Miracle-dz/UniFAR-FTUs huggingface dataset:
The Yield of Community-Based “Retrospective” Tuberculosis Contact Investigation in a High Burden Setting in Ethiopia

Objective To determine the yield and determinants of retrospective TB contact investigation in selected zones in Ethiopia. Materials and Methods This was a community-based cross-sectional study conducted during June-October 2014.Trained lay providers performed symptom screening for close contacts of index cases with all types of TB registered for anti-TB treatment within the last three years. We used logistic regression to determine factors associated with TB diagnosis among the contacts. Results Of 272,441 close contacts of 47, 021 index cases screened, 13,886 and 2, 091 had presumptive and active TB respectively. The yield of active TB was thus 768/100, 000, contributing 25.4% of the 7,954 TB cases reported from the study zones over the study period. The yield was highest among workplace contacts (12,650/100, 000). Active TB was twice more likely among contacts whose index cases had been registered for TB treatment within the last 12 months compared with those who had been registered 24 or more months earlier (adjusted odds ratio, AOR: 1.77 95% CI 1.42–2.21). Sex or clinical type of TB in index cases was not associated with the yield. Smear negative (SS-) index cases (AOR: 1.74 955 CI 1.13–2.68), having index cases who registered for treatment within <12 months (AOR: 2.41 95% CI 1.51–3.84) and being household contact (AOR: 0.072 95% CI 0.01–0.52) were associated with the occurrence of active TB in children. Conclusions The yield of retrospective contact investigation was about six times the case notification in the study zones, contributing a fourth of all TB cases notified over the same period. The yield was highest among workplace contacts and in those with recent past history of contact. Retrospective contact screening can serve as additional strategy to identify high risk groups not addressed through currently recommended screening approaches.
A Cycle Ergometer Exercise Program Improves Exercise Capacity and Inspiratory Muscle Function in Hospitalized Patients Awaiting Heart Transplantation: a Pilot Study

Objective The purpose of this study was to evaluate the effect of a cycle ergometer exercise program on exercise capacity and inspiratory muscle function in hospitalized patients with heart failure awaiting heart transplantation with intravenous inotropic support. Methods Patients awaiting heart transplantation were randomized and allocated prospectively into two groups: 1) Control Group (n=11) - conventional protocol; and 2) Intervention Group (n=7) - stationary cycle ergometer exercise training. Functional capacity was measured by the six-minute walk test and inspiratory muscle strength assessed by manovacuometry before and after the exercise protocols. Results Both groups demonstrated an increase in six-minute walk test distance after the experimental procedure compared to baseline; however, only the intervention group had a significant increase (P=0.08 and P=0.001 for the control and intervention groups, respectively). Intergroup comparison revealed a greater increase in the intervention group compared to the control (P<0.001). Regarding the inspiratory muscle strength evaluation, the intragroup analysis demonstrated increased strength after the protocols compared to baseline for both groups; statistical significance was only demonstrated for the intervention group, though (P=0.22 and P<0.01, respectively). Intergroup comparison showed a significant increase in the intervention group compared to the control (P<0.01). Conclusion Stationary cycle ergometer exercise training shows positive results on exercise capacity and inspiratory muscle strength in patients with heart failure awaiting cardiac transplantation while on intravenous inotropic support.
Evaluation of cluster tool throughput for thin film head production

This paper describes the application of simulation for analyzing cluster tool cycle times and cluster tool capacity planning. The objective of this project was to develop a flexible and expandable tool for rapidly calculating tool cycle times for a multiple step process through alternative tool configurations. The calculated process cycle times are then used to calculate equipment tool set requirements against product demand. The Seagate Industrial Engineering group utilized a simulation based cluster tool model developed to predict cluster tool cycle times and analyze cluster tool capacity across multiple tools and compare with results from static probability based model predictions.
Initiation and execution of lipotoxic ER stress in pancreatic β-cells

Free fatty acids (FFA) cause apoptosis of pancreatic β-cells and might contribute to β-cell loss in type 2 diabetes via the induction of endoplasmic reticulum (ER) stress. We studied here the molecular mechanisms implicated in FFA-induced ER stress initiation and apoptosis in INS-1E cells, FACS-purified primary β-cells and human islets exposed to oleate and/or palmitate. Treatment with saturated and/or unsaturated FFA led to differential ER stress signaling. Palmitate induced more apoptosis and markedly activated the IRE1, PERK and ATF6 pathways, owing to a sustained depletion of ER Ca2+ stores, whereas the unsaturated FFA oleate led to milder PERK and IRE1 activation and comparable ATF6 signaling. Non-metabolizable methyl-FFA analogs induced neither ER stress nor β-cell apoptosis. The FFA-induced ER stress response was not modified by high glucose concentrations, suggesting that ER stress in primary β-cells is primarily lipotoxic, and not glucolipotoxic. Palmitate, but not oleate, activated JNK. JNK inhibitors reduced palmitate-mediated AP-1 activation and apoptosis. Blocking the transcription factor CHOP delayed palmitate-induced β-cell apoptosis. In conclusion, saturated FFA induce ER stress via ER Ca2+ depletion. The IRE1 and resulting JNK activation contribute to β-cell apoptosis. PERK activation by palmitate also contributes to β-cell apoptosis via CHOP.
Know2Look: Commonsense Knowledge for Visual Search

With the rise in popularity of social media, images accompanied by contextual text form a huge section of the web. However, search and retrieval of documents are still largely dependent on solely textual cues. Although visual cues have started to gain focus, the imperfection in object/scene detection do not lead to significantly improved results. We hypothesize that the use of background commonsense knowledge on query terms can significantly aid in retrieval of documents with associated images. To this end we deploy three different modalities - text, visual cues, and commonsense knowledge pertaining to the query - as a recipe for efficient search and retrieval.
Linear and Nonlinear State Estimation in the Czochralski Process

Abstract The Czochralski process is the only method used commercially for production of monocrystalline silicon for semiconductor and solar cell applications. This paper explores the use of mathematical modeling as an aid in estimation of system state variables in the standard Czochralski process. A state-space model of the process is presented, describing the dynamics of the crystal radius and meniscus height with crystal radius as measured output. For the purpose of estimating the actual crystal radius during growth, three types of state estimators are developed based on the state-space model; the Kalman filter, the extended Kalman filter and the unscented Kalman filter. It is found that the latter two provide highly accurate state estimates with excellent noise suppression.
A Design Space Exploration Methodology for Application Specific MPSoC Design

System designers need to perform design-space exploration (DSE) to find appropriate number and type of processing elements (PEs) to be present in a Multiprocessor Systems-on-Chip (MPSoC) to support a throughput-constrained application. This paper presents a DSE methodology that provides application to MPSoC architecture mappings, where different PEs get used. The methodology starts from a mapping using only one type of processors and evaluates different mappings by increasing heterogeneity to improve the performance.
The relationship between perceived stigma, disclosure patterns, support and distress in new attendees at an infertility clinic.

BACKGROUND A model suggesting that high perception of stigma is associated with reduced disclosure to others, leading to lower social support and higher distress in new attendees at an infertility clinic is tested. METHODS Questionnaires measuring stigmatization (Stigma consciousness questionnaire), disclosure of fertility difficulties (Disclosure questionnaire), social support (Duke-UNC Functional Social Support Questionnaire) and fertility-related [Fertility Problem Inventory (FPI)] and generic distress [Hospital Anxiety and Depression Scale (HADS)] were completed by 87 women and 64 men. Data were analysed by gender comparisons, correlations and path analysis. RESULTS Women reported higher stigma and disclosure than men. For women, stigma and disclosure were unrelated but in men higher stigma was associated with lower disclosure. Perceptions of stigma were related to low social support for both genders. Social support was negatively related to anxiety, depression and overall infertility distress and showed greater predictive capacity than satisfaction with partner relationship. Testing the model showed that, for men, stigma was linked to lower disclosure and support and higher fertility-related and generic distress. Disclosure itself did not link to support. For women, greater disclosure linked only to higher generic distress. Stigma was directly linked to fertility-related distress and to low perceived support which mediated a relationship with generic distress. CONCLUSIONS Stigma and the wider social context should be considered when supporting people with fertility problems. Greater disclosure may be associated with higher distress in women.
An End-to-End Block Autoencoder For Physical Layer Based On Neural Networks

Deep Learning has been widely applied in the area of image processing and natural language processing. In this paper, we propose an end-to-end communication structure based on autoencoder where the transceiver can be optimized jointly. A neural network roles as a combination of channel encoder and modulator. In order to deal with input sequences parallelly, we introduce block scheme, which means that the autoencoder divides the input sequence into a series of blocks. Each block contains fixed number of bits for encoding and modulating operation. Through training, the proposed system is able to produce the modulated constellation diagram of each block. The simulation results show that our autoencoder performs better than other autoencoder-based systems under additive Gaussian white noise (AWGN) and fading channels. We also prove that the bit error rate (BER) of proposed system can achieve an acceptable range with increasing the number of symbols.
Improving Brumley and Boneh timing attack on unprotected SSL implementations

Since the remarkable work of Kocher [7], several papers considering different types of timing attacks have been published. In 2003, Brumley and Boneh presented a timing attack on unprotected OpenSSL implementations [2]. In this paper, we improve the efficiency of their attack by a factor of more than 10. We exploit the timing behavior of Montgomery multiplications in the table initialization phase, which allows us to increase the number of multiplications that provide useful information to reveal one of the prime factors of RSA moduli. We also present other improvements, which can be applied to the attack in [2].
Mining Concepts from Incomplete Datasets Utilizing Matrix Factorization

Formal Concept Analysis aims at finding clusters (concepts) with given properties in data. Most techniques of concept analysis require a dense matrix with no missing values on the input. However, real data are often incomplete or inaccurate due to the noise or other unforeseen reasons. This paper focuses on using matrix factorization methods to complete the missing values in the input data such that it can be used with arbitrary concept analysis technique. The used matrix factorization model approximates the sparse object-item data matrix by a product of two dense factor matrices, thus, mapping objects and items to a common latent space. The mentioned object-factor and item-factor matrices are obtained by a simple stochastic gradient optimization method. We also investigate how the amount of missing values influences the output of the concept analysis. Two measures, well-known in the information retrieval community, have been used for the evaluation of the proposed framework. Real datasets from the UCI Machine Learning Repository were used in our experiments.
Paper 265-27 Robust Regression and Outlier Detection with the ROBUSTREG Procedure

Robust regression is an important tool for analyzing data that are contaminated with outliers. It can be used to detect outliers and to provide resistant (stable) results in the presence of outliers. This paper introduces the ROBUSTREG procedure, which is experimental in SAS/STAT Version 9. The ROBUSTREG procedure implements the most commonly used robust regression techniques. These include M estimation (Huber, 1973), LTS estimation (Rousseeuw, 1984), S estimation (Rousseeuw and Yohai, 1984), and MM estimation (Yohai, 1987). The paper will provide an overview of robust regression methods, describe the syntax of PROC ROBUSTREG, and illustrate the use of the procedure to fit regression models and display outliers and leverage points. This paper will also discuss scalability of the ROBUSTREG procedure for applications in data cleansing and data mining. Introduction The main purpose of robust regression is to provide resistant (stable) results in the presence of outliers. In order to achieve this stability, robust regression limits the influence of outliers. Historically, three classes of problems have been addressed with robust regression techniques: • problems with outliers in the y-direction (response direction) • problems with multivariate outliers in the covariate space (i.e. outliers in the x-space, which are also referred to as leverage points) • problems with outliers in both the y-direction and the x-space Many methods have been developed for these problems. However, in statistical applications of outlier detection and robust regression, the methods most commonly used today are Huber M estimation, high breakdown value estimation, and combinations of these two methods. The ROBUSTREG procedure provides four such methods: M estimation, LTS estimation, S estimation, and MM estimation. 1. M estimation was introduced by Huber (1973), and it is the simplest approach both computationally and theoretically. Although it is not robust with respect to leverage points, it is still used extensively in analyzing data for which it can be assumed that the contamination is mainly in the response direction. 2. Least Trimmed Squares (LTS) estimation is a high breakdown value method introduced by Rousseeuw (1984). The breakdown value is a measure of the proportion of contamination that a procedure can withstand and still maintain its robustness. The performance of this method was improved by the FAST-LTS algorithm of Rousseeuw and Van Driessen (1998). 3. S estimation is a high breakdown value method introduced by Rousseeuw and Yohai (1984). With the same breakdown value, it has a higher statistical efficiency than LTS estimation. 4. MM estimation, introduced by Yohai (1987), combines high breakdown value estimation and M estimation. It has both the high breakdown property and a higher statistical efficiency than S estimation. The following example introduces the basic usage of the ROBUSTREG procedure. Growth Study Zaman, Rousseeuw, and Orhan (2001) used the following example to show how these robust techniques 1 SUGI 27 Statistics and Data Analysis
Identification of Candidate Genes and Physiological Pathways Involved in Gonad Deformation in Whitefish (Coregonus spp.) from Lake Thun, Switzerland

In 2000, fishermen reported the appearance of deformed reproductive organs in whitefish (Coregonus spp.) from Lake Thun, Switzerland. Despite intensive investigations, the causes of these abnormalities remain unknown. Using gene expression profiling, we sought to identify candidate genes and physiological processes possibly associated with the observed gonadal deformations, in order to gain insights into potential causes. Using in situ-synthesized oligonucleotide arrays, we compared the expression levels at 21,492 unique transcript probes in liver and head kidney tissue of male whitefish with deformed and normally developed gonads, respectively. The fish had been collected on spawning sites of two genetically distinct whitefish forms of Lake Thun. We contrasted the gene expression profiles of 56 individuals, i.e., 14 individuals of each phenotype and of each population. Gene-by-gene analysis revealed weak expression differences between normal and deformed fish, and only one gene, ictacalcin, was found to be up-regulated in head kidney tissue of deformed fish from both whitefish forms, However, this difference could not be confirmed with quantitative real-time qPCR. Enrichment analysis on the level of physiological processes revealed (i) the involvement of immune response genes in both tissues, particularly those linked to complement activation in the liver, (ii) proteolysis in the liver and (iii) GTPase activity and Ras protein signal transduction in the head kidney. In comparison with current literature, this gene expression pattern signals a chronic autoimmune disease in the testes. Based on the recent observations that gonad deformations are induced through feeding of zooplankton from Lake Thun we hypothesize that a xenobiotic accumulated in whitefish via the plankton triggering autoimmunity as the likely cause of gonad deformations. We propose several experimental strategies to verify or reject this hypothesis.
The Social Context of Health

This thesis presents three empirical essays on the wider social determinants of health status. It analyzes the relevance of cultural, social and psychological determinants of health and their implications for health policy in Germany. These factors reflect the dependence of health status and subjective health assessment on the individual’s social environment. The empirical analyses rely on large-scale survey data from the German Socio-Economic Panel (SOEP). It provides, among others, rich information on socioeconomic status, health, leisure, social activities, personality traits and retrospective data on youth and socialization. Furthermore, respondents are followed over an extended period of time. This feature can be used to reduce the bias emanating from unobserved factors. The first study examines whether social participation measured by the consumption of cultural events (theatre, classical concerts etc.) has direct positive effects on self-reported health status. The existing literature generally suggests positive associations. However, credible evidence on causal effects is rather scarce. Using an elaborate propensity-score-matching approach and exploiting the longitudinal structure of the SOEP data, the results suggest that earlier studies may overestimate the relationship between cultural participation and health. Non-random selection into cultural attendance based on unobserved characteristics may heavily bias the observed associations. The second study is concerned with the indirect effects of social participation. It contributes to the current and large health economic literature on the negative effects of providing care to a family member, i.e. informal care. This study particularly investigates whether social capital attenuates the negative effects of providing informal care on mental health. Fixed-effects regression results indicate that social participation can reduce the negative psychological impact of caregiving. This may have important implications for the design and implementation of public caregiver-support programs. Analogous to the relative-income hypothesis, the third study explores whether relative health – that is own health compared to the average health of a reference group – influences perceived health satisfaction. The importance of relative-health effects is twofold. First, if such externalities exist, the private and social benefits of health investments fall apart. Depending on whether these spillovers are positive or negative, relative health raises or decreases the need for public subsidization of health goods and services. Second, reference group characteristics are a potential source of reporting bias in subjective health assessments, which could lead to erroneous conclusions about the cost effectiveness of alternative policy interventions. However, the empirical analysis suggests that social comparisons in health status are only weakly related to satisfaction with health.
Delay-/Disruption-Tolerant Networking State of the Art and Future Challenges

Networking for challenged environments, or Delay- and Disruption-Tolerant Networking as it is now most commonly referred to, has attracted great attention in the past few years by the networking research community. Connectivity disruptions, limited network capacity, energy and storage constraints of the participating, mobile devices and the arbitrary movement of nodes are only a few of the challenges that the protocol stack has to deal with. Clearly, current Internet protocols (i.e., the TCP/IP protocol stack) suer and can fail under such conditions. In this paper, we initially give the DTN Problem Statement; we contend that not all applications have the same requirements from the system and hence, equal (blind) treatment of all data packets will result in reduced network eciency. Based on that we propose a Design Position for DTN protocols, which states that protocol design has to be done proactively, on the basis of the application’s requirements. We then survey the most recent contributions on the whole spectrum of Delay- and Disruption-Tolerant Networking, from the architectural and the application point of view down to the transport- and the network-layer of the emerging DTN protocol stack. We nd that although not explicitly mentioned
A novel variable step size constant modulus algorithm employing cross correlation between channel output and error signal

A novel variable step size constant modulus algorithm (VSS-CMA) employing cross correlation between channel output and error signal has been proposed as a solution to the problem of slow convergence of CMA algorithm. The new algorithm resolves the conflict between the convergence rate and low steady state error of the fixed step-size conventional CMA algorithm. Computer simulations have been performed to verify the performance of the proposed method in frequency selective Rayleigh fading channels. The proposed technique has been compared with the popular non-blind equalizer, Least Mean Squares (LMS) algorithm, and blind equalizer, the conventional CMA and Zhao's VSS-CMA as benchmarks. The obtained simulation results have demonstrated that the proposed VSS-CMA algorithm has considerably better performance than the conventional CMA and Zhao's VSS-CMA blind equalization algorithms, and the conventional LMS non-blind equalization algorithm.