Miracle-dz/UniFAR-FTUs huggingface dataset:
The First Case of Congenital Prekallikrein Deficiency in Korea With a Novel Pathogenic Variant (c.1198G>T)

Dear Editor, Congenital prekallikrein (PK) deficiency is a rare autosomal recessive coagulation disorder characterized as either type I or II. Type I is associated with a decrease or absence of PK antigen and activity, while type II is associated with low PK activity and varying antigen levels [1]. PK deficiency presents with prolonged isolated activated partial thromboplastin time (aPTT). Other potential causes of isolated aPTT prolongation without bleeding include presence of lupus anticoagulant (LA) and deficiencies of contact phase factors, including factor XII (FXII) and high molecular-weight kininogen (HMWK) [2]. LA and FXII levels can be easily assessed with laboratory tests, whereas PK and HMWK levels and activity are more challenging to assess. Pre-incubating PK deficient plasma with a surface activator in the aPTT reagent, which causes FXII autoactivation, can bypass the fast PK to kallikrein reaction and kallikrein-mediated FXII activation [3]. During pre-incubation in the aPTT assay, only PK deficiency causes aPTT shortening, a finding that can differentiate PK and HMWK deficiencies. We present the first case, to our knowledge, of congenital PK deficiency in Korea. This study was approved by the Institutional Review Board of Seoul National University Hospital (No. 1304-085-481), Seoul, Korea. A four-year-old Korean male with tonsil hypertrophy was scheduled to undergo an elective tonsillectomy at Seoul National University Hospital. During the pre-operative evaluation, a marked prolonged aPTT and normal prothrombin time were noted (Table 1). The patient was taking no medication. Personal and family history revealed no bleeding tendency. Mixing test for aPTT showed complete correction, and coagulation factors FVIII, FIX, FXI, and FXII of the intrinsic pathway were all normal. LA was negative. Upon suspicion of PK deficiency, aPTT assays with various pre-incubation times were performed; the aPTT of samples pre-incubated with an aPTT reagent was dramatically shortened (Fig. 1A). PK antigen level measured by ELISA (SEB801Hu, Cloud-Clone Corporation, Houston, TX, USA) was 0.38 ng/mL, and plasma kallikrein activity measured chromogenically (#K997-100, Biovision, Milpitas, CA, USA) was undetectable. Genomic DNA was extracted from peripheral blood. PCR and direct sequencing of the KLKB1 gene, which encodes prekallikrein, were performed to amplify the coding and flanking regions from exons 2 to 15 [4]. Two variants were detected when comparing the sequence exon with the reference sequence (GenBank Accession Number NM_000892). We detected a novel substitution (c.1198G>T) in exon 11, leading to premature termination of KLKB1 (p.Gly400Ter) (Fig. 1B). This was considered a pathogenic variant according to the 2015 American College of Medical Genetics and Genomics and Association guidelines based on the following factors:
Electrical Tree in Epoxy Resin under Combined DC-harmonic Voltage

Harmonic voltage can be transferred by the converter transformer on AC and DC sides in high-voltage direct-current (HVDC), which can distort the DC voltage quality. This work investigates the electrical treeing process in molded epoxy resin under combined DC-harmonic voltage. A digital microscope system was employed to observe the electrical treeing process. With the DC voltage ranging from −20 kV to +20 kV, different combined waveforms of DC and harmonic voltage were used as the applied voltage. Width/length ratio was employed to describe the propagation process of electrical tree in two dimensions. Results show that the DC voltage amplitude is a key factor for the electrical treeing process. The distribution of tree structures (branch and bush-branch) depends greatly on the polarity of DC voltage in room temperature. Under the same harmonic voltage, the width/length ratio with negative DC voltage is larger than that with positive DC voltage. Meanwhile, positive DC voltage can lead to the electrical breakdown easier. In addition, ‘reverse tree’ has been found in epoxy resin in pure harmonic voltage, which grows from the plate electrode toward the needle electrode when the electrical tree has bridged the insulation.
Kahoot, win the learning race

With the arrival of the millennials and generation Z to the university classrooms, the greatest teaching challenge is to adapt the traditional masterclass and turn the classroom into a collaborative space that takes advantage of Information and communication technology to introduce dynamic elements that encourage the participation of increasingly unmotivated students. With the emergence and penetration of mobile devices in all aspects of everyday life, the teaching-learning process must be changed to encourage participation and to achieve effective results. Thus, the use of gamification tools such as Kahoot can help in this challenge, as they encourage the effective acquisition of competences through play and competition. This study demonstrates the positive results of including gamification processes in the Spanish public and private university classroom, contrasting their effectiveness in different degrees and subjects.
SO2 Donors and Prodrugs, and Their Possible Applications: A Review

SO2 is widely recognized as an air pollutant and is a known cause of acid rain. At a sufficiently high level, it also causes respiratory diseases. A much lesser known side of SO2 is its endogenous nature and possible physiological roles. There is mounting evidence that SO2 is produced during normal cellular metabolism and may possibly function as a signaling molecule in normal physiology. The latter aspect is still at the stage of being carefully examined as to the validity of classifying SO2 as a gasotransmitter with endogenous signaling roles. One difficulty in studying the biological and pharmacological roles of SO2 is the lack of adequate tools for its controllable and precise delivery. Traditional methods of using SO2 gas or mixed sulfite salts do not meet research need for several reasons. Therefore, there has been increasing attention on the need of developing SO2 donors or prodrugs that can be used as tools for the elucidation of SO2's physiological roles, pharmacological effects, and possible mechanism(s) of action. In this review, we aim to review basic sulfur chemistry in the context of sulfur signaling and various chemical strategies used for designing SO2 donors. We will also discuss potential pharmacological applications of SO2 donors, lay out desirable features for such donors and possibly prodrugs, analyze existing problems, and give our thoughts on research needs.
Deep Learned vs. Hand-Crafted Features for Action Classification

The purpose of this study is to determine if the advantage of the deep learned features over the hand-crafted ones, that is evidenced in the state of the art, is still maintained for actions that are carried out in a similar environment, for real applications. The comparison is performed using a dataset created specifically for the study, in which the actions that are carried out are very similar and with a common and noisy environment. The study shows that for a database with a limited number of videos and common environment it is better to consider the hand-crafted features than a shallow CNN architecture as feature extractor.
On Supporting Legacy and RF Energy Harvesting Devices in Two-Tier OFDMA Heterogeneous Networks

Future devices are likely to have the capability to harvest energy from radio-frequency (RF) signals. In this paper, we consider such energy harvesting (EH) devices operating in a two-tier orthogonal frequency-division multiple access-based heterogeneous network. Critically, we investigate how such EH devices can be supported alongside non-RF harvesting or legacy devices. Our aim is to minimize the downlink sum transmit power of both femto and macro base stations and ensure that legacy and EH devices receive a given data rate and amount of energy, respectively. Critically, we study sub-carrier and power allocation to both types of devices and investigate novel questions related to interference, which reduces network capacity but improves the amount of harvested energy by EH devices. To study these questions, we formulate a mixed-integer non-linear program (MINLP) and propose three linear approximations to the MINLP where devices are either assigned one or multiple sub-carriers. Numerical results show that EH devices will not affect network capacity if they can harvest sufficient energy from data transmissions to legacy devices. In addition, if multiple sub-carriers can be assigned to devices, our results show that the sum transmit power decreases by approximately 15% as compared with assigning a single sub-carrier to these devices.
The Changing Epidemiological Profile of HIV-1 Subtype B Epidemic in Ukraine

Abstract While HIV-1 subtype B has caused a large epidemic in the Western world, its transmission in Ukraine remains poorly understood. We assessed the genetic diversity of HIV-1 subtype B viruses circulating in Ukraine, characterized the transmission group structure, and estimated key evolutionary and epidemiological parameters. We analyzed 120 HIV-1 subtype B pol sequences (including 46 newly generated) sampled from patients residing in 11 regions of Ukraine between 2002 and 2017. Phylogenies were estimated using maximum likelihood and Bayesian phylogenetic methods. A Bayesian molecular clock coalescent analysis was used to estimate effective population size dynamics and date the most recent common ancestors of identified clades. A phylodynamic birth–death model was used to estimate the effective reproductive number (Re) of these clades. We identified two phylogenetically distinct predominantly Ukrainian (≥75%) clades of HIV-1 subtype B. We found no significant transmission group structure for either clade, suggesting frequent mixing among transmission groups. The estimated dates of origin of both subtype B clades were around early 1970s, similar to the introduction of HIV-1 subtype A into Ukraine. Re was estimated to be 1.42 [95% highest posterior density (HPD) 1.26–1.56] for Clade 1 and 1.69 (95% HPD 1.49–1.84) for Clade 2. Evidently, the subtype B epidemic in the country is no longer concentrated in specific geographical regions or transmission groups. The study results highlight the necessity for strengthening preventive and monitoring efforts to reduce the further spread of HIV-1 subtype B.
Fast Graph - Organic 3D Graph for Unsupervised Location and Mapping

It is well-known that fingerprinting-based positioning requires an exhaustive calibration phase to create a radio map, which often requires recalibration. Model-based and geometric approaches try to mitigate this effort at the expense of a lower accuracy or high computational cost. This paper introduces FastGraph, where a 3D graph is used to rapidly model the radio propagation environment. By means of unsupervised techniques, FastGraph is able to operate shortly after its deployment without previous knowledge about the environment. The proposed solution uses a novel algorithm to automatically provide location while simultaneously updating the radio map; and learn the position of the Access Points (APs) and location-specific radio propagation parameters. FastGraph has been evaluated in two real-world environments, a factory-plant and a regular university building, with results comparable to those obtained by conventional radio map-based solutions.
Progressive muscle relaxation therapy to relieve dental anxiety: a randomized controlled trial

Dental anxiety causes patients to refuse or delay treatment, which may exacerbate oral diseases. The aim of the current randomized controlled trial was to determine whether progressive muscle relaxation therapy could relieve dental anxiety. The trial included 68 periodontal patients with dental anxiety scores of ≥13 who were randomly assigned to either an intervention group or a control group (n = 34 per group). The intervention group was administered progressive muscle relaxation therapy for 20 min and oral health education for 15 min before periodontal treatment once per week for 4 wk. The control group was provided with oral health education only, for the same duration. Changes in dental anxiety, depression symptoms, blood pressure, heart rate, and salivary cortisol were evaluated 4 wk and 3 months after the intervention. The intervention group exhibited statistically significantly greater reductions in dental anxiety scores than did the control group at the 4-wk (-3.82 vs. -0.89) and 3-month (-4.22 vs. -0.28) assessments. They also exhibited significantly greater reductions in depression symptoms, systolic and diastolic blood pressure, pulse rate, and salivary cortisol levels at both time-points. Progressive muscle relaxation therapy relieves tension and anxiety in dental patients.
Knowledge Seeking on The Shadow Brokers

The Shadow Brokers (TSB) are an infamous group of hackers responsible for major cybercrime incidents. There are currently few studies on TSB, and to prevent their attacks in the future, we believe it is necessary to study them beforehand. This study constructs a relation graph of all the entities concerning TSB using identifiers in the Web. We introduce a systematic approach to finding relations among entities using a case study based on identifiers and clearness of relations. Our investigation covers data from both the Surface Web and Dark Web, with our Dark Web data consisting of over 40 million Dark Web webpages. We have uncovered many hacking forums, hacking groups, and individuals having a relation with TSB using our method. The relation graph of TSB will become a stepping stone in developing a knowledge base of TSB.
Team Performance with Test Scores

Team performance is a ubiquitous area of inquiry in the social sciences, and it motivates the problem of team selection—choosing the members of a team for maximum performance. Influential work of Hong and Page has argued that testing individuals in isolation and then assembling the highest scoring ones into a team is not an effective method for team selection. For a broad class of performance measures, based on the expected maximum of random variables representing individual candidates, we show that tests directly measuring individual performance are indeed ineffective, but that a more subtle family of tests used in isolation can provide a constant-factor approximation for team performance. These new tests measure the “potential” of individuals, in a precise sense, rather than performance; to our knowledge they represent the first time that individual tests have been shown to produce near-optimal teams for a nontrivial team performance measure. We also show families of subdmodular and supermodular team performance functions for which no test applied to individuals can produce near-optimal teams, and we discuss implications for submodular maximization via hill-climbing.
Learning Complex Spatio-Temporal Configurations of Body Joints for Online Activity Recognition

Geometric dynamic configurations of body joints play an essential role in distinguishing different human activities. However, many existing human activity recognition approaches lack the capability of automatically learning these configurations from sequences of joints in four-dimensional space (spatio and temporal). In this paper, the authors propose an automatic joint configuration learning method, based on dictionary learning and sparse representation. The proposed method achieves the following features: 1) it automatically learns dynamic spatio-temporal geometric configurations of body joints, involved in activities, in a simple way; 2) it dispenses with the hand crafted feature designing process and provides a new method to organize joint coordinate data as fixed length column vectors, which are suitable for dictionary learning; 3) it replaces the conventional bag of words model with sparse coding method; words in learned dictionary capture subactivity features, and the frequencies of different words appearing in different activities characterize the categories of global activity; 4) it is robust to time misalignment and can classify any length of video sequence (online classification) in real time; 5) it is easy to combine this method with other forms of data for better performance, because of its data driven nature and flexible framework. The proposed method is tested with three state-of-the-art public human activity recognition datasets and the results are found to be better than those of CAD-60 dataset, and comparable to those of both MSR Action 3D and MSR Daily Activity datasets (source codes are publicly available at https://github.com/jinqijinqi/SparseCodingDictionaryLearningHumanActivityRecognition).
20(S)‐ginsenoside‐Rg3 reverses temozolomide resistance and restrains epithelial‐mesenchymal transition progression in glioblastoma

Glioblastoma multiforme (GBM) is one of the most malignant human intracranial tumors. Temozolomide (TMZ) is the primary alkylating agent for GBM patients. However, many GBM patients are resistant to TMZ. Therefore, patients with GBM urgently need more effective therapeutic options. 20(S)‐ginsenoside‐Rg3 (20(S)‐Rg3) is a natural chemical with anti‐tumor effects, but at present there is little understanding of its functional mechanism. Several research reports have demonstrated that O6‐methylguanine DNA‐methyltransferase (MGMT) repairs damaged DNA and contributes to TMZ resistance in gliomas. In addition, recent studies have shown that MGMT gene expression could be regulated by the Wnt/β‐catenin pathway. However, whether 20(S)‐Rg3 inhibits MGMT expression and augments chemosensitivity to Temozolomide (TMZ) in glioma cells remains unclear. In this study, we explored the modulating effects of 20(S)‐Rg3 on MGMT. We used glioma cell lines, primary cell strain (including T98G, U118 and GBM‐XX; all of them are MGMT‐positive glioma cell lines) and xenograft glioma models to examine whether 20(S)‐Rg3 increased the sensitivity to TMZ and to reveal the underlying mechanisms. We found that the MGMT expression was effectively downregulated by 20(S)‐Rg3 via the Wnt/β‐catenin pathway in glioma cell lines, and TMZ resistance was significantly reversed by 20(S)‐Rg3. Meanwhile, 20(S)‐Rg3 shows no obvious cytotoxicity at its effective dose and is well tolerated in vivo. In addition, we found that 20(S)‐Rg3 significantly restrains the epithelial‐mesenchymal transition (EMT) progression of glioma cells. Taken together, these results indicate that 20(S)‐Rg3 may be a novel agent to use in treatment of GBM, especially in TMZ‐resistant GBM with high MGMT expression.
Frequency Covers for Strings

We study a central problem of string processing: the compact representation of a string by its frequently-occurring substrings. In this paper we propose an effective, easily-computed form of quasi-periodicity in strings, the frequency cover; that is, the longest of those repeating substrings u of w, |u| > 1, that occurs the maximum number of times in w. The advantage of this generalization is that it is not only applicable to all strings but also that it is the only generalized notion of cover yet proposed, which can be computed efficiently in linear time and space. We describe a simple data structure called the repeating substring frequency array (ℛ
On Authority in Academia

This paper describes and discusses the Icelandic experience of maintaining authority in University engineering courses. Professors are experiencing that students are intentionally and continuously challenging their authority; e.g. by ignoring instructions and carrying out tasks relying solely on their own understanding and talent (sometimes collectively). This makes teaching difficult and often frustrating. Several aspects of university teaching are addressed; e.g., different expectations of students and professors, different types of authority, challenges in building Professor-Student Relationships and what influence the relationship can have on the quality of teaching.
An Intelligent Framework for Issue Ticketing System Based on Machine Learning

Due to the rapid shift of companies towards superb customer experience and satisfaction, ticketing systems have come into a prominence and represent a strategic element in business competitiveness. Different software companies have developed very effective software tools for issue tracking, nevertheless, some sub-processes and tasks within the ticketing systems are still performed manually. These manually performed tasks represent bottlenecks, especially at large organizations they result in declined productivity and increased response time. Advancements in machine learning can be used in a novel way in which they are combined with the traditional issue tracking and ticketing systems on the market, to enable optimal operational efficiency in the Customer Service and Support(CSS) Department of large-scale businesses that deal with customer reports. This paper proposes an integrated approach to customer support by treating three seemingly different bottlenecks in the ticketing system: spam detection, ticket assignment and sentiment analysis. We use primary data to implement and apply the proposed machine learning approach. The evaluation shows promising results in terms of accuracy and efficiency of our approach.
Kinect-Based In-Home Exercise System for Lymphatic Health and Lymphedema Intervention

Using Kinect sensors to monitor and provide feedback to patients performing intervention or rehabilitation exercises is an upcoming trend in healthcare. However, the joint positions measured by the Kinect sensor are often unreliable, especially for joints that are occluded by other parts of the body. Also, users’ motion sequences differ significantly even when doing the same exercise and are not temporally aligned, making the evaluation of the correctness of their movement challenging. This paper aims to develop a Kinect-based intervention system, which can guide the users to perform the exercises more effectively. To circumvent the unreliability of the Kinect measurements, we developed a denoising algorithm using a Gaussian Process regression model. We simultaneously capture the joint positions using both a Kinect sensor and a motion capture (MOCAP) system during a training stage and train a Gaussian process regression model to map the noisy Kinect measurements to the more accurate MOCAP measurements. For the sequences alignment issue, we develop a gradient-weighted dynamic time warping approach that can automatically recognize the endpoints of different subsequences from the original user’s motion sequence, and furthermore temporally align the subsequences from multiple actors. During a live exercise session, the system applies the same alignment algorithm to a live-captured Kinect sequence to divide it into subsequences, and furthermore compare each subsequence with its corresponding reference subsequence, and generates feedback to the user based on the comparison results. Our results show that the denoised Kinect measurements by the proposed denoising algorithm are more accurate than several benchmark methods and the proposed temporal alignment approach can precisely detect the end of each subsequence in an exercise with very small amount of delay. These methods have been integrated into a prototype system for guiding patients with risks for breast-cancer related lymphedema to perform a set of lymphatic exercises. The system can provide relevant feedback to the patient performing an exercise in real time.