture information, while the low-frequency area focuses on learning the shape characteristics of DR lesions. By adaptively re-calibrating these frequency representations, our method can effectively differentiate the objects of interest. In the decoder, we introduce a residual attention module (RAM) to enhance lesion feature extraction and efficiently suppress irrelevant information. We evaluate the proposed model's segmentation performance on two public datasets, IDRiD and DDR, and a private dataset, Ultra-wide-field fundus images dataset. Extensive comparative experiments and ablation studies are conducted across multiple datasets. With minimal model parameters, our approach achieves mAP_PR of 60.51%, 34.83%, and 14.35% for the segmentation of EX, HE, and MA on the DDR dataset, and surpass those of the current state-of-the-art methods for EX and HE on the IDRiD dataset, which validates the effectiveness of our network.
Development of a non-invasive augmented reality-based navigation system for total hip arthroplasty in the supine position
Authors: Takada Ryohei , Watanabe Naoto , Miyatake Kazumasa , Sugita Naohiko , Yoshii Toshitaka , Koga Hideyuki 
Published: April 2024

Abstract
Background
A new non-invasive augmented reality (AR)-based portable navigation system was developed for accurate cup positioning during total hip arthroplasty (THA) in the supine position. This study aimed to clarify whether the navigation system supports cup positioning more accurately than a conventional goniometer during surgery. The navigation system may provide more accurate cup alignment than the conventional goniometer.
Methods
Sixty patients who underwent THA in the supine position were enrolled. The navigation system was used for 30 patients (navigation group), and a conventional goniometer was used for 30 patients (control group) to measure radiographic cup inclination and anteversion during surgery. The primary outcome was the absolute value of the difference in cup alignment measured during surgery and by postoperative radiography.
Results
The new non-invasive AR-based navigation system showed superior cup positioning accuracy compared to a conventional goniometer. An assessment of the primary outcome showed no significant difference in the radiographic cup inclination in the navigation and control groups (2.9° vs. 3.2°; mean difference, 0.3°; 95% confidence interval, -1.4–0.9; p = 0.67); however, the positioning in the navigation group was significantly more accurate than that in the control group in terms of radiographic anteversion (3.4° vs. 5.4°; mean difference, 2.0°; 95% confidence interval, 0.4–3.8; p = 0.017).
Conclusions
A new non-invasive AR-based portable navigation system resulted in more accurate cup positioning than the conventional goniometer. Because it is non-invasive, this system should be used for THA in the supine position.
A nomograph model for predicting the risk of diabetes nephropathy
Authors: Liu Moli , Li Zheng , Zhang Xu , Wei Xiaoxing 
Published: April 2024

Abstract

OBJECTIVE
Using machine learning to construct a prediction model for the risk of diabetes kidney disease (DKD) in the American diabetes population and evaluate its effect.
METHODS
Firstly, a dataset of five cycles from 2009 to 2018 was obtained from the National Health and Nutrition Examination Survey (NHANES) database, weighted and then standardized (with the study population in the United States), and the data was processed and randomly grouped using R software. Next, variable selection for DKD patients was conducted using Lasso regression, two-way stepwise iterative regression, and random forest methods. A nomogram model was constructed for the risk prediction of DKD. Finally, the predictive performance, predictive value, calibration, and clinical effectiveness of the model were evaluated through the receipt of ROC curves, Brier score values, calibration curves (CC), and decision curves (DCA). And we will visualize it..
RESULTS
A total of 4371 participants were selected and included in this study. Patients were randomly divided into a training set (n = 3066 people) and a validation set (n = 1305 people) in a 7:3 ratio; Using machine learning algorithms and drawing Venn diagrams, five variables significantly correlated with DKD risk were identified, namely Age, Hba1c, ALB, Scr, and TP; The area under the ROC curve (AUC) of the training set evaluation index for this model is 0.735, the net benefit rate of DCA is 2% -90%, and the Brier score is 0.172; The area under the ROC curve of the validation set (AUC) is 0.717, and the DCA curve shows a good net benefit rate. The Brier score is 0.177, and the calibration curve results of the validation set and training set are almost consistent.
CONCLUSION
The DKD risk line chart model constructed in this study has good predictive performance, which helps to evaluate the risk of DKD as early as possible in clinical practice and formulate relevant intervention and treatment measures. The visual result can be used by doctors or individuals to estimate the probability of DKD risk, as a reference to help make better treatment decisions.
An Optimized RDMA QP Communication Mechanism for Hyperscale AI Infrastructure
Authors: Wang Junliang , Lin Baohong , Zhang Jiao , Sun Mengyu , Pan Yongchen 
Published: April 2024

Abstract
The current artificial intelligence (AI) infrastructure widely employs remote direct memory access (RDMA) protocol for high-performance communication in networks, utilizing Reliable Connection (RC)-based Queue Pairs (QP) to ensure end-to-end correct and ordered data transmission. However, as the scale of AI infrastructure continues to expand, this RC-based QP communication mechanism faces deficiencies in scalability and is prone to congestion, resulting in degraded network transfer performance. In this paper, we propose an optimized RDMA QP communication mechanism to address scalability and congestion issues in hyper-scale AI infrastructure networks. Firstly, we replace RC-based QPs with Reliable Datagram (RD)-based QPs and propose a new reliable mechanism to address scalability problems, eliminating the need for repetitive QP establishment by AI processes during external communication. Additionally, to mitigate congestion caused by a single path, we implement multipath data transmission by introducing a new unordered reception method in the network software stack. Through experiments and simulation tests, the optimized RDMA QP communication in large-scale AI infrastructure exhibits exceptional scalability and significantly reduces the occurrence of congestion, resulting in an overall network performance improvement of over 15%.
Evaluations of drip and furrow irrigation method under deficit irrigation for pepper productivity in Northwest Ethiopia
Authors: Adamtie Temesgen F. , Mitku Demeke T. , Selie Abeba H. 
Published: April 2024

Abstract
The combination of reduced food production and growing water scarcity posed significant difficulties for agriculture and economic development in Ethiopia. To minimize water loss, an experiment was carried out that was both economically viable and technically feasible. Thus, the purpose of this study was to evaluate the irrigation methods (Drip vs furrow) in terms of crop and water productivity, as well as technical implications in the Ethiopian context. A split plot in a randomized complete blocks design with three replications was used to experiment over two consecutive years, 2018 and 2019. Two irrigation systems, furrow irrigation and drip irrigation, were the focus of the plots. The sub-plots, on the other hand, examined four levels of deficit irrigation: 55%, 70%, 85%, and 100%. Drip irrigation resulted in a marketable fresh fruit yield of 6226.1 kg/ha, while furrow irrigation produced 5284.5 kg/ha. Water use efficiency is 20.678 kg/m3 for drip irrigation and 12.827 kg/m3 for furrow irrigation. Under full irrigation, the highest yield of marketable fresh fruit was 9560.2 kg/ha, while the lowest commercial fruit production was 4199.7 kg/ha at 55% of ETc. The results of the analysis suggest that drip irrigation may not be technically feasible for large-scale agricultural operations, as indicated by the substantial evidence from irrigating with 85% ETC using furrow irrigation. Hence, employing 85% ETC for irrigation in water-scarce regions has the potential to improve yield and water productivity.
The Clinical Outcomes and Safety of Tigecycline in Monotherapy or Combination with Cefoperazone/sulbactam for Carbapenem-Resistant Acinetobacter baumannii-Associated Pneumonia: A Multicenter Retrospective Study
Authors: Tian Xiaotong , Lin Jing , Zhou Menglan , Ge Ying , Li Taisheng , Zhang Li , Liu ZhengyL 
Published: April 2024

Abstract

Objective We aimed to evaluate clinical outcomes and safety in tigecycline (TGC) monotherapy or in combination with cefoperazone/sulbactam (CPS) treatment for patients with hospital-acquired pneumonia (HAP) infected by carbapenem-resistant Acinetobacter baumannii(CRAB). Methods This was a retrospective analysis of multicenter data from patients with CRAB HAP in 62 Chinese hospitals. Risk factors of receiving TGC with CPS therapy and predictors of mortality were used multivariate logistic and Cox regression analyses, respectively. Propensity score matching (PSM) evaluated the efficacies and safety of antimicrobial regimens. Results 180 patients included in our study, 95 used TGC monotherapy, and 85 used TGC with CPS therapy. The multivariate logistic regression analysis revealed that the risk factors were significantly associated with TGC with CPS therapy included the older age [P = 0.011], intensive care unit (ICU) admission[P = 0.007]. The multivariate Cox regression demonstrated that there was a significantly higher risk of 90-day mortality [P = 0.031] among subjects in TGC-CPS group. The subgroup of patients who received Standard dose TGC (SDT) plus CPS had a significantly higher rate of SOFA score ≧ 7(P = 0.009), and the 30/90-day mortality rate of patients was also higher. The variation of ALT, TBIL, Cr, Hb, and PLT did not differ between different antimicrobial regimens after PSM. Conclusion The severity of patient conditions and TGC doses were significantly associated with mortality. HDT combined with CPS was the prior treatment option for patients with CRAB HAP who were elderly, had ICU admission. We observed that different antimicrobial regimens had similar safety in liver/kidney/coagulation.
Effect of heat treatment time in oxygen atmosphere on the stabilization of magnetite NPs: synthesis, characterization, and magnetic response
Authors: Perdomo Claudia Patricia Fernandez , Caseiro Ana Laura , Togashi Marina Magro , Kiminami Ruth H.G.A. 
Published: April 2024

Abstract

This study describes the synthesis of magnetite (Fe3O4) nanoparticles (NPs) using the sol-gel method with ethylene glycol as a chelating agent. The use of this agent allowed for the complete crystallization of pure magnetite phase at 200°C, without atmosphere control during the thermal treatment for crystallization. Different thermal treatment times (4, 8, 16, 24, and 48 hours) and their effects on the structure, microstructure, and magnetic properties of the nanoparticles were evaluated. The results showed that the magnetite phase remained stable and pure up to 8 hours of thermal treatment in an air atmosphere, with nanoparticles exhibiting a crystallite size of 30 nm and saturation magnetization of 57 emu/g. After 16 hours, the presence of a magnetite/hematite heterostructure was observed, with approximately 22.5% hematite (α-Fe2O3). The presence of hematite increased with the thermal treatment time, reaching 25.4% at 48 hours, and the saturation magnetization decreased with the reduction of magnetite phase in the nanoparticles. Additionally, the NPs dispersion in different liquid media (isopropyl alcohol, distilled water, and ethylene glycol) was verify to evaluated suspension stability and total magnetic collection time, aiming for potential applications as a magnetic fluid.
Side Channel Based Substitute Adversarial Attack on Deep Learning Models
Authors: Chen Zuohui , Hu Jinxuan , Lu Yao , Zhang Dan , Xiang Yun , Xuan Qi 
Published: April 2024

Abstract

Deep learning models are highly susceptible to adversarial perturbations. Among model attackers, the black-box assumption, where no prior knowledge of the victim model is available, is the most common and practical scenario. Substitute attack, as a mainstream black-box attack method, involves training a surrogate model and leveraging the transferability property of its generated adversarial examples to attack the target model. However, existing approaches typically require a large number of queries to the victim model, resulting in reduced attack efficiency and performance. In this work, we propose an effective side channel attack method to estimate the fine-grained structure of the victim model. We leverage the adjacent intermediate layer perturbation technique to enhance the performance of adversarial attacks. Specifically, we demonstrate that the model structure at the layer level can be analyzed and revealed through a power side channel attack. Our experimental results indicate that the structure information can be successfully restored with an average success rate of 94\%, leading to a significant improvement in the attack success rate across multiple classic datasets.
LULC Dynamics and the effects of Urban Green Spaces in cooling and mitigating micro-climate change and Urban Heat Island Effects: A case study in Addis Ababa city, Ethiopia
Authors: Demisse Mulugeta , Hishe Solomon , Getahun Kefelegn 
Published: April 2024

Abstract

Land Surface Temperature (LST) increment and Urban Heat Islands (UHI) variability are the major urban climatology problems arising in the urban development. Greening urban environment play vital role to combat the effects of micro-climate change. This study attempts to assess the effects of Urban Green Spaces in cooling and mitigating micro-climate change in Addis Ababa City. Three different dates of remotely sensed data from Landsat5 TM (1990) Landsat7 ETM+ (2005) and Landsat8 OLI/TIRS (2021) were used for the study. LST were retrieved from Landsat5 TM and Landsat7 ETM+ using mono window and Landsat8 OLI/TIRS were used split window algorithms. Regression and correlation analysis of LST, Normalized Vegetation Index (NDVI) were performed in SPSS V23. The Results from this study have shown that the proportion of Urban Green Space (UGS) to other LULC were reduced from 120.4 Km2 in 1990 to 76.26 Km2 in 2021. The Result of multiple linear regression analysis clearly indicates that built up and green vegetation contributed 92.2% of the LST variations in Addis Ababa City. The Cooling Efficiency (CE) and Threshold value (TVoE) of green space in Addis Ababa City were calculated as 4.5 ± 0.5 ha. This finding indicated that the city municipality implements urban planning, allocating a green space area of 4.5 ± 0.5 ha is the most efficient to reduce heat effects of the study area. The result of the study indicates that, strengthening of public participation in urban greening as an important strategy to mitigate the effects of micro-climate change and it is important to sustain urban development as well as to provide better quality of life on the urban population.
n-Alkanes hydroisomerization on Ni-HMFI zeolites of different preparation methods
Authors: Patrylak Lyubov , Voloshyna Yuliya , Pertko Oleksandra , Yakovenko Angela 
Published: April 2024

Abstract

A range of bifunctional catalysts based on MFI zeolite was synthesized. The aim of this research was to compare the performance of Ni-HZSM-5 zeolites of different methods of nickel loading in the n-hexane hydroisomerization. It is found that impregnation with nickel salt is more suitable method which allows to obtain active and selective catalysts for hydroisomerization.