Miracle-dz/UniFAR-FTUs huggingface dataset:
In Vivo Fitness Costs of Different Gag CD8 T-Cell Escape Mutant Simian-Human Immunodeficiency Viruses for Macaques

ABSTRACT The kinetics of immune escape and reversion depend upon the efficiency of CD8 cytotoxic T lymphocytes (CTL) and the fitness cost of escape mutations. Escape kinetics of three simian immunodeficiency virus Gag CTL epitopes in pigtail macaques were variable; those of KP9 and AF9 were faster than those of KW9. Kinetics of reversion of escape mutant virus to wild type upon passage to naïve major histocompatibility complex-mismatched macaques also varied. Rapid reversion occurred at KP9, gradual biphasic reversion occurred at AF9, and escape mutant KW9 virus failed to revert. The fitness impact of these mutations is KP9 > AF9 > KW9. These data provide insights into the differential utility of CTL in controlling viremia.
A prospective evaluation of persistence on antihypertensive treatment with different antihypertensive drugs in clinical practice

Persistence on treatment affects the efficacy of antihypertensive treatment. We prospectively investigated the persistence on therapy and the extent of blood pressure (BP) control in 347 hypertensive patients (age 59.4 ± 6 years) randomly allocated to a first-line treatment with: angiotensin-converting enzyme (ACE) inhibitors, calcium-channel blockers (CCBs), ß-blockers, angiotensin-II receptor blockers (ARBs), or diuretics and followed-up for 24-months. Persistence on treatment was higher in patients treated with ARBs (68.5%) and ACE inhibitors (64.5%) vs CCBs (51.6%; p < 0.05), β-blockers (44.8%, p < 0.05), and diuretics (34.4%, p < 0.01). No ARB, ACE inhibitor, β-blocker, or diuretic was associated with a higher persistence in therapy compared with the other molecules used in each therapeutic class. The rate of persistence was significantly higher in patients treated with lercanidipine vs others CCBs (59.3% vs 46.6%, p < 0.05). Systolic and diastolic BP was decreased more successfully in patients treated with ARBs (−11.2/−5.8 mmHg), ACE inhibitors (−10.5/−5.1 mmHg), and CCBs (−8.5/−4.6 mmHg) compared with ß-blockers (−4.0/−2.3 mmHg p < 0.05) and diuretics (−2.3/−2.1 mmHg, p < 0.05). No ARB, ACE inhibitor, β-blocker, or diuretic was associated with a higher BP control compared with the other molecules used in each therapeutic class. A trend toward a better BP control was observed in response to lercanidipine vs other CCBs (p = 0.059). The present results confirm the importance of persistence on treatment for the management of hypertension in clinical practice.
On the Effectiveness of Using Syntactic and Shallow Semantic Tree Kernels for Automatic Assessment of Essays

This paper is concerned with the problem of automatic essay grading, where the task is to grade student written essays given course materials and a set of humangraded essays as training data. Latent Semantic Analysis (LSA) has been used extensively over the years to accomplish this task. However, the major limitation of LSA is that it only retains the frequency of words by disregarding the word sequence, and the syntactic and semantic structure of texts. As a remedy, we propose the use of syntactic and shallow semantic tree kernels for grading essays. Experiments suggest that syntactic and semantic structural information can significantly improve the performance of the state-of-the-art LSAbased models for automatic essay grading.
Similar Exposure to a Tobacco-Specific Carcinogen in Smokeless Tobacco Users and Cigarette Smokers

Smokeless tobacco has been proposed as a reduced risk substitute for smoking, but no large studies have investigated exposure to the powerful carcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) in smokeless tobacco users versus smokers. The purpose of this study was to carry out such a comparison. Levels of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and its glucuronides (total NNAL), a biomarker of NNK exposure, and cotinine, a biomarker of nicotine exposure, were quantified in the urine of 420 smokers and 182 smokeless tobacco users who were participants in studies designed to reduce their use of these products. The measurements were taken at baseline, before intervention. Levels of total NNAL per milliliter of urine were significantly higher in smokeless tobacco users than in smokers (P < 0.0001). When adjusted for age and gender, levels of total NNAL per milligram of creatinine were also significantly higher in smokeless tobacco users than in smokers (P < 0.001). Levels of cotinine per milliliter of urine and per milligram of creatinine were significantly higher in smokeless tobacco users than in smokers (P < 0.001). These results show similar exposures to the potent tobacco-specific carcinogen NNK in smokeless tobacco users and smokers. These findings do not support the use of smokeless tobacco as a safe substitute for smoking. (Cancer Epidemiol Biomarkers Prev 2007;16(8):1567–72)
Understanding Administrative Culture: Some Theoretical and Methodological Remarks

The article highlights five aspects of administrative culture. It first describes different ways of conceptualizing administrative culture. The second aspect is the ontology of administrative culture, two views of which are presented. The first of these sees organizational culture as a dependent variable that can be manipulated and altered to reflect management and leadership preferences; the other views organizations as miniature societies reflecting broader societal culture. Change is more path dependent than rationally arranged at will. The third aspect of administrative culture concerns epistemology, focusing on how knowledge about culture is created, how a researcher may carry out inquiry, and what the inquiry is about. The fourth aspect – the axiology of administrative culture – concerns the appropriate administrative norms and ethical standards of public officials. Finally, the methodological aspect points to how to study and capture different aspects of administrative culture.
Text Cluster Trimming for Better Descriptions and Improved Quality

Text clustering is potentially very useful for exploration of text sets that are too large to study manually. The success of such a tool depends on whether the results can be explained to the user. An automatically extracted cluster description usually consists of a few words that are deemed representative for the cluster. It is preferably short in order to be easily grasped. However, text cluster content is often diverse. We introduce a trimming method that removes texts that do not contain any, or a few of the words in the cluster description. The result is clusters that match their descriptions better. In experiments on two quite different text sets we obtain significant improvements in both internal and external clustering quality for the trimmed clustering compared to the original. The trimming thus has two positive effects: it forces the clusters to agree with their descriptions (resulting in better descriptions) and improves the quality of the trimmed clusters.
Evolution with Explicit Learning

In Genetic Programming (GP) and most other evolutionary computing approaches, the knowledge learned during the evolutionary processing is implicitly encoded in the population. A small family of approaches, known as Estimation of Distribution Algorithms, learn this knowledge directly in the form of probability distributions. In this research, we proposed a new approach for program synthesis – Program Evolution with Explicit Learning (PEEL), belonging to this family. PEEL learns probability distributions from previous generations and stochastically generates new populations according to this distribution. PEEL is intrinsically different from GP systems because it abandons conventional GP genetic operators and does not maintain a population. On the benchmark problems we have studied, this approach shows at least comparable performance to GP.
Simulating Onthe-Job Training using a Collaborative Virtual Environment with Head Slaved Visual Deictic Reference

Aircraft inspection is a vital element in assuring safety and reliability of an air transportation sys tem. The human inspector performing visual inspection of an aircra ft is the backbone of this process. Training of ins pectors is an effective strategy to improve their inspection perf ormance. A drawback of present-day on-the-job train ing provided to aircraft inspectors is the limited exposure to d ifferent defect types. Current research combines th advantages of virtual reality (VR) technology that includes expos ure to a wide variety of defects and the one-on-one tutoring approach of on-the-job training by implementing a c ollaborative virtual training environment. Avatars are used to represent the co-participants in an immersive colla b r tive virtual environment (CVE). In a CVE, infor mation of where the trainer is focusing can be provide to a t rainee as visual deictic reference (VDR) slaved to the head orientation of the trainer. This study evaluates th e effectiveness of simulating on-the-job training i a CVE for aircraft inspection training, providing VDR slaved to the trainer’s head orientation. Results show th at e training was effective in improving inspection performance.
A direct approach for efficiently tracking with 3D morphable models

We present an efficient algorithm for fitting a morphable model to an image sequence. It is built on a projective geometry formulation of perspective projection, which results in a linear mapping from 3D shape to the projective plane, and a factorisation of this mapping into matrices that can be partially computed off-line. This algorithm can cope with full 360 degrees object rotation and linear deformations. We validate our approach using synthetically generated and real sequences. Compared to a plain Lucas-Kanade implementation, we achieve a six fold increase in performance for a rigid object and two fold for a non-rigid face.
An efficient strategy for supporting fully interactive display in a video-on-demand server

Conventional file systems are unable to guarantee that clients can access continuous media in a way that permits delivery deadlines to be met under normal buffering conditions. This is due to the fact that magnetic disks and optical disks have large seek and rotational latencies that can cause a continuous media client to starve if the continuous medium is not buffered and coordinated with other service requests. Therefore, finding a way to support continuous retrieval of multimedia data at its required bandwidth and a way to store the multimedia data is challenging task. One of the most important challenges in a Video-On-Demand system is to support interactive browsing functions such as fast forward and fast backward. There are several possible approaches to implementing these functions which require additional resources, such as, storage space, network bandwidth or a set of scalable data compress algorithms. In this paper, we propose an efficient strategy to support continuous display of multiple objects at different display speed rates, simultaneously, without any additional resource.
Malware Characterization through Alert Pattern Discovery using Factor Analysis

We present initial results of a factor analysis of the alerts generated by a bot detection tool called BotHunter. We examined the Snort rule identifiers (SIDs) of the alerts generated by the BotHunter tool, developed in the Cyber-Threat Analytics (Cyber-TA) project, considering which SIDs co-occur pertaining to the same identified bot instance. Correlation identifies SIDs that occur jointly in the same bot instance, possibly because they are part of a multivector attack. Factor analysis further explains most of the correlation structure across all SIDs as largely due to loadings on a small number of factors. Our initial analysis indicates that some factor patterns correspond to infection stages such as command-and-control activities through IRC channels, and that some malware families have distinguishing factor patterns. Our analysis is useful in identifying redundant signatures, enabling performance tuning of intrusion detection systems. Also, it could facilitate the identification of emerging malware that manifests as multiple attack vectors. Eventually, this approach may enable automated attack pattern discovery. Repository subscribers can query the result of this analysis in near real time, benefiting from identification of emerging malware indicators. Our approach does not require predefined attack models for detecting complex attacks, and discovers potentially unknown relationships among event types. Moreover, it requires relatively little information, as it is based on SIDs and does not consider source or target addresses. As such, it is more suitable for analysis of largescale, privacy-preserving alert repositories such as the Cyber-TA alert repository.
The Fate of the First Galaxies. II. Effects of Radiative Feedback

We use three-dimensional cosmological simulations with radiative transfer to study the formation and evolution of the first galaxies in a ΛCDM cosmology. The simulations include continuum radiative transfer using the optically thin variable Eddington tensor (OTVET) approximation and line radiative transfer in the H2 Lyman-Werner bands of the UV background radiation. Chemical and thermal processes are treated in detail, particularly the ones relevant for H2 formation and destruction. We find that the first luminous objects ("small-halo objects") are characterized by bursting star formation (SF) that is self-regulated by a feedback process acting on cosmological instead of galactic scales. The global SF history is regulated by the mean number of ionizing photons that escape from each source, ϵUV⟨fesc⟩. It is almost independent of the assumed SF efficiency parameter, ϵ*, and the intensity of the dissociating background. The main feedback process that regulates the SF is the reformation of H2 in front of H II regions and inside relic H II regions. The H II regions remain confined inside filaments, maximizing the production of H2 in overdense regions through cyclic destruction/reformation of H2. If ϵUV⟨fesc⟩ > 10-7/ϵ*, the SF is self-regulated, photoevaporation of small-halo objects dominates the metal pollution of the low-density intergalactic medium, and the mass of produced metals depends only on ⟨fesc⟩. If ϵUV⟨fesc⟩ ≲ 10-7/ϵ*, positive feedback dominates, and small-halo objects constitute the bulk of the mass in stars and metals until at least redshift z ~ 10. Small-halo objects cannot reionize the universe because the feedback mechanism confines the H II regions inside the large-scale structure filaments. In contrast to massive objects ("large halos"), which can reionize voids, small-halo objects partially ionize only the dense filaments while leaving the voids mostly neutral.
Aging and Inhibitory Control of Action: Cortico-Subthalamic Connection Strength Predicts Stopping Performance

Diffusion weighted imaging (DWI) studies in humans have shown that seniors exhibit reduced white matter integrity compared with young adults, with the most pronounced change occurring in frontal white matter. It is generally assumed that this structural deterioration underlies inhibitory control deficits in old age, but specific evidence from a structural neuroscience perspective is lacking. Cognitive action control is thought to rely on an interconnected network consisting of right inferior frontal cortex (r-IFC), pre-supplementary motor area (preSMA), and the subthalamic nucleus (STN). Here we performed probabilistic DWI tractography to delineate this cognitive control network and had the same individuals (20 young, 20 older adults) perform a task probing both response inhibition and action reprogramming. We hypothesized that structural integrity (fractional anisotropy) and connection strength within this network would be predictive of individual and age-related differences in task performance. We show that the integrity of r-IFC white matter is an age-independent predictor of stop-signal reaction time (SSRT). We further provide evidence that the integrity of white matter projecting to STN predicts both outright stopping (SSRT) and transient braking of response initiation to buy time for action reprogramming (stopping interference effects). These associations remain even after controlling for Go task performance, demonstrating specificity to the Stop component of this task. Finally, a multiple regression analysis reveals bilateral preSMA–STN tract strength as a significant predictor of SSRT in older adults. Our data link age-related decline in inhibitory control with structural decline of STN projections.
Urinary expression of acute kidney injury biomarkers in patients after RIRS: it is a prospective, controlled study.

OBJECTIVE To evaluated the damage effects of retrograde intra-renal surgery (RIRS) on kidney tissue by measuring kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), N-acetyl-β-D-glucosaminidase (NAG), liver-type fatty acid binding protein (LFABP) expression. MATERIAL AND METHODS We enrolled thirty consecutive patients (Group 1) who underwent RIRS that presented with renal calculi size < 2 cm. Forty-seven control patients (Group 2) with no signs or symptoms of urogenital disease were also enrolled for this study. Patients urine KIM-1, NGAL, NAG, and LFABP and creatinine levels were determined before the surgery, 2 hours after the surgery, and 24 hours after the surgery. RESULTS Demographic data were established and found to be similar between the two groups. Two hours after the surgery KIM-1/Cr and NGAL/Cr levels had increased significantly in urine compared to levels before the surgery (P:0.04, P:0.02 respectively) and decreased 24 hours after the surgery. The NAG/Cr and LFABP/Cr levels did not change significantly after the surgery. CONCLUSION According to acute kidney injury (AKI) markers, this study is suggests that RIRS is a safe method , KIM-1/Cr and NGAL/Cr levels were increased first 2 hours but returned to initial levels within 24 hours after the surgery.
Learning Compact Neural Word Embeddings by Parameter Space Sharing

The word embedding vectors obtained from neural word embedding methods, such as vLBL models and SkipGram, have become an important fundamental resource for tackling a wide variety of tasks in the artificial intelligence field. This paper focuses on the fact that the model size of high-quality embedding vectors is relatively large, i.e., more than 1GB. We propose a learning framework that can provide a set of 'compact' embedding vectors for the purpose of enhancing 'usability' in actual applications. Our proposed method incorporates parameter sharing constraints into the optimization problem. These additional constraints force the embedding vectors to share parameter values, which significantly shrinks model size. We investigate the trade-off between quality and model size of embedding vectors for several linguistic benchmark datasets, and show that our method can significantly reduce the model size while maintaining the task performance of conventional methods.
A Novel Point of Interest (POI) Location Based Recommender System Utilizing User Location and Web Interactions

Location aware mobile devices have increased theavailability of user trajectory information making point ofinterest recommenders a popular service on mobile devices. However, one of the main challenges in this area is sparsity ofthe historical trajectory data. So far, most of the recommendersystems take users' historical trajectory information into considerationto recommend different places. Web interactionsreveal rich information on the user interests, and hence arecommender system should take into consideration such data. In this study, we present a model that combines andassociates users interest/taste information, obtained from theirweb interactions together with location information obtainedfrom the Open Street Map (OSM). Then, we combine thisinformation with the users' real time trajectory information(longitude, latitude and timestamp) to present a list of recommendedpoints of interest close to the current location.
Decomposition Search: A Combinatorial Games Approach to Game Tree Search, with Applications to Solving Go Endgames

We develop a new method called decomposition search for computing minimax solutions to games that can be partitioned into independent subgames. The method does not use traditional minimax search algorithms such as alpha-beta, but relies on concepts from combinatorial game theory to do locally restricted searches. This divide-and-conquer approach allows the exact solution of much larger problems than is possible with alpha-beta. We show an application of decomposition search to the game of Go, which has been traditionally regarded as beyond the range of exact search-based solution methods. Our experiments with solving endgames show that alpha-beta searches already become impractical in positions with about 15 remaining moves. However, an endgame solver based on decomposition search can solve a much larger class of endgame problems with solution lengths exceeding 60 moves.
What drives social sentiment? An entropic measure-based clustering approach towards identifying factors that influence social sentiment polarity

Analyzing the public sentiment over social media streams constitutes an extremely demanding task mainly due to the difficulties that are imposed by the wide spectrum of discussion topics that underlie a given collection of posts. This paper addresses the problem of determining the underlying semantic factors that influence the social sentiment polarity in a given corpus of posts through the utilization of an entropic measure-based clustering approach. Extant studies examine the semantic structure of social network data primarily through topic modeling or sentiment analysis methods. The novelty of our approach lies upon the utilization of a semantically-aware clustering procedure that effectively combines topic modeling and sentiment analysis algorithms. Our approach extends the fundamental assumption behind traditional sentiment analysis methods, according to which sentiment can be associated with low level document features such as words, phrases or sentences. We argue that sentiment can be associated with higher level entities such as the semantic axes that span a given volume of posts, thus performing sentiment analysis at the topic level. Our experimentation provides strong evidence that combining topic modeling and sentiment analysis results by a semantically-aware clustering procedure can reveal the distribution of the overall public sentiment on the underlying semantic axes.
Markov Chains for Exploring Posterior Distributions

Several Markov chain methods are available for sampling from a posterior distribution. Two important examples are the Gibbs sampler and the Metropolis algorithm. In addition, several strategies are available for constructing hybrid algorithms. This paper outlines some of the basic methods and strategies and discusses some related theoretical and practical issues. On the theoretical side, results from the theory of general state space Markov chains can be used to obtain convergence rates, laws of large numbers and central limit theorems for estimates obtained from Markov chain methods. These theoretical results can be used to guide the construction of more efficient algorithms. For the practical use of Markov chain methods, standard simulation methodology provides several variance reduction techniques and also gives guidance on the choice of sample size and allocation