Miracle-dz/UniFAR-FTUs huggingface dataset:
Chromosomal copy number variations in products of conception from spontaneous abortion by next-generation sequencing technology

Abstract Fetal chromosomal abnormalities are considered to be the main cause of spontaneous abortion (SA). We aimed to determine the differences in the rates and numbers of chromosomal abnormalities between samples from women with a history of one versus more than one SA as well as between samples from first- and second-trimester SAs in women from Northeast China. In total, 1210 products of conception (POCs) from patients with a history of one or more SAs were examined. Of these 1210 samples, 434 were from women with a history of 1 SA, and 776 were from women with a history of more than 1 SA. Additionally, 1071 samples were from the first trimester, 118 were from the second trimester, and 21 were from the third trimester. We identified chromosomal abnormalities by next-generation sequencing (NGS) technology. Among the 1210 POCs in women with SA, 607 (50.17%) had fetal chromosomal abnormalities. There were no significant differences in the rates of chromosomal abnormalities according to the abortion frequency. However, first-trimester SA had a significantly higher percentage of fetal chromosomal abnormalities than second-trimester SA (P < .05). Among 663 chromosomal abnormalities, 633 abnormalities occurred in first-trimester SA; the most frequent karyotype was trisomy 16 (14.38%), followed by monosomy X (13.27%), trisomy 22 (7.90%), and trisomy 15 (5.37%). Thirty abnormalities occurred in second-trimester SA; the most frequent karyotype was trisomy 18 (26.67%), followed by monosomy X (16.67%), trisomy 21 (13.33%), and trisomy 13 (10.00%). No chromosomal abnormalities occurred in the third trimester. These findings indicate the importance of determining the genetic cause of abortion in patients with a history of SA. We also identified a trend suggesting that the percentage of fetal chromosomal abnormalities is significantly higher in first- than second-trimester SA. The detection rate of chromosomal abnormalities in POCs from SA can be increased by NGS, which is beneficial for couples with recurrent miscarriages and offers better genetic counseling in the clinical setting.
Medication Use Quality and Safety in Older Adults: 2018 Update

Improving the quality of medication use and medication safety is an important priority for prescribers who care for older adults. The objective of this article was to identify key articles from 2018 that address these issues. In addition, we selected four of these articles to annotate, critique, and discuss their broader implications for clinical practice. The first study highlights a cluster‐randomized trial that utilized a pharmacist‐led education‐based intervention delivered to both patients and physicians to deprescribe four types of inappropriate medications (sedative‐hypnotics, first‐generation antihistamines, selective nonsteroidal anti‐inflammatory drugs, and glyburide). The second study, a nested case‐control study using data from within the UK Clinical Practice Research Datalink, examined the association between anticholinergic exposure, overall and by anticholinergic medication class, and dementia risk in 40 770 older adults. The third study, a longitudinal cohort study of 1028 Swedish older adults, examined the association between antihypertensive medications and incident dementia. The last study was a randomized, double‐blind, placebo‐controlled trial that investigated the effect of daily low‐dose aspirin (100 mg) for primary prevention on cardiovascular events and major hemorrhage in 19 144 community‐dwelling older adults. Collectively, this current article provides insight into the pertinent topics of medication use quality and safety in older adults and helps raise awareness about optimal prescribing in older adults. J Am Geriatr Soc 67:2458–2462, 2019
A Jump Diffusion Model for Volatility and Duration

This paper puts forward a stochastic volatility and stochastic conditional duration with cojumps (SVSDCJ) model to analyze returns and durations. In high frequency data, transactions are irregularly spaced, and the durations between transactions carry information about volatility as suggested by the market microstructure theory. Traditional measures of volatility do not utilize durations. I adopt a jump diffusion process to model the persistence of intraday volatility and conditional duration, and their interdependence. The jump component is disentangled from the continuous part of the price, volatility and conditional duration process. I develop a MCMC algorithm for the inference of irregularly spaced multivariate process with jumps. The algorithm provides smoothed estimates of the latent variables such as spot volatility, jump times and jump sizes. I apply this
Effectiveness of Stress Management and Relaxation Training in Reducing the Negative Affect and in Improving the Life Quality of Women with Breast Cancer

) 10 و شيامزآ رفن 10 لرتنك رفن ( دنتفرگ رارق . همانشسرپ ي ديما هـب يگدنز ردا سرتسا و رديانشا هـب شهوژـپ نـيا رد ،نهوـك هدـش كا ديدرگ هدافتسا رازبا ناونع . كمك هب اه هداد ليلحت و هيزجت نوـمزآ يرامآ ليلحت وك سنايراو تفرگ ماجنا . شهوژپ ياه هتفاي داد ناشن يناـمرد انعم شور هك يـهورگ رـب هدـش كاردا سرتـسا شهاـك و شيازفا رد يگدنز هب ديما ناراميب MS تسا هتشاد ريثأت . هب هجوت اب شهاـكرب يناـمردانعم ندوـب شخبرـثا رب ينبم هدمآ تسد هب جياتن هدش كاردا سرتسا يگدنز هب ديما شيازفا و ناراميب MS ، شهوژپ شهاـك يارـب رثؤـم لـح هار كـي هـب نديـسر رد دـناوت يم رضاح يناور تيعضو دوبهب يلك روط هب و يديماان ،سرتسا نيا هار ناراميب دشاب اشگ .
Effective Inoculation Method and Optimum Concentration of Oryctes Virus to Infect Oryctes rhinoceros Adults

This research was carried out to determine the effective inoculation method and optimum concentration of local Oryctes virus (OrV) for successful infection of Oryctes rhinoceros (L.) adults in laboratory which released to the field to spread the viral disease among healthy larvae and beetles. Percentage of infected beetles were significantly different (P<0.001) among the two methods of inoculation and untreated control. After 21 days of inoculation, oral introduction method, swim method and control recorded 88.8%, 44.4% and 11.1% of OrV infection respectively indicating the most effective inoculation method as oral introduction. Cumulative percentage mortality of O. rhinoceros adults with 10 1 ppm, 10 2 ppm, 10 3 ppm, 10 4 ppm and 10 5 ppm concentrations were recorded as 7.3%, 25.1%, 33.3%, 81.4% and 100% respectively. LC50 was 10 2.7 ppm and LT50 for 10 4 ppm and 10 5 ppm concentrations were 23 days and 12 days respectively. Best concentration to oral inoculation of O. rhinoceros with OrV for field release was selected as 10 4 ppm
Three-Dimensional Matching based Resource Provisioning for the Design of Low-Latency Heterogeneous IoT Networks

Internet-of-Things (IoT) is a networking architecture where promising, intelligent services are designed via leveraging information from multiple heterogeneous sources of data within the network. However, the availability of such information in a timely manner requires processing and communication of raw data collected from these sources. Therefore, the economic feasibility of IoT-enabled networks relies on the efficient allocation of both computational and communication resources within the network. Since fog computing and 5G cellular networks approach this problem independently, there is a need for joint resource-provisioning of both communication and computational resources in the networks. As the solution to this problem, we propose a novel three-dimensional matching based resource provisioning algorithm that minimizes average service latency in the presence of various resource constraints, task deadlines and non-identical preferences at IoT devices, fog access points (FAPs) and small-cell access points (SAPs) in 5G networks. We prove the stability and termination of the proposed algorithm and also demonstrate that our proposed algorithm outperforms other state-of-the-art algorithms through both, simulation and real-world experiments on the laboratory test-bed.
Lazarus: Automatic Management of Diversity in BFT Systems

A long-standing promise of Byzantine Fault-Tolerant (BFT) replication is to maintain the service correctness despite the presence of malicious failures. The key challenge here is how to ensure replicas fail independently, i.e., avoid that a single attack compromises more than f replicas at once. The obvious answer for this is the use of diverse replicas, but most works in BFT simply assume such diversity without supporting mechanisms to substantiate this assumption. Lazarus is a control plane for managing the deployment and execution of diverse replicas in BFT systems. Lazarus continuously monitors the current vulnerabilities of the system replicas (reported in security feeds such as NVD and ExploitDB) and employs a metric to measure the risk of having a common weakness in the replicas set. If such risk is high, the set of replicas is reconfigured. Our evaluation shows that the devised strategy reduces the number of executions where the system becomes compromised and that our prototype supports the execution of full-fledged BFT systems in diverse configurations with 17 OS versions, reaching a performance close to a homogeneous bare-metal setup.
Authorable augmented reality instructions for assistance and training in work environments

Augmented Reality (AR) is a promising technology for assistance and training in work environments, as it can provide instructions and feedback contextualised. Not only, but especially impaired workers can benefit from this technology. While previous work mostly focused on using AR to assist or train specific predefined tasks, "general purpose" AR applications, that can be used to intuitively author new tasks at run-time, are widely missing. The contribution of this work is twofold: First we develop an AR authoring tool on the Microsoft HoloLens in combination with a Smartphone as an additional controller following considerations based on related work, guidelines and focus group interviews. Then, we evaluate the usability of the authoring tool itself and the produced AR instructions on a qualitative level in realistic scenarios and gather feedback. As the results reveal a positive reception, we discuss authorable AR as a viable form of AR assistance or training in work environments.
Modeling Realistic Bit Rates of D2D Communications between Android Devices

Although D2D communications have been extensively investigated in the literature, relatively few works have focused on understanding the capacity of direct links in a real setup. In this paper, we propose an empirical characterization of the currently available high-speed D2D technologies in Android, namelyWi-Fi P2P andGoogle Nearby. To this end, we developed a custom Android application calledOcat which interacts with the available D2D APIs and measures the link's goodput. From the experimental campaign, we derive several useful observations. Concerning communication capacity, the goodput between Android devices ranges between 320~Mbits/s when nodes are within 20 meters of each other and 0.1~Mbits/s when the distance grows to 300 meters. Based on the experimental measurements, we propose a model of the upper-bound goodput as a function of the distance between two devices. Using the wireless signal strength as a link measurement, we combine it with the two-ray ground-reflection model to infer the goodput and obtain a good fit for the characterization of D2D links between Android devices. Our findings provide a reality check in regards to actual direct data-exchange capabilities of Android devices and can help assess system performance of D2D applications.
Role of Bronchoscopy in Diagnosing Sputum Smear Negative Pulmonary Tuberculosis

Background - In patients of pulmonary tuberculosis microscopic examination of sputum is the method of choice. Sputum examination for AFB microscopy is usually positive but even in advanced disease may be negative due to immunosuppression, poor quality of the sample collection, deficient preparation, staining or examination of the sputum smear. Clinical and radiological based diagnosis can lead either to over or under diagnosis of tuberculosis and unnecessary antitubercular treatment may cause drug resistance and economic burden. Fiberoptic bronchoscopy can provide excellent material for diagnosis of suspected cases of pulmonary tuberculosis when smears of expectorated sputum do not reveal mycobacteria. Aim - To assess the role of bronchoscopy in diagnosis of pulmonary tuberculosis among patients who have clinico-radiological suspicion but remain negative for AFB on sputum examination. Settings and design - After consent, 50 patients with sputum smear negative for AFB and chest X-ray suggestive of pulmonary tuberculosis, underwent fiberoptic bronchoscopy. Bronchoalveolar lavage (BAL), Bronchial brushings, Transbronchial lung biopsy (TBLB) and Post bronchoscopy sputum (PBS) were done and samples analyzed by smear microscopy, histopathology and or culture. Result - Sensitivity of BAL, Brushing and PBS by smear and culture are 87.5%, 54.17% and 50% respectively. The final diagnosis of PTB was established in 24/50 (48%) cases. Conclusion - Fiberoptic bronchoscopy with post bronchoscopy sputum is a useful tool for diagnosis and can thereby prompt treatment of sputum smear negative pulmonary tuberculosis patients.
Title: βcell Lipotoxicity in Response to Free Fatty Acid Elevation in Youth: African American versus Caucasian Contrast Running title: Lipid infusion and insulin secretion

1 Division of Weight Management, Children’s Hospital of Pittsburgh, University of Pittsburgh Medical Center; 2 Division of Endocrinology and Metabolic Disease, University of Verona School of Medicine and Azienda Ospedaliera Universitaria Integrata, Verona, Italy; 3 Eunice Kennedy Shriver National Institute of Child Health & Human Development, NIH; 4 Division of Pediatric Endocrinology, Metabolism & Diabetes Mellitus, Children’s Hospital of Pittsburgh
DialPlates: enabling pursuits-based user interfaces with large target numbers

In this paper we introduce a novel approach for smooth pursuits eye movement detection and demonstrate that it allows up to 160 targets to be distinguished. With this work we advance the well-established smooth pursuits technique, which allows gaze interaction without calibration. The approach is valuable for researchers and practitioners, since it enables novel user interfaces and applications to be created that employ a large number of targets, for example, a pursuits-based keyboard or a smart home where many different objects can be controlled using gaze. We present findings from two studies. In particular, we compare our novel detection algorithm based on linear regression with the correlation method. We quantify its accuracy for around 20 targets on a single circle and up to 160 targets on multiple circles. Finally, we implemented a pursuits-based keyboard app with 108 targets as proof-of-concept.
A rare case of arteriovenous malformation of the upper eyelid

Arteriovenous malformations (AVM′s) are serious abnormalities that very rarely affect the eye. They are due to anomalous communications between the arterial and venous systems without any interposing capillaries. Therapeutic management of orbital AVM is not standardized, and it′s most essential to strike a balance between spontaneous and iatrogenic risk of any visual loss. Management has to be very patient-specific and includes observation, embolization, surgical excision or a combined preoperative embolization followed by excision. Surgical intervention can also sometimes lead to life-threatening complications like excessive haemorrhage or cardiovascular instability and /or vision threatening complications like retinal vein occlusions. Here we describe an eyelid AVM in a young lady, with a relatively good vision in the affected eye . She was accurately diagnosed and finally after much deliberation involving multiple specialities was offered a combined embolization followed by complete surgical resection, however she declined any active intervention and is now on a regular follow-up
Are We Still Friends? Evaluating Tie Persistence in Mobility Traces

The advent of mobility networks brought to the surface the need to understand how mobile entities behave, specially considering how the interactions among them are affected. With the arrival of wireless and mobile networks such as 5G, the characterization of contacts between users becomes more and more important. Thereof, protocols, algorithms and applications started to lure on this kind of information. However, we still have to understand better how these interactions change over time and what are the factors that rule them. In this work, we analyze a mobility trace containing data about Wi-Fi users in a university campus over the period of one year to understand how time and space affect their relationships. To do so, we use a set of previously studied metrics to classify contacts between users into random and social. This classification is based on the number of contacts in common between two users and their encounter regularity, i.e., how frequently they meet during their routine. Additionally, this study contributes by going one step further and classifying users into those same categories according to their contacts distribution. Then, we consider how these users and contacts evolve over time, and what is the role of location and time in this evolution. Finally, we discuss how such evolution information can be used to provide better solutions in mobile networks, such as through the ability to predict how long a contact will last, or the ability to measure how qualified a certain user is to deliver a message.
Robust keystroke transcription from the acoustic side-channel

The acoustic emanations from keyboards provide a side-channel attack from which an attacker can recover sensitive user information, such as passwords and personally identifiable information. Previous work has shown the feasibility of these attacks given isolated key strokes, but has not demonstrated robust keystroke detection and segmentation in the presence of realistic noise and fast typing speeds. Common problems include noises like doors closing or speech as well as overlapping keystroke waveforms. Prior work has assumed that isolating the waveform of individual key strokes can be achieved with near 100% accuracy, but we show that these techniques generate a large number of misses and false positives, drastically impacting the downstream keystroke classification task. To solve this problem, we present a deep learning system, leveraging related state-of-the-art techniques from speech transcription, that performs end-to-end, audio-to-keystroke transcription with superior performance. The recurrent architecture enables it to robustly handle overlapping waveforms and adapt to local noise profiles. Furthermore, the joint approach to keystroke detection and classification enables us to both train without ground truth keystroke timings and outperform standard classification approaches even when they have ground truth timings. Due to the paucity of existing datasets, we collected a novel acoustic and keylogger dataset comprising 17 users and 86k keystrokes across various real-world typing tasks. On this dataset, we reduce the end-to-end character error rate on English text from 36.0% to 7.41% for known typists and 41.3% to 15.41% for unknown typists. The keystroke acoustic side-channel attack remains dangerously feasible.
COLiDeR: A Cross-Layer Protocol for Two-Path Relaying

In this work, we present COLiDeR, the first practical system for two-path relaying using off-the-shelf half-duplex radios. While two-path relaying has been mostly studied from a theoretical perspective in the literature, our solution addresses the challenges of making it practical by introducing two key contributions. First, using a measurement-driven approach, we identify the best approach for a radio to handle two overlapping signals. Just as important, we carefully quantify its limits and introduce the concept of the decoding areas. Second, we introduce a cross-layer protocol that seamlessly navigates the decoding areas with the objective of maximizing throughput while minimizing decoding failures. Experiments on a 4-USRP testbed show that COLiDeR delivers between 80-95% of the relaying performance of an ideal full-duplex radio while incurring negligible decoding failures.
GADSA: Decision Support App for Antibiotics Prescribing in Nigeria

GADSA (Gamified Antimicrobial Stewardship Decision Support App) is a decision support tool to improve evidence-based prescribing, designed to be used at the point-of-care to help clinicians comply with guidelines in their everyday practice. The app represents a novel cross-platform, mobile decision support tool, integrating principles from serious games and gamification, to improve compliance with prescription guidelines of Surgical Antibiotic Prophylaxis (SAP) in Nigeria. This paper focuses on the decision support component of the mobile application, integrating the World Health Organisation and Sanford guidelines for SAP prescriptions.
Encouraging Posttreatment Self-Help Group Involvement to Reduce Demand for Continuing Care Services: Two-Year Clinical and Utilization Outcomes

Background:Accumulating evidence indicates that addiction and psychiatric treatment programs that actively promote self-help group involvement can reduce their patients' health care costs in the first year after treatment, but such initially impressive effects may wane over time. This paper examines whether the positive clinical outcomes and reduced health care costs evident 1 year after treatment among substance-dependent patients who were strongly encouraged to attend 12-step self-help groups were sustained at 2-year follow-up. Methods: A 2-year quasi-experimental analysis of matched samples of male substance-dependent patients who were treated in either 12-step-based (n = 887 patients) or cognitive-behavioral (CB, n = 887 patients) treatment programs. The 12-step-based programs placed substantially more emphasis on 12-step concepts, had more staff members “in recovery,” had a more spiritually oriented treatment environment, and promoted self-help group involvement much more extensively than did the CB .
Model inversion attacks against collaborative inference

The prevalence of deep learning has drawn attention to the privacy protection of sensitive data. Various privacy threats have been presented, where an adversary can steal model owners' private data. Meanwhile, countermeasures have also been introduced to achieve privacy-preserving deep learning. However, most studies only focused on data privacy during training, and ignored privacy during inference. In this paper, we devise a new set of attacks to compromise the inference data privacy in collaborative deep learning systems. Specifically, when a deep neural network and the corresponding inference task are split and distributed to different participants, one malicious participant can accurately recover an arbitrary input fed into this system, even if he has no access to other participants' data or computations, or to prediction APIs to query this system. We evaluate our attacks under different settings, models and datasets, to show their effectiveness and generalization. We also study the characteristics of deep learning models that make them susceptible to such inference privacy threats. This provides insights and guidelines to develop more privacy-preserving collaborative systems and algorithms.