Miracle-dz/UniFAR-FTUs huggingface dataset:
Levels of HIV-1 persistence on antiretroviral therapy are not associated with markers of inflammation or activation

Antiretroviral therapy (ART) reduces levels of HIV-1 and immune activation but both can persist despite clinically effective ART. The relationships among pre-ART and on-ART levels of HIV-1 and activation are incompletely understood, in part because prior studies have been small or cross-sectional. To address these limitations, we evaluated measures of HIV-1 persistence, inflammation, T cell activation and T cell cycling in a longitudinal cohort of 101 participants who initiated ART and had well-documented sustained suppression of plasma viremia for a median of 7 years. During the first 4 years following ART initiation, HIV-1 DNA declined by 15-fold (93%) whereas cell-associated HIV-1 RNA (CA-RNA) fell 525-fold (>99%). Thereafter, HIV-1 DNA levels continued to decline slowly (5% per year) with a half-life of 13 years. Participants who had higher HIV-1 DNA and CA-RNA before starting treatment had higher levels while on ART, despite suppression of plasma viremia for many years. Markers of inflammation and T cell activation were associated with plasma HIV-1 RNA levels before ART was initiated but there were no consistent associations between these markers and HIV-1 DNA or CA-RNA during long-term ART, suggesting that HIV-1 persistence is not driving or driven by inflammation or activation. Higher levels of inflammation, T cell activation and cycling before ART were associated with higher levels during ART, indicating that immunologic events that occurred well before ART initiation had long-lasting effects despite sustained virologic suppression. These findings should stimulate studies of viral and host factors that affect virologic, inflammatory and immunologic set points prior to ART initiation and should inform the design of strategies to reduce HIV-1 reservoirs and dampen immune activation that persists despite ART.
Home-Based Psychiatric Outpatient Care Through Videoconferencing for Depression: A Randomized Controlled Follow-Up Trial

Background There is a tremendous opportunity for innovative mental health care solutions such as psychiatric care through videoconferencing to increase the number of people who have access to quality care. However, studies are needed to generate empirical evidence on the use of psychiatric outpatient care via videoconferencing, particularly in low- and middle-income countries and clinically unsupervised settings. Objective The objective of this study was to evaluate the effectiveness and feasibility of home-based treatment for mild depression through psychiatric consultations via videoconferencing. Methods A randomized controlled trial with a 6- and 12-month follow-up including adults with mild depression treated in an ambulatory setting was conducted. In total, 107 participants were randomly allocated to the videoconferencing intervention group (n=53) or the face-to-face group (F2F; n=54). The groups did not differ with respect to demographic characteristics at baseline. The F2F group completed monthly follow-up consultations in person. The videoconferencing group received monthly follow-up consultations with a psychiatrist through videoconferencing at home. At baseline and after 6 and 12 months, in-person assessments were conducted with all participants. Clinical outcomes (severity of depression, mental health status, medication course, and relapses), satisfaction with treatment, therapeutic relationship, treatment adherence (appointment compliance and dropouts), and medication adherence were assessed. Results The severity of depression decreased significantly over the 12-month follow-up in both the groups. There was a significant difference between groups regarding treatment outcomes throughout the follow-up period, with better results in the videoconferencing group. There were 4 relapses in the F2F group and only 1 in the videoconferencing group. No significant differences between groups regarding mental health status, satisfaction with treatment, therapeutic relationship, treatment adherence, or medication compliance were found. However, after 6 months, the rate of dropouts was significantly higher in the F2F group (18.5% vs 5.7% in the videoconferencing group, P<.05). Conclusions Psychiatric treatment through videoconferencing in clinically unsupervised settings can be considered feasible and as effective as standard care (in-person treatment) for depressed outpatients with respect to clinical outcomes, patient satisfaction, therapeutic relationship, treatment adherence, and medication compliance. These results indicate the potential of telepsychiatry to extend access to psychiatric care to remote and underserved populations. ClinicalTrial Clinicaltrials.gov NCT01901315; https://clinicaltrials.gov/ct2/show/NCT01901315 (Archived by WebCite at http://www.webcitation.org/6jBTrIVwg)
Talking less and moving the market more: is this the recipe for monetary policy effectiveness?: evidence from the ECB and the Fed

This paper examines and compares the communication strategies of the Federal Reserve and the European Central Bank, and their effectiveness. First we do a comparative study exercise. We find that on monetary policy committee meeting days both the ECB and the Fed can move market rates using either monetary policy or news shocks. However, the response of the long-end of the American term structure to the surprise component of Fed's statements is significantly larger than the reaction of European long-term yields to ECB's announcements. This result is intimately related to the higher transparency of U.S. Fed statements compared to ECB announcements rather than to the different institutional mandate of the two central banks. Second, we investigate the cross-effects i.e. the Fed's ability to move European interest rates and the corresponding ECB's capacity to move American rates. We find that the Fed has been more able to move the European interst rates of all maturities than the ECB to move American rates. This finding is tied to the predominance of dollar fixed income assets rather than to an attempt of the ECB to mimic the Fed.
Effect of graphene nanoplatelets on the ballistic performance of hybrid Kevlar/Cocos nucifera sheath-reinforced epoxy composites

This study investigated the effect of graphene nanoplatelets (GnPs) on the ballistic performance of Kevlar/Cocos nucifera sheath-reinforced epoxy composites. GnPs with different wt. % (0%, 0.25%, 0.50%) were integrated into epoxy resin through ultra-sonication. Laminated composites were fabricated by incorporating Kevlar and Cocos nucifera sheath layers into the epoxy/GnP mixture by the hand lay-up method followed by hot pressing. Nine- and 12-layered laminates were fabricated with different numbers of Kevlar and Cocos nucifera sheath plies. Ballistic tests were conducted using a single-stage gas gun experimental setup with an 8 mm hemispherical stainless steel projectile. The results showed that the addition of GnPs improved the energy absorption by 8.5% (nine plies) and 12.88% (12 plies) and the ballistic limit by 4.28% (nine plies) and 6.17% (12 plies), respectively of Kevlar/epoxy/GnP composites at 0.25 wt. %. However, hybrid Kevlar/Cocos nucifera sheath/epoxy/GnP composites and Cocos nucifera sheath/epoxy/GnP laminated composites decreased the energy absorption and ballistic limit after the addition of GnPs. This is because the addition of GnPs improved the interfacial interactions between the fiber and GnP modified epoxy matrix, which is inappropriate to absorb and dissipate the kinetic energy of the projectile. Statistical analysis was carried out using one-way analysis of variance and it was shown that there is a statistically significant difference between the obtained ballistic properties of the laminates.
Recognition of Carcinoembryonic Antigen Peptide and Heteroclitic Peptide by Peripheral Blood T Lymphocytes

The carcinoembryonic antigen (CEA)-derived peptide CAP1 and heteroclitic peptide CAP1-6D are stimulators of HLA-A*A0201 restricted CEA-specific T cells in vivo and in vitro. The goal of this study was to evaluate differences in T cell responses to peptide and modified peptide antigens from CEA. The heterogeneity of responses among individuals is potentially important for the design of future CEA-directed immunotherapy trials. Peripheral blood mononuclear cells from blood donors were stimulated with peptide, IL-2, and IL-7. Weekly, microcultures were restimulated with irradiated, autologous peptide-loaded peripheral blood mononuclear cells and expanded in IL-2. Established T cell lines were tested by cytokine release assays using peptide-loaded T2 targets. T cell avidity was measured by cytokine release using targets expressing diminishing concentrations of peptide. Fine specificities were measured using targets loaded with alanine-substituted CAP1 peptide. Tumor recognition was measured using HLA-A*A0201/CAP1-transduced COS tumor targets. Varied responses to CAP1 and CAP1-6D were seen among individuals. The immunogenicity of CAP1 or CAP1-6D was donor dependent. Many T cells recognized one peptide but did not cross-recognize the altered peptide. The avidities of T cell lines were moderate to low, and fine specificities were consistent with a narrow antigen-specific repertoire. CAP1-6D-based immune therapy may not be optimal in some patients with CAP1-specific precursors. The T cell repertoire may be a central contributor to the limited responses seen with CEA-directed immunotherapy to date. Treatment strategies designed to alter or expand the T cell repertoire against CEA should be considered for trials.
The design and implementation of the model constructing satisfiability calculus

We present the design and implementation of the Model Constructing Satisfiability (MCSat) calculus. The MCSat calculus generalizes ideas found in CDCL-style propositional SAT solvers to SMT solvers, and provides a common framework where recent model-based procedures and techniques can be justified and combined. We describe how to incorporate support for linear real arithmetic and uninterpreted function symbols m the calculus. We report encouraging experimental results, where MCSat performs competitive with the state-of-the art SMT solvers without using pre-processing techniques and ad-hoc optimizations. The implementation is flexible, additional plugins can be easily added, and the code is freely available.
Sound onset detection by applying psychoacoustic knowledge

A system was designed, which is able to detect the perceptual onsets of sounds in acoustic signals. The system is general in regard to the sounds involved and was found to be robust for different kinds of signals. This was achieved without assuming regularities in the positions of the onsets. In this paper, a method is first proposed that can determine the beginnings of sounds that exhibit onset imperfections, i.e., the amplitude envelope of which does not rise monothinically. Then the mentioned system is described, which utilizes band-wise processing and a psychoacoustic model of intensity coding to combine the results from the separate frequency bands. The performance of the system was validated by applying it to the detection of onsets in musical signals ranging from rock to classical and big band recordings.
Integration of Multimodal Cues in Perceptual Segmentation

How are multiple, multimodal sensory stimuli used in segmenting spatio-temporal events? For an answer, we measured the effect of various auditory or visual stimuli, in isolation or in combination, on a bistable percept of visual motion. To minimize individual differences, the physical properties of stimuli were adjusted according to reflect each subject’s strength of response. When put into combination, the perceptual influences of stimuli that had been equipotent in isolation, were substantially altered. Predictions from a suite of alternative models of sensory integration were evaluated. The results showed that the state of the bistable percept is governed by a linear sum of cues, but with differential weighting of individual cues within each modality.
Exploiting Multi-modal Curriculum in Noisy Web Data for Large-scale Concept Learning

Learning video concept detectors automatically from the big but noisy web data with no additional manual annotations is a novel but challenging area in the multimedia and the machine learning community. A considerable amount of videos on the web are associated with rich but noisy contextual information, such as the title, which provides weak annotations or labels about the video content. To leverage the big noisy web labels, this paper proposes a novel method called WEbly-Labeled Learning (WELL), which is established on the state-of-the-art machine learning algorithm inspired by the learning process of human. WELL introduces a number of novel multi-modal approaches to incorporate meaningful prior knowledge called curriculum from the noisy web videos. To investigate this problem, we empirically study the curriculum constructed from the multi-modal features of the videos collected from YouTube and Flickr. The efficacy and the scalability of WELL have been extensively demonstrated on two public benchmarks, including the largest multimedia dataset and the largest manually-labeled video set. The comprehensive experimental results demonstrate that WELL outperforms state-of-the-art studies by a statically significant margin on learning concepts from noisy web video data. In addition, the results also verify that WELL is robust to the level of noisiness in the video data. Notably, WELL trained on sufficient noisy web labels is able to achieve a comparable accuracy to supervised learning methods trained on the clean manually-labeled data.
Forecasting Student Admission in Colleges with Neural Networks

Summary In this study, the use of neural networks in predicting the performance of college at the admission counseling, before the actual event takes place, is analyzed. In our model, we aim to convert this forecasting problem into a classification one, i.e., a college based on its its total no. of seats filled during the counseling is classified in one of the five output categories chosen. This model can be used as a powerful decision aid by the university management, the individual college management & the anticipating students. We’ve presented the basic overview of ANN followed by few words on forecasting. The input decision variables & the output classes have been clearly laid out.
Differential Fault Analysis on the Families of SIMON and SPECK Ciphers

In 2013, the US National Security Agency proposed two new families of lightweight block ciphers: SIMON and SPECK. Currently, linear and differential cryptanalytic results for SIMON are available in the literature but no fault attacks have been reported so far on these two cipher families. In this paper, we show that these families of ciphers are vulnerable to differential fault attacks. Specifically, we demonstrate two fault attacks on SIMON and one fault attack on SPECK. The first attack on SIMON assumes a bit-flip fault model and recovers the n-bit last round key of SIMON using n/2 bit faults. The second attack on SIMON uses a more practical, random byte fault model and requires n/8 faults on average to retrieve the last round key. The attack presented on SPECK also assumes a bit-flip fault model and recovers the n-bit last round key of SPECK using n/3 bit faults on average.
Bivariate data with ridges : two-dimensional smoothing of mortality rates

In this article we explore some bivariate smoothing methods with partial differential regularizations designed to handle smooth bivariate surfaces with occasional ridges. We apply our technique to smoothing mortality rates. Mortality rates are typically smoothed over two dimensions: age and time. Occasional ridges occur due to period effects (e.g., deaths due to wars and epidemics) and cohort effects (e.g., the effects of wars and epidemics on the survivors). We propose three new practical methods of smoothing mortality rates over age and time. The first method uses bivariate thin plate splines. The second uses a similar procedure but with lasso-type regularization. The third method also uses bivariate lasso-type regularization, but allows for both period and cohort effects. In these smoothing methods, the logarithms of mortality rates are modelled as the sum of four components: a smooth bivariate function of age and time, smooth one-dimensional cohort effects, smooth one-dimensional period effects and random errors. Cross validation is used to compare these new smoothing methods with existing approaches. Although our methods are designed to smooth logarithms of mortality rates, they are generic enough to be applied to any bivariate data with occasional ridges.
Using Probabilistic Graphical Models to Enhance the Prognosis of Health-Related Quality of Life in Adult Survivors of Critical Illness

Health-related quality of life (HR-QoL) is a subjective concept, reflecting the overall mental and physical state of the patient, and their own sense of well-being. Estimating current and future QoL has become a major outcome in the evaluation of critically ill patients. The aim of this study is to enhance the inference process of 6 weeks and 6 months prognosis of QoL after intensive care unit (ICU) stay, using the EQ-5D questionnaire. The main outcomes of the study were the EQ-5D five main dimensions: mobility, self-care, usual activities, pain and anxiety depression. For each outcome, three Bayesian classifiers were built and validated with 10-fold cross-validation. Sixty and 473 patients (6 weeks and 6 months, respectively) were included. Overall, 6 months QoL is higher than 6 weeks, with the probability of absence of problems ranging from 31% (6 weeks mobility) to 72% (6 months self-care). Bayesian models achieved prognosis accuracies of 56% (6 months, anxiety depression) up to 80% (6 weeks, mobility). The prognosis inference process for an individual patient was enhanced with the visual analysis of the models, showing that women, elderly, or people with longer ICU stay have higher risk of QoL problems at 6 weeks. Likewise, for the 6 months prognosis, a higher APACHE II severity score also leads to a higher risk of problems, except for anxiety depression where the youngest and active have increased risk. Bayesian networks are competitive with less descriptive strategies, improve the inference process by incorporating domain knowledge and present a more interpretable model. The relationships among different factors extracted by the Bayesian models are in accordance with those collected by previous state-of-the-art literature, hence showing their usability as inference model.
Hepatocellular carcinoma: iodized-oil CT TNM classification.

OBJECTIVE We conducted a prospective study to determine the efficacy of iodized-oil enhancement in CT for preoperative assessment of the T factor of the TNM classification in patients with hepatocellular carcinoma (HCC). SUBJECTS AND METHODS Iodized-oil CT was performed as a part of preoperative staging in 28 patients with HCC. We determined the radiologic T factor. Pathologic correlation was obtained after orthotopic liver transplantation (n = 25) or autopsy (n = 3), and the histologic T factor was determined. RESULTS The sensitivity of iodized-oil CT accurately distinguishing between stage-T4 tumors and tumors in stages T1-T3 was 88% (7/8) with a positive predictive value of 100% (7/7). CONCLUSION Iodized-oil CT is useful for preoperatively assessing the T factor of the TNM classification in HCC, and it improves the accuracy of radiologic staging. Iodized-oil CT can help to better patients with HCC for orthotopic liver transplantation.
A relevance-novelty combined model for genomics search result diversification

Traditional retrieval models assume that the relevance of a document is independent of the relevance of other documents. However, this assumption may result in high redundancy and low diversity in a ranked list. In order to provide comprehensive and diverse answers to fulfill biologists' information need, we propose a relevance-novelty combined model, named RelNov model, based on the framework of an undirected graphical model. Experiments conducted on the TREC 2006 and 2007 Genomics collections show that the proposed approach is effective in promoting both diversity and relevance of retrieval ranked lists.
Assessing Movement Factors in Upper Limb Kinematics Decoding from EEG Signals

The past decades have seen the rapid development of upper limb kinematics decoding techniques by performing intracortical recordings of brain signals. However, the use of non-invasive approaches to perform similar decoding procedures is still in its early stages. Recent studies show that there is a correlation between electroencephalographic (EEG) signals and hand-reaching kinematic parameters. From these studies, it could be concluded that the accuracy of upper limb kinematics decoding depends, at least partially, on the characteristics of the performed movement. In this paper, we have studied upper limb movements with different speeds and trajectories in a controlled environment to analyze the influence of movement variability in the decoding performance. To that end, low frequency components of the EEG signals have been decoded with linear models to obtain the position of the volunteer’s hand during performed trajectories grasping the end effector of a planar manipulandum. The results confirm that it is possible to obtain kinematic information from low frequency EEG signals and show that decoding performance is significantly influenced by movement variability and tracking accuracy as continuous and slower movements improve the accuracy of the decoder. This is a key factor that should be taken into account in future experimental designs.
Probabilistic Inference for Occluded and Multiview On-road Vehicle Detection

Visual-based approaches have been extensively studied for on-road vehicle detection; however, it faces great challenges as the visual appearance of a vehicle may greatly change across different viewpoints and as a partial observation sometimes happens due to occlusions from infrastructure or scene dynamics and/or a limited camera vision field. This paper presents a visual-based on-road vehicle detection algorithm for a multilane traffic scene. A probabilistic inference framework based on part models is proposed to overcome the challenges from a multiview and partial observation. Geometric models are learned for each dominant viewpoint to describe the configuration of vehicle parts and their spatial relations in probabilistic representations. Viewpoint maps are generated based on the knowledge of the road structure and driving patterns, which provide a prediction of the viewpoints of a vehicle whenever it happens at a certain location. Extensive experiments are conducted using an onboard camera on multilane motor ways in Beijing. A large-scale data set that contains more than 30 000 labeled ground truths for both fully and partially observed vehicles in different viewpoints across various traffic density scenes is developed. The data set will be opened to the society together with this publication.