bluuebunny/crossref_metadata_2025 huggingface dataset:
Establishment of a thermal compensation model for a turning–milling compound machine
Authors: Chuang Chia-Chin , Kuo Tzu-Chien , Jhang Yu-Hao , Peng Chuan , Hsieh Wen-Hsin
Published: April 2024

Thermal errors are considered the most significant errors in precision machining. The heat sources of a turning– milling compound machine (TMCM) are diverse and complex. This study proposes a new thermal compensation model in a TMCM to compensate for thermal errors. First, the relationship between the tool tip deformation direction and the temperature increase trend is considered. The temperature points that have a pronounced influence on the tool tip deformation are selected from 76 measured temperature points. Temperature sensitive points are then identified through F-test feature engineering. The temperature increases and thermal deformation data of the temperature sensitive points under the characteristic and training conditions are used as the input and output in training a recurrent neural network model. When the recurrent neural network model was trained, the automatic parameter adjustment method of the RandomSearch in the Keras tuner toolbox was used. The minimum mean-square error (MSE) of the verified operating conditions was taken as the evaluation index, and a thermal compensation model was established. Compared with the average root-mean-square errors (RMSE) of the validation and test conditions along the six axes, the maximum difference in the average RMSE (only 2.12 μm) occurred in the direction of the spindle Y-axis. Noticeably, the thermal compensation model established in this study has high robustness and good universality. In addition, the average accuracy of the validation and testing conditions along the six axes improved by 84.3%. The proposed thermal compensation model is highly reliable and can effectively improve the thermal compensation accuracy.
HRI Inhibition by Hemin as a Novel Targeted Therapy for Glioblastoma via the Integrated Stress Response
Authors: Ma Xingchuan
Published: April 2024

The treatment of glioblastoma (GBM), a highly malignant brain tumor, is critically hindered by the ineffectiveness of current modalities such as surgery, radiation, and chemotherapy. These traditional methods fail to completely remove the tumor mass and lack the ability to discriminate between cancerous and normal brain cells, often resulting in collateral damage to healthy tissue and recurrence of the disease. This underscores an urgent necessity to develop novel therapeutic strategies that can target tumor cells with precision, offering hope for improved survival rates and quality of life for GBM patients. This study investigates targeted therapy, focusing on the Integrated Stress Response (ISR) that cancer cells harness to survive hypoxic stress. Specifically, it demonstrates that EIF2AK1, which encodes Heme-regulated eIF2α kinase (HRI), is activated under hypoxia and co-expressed with the glioma stem cell marker SOX2, which specifically happens in glioma cells, increasing the targeted accuracy of the repurposing drug. This correlation, indicating hypoxia-driven stemness, is confirmed at both the genetic level and through Gene Set Enrichment Analysis (GSEA). Furthermore, GSEA in Spatial Transcriptomics shows hypoxia-induced glycolysis, disrupting the tumor microenvironment and causing necrotic cell death. Stemness phenotype is induced in the peripheral cells due to the unfavorable hypoxic environment. Hemin, an HRI inhibitor, has been repurposed to inhibit ISR and mitigate hypoxia. Treatment with Hemin on the U87 cell line resulted in IC50 values of 23.50 µM and 52.46 µM at 24 and 48 hours, respectively, surpassing Temozolomide's efficacy. A decrease in HRI expression after the Hemin treatment suggests the and ISR activity and, potentially, hypoxia. This would reverse the unfavorable microenvironment so that the stemness phenotype doesn’t spread. Potentially, invasiveness and recurrences of GBM in clinic situation would decrease, thus potentially improving patient prognosis. The therapeutic potential of Hemin is enhanced by its ability to kill glioma cells directly and accurately in the glioma cell in original TME when cells are proliferating with adequate oxygen. Therefore, this study demonstrates the therapeutic potential of repurposing Hemin, an HRI inhibitor, to precisely target hypoxia-induced glioma stem cells in glioblastomas, disrupting the aggressive tumor microenvironment to potentially improve patient prognosis.
Finite-time Neural Network Adaptive Sliding Mode Control of Fractional-Order Chaotic Systems With Disturbance Observer And Fractional Robust Differentiator
Authors: Cui JinWei , Zhao XiaoShan
Published: April 2024

This paper proposes a neural network adaptive sliding mode control scheme with a fractional-order disturbance observer and a robust differentiator for fractional-order chaotic systems with uncertain functions and random noise. The solution combines fractional-order differential controllers, gradient descent neural networks, and fractional-order disturbance observer techniques. A gradient descent neural network handles uncertain nonlinear functions, while fractional-order disturbance observers are presented to solve unexpected noise problems. On the premise that the above problems are solved, the state vector is used as the original signal, the ideal function as the tracking signal, and the design error function as the sliding mode surface. Meanwhile, in order to eliminate the vibration caused by the sliding mode functions, a fractional order robust differential observer is introduced in the estimation error. Adaptive rates are introduced in the design of the controller so that the control converges to a small interval in a finite time.
Long-Term Cardiopulmonary Outcomes and Quality of Life: A Comparative Study Among Cigarette, Electronic Cigarette and Dual use Consumers.
Authors: Krishna Gokul , Jose Ann Mary , Rahali Weaam , Alyamani Wejdan , Mohammed Manahel , Alghamdi Basmah , Homoud Mazen , AlAhmari Mohammed , Alwadeai Khalid , Alkhathami Saleh , Sreedha Jithin , Alahmari Ayedh
Published: April 2024

Background: Smoking is a leading cause of heart and lung diseases worldwide. Though there is a decline in tobacco smoking the use of e-cigarettes is growing in popularity.Objective:To assess the long-term effects of smoking, vaping, and dual use on cardiopulmonary function capacity and quality of life.Methods:The study participants were classified into five groups: Control, Individuals who use cigarettes only, Individuals who only use e-cigarettes, Individuals who previously smoked and currently use only e-cigarettes and Individuals who use both cigarettes and e-cigarettes. Participants performed PFT, 6MWT and completed HRQoL questionnaire.Results:A total of 175(85.7% male) participants with an average age of mean(±SD)27.38(±5.75) enrolled in the study. PFT results showed significant difference in FEV1(L/Sec) [F (4,170) = 7.73, p≤ 0.001] η²=0.15; FEV1(%predicted) [F (4,170) =24.63, p≤0.001] η²=0.36; and FEV1/FVC (%) [F (4,170) =16.85, p≤0.001] η² = 0.28 values across the five groups. On further analysis, FEV1and FEV1(%predicted) significantly decreased in all user groups. Similarly, in FEV1/FVC (%) significant difference was observed with user groups except those who only use e-cigarettes. 6MWT showed a significant difference in walking distance [F (4,170) = 19.93, p≤0.001] η² =0.31. HRQoL reported a significant difference in physical function [H (4) =20.55, p≤0.001], emotional well-being [H (4) =19.21, p=0.001], and social function [H (4) =29.63, p≤0.001] domains. Individuals who use both cigarettes and e-cigarettes reported a significant decline in lung function [(FEV12.96±0.41, FEV1(% predicted) 72.6±6.07 and FEV1/FVC (%) 73.65±11], 6MWT walking distance(m) (mean ±SD) 416.26±53.49.Conclusion: Weobserved a significant long-term impairment in lung function, cardiopulmonary capacity, and quality of life among individuals who use cigarettes and e-cigarettes compared to those who never smoked or vaped. Also, individuals who use both cigarettes and e-cigarettes showed the worst health outcomes.
Energy Forecasting In LED Video Display Panels Using Deep Learning
Authors: R RAMESH , A Bazilabanu
Published: April 2024

In recent years, energy usage in LED Video Wall Display Panels (LED-VWDPs) has increased massively; Predicting energy consumption is a challenging and crucial task for LED-VWDPs. Hence Real-time energy usage issues can be resolved by predicting future energy demand. Deep learning plays an important role in more accurate prediction in energy forecasting. In this article, two approaches are presented: the first makes use of a recurrent neural network (RNN), and the other utilizes a long short-term memory (LSTM) network.In comparison to other existing machine learning techniques, such as ARIMA and Facebook Prophet, Long Short-Term Memory (LSTM) in deep learning is better at handling time-series datasets and projecting future energy demand. It predicts the actual energy usage of LED-VWDP and forecasts the futureenergydemandofLED-VWDP. A vast dataset of LED-VWDP energy consumption is utilized in this paper. Through the proposed RNN and LSTM methods, users can identify the individual energy usage of LED-VWDP and predict its future energy demand.The results of the proposed methods are evaluated alongside those of the existing methods in order to forecast energy usage. The results are used to evaluate the performance of forecasting future energy demands, depending on the number of epochs. The accuracy of RNN and LSTM ranges from 82.02–95.86%. The predictions have been made for a period of two months, encompassing short-and mid-term forecasts.In evaluating the comparison of various machine and deep learning models, LSTM is found to be accurate with an average root mean square error of 0.5 in forecasting energy consumption.
Analysis of Radiological Findings and Pathological Correlation in Secondary Intracranial Hypertension
Authors: Kuzan Beyza Nur , Şahin Yener , Çiftçi Tufan , Günbey Hediye Pınar
Published: April 2024

Background
Intracranial hypertension is a potentially fatal entity that can occur due to idiopathic or secondary causes and can be detected by radiological methods. To determine radiological findings of intracranial hypertension secondary to primary intracranial tumors and to reveal the relationship between tumor pathology and radiological findings.
Methods
This retrospective study included 105 adult patients with a histopathologically confirmed primary intracranial tumor. Tumor characteristics and MRI findings associated with intracranial hypertension were noted. The relationship between tumor type and radiological findings was evaluated.
Results
Glioblastoma (41.0%) followed by meningioma (29.5%) was the most common primary brain tumor. There was a statistically significant difference between intraaxial tumors and extraaxial tumors in terms of pituitary gland height (p = 0.016) and sinus invasion area of the tumor (p = 0.010). In the subgroup analysis, there was a significant difference between meningioma, the most common extraaxial tumor, and other tumors in terms of pituitary gland height and dural sinus invasion area (p = 0.008 and p = 0.029, respectively).
Conclusion
It is important to know the radiological findings of secondary intracranial hypertension caused by intracranial masses and to keep in mind that it may be associated with tumor pathology and some secondary intracranial hypertension findings may be detected more frequently in extraaxial tumors.
Predictive factors for developing acute cholangitis due to choledocholithiasis
Authors: Li Feifan , Dai Meiqing , Xu Min , Cui Qidong , Shen Rongwei , Zhou Xulin , Wang Zhihua
Published: April 2024

Background
Acute cholangitis, an acute and severe disease in the clinic, is mostly caused by choledocholithiasis. This study aimed to identify the predictive factors for developing acute cholangitis due to choledocholithiasis.
Methods
A total of 558 patients with choledocholithiasis were enrolled. The patients were divided into a choledocholithiasis group (n = 399) and an acute cholangitis group (n = 159) according to whether they had acute cholangitis. The clinical data were analyzed, and logistic regression was used to predict acute cholangitis. Receiver operating characteristic (ROC) curves were generated to identify predictive factors for acute cholangitis.
Results
The acute cholangitis group had a greater prevalence of male sex, age ≥ 70 years, smoking history, hypertension, fever (> 38°C) and/or shaking chills, duodenal peripapillary diverticulum, and maximum diameter of choledocholithiasis (≥ 10 mm). Furthermore, the acute cholangitis group had higher WBC, CRP, T-Bil, ALT, AST, ALP, GGT, serum creatinine (Scr), prothrombin time (PT) and D-dimer (D-D) levels and lower albumin levels. Logistic regression analysis revealed that the maximum diameter of choledocholithiasis (≥ 10 mm), T-Bil, CRP, WBC, fever (> 38°C) and/or shaking chills, male sex, AST, and ALP were independent risk factors for developing acute cholangitis, with an area under the ROC curve (AUC) of 0.869 for CRP, 0.858 for T-Bil, 0.835 for WBC, 0.765 for AST and 0.743 for ALP.
Conclusions
Attention should be given to choledocholithiasis patients who have a maximum diameter of choledocholithiasis (≥ 10 mm), T-Bil > 34.25 µmol/L, CRP > 10.85 mg/L, WBC > 9.95×109/L, fever (> 38℃) and/or shaking chills, male sex, ALP > 162.5 U/L and AST > 57.8 U/L. Interventions may be taken to prevent acute cholangitis.
Impact of Frailty on Outcomes Following Emergency Laparotomy: A Retrospective Analysis Across Diverse Clinical Conditions
Authors: Isand Karl Gunnar , Hussain Shoaib Fahad , Sadiqi Maseh , Kirsimägi Ülle , Bond-Smith Giles , Kolk Helgi , Saar Sten , Lepner Urmas , Talving Peep
Published: April 2024

Purpose
Emergency laparotomy (EL) encompasses procedures of varying complexity and urgency, undertaken in different clinical scenarios, leading to different risks of morbidity and mortality. We hypothesized that the increased mortality and longer postoperative length of stay (LoS) observed in frail patients are related to differences in indication for operation, a higher rate of sepsis, worse intraperitoneal soiling, and more advanced malignancy in this group.
Methods
This retrospective cohort study analysed patients entered into the National Emergency Laparotomy Audit database between January 1, 2018, and June 15, 2021, in Oxford. The primary outcome was 180-day survival analysed using multivariable Cox regression. The secondary outcomes, delay to surgery (DtS) and postoperative LoS, were analysed using logarithmically transformed multivariable linear regression.
Results
Of the 803 patients analysed, 396 (49.3%) were male. The median age was 66, and 337 (42%) were living with at least very mild frailty. Mortality hazard ratios for Clinical Frailty Scale grades 4 (3.93, 95% CI 1.89–8.20), 5 (5.86, 95% CI 2.87–11.97), and 6–7 (14.17, 95% CI 7.33–27.40) were not confounded by indication, sepsis, intraperitoneal soiling, or malignancy status. Frail patients experienced a 1.38-fold longer DtS and a 1.24-fold longer postoperative LoS, even after adjusting for indication, sepsis, intraperitoneal soiling, malignancy status, and DtS.
Conclusion
Our results indicate that frail patients have a poorer prognosis and longer postoperative LoS, independent of DtS, indication, sepsis, intraperitoneal soiling, and malignancy status. Patient frailty is also associated with longer DtS.
Assessment of genetic alterations found in type 2 diabetes mellitus patients with pancreatic adenocarcinoma
Authors: Calderon-Martinez Evelyn , Zevallos-Delgado Christian , Gavilanes Diana , Landazuri-Navas Samanta , Guzman Adalberto , Ng Wern , Marquez Luis , Atrash Anas
Published: April 2024

Purpose In this study, we aim to determine the prognostic effects of genetic mutations identified in pancreatic adenocarcinoma (PAAD) patients with type 2 diabetes mellitus.Method Gepia and cBioPortal were used to assess the TCGA data and other PAAD studies. CBioPortal showed 2963 patients in 14 related studies. Among these participants, 37 had a positive medical history of type 2 diabetes mellitus, while 104 did not.Results Fifteen genes (HTRA3, FANCA, IL4R, NRXN2, GRPEL1, KIAA0232, TTK, WFS1, TBC1D14, CCDC96, USP25, STOML2, TPI1, PLAA, and FASTKD2) have been identified to be involved in the prognosis of the disease based on the number of mutations and survival prognosis in this study. Furthermore, the adverse impact of diabetes mellitus and PAAD are linked to single nucleotide polymorphisms.Conclusion Several genetic alterations found in patients with PAAD and type 2 diabetes mellitus have an impact on the prognosis of PAAD patients.
Errorless Robust JPEG Steganography Using Steganographic Polar Codes
Authors: Zhang Jimin , Cao Yun , He Xiaolei
Published: April 2024

Recently, a robust steganographic algorithm that achieves errorless robustness against JPEG recompression has been proposed. The method employs a lattice embedding scheme and utilizes the syndrome-trellis code (STC) for practical embedding. However, we have noticed that errorless robust embedding with STC may encounter failures due to modifications on wet coefficients, especially when a high quality factor is used by the compression channel. To solve this problem, we have discovered that using steganographic polar code (SPC) for embedding has better performance in avoiding modifications on wet coefficients.In this paper, we conduct theoretical analysis to prove the better performance of SPC in wet paper embedding.We establish the condition of avoiding modifications on wet coefficients, followed by presenting a recursive calculation method for determining the distribution of columns in the generator matrix of SPC. The findings reveal that SPC can avoid modifications on wet coefficients under a larger number of wet coefficients compared with STC, and therefore we propose a better errorless robust embedding method employing SPC. The experimental results demonstrate that under close security performance, the proposed method achieves a higher success rate compared with embedding with STC.Specifically, when the quality factor of the compressor is 95, our method achieves a success rate of 99.85%, surpassing the 91.95% success rate of the embedding with STC.
Massive MIMO-FDD Self-Attention CSI Feedback Networkfor Outdoor Environments
Authors: Wang Linyu , Cao Yi , Xiang Jianhong , Jiang Hanyu , Zhong Yu
Published: April 2024

In situations where traditional feedback methods face high complexity and rely on channel sparsity,many deep learning-based CSI compression feedback methods have shown the potential to fullycapture the diversity and multiplexing gains of Massive MIMO technology in Frequency DivisionDuplex (FDD) mode. In order to further enhance the accuracy of obtaining downlink channel stateinformation at the base station side, this paper proposes a novel neural network, SfNet. Basedon visual analysis, the convolutional layers in the encoder are designed to extract long-distancetime-delay correlations of the same antenna across different subcarriers. The SimAM module isintroduced to mitigate clustering effects. In the decoder, a spatial-temporal joint modeling approachis presented, utilizing the CBAM module to preserve the original spatial structure features lostduring the dimension reduction of the CSI matrix to sequence data. Subsequently, two layers ofTransformer multi-head attention are employed to achieve more global time-series modeling.Theexperimental results indicate that, compared to CLNet, the average complexity of SfNet’s encoder isreduced by 3.4%. It meets the demand for lightweight devices on the user side. In terms of accuracy,SfNet exhibits an average improvement of 30.17% compared to TransNet in outdoor environments.
Effects of juniperus root extract on wound healing, gene expression and biochemical response in yellowtail acei Pseudotropheus sp. fish
Authors: Önalan Şükrü , Kankaya Ertuğrul
Published: April 2024

Injuries are an undesirable situation in fish kept in aquariums. The use of antibiotics in wound healing is not preferred due to the development of antibiotic resistance in bacteria and for economic reasons. This study was conducted to demonstrate the efficacy of Juniperus root extract in healing experimentally induced incised wounds in yellowtail acei. Wound closure time, morphology, malondialdehyde content, glutathione s-transferase activity, changes in gene expression levels, non-dispersibility of the extract in aqueous environment, and retention on the wound were analyzed. In the antibiotic group, the scarring of the wound was observed on the sixth day. On the other hand, in the root extract group, wound healing was completed on the second day. In addition, a significant decrease in malondialdehyde and glutathione s-transferase levels was observed in the extract group compared to the control group. It was found that SWS2a, RH2B and LWS gene expression levels started to decrease during wound healing at the end of the second day in the extract-treated group. In the antibiotic group, the same effect was observed on the seventh day. Important properties of the extract used, such as not dispersing in the water environment and remaining on the wound, were also determined. It would be useful to continue the experiments with Juniperus root extract, which has been shown to be effective on wound healing, with microscopic studies on aquaculture and diseases.
Gene trail of polarized M2 macrophage decodes tumor heterogeneity and recommendation for therapy of hepatocellular carcinoma
Authors: Zhao Minjie , Tang Changlin , Liang Hang , Sun Xinyu , Wu Kun
Published: April 2024

M2 macrophages are known to substantially influent tumor invasion and metastasis, leading to diverse clinical outcomes. This study aimed to identify a novel biomarker associated with M2 macrophages and assess its prognostic significance in hepatocellular carcinoma. Genes exhibiting a significant correlation with M2 cells were identified using single-cell RNA sequencing (scRNA-Seq) and WGCNA analysis. Subsequently, patients were stratified into two distinct groups using the consistency clustering method. Following this categorization, an in-depth investigation was conducted to explore the prognostic implications, biological attributes, genomic variations, and the immune landscape within each cluster. Moreover, the potential efficacy of drug treatment and the anticipated impact of immunotherapy were also assessed.Based on the analysis of M2 cell development trace genes and the findings from WGCNA, a total of 17 target genes were identified, leading to the identification of two distinct clusters with varying clinical outcomes. Group C1 was characterized as cell proliferative liver cancer, whereas Group C2 was classified as lipid metabolic liver cancer. Furthermore, two anticancer drugs were analyzed. Patients in group C1 exhibited a higher sensitivity to erlotinib, whereas patients in group C2 demonstrated a greater sensitivity to rapamycin. Moreover, immunotherapy appeared to be more suitable for patients in group C1. This study significantly enhances the existing understanding of the involvement of M2 cells in the biological and prognostic prediction of hepatocellular carcinoma, thereby offering potential benefits in terms of enhancing clinical management and ultimately improving clinical outcomes.
Dual Representation Propagation Comparative Learning for Recommendations
Authors: Huang Huohui , Fan Xin , Tian Shengwei , Yu Long
Published: April 2024

Graph neural networks combined with comparative learning have become a very popular paradigm in recommender systems. However, most methods still suffer from data sparsity, noise and difficulty in extracting multi-granularity information. To address these limitations, we propose a Dual Representation Propagation Comparative Learning(DRPCL) method, which uses propagation representations based on two rules to extract multi-granularity signals, including graph convolutional propagation pathway and message node propagation pathway. Where the message node propagation pathway extracts and integrates local and global signals. And the dual-pathway node representations generate misaligned contrastive views and denoising auxiliary supervision signals to mitigate the negative effects of data sparsity and noise. Experimental results show that our DRPCL is able to demonstrate performance superiority over other bases on different datasets. Some in-depth experimental analysis demonstrates the robustness of DRPCL against data sparsity and noise.
Multi-task Dual-Graph Network framework for Aspect Sentiment Triplet Extraction
Authors: Wang Liuxuan , Zheng Yanqian , Tang Mingwei , Meng Kezhu , Zhao Mingfeng , Zhou Wei
Published: April 2024

Aspect Sentiment Triplet Extraction is a task that extracts aspects, opinion words corresponding to aspects and sentiment polarity from review sentences. Although aspect emotion triple extraction technology has achieved remarkable results in recent years, it still has a few drawbacks. Many existing research methods ignore important issues, such as the dependency relationships between emotional triad elements and the detection of aspect and viewpoint boundary terms at the phrase-level. Due to the above problems, this paper proposes a multi-task joint learning architecture based on dual graph networks (DGNMT). Firstly, a shared sentence encoder module is proposed in the model. The middle-layer and top-level feature representations of the BERT encoder are applied to two different subtasks in this module. Secondly, Dual-Graph Network Learning module has been designed. As part of the dual-graph network learning layer, this module introduces a graph attention network based on phrase structure trees in the joint extraction task branch. For the triple extraction task branch, a graph convolution network is used based on the modified syntactic dependency graph in order to determine the syntactic relationships between triple elements. Additionally , the Multi-Task Tagging module is proposed to extract labels for tasks as well as triplet extraction tasks. And then, predictions are made based on 1D sequence tags and 2D word pair matrices, and the tag information of the two tasks is transformed into each other through a conversion strategy. According to the final experimental results, the model outperforms a state-of-the-art baseline model on four commonly used evaluation indicators.
Modeling of Quantum Cognitive Perceptual Associative Memory for Conscious Agents: A Parametrized Circuit of Quantum Neural Networks
Authors: Mahmood Tariq , Sherwani Rehan Ahmad Khan
Published: April 2024

Perceptual memory is important for human cognition and learning because it plays an important role in forming consciousness, memories, learning, and stimulus awareness. Perceptual memory assists in the recognition of items or faces, as well as in the sorting, differentiating, and combining of new information with context and emotions. Perceptual-associative memory encodes information structurally, semantically, and phonetically. It interacts with the conscious artifact's medium-term memory (MTM), long-term memory (LTM), workspace, and emotion modules to construct meanings and connections to chosen sensory information. This research developed a quantum cognitive model of perceptual associative memory to replicate human-like perceptual cognitions in conscious artifacts. Moreover, for the quantum neural correlates of consciousness, this research has also simulated the circuit of the quantum neural network for non-linear learning of an XOR gate using the qiskit library.
Repurposing pitavastatin and atorvastatin to overcome chemoresistance of metastatic colorectal cancer under high glucose conditions
Authors: Cheng Wei-Ming , Li Po-Chen , Nguyen Minh Tran-Binh , Lin Yu-Teng , Huang Yu-Tang , Cheng Tai-Shan , Nguyen Thi-Huong , Tran Thu-Ha , Huang Tzu-Yi , Hoang Thu-Huyen , Chen Sin-Yu , Chu Yu-Chieh , Wu Chih-Wei , Lee Ming-Fen , Chiou Yi-Shiou , Liu Hsiao-Sheng , Hong Yi-Ren , Chang Peter Mu-Hsin , Hu Yu‑Feng , Chang Ying-Chih , Lai Jin-Mei , Huang Chi-Ying F.
Published: April 2024

Background
Colorectal cancer (CRC) poses a significant clinical challenge because of drug resistance, which can adversely impact patient outcomes. Recent research has shown that abnormalities within the tumor microenvironment, especially hyperglycemia, play a crucial role in promoting metastasis and chemoresistance, and thereby determine the overall prognosis of patients with advanced CRC.
Methods
This study employs data mining and consensus molecular subtype (CMS) techniques to identify potential drugs for targeting high glucose-induced drug resistance in advanced CRC cells. CRC cells maintained in low or high glucose conditions were established and were used to evaluate the cytotoxic effects of potential drugs with or without 5-FU. CRC 3D spheroids cultured were also included to demonstrate the anti-drug resistance of these potential drugs.
Results
A bioinformatics analysis identified pitavastatin and atorvastatin as promising drug candidates. We established the CMS4 CRC cell line SW480 (SW480-HG) cultured under high glucose conditions to simulate hyperglycemia-induced drug resistance and metastasis in CRC patients. We showed that both pitavastatin and atorvastatin can effectively inhibit cell proliferation and 3D spheroid formation of CMS4 CRC cells under high glucose conditions. In addition, both pitavastatin and atorvastatin can synergistically promote the 5-FU-mediated cytotoxic effect and inhibit the growth of 5-FU-resistant CRC cells. Mechanistically, pitavastatin and atorvastatin can induce apoptosis and synergistically promote the 5-FU-mediated cytotoxic effect by activating autophagy, as well as the PERK/ATF4/CHOP signaling pathway while decreasing YAP expression.
Conclusion
This study highlights the biomarker-guided precision medicine strategy for drug repurposing. We showcase pitavastatin and atorvastatin with the moonlighting role for treating advanced CRC, particularly with CMS4 subtype CRC patients who also suffer from hyperglycemia. Pitavastatin, with an achievable dosage used for clinical interventions, is highly recommended for a novel CRC therapeutic strategy.
A Holistic Physics-Informed Neural Network Solution for Precise Destruction of Breast Tumors using High-Intensity Focused Ultrasound on a Realistic Breast Model
Authors: Lari Salman , Rajabzadeh Hossein , Kohandel Mohammad , Kwon Hyock Ju
Published: April 2024

This study presents a novel approach for the precise ablation of breast tumors using high-intensity focused ultrasound (HIFU), leveraging a physics-informed neural network (PINN) integrated with a realistic breast model. The potential of high-intensity focused ultrasound (HIFU) in eliminating breast tumors has shown significant promise. This technique employs concentrated ultrasonic waves to generate intense heat, effectively destroying cancerous tissue. In previous finite element method (FEM) models, the computational demands of handling extensive datasets, multiple dimensions, and discretization pose significant challenges. Our PINN-based solution operates efficiently in a mesh-free domain, achieving remarkable accuracy with significantly reduced computational demands, compared to conventional FEM techniques. Additionally, employing PINN for estimating partial differential equations (PDE) solutions can notably decrease the enormous number of discretized elements needed. The model employs a bowlshaped acoustic transducer to focus ultrasound waves accurately on the tumor location. The simulation results offer detailed insights into each step of the HIFU process. By applying a 3.8 nm displacement amplitude of transducer input pulse at a frequency of 1.1 MHz for 1 second at the focal point, the focal point temperature reaches 38.4 °C. Subsequently, continuing the ablation process for another 90 seconds without the acoustic source leads to extensive necrosis of the tumor tissues. Validation of the PINN model’s accuracy was conducted through FEM analysis and a series of tests using ex vivo bovine liver, aligning closely with real-world HIFU therapy scenarios. This innovative platform provides physicians with a predictive tool to estimate the necrosis of tumor tissue, facilitating the customization of HIFU treatment strategies for individual breast cancer patients.
Has Anti-corruption Efforts lowered Enterprises’ Innovation Efficiency? — An Empirical Analysis from China
Authors: liu lunwu
Published: April 2024

This study adopts the fixed effects panel model and provincial panel data on anticorruption and the innovation efficiency of high-level technology and new technology enterprises in China from 2005 to 2014, to estimate the effects of the anticorruption movement on the innovation efficiency of enterprises at different corruption levels. The empirical results show that anticorruption is positively correlated with the innovation efficiency of enterprises; however, the correlation is differentiated according to different corruption levels and business natures. At a high level of corruption, anticorruption has positive impacts on enterprises’ innovation; at a low level of corruption, it negatively affects innovation efficiency. However, anticorruption has negative effects on the innovation efficiency of state-owned enterprises at both high and low corruption levels; for nonstate-owned enterprises, its effects are positive at a high corruption level and negative at a low corruption level. The effects remain the same across different regions.
Artificial Intelligence in Mental Control: the dialectical antilogies
Authors: Popov V. D.
Published: April 2024

The paper examines the influence technics on human conscience, the dialectic of interaction between artificial intelligence (AI) and human intelligence within the system: information technologies –mentality and psychology of society – control over people’s minds and technologies. The emphasis is on the essence of the concept of mentality, mental identity, the role of informal institutions, and the speed of changes in biological and information time. The author represents a conceptual view on the problem of the role of artificial intelligence in the development of mental management as a humanitarian problem in the logic of the development of ideas and approaches previously considered by the author in the following works: The Future of Russia: transition to a new formation; Strategy of Reforms in Russia: from leader to leader; and in the context of the idea of social justice and economic growth, discussed at the First Russian Economic Forum.
Lieb-Schultz-Mattis anomalies as obstructions to gauging (non-on-site) symmetries
Authors: Seifnashri Sahand
Published: April 2024

We study ’t Hooft anomalies of global symmetries in 1+1d lattice Hamiltonian systems. We consider anomalies in internal and lattice translation symmetries. We derive a microscopic formula for the “anomaly cocycle” using topological defects implementing twisted boundary conditions. The anomaly takes value in the cohomology group H^3(G,U(1)) × H^2(G,U(1))H3(G,U(1))×H2(G,U(1)). The first factor captures the anomaly in the internal symmetry group G, and the second factor corresponds to a generalized Lieb-Schultz-Mattis anomaly involving G and lattice translation. We present a systematic procedure to gauge internal symmetries (that may not act on-site) on the lattice. We show that the anomaly cocycle is the obstruction to gauging the internal symmetry while preserving the lattice translation symmetry. As an application, we construct anomaly-free chiral lattice gauge theories. We demonstrate a one-to-one correspondence between (locality-preserving) symmetry operators and topological defects, which is essential for the results we prove. We also discuss the generalization to fermionic theories. Finally, we construct non-invertible lattice translation symmetries by gauging internal symmetries with a Lieb-Schultz-Mattis anomaly.
On correlation functions for the open XXZ chain with non-longitudinal boundary fields: The case with a constraint
Authors: Niccoli Giuliano , Terras Véronique
Published: April 2024

This paper is a continuation of [J. Phys. A: Math. Theor. 55, 405203 (2022)], in which a set of matrix elements of local operators was computed for the XXZ spin-1/2 open chain with a particular case of unparallel boundary fields. Here, we extend these results to the more general case in which both fields are non-longitudinal and related by one constraint, allowing for a partial description of the spectrum by usual Bethe equations. More precisely, the complete spectrum and eigenstates can be characterized within the Separation of Variables (SoV) framework. One uses here the fact that, under the constraint, a part of this SoV spectrum can be described via solutions of a usual, homogeneous, TQ-equation, with corresponding transfer matrix eigenstates coinciding with generalized Bethe states. We explain how to generically compute the action of a basis of local operators on such kind of states, and this under the most general boundary condition on the last site of the chain. As a result, we can compute the matrix elements of some of these basis elements in any eigenstate described by the homogenous TQ-equation. Assuming, following a conjecture of Nepomechie and Ravanini, that the ground state itself can be described in this framework, we obtain multiple integral representations for these matrix elements in the half-infinite chain limit, generalizing those previously obtained in the case of longitudinal boundary fields and in the case of the special boundary conditions considered in [J. Phys. A: Math. Theor. 55, 405203 (2022)].
Hidden symmetry of Bogoliubov de Gennes quasi-particle eigenstates and universal relations in flat band superconducting bipartite lattices
Authors: Bouzerar Georges , Thumin Maxime
Published: April 2024

Unconventional flat band (FB) superconductivity, as observed in van der Waals heterostructures, could open promising avenues towards high-T_cc materials. In FBs, pairings and superfluid weight scale linearly with the interaction parameter, such an unusual behaviour justifies and encourages strategies to promote FB engineering. Bipartite lattices (BLs) which naturally host FBs could be particularly interesting candidates. Within Bogoliubov de Gennes theory and in the framework of the attractive Hubbard model in BLs, a hidden symmetry of the quasi-particle eigenstates is revealed. As a consequence, we demonstrate universal relations for the pairings and the superfluid weight that are independent of the characteristics of the hopping term. Remarkably, it is shown that these general properties are insensitive to disorder as long as the bipartite character is protected.
Understanding the Roles of Non-coding RNAs and Exosomal Non-Coding RNAs in Diabetic Nephropathy
Authors: Zhu Yuye , Liu Chunying , Hallajzadeh Jamal
Published: April 2024

One of the greatest serious side effects of diabetes is diabetic nephropathy
(DN), which is also the key factor in the sometimes-deadly diabetic end-stage renal
disease. Progressive renal interstitial fibrosis is closely associated with oxidative
stress, and the extracellular matrix is typically a feature of DN. Some RNAs formed by
genome transcription that are not translated into proteins are recognized as noncoding
RNAs. It has been shown that ncRNAs control apoptosis, inflammatory
response, cell proliferation, autophagy, and other pathogenic processes, contributing
to the pathogenesis of DN. Exosomes are nano-carriers vesicles that variety in size
from 40 to 160 nm. Exosomes are widely present and dispersed in different bodily
fluids, plentiful in nucleic acids, lipids, and proteins (microRNA, mRNA, tRNA, lncRNA,
circRNA, etc.). Exosomes play a crucial role as messengers for cellular
communication. They transport and transmit key signaling molecules, participate in the
transfer of information and materials between cells, control cellular physiological
processes, and are carefully linked to the beginning and development of many
diseases. Herein, we summarized the role of different ncRNAs in DN. Moreover, we
highlighted the role of the exosomal form of ncRNAs in the DN pathogenesis.
Novel Compound Heterozygous Mutations of LIG4 Gene in an Indian
LIG4 Syndrome Patient with Severe Microcephaly: Case Report, In-silico
Analysis and Systematic Review
Authors: Singh Amit , Senapati Sabyasachi , Panigrahi Inusha , Khetarpal Preeti
Published: April 2024

Background::

LIG4 syndrome, characterized by immunodeficiency, sensitivity to ionizing
radiations, intrauterine growth retardation, postnatal growth retardation, and microcephaly, is
a rare genetic disorder caused by pathogenic variants of the LIG4 gene. Few patients are presented
with no immune dysregulation as well.

Case Study:

We present here a male child of 2 years and 4 months of age with severe microcephaly
and short stature. His birth weight was 1.9 Kg, and his current height, weight, and head circumference
are 83.2 cm (z score = -2.37), 9.5 Kg (z score = -2.76), and 36 cm (z score = -9.24), respectively.
Possible causative pathogenic compound heterozygous variants of the LIG4 gene, which
were inherited from the parents, were identified by whole exome sequencing of the DNA of the patient
and his parents. A systematic review of the literature is also performed to summarize the patients
of LIG4 syndrome reported worldwide and summarize the associated genetic mutations of
the LIG4 gene. Compound heterozygous variants (c.597_600delTCAG/ c.342del) of LIG4 gene
were identified. The parents were found to be heterozygous carriers of one variant each.

Conclusion::

The in-silico analysis of identified variants explains their effect on the structure and
function of the LIG4 protein hence explaining the genotype-phenotype correlation.
Cognitive Behavioral Therapy for Symptom Preoccupation Among Patients With Premature Ventricular Contractions: Nonrandomized Pretest-Posttest Study
Authors: Liliequist Björn E , Särnholm Josefin , Skúladóttir Helga , Ólafsdóttir Eva , Ljótsson Brjánn , Braunschweig Frieder
Published: April 2024

Background

Premature ventricular contractions (PVCs) are a common cardiac condition often associated with disabling symptoms and impaired quality of life (QoL). Current treatment strategies have limited effectiveness in reducing symptoms and restoring QoL for patients with PVCs. Symptom preoccupation, involving cardiac-related fear, hypervigilance, and avoidance behavior, is associated with disability in other cardiac conditions and can be effectively targeted by cognitive behavioral therapy (CBT).

Objective

The aim of this study was to evaluate the effect of a PVC-specific CBT protocol targeting symptom preoccupation in patients with symptomatic idiopathic PVCs.

Methods

Nineteen patients diagnosed with symptomatic idiopathic PVCs and symptom preoccupation underwent PVC-specific CBT over 10 weeks. The treatment was delivered by a licensed psychologist via videoconference in conjunction with online text-based information and homework assignments. The main components of the treatment were exposure to cardiac-related symptoms and reducing cardiac-related avoidance and control behavior. Self-rated measures were collected at baseline, post treatment, and at 3- and 6-month follow-ups. The primary outcome was PVC-specific QoL at posttreatment assessment measured with a PVC-adapted version of the Atrial Fibrillation Effects on Quality of Life questionnaire. Secondary measures included symptom preoccupation measured with the Cardiac Anxiety Questionnaire. PVC burden was evaluated with 5-day continuous electrocardiogram recordings at baseline, post treatment, and 6-month follow-up.

Results

We observed large improvements in PVC-specific QoL (Cohen d=1.62, P<.001) and symptom preoccupation (Cohen d=1.73, P<.001) post treatment. These results were sustained at the 3- and 6-month follow-ups. PVC burden, as measured with 5-day continuous electrocardiogram, remained unchanged throughout follow-up. However, self-reported PVC symptoms were significantly lower at posttreatment assessment and at both the 3- and 6-month follow-ups. Reduction in symptom preoccupation had a statistically significant mediating effect of the intervention on PVC-specific QoL in an explorative mediation analysis.

Conclusions

This uncontrolled pilot study shows preliminary promising results for PVC-specific CBT as a potentially effective treatment approach for patients with symptomatic idiopathic PVCs and symptom preoccupation. The substantial improvements in PVC-specific QoL and symptom preoccupation, along with the decreased self-reported PVC-related symptoms warrant further investigation in a larger randomized controlled trial.

Trial Registration

ClinicalTrials.gov NCT05087238; https://clinicaltrials.gov/study/NCT05087238
Robotic Process Automation: A Burgeoning Technology with Promising Prospects
Authors: Singh Om Radheshyam
Published: April 2024

Abstract: Robotic Process Automation, a novel technology centered on automating repetitive, routine, and rule-based human tasks, offers significant benefits to organizations that adopt it. However, due to its relative infancy in the market, scientific research on RPA remains limited. This paper delves into the academic community's definition of RPA and its exploration in the literature, examining its current state, trends, and applications. It also elucidates the distinction between RPA and business process management. To achieve these objectives, a systematic literature review (SLR) was conducted using the Web of Science and Scopus databases. The paper presents the findings of the SLR on RPA, providing an overview of RPA definitions and practical applications, along with the benefits of its implementation across various industries.
Real-Time News Aggregation Using ReactJS: A Case Study and Analysis
Authors: Dhamdhere Tejas
Published: April 2024

Abstract: Real-time news aggregation has become increasingly vital in the digital age, where access to upto-date information is crucial for individuals and organizations alike. This paper presents a comprehensive study on the development of a real-time news application using ReactJS, a popular JavaScript library for building user interfaces. The study explores the significance of real-time updates in news applications, reviews existing literature on news aggregation platforms and ReactJS development, and identifies gaps in the current research landscape. The proposed system leverages ReactJS's flexibility and efficiency to create an intuitive user interface for accessing and interacting with real-time news content. Through integration with external APIs such as News API, the application fetches news articles from various sources and presents them to users in a timely manner.
Comparative Analysis of ML Algorithms and Neural Networks to Classify Student Based on Academic Performance
Authors: Sruthi Pinamala , Kumar Divya Hari , Nishanth N. Sai , Najmuddin Md.
Published: April 2024

Abstract: In recent years, there has been an interesting surge in interest in both educational data and deep learning. This research proposes a Neural Network (NN) architecture that visually represents the class category to students. This gives information to the school so they can help the pupils who might otherwise fail. A comparative analysis between the suggested model and present-day techniques of machine learning that utilizes the identical dataset. Compared to existing machine learning algorithms, the accuracy of the suggested neural network model is higher.