Miracle-dz/UniFAR-FTUs huggingface dataset:
High Prevalence and Incidence of Asymptomatic Sexually Transmitted Infections During Pregnancy and Postdelivery in KwaZulu Natal, South Africa

Background We report the prevalence and incidence of 3 treatable sexually transmitted pathogens (Neiserria gonorrhoeae, Chlamydia trachomatis, and Trichomonas vaginalis) in women who were HIV infected or at high risk for HIV infection, in pregnancy and postpartum, respectively. Method Vulvovaginal specimens collected at the first antenatal visit and again at 14 weeks postpartum were tested for N. gonorrhoeae, C. trachomatis, and T. vaginalis in the laboratory. Women were routinely tested for HIV-1 with a point-of-care test. Results Among 1480 women, 32.3% (95% confidence interval, 29.9–34.7) tested positive for any of the sexually transmitted infections (STIs) in pregnancy and 19.2% (95% confidence interval, 16.9–21.5) were positive when retested 14 weeks postpartum (incidence rate, 79.2 per 100 person-years). The prevalence of N. gonorrhoeae and T. vaginalis infections in pregnancy and the incidence rate of any STI at 14 weeks postpartum were significantly higher in HIV-1–infected women (P < 0.0001 amd P = 0.0079). More than 50% of N. gonorrhoeae, T. vaginalis, and C. trachomatis infections in pregnancy were asymptomatic. Conclusions The high prevalence of asymptomatic STIs in pregnancy is compelling evidence that demands the development and validation of point-of-care tests for STIs be expedited. In addition, the high incidence of STIs 3 months postpartum suggests that women in this study setting resume unprotected sexual intercourse soon after delivery.
A MODIFICATION OF GOMORI'S METHOD FOR DEMONSTRATION OF PHOSPHAMIDASE IN TISSUE SECTIONS

The shortcomings of Gomori's method for the histochemical demonstration of the enzyme phosphamidase lie in the need for a long incubation period and for the distinction between reaction products due to enzyme action and those due to spontaneous hydrolysis of the substrate. A modification of his method is suggested which permits reduction of the incubation period to about a fourth and eliminates the need to distinguish between reaction products of different origin in brief incubations. The suggested procedure is free from erratic failures to obtain positive results. As tested for brain, the overall pictures of enzyme distribution are uniformly reproducible. Variability of results is reduced to the occasional occurrence of small areas in which no precipitate has formed. It is felt that with these changes the method is ready to be subjected to a more advanced stage of investigation, as for instance the preservation of the enzyme content during fixing, dry-freezing, etc.
Machine Learning Techniques for Heart Disease Datasets: A Survey

Heart Failure (HF) has been proven one of the leading causes of death that is why an accurate and timely prediction of HF risks is extremely essential. Clinical methods, for instance, angiography is the best and most effective way of diagnosing HF, however, studies show that it is not only costly but has side effects as well. Lately, machine learning techniques have been used for the stated purpose. This survey paper aims to present a systematic literature review based on 35 journal articles published since 2012, where state of the art machine learning classification techniques have been implemented on heart disease datasets. This study critically analyzes the selected papers and finds gaps in the existing literature and is assistive for researchers who intend to apply machine learning in medical domains, particularly on heart disease datasets. The survey finds out that the most popular classification techniques are Support Vector Machine, Neural Networks, and ensemble classifiers.
Cell division cycle 6, a mitotic substrate of polo-like kinase 1, regulates chromosomal segregation mediated by cyclin-dependent kinase 1 and separase

Defining the links between cell division and DNA replication is essential for understanding normal cell cycle progression and tumorigenesis. In this report we explore the effect of phosphorylation of cell division cycle 6 (Cdc6), a DNA replication initiation factor, by polo-like kinase 1 (Plk1) on the regulation of chromosomal segregation. In mitosis, the phosphorylation of Cdc6 was highly increased, in correlation with the level of Plk1, and conversely, Cdc6 is hypophosphorylated in Plk1-depleted cells, although cyclin A- and cyclin B1-dependent kinases are active. Binding between Cdc6 and Plk1 occurs through the polo-box domain of Plk1, and Cdc6 is phosphorylated by Plk1 on T37. Immunohistochemistry studies reveal that Cdc6 and Plk1 colocalize to the central spindle in anaphase. Expression of T37V mutant of Cdc6 (Cdc6-TV) induces binucleated cells and incompletely separated nuclei. Wild-type Cdc6 but not Cdc6-TV binds cyclin-dependent kinase 1 (Cdk1). Expression of wild-type Plk1 but not kinase-defective mutant promotes the binding of Cdc6 to Cdk1. Cells expressing wild-type Cdc6 display lower Cdk1 activity and higher separase activity than cells expressing Cdc6-TV. These results suggest that Plk1-mediated phosphorylation of Cdc6 promotes the interaction of Cdc6 and Cdk1, leading to the attenuation of Cdk1 activity, release of separase, and subsequent anaphase progression.
Out-of-store Object Detection Based on Deep Learning

In the field of urban management, out-of-store operation is one of the key governance objects. Although there are many monitoring probes and large amounts video data, the management process is difficult due to the traditional technology used and the low efficiency of evidence collection. The concept of "Smart Urban Management" has introduced technologies such as mobile internet and cloud computing to realize the transformation of urban management into intelligent management. This paper proposed an out-of-store detection method, which combines image processing technology with deep learning model. The Faster R-CNN model is used to detect store locations and identify the out-of-store objects, and Visual Background Extractor (ViBe) method is applied to determine whether there is object outside of the store or not. Finally, a certain data processing method is used to record and collect evidence of the out-of-store operation phenomenon. The method is verified on the test data and the results show that it has a good detection effect which also prove its application value.
Noncontact holographic detection for photoacoustic tomography

Abstract. A holographic method for high-speed, noncontact photoacoustic tomography is introduced and evaluated. Relative changes of the object’s topography, induced by the impact of thermoelastic pressure waves, were determined at nanometer sensitivity without physical contact. The object’s surface was illuminated with nanosecond laser pulses and imaged with a high-speed CMOS camera. From two interferograms measured before and after excitation of the acoustic wave, surface displacement was calculated and then used as the basis for a tomographic reconstruction of the initial pressure caused by optical absorption. The holographic detection scheme enables variable sampling rates of the photoacoustic signal of up to 50 MHz. The total acquisition times for complete volumes with 230 MVoxel is far below 1 s. Measurements of silicone and porcine skin tissue phantoms with embedded artificial absorbers, which served as a model for human subcutaneous vascular networks, were possible. Three-dimensional reconstructions of the absorbing structures show details with a diameter of 310 μm up to a depth of 2.5 mm. Theoretical limitations and the experimental sensitivity, as well as the potential for in vivo imaging depending on the detection repetition rate, are analyzed and discussed.
The Development of Learning Resource Center Administration Management Model, Center for Educational Media and Technology, Thailand

The administration management model was important work process for every organization. The learning resource center was support education with educational Technology in educational organization. Therefore, this research will be to develop quality organization management to added value in organization and up to business model in the future. This paper aimed to develop learning resource center administration management model by using content analysis and mix-methodology. The samplings were 7 expert persons in administration educational technology organization. Data was analyzed by using content analysis and focus group discussion. The results found that CEMT Management Model consisted 3 units; 1) work process unit (production and learning media services sector, educational technology development sector and office of the director), 2) administration and assessment unit (quality management and following & evaluation), and 3) administration management organization unit (C: Creativity E: Ethics, M: Modern Technology and T:Team Work).
ClassVector: A Parameterized Prototype-Based Model for Text Classification

We propose a parameterized prototype-based model for text classification, named ClassVector. Unlike prototype-based models, we parameterize the prototype vector of each class. We also apply convolutional neural network to map a text to a feature vector. Classification is then conducted by computing distances between the prototype vector of each class and the feature vector of a text. We test our model on four different datasets, involving tasks of topic classification and sentiment analysis. The results demonstrate that the proposed model has a competitive performance compared with the previous state-of-the-art methods.
Lack of TRPV2 impairs thermogenesis in mouse brown adipose tissue

Brown adipose tissue (BAT), a major site for mammalian non‐shivering thermogenesis, could be a target for prevention and treatment of human obesity. Transient receptor potential vanilloid 2 (TRPV2), a Ca2+‐permeable non‐selective cation channel, plays vital roles in the regulation of various cellular functions. Here, we show that TRPV2 is expressed in brown adipocytes and that mRNA levels of thermogenic genes are reduced in both cultured brown adipocytes and BAT from TRPV2 knockout (TRPV2KO) mice. The induction of thermogenic genes in response to β‐adrenergic receptor stimulation is also decreased in TRPV2KO brown adipocytes and suppressed by reduced intracellular Ca2+ concentrations in wild‐type brown adipocytes. In addition, TRPV2KO mice have more white adipose tissue and larger brown adipocytes and show cold intolerance, and lower BAT temperature increases in response to β‐adrenergic receptor stimulation. Furthermore, TRPV2KO mice have increased body weight and fat upon high‐fat‐diet treatment. Based on these findings, we conclude that TRPV2 has a role in BAT thermogenesis and could be a target for human obesity therapy.
Use Case Scenarios on Legal Text Mining

Europe's vision is to establish a well-functioning Digital Single Market, where Europeans are able to move and trade among the EU member states. On the other hand the large amount of information about laws that apply in each EU country has posed significant barriers in this vision. Moreover only legal experts can follow the latest legislation in each country consuming a large amount of business resources in order to follow the current legislation. However, Mass customization tools can help to filter and thereby reduce the flood of legal information and make it easier to be followed from businesses and citizens without legal expertise. The proposed solution is a novel ICT architecture utilising and built upon text mining, advanced processing and semantic analysis of legal information towards the provision of a set of services for citizens, businesses, and administrations of the European Union. In order to provide the most appealing, comprehensive and added value services in the legal domain, this paper presents six use case scenarios based on the opinion of different target groups. Conducting interviews and focus groups, we were able to identify the novel functionalities and services of great importance for the users highlighting and addressing users' daily problems regarding legal information. Generally, interviews with the different target groups reveal that at this point, users prioritise their needs towards more basic services such as search functionalities and correlation with previous laws. Lawyers on the other hand as more competent target group asked for summarisation and reporting services. All target groups where eager on the implementation of this service which as it seems it will directly impact their everyday professional and personal use of legal information.
How Machine Learning is Changing e-Government

Big Data is, clearly, an integral part of modern information societies. A vast amount of data is daily produced and it is estimated that, for the years to come, this number will grow dramatically. In an effort to transform the hidden information in this ocean of data into a useful one, the use of advanced technologies, such as Machine Learning, is deemed appropriate. Machine Learning is a technology that can handle Big Data classification for statistical or even more complex purposes, such as decision making. This fits perfectly with the scope of the new generation of government, Government 3.0, which explores all the new opportunities to tackle any challenge faced by contemporary societies, by utilizing new technologies for data-driven decision making. Boosted by the opportunities, Machine Learning can facilitate more and more governments participate in the development of such applications in different governmental domains. But is the Machine Learning only beneficial for public sectors? Although there is a huge number of researches in the literature related to Machine Learning applications, there is lack of a comprehensive study focusing on the usage of this technology within governmental applications. The current paper moves towards this research question, by conducting a comprehensive analysis of the use of Machine Learning by governments. Through the analysis, quite interesting findings have been identified, containing both benefits and barriers from the public sectors' perspective, pinpointing a wide adoption of Machine Learning approaches in the public sector.
HomeSnitch: behavior transparency and control for smart home IoT devices

The widespread adoption of smart home IoT devices has led to a broad and heterogeneous market with flawed security designs and privacy concerns. While the quality of IoT device software is unlikely to be fixed soon, there is great potential for a network-based solution that helps protect and inform consumers. Unfortunately, the encrypted and proprietary protocols used by devices limit the value of traditional network-based monitoring techniques. In this paper, we present HomeSnitch, a building block for enhancing smart home transparency and control by classifying IoT device communication by semantic behavior (e.g., heartbeat, firmware check, motion detection). HomeSnitch ignores payload content (which is often encrypted) and instead identifies behaviors using features of connection-oriented application data unit exchanges, which represent application-layer dialog between clients and servers. We evaluate HomeSnitch against an independent labeled corpus of IoT device network flows and correctly detect over 99% of behaviors. We further deployed HomeSnitch in a home environment and empirically evaluated its ability to correctly classify known behaviors as well as discover new behaviors. Through these efforts, we demonstrate the utility of network-level services to classify behaviors of and enforce control on smart home devices.
Iterative Blind Deconvolution Algorithm for Support Domain Based on Information Entropy

Aiming at the problem of deconvolution which is easy to appear in traditional iterative blind deconvolution algorithm, an improved iterative blind deconvolution algorithm is proposed. The information entropy algorithm is used to calculate the limited support domain of the image, and the iterative replacement of the space and the frequency domain is performed in the support domain, thereby effectively solving the fuzzy problem. The simulation results show that compared with the original iterative blind deconvolution algorithm, the image has higher peak signal-to-noise ratio (SNR), faster convergence and better recovery.
Abnormal Phone Analysis Based on Learning to Rank and Ensemble Learning in Environment of Telecom Big Data

With the rapid development of Telecom era, the number of telecom users has increased dramatically. User phone have been widely recognized as user identities and are registered in a large number of Internet applications. Due to the leakage of user information, a series of social problems have arisen. Abnormal telephone has become a social problem to be solved. Current methods are mostly passive detection methods, and some of them are extremely expensive and do not meet the requirements of practical application. Our current situation of lack of effective control measures and active detection methods for abnormal phones. Based on the existing telecommunication big data, an abnormal phone active detection method is designed based on learning to rank and ensemble learning algorithm. The experimental results on the real dataset show that the proposed method has higher accuracy than the experimental results obtained by a single learning algorithm.
Analysis and Prediction of CET4 Score Based on RNN

Educational data mining technology is an application technology to improve and assist teaching practice and educational research and it is gradually concerned by researchers at home and abroad. However, the research of educational data mining in China started late, especially the research based on recurrent neural network and other deep learning algorithms. In recent years, recurrent neural network introduces a feedback mechanism in the hidden layer to realize the effective information analysis and data processing. Recurrent neural network has become a research hotspot in the fields of classification and prediction, natural language processing, computer vision and so on. Firstly, this paper makes a correlation analysis of College English Test Band 4 (CET4) scores and students' basic information for 7367 undergraduates in a Shanghai university. And then this paper realizes the prediction of CET4 scores based on students' basic information by recurrent neural network. It is an effective of recurrent neural network in educational data mining. The prediction results will provide guidance for graded teaching of college English.
PCATMIP: Enhancing signal intensity in diffusion‐weighted magnetic resonance imaging

Diffusion‐weighted MRI studies generally lose signal intensity to physiological motion, which can adversely affect quantification/diagnosis. Averaging over multiple repetitions, often used to improve image quality, does not eliminate the signal loss. In this article, PCATMIP, a combined principal component analysis and temporal maximum intensity projection approach, is developed to address this problem. Data are first acquired for a fixed number of repetitions. Assuming that physiological fluctuations of image intensities locally are likely temporally correlated unlike random noise, a local moving boxcar in the spatial domain is used to reconstruct low‐noise images by considering the most relevant principal components in the temporal domain. Subsequently, a temporal maximum intensity projection yields a high signal‐intensity image. Numerical and experimental studies were performed for validation and to determine optimal parameters for increasing signal intensity and minimizing noise. Subsequently, a combined principal component analysis and temporal maximum intensity projection approach was used to analyze diffusion‐weighted porcine liver MRI scans. In these scans, the variability of apparent diffusion coefficient values among repeated measurements was reduced by 59% relative to averaging, and there was an increase in the signal intensity with higher intensity differences observed at higher b‐values. In summary, a combined principal component analysis and temporal maximum intensity projection approach is a postprocessing approach that corrects for bulk motion‐induced signal loss and improves apparent diffusion coefficient measurement reproducibility. Magn Reson Med, 2011. © 2010 Wiley‐Liss, Inc.
Predicting Customer Churn in the Telecommunication Industry by Analyzing Phone Call Transcripts with Convolutional Neural Networks

For telecommunication service providers, a principle method for reducing costs and generating revenue is to focus on retaining existing customers rather than acquiring new customers. To support this strategy, it is important to understand customer concerns as early as possible to prevent churn: The customer action of canceling a subscription and moving to a new provider. In this paper, we use actual customer phone call data and develop the convolutional neural network (CNN)-based predictive model to detect churn signals from transcript data of phone calls. Experimental results show that when sufficient training data is provided with our text annotation method, our CNN-based predictive model generates state-of-the-art performance in churn prediction.
Complement-Dependent Acute-Phase Expression of C-Reactive Protein and Serum Amyloid P-Component1

The acute-phase response (APR) is regulated by TNF-α, IL-1β, and IL-6 acting alone, in combination, or in concert with hormones. The anaphylotoxin C5a, generated during complement activation, induces in vitro the synthesis of these cytokines by leukocytes and of acute-phase proteins by HepG2 cells. However, there is no clear evidence for a role of C5a or any other complement activation product in regulation of the APR in vivo. In this study, using human C-reactive protein (CRP) transgenic mice deficient in C3 or C5, we investigated whether complement activation contributes to induction of the acute-phase proteins CRP and serum amyloid P-component (SAP). Absence of C3 or C5 resulted in decreased LPS-induced up-regulation of the CRP transgene and the mouse SAP gene. Also, LPS induced both the IL-1β and IL-6 genes in normocomplementemic mice, but in complement-deficient mice it significantly induced only IL-6. Like LPS injection, activation of complement by cobra venom factor led to significant elevation of serum CRP and SAP in normocomplementemic mice but not in complement-deficient mice. Injection of recombinant human C5a into human CRP transgenic mice induced the IL-1β gene and caused significant elevation of both serum CRP and SAP. However, in human CRP transgenic IL-6-deficient mice, recombinant human C5a did not induce the CRP nor the SAP gene. Based on these data, we conclude that during the APR, C5a generated as a consequence of complement activation acts in concert with IL-6 and/or IL-1β to promote up-regulation of the CRP and SAP genes.
A Comparison of Blood Damage Potential of Pulsatile LVAD System with Different Types of Valve

The paper presents the results of the mathematical modelling of the haemolysis and thrombus formation process in a blood pump chamber of the pulsatile type Ventricle Assist Device system with a 30 CM3 ejection volume by using two types of the Russian national mechanical heart valves. We show the influence of the inlet valve type on the haemolysis level and the process of thrombus formation during the operation of the blood pump. Based on the obtained results, we give some recommendations on the valve type selection, which ensure a minimum haemolysis level and the minimization of stagnant zones inside the blood pump chamber.