Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Genes Involved in Biogenesis of Outer Membrane Vesicles (OMVs) in Salmonella enterica Serovar Typhi

Outer membrane vesicles (OMVs) are nano-sized proteoliposomes discharged from the cell envelope of Gram-negative bacteria. OMVs normally contain toxins, enzymes and other factors, and are used as vehicles in a process that has been considered a generalized, evolutionarily conserved delivery system among bacteria. Furthermore, OMVs can be used in biotechnological applications that require delivery of biomolecules, such as vaccines, remarking the importance of their study. Although it is known that Salmonella enterica serovar Typhi (S. Typhi), the etiological agent of typhoid fever in humans, delivers toxins (e.g., HlyE) via OMVs, there are no reports identifying genetic determinants of the OMV biogenesis in this serovar. In the present work, and with the aim to identify genes participating in OMV biogenesis in S. Typhi, we screened 15,000 random insertion mutants for increased HlyE secretion. We found 9 S. Typhi genes (generically called zzz genes) determining an increased HlyE secretion that were also involved in OMV biogenesis. The genes corresponded to ompA, nlpI, and tolR (envelope stability), rfaE and waaC (LPS synthesis), yipP (envC), mrcB (synthesis and remodeling of peptidoglycan), degS (stress sensor serine endopeptidase) and hns (global transcriptional regulator). We found that S. Typhi Δzzz mutants were prone to secrete periplasmic, functional proteins with a relatively good envelope integrity. In addition, we showed that zzz genes participate in OMV biogenesis, modulating different properties such as OMV size distribution, OMV yield and OMV protein cargo.
Average cost optimal control under weak hypotheses: Relative value iterations

We study the well-posedness of the Bellman equation for the ergodic control problem for a controlled Markov process in $R^d$ for a near-monotone cost and establish convergence results for the associated 'relative value iteration' algorithm which computes its solution recursively. In addition, we present some results concerning the stability and asymptotic optimality of the associated rolling horizon policies.
Jumping Manifolds: Geometry Aware Dense Non-Rigid Structure From Motion

Given dense image feature correspondences of a non-rigidly moving object across multiple frames, this paper proposes an algorithm to estimate its 3D shape for each frame. To solve this problem accurately, the recent state-of-the-art algorithm reduces this task to set of local linear subspace reconstruction and clustering problem using Grassmann manifold representation [34]. Unfortunately, their method missed on some of the critical issues associated with the modeling of surface deformations, for e.g., the dependence of a local surface deformation on its neighbors. Furthermore, their representation to group high dimensional data points inevitably introduce the drawbacks of categorizing samples on the high-dimensional Grassmann manifold [32, 31]. Hence, to deal with such limitations with [34], we propose an algorithm that jointly exploits the benefit of high-dimensional Grassmann manifold to perform reconstruction, and its equivalent lower-dimensional representation to infer suitable clusters. To accomplish this, we project each Grassmannians onto a lower-dimensional Grassmann manifold which preserves and respects the deformation of the structure w.r. t its neighbors. These Grassmann points in the lower-dimension then act as a representative for the selection of high-dimensional Grassmann samples to perform each local reconstruction. In practice, our algorithm provides a geometrically efficient way to solve dense NRSfM by switching between manifolds based on its benefit and usage. Experimental results show that the proposed algorithm is very effective in handling noise with reconstruction accuracy as good as or better than the competing methods.
Robustness Certificates Against Adversarial Examples for ReLU Networks

While neural networks have achieved high performance in different learning tasks, their accuracy drops significantly in the presence of small adversarial perturbations to inputs. Defenses based on regularization and adversarial training are often followed by new attacks to defeat them. In this paper, we propose attack-agnostic robustness certificates for a multi-label classification problem using a deep ReLU network. Although computing the exact distance of a given input sample to the classification decision boundary requires solving a non-convex optimization, we characterize two lower bounds for such distances, namely the simplex certificate and the decision boundary certificate. These robustness certificates leverage the piece-wise linear structure of ReLU networks and use the fact that in a polyhedron around a given sample, the prediction function is linear. In particular, the proposed simplex certificate has a closed-form, is differentiable and is an order of magnitude faster to compute than the existing methods even for deep networks. In addition to theoretical bounds, we provide numerical results for our certificates over MNIST and compare them with some existing upper bounds.
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free

We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal in terms of dynamic regret. Specifically, our algorithm achieves dynamic regret $\mathcal{O}(\min\{\sqrt{ST}, \Delta^{\frac{1}{3}}T^{\frac{2}{3}}\})$ for a contextual bandit problem with $T$ rounds, $S$ switches and $\Delta$ total variation in data distributions. Importantly, our algorithm is adaptive and does not need to know $S$ or $\Delta$ ahead of time, and can be implemented efficiently assuming access to an ERM oracle. Our results strictly improve the $\mathcal{O}(\min \{S^{\frac{1}{4}}T^{\frac{3}{4}}, \Delta^{\frac{1}{5}}T^{\frac{4}{5}}\})$ bound of (Luo et al., 2018), and greatly generalize and improve the $\mathcal{O}(\sqrt{ST})$ result of (Auer et al, 2018) that holds only for the two-armed bandit problem without contextual information. The key novelty of our algorithm is to introduce replay phases, in which the algorithm acts according to its previous decisions for a certain amount of time in order to detect non-stationarity while maintaining a good balance between exploration and exploitation.
Insertion-based Decoding with Automatically Inferred Generation Order

Abstract Conventional neural autoregressive decoding commonly assumes a fixed left-to-right generation order, which may be sub-optimal. In this work, we propose a novel decoding algorithm— InDIGO—which supports flexible sequence generation in arbitrary orders through insertion operations. We extend Transformer, a state-of-the-art sequence generation model, to efficiently implement the proposed approach, enabling it to be trained with either a pre-defined generation order or adaptive orders obtained from beam-search. Experiments on four real-world tasks, including word order recovery, machine translation, image caption, and code generation, demonstrate that our algorithm can generate sequences following arbitrary orders, while achieving competitive or even better performance compared with the conventional left-to-right generation. The generated sequences show that InDIGO adopts adaptive generation orders based on input information.
Proteomic Analysis of Paracoccidioides brasiliensis During Infection of Alveolar Macrophages Primed or Not by Interferon-Gamma

Although members of the Paracoccidioides complex are not obligate intracellular pathogens, they present the ability to survive and multiply inside epithelial cells and phagocytes of mammals, which may favor the spread of the fungus in host tissues. Macrophages resident in the lung are the first line of defense against paracoccidioidomycosis (PCM), presenting mechanisms to control the pathogen dissemination through the granuloma formation or eliminating the fungus through phagocytosis. Phagocytosis triggers an oxidative burst, in which there is an increase in the production of toxic elements, derived from oxygen and nitrogen. The interior of the phagolysosome is a harsh environment to the internalized pathogens, since in addition to the oxygen and nitrogen reactive species, microorganisms face nutrient shortages and proteases activity. Through the NanoUPLC-MSE technology, we analyzed the proteomic response of Paracoccidioides brasiliensis during the infection of alveolar macrophages primed or not by interferon gamma (IFN-γ). At 6 hs post-infection, only (IFN-γ)-primed macrophages were able to kill the fungus. We observed the regulation of amino acids degradation, tricarboxylic acid cycle, respiratory chain, ATP synthesis, glyoxylate cycle, as well as an increase in the expression of defense proteins related to oxidative stress, heat shock, and virulence factors under both conditions analyzed. However, some pathways described as essential for the survival of pathogens inside macrophages were observed only or with higher intensity in yeast cells recovered from non-primed macrophages, as phosphate pentoses pathway, methylcitrate cycle, synthesis of cell wall components, and mitochondrial activity. The data indicate that the intracellular environment of non-primed macrophages could be more permissive to the survival and multiplication of P. brasiliensis. The identification of key molecules for the establishment of infection can help the understanding of the nature of the parasite–host relationship and pathogenesis of PCM.
On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning

Generalization error (also known as the out-of-sample error) measures how well the hypothesis learned from training data generalizes to previously unseen data. Proving tight generalization error bounds is a central question in statistical learning theory. In this paper, we obtain generalization error bounds for learning general non-convex objectives, which has attracted significant attention in recent years. We develop a new framework, termed Bayes-Stability, for proving algorithm-dependent generalization error bounds. The new framework combines ideas from both the PAC-Bayesian theory and the notion of algorithmic stability. Applying the Bayes-Stability method, we obtain new data-dependent generalization bounds for stochastic gradient Langevin dynamics (SGLD) and several other noisy gradient methods (e.g., with momentum, mini-batch and acceleration, Entropy-SGD). Our result recovers (and is typically tighter than) a recent result in Mou et al. (2018) and improves upon the results in Pensia et al. (2018). Our experiments demonstrate that our data-dependent bounds can distinguish randomly labelled data from normal data, which provides an explanation to the intriguing phenomena observed in Zhang et al. (2017a). We also study the setting where the total loss is the sum of a bounded loss and an additional \ell_2 regularization term. We obtain new generalization bounds for the continuous Langevin dynamic in this setting by developing a new Log-Sobolev inequality for the parameter distribution at any time. Our new bounds are more desirable when the noisy level of the process is not small, and do not become vacuous even when T tends to infinity.
Towards Safer Smart Contracts: A Sequence Learning Approach to Detecting Security Threats.

Symbolic analysis of security exploits in smart contracts has demonstrated to be valuable for analyzing predefined vulnerability properties. While some symbolic tools perform complex analysis steps, they require a predetermined invocation depth to search vulnerable execution paths, and the search time increases with depth. The number of contracts on blockchains like Ethereum has increased 176 fold since December 2015. If these symbolic tools fail to analyze the increasingly large number of contracts in time, entire classes of exploits could cause irrevocable damage. In this paper, we aim to have safer smart contracts against emerging threats. We propose the approach of sequential learning of smart contract weaknesses using machine learning---long-short term memory (LSTM)---that allows us to be able to detect new attack trends relatively quickly, leading to safer smart contracts. Our experimental studies on 620,000 smart contracts prove that our model can easily scale to analyze a massive amount of contracts; that is, the LSTM maintains near constant analysis time as contracts increase in complexity. In addition, our approach achieves $99\%$ test accuracy and correctly analyzes contracts that were false positive (FP) errors made by a symbolic tool.
Beta 3 Adrenergic Receptor Activation Rescues Metabolic Dysfunction in Female Estrogen Receptor Alpha-Null Mice

Metabolic disease risk escalates following menopause. The mechanism is not fully known, but likely involves reduced signaling through estrogen receptor alpha (ERα), which is highly expressed in brown and white adipose tissue (BAT and WAT). Objective: Test the hypothesis that uncoupling protein (UCP1) activation mitigates metabolic dysfunction caused by loss of signaling through ERα. Methods: At 8 weeks of age, female ERα knock out (KO) and wild-type mice were housed at 28°C and fed a Western-style high-fat, high sucrose diet (HFD) or a normal low-fat chow diet (NC) for 10 weeks. During the final 2 weeks, they received daily injections of CL 316,256 (CL), a selective β3 adrenergic agonist, or vehicle control (CTRL), creating eight groups: WT-CTRL, WT-CL, KO-CTRL, and KO-CL on HFD or NC; n = 4–10/group. Results: ERαKO demonstrated exacerbated HFD-induced adiposity gain (P < 0.001) and insulin resistance (P = 0.006). CL treatment improved insulin sensitivity (P < 0.05) and normalized ERαKO-induced adiposity increase (P < 0.05). In both genotypes, CL increased resting energy expenditure (P < 0.05) and induced WAT beiging indicated by increased UCP1 protein in both perigonadal (PGAT) and subcutaneous (SQAT) depots. These effects were attenuated under HFD conditions (P < 0.05). In KO, CL reduced HFD energy consumption compared to CTRL (P < 0.05). Remarkably, CL increased WAT ERβ protein levels of both WT and KO (P < 0.001), revealing CL-mediated changes in estrogen signaling may have protective metabolic effects. Conclusion: CL completely restored metabolic dysfunction in ERαKO mice. Thus, UCP1 may be a therapeutic target for treating metabolic dysfunction following loss of estrogen receptor signaling.
Activin A Signaling Regulates IL13Rα2 Expression to Promote Breast Cancer Metastasis

Metastatic dissemination of cancer cells to distal organs is the major cause of death for patients suffering from the aggressive basal-like breast cancer (BLBC) subtype. Recently, we have shown that interleukin 13 receptor alpha 2 (IL13Rα2) is a critical gene that is overexpressed in a subset of BLBC primary tumors associated with poor distant metastasis-free survival (DMFS) and can promote extravasation and metastasis of breast cancer cells to the lungs. However, the upstream signaling mechanisms that promote aberrant IL13Rα2 expression during tumor progression remain unknown. Driven by our previously published gene expression microarray data derived from a well-characterized cell line model for BLBC progression, we show that both Inhibin βA (INHBA) and IL13Rα2 genes exhibit similarly higher expression levels in metastatic compared to non-metastatic cells and that overexpression of both genes predicts worse metastasis-free survival of patients with high grade tumors. Activin A, a member of the TGFβ superfamily comprising two INHBA subunits, has been shown to play context-depended roles in cancer progression. Here, we demonstrate that INHBA depletion downregulates IL13Rα2 expression in metastatic breast cancer cells, whereas treatment with Activin A in non-metastatic cells increases its expression levels. We also find that Activin A predominantly induces Smad2 phosphorylation and to a lesser extent activates Smad3 and Akt. Interestingly, we also show that Activin A-mediated upregulation of IL13Rα2 is Smad2-dependent since knocking down Smad2 or using the ALK4/ALK5 inhibitors EW-7197 and SB-505124 abolishes this effect. Most importantly, our data indicate that knocking down INHBA levels in breast cancer cells delays primary tumor growth, suppresses migration in vitro and inhibits the formation of lung metastases in vivo. Conclusively, our findings presented here suggest that the development of therapeutic interventions employing small molecule inhibitors against Activin receptors or neutralizing antibodies targeting Activin A ligand, could serve as alternative approaches against breast tumors overexpressing INHBA and/or IL13Rα2.
HIF-1α-Deficiency in Myeloid Cells Leads to a Disturbed Accumulation of Myeloid Derived Suppressor Cells (MDSC) During Pregnancy and to an Increased Abortion Rate in Mice

Abortions are the most important reason for unintentional childlessness. During pregnancy, maternal immune cells are in close contact to cells of the semi-allogeneic fetus. Dysregulation of the maternal immune system leading to defective adaptation to pregnancy often plays a role in pathogenesis of abortions. Myeloid-derived suppressor cells (MDSC) are myeloid cells that suppress functions of other immune cells, especially T-cells, thereby negatively affecting diseases such as cancer, sepsis or trauma. They seem, however, also necessary for maintenance of maternal-fetal tolerance. Mechanisms regulating MDSC expansion and function during pregnancy are only incompletely understood. In tumor environment, hypoxia is crucial for MDSC accumulation and activation. Hypoxia is also important for early placenta and embryo development. Effects of hypoxia are mediated through hypoxia-inducible factor 1α (HIF-1α). In the present study we aimed to examine the role of HIF-1α in myeloid cells for MDSC accumulation and MDSC function during pregnancy and for pregnancy outcome. We therefore used a mouse model with targeted deletion of HIF-1α in myeloid cells (myeloid HIF-KO) and analyzed blood, spleens and uteri of pregnant mice at gestational day E 10.5 in comparison to non-pregnant animals and wildtype (WT) animals. Further we analyzed pregnancy success by determining rates of failed implantation and abortion in WT and myeloid HIF-KO animals. We found that myeloid HIF-KO in mice led to an abrogated MDSC accumulation in the pregnant uterus and to impaired suppressive activity of MDSC. While expression of chemokine receptors and integrins on MDSC was not affected by HIF-1α, myeloid HIF-KO led to increased apoptosis rates of MDSC in the uterus. Myeloid-HIF-KO resulted in increased proportions of non-pregnant animals after positive vaginal plug and increased abortion rates, suggesting that activation of HIF-1α dependent pathways in MDSC are important for maintenance of pregnancy.
A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks

Empirical studies show that gradient based methods can learn deep neural networks (DNNs) with very good generalization performance in the over-parameterization regime, where DNNs can easily fit a random labeling of the training data. While a line of recent work explains in theory that gradient-based methods with proper random initialization can find the global minima of the training loss in over-parameterized DNNs, it does not explain the good generalization performance of the gradient-based methods for learning over-parameterized DNNs. In this work, we take a step further, and prove that under certain assumption on the data distribution that is milder than linear separability, gradient descent (GD) with proper random initialization is able to train a sufficiently over-parameterized DNN to achieve arbitrarily small expected error (i.e., population error). This leads to an algorithmic-dependent generalization error bound for deep learning. To the best of our knowledge, this is the first result of its kind that can explain the good generalization performance of over-parameterized deep neural networks learned by gradient descent.
Stagewise Training Accelerates Convergence of Testing Error Over SGD

Stagewise training strategy is widely used for learning neural networks, which runs a stochastic algorithm (e.g., SGD) starting with a relatively large step size (aka learning rate) and geometrically decreasing the step size after a number of iterations. It has been observed that the stagewise SGD has much faster convergence than the vanilla SGD with a polynomially decaying step size in terms of both training error and testing error. {\it But how to explain this phenomenon has been largely ignored by existing studies. } This paper provides some theoretical evidence for explaining this faster convergence. In particular, we consider a stagewise training strategy for minimizing empirical risk that satisfies the Polyak-\L ojasiewicz (PL) condition, which has been observed/proved for neural networks and also holds for a broad family of convex functions. For convex loss functions and two classes of "nice-behaviored" non-convex objectives that are close to a convex function, we establish faster convergence of stagewise training than the vanilla SGD under the PL condition on both training error and testing error. Experiments on stagewise learning of deep residual networks exhibits that it satisfies one type of non-convexity assumption and therefore can be explained by our theory. Of independent interest, the testing error bounds for the considered non-convex loss functions are dimensionality and norm independent.
Relationship Between Depression and Subtypes of Early Life Stress in Adult Psychiatric Patients

Numerous studies have researched the aggravating and maintainer effect of Early Life Stress in patients adults with psychiatric disorders. This study examined the relationship between depression and subtypes of early life stress among 81 psychiatric patients treated at the inpatient Day Hospital Unit of a University General Hospital. Psychiatric diagnosis was confirmed according to the MINI International Neuropsychiatric Interview (MINI). The Childhood Trauma Questionnaire (CTQ) was used for evaluating as retrospective assessment of the presence of ELS on these patients, and we also evaluated the severity of hopelessness with the Beck Hopelessness Scale (BHS). Our results suggested that the occurrence of depression in adulthood is related to situations of emotional abuse, sexual, and physical neglect during childhood. The analysis between depression and childhood emotional abuse was significant after a multiple logistic regression analysis OR (IC 95%): 4.4 (1.7–11.2), even accounting for gender adjusted OR [AOR] 4.0; (IC 1.5–10.5); psychiatry family history AOR 3.8 (1.4–10.5); previous suicide attempted AOR 3.7; (1.4–10.5) and Hopelessness AOR 3.2 (1.11–9.4). Thus, these findings demonstrate emotional abuse as a significant risk factor to be part of the mechanism involved in the pathogenesis of depression related to early life stress.