Miracle-dz/UniFAR-FTUs huggingface dataset:
Reactive oxygen species production induced by pore opening in cardiac mitochondria: The role of complex II

Succinate-driven reverse electron transport (RET) through complex I is hypothesized to be a major source of reactive oxygen species (ROS) that induces permeability transition pore (PTP) opening and damages the heart during ischemia/reperfusion. Because RET can only generate ROS when mitochondria are fully polarized, this mechanism is self-limiting once PTP opens during reperfusion. In the accompanying article (Korge, P., Calmettes, G., John, S. A., and Weiss, J. N. (2017) J. Biol. Chem. 292, 9882–9895), we showed that ROS production after PTP opening can be sustained when complex III is damaged (simulated by antimycin). Here we show that complex II can also contribute to sustained ROS production in isolated rabbit cardiac mitochondria following inner membrane pore formation induced by either alamethicin or calcium-induced PTP opening. Two conditions are required to maximize malonate-sensitive ROS production by complex II in isolated mitochondria: (a) complex II inhibition by atpenin A5 or complex III inhibition by stigmatellin that results in succinate-dependent reduction of the dicarboxylate-binding site of complex II (site IIf); (b) pore opening in the inner membrane resulting in rapid efflux of succinate/fumarate and other dicarboxylates capable of competitively binding to site IIf. The decrease in matrix [dicarboxylate] allows O2 access to reduced site IIf, thereby making electron donation to O2 possible, explaining the rapid increase in ROS production provided that site IIf is reduced. Because ischemia is known to inhibit complexes II and III and increase matrix succinate/fumarate levels, we hypothesize that by allowing dicarboxylate efflux from the matrix, PTP opening during reperfusion may activate sustained ROS production by this mechanism after RET-driven ROS production has ceased.
Robust Inference of Forces in Heterogeneous Environments

We devise a method to obtain a robust estimate of forces in a heterogeneous environment based on experimentally recorded trajectories. In particular, we focus on systems modeled by the heterogeneous overdamped Langevin equation. Here, the observed force includes a "spurious" force term due to heterogeneous diffusivity. We show how Bayesian inference can be leveraged to reliably infer forces by taking into account various noise sources including such spurious forces. The method is based on the marginalization of the force posterior over all possible spurious force contributions. The approach is combined with a Bayes factor statistical test for the presence of forces. The performance of our method is investigated analytically, numerically and tested on experimental data sets. The main results are obtained in a closed form allowing for direct exploration of their properties and fast computation. The method is incorporated into an open-source software platform for automated analysis of random walks.
A role for ceramide in driving cancer cell resistance to doxorubicin

Advanced cancers acquire resistance to chemotherapy, and this results in treatment failure. The cellular mechanisms of chemotherapy resistance are not well understood. Here, for the first time, we show that ceramide contributes to cellular resistance to doxorubicin through up‐regulating the gene expression of glucosylceramide synthase (GCS). Ceramide, a cellular lipid messenger, modulates doxorubicin‐induced cell death. GCS catalyzes ceramide glycosylation, converting ceramide to glucosylceramide; this process hastens ceramide clearance and limits ceramide‐induced apoptosis. In the present study, we evaluated the role of the GCS gene in doxorubicin resistance using several paired wild‐type and drug‐resistant (doxorubicin‐selected) cancer cell lines, including breast, ovary, cervical, and colon. GCS was overexpressed in all drug‐resistant counterparts, and suppressing GCS overexpression using antisense oligonucleotide restored doxorubicin sensitivity. Characterizing the effect mechanism showed that doxorubicin exposure increased ceramide levels, enhanced GCS expression, and imparted cellular resistance. Exogenous C6‐ceramide and sphingomyelinase treatments mimicked the influence of doxorubicin on GCS, activating the GCS promoter and upregulating GCS gene expression. Fumonisin B1, an inhibitor of ceramide synthesis, significantly suppressed doxorubicin‐up‐regulated GCS expression. Promoter truncation, point mutation, gel‐shift, and protein‐DNA ELISA analysis showed that transcription factor Sp1 was essential for ceramide‐induced GCS up‐regulation. These data indicate that ceramide‐governed GCS gene expression drives cellular resistance to doxorubicin. —Liu, Y. ‐Y. , Yu, J. Y. , Yin, D. , Patwardhan, G. A. , Gupta, V. , Hirabayashi, Y. , Holleran, W. M. , Giuliano, A. E. , Jazwinski, S. M. , Gouaze‐Andersson, V. , Consoli, D. P., Cabot, M. C. A role for ceramide in driving cancer cell resistance to doxorubicin. FASEB J. 22, 2541–2551 (2008)
Security Issues in Language-based Software Ecosystems

Language-based ecosystems (LBE), i.e., software ecosystems based on a single programming language, are very common. Examples include the npm ecosystem for JavaScript, and PyPI for Python. These environments encourage code reuse between packages, and incorporate utilities - package managers - for automatically resolving dependencies. However, the same aspects that make these systems popular - ease of publishing code and importing external code - also create novel security issues, which have so far seen little study. We present an a systematic study of security issues that plague LBEs. These issues are inherent to the ways these ecosystems work and cannot be resolved by fixing software vulnerabilities in either the packages or the utilities, e.g., package manager tools, that build these ecosystems. We systematically characterize recent security attacks from various aspects, including attack strategies, vectors, and goals. Our characterization and in-depth analysis of npm and PyPI ecosystems, which represent the largest LBEs, covering nearly one million packages indicates that these ecosystems make an opportune environment for attackers to incorporate stealthy attacks. Overall, we argue that (i) fully automated detection of malicious packages is likely to be unfeasible; however (ii) tools and metrics that help developers assess the risk of including external dependencies would go a long way toward preventing attacks.
Why replication is important in landscape genetics: American black bear in the Rocky Mountains

We investigated how landscape features influence gene flow of black bears by testing the relative support for 36 alternative landscape resistance hypotheses, including isolation by distance (IBD) in each of 12 study areas in the north central U.S. Rocky Mountains. The study areas all contained the same basic elements, but differed in extent of forest fragmentation, altitude, variation in elevation and road coverage. In all but one of the study areas, isolation by landscape resistance was more supported than IBD suggesting gene flow is likely influenced by elevation, forest cover, and roads. However, the landscape features influencing gene flow varied among study areas. Using subsets of loci usually gave models with the very similar landscape features influencing gene flow as with all loci, suggesting the landscape features influencing gene flow were correctly identified. To test if the cause of the variability of supported landscape features in study areas resulted from landscape differences among study areas, we conducted a limiting factor analysis. We found that features were supported in landscape models only when the features were highly variable. This is perhaps not surprising but suggests an important cautionary note – that if landscape features are not found to influence gene flow, researchers should not automatically conclude that the features are unimportant to the species’ movement and gene flow. Failure to investigate multiple study areas that have a range of variability in landscape features could cause misleading inferences about which landscape features generally limit gene flow. This could lead to potentially erroneous identification of corridors and barriers if models are transferred between areas with different landscape characteristics.
SAVIME: A Multidimensional System for the Analysis and Visualization of Simulation Data

Scientific applications produce a huge amount of data, which imposes serious management and analysis challenges. In particular, limitations in current database management systems prevent their adoption in simulation applications, in which in-situ analysis libraries, in-transit I/O interfaces and scientific format files are preferred over DBMSs. In order to make simulation applications benefit from DBMS support, the author proposes the development of a system called SAVIME in the context of his PhD thesis. SAVIME is an array database system designed to manage numerical simulation data. In this document, the author presents all work conducted so far and the current state of development.
IMEXnet: A Forward Stable Deep Neural Network

Deep convolutional neural networks have revolutionized many machine learning and computer vision tasks, however, some remaining key challenges limit their wider use. These challenges include improving the network's robustness to perturbations of the input image and the limited ``field of view'' of convolution operators. We introduce the IMEXnet that addresses these challenges by adapting semi-implicit methods for partial differential equations. Compared to similar explicit networks, such as residual networks, our network is more stable, which has recently shown to reduce the sensitivity to small changes in the input features and improve generalization. The addition of an implicit step connects all pixels in each channel of the image and therefore addresses the field of view problem while still being comparable to standard convolutions in terms of the number of parameters and computational complexity. We also present a new dataset for semantic segmentation and demonstrate the effectiveness of our architecture using the NYU Depth dataset.
Assessing the complex architecture of polygenic traits in diverged yeast populations

Phenotypic variation arising from populations adapting to different niches has a complex underlying genetic architecture. A major challenge in modern biology is to identify the causative variants driving phenotypic variation. Recently, the baker’s yeast, Saccharomyces cerevisiae has emerged as a powerful model for dissecting complex traits. However, past studies using a laboratory strain were unable to reveal the complete architecture of polygenic traits. Here, we present a linkage study using 576 recombinant strains obtained from crosses of isolates representative of the major lineages. The meiotic recombinational landscape appears largely conserved between populations; however, strain‐specific hotspots were also detected. Quantitative measurements of growth in 23 distinct ecologically relevant environments show that our recombinant population recapitulates most of the standing phenotypic variation described in the species. Linkage analysis detected an average of 6.3 distinct QTLs for each condition tested in all crosses, explaining on average 39% of the phenotypic variation. The QTLs detected are not constrained to a small number of loci, and the majority are specific to a single cross‐combination and to a specific environment. Moreover, crosses between strains of similar phenotypes generate greater variation in the offspring, suggesting the presence of many antagonistic alleles and epistatic interactions. We found that subtelomeric regions play a key role in defining individual quantitative variation, emphasizing the importance of the adaptive nature of these regions in natural populations. This set of recombinant strains is a powerful tool for investigating the complex architecture of polygenic traits.
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification From the Bottom Up

Given a training dataset composed of images and corresponding category labels, deep convolutional neural networks show a strong ability in mining discriminative parts for image classification. However, deep convolutional neural networks trained with image level labels only tend to focus on the most discriminative parts while missing other object parts, which could provide complementary information. In this paper, we approach this problem from a different perspective. We build complementary parts models in a weakly supervised manner to retrieve information suppressed by dominant object parts detected by convolutional neural networks. Given image level labels only, we first extract rough object instances by performing weakly supervised object detection and instance segmentation using Mask R-CNN and CRF-based segmentation. Then we estimate and search for the best parts model for each object instance under the principle of preserving as much diversity as possible. In the last stage, we build a bi-directional long short-term memory (LSTM) network to fuze and encode the partial information of these complementary parts into a comprehensive feature for image classification. Experimental results indicate that the proposed method not only achieves significant improvement over our baseline models, but also outperforms state-of-the-art algorithms by a large margin (6.7%, 2.8%, 5.2% respectively) on Stanford Dogs 120, Caltech-UCSD Birds 2011-200 and Caltech 256.
An Inverting-Tube Clutching Contractile Soft Pneumatic Actuator

This paper presents the simple synergistic combination of a novel contracting soft pneumatic actuator with a soft clutch (linear brake). The device is designated the Inverting-tube Vacuum ACtuator with Clutch (InVACC). The actuator alone (no clutch) is designated "InVAC" and uses vacuum pressure to invert a thin tube into a shorter section of reinforced flexible tubing. The inverting tube acts as rolling diaphragm and a flexible tendon. This allows the actuator to contract to one third of its extended length. The contractile-force-per-unit-pressure is approximately constant over the stroke. The theoretical maximum of this force is the product of the vacuum gauge pressure and half the interior cross-sectional area of the tube. The experimental evaluation revealed hysteretic losses that depend on the actuation direction and rate. With -81 kPa, the prototype produced 12.7 N of tension during extension and 7.5 N during retraction. The reinforced tubing of the InVAC was integrated with an inner collapsible "clutching" tube to create an InVACC. The clutch is engaged by applying a positive pressure between the reinforced tube and the clutching tube, which collapses the clutching tube onto the flexible tendon. With a pressure of 50 kPa, the InVACC clutch tested in this work was able to support a peak tensile load of 120 N before slipping. Though the fatigue life of the current prototypes is limited, improved fabrication methods for this novel actuator/clutch concept will enable new applications in robotics and wearable haptic systems.
Separated by sand, fused by dropping water: habitat barriers and fluctuating water levels steer the evolution of rock‐dwelling cichlid populations in Lake Tanganyika

The conditions of phenotypic and genetic population differentiation allow inferences about the evolution, preservation and loss of biological diversity. In Lake Tanganyika, water level fluctuations are assumed to have had a major impact on the evolution of stenotopic littoral species, though this hypothesis has not been specifically examined so far. The present study investigates whether subtly differentiated colour patterns of adjacent Tropheus moorii populations are maintained in isolation or in the face of continuous gene flow, and whether the presumed influence of water level fluctuations on lacustrine cichlids can be demonstrated in the small‐scale population structure of the strictly stenotopic, littoral Tropheus. Distinct population differentiation was found even across short geographic distances and minor habitat barriers. Population splitting chronology and demographic histories comply with our expectation of old and rather stable populations on steeper sloping shore, and more recently established populations in a shallower region. Moreover, population expansions seem to coincide with lake level rises in the wake of Late Pleistocene megadroughts ∼100 KYA. The imprint of hydrologic events on current population structure in the absence of ongoing gene flow suggests that phenotypic differentiation among proximate Tropheus populations evolves and persists in genetic isolation. Sporadic gene flow is effected by lake level fluctuations following climate changes and controlled by the persistence of habitat barriers during lake level changes. Since similar demographic patterns were previously reported for Lake Malawi cichlids, our data furthermore strengthen the hypothesis that major climatic events synchronized facets of cichlid evolution across the East African Great Lakes.
Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems

One of the fundamental challenges to realize massive Multiple-Input Multiple-Output (MIMO) communications is the accurate acquisition of channel state information for a plurality of users at the base station. This is usually accomplished in the UpLink (UL) direction profiting from the time division duplexing mode. In practical base station transceivers, there exist inevitably nonlinear hardware components, like signal amplifiers and various analog filters, which complicates the calibration task. To deal with this challenge, we design a deep neural network for channel calibration between the UL and DownLink (DL) directions. During the initial training phase, the deep neural network is trained from both UL and DL channel measurements. We then leverage the trained deep neural network with the instantaneously estimated UL channel to calibrate the DL one, which is not observable during the UL transmission phase. Our numerical results confirm the merits of the proposed approach, and show that it can achieve performance comparable to conventional approaches, like the Agros method and methods based on least squares, that however assume linear hardware behavior models. More importantly, considering generic nonlinear relationships between the UL and DL channels, it is demonstrated that our deep neural network approach exhibits robust performance, even when the number of training sequences is limited.
Intelligent Knowledge Distribution: Constrained-Action POMDPs for Resource-Aware Multiagent Communication

This article addresses a fundamental question of multiagent knowledge distribution: what information should be sent to whom and when with the limited resources available to each agent? Communication requirements for multiagent systems can be rather high when an accurate picture of the environment and the state of other agents must be maintained. To reduce the impact of multiagent coordination on networked systems, for example, power and bandwidth, this article introduces two concepts for the partially observable Markov decision processes (POMDPs): 1) action-based constraints that yield constrained-action POMDPs (CA-POMDPs) and 2) soft probabilistic constraint satisfaction for the resulting infinite-horizon controllers. To enable constraint analysis over an infinite horizon, an unconstrained policy is first represented as a finite-state controller (FSC) and optimized with policy iteration. The FSC representation then allows for a combination of the Markov chain Monte Carlo and discrete optimization to improve the probabilistic constraint satisfaction of the controller while minimizing the impact on the value function. Within the CA-POMDP framework, we then propose intelligent knowledge distribution (IKD) which yields per-agent policies for distributing knowledge between agents subject to interaction constraints. Finally, the CA-POMDP and IKD concepts are validated using an asset tracking problem where multiple unmanned aerial vehicles (UAVs) with heterogeneous sensors collaborate to localize a ground asset to assist in avoiding unseen obstacles in a disaster area. The IKD model was able to maintain asset tracking through multiagent communications while only violating soft power and bandwidth constraints 3% of the time, while greedy and naive approaches violated constraints more than 60% of the time.
Quantum hardness of learning shallow classical circuits

In this paper we study the quantum learnability of constant-depth classical circuits under the uniform distribution and in the distribution-independent framework of PAC learning. In order to attain our results, we establish connections between quantum learning and quantum-secure cryptosystems. We then achieve the following results. 1) Hardness of learning AC$^0$ and TC$^0$ under the uniform distribution. Our first result concerns the concept class TC$^0$ (resp. AC$^0$), the class of constant-depth and polynomial-sized circuits with unbounded fan-in majority gates (resp. AND, OR, NOT gates). We show that if there exists no quantum polynomial-time (resp. strong sub-exponential time) algorithm to solve the Ring Learning with Errors (RLWE) problem, then there exists no polynomial-time quantum learning algorithm for TC$^0$ (resp. AC$^0$) under the uniform distribution (even with access to quantum membership queries). The main technique in this result uses explicit pseudo-random functions that are believed to be quantum-secure to construct concept classes that are hard to learn quantumly under the uniform distribution. 2) Hardness of learning TC$^0_2$ in the PAC setting. Our second result shows that if there exists no quantum polynomial time algorithm for the LWE problem, then there exists no polynomial time quantum PAC learning algorithm for the class TC$^0_2$, i.e., depth-2 TC$^0$ circuits. The main technique in this result is to establish a connection between the quantum security of public-key cryptosystems and the learnability of a concept class that consists of decryption functions of the cryptosystem. This gives a strong (conditional) negative answer to one of the "Ten Semi-Grand Challenges for Quantum Computing Theory" raised by Aaronson [Aar05], who asked if AC$^0$ and TC$^0$ can be PAC-learned in quantum polynomial time.
Essential role of the flexible linker on the conformational equilibrium of bacterial peroxiredoxin reductase for effective regeneration of peroxiredoxin

Reactive oxygen species (ROS) can damage DNA, proteins, and lipids, so cells have antioxidant systems that regulate ROS. In many bacteria, a dedicated peroxiredoxin reductase, alkyl hydroperoxide reductase subunit F (AhpF), catalyzes the rapid reduction of the redox-active disulfide center of the antioxidant protein peroxiredoxin (AhpC) to detoxify ROS such as hydrogen peroxide, organic hydroperoxide, and peroxynitrite. AhpF is a flexible multidomain protein that enables a series of electron transfers among the redox centers by accepting reducing equivalents from NADH. A flexible linker connecting the N-terminal domain (NTD) and C-terminal domain (CTD) of AhpF suggests that the enzyme adopts a large-scale domain motion that alternates between the closed and open states to shuttle electrons from the CTD via the NTD to AhpC. Here, we conducted comprehensive mutational, biochemical, and biophysical analyses to gain insights into the role of the flexible linker and the residues critical for the domain motions of Escherichia coli AhpF (EcAhpF) during electron transfer. Small-angle X-ray scattering studies of linker mutants revealed that a group of charged residues, 200EKR202, is crucial for the swiveling motion of the NTD. Moreover, NADH binding significantly affected EcAhpF flexibility and the movement of the NTD relative to the CTD. The mutants also exhibited a decrease in H2O2 reduction by the AhpF-AhpC ensemble. We propose that a concerted movement involving the NTD, C-terminal NADH, and FAD domains, and the flexible linker between them is essential for optimal intra-domain cross-talk and for efficient electron transfer to the redox partner AhpC required for peroxidation.