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Downregulation of nc886 contributes to prostate cancer cell invasion and TGFβ1-induced EMT

Genes & Diseases 2022年第4期9卷 1086-1098页

作者： Ronghui Yang Lingkun Zuo Hui Ma Ying Zhou Ping Zhou Liyong Wang Miao Wang mahrukh latif Lu Kong Department of Biochemistry and Molecular Biology Capital Medical UniversityBeijing 100069PR China Biomedical Engineering Institute of Capital Medical University Capital Medical UniversityBeijing 100069PR China Department of Pathology Beijing Friendship HospitalThe Second Clinical Medical College of Capital Medical UniversityBeijing 100050PR China Department of Nuclear Medicine Fourth Hospital of Hebei Medical UniversityShijiazhuangHebei 050010PR China

Epithelial-to-mesenchymal transition (EMT) activation is important in cancer progression and metastasis. Evidence indicates that nc886 is a representative Pol III gene that processes microRNA products via Dicer and further downregulates its target gene transforming growth factor- β1 (TGF-β1), which is the most prominent inducer of EMT in prostate cancer (PC). Consistent with the previous literature, we found that nc886 downregulation was strongly associated with metastatic behavior and showed worse outcomes in PC patients. However, little is known about the association between nc886 and the EMT signaling pathway. We developed a PC cell model with stable overexpression of nc886 and found that nc886 changed cellular morphology and drove MET. The underlying mechanism may be related to its promotion of SNAIL protein degradation via ubiquitination, but not to its neighboring genes, TGFβ-induced protein (TGFBI) and SMAD5, which are Pol II-transcribed. TGF-β1 also override nc886 promotion of MET via transient suppression the transcription of nc886, promotion of TGFBI or increase in SMAD5 phosphorylation. Both nc886 inhibition and TGFBI activation occur regardless of their methylation status. The literature suggests that MYC inhibition by TGF-β1 is attributed to nc886 downregulation. We incidentally identified MYC-associated zinc finger protein (MAZ) as a suppressive transcription factor of TGFBI, which is controlled by TGF-β1. We elucidate a new mechanism of TGF-β1 differential control of Pol II and the transcription of its neighboring Pol III gene and identify a new EMT unit consisting of nc886 and its neighboring genes.

关键词： EMT MET Non-coding RNA Prostate cancer TGF-β1

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Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems

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China Communications 2024年第8期21卷 115-126页

作者： Talha Younas Shen Jin Muluneh Mekonnen Gao Mingliang Saqib Saleem Sohaib Tahir mahrukh Liaqat School of Electrical and Electronic Engineering Shandong University of TechnologyZibo 255000China Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal Campus Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah 211Oman Department of Electrical and Computer Engineering Addis Ababa Science and Technology UniversityAddis AbabaEthiopia Department of Electrical Engineering College of Electrical and Mechanical EngineeringNUSTIslamabadPakistan

Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power *** power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low *** this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician *** start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in *** also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the *** emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining *** also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable *** emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

关键词： low-bit analog-digital converter massive(multiple-input-multiple-output)MIMO minimum mean square error(MMSE) regularized zero forcing zero forcing

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The role of predictive and prognostic values of inflammatory markers in acute pancreatitis: a narrative review

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胰腺病学杂志(英文) 2024年第1期7卷 72-85页

作者： Rafaqat Saira Sattar Aqsa Anjum Farhan Gilani mahrukh Rafaqat Sana Department of Zoology (Molecular Physiology) Lahore College for Women University Lahore Pakistan Institute of Zoology University of the Punjab Quaid-i-Azam Campus Lahore Pakistan Department of Zoology Lahore College for Women University Lahore Pakistan Department of Biotechnology (Human Genetics) Lahore College for Women University Lahore Pakistan

Pancreatitis is an inflammatory condition affecting the pancreas and is classified into 2 types, acute and chronic, which can manifest in various forms. This review article summarizes the role of predictive and prognostic values of inflammatory markers in the pathogenesis of acute pancreatitis, mainly focused on preclinical and clinical studies. It includes serum amyloid A (SAA), monocyte chemotactic protein-1 (MCP-1), erythrocyte sedimentation rate (ESR), interleukin-6 (IL-6), C-reactive protein (CRP), IL-10, myeloperoxidase, pentraxin 3, and plasminogen activator inhibitor 1. SAA3 plays a crucial role in developing acute pancreatitis by triggering a receptor-interacting protein 3-dependent necroptosis pathway in acinar cells. Targeting SAA3 could be a potential strategy for treating acute pancreatitis. The recruitment of monocytes/macrophages and the activation of the systemic MCP-1 signaling pathway play a role in the progression of pancreatitis, and blocking MCP-1 may have a suppressive effect on the development of pancreatic fibrosis. The ESR can predict severe acute pancreatitis with slightly lower accuracy than CRP. When ESR and CRP levels are combined at 24 hours, they predict severe acute pancreatitis accurately. IL-6 plays a crucial role in activating the Janus kinase/signal transducers and activators of the transcription pathway, exacerbating pancreatitis and contributing to the initiation and progression of pancreatic cancer. Endogenous IL-10 plays a crucial role in controlling the regenerative phase and limiting the severity of fibrosis and glandular atrophy induced by repeated episodes of acute pancreatitis in mice. The predictive and diagnostic roles of these inflammatory factors in pancreatitis were introduced in detail in this review.

关键词： Acute pancreatitis Inflammatory markers Pathogenesis

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Physico-chemical properties and macrofauna of soils under various farming systems of cold arid region in Balochistan,Pakistan

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Journal of Mountain Science 2024年第8期21卷 2618-2630页

作者： KHAN Mehmood GUL Shamim KAKAR Hidayatullah PANEZAI Sanaullah KHAN Nayab ZIAD Tariq NASEEM mahrukh SHAHEEN Umbreen Department of Botany University of BalochistanQuetta 87300Pakistan Department of Natural Resource Sciences McGill University21111 Lakeshore Road Ste.Anne de BellevueQuébec H9X 3V9Canada Directorate of Agriculture Research Soil and Water TestingAgricultural Research InstituteQuetta 87300Pakistan Department of Geography and Regional Planning University of BalochistanQuetta 87300Pakistan Department of Zoology University of BalochistanQuetta 87300Pakistan

Barshore is a small village in the Pishin District,Balochistan,Pakistan,with dry summers and cold rainy *** is an agrarian region,mostly with orchards of various fruit *** study investigated the physico-chemical properties and macrofauna of soils under various agricultural management practices of this *** concentrations of soil organic matter(SOM),soil organic carbon(SOC),nutrients,pH,electrical conductivity,soil texture,and the abundance and number of species of soil macrofauna of the agricultural fields were *** agricultural fields were *** fields were orchards of apple,apricot or the mixture of apple and apricot trees and one field was a cropland,cultivated with wheat as a *** orchards were under conservation agricultural practices;whereas,the cropland was under conventional *** agricultural lands were 2-26 years *** concentration of soil organic matter(SOM)in the upper 0-10 cm depth of these field sites ranged from 11.6 g kg^(-1)to 32.8 g kg^(-1)*** compared to cropland,orchards had significantly higher concentration of SOM and SOC.A total of 18 soil macrofauna species were found and the most common and abundant were ants(Monomorium minimum,Camponotus pennsylvanicus,Solenopsis invicta,and Lasius niger)followed by Arion ssp.(Brown Slug)and earthworm Lumbricus *** analysis revealed non-significant relationship of the age and the concentration of SOM with the number of macrofauna species and with the concentrations of total mineral nitrogen,bioavailable phosphorus and *** existence of ants had no relationship with the concentration of SOM;whereas,existence of Lumbricus terrestris tended to had a positive relationship with the concentration of *** field of tree-based intercropping system was 2 years of age since the land was converted from rangeland to a cropland,had two ant species *** indicates the positive influence of crop diversification on soil

关键词： Soil organic matter Macrofauna Land use history Tree-based intercropping Conservation agriculture

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Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1341-1364页

作者： Mohamed Zarouan Ibrahim M.Mehedi Shaikh Abdul latif Md.Masud Rana Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Nuclear Engineering King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Electronic Engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry ***, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of *** diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel *** experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods

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Intelligent Automation & Soft Computing 2024年第1期39卷 57-71页

作者： Ghulam Gilanie Sana Cheema Akkasha latif AnumSaher Muhammad Ahsan Hafeez Ullah Diya Oommen Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan Biophotonics Imaging Techniques Laboratory Institute of PhysicsThe Islamia University of BahawalpurBahawalpur63100Pakistan West High School Salt Lake CityUT84103USA

Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the *** affects a large percentage of people globally,who fluctuate between depression and mania,or vice versa.A pleasant or unpleasant mood is more than a reflection of a state of ***,it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio,so automated procedures are the best options to diagnose and verify the severity of *** this research work,facial microexpressions have been used for bipolar detection using the proposed Convolutional Neural Network(CNN)-based *** Action Coding System(FACS)is used to extract micro-expressions called Action Units(AUs)connected with sad,happy,and angry *** have been conducted on a dataset collected from Bahawal Victoria Hospital,Bahawalpur,Pakistan,Using the Patient Health Questionnaire-15(PHQ-15)to infer a patient’s mental *** experimental results showed a validation accuracy of 98.99%for the proposed CNN modelwhile classification through extracted featuresUsing SupportVectorMachines(SVM),K-NearestNeighbour(KNN),and Decision Tree(DT)obtained 99.9%,98.7%,and 98.9%accuracy,***,the outcomes demonstrated the stated method’s superiority over the current best practices.

关键词： Bipolar mental illness detection facial micro-expressions facial landmarked images

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： Muhammad Armghan latif Zohaib Mushtaq Saad Arif Sara Rehman Muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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Redefining biofuels:Investigating oil palm biomass as a promising cellulose feedstock for nitrocellulose-based propellant production

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Defence Technology（防务技术） 2024年第7期37卷 111-132页

作者： Khoirul Solehah binti Abdul Rahim Alinda binti Samsuri Siti Hasnawati binti Jamal Siti Aminah binti Mohd Nor Siti Nor Ain binti Rusly Hafizah binti Ariff Nur Shazwani binti Abdul latif Centre for Tropicalization(CENTROP) Defence Research InstituteUniversiti Pertahanan Nasional MalaysiaKem Sungai Besi57000 Kuala LumpurMalaysia Department of Chemistry and Biology Centre for Defence Foundation StudiesUniversiti Pertahanan Nasional MalaysiaKem Sungai Besi57000 Kuala LumpurMalaysia

This review paper explores the potential of oil palm biomass as a valuable cellulose source for the production of nitrocellulose-based propellants,contributing to the green revolution and sustainable energy *** highlights the availability of the corresponding biomass in Malaysia and in line with global studies,the chemical compositions,as well as a brief description of current technologies for converting biomass of oil palm into value added products specifically *** to achieve maximum utilization of biomass from oil palm industry for cellulose production and prospective source for nitrocellulose-based propellant are also *** methodology section outlines the pretreatment of lignocellulosic fibres,cellulose extraction,and nitrocellulose production ***,the review underscores the prospective of palm oil biomass as a sustainable cellulose source for propellant manufacturing,while acknowledging the need for further research and advancements in the field.

关键词： Biopolymer Cellulose nitrate Energetic materials Green technology Palm oil

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Geospatial Variability of Cholera Cases in Malawi Based on Climatic and Socioeconomic Influences

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Journal of Geographic Information System 2024年第1期16卷 1-20页

作者： Emmanuel Chinkaka Francis Chauluka Ruth Chinkaka Billy Kachingwe Esther Banda latif Department of Geography and Spatial Sciences University of Delaware Newark USA Department of Earth Sciences Malawi University of Science and Technology Limbe Malawi Department of Water Resources Management Malawi University of Science and Technology Limbe Malawi Department of Obstetrics and Gynecology Queen Elizabeth Central Hospital Blantyre Malawi Department of Public Health Nkhoma Mission Hospital Nkhoma Malawi

Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity and mortality rates than the past two decades. Lack of spatiotemporal-based technology and variability assessment tools in Malawi’s Cholera monitoring and management, limit our understanding of the disease’s epidemiology. The present work developed a spatiotemporal variability model for Cholera disease at district level and its relationship to socioeconomic and climatic factors based on cumulative confirmed Cholera cases in Malawi from March 2022 to July 2023 using Z-score statistic and multiscale geographically weighted regression (MGWR) in a Geographical Information System (GIS). We found out that socioeconomic factors such as access to safe drinking water, population density and poverty level, and climatic factors including temperature and rainfall strongly influenced Cholera prevalence in a complex and multifaceted manner. The model shows that Lilongwe, Mangochi, Blantyre and Balaka districts were highly vulnerable to Cholera disease followed by lakeshore districts of Salima, Nkhotakota, Nkhata-Bay and Karonga than other districts. We recommend strategic measures such as Water, Sanitation, and Hygiene (WASH) interventions, community awareness on proper water storage, Cholera case management, vaccination campaigns and spatial-based surveillance systems in the most affected districts. This research has shown that MGWR, as a surveillance system, has the potential of providing insights on the disease’s spatial patterns for public health authorities to identify high-risk districts and implement early response interventions to reduce the spread of the disease.

关键词： Cholera Geospatial Variability Prevalence GIS MGWR Vulnerability Malawi

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