Miracle-dz/UniFAR-FTUs huggingface dataset:
Underlying mechanisms of two catalase-dependent apoptotic pathways in plasma-treated cancer cells

Cancer treatment by cold atmospheric plasma (CAP) offers the possibility to selectively eliminate cancer cells while leaving surrounding, normal cells, unaffected. The mechanisms underlying the CAP selectivity towards cancer cells are yet not fully understood, but it has been proposed that two apoptosis-inducing signaling pathways in the extracellular compartment are reactivated when catalase in the cell membrane of cancer cells is inactivated. Since CAP has the capacity to inactivate catalase, CAP may thus be used to induce cancer cell death by reactivation of these signaling pathways. A better insight of this specific apoptosis-inducing mechanism - especially the role of catalase inactivation - could be useful to increase our understanding of possible roles of CAP constituents in cancer treatment. In the present study, a mathematical model describing the reaction kinetics of the two apoptosis-inducing signaling pathways is developed and used to theoretically investigate their catalase-dependent reactivation. The results show that when only the reaction kinetics is considered, just one of the pathways may be responsible for apoptosis induction. This pathway requires a high catalase concentration (in the order of mM) to be inhibited, which thus can be assumed to be the condition of cancer cells before CAP treatment, and it can only be reactivated by inactivation of about 99% of the catalase. This model is a first step into the efforts that should be taken to quantitatively understand the complex mechanisms of the role of CAP and catalase in the apoptosis-inducing signaling pathways, but will need to be further extended before it can capture the entire mechanism. Still, it gives some important insights regarding necessary conditions in order for the proposed mechanism to represent a feasible explanation of selective CAP treatment of cancer cells.
Combined activities of JNK1 and JNK2 in hepatocytes protect against toxin-induced liver injury

Manuscript Number: GASTRO-D-14-01639R2 Full Title: Combined activities of JNK1 and JNK2 in hepatocytes protect against toxin-induced liver injury Article Type: Basic Liver Section/Category: Basic Corresponding Author: Christian Trautwein, M.D. University Hospital, RWTH Aachen Aachen, GERMANY Corresponding Author Secondary Information: Corresponding Author's Institution: University Hospital, RWTH Aachen Corresponding Author's Secondary Institution: First Author: Francisco Javier Cubero First Author Secondary Information: Order of Authors: Francisco Javier Cubero Miguel Eugenio Zoubek Jin Peng Wei Hu Gang Zhao Yulia A. Nevzorova Malika Al Masaoudi Lars P. Bechmann Mark V. Boekschoten Michael Muller Nikolaus Gassler Christian Preisinger Ali E. Canbay Tom Luedde Roger J Davis Christian Liedtke Christian Trautwein, M.D. Order of Authors Secondary Information: Abstract: Background&Aims: c-Jun N-terminal kinase (JNK)1 and JNK2 are expressed in hepatocytes and have overlapping and distinct functions. JNK proteins are activated, via phosphorylation, in response to acetaminophenor CCl4-induced liver damage; the level of activation correlates with the degree of injury. SP600125, a JNK inhibitor, has been reported to block acetaminophen-induced liver injury. We investigated the role of JNK in drug-induced liver injury (DILI) in liver tissues from patients and in mice with genetic deletion of JNK in hepatocytes. Methods: We studied liver sections from
Comparison Betweeu Sparsely Distributed Memory and Hopfield-type Neural Network Models

: The Sparsely Distributed Memory (SDM) model (Kanerva, 1984) is compared to Hopfield-type neural-network models. A mathematical framework for comparing the two is developed, and the capacity of each model is investigated. The capacity of the SDM can be increased independently of the dimension of the stored vectors, whereas the Hopfield capacity is limited to a fraction of this dimension. However, the total number of stored bits per matrix element is the same in the two models, as well as for extended models with higher order interactions. The models are also compared in their ability to store sequences of patterns.
Modelling the Relation between Managers, Shadow Cost of External Finance and Corporate Investment

This paper provides a theoretical framework for studying the impact of self-interested managers on the level of corporate investment. I extend the standard neoclassical model of firm value maximization to incorporate the effect of misaligned managers on corporate investment via a firm’s profit, adjustment costs of capital and shadow cost of external finance. Under some assumptions, commonly made by the relevant literature, the model shows that the intensity of agency conflicts between misaligned managers and outside shareholders affects a firm’s investment decisions generating either under or overinvestment with respect to a perfect capital market and driving a higher cost of external finance.
Is the World Becoming a Better or a Worse Place? A Data-Driven Analysis

Is the World becoming a better or a worse place to live? In this paper, we propose a tool that can help to answer the question by combining a number of global indicators belonging to multiple categories. The proliferation of statistical data about various aspects of the World performance may suggest that it should be “easy” to evaluate the overall success of human enterprise on this planet. Moreover, it also points out the intrinsic importance in the selection of indicators. However, people have different values, biases, and preferences about the importance of various indicators, making it almost impossible to find an objective answer to this question. To address the variety and the heterogeneity of available indicators and world views, we present the analysis of global World performance as a multi-criteria decision problem, making sure that the assessment method remains as transparent as possible. By dynamically selecting a set of indicators of interest, defining the weights that we attach to various indicators and specifying the desired trends associated with each indicator, we make the assessment adaptive to individual values. We also try to deal with the inherent bias that may exist in the set of indicators that are chosen. As a study case, from various data sets that are openly available online, we have selected several that are most relevant and easy to interpret in the context of the question in the title of the paper. We demonstrate how the choice of personal preferences, or weights, can strongly change the result. Our method also provides analysis of the weights space, showing how results for particular value sets compare to the average and extreme (optimistic and pessimistic) combinations of weights that may be chosen by users.
Multi-Task Learning for Voice Trigger Detection

We describe the design of a voice trigger detection system for smart speakers. In this study, we address two major challenges. The first is that the detectors are deployed in complex acoustic environments with external noise and loud playback by the device itself. Secondly, collecting training examples for a specific keyword or trigger phrase is challenging resulting in a scarcity of trigger phrase specific training data. We describe a two-stage cascaded architecture where a low-power detector is always running and listening for the trigger phrase. If a detection is made at this stage, the candidate audio segment is re-scored by larger, more complex models to verify that the segment contains the trigger phrase. In this study, we focus our attention on the architecture and design of these second-pass detectors. We start by training a general acoustic model that produces phonetic transcriptions given a large labelled training dataset. Next, we collect a much smaller dataset of examples that are challenging for the baseline system. We then use multi-task learning to train a model to simultaneously produce accurate phonetic transcriptions on the larger dataset and discriminate between true and easily confusable examples using the smaller dataset. Our results demonstrate that the proposed model reduces errors by half compared to the baseline in a range of challenging test conditions without requiring extra parameters.
Explainable artificial intelligence and machine learning: A reality rooted perspective

As a consequence of technological progress, nowadays, one is used to the availability of big data generated in nearly all fields of science. However, the analysis of such data possesses vast challenges. One of these challenges relates to the explainability of methods from artificial intelligence (AI) or machine learning. Currently, many of such methods are nontransparent with respect to their working mechanism and for this reason are called black box models, most notably deep learning methods. However, it has been realized that this constitutes severe problems for a number of fields including the health sciences and criminal justice and arguments have been brought forward in favor of an explainable AI (XAI). In this paper, we do not assume the usual perspective presenting XAI as it should be, but rather provide a discussion what XAI can be. The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics.
The Atacama Cosmology Telescope: SZ-based masses and dust emission from IR-selected cluster candidates in the SHELA survey

We examine the stacked thermal Sunyaev–Zel’dovich (SZ) signals for a sample of galaxy group and cluster candidates from the 24 deg2 infrared Spitzer-HETDEX Exploratory Large Area (SHELA) survey. We identify the objects in combination with optical data using the redMaPPer algorithm, and divide them into three richness bins (λ in 10–20, 20–30, and 30–76 with average photometric redshifts of 0.80, 0.73, and 0.70, respectively). All richness bins show evidence for dust emission, which we fit using stacked profiles from Herschel Stripe 82 data. We fit for synchrotron emission using stacked profiles created by binning source fluxes from NRAO VLA Sky Survey data. We can confidently detect the SZ decrement only in the highest richness bin, finding MSZ,500 = $8.7^{+1.7}_{-1.3} \times 10^{13}\, \mathrm{ M}_\odot$. Neglecting the correction for dust and synchrotron depresses the inferred mass by 26 per cent, indicating a partial fill-in of the SZ decrement from dust and synchrotron emission. We compare our corrected SZ masses to two redMaPPer mass–richness scaling relations and find that the SZ mass is lower than predicted by the richness. For the lower richness bins, mass bias factors as low as 1 − b = 0.6 are not enough to bring the mass limits into agreement. We discuss possible explanations for this discrepancy. The SHELA richnesses may differ from previous richness measurements due to the inclusion of infrared data in redMaPPer. To connect the SZ signal to the mass, we use a universal gas pressure profile that is calibrated to massive clusters at low redshift. It may not be applicable to our lower mass, higher redshift sample.
On Yang-Mills connections on compact Kähler surfaces

We extend an $L^{2}$-energy gap of Yang-Mills connections on principal $G$-bundles $P$ over a compact Riemannian manfold with a $good$ Riemannian metric to the case of a compact Kahler surface with a $generic$ Kahler metric $g$, which guarantees that all ASD connections on the principal bundle $P$ over $X$ are irreducible.
Neural Autopoiesis: Organizing Self-Boundaries by Stimulus Avoidance in Biological and Artificial Neural Networks

Living organisms must actively maintain themselves in order to continue existing. Autopoiesis is a key concept in the study of living organisms, where the boundaries of the organism are not static but dynamically regulated by the system itself. To study the autonomous regulation of a self-boundary, we focus on neural homeodynamic responses to environmental changes using both biological and artificial neural networks. Previous studies showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) learn an action as they avoid stimulation from outside. In this article, as a result of our experiments using embodied cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: If the agent cannot learn an action to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes, as if to ignore the uncontrollable input. We also show such a behavior is reproduced by spiking neural networks with asymmetric STDP. We consider that these properties are to be regarded as autonomous regulation of self and nonself for the network, in which a controllable neuron is regarded as self, and an uncontrollable neuron is regarded as nonself. Finally, we introduce neural autopoiesis by proposing the principle of stimulus avoidance.
Convergence analysis of subdivision processes on the sphere

We analyse the convergence of nonlinear Riemannian analogues of linear subdivision processes operating on data in the sphere. We show how for curve subdivision rules we can derive bounds guaranteeing convergence if the density of input data is below that threshold. Previous results only yield thresholds that are several magnitudes smaller and are thus useless for a priori checking of convergence. It is the first time that such a result has been shown for a geometry with positive curvature and for subdivision rules not enjoying any special properties like being interpolatory or having non-negative mask.
SICK BUILDING SYNDROME (SBS) AMONG SCHOOL EMPLOYEES IN THE COUNTY OF UPPSALA, SWEDEN

Sick building syndrome (SBS) in school employees was investigated in 38 randomly selected schools in mid-Sweden. A questionnaire was mailed in 1993, with follow-up questionnaires in 1995 and 1997. Exposure was measured in 98 classrooms in 1993, and 101 in 1995. In 1993, 1410 employees participated (85%). The prevalence of weekly ocular, dermal, upperrespiratory, and general symptoms in 1993 was 8%, 13%, 26%, 28%, respectively. General symptoms were more prevalent at higher temperature. Eye symptoms and tiredness were more common at lower lightning effect. Tiredness was more common at lower illuminance. Headache was more common at less daylight. Eye symptoms were related to total air concentration of bacteria. A relationship between observed building dampness and SBSsymptoms was found, but only in schools with an air exchange rate below the median value (<1.8 ach). The study indicates that bacteria, building dampness, temperature and illumination in schools, are associated with SBS. INDEX TERMS Building dampness, Illumination, School environment, Room temperature, Sick Building Syndrome (SBS) INTRODUCTION The school environment has been paid an increased attention. Poor ventilation, lack of maintenance and unsatisfactory cleaning are though to be common. Studies have shown that the air exchange rate is low, resulting in high concentrations of carbon dioxide (CO2) and dust (Lee and Chang, 1999; Norbäck et al., 1990; Ruotsalainen et al., 1995; Smedje et al., 1997). Disorders that have been associated with indoor air pollution include asthma, allergies, and non-specific symptoms from eyes, upper airways and facial skin. These non-specific symptoms are sometimes referred to as the sick building syndrome (SBS) (Hodgson, 1995; Apter et al., 1995). Most studies on SBS have been dealing with office workers, and there are few studies on SBS in relation to different aspects of the school environment (Cooley et al., 1998; Norbäck et al., 1990, Lander et al., 2001). In the Swedish School Environment Project we have undertaken a number of studies of health effects and on the subjective perception of the indoor environment, among school personnel and pupils. The aim of the study was to investigate the prevalence and incidence of SBS, and frequent respiratory infections, in relation to the school environment. The following hypotheses were tested. SBS is related to different characteristics of the school building, fittings and furniture, and pollutants in the classroom air. * Contact author email: bo.sahlberg@arbmed.uas. lul.se Proceedings: Indoor Air 2002
End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning

This paper presented a deep reinforcement learning method named Double Deep Q-networks to design an end-to-end vision-based adaptive cruise control (ACC) system. A simulation environment of a highway scene was set up in Unity, which is a game engine that provided both physical models of vehicles and feature data for training and testing. Well-designed reward functions associated with the following distance and throttle/brake force were implemented in the reinforcement learning model for both internal combustion engine (ICE) vehicles and electric vehicles (EV) to perform adaptive cruise control. The gap statistics and total energy consumption are evaluated for different vehicle types to explore the relationship between reward functions and powertrain characteristics. Compared with the traditional radar-based ACC systems or human-in-the-loop simulation, the proposed vision-based ACC system can generate either a better gap regulated trajectory or a smoother speed trajectory depending on the preset reward function. The proposed system can be well adaptive to different speed trajectories of the preceding vehicle and operated in real-time.
Stochastic thermodynamics of all-to-all interacting many-body systems

We provide a stochastic thermodynamic description across scales for N identical units with all-to-all interactions that are driven away from equilibrium by different reservoirs and external forces. We start at the microscopic level with Poisson rates describing transitions between many-body states. We then identify an exact coarse graining leading to a mesoscopic description in terms of Poisson transitions between system occupations. We proceed studying macroscopic fluctuations using the Martin–Siggia–Rose formalism and large deviation theory. In the macroscopic limit (N → ∞), we derive the exact nonlinear (mean-field) rate equation describing the deterministic dynamics of the most likely occupations. We identify the scaling of the energetics and kinetics ensuring thermodynamic consistency (including the detailed fluctuation theorem) across microscopic, mesoscopic and macroscopic scales. The conceptually different nature of the ‘Shannon entropy’ (and of the ensuing stochastic thermodynamics) at different scales is also outlined. Macroscopic fluctuations are calculated semi-analytically in an out-of-equilibrium Ising model. Our work provides a powerful framework to study thermodynamics of nonequilibrium phase transitions.
GEDDnet: A Network for Gaze Estimation with Dilation and Decomposition

Appearance-based gaze estimation from RGB images provides relatively unconstrained gaze tracking from commonly available hardware. The accuracy of subject-independent models is limited partly by small intra-subject and large inter-subject variations in appearance, and partly by a latent subject-dependent bias. To improve estimation accuracy, we propose to use dilated-convolutions in a deep convolutional neural network to capture subtle changes in the eye images, and a novel gaze decomposition method that decomposes the gaze angle into the sum of a subject-independent gaze estimate from the image and a subject-dependent bias. To further reduce estimation error, we propose a calibration method that estimates the bias from a few images taken as the subject gazes at only a few or even just a single gaze target. This significantly redues calibration time and complexity. Experiments on four datasets, including a new dataset we collected containing large variations in head pose and face location, indicate that even without calibration the estimator already outperforms state-of-the-art methods by more than 6.3%. The proposed calibration method is robust to the location of calibration target and reduces estimation error significantly (up to 35.6%), achieving state-of-the-art performance with much less calibration data than required by previously proposed methods.
Stability of dynamic load balancing on a hyper-graph

We consider Poisson streams of exponentially distributed jobs arriving at each edge of an hypergraph of queues. Upon arrival, an incoming job chooses the shortest queue among the corresponding vertices. This generalizes many known models such as power-of-d load balancing and join the shorstest queue on generic graphs. We give a generic stability condition for this model and prove positive recurrence and transience of the underlying Markov process. We show that some graph topologies lead to a loss of capacity, implying more restrictive stability conditions than in, e.g., complete graphs.
Globalizing Extraction and Indigenous Rights in the Russian Arctic: The Enduring Role of the State in Natural Resource Governance

The governance of extractive industries has become increasingly globalized. International conventions and multi-stakeholder institutions set out rules and standards on a range of issues, such as environmental protection, human rights, and Indigenous rights. Companies’ compliance with these global rules may minimize risks for investors and shareholders, while offering people at sites of extraction more leverage. Although the Russian state retains a significant stake in the oil and gas industries, Russian oil and gas companies have globalized as well, receiving foreign investment, participating in global supply chains, and signing on to global agreements. We investigate how this global engagement has affected Nenets Indigenous communities in Yamal, an oil- and gas-rich region in the Russian Arctic, by analyzing Indigenous protests and benefit-sharing arrangements. Contrary to expectations, we find that Nenets Indigenous communities have not been empowered by international governance measures, and also struggle to use domestic laws to resolve problems. In Russia, the state continues to play a significant role in determining outcomes for Indigenous communities, in part by working with Indigenous associations that are state allies. We conclude that governance generating networks in the region are under-developed.
Inhibition Mechanism of Indoleamine 2, 3-Dioxygenase 1 (IDO1) by Amidoxime Derivatives and Its Revelation in Drug Design: Comparative Molecular Dynamics Simulations

For cancer treatment, in addition to the three standard therapies of surgery, chemotherapy, and radiotherapy, immunotherapy has become the fourth internationally-recognized alternative treatment. Indoleamine 2, 3-dioxygenase 1 (IDO1) catalyzes the conversion of tryptophan to kynurenine causing lysine depletion, which is an important target in the research and development of anticancer drugs. Epacadostat (INCB024360) is currently one of the most potent IDO1 inhibitors, nevertheless its inhibition mechanism still remains elusive. In this work, comparative molecular dynamics simulations were performed to reveal that the high inhibitory activity of INCB024360 mainly comes from two aspects: disturbing the ligand delivery tunnel and then preventing small molecules such as oxygen and water molecules from accessing the active site, as well as hindering the shuttle of substrate tryptophan with product kynurenine through the heme binding pocket. The scanning of key residues showed that L234 and R231 residues both were crucial to the catalytic activity of IDO1. With the association with INCB024360, L234 forms a stable hydrogen bond with G262, which significantly affects the spatial position of G262-A264 loop and then greatly disturbs the orderliness of ligand delivery tunnel. In addition, the cleavage of hydrogen bond between G380 and R231 increases the mobility of the GTGG conserved region, leading to the closure of the substrate tryptophan channel. This work provides new ideas for understanding action mechanism of amidoxime derivatives, improving its inhibitor activity and developing novel inhibitors of IDO1.