Miracle-dz/UniFAR-FTUs huggingface dataset:
[PP.LB01.10] PREFERRED DRUG CLASSES TO INITIATE ANTIHYPERTENSIVE TREATMENT IN SWEDISH PRIMARY CARE: RESULTS FROM THE SWEDISH PRIMARY CARE CARDIOVASCULAR DATABASE

Objective: To assess which antihypertensive drug classes are the most preferred in patients newly initiated on treatment in Swedish primary care. Design and method: The Swedish Primary Care Cardiovascular Database (SPCCD) comprises almost 75000 patients attending primary care with a diagnosis of hypertension in two geographically defined rural and urban regions in Sweden. All primary care physicians and patients at the 48 primary care centres are included and data are automatically extracted, minimizing potential selection bias due to voluntary participation. The current observational cohort study included patients aged > = 30 years with previously untreated hypertension and a first prescription of an antihypertensive drug during 2006–2007. Preferred drug class was analysed according to sex, age and concurrent diseases. Results: We identified 5225 patients (mean age 61.4 years, 55% women) with first time prescribed antihypertensive treatment. Around one in ten patients had diabetes, with a lower prevalence in women than men (8.0 vs 11.1%, P < 0.01). The most preferred drug class was angiotensin converting enzyme inhibitors (36.2%), although substantially less common in women than in men (30.5 vs 43.1%; P < 0.001). However, diuretics were more common in women (32.4 vs. 19.7%; P < 0.001), while we observed no differences between women and men in the prescriptions of angiotensin receptor blockers (3.6 vs 4.3%), beta blockers (21.8 vs 19.5%), calcium channel blockers (7.0 vs 8.0%), fixed combination therapy (1.6 vs 2.2%), and those with more than one antihypertensive drug prescription (3.1 vs 3.2%). Furthermore, there were less diabetic women than men with a prescription of an angiotensin converting enzyme inhibitor (60.2 vs 73.9%, < 0.01). Conclusions: In conclusion, angiotensin converting enzyme inhibitors is the most preferred initial antihypertensive drug class in primary care, indicating a high concordance with contemporary guidelines.
Anti PD-1 (nivolumab, pembrolizumab) or anti PD-L1 (atezolizumab) versus docetaxel for previously treated patients with advanced NSCLC: A meta-analysis.

e20555Background: In phase I studies, Immune Checkpoint Inhibitors (ICIs) targeting programmed death protein 1 (PD-1) receptor and its ligand, PD-L1, have recently led to significant efficacy in advanced NSCLC in second and more lines setting. We undertook a meta-analysis of all published randomized studies in this setting to assess the range of improvements. Methods: We did a PubMed query using keywords simultaneously (NSCLC, anti PD-1, anti PDL-1, docetaxel, survival, and RCTs). We also screened ASCO and ESMO proceedings. The efficacy outcomes were OS, response rate and PFS. Grade 3-5 toxicity was also examined. Hazard ratios (HRs) with their 95 % confidence interval (CI) were collected from the studies and pooled. A fixed-effect model was used. Results: Two studies assessed nivolumab (CheckMate 017 and 057), one atezolizumab (Poplar), and one pembrolizumab (Keynote 010). The four eligible studies included 2174 patients (1330 males, 844 females, mostly smokers (81%), median age 62 years), with 1496 nons.
DATA PROTECTION ON MOBILE APPLICATIONS

Piracy has been a huge problem to the entire IT industry. In the recent years there has been an increase in the usage of mobile based applications and many a times these applications have been illegally copied and used a lot. Apart from applications, piracy also affects video, audio producers, digital book etc. This results in huge revenue loss to the application developers and also in many cases causes the spread of viruses and backdoor programs causing hijacking of personal data. The most commonly pirated mobile apps are games and utility apps. In this paper we are proposing a new method of protecting mobile apps using steganography which will help in protecting the mobile apps against piracy.
Record linkage of banks and municipalities through multiple criteria and neural networks

Record linkage aims to identify records from multiple data sources that refer to the same entity of the real world. It is a well known data quality process studied since the second half of the last century, with an established pipeline and a rich literature of case studies mainly covering census, administrative or health domains. In this paper, a method to recognize matching records from real municipalities and banks through multiple similarity criteria and a Neural Network classifier is proposed: starting from a labeled subset of the available data, first several similarity measures are combined and weighted to build a feature vector, then a Multi-Layer Perceptron (MLP) network is trained and tested to find matching pairs. For validation, seven real datasets have been used (three from banks and four from municipalities), purposely chosen in the same geographical area to increase the probability of matches. The training only involved two municipalities, while testing involved all sources (municipalities vs. municipalities, banks vs banks and and municipalities vs. banks). The proposed method scored remarkable results in terms of both precision and recall, clearly outperforming threshold-based competitors.
Caracterización Molecular del gen F9 y correlación genotipo-fenotipo en una muestra de pacientes con hemofilia B en Colombia

La Hemofilia B es un trastorno de la coagulacion con patron de herencia recesiva ligada al X, causado por la deficiencia de FIX en plasma debido a variantes patogenicas en el gen F9. En Colombia, la HB se considera una enfermedad huerfana y de alto costo, con un reporte para el ano 2017 de 362 varones en total. En este estudio se incluyeron 20 pacientes masculinos no relacionados con edades entre 5 y 68 anos, pertenecientes a los programas de tratamiento Integral de Hemofilia en diferentes instituciones hospitalarias del pais. Se realizo aislamiento de ADN en sangre periferica, posteriormente se realizo una reaccion de PCR para los 8 exones, limites exon-intron, las regiones UTR 5’ y 3’ del gen F9, verificacion mediante electroforesis en gel de agarosa, y secuenciacion Sanger. Los datos obtenidos se compararon con la secuencia de referencia y las variantes encontradas se cotejaron con bases de datos internacionales y literatura cientifica. Se identificaron las mutaciones en todos los pacientes analizados, siendo 15 mutaciones en total, ya que una misma mutacion se detecto en varios pacientes. De ellas, ocho son variantes tipo missense, tres tipo nonsense, una mutacion en la region UTR 3’, dos grandes deleciones 50pb y una sustitucion intronica que afecta el splicing. Todas estas variantes han sido reportadas previamente en la literatura, a excepcion de la delecion de los exones 3 y 4 presente en un paciente, que no se encuentra reportada en las bases de datos internacionales. Siete pacientes presentan una mutacion en IVS5+13 que afecta el splicing, siendo la frecuencia de mutacion en splicing mas alta reportada en una serie de pacientes con hemofilia B (35%) y dos pacientes no relacionados presentaron la misma mutacion missense. La correlacion genotipo-fenotipo observada coincide con la reportada en la literatura con excepcion de un paciente, con HB leve que posee una variante tipo missense asociada en la literatura a fenotipo moderado. En este primer estudio, se evidencia el amplio espectro mutacional en F9, lo cual se refleja en el comportamiento clinico de la enfermedad (severidad, presencia de inhibidores) y resalta la necesidad de realizar el analisis mutacional en todos los casos, para poder identificar mujeres portadoras y brindar un adecuado asesoramiento genetico. En este trabajo realizado en el pais, se reportan las primeras mutaciones en poblacion colombiana con Hemofilia B.
Foreign Accent Syndrome presenting as Conversion Disorder: A rare presentation

Foreign accent syndrome (FAS) is a rare disorder in the production of speech with a change in articulation and prosody that is perceived by listeners to have a different accent, hence the name foreign accent syndrome. FAS incidents have been mostly reported secondary to traumatic brain injury or stroke affecting the speech producing regions of the brain. Very few reported cases of FAS have been associated with psychiatric conditions. We report a case of FAS with comorbid transient stuttering presenting as conversion disorder in a 45- year- old right handed monolingual female with an American English accent who presented to the emergency room with a British cockney or Australian accent. The onset of the syndrome occurred after the dissolution of her second marriage. Repeated comprehensive assessments including physical and neurological examinations, laboratory tests, neuroimaging, and electroencephalogram were all normal. Personality testing and two independent neuropsychological and psychiatric evaluations support a psychiatric etiology, namely FAS secondary to conversion disorder. Journal of Nature and Science (JNSCI), 2(7):e197, 2016
Association between Stress and Antiretroviral Therapy Adherence among Women Living with HIV in Toronto, Ontario: Assessment of Correlates, Mediation and Moderation

This cross-sectional study sought to: “Determine the association between stress, depression and ART adherence among women living with and accessing care for HIV in Toronto, Ontario, between 2007 and 2012”. Using OCS data, logistic regression models were used to identify covariates of (≥95%) adequate adherence and mediators and moderators in the association between stress and adherence. Among 307 women, 34.5% and 65.5% had poor and adequate adherence, respectively. Women with poor versus adequate adherence had more reports of hazardous alcohol use, stress events and depressive symptoms. No hazardous alcohol use (aOR=2.20, 95%CI:1.124.32) and fewer stress events (aOR=0.56, 95%CI:0.33-0.94) were associated with increased odds of adequate adherence. The association between environmental stress and adequate adherence was attenuated among women ≤35 years (aOR=0.73, 95%CI:0.56-0.94) versus ≥45 years (aOR=0.51, 95%CI:0.34-0.77). Interventions to improve adherence and ensure levels ≥95% must address stress and promote adaptive coping strategies to reduce or eliminate hazardous alcohol use.
Life Cycle Sustainability Analysis for Circular Economy

A Life Cycle Sustainability Analysis is a complex assessment that requires time, expertise and quality data. Decision-making boards of industries required live data to manage their business. Although planned changes can be made pursuing innovation and sustainability within a wide timeframe, daily decisions are often driven just by economic indicators. However, many industries are already implementing systems, simple or complex, that allows them to obtain some environmental or social information related to their activities aware that not only economic value foster ther circular economy that our planet needs. Key performance indicators are excellent information suppliers that can be define either in the economic, social or environmental area of a sustainable analysis. Willing to develop a methodology easy to apply in existing decision-making panels that incorporates social and environmental indicators to fill the gap of a sustianibility analisys, this research group is exploring new protocols and procedures to define customized key performance indicators. The inclusion of key performance indicators based in Life Cycle Assessment in existing management panels will serve as a tool to make the commitment of our European industries with circular economy come true.
Method of determining vehicle speed according to video stream data

This article presents a method of determining the speed of a dynamic object from the data of a video stream. The determination of speed occurs based on the analysis of repeated objects of the environment, the distance between which varies according to a previously known law, such as lamp posts, milestones or bump stops on roads, intermittent road marking lines, emergency exits in tunnels. The method of determining vehicle speed includes three steps: pre-processing of the frame, searching for a pattern in the image, calculating the average vehicle speed obtained on the basis of the number of repeated objects, taking into account the law of their repetition and the distance between them for some fixed time. The advantage of the proposed method is that there is no need to install any additional sensors on the dynamic object except video camera. The development of an additional method for calculating speed has a significant application value for the complexes of control of autonomous traffic, as the existing methods of determining speed have their disadvantages (not high enough accuracy, the requirement for the presence of additional equipment). This algorithm was studied on regularly recurring objects (lampposts) under different lighting conditions (day, night), the distance between repeated objects and different weather conditions. It was also compared with other methods of determining the speed of the data from the video stream, the results of which the proposed method has a significant advantage in accuracy as compared with analogs in the absence of interference with video.
Neuromuscular Electrical Stimulator Design

References [1] National Spinal Cord Injury Statistical Center, “Spinal cord injury facts and figures at a glance,” The Journal of Spinal Cord Medicine, vol. 36, no. 1, pp. 1-2, 2013. [2] R. Kobetic, R. Triolo, and E. Marsolais, “Muscle selection and walking performance of multichannel FES systems for ambulation in paraplegia,” IEEE Transactions on Rehabilitation Engineering, vol. 5, no. 1, pp. 23-29, 1997. [3] Seel, Thomas, “Controlled Functional Electric Stimulation for Rehabilitaion Purposes,” Technische Universitat Berlin, 2013. Background Motivation System Design Results
A lightweight portable intrusion detection communication system for auditing applications

The goal of this paper is to develop, deploy, test, and evaluatea a lightweight portable intrusion detection system (LPIDS) over wireless networks by adopting two different string matching algorithms: Aho‐Corasick algorithm and Knuth‐Morris‐Pratt algorithm (KMP). Thus, this research contributes in three ways. First, an efficient and lightweight IDS (LPIDS) is proposed. Second, the LPIDS was developed, implemented, tested, and evaluated using Aho‐Corasick and KMP on two different hardware platforms: Wi‐Fi Pineapple and Raspberry Pi. Third, a comparative analysis of proposed LPIDS is done in terms of network metrics such as throughput, power consumption, and response time with regard to their counterparts. Additionally, the proposed LPIDS is suggested for consultants while performing security audits. The experimental results reveal that Aho‐Corasick performs better than KMP throughout the majority of the process, but KMP is typically faster in the beginning with fewer rules. Similarly, Raspberry Pi shows remarkably higher performance than Wi‐Fi Pineapple in all of the measurements. Moreover, we compared the throughput between LPIDS and Snort, it is observed and analyzed that former has significantly higher throughput than later when most of the rules do not include content parameters. This paper concludes that due to computational complexity and slow hardware processing capabilities of Wi‐Fi Pineapple, it could not become suitable IDS in the presence of different pattern matching strategies. Finally, we propose modification of Snort to increase the throughput of the system.
Heart failure in palliative care

In recent years, there has been increased interest in palliative care of patients with heart disease. It is associated with a large number of patients, a significant need for care, important end-stage infirmity in this group of patients, and a huge number of deaths due to heart failure. There is also limited access to palliative care specialists for patients with declining heart disease, including heart failure, in the end of life. Despite relatively low costs of treatment of palliative medicine patients with heart disease, compared with other high technology departments and procedures, it is difficult to apply it and it is not generally advised. The article aims to broaden knowledge of use of palliative medicine for heart disease, and analyze legal conditions and medical possibilities of use of palliative medicine in this group of patients.
Comparative Studies of Using Nano Zerovalent Iron, Activated Carbon, and Green Synthesized Nano Zerovalent Iron for Textile Wastewater Color Removal Using Artificial Intelligence, Regression Analysis, Adsorption Isotherm, and Kinetic Studies

Daily, a big extent of colored, partially treated textile effluents drained into the sanitation systems causing serious environmental concerns. Therefore, the decolorization treatment process of wastewater is crucial to improve effluent quality. In the present study, 3 different sorbent materials, nano zerovalent iron (nZVI), activated carbon (AC), and green-synthesized nano zerovalent iron (GT-nZVI), have been prepared for raw textile wastewater decolourization. The prepared nanomaterials were characterized via X-ray diffraction (XRD) spectroscopy, scanning electron microscopy (SEM), energy dispersive X-ray (EDX) analysis, and UV-Vis absorption spectroscopy. In addition, the effect of different operating parameters such as pH, contact time, and stirring rate on the color removal efficiency was extensively studied to identify the optimum removal conditions. The reaction temperature, adsorbent dose, and initial color concentration were fixed during the experiments at room temperature, 0.7 g/L, and 350 and 50 mg/L Pt/Co color unit, respectively. Moreover, adsorption and reaction kinetics were analyzed using different isotherms and models. For simulating the adsorption process, artificial neural network (ANN) data were compatible with the result of regression analysis derived from response surface methodology (RSM) optimization. Our results showed the higher ability of nZVI, AC, and GT-nZVI in textile wastewater color removal. At pH 5, contact time 50 minutes, and stirring rate 150 rpm, nZVI showed good color removal efficiency of about 71% and 99% for initial color concentrations of 350 and 50 mg/L Pt/Co color unit, respectively. While slightly higher color removal ability of about 72% and 100% was achieved by using AC at pH 8, contact time 70 minutes, and stirring rate 250 rpm. Finally, the largest ability of color removal about 85% and 100% was recorded for GT-nZVI at pH 7, contact time 40 minutes, and stirring rate 150 rpm. This work shows the enhanced color removal ability of GT-nZVI as a potential textile wastewater decolourization material, opening the way for many industrial and environmental applications.
Extremal Region Analysis based Deep Learning Framework for Detecting Defects

A maximally stable extreme region (MSER) analysis based convolutional neural network (CNN) for unified defect detection framework is proposed in this paper. Our proposed framework utilizes the generality and stability of MSER to generate the desired defect candidates. Then a specific trained binary CNN classifier is adopted over the defect candidates to produce the final defect set. Defect datasets over different categories \blue{are used} in the experiments. More generally, the parameter settings in MSER can be adjusted to satisfy different requirements in various industries (high precision, high recall, etc). Extensive experimental results have shown the efficacy of the proposed framework.
Probing Axial Symmetry Breaking in the Galaxy with Gaia Data Release 2

We study a set of solar neighborhood (d < 3 kpc) stars from Gaia Data Release 2 to determine azimuthal star count differences, i.e., left and right of the line from the Galactic Center (GC) through the Sun—and compare these differences north and south. In this companion paper to Gardner et al., we delineate our procedures to remove false asymmetries from sampling effects, incompleteness, and/or interloper populations, as this is crucial to tests of axisymmetry. Particularly, we have taken care to make appropriate selections of magnitude, color, in-plane Galactocentric radius, and Galactic and . We find that requiring parallax determinations of high precision induces sampling biases, so that we eschew such requirements and exclude, e.g., regions around the lines of sight to the Magellanic Clouds, along with their mirror-image lines of sight, to ensure well-matched data sets. After making conservative cuts, we demonstrate the existence of azimuthal asymmetries and find differences in those, north and south. These asymmetries give key insights into the nature and origins of the perturbations on Galactic matter, allowing us to assess the relative influence of the Magellanic Clouds (LMC and SMC), the Galactic bar, and other masses on the Galactic mass distribution, as described in Gardner et al. The asymmetry’s radial dependence reveals variations that we attribute to the Galactic bar, and it changes sign at a radius of (0.95 ± 0.03)R0, with R0 the Sun–GC distance, to give us the first direct assessment of the outer Lindblad resonant radius.
RADIOGAN:Deep Convolutional Conditional Generative Adversarial Network to Generate PET Images

One of the most challenges in medical imaging is the lack of data. It is proven that classical data augmentation methods are useful but still limited due to the huge variation in images. Using generative adversarial networks (GAN) is a promising way to address this problem, however, it is challenging to train one model to generate different classes of lesions. In this paper, we propose a deep convolutional conditional generative adversarial network to generate MIP positron emission tomography image (PET) which is a 2D image that represents a 3D volume for fast interpretation, according to different lesions or non-lesion (normal). The advantage of our proposed method consists of one model that is capable of generating different classes of lesions trained on a small sample size for each class of lesion and showing a very promising result. In addition, we show that a walk through a latent space can be used as a tool to evaluate the images generated.
Domain-Adaptive Few-Shot Learning

Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples. However, in practice, this assumption is often invalid –the target classes could come from a different domain. This poses an additional challenge of domain adaptation (DA) with few training samples. In this paper, the problem of domain-adaptive few-shot learning (DA-FSL) is tackled, which is expected to have wide use in real-world scenarios and requires solving FSL and DA in a unified framework. To this end, we propose a novel domain-adversarial prototypical network (DAPN) model. It is designed to address a specific challenge in DA-FSL: the DA objective means that the source and target data distributions need to be aligned, typically through a shared domain-adaptive feature embedding space; but the FSL objective dictates that the target domain per class distribution must be different from that of any source domain class, meaning aligning the distributions across domains may harm the FSL performance. How to achieve global domain distribution alignment whilst maintaining source/target per-class discriminativeness thus becomes the key. Our solution is to explicitly enhance the source/target per-class separation before domain-adaptive feature embedding learning, to alleviate the negative effect of domain alignment on FSL. Extensive experiments show that our DAPN outperforms the state-of-the-arts. The code is available at https://github.com/dingmyu/DAPN.