Miracle-dz/UniFAR-FTUs huggingface dataset:
HUBBLE SPACE TELESCOPE COMBINED STRONG AND WEAK LENSING ANALYSIS OF THE CLASH SAMPLE: MASS AND MAGNIFICATION MODELS AND SYSTEMATIC UNCERTAINTIES

We present results from a comprehensive lensing analysis in Hubble Space Telescope (HST) data of the complete Cluster Lensing And Supernova survey with Hubble cluster sample. We identify previously undiscovered multiple images, allowing improved or first constraints on the cluster inner mass distributions and profiles. We combine these strong lensing constraints with weak lensing shape measurements within the HST field of view (FOV) to jointly constrain the mass distributions. The analysis is performed in two different common parameterizations (one adopts light-traces-mass for both galaxies and dark matter while the other adopts an analytical, elliptical Navarro–Frenk–White form for the dark matter) to provide a better assessment of the underlying systematics—which is most important for deep, cluster-lensing surveys, especially when studying magnified high-redshift objects. We find that the typical (median), relative systematic differences throughout the central FOV are ∼40% in the (dimensionless) mass density, κ, and ∼20% in the magnification, μ. We show maps of these differences for each cluster, as well as the mass distributions, critical curves, and two-dimensional (2D)-integrated mass profiles. For the Einstein radii (zs = 2) we find that all typically agree within 10% between the two models, and Einstein masses agree, typically, within ∼15%. At larger radii, the total projected, 2D-integrated mass profiles of the two models, within r ∼ 2′, differ by ∼30%. Stacking the surface-density profiles of the sample from the two methods together, we obtain an average slope of dlog (Σ)/dlog (r) ∼ −0.64 ± 0.1, in the radial range [5350] kpc. Last, we also characterize the behavior of the average magnification, surface density, and shear differences between the two models as a function of both the radius from the center and the best-fit values of these quantities. All mass models and magnification maps are made publicly available for the community.
Brain stem NOS and ROS in neural mechanisms of hypertension.

SIGNIFICANCE There is now compelling evidence to substantiate the notion that by depressing baroreflex regulation of blood pressure and augmenting central sympathetic outflow through their actions on the nucleus tractus solitarii (NTS) and rostral ventrolateral medulla (RVLM), brain stem nitric oxide synthase (NOS) and reactive oxygen species (ROS) are important contributing factors to neural mechanisms of hypertension. This review summarizes our contemporary views on the impact of NOS and ROS in the NTS and RVLM on neurogenic hypertension, and presents potential antihypertensive strategies that target brain stem NOS/ROS signaling. RECENT ADVANCES NO signaling in the brain stem may be pro- or antihypertensive depending on the NOS isoform that generates this gaseous moiety and the site of action. Elevation of the ROS level when its production overbalances its degradation in the NTS and RVLM underlies neurogenic hypertension. Interventional strategies with emphases on alleviating the adverse actions of these molecules on blood pressure regulation have been investigated. CRITICAL ISSUES The pathological roles of NOS in the RVLM and NTS in neural mechanisms of hypertension are highly complex. Likewise, multiple signaling pathways underlie the deleterious roles of brain-stem ROS in neurogenic hypertension. There are recent indications that interactions between brain stem ROS and NOS may play a contributory role. FUTURE DIRECTIONS Given the complicity of action mechanisms of brain-stem NOS and ROS in neural mechanisms of hypertension, additional studies are needed to identify the most crucial therapeutic target that is applicable not only in animal models but also in patients suffering from neurogenic hypertension.
Optimal global planning for cognitive factories with multiple teams of heterogeneous robots

We consider a cognitive factory domain with multiple teams of heterogeneous robots where the goal is for all teams to complete their tasks as soon as possible to achieve overall shortest delivery time for a given manufacturing order. Should the need arise, teams help each other by lending robots. This domain is challenging in the following ways: different capabilities of heterogeneous robots need to be considered in the model; discrete symbolic representation and reasoning need to be integrated with continuous external computations to find feasible plans (e.g., to avoid collisions); a coordination of the teams should be found for an optimal feasible global plan (with minimum makespan); in case of an encountered discrepancy/failure during plan execution, if the discrepancy/failure prevents the execution of the rest of the plan, then finding a diagnosis for the discrepancy/failure and recovering from the plan failure is required to achieve the goals. We introduce a formal planning, execution and monitoring framework to address these challenges, by utilizing logic-based formalisms that allow us to embed external computations in continuous spaces, and the relevant state-of-the-art automated reasoners. To find a global plan with minimum makespan, we propose a semi-distributed approach that utilizes a mediator subject to the condition that the teams and the mediator do not know about each other’s workspaces or tasks. According to this approach, 1) the mediator gathers sufficient information from the teams about when they can/need lend/borrow how many and what kind of robots, 2) based on this information, the mediator computes an optimal coordination of the teams and informs each team about this coordination, 3) each team computes its own optimal local plan to achieve its own tasks taking into account the information conveyed by the mediator as well as external computations to avoid collisions, 4) these optimal local plans are merged into an optimal global plan. For the first and the third stages, we utilize methods and tools of hybrid reasoning. For the second stage, we formulate the problem of finding an optimal coordination of teams that can help each other, prove its intractability, and describe how to solve this problem using existing automated reasoners. For the last stage, we prove the optimality of the global plan. For execution and monitoring of an optimal global plan, we introduce a formal framework that provides methods to diagnose failures due to broken robots, and to handle changes in manufacturing orders and in workspaces. We illustrate the applicability of our approaches on various scenarios of cognitive factories with dynamic simulations and physical implementation.
Structural and Biochemical Characterization of Compounds Inhibiting Mycobacterium tuberculosis Pantothenate Kinase*

Background: Pantothenate kinase (PanK), an essential enzyme for Mycobacterium tuberculosis, catalyzes the rate-limiting step in the CoA pathway. Results: Structures of M. tuberculosis PanK, complexed with new inhibitors, were determined, and their inhibitions were evaluated biochemically. Conclusion: Inhibitor binding overlaps with the substrate/product sites; also, an alternative mode of ATP binding is proposed. Significance: These are the first structures of a type I PanK complexed with inhibitors. Mycobacterium tuberculosis, the bacterial causative agent of tuberculosis, currently affects millions of people. The emergence of drug-resistant strains makes development of new antibiotics targeting the bacterium a global health priority. Pantothenate kinase, a key enzyme in the universal biosynthesis of the essential cofactor CoA, was targeted in this study to find new tuberculosis drugs. The biochemical characterizations of two new classes of compounds that inhibit pantothenate kinase from M. tuberculosis are described, along with crystal structures of their enzyme-inhibitor complexes. These represent the first crystal structures of this enzyme with engineered inhibitors. Both classes of compounds bind in the active site of the enzyme, overlapping with the binding sites of the natural substrate and product, pantothenate and phosphopantothenate, respectively. One class of compounds also interferes with binding of the cofactor ATP. The complexes were crystallized in two crystal forms, one of which is in a new space group for this enzyme and diffracts to the highest resolution reported for any pantothenate kinase structure. These two crystal forms allowed, for the first time, modeling of the cofactor-binding loop in both open and closed conformations. The structures also show a binding mode of ATP different from that previously reported for the M. tuberculosis enzyme but similar to that in the pantothenate kinases of other organisms.
Frontiers in Computational Neuroscience

time constraints are highly relevant behaviorally, because in natural environments fast responses are often crucial for survival (Moss et al., 2006; Ghose et al., 2009). The same is true in sports, where both perceptual and motor systems must operate at high speeds Recently, we developed a two-alternative forced-choice task that realizes this time-limited approach with high accuracy and minimal complexity (Stanford et al., 2010). The version that we have implemented and which we will discuss here is called the compelled-saccade task, but whether eye movements or other motor actions are used is not crucial; the issue at hand is whether subjects performing the task can do so with different behavioral or cognitive strategies (see Hernández et al., 1997). In neurophysi-ological terms the question is, what oculomotor choice mechanisms could be engaged during task performance? To appreciate the problem, consider the sequence of events in the task (Figure 1). The subject first fixates on the central spot, which is either red or green. Then two yellow spots appear, one on each side; these are the two possible saccadic choices. Next, the disappearance of the fixation point (go) instructs the subject to make an eye movement. Crucially, however, because the two available spots are still yellow, at this point the identities of the target and distracter are not known yet; these are revealed (cue) after a delay interval called the gap. Thus, the subject is compelled to initiate an eye movement first, before the cue is presented. The choice is correct if the eye movement is to the spot that matches
Automated segmentation and classification technique for brain stroke

Difussion-Weighted Imaging (DWI) plays an important role in the diagnosis of brain stroke by providing detailed information regarding the soft tissue contrast in the brain organ. Conventionally, the differential diagnosis of brain stroke lesions is performed manually by professional neuroradiologists during a highly subjective and time- consuming process. This study proposes a segmentation and classification technique to detect brain stroke lesions based on diffusion-weighted imaging (DWI). The type of stroke lesions consists of acute ischemic, sub-acute ischemic, chronic ischemic and acute hemorrhage. For segmentation, fuzzy c-Means (FCM) and active contour is proposed to segment the lesion’s region. FCM is implemented with active contour to separate the cerebral spinal fluid (CSF) with the hypointense lesion. Pre-processing is applied to the DWI for image normalization, background removal and image enhancement. The algorithm performance has been evaluated using Jaccard Index, Dice Coefficient (DC) and both false positive rate (FPR) and false negative rate (FNR). The average results for the Jaccard index, DC, FPR and FNR are 0.55, 0.68, 0.23 and 0.23, respectively. First statistical order method is applied to the segmentation result to obtain the features for the classifier input. For classification technique, bagged tree classifier is proposed to classify the type of stroke. The accuracy results for the classification is 90.8%. Based on the results, the proposed technique has potential to segment and classify brain stroke lesion from DWI images.
The transcription factor FOXO 3 a is a crucial cellular target of gefitinib ( Iressa ) in breast cancer cells

Gefitinib is a specific inhibitor of the epidermal growth factor receptor (EGFR) that causes growth delay in cancer cell lines and human tumor xenografts expressing high levels of EGFR. An understanding of the downstream cellular targets of gefitinib will allow the discovery of biomarkers for predicting outcomes and monitoring antiEGFR therapies and provide information for key targets for therapeutic intervention. In this study, we investigated the role of FOXO3a in gefitinib action and resistance. Using two gefitinib-sensitive (i.e., BT474 and SKBR3) as well as three other resistant breast carcinoma cell lines (i.e., MCF-7, MDA-MB-231, and MDA-MB-453), we showed that gefitinib targets the transcription factor FOXO3a to mediate cell cycle arrest and cell death in sensitive breast cancer cells. In the sensitive cells, gefitinib treatment causes cell cycle arrest predominantly at the G0-G1 phase and apoptosis, which is associated with FOXO3a dephosphorylation at Akt sites and nuclear translocation, whereas in the resistant cells, FOXO3a stays phosphorylated and remains in the cytoplasm. The nuclear accumulation of FOXO3a in response to gefitinib was confirmed in tumor tissue sections from breast cancer patients presurgically treated with gefitinib as monotherapy. We also showed that knockdown of FOXO3a expression using small interfering RNA (siRNA) can rescue sensitive BT474 cells from gefitinib-induced cell-proliferative arrest, whereas reintroduction of active FOXO3a in resistant MDA-MB231 cells can at least partially restore cell-proliferative arrest and sensitivity to gefitinib. These results suggest that the FOXO3a dephosphorylation and nuclear localization have a direct role in mediating the gefitinib-induced proliferative arrest and in determining sensitivity to gefitinib. [Mol Cancer Ther 2007;6(12):3169–79]
Quantum Memory Hierarchies: Efficient Designs to Match Available Parallelism in Quantum Computing

The assumption of maximum parallelism support for the successful realization of scalable quantum computers has led to homogeneous, "sea-of-qubits" architectures. The resulting architectures overcome the primary challenges of reliability and scalability at the cost of physically unacceptable system area. We find that by exploiting the natural serialization at both the application and the physical microarchitecture level of a quantum computer, we can reduce the area requirement while improving performance. In particular we present a scalable quantum architecture design that employs specialization of the system into memory and computational regions, each individually optimized to match hardware support to the available parallelism. Through careful application and system analysis, we find that our new architecture can yield up to a factor of thirteen savings in area due to specialization. In addition, by providing a memory hierarchy design for quantum computers, we can increase time performance by a factor of eight. This result brings us closer to the realization of a quantum processor that can solve meaningful problems
Epidural Cortical Stimulation as a Treatment for Poststroke Aphasia

Background. Nearly 15 million people suffer from stroke every year worldwide, with about 20% of the survivors retaining chronic aphasic symptoms. Spontaneous recovery is limited to 3 to 6 months. Cortical stimulation techniques have been proposed to enhance the recovery process. Objective. The goal of this study was to evaluate the benefit of epidural cortical stimulation for the treatment of poststroke aphasia, based on a systematic review of the literature. Methods. An extensive PubMed search was performed for English language articles published from 1990 to 2014 with the keywords (cortical OR epidural) AND stimulation AND stroke AND (aphasia OR language OR speech). The criteria analyzed included the type of study, epidemiology of patients, stroke, aphasia, stimulation protocol, concurrent rehabilitation therapies, language evaluations, results observed, and follow-up. Results. Seven cases were reported to date (3 case reports, 1 randomized controlled trial). All patients experienced nonfluent aphasia following an ischemic stroke. All four studies reported encouraging effects of the stimulation with improved lexical access and fluency for all patients. The effects were specific, independent of the motor recovery or of the pain reported by the patients, and they were linked to the stimulation parameters. Conclusions. Due to the small number of existing cases in the literature, the strength of the evidence is still low. Two main hypotheses of neurobiological mechanisms have been explored: either using continuous stimulation to modify cortical perilesional inhibition or using intermittent stimulation during the speech and language therapy sessions to explore synaptic plasticity and long-term potentiation or depression. To establish the role of epidural stimulation and the relevant stimulation protocols and parameters, large randomized controlled trials are mandatory. We suggest avenues of investigation.
Geometric clustering for line drawing simplification

We present a new approach to the simplification of line drawings, in which a smaller set of lines is created to represent the geometry of the original lines. An important feature of our method is that it maintains the morphological structure of the original drawing while allowing user-de ned decisions about the appearance of lines. The technique works by analyzing the structure of the drawing at a certain scale and identifying clusters of lines that can be merged given an extendable error threshold. These clusters are then processed to create new lines, in a separate stage where different scenarios can be favored based on the application. Successful results are presented for a variety of drawings including scanned and vectorized artwork, original vector drawings, drawings created from 3d models, and hatching marks. The clustering technique is shown to be effective in all these situations.
Urinary biomarkers for the detection of prostate cancer in patients with high‐grade prostatic intraepithelial neoplasia

High‐grade prostatic intraepithelial neoplasia (HGPIN) is a recognized precursor stage of PCa. Men who present HGPIN in a first prostate biopsy face years of active surveillance including repeat biopsies. This study aimed to identify non‐invasive prognostic biomarkers that differentiate early on between indolent HGPIN cases and those that will transform into actual PCa.
Further results on geometric properties of a family of relative entropies

This paper extends some geometric properties of a one-parameter family of relative entropies. These arise as redundancies when cumulants of compressed lengths are considered instead of expected compressed lengths. These parametric relative entropies are a generalization of the Kullback-Leibler divergence. They satisfy the Pythagorean property and behave like squared distances. This property, which was known for finite alphabet spaces, is now extended for general measure spaces. Existence of projections onto convex and certain closed sets is also established. Our results may have applications in the Rényi entropy maximization rule of statistical physics.
Compositions partially deacetylated chitin derivatives

1. A method for extracting a highly purified and fully soluble partially deacetylated chitin polymer having a degree of deacetylation (AN) of 30-60%, wherein the method comprises the steps of: neutralization of partially deacetylated chitin; ! dissolving the partially deacetylated chitin in acidic solution; ! removal of undissolved particles through sequential filtering steps; ! adjusting the pH of the solution to a value greater than 8; ! deposition fully dissolved and purified partially deacetylated chitin into a precipitate by raising the temperature and the addition salts! wherein the precipitate is recovered and washed after precipitation through sieving or centrifugation and wherein the temperature of washing water and the precipitate is above 40 ° C. 2. A method according to claim 1 or 2, wherein the molecular weight of the partially deacetylated chitin is above 10 kDa. 3. The method of claim 1 wherein the elevated temperature of the partially deacetylated chitin solution comprises raising the temperature to 40-100 ° C. 4. The method of claim 1, wherein adjusting partially deacetylated chitin solution comprises raising the pH to a value from 8 to 13. 5. The method of claim 1 wherein the salt used for salting out, is any chaotropic salt with a high, e.g., sodium chloride, or any acid salt, used to dissolve the polymer, preferably a salt of tricarboxylic or dicarboxylic or monocarboxylic organic acid such as citric acid , malic acid or acetic acid or any other salt, e.g., ammonium sulfate, urea, guanidinium chloride. 6. The method of claim 1, wherein the salt concentration is between 2% and saturation. 7. The method of claim 1, wherein chitin is treated with inorganic or organiches
The Persistence of L1 Patterns in SLA: the Boundary Crossing Constraint and Incidental Learning

Based on the theoretical framework of Talmy (1985 et passim) and Slobin (1987 et passim) the present study analyses the influence of L1 patterns on the description of motion events with boundary crossings. Arab speakers avoid the use of manner of motion verbs in the description of these events and use simple path verbs (e.g. enter, go etc.), whereas speakers of English mostly use manner verbs (run, crawl etc.). These deeply engrained differences between L1 and L2 are a learning challenge in SLA. We analyse the use of manner verbs by an intermediate and an advanced group of Arab EFL learners, who live in the UK. Most learners either avoid the description or use path verbs as in Arabic. As the learners do not produce ungrammatical sentences, they will not receive negative feedback (e.g. from a teacher) and rely entirely on incidental learning from the input. However, despite the high frequency of these manner verbs in the daily input of the learners, they do not acquire the patterns of the target language even at a high proficiency level. Implicit learning in this context is hardly possible and explicit teaching and learning is needed to overcome the influence of the first language.
Hybrid Long- and Short-Term Models of Folk Melodies

In this paper, we present the results of a study on dynamic models for predicting sequences of musical pitch in melod-ies. Such models predict a probability distribution over the possible values of the next pitch in a sequence, which is obtained by combining the prediction of two components (1) a long-term model (LTM) learned ofﬂine on a corpus of melodies, as well as (2) a short-term model (STM) which incorporates context-speciﬁc information available during prediction. Both the LTM and the STM learn regularities in pitch sequences solely from data. The models are combined in an ensemble, wherein they are weighted by the relative entropies of their respective predictions. Going by previous work that demonstrates the success of Connec-tionist LTMs, we employ the recently proposed Recurrent Temporal Discriminative Restricted Boltzmann Machine (RTDRBM) as the LTM here. While it is indeed possible for the same model to also serve as an STM, our experiments showed that n -gram models tended to learn faster than the RTDRBM in an online setting and that the hybrid of an RTDRBM LTM and an n -gram STM gives us the best predictive performance yet on a corpus of mono-phonic chorale and folk melodies.