Miracle-dz/UniFAR-FTUs huggingface dataset:
Tissue-Nonspecific Alkaline Phosphatase, a Possible Mediator of Cell Maturation: Towards a New Paradigm

Alkaline phosphatase (ALP) is a ubiquitous membrane-bound glycoprotein capable of providing inorganic phosphate by catalyzing the hydrolysis of organic phosphate esters, or removing inorganic pyrophosphate that inhibits calcification. In humans, four forms of ALP cDNA have been cloned, among which tissue-nonspecific ALP (TNSALP) (TNSALP) is widely distributed in the liver, bone, and kidney, making it an important marker in clinical and basic research. Interestingly, TNSALP is highly expressed in juvenile cells, such as pluripotent stem cells (i.e., embryonic stem cells and induced pluripotent stem cells (iPSCs)) and somatic stem cells (i.e., neuronal stem cells and bone marrow mesenchymal stem cells). Hypophosphatasia is a genetic disorder causing defects in bone and tooth development as well as neurogenesis. Mutations in the gene coding for TNSALP are thought to be responsible for the abnormalities, suggesting the essential role of TNSALP in these events. Moreover, a reverse-genetics-based study using mice revealed that TNSALP is important in bone and tooth development as well as neurogenesis. However, little is known about the role of TNSALP in the maintenance and differentiation of juvenile cells. Recently, it was reported that cells enriched with TNSALP are more easily reprogrammed into iPSCs than those with less TNSALP. Furthermore, in bone marrow stem cells, ALP could function as a “signal regulator” deciding the fate of these cells. In this review, we summarize the properties of ALP and the background of ALP gene analysis and its manipulation, with a special focus on the potential role of TNSALP in the generation (and possibly maintenance) of juvenile cells.
Microglial Potassium Channels: From Homeostasis to Neurodegeneration

The growing interest in the role of microglia in the progression of many neurodegenerative diseases is developing in an ever-expedited manner, in part thanks to emergent new tools for studying the morphological and functional features of the CNS. The discovery of specific biomarkers of the microglia phenotype could find application in a wide range of human diseases, and creates opportunities for the discovery and development of tailored therapeutic interventions. Among these, recent studies highlight the pivotal role of the potassium channels in regulating microglial functions in physiological and pathological conditions such as Alzheimer’s Disease, Parkinson’s Disease, and Amyotrophic Lateral Sclerosis. In this review, we summarize the current knowledge of the involvement of the microglial potassium channels in several neurodegenerative diseases and their role as modulators of microglial homeostasis and dysfunction in CNS disorders.
Sparse convolutional neural network acceleration with lossless input feature map compression for resource-constrained systems

Many recent research efforts have exploited data sparsity for the acceleration of convolutional neural network (CNN) inferences. However, the effects of data transfer be-tween main memory and the CNN accelerator have been largely overlooked. In this work, the authors propose a CNN acceleration technique that leverages hardware/software co ‐ design and exploits the sparsity in input feature maps (IFMs). On the software side, the authors' technique employs a novel lossless compression scheme for IFMs, which are sent to the hardware accelerator via direct memory access. On the hardware side, the authors' technique uses a CNN inference accelerator that performs convolutional layer operations with their compressed data format. With several design optimization techniques, the authors have implemented their technique in a field ‐ programmable gate array (FPGA) system ‐ on ‐ chip platform and evaluated their technique for six different convolutional layers in SqueezeNet. Results reveal that the authors' technique improves the performance by 1.1 � –22.6 � while reducing energy consumption by 47.7%–97.4% as compared to the CPU ‐ based execution. Furthermore, results indicate that the IFM size and transfer latency are reduced by 34.0%–85.2% and 4.4%–75.7%, respectively, compared to the case without data compression. In addition, the authors' hardware accelerator shows better performance per hardware resource with less than or comparable power consumption to the state ‐ of ‐ the ‐ art FPGA ‐ based designs.
Behavioral Responses of Castrated Buck Kids at Different Ages By Using Different Methods of Castration

To evaluate the behavioral responses of kids to castration by burdizzo (Bur), rubber ring (RR) or control handled, 90 buck kids were used. Behavioral responses of kids throughout the 180 minutes after castration or control handled were assessed by using a video camera. Castration of younger kids (7 and 21 days) took significantly shorter time than 42 days old. RR produced the highest values of active pain behavior, frequency of elimination, less frequently suck, teat seeking or nibble at feed. The scrotal sloughing was delayed in older kids. Results of questionnaire indicated that most castrations were done at the farm; castration failure was only in some of Burdizzo especially those older than 8 weeks. It was concluded that kids castration must be done as early as possible to reduce the total pain response and for achievement of animal welfare, and Burdizzo method is the preferable technique for castration of kids at all ages.
Delivery of Ecosystem Services by Community Woodland Groups and Their Networks

The Millennium Ecosystem Assessment stresses that it is possible to manage ecosystems so as to strengthen their capacity to provide a range of goods and services. In reality, the delivery of ecosystem services reflects policy and delivery mechanisms, the environment, and the objectives of landowners and managers. Amid gradual changes to forest policy and more recent periods of austerity, the management of treescapes by locally led groups, such as Community Woodland Groups (CWGs), has become increasingly common. Through document analysis and interviews we explore the objectives and activities of British-based CWGs, and the implications these have for the delivery of ecosystem services. Additionally, we explore CWGs involvement with three types of networks and the ways in which each facilitate CWGs’ establishment, operations and ecosystem service provision. We conclude that, while CWGs are capable of delivering a range of ecosystem services, their focus is typically on: (i) cultural services for the benefit of the local community, and (ii) biodiversity. Since these foci parallel the goods and services emphasised in contemporary forest policy agendas, it is apparent that CWGs represent a promising model for woodland management. However, to realise their potential and confront management challenges, CWGs often rely on access to advice, labour, equipment and funding from across multiple networks.
Recycling of Scrap Solid (Metal) Industrial Waste in Fabrication of Tile Making Machine as a Strategy in Waste Management in Kaduna, Nigeria.

In many third world or developing countries like Nigeria, there is abundant solid waste littered all over the cities. These wastes are generated without control due to lack of enforcement of existing laws to prevent waste generation or proper waste disposal. Generally, Nigerians have a poor attitude towards enacted laws. This attitude certainly leads to generation of more wastes and consequent poor management. Experts initiated and proffered strategies to reduce the volume of waste generation such as waste: reduction, prevention, recovering, repackaging, composting, recycling, reused, landfill, incineration, etc. – these are some of the major ways currently used to manage wastes generally.
Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic

The recent advance in information technology has created a new era named the Internet of Things (IoT). This new technology allows objects (things) to be connected to the Internet, such as smart TVs, printers, cameras, smartphones, smartwatches, etc. This trend provides new services and applications for many users and enhances their lifestyle. The rapid growth of the IoT makes the incorporation and connection of several devices a predominant procedure. Although there are many advantages of IoT devices, there are different challenges that come as network anomalies. In this research, the current studies in the use of deep learning (DL) in DDoS intrusion detection have been presented. This research aims to implement different Machine Learning (ML) algorithms in WEKA tools to analyze the detection performance for DDoS attacks using the most recent CICDDoS2019 datasets. CICDDoS2019 was found to be the model with best results. This research has used six different types of ML algorithms which are K_Nearest_Neighbors (K-NN), super vector machine (SVM), naïve bayes (NB), decision tree (DT), random forest (RF) and logistic regression (LR). The best accuracy result in the presented evaluation was achieved when utilizing the Decision Tree (DT) and Random Forest (RF) algorithms, 99% and 99%, respectively. However, the DT is better than RF because it has a shorter computation time, 4.53 s and 84.2 s, respectively. Finally, open issues for further research in future work are presented.
Cochrane Review Summary on “Vitamin D Supplementation for the Treatment of COVID‐19: A Living Systematic Review”

Coronavirus disease (COVID-19) can cause an exaggerated inflammatory response, damage to blood vessels, and a hyper-coagulable state, which can contribute to the formation of blood clots, impaired breathing, and death (1, 2). A recent systematic review reported that most confirmed COVID‐19 patients in the study had vitamin D deficiency/insufficiency, and individuals deficient in vitamin D were nearly three times more likely to become infected with SARS‐CoV‐2 and about five times more likely to develop severe disease (3). It is now known that vitamin D has important roles in both immune and lung functions, and lower levels of vitamin D have been associated with increased infections (4) and worse performance on lung function tests (5). Despite this, the effectiveness and safety of vitamin D as a treatment for COVID-19 infections is not yet known. Here, we summarize a Cochrane living systematic review examining the effectiveness and safety of using vitamin D supplementation to treat individuals diagnosed with COVID-19 (6). Living systematic review is a new systematic review approach which is continuously updated as new relevant evidence becomes available. Up to May 24 of 2021, three randomized controlled trials with 356 total participants who were asymptomatic or with mild to severe COVID-19 symptoms were included in the review. Participants in two of the studies were vitamin D‐deficient, but vitamin D status was not reported for the other study. Vitamin D was administered to 183 participants as either multiple doses of oral calcifediol or cholecalciferol or as a single high dose of oral cholecalciferol. Control participants were given placebos or standard care. Study results could not be pooled, so the authors reported results for individual studies. It is worth noting that the number of total events across study groups ranged from <1% to 11% for all outcomes analyzed in the review. Regarding effectiveness in participants with moderate to severe disease, the authors found very low-certainty evidence and mixed results among two studies for the effect of vitamin D on all-cause mortality at hospital discharge [study 1: Risk ratio (RR) 0.11, 95% confidence interval (CI) 0.01 to 2.13; study 2: RR 1.49, 95% CI 0.55 to 4.04].
Climate change governance in agroforestry systems: a systematic review

Agroforestry has the potential to fundamentally transform socio-ecological systems to address the root causes of climate vulnerability. Although there is increasing interest in agroforestry as a transformative adaptation strategy, its implementation is often discouraged by the need to involve multiple stakeholders, sectors, and governance levels with potentially different interests. We draw on a systematic review of sixty-four peer-reviewed papers on climate change governance in agroforestry systems to (1) outline the current state of the literature, (2) characterise how governance is conceptualised, (3) investigate governance challenges, and (4) provide insights into effective governance. The review finds that most relevant papers have been published in the past three years, and most of these papers are found in interdisciplinary journals. The main governance challenges include coordinating polycentricity, overcoming power imbalances, and sharing, translating, and integrating different types of knowledge. However, few empirical studies of agroforestry governance have been completed. A richer conceptual framework of governance is required to improve our ability to navigate the role of sustainable land management practices such as agroforestry in successful climate change adaptation and mitigation.
The modelling and simulation of vehicle handling Part 3: Tyre modelling

Abstract This is the third in a series of four papers (Parts 1 to 4) looking at the application of computer-based analysis methods to model vehicles and simulate vehicle handling. The material contained in these papers is based on a study carried out in order to investigate the influence of suspension and tyre modelling on the outputs predicted by vehicle handling simulations. The papers deal with analysis methods, vehicle modelling (both in the previous Issue), tyre modelling and handling simulation. In this paper an overview of the use of tyre models in vehicle dynamics is provided. This is followed by a more detailed description of three tyre modelling approaches that can be used for handling simulations. A description is also provided of a computer-based modelling system where FORTRAN routines represent the various models and a computer model of a tyre test rig is used to interrogate the models and data before integrating these into a full vehicle handling simulation. The use of this system to compare the accuracy of the tyre models under consideration is also presented. The examples used to illustrate the concepts explained throughout this series of papers have been generated using the ADAMS (Automatic Dynamic Analysis of Mechanical Systems) program.
q‐Rung double hierarchy linguistic term set fuzzy AHP; applications in the security system threats features of social media platforms

In this modern age, information technology has developed tremendously. This development becomes very fast as it is the best way to share information. Social media users' concerns about their privacy have spiked in recent years. Incidents of data breaches have alarmed many users and forced them to rethink their relationships to social media and the security of their personal information. There can be many risks to information sharing, which is considered an attack on software and information hacking. Today, the password has a threat to any security system. Hackers steal people's passwords and misuse them. Peoples are worried about hacking their information and data. In this paper, we present the analytic hierarchy process (AHP) approach under the environment of q‐rung orthopair double hierarchy linguistic term set (q‐RODHLTS) and its some fundamental operations. For this purpose, we use the q‐RODHLAHP in the multi‐attribute decision‐making process. Examples of security systems are demonstrated to illustrate the validity of the proposed approach. Security has become a significant issue as information technology is spreading rapidly in our daily lives. Finally, numerical applications of the security system have been presented to show the validity of our proposed method.
Estimation of Trip Generation in Mexico City, Mexico, with Spatial Effects and Urban Densities

Coefficients for work, shopping, and school trip generation models are presented for Mexico City, Mexico. These results were generated for the first stage of a four-stage model that was built for the city for 1994, the most recent year for which data are available. Because no complete urban transportation model that incorporated behavioral aspects of urban travel could be found at the time of this study, these estimates are the first results of their kind to be published in the literature for recent years. Attempts to account for spatial processes have not been undertaken as a general rule in transportation modeling. Therefore, there is a bias in coefficients estimated without taking spatial autocorrelation into account when spatially defined data are used. To address this peculiarity, spatial regression was used instead of the traditional ordinary least-squares estimation method. Trip generation is normally defined on a per capita or per household basis. Surprisingly, these results show that models specified for trip generation densities (trips per unit of area) were as good as or better than more traditional specifications in explaining trip generation. The implication of these findings could potentially be of importance in other cities.
Quantification of Fiber Releases for Various Floor Tile Removal Methods

Abstract The risk of human exposure from asbestos-containing material is directly related to the condition of the material, custodial/maintenance activities, and the methods used to reduce the potential exposure to workers, bystander employees, and occupants of the area containing the asbestos. Many consider floor tile to be nonfriable, which is in accordance with the National Emission Standards for Hazardous Air Pollutants regulations, although there is some documentation of fiber releases from deterioration of such asbestos-containing materials during polishing, removal, or routine walking, due to the friction generated by such activities. This study evaluates several commonly used procedures for removal of floor tile and quantifies the fibers released during those procedures using data obtained by analyses from phase contrast microscopy and analytical transmission electron microscopy. These data document that fibers are released when floor tile is broken and/or abraded during removal procedures. Fiber .
The Study of Military Conceptual Model Based on Tactical Decision-Making Simulation

The military conceptual model (MCM) provides an unambiguous and credible reference for the development of the combat simulation system. According to the characteristics and regularities of the tactical decision-making (TDM) process, this process is divided into five abstract stages, which analyze the modeling method of the simulated military conceptual model in detail. This method reduces the common attributes and action of the TDM entity to that of a super one, which avoids the appearance of a multi-inheritable complex construction. Then the super TDM entity is analyzed so as to obtain a description of its properties and action. By summing up the functions of the TDM simulation entity, it can point out that this modeling method has very strong feasibility.
A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness

Background A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. Objective The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. Methods We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. Results Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. Conclusions Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses.
Installing an ERP System with a methodology based on the principles of goal directed project management

This paper describes a generic methodology to support the process of modelling, adaptation and implementation (MAI) of Enterprise Resource Planning Systems (ERPS) based on the principles of goal directed project management (GDPM). The proposed methodology guides the project manager through specific stages in order to successfully complete the ERPS implementation. The development of the proper MAI methodology is deemed necessary because it will simplify the installation process of ERPS. The goal directed project management method was chosen since it provides a way of focusing all changes towards a predetermined goal. The main stages of the methodology are the promotion and preparation steps, the proposal, the contract, the implementation and the completion. The methodology was applied as a pilot application by a major ERPS development company. Important benefits were the easy and effective guidance for all installation and analysis stages, the faster installation for the ERPS and the control and cost reduction for the installation, in terms of time, manpower, technological equipment and other resources.
Association between Neutrophilic Granulocyte Percentage and Diabetes Mellitus in Cushing's Syndrome Patients: A Cross-Sectional Study

Background Glucose metabolism is frequently impaired in patients with Cushing's syndrome (CS) due to chronic exposure to excess glucocorticoids. Inflammation plays an essential role in the pathophysiology of diabetes mellitus (DM). The present study aimed to investigate the potential associations of inflammatory blood cell parameters, including white blood cell (WBC) count, neutrophil count, neutrophilic granulocyte percentage (NEUT%), lymphocyte count (LYM), and lymphocyte proportion (LYM%), with diabetes mellitus in Cushing's syndrome patients. Materials and Methods The cross-sectional study was conducted in Zhongshan Hospital of Fudan University, China. A total of 150 patients with Cushing's syndrome were retrospectively screened from 2017 to 2019. The demographic data, clinical data, and blood samples (lipids, adrenal, glucose, and inflammatory blood cell parameters) were recorded. Statistical analyses were carried out by using the SPSS software package, version 13.0. Results In this study, the prevalence of diabetes mellitus was 38.7% in patients with Cushing's syndrome. Patients with DM had higher WBC, neutrophil, NEUT% levels than patients without DM (p < 0.05). As the NEUT% increased, a stepwise increase in glucose and glycated hemoglobin (HbA1c) level was observed. In addition, in the multivariate logistic regression, NEUT% was a significant independent risk factor for DM, regardless of gender, age, body mass index (BMI), and triglyceride and 12 midnight cortisol (12 MN cortisol) level (OR = 2.542, 95% CI 1.337–4.835, p < 0.001). Conclusions In conclusion, elevated NEUT% level was linked to diabetes in patients with Cushing's syndrome. The neutrophilic granulocyte percentage may be referred to as a new predictor for diabetes in Cushing's syndrome patients.