Miracle-dz/UniFAR-FTUs huggingface dataset:
Myosin II and the Gal‐GalNAc lectin play a crucial role in tissue invasion by Entamoeba histolytica

Entamoeba histolytica is the human parasite responsible of amoebiasis, during which highly motile trophozoites invade the intestinal epithelium leading to amoebic colitis, and disseminate via the blood circulation causing liver abscesses. The invasive process, central to the pathogenesis, is known to be driven by parasites motility. To investigate molecules responsible for in vivo motion, we performed a high resolution dynamic imaging analysis using two‐photon laser scanning microscopy. Image analysis of the parasites during invasion of Caco‐2 cell monolayers, an enterocyte‐like model, and hamster liver shows that E. histolytica undergoes non‐Brownian motion. However, studies of movements of parasite strains dominant negative for myosin II, a central component of the cytoskeleton, and for Gal‐GalNAc lectin, a major adhesion molecule, indicate that myosin II is essential for E. histolytica intercellular motility through intestinal cell monolayers and for its motility in liver. In contrast, the Gal‐GalNAc lectin exclusively triggers invasion of the liver. These observations are in agreement with emerging studies that highlight marked differences in the way that cells migrate in vitro in two dimensions versus in vivo in three dimensions. The approach that we have developed should be powerful to identify adhesive complexes required for in vivo cell migration in normal and pathogenic situations and may, thereby, lead to new therapeutic drug, for pathologies based on cell motility and adhesion.
Distributional Reinforcement Learning for Efficient Exploration

In distributional reinforcement learning (RL), the estimated distribution of value function models both the parametric and intrinsic uncertainties. We propose a novel and efficient exploration method for deep RL that has two components. The first is a decaying schedule to suppress the intrinsic uncertainty. The second is an exploration bonus calculated from the upper quantiles of the learned distribution. In Atari 2600 games, our method outperforms QR-DQN in 12 out of 14 hard games (achieving 483 \% average gain across 49 games in cumulative rewards over QR-DQN with a big win in Venture). We also compared our algorithm with QR-DQN in a challenging 3D driving simulator (CARLA). Results show that our algorithm achieves near-optimal safety rewards twice faster than QRDQN.
Polydatin prevents the induction of secondary brain injury after traumatic brain injury by protecting neuronal mitochondria

Polydatin is thought to protect mitochondria in different cell types in various diseases. Mitochondrial dysfunction is a major contributing factor in secondary brain injury resulting from traumatic brain injury. To investigate the protective effect of polydatin after traumatic brain injury, a rat brain injury model of lateral fluid percussion was established to mimic traumatic brain injury insults. Rat models were intraperitoneally injected with polydatin (30 mg/kg) or the SIRT1 activator SRT1720 (20 mg/kg, as a positive control to polydatin). At 6 hours post-traumatic brain injury insults, western blot assay was used to detect the expression of SIRT1, endoplasmic reticulum stress related proteins and p38 phosphorylation in cerebral cortex on the injured side. Flow cytometry was used to analyze neuronal mitochondrial superoxide, mitochondrial membrane potential and mitochondrial permeability transition pore opened. Ultrastructural damage in neuronal mitochondria was measured by transmission electron microscopy. Our results showed that after treatment with polydatin, release of reactive oxygen species in neuronal mitochondria was markedly reduced; swelling of mitochondria was alleviated; mitochondrial membrane potential was maintained; mitochondrial permeability transition pore opened. Also endoplasmic reticulum stress related proteins were inhibited, including the activation of p-PERK, spliced XBP-1 and cleaved ATF6. SIRT1 expression and activity were increased; p38 phosphorylation and cleaved caspase-9/3 activation were inhibited. Neurological scores of treated rats were increased and the mortality was reduced compared with the rats only subjected to traumatic brain injury. These results indicated that polydatin protectrd rats from the consequences of traumatic brain injury and exerted a protective effect on neuronal mitochondria. The mechanisms may be linked to increased SIRT1 expression and activity, which inhibits the p38 phosphorylation-mediated mitochondrial apoptotic pathway. This study was approved by the Animal Care and Use Committee of the Southern Medical University, China (approval number: L2016113) on January 1, 2016.
The Relationship of the Amount of Physical Therapy to Time Lost from Work and Costs in the Workers' Compensation System.

: Physical therapy (PT) is perceived as a cost driver in the U.S. workers' compensation system. We conducted a 5-year (2013-2017) retrospective analysis utilizing 192,197 claims from a large Texas based workers' compensation insurance company to describe the relationship between the amount of physical therapy delivered and workers' compensation costs and lost-time. Closed, indemnity claims with 15 or more PT visits were 6 times more likely (95% CI: 5.50, 86.58) to result in high medical costs (>$7,000, excluding PT costs) and were 4 times more likely (95% CI: 3.77, 4.42) to result in ≥6 months of lost-time, when controlling for confounders. When the number of PT visits ≥15 visits for a lost time claim, this level of PT exceeds all other predictors (opioid use, co-morbidities, legal involvement, surgery, etc.) of medical cost and extended time out from work.
Restricted Max-Min Allocation: Approximation and Integrality Gap

Asadpour, Feige, and Saberi proved that the integrality gap of the configuration LP for the restricted max-min allocation problem is at most $4$. However, their proof does not give a polynomial-time approximation algorithm. A lot of efforts have been devoted to designing an efficient algorithm whose approximation ratio can match this upper bound for the integrality gap. In ICALP 2018, we present a $(6 + \delta)$-approximation algorithm where $\delta$ can be any positive constant, and there is still a gap of roughly $2$. In this paper, we narrow the gap significantly by proposing a $(4+\delta)$-approximation algorithm where $\delta$ can be any positive constant. The approximation ratio is with respect to the optimal value of the configuration LP, and the running time is $\mathit{poly}(m,n)\cdot n^{\mathit{poly}(\frac{1}{\delta})}$ where $n$ is the number of players and $m$ is the number of resources. We also improve the upper bound for the integrality gap of the configuration LP to $3 + \frac{21}{26} \approx 3.808$.
RNA targeting by an anthracycline drug: spectroscopic and in silico evaluation of epirubicin interaction with tRNA

Abstract Anthracyclines are putative anticancer agents used to treat a wide range of cancers. Among these anthracyclines, epirubicin is derived from the doxorubicin by the subtle difference in the orientation of C4-hydroxyl group at sugar molecule. Epirubicin has great significance as it has propitious anticancer potential with lesser cardiotoxicity and faster elimination from the body. The present study is done to understand the molecular aspect of epirubicin binding to tRNA. We have used various spectroscopic techniques like Fourier transform infrared spectroscopy (FTIR), absorption spectroscopy and circular dichroism to illustrate the binding sites, the extent of binding and conformational changes associated with tRNA after interacting with epirubicin. From infrared studies, we infer that epirubicin interacts with guanine and uracil bases of tRNA. Results obtained from infrared and CD studies suggest that epirubicin complexation with tRNA does not result in any conformational change in tRNA structure. Binding constant (2.1 × 103 M–1) calculated from the absorbance data illustrates that epirubicin has a weak interaction with tRNA molecule. These spectroscopic results like the binding site of epirubicin and binding energy of epirubicin-tRNA complex were also verified by the molecular docking. Results of the present study provide information that aids in the development of efficient RNA targeted drugs from the existing drugs by certain chemical modification in their structure resulting in lesser side effects and better efficacy. Communicated by Ramaswamy H. Sarma
The influence of maternal body mass index, maternal diabetes mellitus, and maternal smoking during pregnancy on the risk of childhood‐onset type 1 diabetes mellitus in the offspring: Systematic review and meta‐analysis of observational studies

There is emerging evidence that events occurring before and shortly after birth may be important in determining the risk of childhood‐onset type 1 diabetes mellitus (T1DM). We aimed to summarize and synthesize the associations between maternal body mass index (BMI), maternal diabetes mellitus (DM), and maternal smoking during pregnancy and the risk of childhood‐onset T1DM in the offspring by performing a systematic review and meta‐analysis of observational studies. A random effects model was used to generate the summary risk estimates. The PubMed and Web of Science databases were searched to identify relevant observational studies. Twenty one observational studies were included in the present meta‐analysis. Compared with offspring of mothers with normal weight, offspring of women with overweight or obesity were at an increased risk of developing childhood‐onset T1DM (overweight: relative risk [RR] 1.09, 95% confidence interval [CI], 1.03‐1.15; obesity: RR 1.25, 95% CI, 1.16‐1.34; per 5 kg m−2 increase in BMI: RR 1.10, 95% CI, 1.06‐1.13). No association was found for maternal underweight (RR 0.92, 95% CI, 0.75‐1.13). Maternal DM was associated with an increased risk of childhood‐onset T1DM (RR 3.26, 95% CI, 2.84‐3.74). Regarding the type of maternal DM, the greatest risk of T1DM in the offspring appeared to be conferred by maternal T1DM (RR 4.46, 95% CI, 2.89‐6.89), followed by maternal gestational diabetes mellitus (RR 1.66, 95% CI, 1.16‐2.36), and lastly by maternal type 2 diabetes mellitus (RR 1.11, 95% CI, 0.69‐1.80). Additional analysis of studies comparing maternal versus paternal T1DM within the same population revealed that offspring of fathers with T1DM had a 1.5 times higher risk of developing childhood‐onset T1DM than offspring of mothers with T1DM (RR 9.58, 95% CI, 6.33‐14.48 vs. RR 6.24, 95% CI, 5.52‐7.07). Furthermore, a reduced risk of childhood‐onset T1DM was observed in infants born to mothers who smoked during pregnancy compared with infants born to mothers who did not smoke during pregnancy (RR 0.79, 95% CI, 0.71‐0.87). In summary, our findings add further evidence that early‐life events or environmental factors may play a role in modulating infants' risk of developing T1DM later in life.
Targeting Hypoxia-Induced Carbonic Anhydrase IX Enhances Immune-Checkpoint Blockade Locally and Systemically

The prognosis for breast cancer and melanoma patients is worse if their tumors metabolically regulate pH, promoting an acidic microenvironment, through enzymes like CAIX. Inhibition of CAIX plus blockade of immune checkpoints boosts antitumor responses by buffering acidosis. Treatment strategies involving immune-checkpoint blockade (ICB) have significantly improved survival for a subset of patients across a broad spectrum of advanced solid cancers. Despite this, considerable room for improving response rates remains. The tumor microenvironment (TME) is a hurdle to immune function, as the altered metabolism-related acidic microenvironment of solid tumors decreases immune activity. Here, we determined that expression of the hypoxia-induced, cell-surface pH regulatory enzyme carbonic anhydrase IX (CAIX) is associated with worse overall survival in a cohort of 449 patients with melanoma. We found that targeting CAIX with the small-molecule SLC-0111 reduced glycolytic metabolism of tumor cells and extracellular acidification, resulting in increased immune cell killing. SLC-0111 treatment in combination with immune-checkpoint inhibitors led to the sensitization of tumors to ICB, which led to an enhanced Th1 response, decreased tumor growth, and reduced metastasis. We identified that increased expression of CA9 is associated with a reduced Th1 response in metastatic melanoma and basal-like breast cancer TCGA cohorts. These data suggest that targeting CAIX in the TME in combination with ICB is a potential therapeutic strategy for enhancing response and survival in patients with hypoxic solid malignancies.
The p75 Neurotrophin Receptor Is an Essential Mediator of Impairments in Hippocampal-Dependent Associative Plasticity and Memory Induced by Sleep Deprivation

Sleep deprivation (SD) interferes with hippocampal structural and functional plasticity, formation of long-term memory and cognitive function. The molecular mechanisms underlying these effects are incompletely understood. Here, we show that SD impaired synaptic tagging and capture and behavioral tagging, two major mechanisms of associative learning and memory. Strikingly, mutant male mice lacking the p75 neurotrophin receptor (p75NTR) were resistant to the detrimental effects of SD on hippocampal plasticity at both cellular and behavioral levels. Mechanistically, SD increased p75NTR expression and its interaction with phosphodiesterase. p75NTR deletion preserved hippocampal structural and functional plasticity by preventing SD-mediated effects on hippocampal cAMP–CREB-BDNF, cAMP-PKA-LIMK1-cofilin, and RhoA-ROCK2 pathways. Our study identifies p75NTR as an important mediator of hippocampal structural and functional changes associated with SD, and suggests that targeting p75NTR could be a promising strategy to limit the memory and cognitive deficits that accompany sleep loss. SIGNIFICANCE STATEMENT The lack of sufficient sleep is a major health concern in today's world. Sleep deprivation (SD) affects cognitive functions such as memory. We have investigated how associative memory mechanisms, synaptic tagging and capture (STC), was impaired in SD mice at cellular and behavioral level. Interestingly, mutant male mice that lacked the p75 neurotrophin receptor (p75NTR) were seen to be resistant to the SD-induced impairments in hippocampal synaptic plasticity and STC. Additionally, we elucidated the molecular pathways responsible for this rescue of plasticity in the mutant mice. Our study has thus identified p75NTR as a promising target to limit the cognitive deficits associated with SD.
Encoding and reconstruction in parallel MRI

The advent of parallel MRI over recent years has prompted a variety of concepts and techniques for performing parallel imaging. A main distinguishing feature among these is the specific way of posing and solving the problem of image reconstruction from undersampled multiple‐coil data. The clearest distinction in this respect is that between k‐space and image‐domain methods. The present paper reviews the basic reconstruction approaches, aiming to emphasize common principles along with actual differences. To this end the treatment starts with an elaboration of the encoding mechanisms and sampling strategies that define the reconstruction task. Based on these considerations a formal framework is developed that permits the various methods to be viewed as different solutions of one common problem. Besides the distinction between k‐space and image‐domain approaches, special attention is given to the implications of general vs lattice sampling patterns. The paper closes with remarks concerning noise propagation and control in parallel imaging and an outlook upon key issues to be addressed in the future. Copyright © 2006 John Wiley & Sons, Ltd.
Feedback MPC for Torque-Controlled Legged Robots

The computational power of mobile robots is currently insufficient to achieve torque level whole-body Model Predictive Control (MPC) at the update rates required for complex dynamic systems such as legged robots. This problem is commonly circumvented by using a fast tracking controller to compensate for model errors between updates. In this work, we show that the feedback policy from a Differential Dynamic Programming (DDP) based MPC algorithm is a viable alternative to bridge the gap between the low MPC update rate and the actuation command rate. We propose to augment the DDP approach with a relaxed barrier function to address inequality constraints arising from the friction cone. A frequency-dependent cost function is used to reduce the sensitivity to high-frequency model errors and actuator bandwidth limits. We demonstrate that our approach can find stable locomotion policies for the torque-controlled quadruped, ANYmal, both in simulation and on hardware.
Spitzer Parallax of OGLE-2018-BLG-0596: A Low-mass-ratio Planet around an M Dwarf

We report the discovery of a Spitzer microlensing planet OGLE-2018-BLG-0596Lb, with preferred planet-host mass ratio q ∼ 2 × 10−4. The planetary signal, which is characterized by a short (∼1 day) “bump” on the rising side of the lensing light curve, was densely covered by ground-based surveys. We find that the signal can be explained by a bright source that fully envelops the planetary caustic, i.e., a “Hollywood” geometry. Combined with the source proper motion measured from Gaia, the Spitzer satellite parallax measurement makes it possible to precisely constrain the lens physical parameters. The preferred solution, in which the planet perturbs the minor image due to lensing by the host, yields a Uranus-mass planet with a mass of Mp = 13.9 ± 1.6 M⊕ orbiting a mid M-dwarf with a mass of Mh = 0.23 ± 0.03 M⊙. There is also a second possible solution that is substantially disfavored but cannot be ruled out, for which the planet perturbs the major image. The latter solution yields Mp = 1.2 ± 0.2 M⊕ and Mh = 0.15 ± 0.02 M⊙. By combining the microlensing and Gaia data together with a Galactic model, we find in either case that the lens lies on the near side of the Galactic bulge at a distance DL ∼ 6 ± 1 kpc. Future adaptive optics observations may decisively resolve the major image/minor image degeneracy.
Extrapolation of nuclear structure observables with artificial neural networks

Calculations of nuclei are often carried out in finite model spaces. Thus, finite-size corrections enter, and it is necessary to extrapolate the computed observables to infinite model spaces. In this work, we employ extrapolation methods based on artificial neural networks for observables such as the ground-state energy and the point-proton radius. We extrapolate results from no-core shell model and coupled-cluster calculations to very large model spaces and estimate uncertainties. Training the network on different data typically yields extrapolation results that cluster around distinct values. We show that a preprocessing of input data, and the inclusion of correlations among the input data, reduces the problem of multiple solutions and yields more stable extrapolated results and consistent uncertainty estimates. We perform extrapolations for ground-state energies and radii in $^{4}$He, $^{6}$Li, and $^{16}$O, and compare the predictions from neural networks with results from infrared extrapolations.
Intellectual disability and autism spectrum disorders ‘on the fly’: insights from Drosophila

ABSTRACT Intellectual disability (ID) and autism spectrum disorders (ASD) are frequently co-occurring neurodevelopmental disorders and affect 2-3% of the population. Rapid advances in exome and genome sequencing have increased the number of known implicated genes by threefold, to more than a thousand. The main challenges in the field are now to understand the various pathomechanisms associated with this bewildering number of genetic disorders, to identify new genes and to establish causality of variants in still-undiagnosed cases, and to work towards causal treatment options that so far are available only for a few metabolic conditions. To meet these challenges, the research community needs highly efficient model systems. With an increasing number of relevant assays and rapidly developing novel methodologies, the fruit fly Drosophila melanogaster is ideally positioned to change gear in ID and ASD research. The aim of this Review is to summarize some of the exciting work that already has drawn attention to Drosophila as a model for these disorders. We highlight well-established ID- and ASD-relevant fly phenotypes at the (sub)cellular, brain and behavioral levels, and discuss strategies of how this extraordinarily efficient and versatile model can contribute to ‘next generation’ medical genomics and to a better understanding of these disorders. Summary: This Review summarizes some of the exciting work that has used Drosophila as a model for the ever-expanding field of intellectual disability and autism spectrum disorders. It highlights disease-relevant assays, emerging themes and future challenges.
PU-LP: A novel approach for positive and unlabeled learning by label propagation

For the positive and unlabeled learning algorithms, when there is only small amount of labeled positive examples available, the algorithms can hardly extract reliable negative examples from the unlabeled examples in step one, which makes it hard to build the classifier with good performance in step two. Based on the same label assumption from graph based semi-supervised learning, we propose a novel graph-based PU learning algorithm, PU-LP, which takes Katz index to measure the similarities between vertices. After enlarging labeled positive set and extracting reliable negative examples, PU-LP build the classifier by label propagation algorithm. Experiments on UCI datasets shows that PU-LP has excellent performance when there is only small amount of labeled positive examples available, and it outperforms than PNB algorithm.
New algorithms for solving stochastic games.

Stochastic games are a classical model in game theory in which two opponents interact and the environment changes in response to the players' behavior. The central solution concepts for these games are the discounted values and the value, which represent what playing the game is worth to the players for different levels of impatience. In the present manuscript, we provide algorithms for computing exact expressions for the discounted values and for the value, which are polynomial in the number of pure stationary strategies of the players. This result considerably improves all the existing algorithms, including the most efficient one, due to Hansen, Kouck\'y, Lauritzen, Miltersen and Tsigaridas (STOC 2011).
Adaptive Approach for a Maximum Entropy Algorithm in Ecological Niche Modeling

This paper presents an Adaptive Maximum Entropy (AME) approach for modeling biological species. The Maximum Entropy algorithm (MaxEnt) is one of the most used methods in modeling biological species geographical distribution. The approach presented here is an alternative to the classical algorithm. Instead of using the same set features in the training, the AME approach tries to insert or to remove a single feature at each iteration. The aim is to reach the convergence faster without affect the performance of the generated models. The preliminary experiments were well performed. They showed an increasing on performance both in accuracy and in execution time. Comparisons with other algorithms are beyond the scope of this paper. Some important researches are proposed as future works.