Miracle-dz/UniFAR-FTUs huggingface dataset:
Matrix Metalloproteinase-9-Generated COOH-, but Not NH2-Terminal Fragments of Serum Amyloid A1 Retain Potentiating Activity in Neutrophil Migration to CXCL8, With Loss of Direct Chemotactic and Cytokine-Inducing Capacity

Serum amyloid A1 (SAA1) is a prototypic acute phase protein, induced to extremely high levels by physical insults, including inflammation and infection. Human SAA and its NH2-terminal part have been studied extensively in the context of amyloidosis. By contrast, little is known about COOH-terminal fragments of SAA. Intact SAA1 chemoattracts leukocytes via the G protein-coupled receptor formyl peptide receptor like 1/formyl peptide receptor 2 (FPR2). In addition to direct leukocyte activation, SAA1 induces chemokine production by signaling through toll-like receptor 2. We recently discovered that these induced chemokines synergize with intact SAA1 to chemoattract leukocytes in vitro and in vivo. Gelatinase B or matrix metalloproteinase-9 (MMP-9) is also induced by SAA1 during infection and inflammation and processes many substrates in the immune system. We demonstrate here that MMP-9 rapidly cleaves SAA1 at a known consensus sequence that is also present in gelatins. Processing of SAA1 by MMP-9 at an accessible loop between two alpha helices yielded predominantly three COOH-terminal fragments: SAA1(52–104), SAA1(57–104), and SAA1(58–104), with a relative molecular mass of 5,884.4, 5,327.3, and 5,256.3, respectively. To investigate the effect of proteolytic processing on the biological activity of SAA1, we chemically synthesized the COOH-terminal SAA fragments SAA1(52–104) and SAA1(58–104) and the complementary NH2-terminal peptide SAA1(1–51). In contrast to intact SAA1, the synthesized SAA1 peptides did not induce interleukin-8/CXCL8 in monocytes or fibroblasts. Moreover, these fragments possessed no direct chemotactic activity for neutrophils, as observed for intact SAA1. However, comparable to intact SAA1, SAA1(58–104) cooperated with CXCL8 in neutrophil activation and migration, whereas SAA1(1–51) lacked this potentiating activity. This cooperative interaction between the COOH-terminal SAA1 fragment and CXCL8 in neutrophil chemotaxis was mediated by FPR2. Hence, proteolytic cleavage of SAA1 by MMP-9 fine tunes the inflammatory capacity of this acute phase protein in that only the synergistic interactions with chemokines remain to prolong the duration of inflammation.
On the dynamics of small and medium-sized enterprises: Evidence from Japan

We examine how changes in macroeconomic conditions (the global financial crisis) relate to investment and financial decision making for each of the different size categories of small and medium-sized enterprises (SMEs). We use a large dataset of 764,963 observations in Japan for the time period from 2006 to 2014 to understand the heterogeneity of SMEs in financing and investment decision making, such in size, industry, and region. Our findings are of particular importance for regulators because we show that SMEs are dynamic in nature where they change their financial behavior in response to any macroeconomic shock. In addition, we report differences among the different size subsample at the sales growth and state/industry GDP growth levels. Hence, this requires designing a unique set of regulations for each group accordingly to effectively enhance the growth potential for each group and for SMEs as a whole. These findings have implications for lenders, especially banks, which should treat each size group within SMEs differently while lending or assessing creditworthiness.
Urban food webs Evolution mediates the effects of apex predation on aquatic Supplementary data tml

Supplementary data tml http://rspb.royalsocietypublishing. org/content/suppl/2013/05/23/rspb.2013. 0859.DC1. h "Data Supplement" References http://rspb.royalsocietypublishing. org/content/280/1763/20130859.full. html#ref-list-1 This article cites 42 articles, 9 of which can be accessed free Subject collections (1595 articles) evolution  (1477 articles) ecology  Articles on similar topics can be found in the following collections
The effect of fishmeal inclusion and prebiotic supplementation on the hindgut faecal microbiota of farmed Tasmanian Atlantic salmon (Salmo salar L.)

Factors such as seasonal temperature and diet components, for example, fishmeal (FM) inclusion, can influence the composition of the gut microbiota of fish. In this study, we examined changes in the gut bacterial populations, in particular lactic acid bacteria (LAB), of farmed Tasmanian Atlantic salmon in response to different diets, during periods of higher water temperature.
Towards Restoring Locomotion for Paraplegics: Realizing Dynamically Stable Walking on Exoskeletons

This paper presents the first experimental results of crutch-less dynamic walking with paraplegics on a lower-body exoskeleton: ATALANTE, designed by the French start-up company Wandercraft. The methodology used to achieve these results is based on the partial hybrid zero dynamics (PHZD) framework for formally generating stable walking gaits. A direct collocation optimization formulation is used to provide fast and efficient generation of gaits tailored to each patient. These gaits are then implemented on the exoskeleton for three paraplegics. The end result is dynamically stable walking in an exoskeleton without the need for crutches. After a short period of tuning by the engineers and practice by the subjects, each subject was able to dynamically walk across a room of about 10 m up to a speed of 0.15 m/s (0.5 km/h) without the need for crutches or any other kind of assistance.
Semi-supervised Energy Disaggregation Framework using General Appliance Models

Providing detailed appliance-level energy consumption information helps consumers to understand their usage behavior and encourages them to optimize their energy usage. Non-intrusive load monitoring (NILM) or energy disaggregation aims to estimate appliance-level energy consumption data from the aggregate consumption data of households. NILM algorithms can be broadly classified into supervised and unsupervised (or semi-supervised) techniques. The former requires a large amount of prior data for each appliance and the latter relies on manual tuning of models of appliances based on some metadata information. While there is a significant interest from academia and industry, NILM techniques are still not adopted widely across households. This is mainly because the techniques developed for one household cannot be generalized and applied in other households (applicability), require tremendous manual-tuning to apply across households (scalability), and cannot run in real-time. To overcome the above issues, we propose a novel semi-supervised energy disaggregation framework – UniversalNILM. The key idea of UniversalNILM is to model appliances in a few (3-10) training houses, which has detailed appliance-level data and transfer this learning to test houses (blind disaggregation), which has only aggregate house consumption data to derive fine-grained appliance energy consumption. To this end, we develop an automated appliance modeling technique that creates general appliance models across various appliance brands and models. The general appliance models are analytical models which describe power consumption of each appliance. These general appliance models are then fine-tuned automatically on test houses to accurately disaggregate the energy consumption in real-time. To test the robustness of UniversalNILM, we empirically evaluated it across three publicly available real-world datasets. We show that the general appliance models learnt on a few households is able to accurately disaggregate on unseen test houses in the same dataset, as well as unseen houses from different datasets. This is the first work in NILM which is able to perform disaggregation across datasets. Another improvement is that UniversalNILM outperforms the reported accuracy from both state-of-the-art supervised and unsupervised NILM techniques.
Dyna: A Method of Momentum for Stochastic Optimization

An algorithm is presented for momentum gradient descent optimization based on the first-order differential equation of the Newtonian dynamics. The fictitious mass is introduced to the dynamics of momentum for regularizing the adaptive stepsize of each individual parameter. The dynamic relaxation is adapted for stochastic optimization of nonlinear objective functions through an explicit time integration with varying damping ratio. The adaptive stepsize is optimized for each individual neural network layer based on the number of inputs. The adaptive stepsize for every parameter over the entire neural network is uniformly optimized with one upper bound, independent of sparsity, for better overall convergence rate. The numerical implementation of the algorithm is similar to the Adam Optimizer, possessing computational efficiency, similar memory requirements, etc. There are three hyper-parameters in the algorithm with clear physical interpretation. Preliminary trials show promise in performance and convergence.
Noise-Robust Morphological Disambiguation for Dialectal Arabic

User-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging. The challenging nature of noisy text processing is exacerbated for dialectal content, where in addition to spelling and lexical differences, dialectal text is characterized with morpho-syntactic and phonetic variations. These issues increase sparsity in NLP models and reduce accuracy. We present a neural morphological tagging and disambiguation model for Egyptian Arabic, with various extensions to handle noisy and inconsistent content. Our models achieve about 5% relative error reduction (1.1% absolute improvement) for full morphological analysis, and around 22% relative error reduction (1.8% absolute improvement) for part-of-speech tagging, over a state-of-the-art baseline.
Long-term outcomes in patients with ypT0 rectal cancer after neoadjuvant chemoradiotherapy and curative resection.

Objective For patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy (NCRT), significant pathological response of the primary tumor has been proposed to identify candidates for organ preservation. However, this does not address metastatic lymph nodes in the mesorectum. The aim of this study was to assess the incidence of lymph node metastases in ypT0 patients treated with NCRT and curative resection and to explore risk factors associated with survival. Methods This was a retrospective study of patients with ypT0 rectal cancer after NCRT and curative resection at a tertiary care center in China from 2005 to 2014. Results A total of 60 (18.6%) patients who underwent surgery after NCRT and achieved ypT0 were enrolled in this study; one patient was excluded owing to lack of follow-up. Of these 59 patients, lymph node metastases were found in the mesorectum (ypT0N+) in eight (13.6%) patients. After a median follow-up of 52 months, 5-year recurrence-free survival (82.7% vs. 62.5%, P=0.014) and overall survival (OS) (90.9% vs. 70.0%, P=0.032) were much higher in ypN0 than ypN+ patients. Multivariate analyses showed that ypN+ status (P=0.009) and perioperative blood transfusion (BT) (P=0.001) were significantly independent risk factors associated with recurrence; however, no factor was correlated with 5-year OS. Conclusions Patients with ypT0N0 rectal cancer can achieve excellent long-term outcomes; however, positive lymph nodes or tumor deposits can still be found in 13.6% of ypT0 patients. Nodal positivity in the mesorectum and perioperative BT are independent risk factors for recurrence.
Practical personalized genomics in the encrypted domain

In this paper, we examine and propose a solution for the challenges of sharing of genome sequence data and of data querying on the genome sequence data on a cloud server in per-sonalized medicine scenarios. We develop a privacy-preserving, a secure and efficient solution for personalized medicine. The solution that we propose making use of stream cipher-based homomorphic transciphering in a cloud server, and to show the effectiveness of transciphering solution in the personalized medicine scenario. This paper also provides the comparative analysis of well-known existing homomorphic encryption solutions BGV and FV schemes combined with the FLIP stream cipher to demonstrate the efficiency and privacy of our solution.
Bipolar H ii regions produced by cloud-cloud collisions

We suggest that Bipolar H{\sc ii} Regions may be the aftermath of collisions between clouds. Such a collision will produce a shock-compressed layer, and a star cluster can then condense out of the dense gas near the centre of the layer. If the clouds are sufficiently massive, the star cluster is likely to contain at least one massive star, which emits ionising radiation, and excites an H{\sc ii} region, which then expands, sweeping up the surrounding neutral gas. Once most of the matter in the clouds has accreted onto the layer, expansion of the H{\sc ii} Region meets little resistance in directions perpendicular to the mid-plane of the layer, and so it expands rapidly to produce two lobes of ionised gas, one on each side of the layer. Conversely, in directions parallel to the mid-plane of the layer, expansion of the H{\sc ii} Region stalls due to the ram-pressure of the gas that continues to fall towards the star cluster from the outer parts of the layer; a ring of dense neutral gas builds up around the waist of the Bipolar H{\sc ii} Region, and may spawn a second generation of star formation. In this paper we present a dimensionless model for the flow of ionised gas in a Bipolar H{\sc ii} Region created according to the above scenario, and predict the characteristics of the resulting freefree continuum and recombination-line emission. This dimensionless model can be scaled to the physical parameters of any particular system. Our intention is that these predictions will be useful in testing the scenario outlined above, and thereby providing indirect support for the role of cloud/cloud collisions in triggering star formation.
Nonlinear Equalization with Deep Learning for Multi-Purpose Visual MIMO Communications

A major challenge of screen-camera visual multiinput multi-output (MIMO) communications is to increase the achievable throughput by reducing nonlinear channel effects including perspective distortion, ambient lights, and color mixing. To mitigate such nonlinear effects, an existing transmission method uses linear or simple nonlinear equalizations in decoding operations. However, the throughput improvement from the equalization techniques is often limited because the effects are composed of a combination of various nonlinear distortions. In addition to the above issue, the existing studies consider specific environments, such as indoor and static communications, although screen-camera communications can be used for a variety of applications including outdoor and mobile scenarios. In this study, we propose 1) deep neural network (DNN)- based decoding for screen-camera communications to increase the achievable throughput and 2) Unity 3D-based evaluation methodology to synthetically learn the DNN for being robust against many different screen-camera environments. The DNN finds the best nonlinear kernels for equalization from numerously captured images, and then decodes original bits from newly captured images based on the trained nonlinear kernels. In the Unity-based evaluation tool, we can easily capture numerous photo-realistic images in different screen-camera scenarios to learn the impact of perspective distortion, screen- to-camera distance, motion blur, and ambient lights on the throughput since Unity-based environment can freely set programmable screens, cameras, and ambient lights on a 3D space. As an initial proof of concept, we demonstrate that the proposed DNN-based decoder scheme improves the achievable throughput by up to 148% compared to existing methods by equalizing nonlinear effects.
A Transition Strategy from Fossil Fuels to Renewable Energy Sources in the Mexican Electricity System

Renewable energy sources exploitation acquires special importance for creating low-carbon energy systems. In Mexico a national regulation limits the fossil fuel-based electricity generation to 65%, 60% and 50% by years 2024, 2030 and 2050 respectively. This study evaluates several scenarios of renewables incorporation into the Mexican electricity system to attend those targets as well as a 75% renewables-based electricity share target towards a 100% renewable system. By its size, the Mexican electricity system, with a generation of 260.4 TWh/year (85% based on fossil fuels), can be regarded as an illustrating reference. The impact of increasing amounts of wind, photovoltaic solar, biomass, biogas, geothermal, hydro and concentrating solar power on the system’s capacity to attend demand on a one-hour timescale resolution is investigated utilizing the EnergyPLAN model and the minimum total mix capacity method. Possible excess of electricity production is also assessed. For every target year, a solution is obtained corresponding to the combination resulting in the minimum total generation capacity for the electricity system. A transition strategy to a system with a high share of renewables-based electricity is designed where every transition step corresponds to the optimal energy mix for each of the target years.
Mining gold from implicit models to improve likelihood-free inference

Significance In scientific fields as diverse as particle physics, genetics, and epidemiology, complex computer simulations provide the most accurate description of phenomena, but the corresponding likelihood function cannot be computed. This is a major challenge for statistical inference. We present inference techniques for this case that combine the insight that additional latent information can be extracted from the simulator with the power of neural networks in regression and density estimation tasks. In toy problems and a real-life particle physics problem, these techniques lead to better sample efficiency than established methods, demonstrating the potential to enable more precise scientific results from large-scale experiments across many disciplines. Simulators often provide the best description of real-world phenomena. However, the probability density that they implicitly define is often intractable, leading to challenging inverse problems for inference. Recently, a number of techniques have been introduced in which a surrogate for the intractable density is learned, including normalizing flows and density ratio estimators. We show that additional information that characterizes the latent process can often be extracted from simulators and used to augment the training data for these surrogate models. We introduce several loss functions that leverage these augmented data and demonstrate that these techniques can improve sample efficiency and quality of inference.
Association between IL-6 production in synovial explants from rheumatoid arthritis patients and clinical and imaging response to biologic treatment: A pilot study

Introduction The need for biomarkers which can predict disease course and treatment response in rheumatoid arthritis (RA) is evident. We explored whether clinical and imaging responses to biologic disease modifying anti-rheumatic drug treatment (bDMARD) were associated with the individual’s mediator production in explants obtained at baseline. Methods RA Patients were evaluated by disease activity score 28 joint C-reactive protein (DAS 28-)), colour Doppler ultrasound (CDUS) and 3 Tesla RA magnetic resonance imaging scores (RAMRIS). Explants were established from synovectomies from a needle arthroscopic procedure prior to initiation of bDMARD. Explants were incubated with the bDMARD in question, and the productions of interleukin-6 (IL-6), monocyte chemo-attractive protein-1 (MCP-1) and macrophage inflammatory protein-1-beta (MIP-1b) were measured by multiplex immunoassays. The changes in clinical and imaging variables following a minimum of 3 months bDMARD treatment were compared to the baseline explant results. Mixed models and Spearman’s rank correlations were performed. P-values below 0.05 were considered statistically significant. Results 16 patients were included. IL-6 production in bDMARD-treated explants was significantly higher among clinical non-responders compared to responders (P = 0.04), and a lack of suppression of IL-6 by the bDMARDS correlated to a high DAS-28 (ρ = 0.57, P = 0.03), CDUS (ρ = 0.53, P = 0.04) and bone marrow oedema (ρ = 0.56, P = 0.03) at follow-up. No clinical association was found with explant MCP-1 production. MIP-1b could not be assessed due to a large number of samples below the detection limit. Conclusions Synovial explants appear to deliver a disease-relevant output testing which when carried out in advance of bDMARD treatment can potentially pave the road for a more patient tailored treatment approach with better treatment effects.
Regulated Cell Death as a Therapeutic Target for Novel Antifungal Peptides and Biologics

The rise of microbial pathogens refractory to conventional antibiotics represents one of the most urgent and global public health concerns for the 21st century. Emergence of Candida auris isolates and the persistence of invasive mold infections that resist existing treatment and cause severe illness has underscored the threat of drug-resistant fungal infections. To meet these growing challenges, mechanistically novel agents and strategies are needed that surpass the conventional fungistatic or fungicidal drug actions. Host defense peptides have long been misunderstood as indiscriminant membrane detergents. However, evidence gathered over the past decade clearly points to their sophisticated and selective mechanisms of action, including exploiting regulated cell death pathways of their target pathogens. Such peptides perturb transmembrane potential and mitochondrial energetics, inducing phosphatidylserine accessibility and metacaspase activation in fungi. These mechanisms are often multimodal, affording target pathogens fewer resistance options as compared to traditional small molecule drugs. Here, recent advances in the field are examined regarding regulated cell death subroutines as potential therapeutic targets for innovative anti-infective peptides against pathogenic fungi. Furthering knowledge of protective host defense peptide interactions with target pathogens is key to advancing and applying novel prophylactic and therapeutic countermeasures to fungal resistance and pathogenesis.
Preventing Zeroizing Attacks on GGH15

The GGH15 multilinear maps have served as the foundation for a number of cutting-edge cryptographic proposals. Unfortunately, many schemes built on GGH15 have been explicitly broken by so-called “zeroizing attacks,” which exploit leakage from honest zero-test queries. The precise settings in which zeroizing attacks are possible have remained unclear. Most notably, none of the current indistinguishability obfuscation (iO) candidates from GGH15 have any formal security guarantees against zeroizing attacks. In this work, we demonstrate that all known zeroizing attacks on GGH15 implicitly construct algebraic relations between the results of zero-testing and the encoded plaintext elements. We then propose a “GGH15 zeroizing model” as a new general framework which greatly generalizes known attacks. Our second contribution is to describe a new GGH15 variant, which we formally analyze in our GGH15 zeroizing model. We then construct a new iO candidate using our multilinear map, which we prove secure in the GGH15 zeroizing model. This implies resistance to all known zeroizing strategies. The proof relies on the Branching Program Un-Annihilatability (BPUA) Assumption of Garg et al. [TCC 16-B] (which is implied by PRFs in NC secure against P/poly) and the complexity-theoretic p-Bounded Speedup Hypothesis of Miles et al. [ePrint 14] (a strengthening of the Exponential Time Hypothesis).