Miracle-dz/UniFAR-FTUs huggingface dataset:
Hepatic Stellate Cells as a Target for the Treatment of Liver Fibrosis

Following chronic liver injury of any etiology, there is progressive fibrosis. To date, removing the causative agent is the only effective therapy to stop or even reverse liver fibrosis. Therefore, the development of effective antifibrotic therapies represents a challenge for modern hepatology. In the past decade, dramatic advances have been made in the understanding of the cellular and molecular mechanisms underlying liver fibrogenesis. The identification of activated hepatic stellate cells (HSCs) as the major fibrogenic cell type in the injured liver, as well as the recognition of key cytokines involved in this process, have facilitated the design of promising new antifibrotic therapies. These therapies are aimed at inhibiting the accumulation of activated HSCs at the sites of liver injury and preventing the deposition of extracellular matrix. Although many of these approaches are effective in experimental models of liver fibrosis, their efficacy and safety in humans are still unknown. This review describes the current therapeutic approaches for liver fibrosis and discusses different features of activated HSCs as a target to design new treatments to inhibit scar formation in chronic liver diseases.
Effective Emotional Model of Pet-type Robot in Interactive Emotion Communication

In order for the robot and human to live together, the flexible understanding ability of human intention is required for the robot. In this research, we aim to realize “Interactive Emotion Communication” (IEC) which is a bidirectional communication based on emotional behaviors between human and robot. The purpose of IEC is to raise the personal affinity which the robot gives to the human by interactive emotional behaviors between both of them. IEC consists of three processes: (1) recognizing human emotion, (2) generating robot emotion, and (3) expressing robot emotion. In this research, we mainly developed the process of generating robot emotion. We consider that patterns of emotional behaviors desired to the robot vary from person to person in IEC. In this research, we perform the individual preference analysis for emotional behaviors.
Probabilistic and Rule-Based Tagger of an Inflective Language- a Comparison

We present results of probabilistic tagging of Czech texts in order to show how these techniques work for one of the highly morphologically ambiguous inflective languages. After description of the tag system used, we show the results of four experiments using a simple probabilistic model to tag Czech texts (unigram, two bigram experiments, and a trigram one). For comparison, we have applied the same code and settings to tag an English text (another four experiments) using the same size of training and test data in the experiments in order to avoid any doubt concerning the validity of the comparison. The experiments use the source channel model and maximum likelihood training on a Czech hand-tagged corpus and on tagged Wall Street Journal (WSJ) from the LDC collection. The experiments show (not surprisingly) that the more training data, the better is the success rate. The results also indicate that for inflective languages with 1000+ tags we have to develop a more sophisticated approach in order to get closer to an acceptable error rate. In order to compare two different approaches to text tagging---statistical and rule-based --- we modified Eric Brill's rule-based part of speech tagger and carried out two more experiments on the Czech data, obtaining similar results in terms of the error rate. We have also run three more experiments with greatly reduced tagset to get another comparison based on similar tagset size.
Virtual Extended Memory Symmetric Multiprocessor ( SMP ) Organization Design Using LC3 Processor : Case Study of Dual Processor

This paper presents the Symmetric Processor Organization design with the Little Computer (LC-3) processor. The physical memory space over 16 address bus lines of 64K is shares by the processors over various bank size of say 16K each to share the memory for parallelism. The paper discusses entire design architecture of the LC-3 processor and discusses the organization detail of Quad core LC-3 processor. The customized case of dual core LC-3 processor has been design sharing the memory over 32K back size. The instructions are design such that each one ends in exactly 5 clock cycles.
A nonlinear observer for 6 DOF pose estimation from inertial and bearing measurements

This paper considers the problem of estimating pose from inertial and bearing-only vision measurements. We present a non-linear observer that evolves directly on the Special Euclidean group SE(3) from inertial measurements and bearing measurements, such as provided by a visual system tracking known landmarks. Local asymptotic convergence of the observer is proved. The observer is computationally simple and its gains are easy to tune. Simulation results demonstrate robustness to measurement noise and initial conditions.
eview loud manufacturing : Strategic vision and state-ofthe-art

Cloud manufacturing, a service oriented, customer centric, demand driven manufacturing model is explored in both its possible future and current states. A unique strategic vision for the field is documented, and the current state of technology is presented from both industry and academic viewpoints. Key commercial implementations are presented, along with the state of research in fields critical to
Fundamentals of fluid lubrication

The aim is to coordinate the topics of design, engineering dynamics, and fluid dynamics in order to aid researchers in the area of fluid film lubrication. The lubrication principles that are covered can serve as a basis for the engineering design of machine elements. The fundamentals of fluid film lubrication are presented clearly so that students that use the book will have confidence in their ability to apply these principles to a wide range of lubrication situations. Some guidance on applying these fundamentals to the solution of engineering problems is also provided.
How To Elect a Leader Faster than a Tournament

The problem of electing a leader from among n contenders is one of the fundamental questions in distributed computing. In its simplest formulation, the task is as follows: given n processors, all participants must eventually return a win or lose indication, such that a single contender may win. Despite a considerable amount of work on leader election, the following question is still open: can we elect a leader in an asynchronous fault-prone system faster than just running a Θ(log n)-time tournament, against a strong adaptive adversary? In this paper, we answer this question in the affirmative, improving on a decades-old upper bound. We introduce two new algorithmic ideas to reduce the time complexity of electing a leader to O( log* n), using O(n2) point-to-point messages. A non-trivial application of our algorithm is a new upper bound for the tight renaming problem, assigning n items to the n participants in expected O(log2 n ) time and O(n2) messages. We complement our results with lower bound of Ω(n2) messages for solving these two problems, closing the question of their message complexity.
Power-aware control speculation through selective throttling

With the constant advances in technology that lead to the increasing of the transistor count and processor frequency, power dissipation is becoming one of the major issues in high-performance processors. These processors increase their clock frequency by lengthening the pipeline, which puts more pressure on the branch prediction engine since branches take longer to be resolved. Branch mispredictions are responsible for around 28% of the power dissipated by a typical processor due to the useless activities performed by instructions that are squashed. This work focuses on reducing the power dissipated by mis-speculated instructions. We propose selective throttling as an effective way of triggering different power-aware techniques (fetch throttling, decode throttling or disabling the selection logic). The particular set of techniques applied to each branch is dynamically chosen depending on the branch prediction confidence level. For branches with a low confidence on the prediction, the most aggressive throttling mechanism is used whereas high confidence branch predictions trigger the least aggressive techniques. Results show that combining fetch bandwidth reduction along with select logic disabling provides the best performance both in terms of energy reduction and energy-delay improvement (14% and 9% respectively for 14 stages, and 17% and 12% respectively for 28 stages).
Meeting the photosynthetic demand for inorganic carbon in an alga–invertebrate association: preferential use of CO2 by symbionts in the giant clam Tridacna gigas

Unlike most marine invertebrates which excrete respiratory CO2, giant clams (Tridacna gigas) must acquire inorganic carbon (Ci) in order to support their symbiotic population of photosynthetic dinoflagellates. Their capacity to meet this demand will be reflected in the Ci concentration of their haemolymph during periods of high photosynthesis. The Ci concentration in haemolymph was found to be inversely proportional to irradiance with a minimum Ci concentration of 0.75 mM at peak light levels increasing to 1.2 mM in the dark. The photosynthetic rate of isolated zooxanthellae under conditions that prevail in the haemolymph at peak light levels was significantly less than the potential Pmax (maximum photosynthetic rate) indicating that zooxanthellae are carbon limited in hospite. This is consistent with previous studies on the hermatypic coral symbiosis. The Pmax was not affected by pH but there was a dramatic increase in the half–saturation constant for Ci (K0.5(Ci)) with increasing pH (6.5–9.0) and only a small decrease in K0.5(CO2) over the same range. These results indicate that zooxanthellae in giant clams use CO2 as the primary source of their Ci in contrast to symbionts in corals, which use bicarbonate. The physiological implications are discussed and comparison is made with the coral symbiosis.
Infection with Hepatitis C Virus among HIV-Infected Pregnant Women in Thailand

Objective. The purpose of this study was to describe the epidemiology of coinfection with hepatitis C virus (HCV) and HIV among a cohort of pregnant Thai women. Methods. Samples from 1771 pregnant women enrolled in three vertical transmission of HIV studies in Bangkok, Thailand, were tested for HCV. Results. Among HIV-infected pregnant women, HCV seroprevelance was 3.8% and the active HCV infection rate was 3.0%. Among HIV-uninfected pregnant women, 0.3% were HCV-infected. Intravenous drug use by the woman was the factor most strongly associated with HCV seropositivity. Among 48 infants tested for HCV who were born to HIV/HCV coinfected women, two infants were HCV infected for an HCV transmission rate of 4.2% (95% 0.51–14.25%). Conclusions. HCV seroprevalence and perinatal transmission rates were low among this Thai cohort of HIV-infected pregnant women.
Virtual reality exposure therapy for the treatment of posttraumatic stress disorder following September 11, 2001.

OBJECTIVE This preliminary study endeavored to evaluate the use of virtual reality (VR) enhanced exposure therapy for the treatment of posttraumatic stress disorder (PTSD) consequent to the World Trade Center attacks of September 11, 2001. METHOD Participants were assigned to a VR treatment (N = 13) or a waitlist control (N = 8) group and were mostly middle-aged, male disaster workers. All participants were diagnosed with PTSD according to DSM-IV-TR criteria using the Clinician-Administered PTSD Scale (CAPS). The study was conducted between February 2002 and August 2005 in offices located in outpatient buildings of a hospital campus. RESULTS Analysis of variance showed a significant interaction of time by group (p < .01) on CAPS scores, with a between-groups posttreatment effect size of 1.54. The VR group showed a significant decline in CAPS scores compared with the waitlist group (p < .01). CONCLUSIONS Our preliminary data suggest that VR is an effective treatment tool for enhancing exposure therapy for both civilians and disaster workers with PTSD and may be especially useful for those patients who cannot engage in imaginal exposure therapy.
Hierarchical Feature Subset Selection for Features Computed from the Continuous Wavelet Transform

An algorithm for feature subset selection is proposed in which the correlation structure of the features is exploited. Especially in pattern recognition applications when features are computed from the continuous wavelet transform features are highly correlated and the algorithm is shown to be performing better. The algorithm is a hybrid filter/wrapper approach for feature subset selection. The filter removes irrelevant and redundant features. The wrapper part of the algorithm can be conceived as a hierarchical search for features: a search at the cluster level followed by a search at within-cluster level. It is shown that a significant increase in performance for the ACO (ant colony optimization) and the GA (genetic algorithm) optimization algorithms are obtained, both examples of meta heuristic optimization algorithms. However our approach is not limited to meta-heuristic search algorithms. Essentially any search algorithm can be plugged into the proposed algorithm
Dietary Intake of High-Protein Foods and Other Major Foods in Meat-Eaters, Poultry-Eaters, Fish-Eaters, Vegetarians, and Vegans in UK Biobank

Vegetarian diets are defined by the absence of meat and fish, but differences in the intake of other foods between meat-eaters and low or non-meat eaters are also important to document. We examined intakes of high-protein foods (meat, poultry, fish, legumes, nuts, vegetarian protein alternatives, dairy products, and eggs) and other major food groups (fruit, vegetables, bread, pasta, rice, snack foods, and beverages) in regular meat-eaters, low meat-eaters, poultry-eaters, fish-eaters, vegetarians, and vegans of white ethnicity participating in UK Biobank who had completed at least one web-based 24-h dietary assessment (n = 199,944). In regular meat-eaters, around 25% of total energy came from meat, fish, dairy and plant milk, cheese, yogurt, and eggs. In vegetarians, around 20% of energy came from dairy and plant milk, cheese, yoghurt, eggs, legumes, nuts, and vegetarian protein alternatives, and in vegans around 15% came from plant milk, legumes, vegetarian alternatives, and nuts. Low and non-meat eaters had higher intakes of fruit and vegetables and lower intakes of roast or fried potatoes compared to regular meat-eaters. The differences in the intakes of meat, plant-based high-protein foods, and other foods between meat-eaters and low and non-meat eaters in UK Biobank may contribute to differences in health outcomes.
Many Dropouts ? Never Mind ! - Employment Prospects of Dropouts from Training Programs

The employment effects of training programs for the unemployed are frequently studied, but little is known about the number, the characteristics, and the labor market prospects of those who drop out of these programs. The aim of this paper is to contribute to filling this gap. Dropouts are identified in German administrative data. The medium- and long-term effect of dropout is estimated using a bivariate dynamic random effects probit model which takes into account state dependence and selection on unobservables. The model is estimated with Markov Chain Monte Carlo (MCMC) methods.
MTMR channel estimation and pilot design in the context of space-time block coded OFDM-based WLANs

In this paper, we focus on pilot-aided channel estimation methods in the context of OFDM-based WLANs with Multiple Transmit Multiple Receive (MTMR) antennas, and we compare several solutions together with several pilot designs in a realistic context. In particular, we take into account null subcarriers as specified in the WLANs standards. We consider channel estimation based on the Minimum Mean Square Error (MMSE) and Least Square (LS) criteria. For the latter, we present an optimality criterion for the pilots, and corresponding optimal designs enabling complexity reductions. In addition, suboptimal performance/complexity trade-offs are studied. Finally, the comparison of the performance of different space-time block coded schemes using 2 and 4 transmit antennas, combined with these various options, complements the study by highlighting the impact of channel estimation errors on the Packet Error Rate (PER).
CSF contamination-invariant statistics in diffusion-weighted MRI

In diffusion-weighted magnetic resonance imaging (DW-MRI), the detection of microstructural change in brain white matter structures that border cerebrospinal fluid (CSF) is complicated by partial volume effects. The conventional diffusion tensor model is very sensitive to CSF contamination, which may leave subtle microstructural change undetected or may lead to detection of spurious microstructural change due to white matter atrophy. In this paper we present a novel method to detect microstructural change in CSF contaminated voxels of white matter structures imaged with conventional DW-MRI protocols. To the diffusion-weighted images (DWIs) a two-compartment tensor model, which has a tissue and a CSF compartment, is fitted by maximum likelihood estimation. However, this estimation problem has an infinite set of (almost) equally likely solution for DWIs acquired at a single b-value. We use statistical properties of this infinite set as statistics by fitting a trace-constrained bi-tensor model. We demonstrate on simulated diffusion-weighted data that our method is more sensitive to detect subtle changes in the microstructure of CSF contaminated voxels than the conventional single tensor model. In a small pilot study, using data of aging subjects to investigate the fornix with the proposed method, we found that the compartment fraction of the tissue compartment decreases significantly with age, whereas the anisotropy of the tissue compartment did not. Our method enables studying the microstructure of white matter regions that may contain CSF contamination.
Pressure marks

Selections and actions in GUI's are often separated -- i.e. an action or command typically follows a selection. This sequence imposes a lower bound on the interaction time that is equal to or greater than the sum of its parts. In this paper, we introduce pressure marks -- pen strokes where the variations in pressure make it possible to indicate both a selection and an action simultaneously. We propose a series of design guidelines from which we develop a set of four basictypes of pressure marks. We first assess the viability of this set through an exploratory study that looks at the way users draw straight and lasso pressure marks of different sizes and orientations. We then present the results of a quantitative experiment that shows that users perform faster selection-action interactions with pressure marks than with a combination of lassos and pigtails. Based on these results, we present and discuss a number of interaction designs that incorporate pressure marks.
Supervised and unsupervised feature selection for inferring social nature of telephone conversations from their content

The ability to reliably infer the nature of telephone conversations opens up a variety of applications, ranging from designing context-sensitive user interfaces on smartphones, to providing new tools for social psychologists and social scientists to study and understand social life of different subpopulations within different contexts. Using a unique corpus of everyday telephone conversations collected from eight residences over the duration of a year, we investigate the utility of popular features, extracted solely from the content, in classifying business-oriented calls from others. Through feature selection experiments, we find that the discrimination can be performed robustly for a majority of the calls using a small set of features. Remarkably, features learned from unsupervised methods, specifically latent Dirichlet allocation, perform almost as well as with as those from supervised methods. The unsupervised clusters learned in this task shows promise of finer grain inference of social nature of telephone conversations.
U-SPECT-I: a novel system for submillimeter-resolution tomography with radiolabeled molecules in mice.

UNLABELLED A major advance in biomedical science and diagnosis was accomplished with the development of in vivo techniques to image radiolabeled molecules, but limited spatial resolution has slowed down applications to small experimental animals. Here, we present a SPECT system (U-SPECT-I) dedicated to radionuclide imaging of murine organs at a submillimeter resolution. METHODS The high performance of U-SPECT-I is based on a static triangular detector setup, with a cylindric imaging cavity in the center and 75 gold micropinhole apertures in the cavity wall. The pinholes are focused on a small volume of interest such as the mouse heart or spine to maximize the detection yield of gamma-photons. Three-dimensional molecular distributions are iteratively estimated using the detector data and a statistical reconstruction algorithm that takes into account system blurring and data noise to increase resolution and reduce image noise. RESULTS With 0.6-mm-diameter pinholes, the maximum fraction of detected photons emitted by a point source (peak sensitivity) is 0.22% for a 15%-wide energy window and remains higher than 0.12% in the central 12 mm of the central plane. In a resolution phantom, radioactively filled capillaries as small as 0.5 mm and separated by 0.5 mm can be distinguished clearly in reconstructions. Projection data needed for the reconstruction of cross sections of molecular distributions in mouse organs can readily be obtained without the need for any mechanical movements. Images of a mouse spine show 99mTc-hydroxymethylene diphosphonate uptake down to the level of tiny parts of vertebral processes. These are separated clearly from the vertebral and intervertebral foramina. Using another tracer, one can monitor myocardial perfusion in the left and right ventricular walls, even in structures as small as the papillary muscles. CONCLUSION U-SPECT-I allows discrimination between molecular concentrations in adjacent volumes of as small as about 0.1 muL, which is significantly smaller than can be imaged by any existing SPECT or PET system. Our initial in vivo images of the mouse heart and spine show that U-SPECT-I can be used for novel applications in the study of dynamic biologic systems with a clear projection to clinical applications. The combination of high resolution and detection efficiency of U-SPECT-I opens up new possibilities for the suborgan-level study of radiotracers in mouse models.