Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel mechanisms to inhibit HIV reservoir seeding using Jak inhibitors

Despite advances in the treatment of HIV infection with ART, elucidating strategies to overcome HIV persistence, including blockade of viral reservoir establishment, maintenance, and expansion, remains a challenge. T cell homeostasis is a major driver of HIV persistence. Cytokines involved in regulating homeostasis of memory T cells, the major hub of the HIV reservoir, trigger the Jak-STAT pathway. We evaluated the ability of tofacitinib and ruxolitinib, two FDA-approved Jak inhibitors, to block seeding and maintenance of the HIV reservoir in vitro. We provide direct demonstration for involvement of the Jak-STAT pathway in HIV persistence in vivo, ex vivo, and in vitro; pSTAT5 strongly correlates with increased levels of integrated viral DNA in vivo, and in vitro Jak inhibitors reduce the frequency of CD4+ T cells harboring integrated HIV DNA. We show that Jak inhibitors block viral production from infected cells, inhibit γ-C receptor cytokine (IL-15)-induced viral reactivation from latent stores thereby preventing transmission of infectious particles to bystander activated T cells. These results show that dysregulation of the Jak-STAT pathway is associated with viral persistence in vivo, and that Jak inhibitors target key events downstream of γ-C cytokine (IL-2, IL-7 and IL-15) ligation to their receptors, impacting the magnitude of the HIV reservoir in all memory CD4 T cell subsets in vitro and ex vivo. Jak inhibitors represent a therapeutic modality to prevent key events of T cell activation that regulate HIV persistence and together, specific, potent blockade of these events may be integrated to future curative strategies.
Ripple Minimization Through Harmonic Elimination in Asymmetric Interleaved Multiphase DC–DC Converters

Symmetric multiphase dc-dc converters are widely used in power electronics, as they enable the processing of high power through splitting the overall load-current into multiple phases. Distributing the processed power symmetrically between the phases and performing ripple minimization through interleaving is well understood. However, in recent applications such as maximum power point tracking for solar photovoltaic, converters are forced to operate under asymmetric conditions, due to differences in the sources or loads of each converter. This study presents a control technique, based on harmonic elimination, that allows for ripple minimization under asymmetric conditions. The mathematical derivations are outlined and simulations are used to evaluate the performance of the proposed technique. Measurements taken from an experimental prototype, consisting of three dc-dc buck converters, demonstrate significant improvements in ripple reduction over conventional interleaving techniques. When the multiphase converter is operated at the optimum asymmetric phase-shift found through the techniques presented here, a more than 3x reduction in net current ripple is observed under realistic operating conditions. Additionally, the undesirable first harmonic ripple component is reduced by 14.8 dB with the proposed technique.
Simulation Analysis of Order and Kanban Sequencing Rules in a Kanban-Controlled Flow Shop

This paper presents a simulation study of several customer order and kanban sequencing/selection rules in a kanban-controlled flow shop. Kanbans are used to authorize production within a work center and withdrawals from previous work cen ters. In addition to single queue sequencing rules such as first-come, first-served (FCFS) and shortest processing time first (SPT), a set ofmul tiple queue selection rules are defined for produc tion kanbans. Furthermore, assuming that the orders wait in separate queues determined by part type, single queue sequencing rules are ap plied to customer orders within each queue. A simulation model is developed to investigate the effect of kanban-order sequencing rule combina tions on performance measures such as mean order cycle time, lateness, and tardiness. The re sults indicate that the shortest processing time first-smallest order quantity combination (SPT- SOQ) is the best operating policy.
Modeling of Smoke from a Single View

This paper presents a simple method for modeling smoke from a single view, which could preserve fine realistic look observed from different views. Thin translucent smoke, generated by cigarette, candle, joss stick et al, is the main focus of this paper. The proposed method initially computes the smoke intensity from the input key frame image, then partitions the smoke into multiple segments. For each segment of smoke, the principal direction is calculated by principal component analysis, and two basic functions are generated. Depth of each pixel in the image is estimated with the basic functions. Then, three-dimensional density distributions are constructed by referring to the intensity and depth. Finally, smoke density distributions at key frames are used to generate animated smoke. Experiment results indicate that our method can synthesize visually realistic smoke from a single view with low computational cost.
High-speed X-ray imaging of needle-free jet injections

A custom-built high-speed X-ray imaging system with a maximum frame rate of 2,000 fps was used to visualize the flow of fluid injected using a 27 gauge needle and a needle-free jet injector. Two different needle injection techniques (injection after partial retraction of the needle, and injection after no needle retraction) were evaluated to demonstrate the different flow patterns. For needle-free jet injections, we demonstrate how a two-phase injection velocity profile affects tissue penetration and fluid delivery. We show that a high initial jet speed will first puncture the skin, deliver volume to the fat, and then puncture the muscle, and a slower following speed will deliver the remaining fluid to the muscle. In all cases, due to the inhomogeneous anisotropic nature of tissue, the injectate preferentially follows paths of least resistance.
Effects of a Psychological Internet Intervention in the Treatment of Mild to Moderate Depressive Symptoms: Results of the EVIDENT Study, a Randomized Controlled Trial

Background: Mild to moderate depressive symptoms are common but often remain unrecognized and treated inadequately. We hypothesized that an Internet intervention in addition to usual care is superior to care as usual alone (CAU) in the treatment of mild to moderate depressive symptoms in adults. Methods: This trial was controlled, randomized and assessor-blinded. Participants with mild to moderate depressive symptoms (Patient Health Questionnaire, PHQ-9, score 5-14) were recruited from clinical and non-clinical settings and randomized to either CAU or a 12-week Internet intervention (Deprexis) adjunctive to usual care. Outcomes were assessed at baseline, 3 months (post-assessment) and 6 months (follow-up). The primary outcome measure was self-rated depression severity (PHQ-9). The main analysis was based on the intention-to-treat principle and used linear mixed models. Results: A total of 1,013 participants were randomized. Changes in PHQ-9 from baseline differed signiﬁcantly between groups (t825 = 6.12, p < 0.001 for the main effect of group). The post-assessment between-group effect size in favour of the intervention was d = 0.39 (95% CI: 0.13-0.64). It was stable at follow-up, with d = 0.32 (95% CI: 0.06-0.69). The rate of participants experiencing at least minimally clinically important PHQ-9 change at the post-assessment was higher in the intervention group (35.6 vs. 20.2%) with a number needed to treat of 7 (95% CI: 5-10). Conclusions: The Internet intervention examined in this trial was superior to CAU alone in reducing mild to moderate depressive symptoms. The magnitude of the effect is clinically important and has public health implications.
Driver Distraction Assessment Using Driver Modeling

Characterizing individual human drivers is of increasing interest for applications like adaptive driver assistance or monitoring. Describing the human driver by means of control-theoretic driver models constitutes a promising approach. In this paper, we apply a driver model adopted from literature to real-road driving of a distraction experiment in order to assess the driver state. The control-theoretic driver model features an anticipatory and a compensatory tracking component as well as a processing delay and a neuromuscular motor component. The distraction experiment data comprises real road driving with a visuomotor and an auditory secondary task, as well as reference driving. By means of prediction error identification, we continuously and individually estimate the model parameters from driving data of eleven drivers. We evaluate the distributions of the driver model parameters and the predictive capability of the estimated driver models. The estimated driver model parameters reflect distracted driving behavior according to the driving task. As a promising experimental result, the driver model parameters and predictive performance are significantly associated with driver distraction.
Entrance of tetrahydropterin derivatives in brain after peripheral administration: effect on biogenic amine metabolism.

The ability of various structural analogs of tetrahydrobiopterin to enter the rat brain and remain in the reduced form after i.p. administration was tested because tetrahydrobiopterin itself enters poorly. The total content of various pterins in different brain areas was measured by high-pressure liquid chromatography-fluorescence detection, whereas high-pressure liquid chromatography-electrochemical detection was used to measure the content of reduced pterins. The effect of injection of certain tetrahydropterins on brain content of biogenic amines and their major metabolites was also monitored. In general, when the position 6 side-chain of tetrahydrobiopterin was made shorter and smaller in size, entrance into the brain from the periphery was markedly enhanced. Increasing the lipophilicity or size of the position 6 side-chain did not allow for better entry into brain and, in some cases, hindered entry. Under the conditions tested, none of the tetrahydropterins influenced the brain content of biogenic amines or their major metabolites; higher brain concentrations of tetrahydropterins are probably necessary to modify central nervous system biogenic amine metabolism. Other types of structural modifications or experimental approaches may be necessary to achieve higher brain concentrations of active cofactors, which may be required for the successful treatment of certain human diseases with tetrahydropterins.
A runtime fault survival method for deployed software during production runs

Runtime memory faults during production run should be more thoroughly addressed because they severely affect system availability. This paper proposes a method for mitigating memory faults during production runs of deployed software, thereby ensuring normal system operation until patches to fix the faults are delivered. Furthermore, the method helps enhance debugging efficiency by providing accurate on‐site fault information used by developers to release timely patches. The core of the method is to offer information tagging to identify runtime faults and a fault survival algorithm to provide differentiated fault mitigation according to the runtime state. We implemented ROPHE on a Linux 2.6 platform and conducted an empirical study of representative Linux applications. The results show that the average fault‐handling rate among the applications is 35.75%, whereas the RemOte runtime Protection for High‐risk Error (ROPHE) greatly improves capacity to an average of 91.94%. Specifically, the fault‐handling rates of the applications ranged widely from 7.32% to 62.96%, while ROPHE provided fault‐survival rates in the relatively narrow range of 82.35–97.44%. The experimental results show that the proposed method guarantees the same level of reliability for all applications regardless of their individual fault handling capacity. Copyright © 2016 John Wiley & Sons, Ltd.
Recognizing and Segmenting Objects in the Presence of Occlusion and Clutter

Author(s): Ghiasi, Golnaz | Advisor(s): Fowlkes, Charless C | Abstract: One of the fundamental problems of computer vision is to detect and localize objectssuch as humans and faces in images. Object detection is a building block for a widerange of applications including self-driving cars, robotics and face recognition. Thoughsignificant progress has been achieved in these tasks, it is still challenging to obtainrobust results in unconstrained images. Real world scenes usually contain more than oneobject and it is very likely that some parts of an object are occluded by other objectsin the scene. To tackle occlusion, image features generated by occlusion should be explicitlymodeled rather than treated as noise. In this thesis, a deformable part model for detectionand keypoint localization is introduced that explicitly models part occlusion. The proposedmodel structure makes it possible to augment positive training data with large numbers ofsynthetically occluded instances. This allows us to easily incorporate the statistics ofocclusion patterns in a discriminatively trained model. To exploit bottom-up cues such asoccluding contours and image segments, we extend the proposed model to utilize bottom-upclass-specific segmentation in order to jointly detect and segment out the foregroundpixels belonging to the object.In these approaches, a detector for a single object category is trained which operatesindependently of other detections in the scene. An appealing alternative approach fordetection in cluttered images is to move from single object detection to whole-imageparsing. The presence of occlusion can then be “explained away” by the presence of anoccluding object. We model multi-object detection by classifying each pixel of the image(semantic segmentation) using Convolutional Neural Network. CNN architectures have terrificrecognition performance but rely on spatial pooling which makes it difficult to adapt themto tasks that require dense, pixel-accurate labeling. We demonstrate that while theapparent spatial resolution of convolutional feature maps is low, the high-dimensional featurerepresentation contains significant sub-pixel localization information. We describe amulti-resolution reconstruction architecture based on a Laplacian pyramid that uses skipconnections from higher resolution feature maps and multiplicative gating to successivelyrefine segment boundaries reconstructed from lower-resolution maps. We demonstrate that this approach yields state-of-the-art semantic segmentation results without resorting to morecomplex random-field inference or instance detection driven architectures.
Fractional convolution and correlation via operator methods and an application to detection of linear FM signals

Using operator theory methods together with our previously introduced unitary fractional operator, we derive explicit definitions of fractional convolution and correlation operations in a systematic and comprehensive manner. Via operator manipulations, we also provide alternative formulations of those fractional operations that suggest efficient algorithms for discrete implementation. Through simulation examples, we demonstrate how well the proposed efficient method approximates the continuous formulation of fractional autocorrelation. It is also shown that the proposed fractional autocorrelation corresponds to radial slices of the ambiguity function (AF). We also suggest an application of the fast fractional autocorrelation for detection and parameter estimation of linear FM signals.
Patch-based Video Denoising with Optical Flow Estimation

— Over the years a variety of methods have been introduced to remove noise from digital images, such as Gaussian filtering, anisotropic filtering, and Total Variation minimization. However, many of these algorithms remove the fine details and structure of the image in addition to the noise because of assumptions made about the frequency content of the image. The non-local means algorithm does not make these assumptions, but instead assumes that the image contains an extensive amount of redundancy. These redundancies can then be exploited to remove the noise in the image. This project will implement the non-local means algorithm and compare it to other denoising methods using the method noise measurement.
Flavonoid intake from vegetables and fruits is inversely associated with colorectal cancer risk: a case–control study in China

Abstract Flavonoids may play an important role in the protective effects of vegetables, fruits and tea against colorectal cancer. However, associations between flavonoids and colorectal cancer risk are inconsistent, and a few studies have evaluated the effect of flavonoids from different dietary sources separately. This study aimed to evaluate associations of flavonoids intake from different dietary sources with colorectal cancer risk in a Chinese population. From July 2010 to December 2015, 1632 eligible colorectal cancer cases and 1632 frequency-matched controls (age and sex) completed in-person interviews. A validated FFQ was used to estimate dietary flavonoids intake. Multivariate logistical regression models were used to calculate the OR and 95 % CI of colorectal cancer risk after adjusting for various confounders. No significant association was found between total flavonoids and colorectal cancer risk, with an adjusted OR of 1·06 (95 % CI 0·85, 1·32) comparing the highest with the lowest quartile. Anthocyanidins, flavanones and flavones intakes from total diet were found to be inversely associated with colorectal cancer risk. Compared with the lowest quartile, the adjusted OR for the highest quartile were 0·80 (95 % CI 0·64, 1·00) for anthocyanidins, 0·28 (95 % CI 0·22, 0·36) for flavanones and 0·54 (95 % CI 0·43, 0·67) for flavones. All subclasses of flavonoids from vegetables and fruits were inversely associated with colorectal cancer. However, no significant association was found between tea flavonoids and colorectal cancer risk. These data indicate that specific flavonoids, specifically flavonoids from vegetables and fruits, may be linked with the reduced risk of colorectal cancer.
Do Loyalty Programs Enhance Behavioral Loyalty ? A Market-Wide Analysis Accounting for Endogeneity

This paper studies the effects of loyalty programs on behavioral loyalty using market-wide panel data on supermarket purchases. We present a conceptual framework relating loyalty program membership to share-of-wallet. To account for the two-sided causality between these variables, we specify a model for the loyalty program decision and use instrumental variables such as privacy concerns and loyalty program enjoyment. In general, loyalty programs are shown to influence share-of-wallet positively; ignoring endogeneity leads to substantially overestimate of effects. Not all loyalty programs are effective, however; four out of seven loyalty programs under study give greater value away to their members than they earn back in terms of additional customer revenues. The effectiveness of loyalty programs increases with value given through saving features. In contrast, the effectiveness of a program diminishes with higher price discounts to program members.
Radial basis function networks for classifying process faults

It is shown that under certain conditions the backpropagation network classifier can produce nonintuitive, nonrobust decision surfaces. These result from the inherent nature of the sigmoid transfer function, the definition of the training set, and the error function used for training. The backpropagation network has no mechanism in the standard training scheme for identifying regions not in any known classes. The radial basis function network overcomes these difficulties by using a nonmonotonic transfer function based on the Gaussian density function. While producing robust decision surfaces, the radial basis function also provides an estimate of how close a test case is to the original training data, allowing the classifier to signal that a test case potentially represents a novel class while still presenting the most plausible classification. For applications where this type of behavior is important, such as fault diagnosis, the radial basis function network is shown to offer clear advantages over the backpropagation network. The radial basis function is also faster to train because the training of the two layers is decoupled. <<ETX>>
Human Papillomavirus (HPV) Profiles of Vulvar Lesions: Possible Implications for the Classification of Vulvar Squamous Cell Carcinoma Precursors and for the Efficacy of Prophylactic HPV Vaccination

The term vulvar intraepithelial neoplasia (VIN) introduced in 1986 incorporates 3 grades of usual VIN (u-VIN I-III) and the differentiated VIN (d-VIN). Although u-VIN is etiologically associated with the human papillomavirus (HPV) infection, d-VIN represents an alternative HPV negative pathway of vulvar carcinogenesis. In 2004, the u-VIN I category was abandoned and u-VIN II and III were merged. Further, an alternative Bethesda-like terminology scheme presenting the term vulvar intraepithelial lesion was proposed recently. To analyze the impact of HPV profiles of vulvar precancerous lesions for their classification and to assess the presumable efficacy of the prophylactic HPV vaccination, 269 vulvar excisions representing lichen sclerosus, lichen simplex chronicus, condylomata acuminata, d-VIN, all grades of u-VIN and squamous cell carcinomas were subjected to the HPV typing by use of GP5+/6+ polymerase chain reaction and reverse line blot hybridization. The results showed different HPV profiles, and also differing frequency of multiple-type HPV infection and the age structure in patients with u-VIN II and III. The biologic heterogeneity within the u-VIN II category was also demonstrated. u-VIN I was distinguished as a rare disorder associated with high-risk HPV infection. We conclude that the original VIN terminology proposed in 1986 seems to be appropriate for the classification of vulvar squamous dysplastic lesions. The spectrum of HPV types found in vulvar squamous cell carcinomas indicates that the efficacy of HPV vaccination in preventing vulvar cancer might be diminished in the studied population, because the recently developed prophylactic vaccines are targeted against a limited number of HPV types.
Comprehensive Research on Privacy Preserving Emphasizing on Distributed Clustering

: Often, the information is sensitive or private in nature and these sensitive data when mined violates the privacy of the individuals. Privacy preserving data mining (PPDM) mines the data but intends to preserve the privacy of susceptible data without ever actually seeing it. This paper recaps the important techniques in PPDM like anonymization, perturbation and cryptography. Nowadays, data mining is extensively used when the data is distributed among multiple parties. This paper highlights the research carried out in privacy preserving distributed clustering. Clustering is an effective method to discover data distribution and patterns in datasets. Significant research in privacy preserving distributed clustering is shaped on k-means clustering algorithm with secure multiparty computation (SMC). This work focuses on the previous development, existing challenges, and upcoming trends in privacy preserving k-means clustering with horizontally and vertically distributed data.
Observer-based sensorless field-oriented control of induction machines

This paper provides an improvement in sensorless control of induction machines (IM). The proposed method uses a reduced-order linear observer that simplifies design and hardware implementation. Its execution time is less than that we have realized in the previous works. Furthermore, it also estimates the load torque. We have obtained good results until 60 rpm. The simulation and experimental results illustrate the validity and efficiency of the proposed method.