Miracle-dz/UniFAR-FTUs huggingface dataset:
Addition of Cryoprotectant Significantly Alters the Epididymal Sperm Proteome

Although cryopreservation has been developed and optimized over the past decades, it causes various stresses, including cold shock, osmotic stress, and ice crystal formation, thereby reducing fertility. During cryopreservation, addition of cryoprotective agent (CPA) is crucial for protecting spermatozoa from freezing damage. However, the intrinsic toxicity and osmotic stress induced by CPA cause damage to spermatozoa. To identify the effects of CPA addition during cryopreservation, we assessed the motility (%), motion kinematics, capacitation status, and viability of epididymal spermatozoa using computer-assisted sperm analysis and Hoechst 33258/chlortetracycline fluorescence staining. Moreover, the effects of CPA addition were also demonstrated at the proteome level using two-dimensional electrophoresis. Our results demonstrated that CPA addition significantly reduced sperm motility (%), curvilinear velocity, viability (%), and non-capacitated spermatozoa, whereas straightness and acrosome-reacted spermatozoa increased significantly (p < 0.05). Ten proteins were differentially expressed (two decreased and eight increased) (>3 fold, p < 0.05) after CPA, whereas NADH dehydrogenase flavoprotein 2, f-actin-capping protein subunit beta, superoxide dismutase 2, and outer dense fiber protein 2 were associated with several important signaling pathways (p < 0.05). The present study provides a mechanistic basis for specific cryostresses and potential markers of CPA-induced stress. Therefore, these might provide information about the development of safe biomaterials for cryopreservation and basic ground for sperm cryopreservation.
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models

We introduce an off-policy evaluation procedure for highlighting episodes where applying a reinforcement learned (RL) policy is likely to have produced a substantially different outcome than the observed policy. In particular, we introduce a class of structural causal models (SCMs) for generating counterfactual trajectories in finite partially observable Markov Decision Processes (POMDPs). We see this as a useful procedure for off-policy "debugging" in high-risk settings (e.g., healthcare); by decomposing the expected difference in reward between the RL and observed policy into specific episodes, we can identify episodes where the counterfactual difference in reward is most dramatic. This in turn can be used to facilitate review of specific episodes by domain experts. We demonstrate the utility of this procedure with a synthetic environment of sepsis management.
Different mechanisms for selective transport of fatty acids using a single class of lipoprotein in Drosophila[S]

In mammals, lipids are selectively transported to specific sites using multiple classes of lipoproteins. However, in Drosophila, a single class of lipoproteins, lipophorin, carries more than 95% of the lipids in the hemolymph. Although a unique ability of the insect lipoprotein system for cargo transport has been demonstrated, it remains unclear how this single class of lipoproteins selectively transports lipids. In this study, we carried out a comparative analysis of the fatty-acid composition among lipophorin, the CNS, and CNS-derived cell lines and investigated the transport mechanism of fatty acids, particularly focusing on the transport of PUFAs in Drosophila. We showed that PUFAs are selectively incorporated into the acyl chains of lipophorin phospholipids and effectively transported to CNS through lipophorin receptor-mediated endocytosis of lipophorin. In addition, we demonstrated that C14 fatty acids are selectively incorporated into the diacylglycerols (DAGs) of lipophorin and that C14 fatty-acid-containing DAGs are spontaneously transferred from lipophorin to the phospholipid bilayer. These results suggest that PUFA-containing phospholipids and C14 fatty-acid-containing DAGs in lipophorin could be transferred to different sites by different mechanisms to selectively transport fatty acids using a single class of lipoproteins.
Dynamical stability of water distribution networks

Water distribution networks are hydraulic infrastructures that aim to meet water demands at their various nodes. Water flows through pipes in the network create nonlinear dynamics on networks. A desirable feature of water distribution networks is high resistance to failures and other shocks to the system. Such threats would at least transiently change the flow rate in various pipes, potentially undermining the functionality of the whole water distribution system. Here we carry out a linear stability analysis for a nonlinear dynamical system representing the flow rate through pipes that are interconnected through an arbitrary pipe network with reservoirs and consumer nodes. We show that the steady state is always locally stable and develop a method to calculate the eigenvalue that corresponds to the mode that decays the most slowly towards the equilibrium, which we use as an index for resilience of the system. We show that the proposed index is positively correlated with the recovery rate of the pipe network, which was derived from a realistic and industrially popular simulator. The present analytical framework is expected to be useful for deploying tools from nonlinear dynamics and network analysis in the design, resilience management and scenario testing of water distribution networks.
Robust Feedback Control of Nonlinear PDEs by Numerical Approximation of High-Dimensional Hamilton-Jacobi-Isaacs Equations

We propose an approach for the synthesis of robust and optimal feedback controllers for nonlinear PDEs. Our approach considers the approximation of infinite-dimensional control systems by a pseudospectral collocation method, leading to high-dimensional nonlinear dynamics. For the reduced-order model, we construct a robust feedback control based on the $\cH_{\infty}$ control method, which requires the solution of an associated high-dimensional Hamilton-Jacobi-Isaacs nonlinear PDE. The dimensionality of the Isaacs PDE is tackled by means of a separable representation of the control system, and a polynomial approximation ansatz for the corresponding value function. Our method proves to be effective for the robust stabilization of nonlinear dynamics up to dimension $d\approx 12$. We assess the robustness and optimality features of our design over a class of nonlinear parabolic PDEs, including nonlinear advection and reaction terms. The proposed design yields a feedback controller achieving optimal stabilization and disturbance rejection properties, along with providing a modelling framework for the robust control of PDEs under parametric uncertainties.
Superior early diagnostic performance of a sensitive cardiac troponin assay as compared to a standard troponin test in the diagnosis of acute myocardial infarction.

BACKGROUND New generation cardiac troponin assays have sufficient precision to detect and quantify plasma troponin concentrations below the lower threshold of detection of the currently employed troponin tests. However, diagnostic performance of the newer generation assays in daily clinical practice is not well established. AIM To evaluate the diagnostic performance of a sensitive assay as compared to a standard assay in a single reading at admission in the diagnosis of acute myocardial infarction (AMI) in patients presenting to the Emergency Department with chest pain. METHODS The study comprised 187 consecutive patients admitted to the Institute of Cardiology in Warsaw in June and July 2010 with chest pain in whom the attending physician ordered troponin assay to rule AMI in or out. In all of these patients, in addition to the standard Dimension Flex Troponin I (Siemens Healthcare Diagnostics, Inc.) the sensitive Architect Stat Troponin I (Abbott Diagnostics) test was assayed. The triage of patients as well as all diagnostic and treatment decisions were left to the discretion of the attending physician who was blinded to the sensitive troponin test readings. The final diagnosis was adjudicated by a team of two cardiologists on the basis of all the available medical records except for sensitive troponin test results. RESULTS Mean age of the study cohort (n = 187) was 64.3 ± 13.9 years and 119 (63.6%) were males. The final diagnosis of AMI was adjudicated in 84 (44.9%) patients (mean age 67.5 ± 12.9 years; 119 [63.6%] males). Receiver operating characteristic (ROC) analysis showed greater area under the curve (AUC) for the sensitive cardiac troponin assay compared to the standard assay (AUC = 0.916, 95% CI = 0.866-0.951 vs AUC = 0.863, 95% CI = 0.806-0.909, respectively; p = 0.02) in a single reading at admission. Sensitive assay was characterised by higher sensitivity (87%), specificity (88%), positive (86%) and negative (89%) predictive values in the detection of AMI compared to the standard troponin test (82%, 81%, 78%, and 85% respectively). CONCLUSIONS The newer generation sensitive cardiac troponin assay presented superior diagnostic accuracy in the diagnosis of AMI compared to the standard troponin test in a single reading at admission with improved sensitivity and specificity. The sensitive troponin assay has the potential to improve early detection and/or exclusion of AMI.
Identification of a nuclear localization signal and importin beta members mediating NUAK1 nuclear import inhibited by oxidative stress

NUAK1 is a serine/threonine kinase member of the AMPK‐α family. NUAK1 regulates several processes in tumorigenesis; however, its regulation and molecular targets are still poorly understood. Bioinformatics analysis predicted that the majority of NUAK1 localizes in the nucleus. However, there are no studies about the regulation of NUAK1 subcellular distribution. Here, we analyzed NUAK1 localization in several human cell lines, mouse embryo fibroblasts, and normal mouse tissues. We found that NUAK1 is located in the nucleus and also in the cytoplasm. Through bioinformatics analysis and studies comparing subcellular localization of wild type and NUAK1 mutants, we identified a conserved bipartite nuclear localization signal at the N‐terminal domain of NUAK1. Based on mass spectrometry analysis, we found that NUAK1 interacts with importin‐β members including importin‐β1 (KPNB1), importin‐7 (IPO7), and importin‐9 (IPO9). We confirmed that importin‐β members are responsible for NUAK1 nuclear import through the inhibition of importin‐β by Importazole and the knockdown of either IPO7 or IPO9. In addition, we found that oxidative stress induces NUAK1 cytoplasmic accumulation, indicating that oxidative stress affects NUAK1 nuclear transport. Thus, our study is the first evidence of an active nuclear transport mechanism regulating NUAK1 subcellular localization. These data will lead to investigations of the molecular targets of NUAK1 according to its subcellular distribution, which could be new biomarkers or targets for cancer therapies.
A pilot study of Helicobacter pylori infection and risk of laryngopharyngeal cancer

Squamous cell carcinoma of the laryngopharynx has been linked to laryngopharyngeal reflux disease. Helicobacter pylori corpus gastritis decreases gastric acid secretion and provides some protection against complications of gastroesophageal reflux, including adenocarcinoma of the distal esophagus. The aim of this study was to investigate whether H. pylori infection also protects against laryngopharyngeal carcinoma.
Lyral® has been included in the patch test standard series in Germany

Lyral® 5% pet. was tested in 3245 consecutive patch test patients in 20 departments of dermatology in order (i) to check the diagnostic quality of this patch test preparation, (ii) to examine concomitant reactions to Lyral and fragrance mix (FM), and (iii) to assess the frequency of contact allergy to Lyral in an unselected patch test population of German dermatological clinics. 62 patients reacted to Lyral, i.e. 1.9%. One third of the positive reactions were + + and + + + . The reaction index was 0.27. Thus, the test preparation can be regarded a good diagnostic tool. Lyral and fragrance mix (FM) were tested in parallel in 3185 patients. Of these, 300 (9.4%) reacted to FM, and 59 (1.9%) to Lyral. In 40 patients, positive reactions to both occurred, which is 13.3% of those reacting to FM, and 67.8% of those reacting to Lyral. So the concordance of positive test reactions to Lyral and FM was only slight. Based on these results, the German Contact Dermatitis Research Group (DKG) decided to add Lyral 5% pet. to the standard series.
Bypassing Osmotic Shock Dilemma in a Polystyrene Resin Using the Green Solvent Cyclopentyl methyl Ether (CPME): A Morphological Perspective

The “osmotic shock” phenomenon is the main thing that is responsible for morphological structure alteration, which can jeopardize the use of a polymer in a chemical process. This is extremely important in solid-phase peptide synthesis (SPPS), which is the method of choice for the preparation of these important biologically active compounds. Herein, we have used Hildebrand solubility parameters (δ) to investigate the influence of different ethers that are used in the precipitation step of the SPPS using a polystyrene resin. The green cyclopentyl methyl ether (CPME) has shown to be slightly superior to 2-methyltetrahydrofurane, which is also a green ether and clearly better than the hazardous diethyl ether and tert-butyl methyl ether. These results have been corroborated by scanning electron microscope (SEM) analysis and computational studies. All together, these confirm the adequacy of CPME for being the ether of choice to be used in SPPS.
TFAM Enhances Fat Oxidation and Attenuates High-Fat Diet–Induced Insulin Resistance in Skeletal Muscle

Diet-induced insulin resistance (IR) adversely affects human health and life span. We show that muscle-specific overexpression of human mitochondrial transcription factor A (TFAM) attenuates high-fat diet (HFD)–induced fat gain and IR in mice in conjunction with increased energy expenditure and reduced oxidative stress. These TFAM effects on muscle are shown to be exerted by molecular changes that are beyond its direct effect on mitochondrial DNA replication and transcription. TFAM augmented the muscle tricarboxylic acid cycle and citrate synthase facilitating energy expenditure. TFAM enhanced muscle glucose uptake despite increased fatty acid (FA) oxidation in concert with higher β-oxidation capacity to reduce the accumulation of IR-related carnitines and ceramides. TFAM also increased pAMPK expression, explaining enhanced PGC1α and PPARβ, and reversing HFD-induced GLUT4 and pAKT reductions. TFAM-induced mild uncoupling is shown to protect mitochondrial membrane potential against FA-induced uncontrolled depolarization. These coordinated changes conferred protection to TFAM mice against HFD-induced obesity and IR while reducing oxidative stress with potential translational opportunities.
Reinforcement Learning for Robotics and Control with Active Uncertainty Reduction

Model-free reinforcement learning based methods such as Proximal Policy Optimization, or Q-learning typically require thousands of interactions with the environment to approximate the optimum controller which may not always be feasible in robotics due to safety and time consumption. Model-based methods such as PILCO or BlackDrops, while data-efficient, provide solutions with limited robustness and complexity. To address this tradeoff, we introduce active uncertainty reduction-based virtual environments, which are formed through limited trials conducted in the original environment. We provide an efficient method for uncertainty management, which is used as a metric for self-improvement by identification of the points with maximum expected improvement through adaptive sampling. Capturing the uncertainty also allows for better mimicking of the reward responses of the original system. Our approach enables the use of complex policy structures and reward functions through a unique combination of model-based and model-free methods, while still retaining the data efficiency. We demonstrate the validity of our method on several classic reinforcement learning problems in OpenAI gym. We prove that our approach offers a better modeling capacity for complex system dynamics as compared to established methods.
Recommending Dream Jobs in a Biased Real World

Machine learning models learn what we teach them to learn. Machine learning is at the heart of recommender systems. If a machine learning model is trained on biased data, the resulting recommender system may reflect the biases in its recommendations. Biases arise at different stages in a recommender system, from existing societal biases in the data such as the professional gender gap, to biases introduced by the data collection or modeling processes. These biases impact the performance of various components of recommender systems, from offline training, to evaluation and online serving of recommendations in production systems. Specific techniques can help reduce bias at each stage of a recommender system. Reducing bias in our recommender systems is crucial to successfully recommending dream jobs to hundreds of millions members worldwide, while being true to LinkedIn's vision: "To create economic opportunity for every member of the global workforce".
Impact of rpoB Mutations on Reduced Vancomycin Susceptibility in Staphylococcus aureus

ABSTRACT Of 38 vancomycin-intermediate Staphylococcus aureus (VISA) clinical strains, 27 (71%) possessed a mutation(s) in rpoB encoding the β-subunit of RNA polymerase. Furthermore, 95.6% of the rifampin-resistant mutants obtained from 9 methicillin-resistant S. aureus (MRSA) clinical isolates showed decreased vancomycin susceptibilities. These data indicate the involvement of an rpoB mutation in VISA phenotype expression.
Searches for physics beyond the standard model at the LHC.

At the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN), protons and heavy ions are accelerated to velocities close to the speed of light and collided in order to study particle interactions and give us an insight to the fundamental laws of nature. The energy and intensity of the particle beams at the LHC are unprecedented, and a tremendous amount of data is collected by three experiments on the circular ring of the LHC that are specialized in proton-proton collisions. The data confirm the most successful theory of particle physics to date known as the standard model of particle physics to very good precision, including the long expected and recently discovered Higgs boson. The standard model cannot, however, accommodate experimentally observed phenomena like gravity, neutrino masses, and dark matter. The theory can also be theoretically unsatisfying as a result of parameters that go unexplained, such as the relatively low value of the Higgs mass despite its large quantum corrections, implying a lack of understanding. For this reason, in addition to precision measurements of standard model observables, experiments search for new physics beyond the standard model that could explain some of the shortcomings of the standard model. A selection of results for searches for new physics beyond the standard model using data recorded by three experiments on the LHC are presented in this talk.
Physiological function and transplantation of scaffold-free and vascularized human cardiac muscle tissue

Success of human myocardial tissue engineering for cardiac repair has been limited by adverse effects of scaffold materials, necrosis at the tissue core, and poor survival after transplantation due to ischemic injury. Here, we report the development of scaffold-free prevascularized human heart tissue that survives in vivo transplantation and integrates with the host coronary circulation. Human embryonic stem cells (hESCs) were differentiated to cardiomyocytes by using activin A and BMP-4 and then placed into suspension on a rotating orbital shaker to create human cardiac tissue patches. Optimization of patch culture medium significantly increased cardiomyocyte viability in patch centers. These patches, composed only of enriched cardiomyocytes, did not survive to form significant grafts after implantation in vivo. To test the hypothesis that ischemic injury after transplantation would be attenuated by accelerated angiogenesis, we created “second-generation,” prevascularized, and entirely human patches from cardiomyocytes, endothelial cells (both human umbilical vein and hESC-derived endothelial cells), and fibroblasts. Functionally, vascularized patches actively contracted, could be electrically paced, and exhibited passive mechanics more similar to myocardium than patches comprising only cardiomyocytes. Implantation of these patches resulted in 10-fold larger cell grafts compared with patches composed only of cardiomyocytes. Moreover, the preformed human microvessels anastomosed with the rat host coronary circulation and delivered blood to the grafts. Thus, inclusion of vascular and stromal elements enhanced the in vitro performance of engineered human myocardium and markedly improved viability after transplantation. These studies demonstrate the importance of including vascular and stromal elements when designing human tissues for regenerative therapies.
Automatic Model Selection for Neural Networks

Neural networks and deep learning are changing the way that artificial intelligence is being done. Efficiently choosing a suitable network architecture and fine-tune its hyper-parameters for a specific dataset is a time-consuming task given the staggering number of possible alternatives. In this paper, we address the problem of model selection by means of a fully automated framework for efficiently selecting a neural network model for a given task: classification or regression. The algorithm, named Automatic Model Selection, is a modified micro-genetic algorithm that automatically and efficiently finds the most suitable neural network model for a given dataset. The main contributions of this method are a simple list based encoding for neural networks as genotypes in an evolutionary algorithm, new crossover, and mutation operators, the introduction of a fitness function that considers both, the accuracy of the model and its complexity and a method to measure the similarity between two neural networks. AMS is evaluated on two different datasets. By comparing some models obtained with AMS to state-of-the-art models for each dataset we show that AMS can automatically find efficient neural network models. Furthermore, AMS is computationally efficient and can make use of distributed computing paradigms to further boost its performance.