Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks

We propose a population-based Evolutionary Stochastic Gradient Descent (ESGD) framework for optimizing deep neural networks. ESGD combines SGD and gradient-free evolutionary algorithms as complementary algorithms in one framework in which the optimization alternates between the SGD step and evolution step to improve the average fitness of the population. With a back-off strategy in the SGD step and an elitist strategy in the evolution step, it guarantees that the best fitness in the population will never degrade. In addition, individuals in the population optimized with various SGD-based optimizers using distinct hyper-parameters in the SGD step are considered as competing species in a coevolution setting such that the complementarity of the optimizers is also taken into account. The effectiveness of ESGD is demonstrated across multiple applications including speech recognition, image recognition and language modeling, using networks with a variety of deep architectures.
The Family and Peer Origins of Coercion Within Adult Romantic Relationships: A Longitudinal Multimethod Study Across Relationships Contexts

Using coercive strategies to resolve conflicts with romantic partners has toxic effects on relationships. Coercion predicts relationship dissatisfaction, instability, and intimate partner violence. The early adult romantic relationships model hypothesizes that such strategies first emerge within the family and continue to affect romantic relationships into adulthood. We tested whether adolescent antisocial behaviors and deviancy training with peers mediated between early disruptive parenting and adult romantic relationship coercion. Furthermore, we tested the impact of trauma in this longitudinal model. We studied 230 adults in committed relationships, whom we initially recruited and studied when they were age 11. We collected videotaped observations with friends (when participants were ages 16–17) and with intimate partners (when participants were ages 28–30). As hypothesized, disruptive parenting predicted antisocial behaviors and deviancy training with friends in adolescence, which in turn predicted coercion within intimate adult relationships. Moreover, disruptive parenting in early adolescence also directly predicted romantic partner coercion 15 years later. No significant effects were found for trauma. Findings suggest the promise of promoting healthy adult intimate relationships through early relationships with parents and friends.
T-2 Toxin Exposure Induces Apoptosis in TM3 Cells by Inhibiting Mammalian Target of Rapamycin/Serine/Threonine Protein Kinase(mTORC2/AKT) to Promote Ca2+Production

Although mTOR (the mammalian target of rapamycin) can regulate intracellular free Ca2+concentration in normal cultured podocytes, it remains elusive as to how mTORC2/AKT-mediated Ca2+participates in the process of T-2 toxin-induced apoptosis. The potential signaling responsible for intracellular Ca2+ concentration changes was investigated using immunoblot assays in an in vitro model of TM3 cell injury induced by T-2 toxin. Changes in Ca2+ were assessed using the Ca2+-sensitive fluorescent indictor dye Fura 2-AM. The cytotoxicity of TM3 cells was assessed with an MTT bioassay, and apoptosis was measured using Annexin V-FITC staining. Following T-2 toxin treatment, the growth of cells, phospho-mTORSer2481, phospho-mTORSer2448, and phospho-AktSer473 were significantly decreased in a time-dependent manner, whereas Ca2+ and apoptosis were increased. T-2 toxin-induced apoptosis was prevented by BAPTA-AM (a Ca2+chelator) and MHY1485 (an mTOR activator), and the application of mTOR activator MHY1485 also prevented the increase of intracellular free Ca2+concentration in TM3 cells. Our results strongly suggest that T-2 toxin exposure induces apoptosis in TM3 cells by inhibiting mTORC2/AKT to promote Ca2+ production.
Exploring teachers' needs and the existing barriers to the adoption of Learning Design methods and tools: A literature survey

Learning Design (LD) research is oriented to support teachers in designing their teaching with the aim to provide a sound pedagogical background and to make effective use of resources and technologies. In spite of the significant number of LD approaches and tools proposed so far, their adoption is still very limited and this represents an unsolved challenge in the field of LD. This paper presents a systematic review of the literature about learning design tools, tackling the issue of adoption from two points of view: teachers' needs in relation to LD tools and methods and possible barriers to their adoption. The review includes only research papers where teachers' behaviours and opinions are directly explored and not purely theoretical papers. The search included five main academic databases in Technology‐Enhanced Learning (TEL) plus a search on Google about project reports; the resulting corpus included 423 papers: 26 of these, plus 3 reports were included in the final list for the analysis. The review provides a systematic overview of the knowledge developed in the LD field, focusing on a set of research gaps that need further exploration in the future. [ABSTRACT FROM AUTHOR]
ClinicalVis: Supporting Clinical Task-Focused Design Evaluation

Making decisions about what clinical tasks to prepare for is multi-factored, and especially challenging in intensive care environments where resources must be balanced with patient needs. Electronic health records (EHRs) are a rich data source, but are task-agnostic and can be difficult to use as summarizations of patient needs for a specific task, such as "could this patient need a ventilator tomorrow? " In this paper, we introduce ClinicalVis, an open-source EHR visualization-based prototype system for task-focused design evaluation of interactions between healthcare providers (HCPs) and EHRs. We situate ClinicalVis in a task-focused proof-of-concept design study targeting these interactions with real patient data. We conduct an empirical study of 14 HCPs, and discuss our findings on usability, accuracy, preference, and confidence in treatment decisions. We also present design implications that our findings suggest for future EHR interfaces, the presentation of clinical data for task-based planning, and evaluating task-focused HCP/EHR interactions in practice.
Serum Interleukin-18 Level is Associated With Disease Activity and Interstitial Lung Disease in Patients With Dermatomyositis.

Objectives This study aims to demonstrate the utilization of serum interleukin-18 (IL-18) level as a biomarker for disease activity and organ damage in patients with dermatomyositis (DM) or polymyositis (PM) using the Myositis Disease Activity Assessment Tool. Patients and methods Forty-nine patients (13 males, 36 females) with idiopathic inflammatory myopathies were enrolled in the study. Patients were diagnosed with DM (n=33) or PM (n=16). Twenty age- and sex-matched healthy volunteers (9 males, 11 females) who were determined to be free of autoimmune disease were included as a control group. Disease activity and organ damage were assessed with Myositis Disease Activity Assessment Tool. Serum IL-18 level was measured with enzyme-linked immunosorbent assay and the differences between patient groups were determined. Results The mean ages of DM or PM patients were 44 and 45, respectively. Interstitial lung disease (ILD) was found in 24 patients (48.98%), including 18 patients with DM (54.55%) and six patients with PM (37.5%). There was no significant difference of disease duration between DM patients (17.5±22.3 months) and PM patients (14.9±28.7 months). Serum IL-18 level in DM (669.2±528.1 pg/mL) was significantly higher than that in the control group (388.3±139.4 pg/mL, p=0.017), but no significant difference was observed between the PM and control group. IL-18 was significantly higher in DM patients with ILD (890.5±618.8 pg/mL) than in patients without ILD (403.8±84.8 pg/mL, p=0.003). It was significantly elevated in patients with severe disease activity (1001.71±694.2 pg/mL) than in those with moderate (485.3±102.2 pg/mL) or mild disease activity (421.0±270.2 pg/mL) (p=0.039). In DM patients, IL-18 was significantly correlated with global disease activity and disease activity in the skin, lung, and heart. Conclusion Serum IL-18 level was higher in patients with DM and this was associated with disease activity and ILD complication. Thus, it can be used as a valuable serological marker for DM disease activity and ILD complication.
Improved Network Robustness with Adversary Critic

Ideally, what confuses neural network should be confusing to humans. However, recent experiments have shown that small, imperceptible perturbations can change the network prediction. To address this gap in perception, we propose a novel approach for learning robust classifier. Our main idea is: adversarial examples for the robust classifier should be indistinguishable from the regular data of the adversarial target. We formulate a problem of learning robust classifier in the framework of Generative Adversarial Networks (GAN), where the adversarial attack on classifier acts as a generator, and the critic network learns to distinguish between regular and adversarial images. The classifier cost is augmented with the objective that its adversarial examples should confuse the adversary critic. To improve the stability of the adversarial mapping, we introduce adversarial cycle-consistency constraint which ensures that the adversarial mapping of the adversarial examples is close to the original. In the experiments, we show the effectiveness of our defense. Our method surpasses in terms of robustness networks trained with adversarial training. Additionally, we verify in the experiments with human annotators on MTurk that adversarial examples are indeed visually confusing. Codes for the project are available at this https URL.
Dbx1-Derived Pyramidal Neurons Are Generated Locally in the Developing Murine Neocortex

The neocortex (NCx) generates at the dorsal region of the pallium in the forebrain. Several adjacent structures also contribute with neurons to NCx. Ventral pallium (VP) is considered to generate several populations of neurons that arrive through tangential migration to the NCx. Amongst them are the Cajal-Retzius cells and some transient pyramidal neurons. However, the specific site and timing of generation, trajectory of migration and actual contribution to the pyramidal population remains elusive. Here, we investigate the spatio-temporal origin of neuronal populations from VP in an in vivo model, using a transposase mediated in utero electroporation method in embryonic mouse. From E11 to E14 cells born at the lateral corner of the neocortical neuroepithelium including the VP migrated ventro-laterally to settle all areas of the ventral telencephalon. Specifically, neurons migrated into amygdala (Ag), olfactory cortices, and claustrum (Cl). However, we found no evidence for any neurons migrating tangentially toward the NCx, regardless the antero-posterior level and developmental time of the electroporation. Our results challenge the described ventral-pallial origin of the transient pyramidal neuron population. In order to find the exact origin of cortical neurons that were previously Dbx1-fate mapped we used the promoter region of the murine Dbx1 locus to selectively target Dbx1-expressing progenitors and label their lineage. We found these progenitors in low numbers in all pallial areas, and not only in the ventral pallial ventricular zone. Our findings on the local cortical origin of the Dbx1-derived pyramidal neurons reconcile the observation of Dbx1-derived neurons in the cortex without evidence of dorsal tangential migration from VP and provide a new framework for the origin of the transient Dbx1-derived pyramidal neuron population. We conclude that these neurons are born locally within the dorsal pallial neuroepithelium.
Supervised COSMOS Autoencoder: Learning Beyond the Euclidean Loss!

Autoencoders are unsupervised deep learning models used for learning representations. In literature, autoencoders have shown to perform well on a variety of tasks spread across multiple domains, thereby establishing widespread applicability. Typically, an autoencoder is trained to generate a model that minimizes the reconstruction error between the input and the reconstructed output, computed in terms of the Euclidean distance. While this can be useful for applications related to unsupervised reconstruction, it may not be optimal for classification. In this paper, we propose a novel Supervised COSMOS Autoencoder which utilizes a multi-objective loss function to learn representations that simultaneously encode the (i) "similarity" between the input and reconstructed vectors in terms of their direction, (ii) "distribution" of pixel values of the reconstruction with respect to the input sample, while also incorporating (iii) "discriminability" in the feature learning pipeline. The proposed autoencoder model incorporates a Cosine similarity and Mahalanobis distance based loss function, along with supervision via Mutual Information based loss. Detailed analysis of each component of the proposed model motivates its applicability for feature learning in different classification tasks. The efficacy of Supervised COSMOS autoencoder is demonstrated via extensive experimental evaluations on different image datasets. The proposed model outperforms existing algorithms on MNIST, CIFAR-10, and SVHN databases. It also yields state-of-the-art results on CelebA, LFWA, Adience, and IJB-A databases for attribute prediction and face recognition, respectively.
On the Difficulty of FSM-based Hardware Obfuscation

In today’s Integrated Circuit (IC) production chains, a designer’s valuable Intellectual Property (IP) is transparent to diverse stakeholders and thus inevitably prone to piracy. To protect against this threat, numerous defenses based on the obfuscation of a circuit’s control path, i.e. Finite State Machine (FSM), have been proposed and are commonly believed to be secure. However, the security of these sequential obfuscation schemes is doubtful since realistic capabilities of reverse engineering and subsequent manipulation are commonly neglected in the security analysis. The contribution of our work is threefold: First, we demonstrate how high-level control path information can be automatically extracted from third-party, gate-level netlists. To this end, we extend state-of-the-art reverse engineering algorithms to deal with Field Programmable Gate Array (FPGA) gate-level netlists equipped with FSM obfuscation. Second, on the basis of realistic reverse engineering capabilities we carefully review the security of state-of-the-art FSM obfuscation schemes. We reveal several generic strategies that bypass allegedly secure FSM obfuscation schemes and we practically demonstrate our attacks for a several of hardware designs, including cryptographic IP cores. Third, we present the design and implementation of Hardware Nanomites, a novel obfuscation scheme based on partial dynamic reconfiguration that generically mitigates existing algorithmic reverse engineering.
Simultaneous Inhibition of MEK and Hh Signaling Reduces Pancreatic Cancer Metastasis

Pancreatic cancer, mostly pancreatic ductal adenocarcinoma (PDAC), is one of the most lethal cancer types, with an estimated 44,330 death in 2018 in the US alone. While targeted therapies and immune checkpoint inhibitors have significantly improved treatment options for patients with lung cancer and renal cell carcinomas, little progress has been made in pancreatic cancer, with a dismal 5-year survival rate currently at ~8%. Upon diagnosis, the majority of pancreatic cancer cases (~80%) are already metastatic. Thus, identifying ways to reduce pancreatic cancer metastasis is an unmet medical need. Furthermore, pancreatic cancer is notorious resistant to chemotherapy. While Kirsten RAt Sarcoma virus oncogene (K-RAS) mutation is the major driver for pancreatic cancer, specific inhibition of RAS signaling has been very challenging, and combination therapy is thought to be promising. In this study, we report that combination of hedgehog (Hh) and Mitogen-activated Protein/Extracellular Signal-regulated Kinase Kinase (MEK) signaling inhibitors reduces pancreatic cancer metastasis in mouse models. In mouse models of pancreatic cancer metastasis using human pancreatic cancer cells, we found that Hh target gene Gli1 is up-regulated during pancreatic cancer metastasis. Specific inhibition of smoothened signaling significantly altered the gene expression profile of the tumor microenvironment but had no significant effects on cancer metastasis. By combining Hh signaling inhibitor BMS833923 with RAS downstream MEK signaling inhibitor AZD6244, we observed reduced number of metastatic nodules in several mouse models for pancreatic cancer metastasis. These two inhibitors also decreased cell proliferation significantly and reduced CD45+ cells (particularly Ly6G+CD11b+ cells). We demonstrated that depleting Ly6G+ CD11b+ cells is sufficient to reduce cancer cell proliferation and the number of metastatic nodules. In vitro, Ly6G+ CD11b+ cells can stimulate cancer cell proliferation, and this effect is sensitive to MEK and Hh inhibition. Our studies may help design novel therapeutic strategies to mitigate pancreatic cancer metastasis.
The classical Bordetella species and MALDI‐TOF technology: a brief experience

Purpose. The aim of this work was to evaluate and optimize the identification of Bordetella pertussis, Bordetella parapertussis and Bordetella bronchiseptica (usually known as the classical Bordetella species) using Bruker Biotyper matrix‐assisted laser desorption ionization/time‐of‐flight mass spectrometry (MALDI‐TOF MS). Methodology. A set of 106 previously characterized clinical isolates was used. The results were interpreted according to the manufacturer's recommendations and, in addition, a new score value cutoff was used for species identification. Further, the 10 % rule (previously adopted by other authors) and the new 5 % breaking point (proposed in this work) were evaluated in order to optimize identification rates. Results/Key findings. Our results suggest that it is possible to distinguish different species of the classical Bordetella species by following a simple algorithm without additional testing being required. Conclusion. MALDI‐TOF might be a reliable tool for the identification of this group of bacteria when a combination of cutoff scores is used. This procedure allows us to increase the identification rates for the classical Bordetella species significantly; however, more studies will be required to determine the applicability of the method to other difficult‐to‐distinguish organisms.
Nausea intensity as a reflector of early physical recovery after surgery

AIM To compare different levels of self-rated average nausea intensity with early physical recovery and determine if nausea can reflect recovery in patients undergoing general or orthopaedic surgery. BACKGROUND Nausea has been found to influence postoperative physical recovery. Despite the incidence of nausea in postoperative care, there is a knowledge gap about the possibility of using average nausea intensity to reflect recovery, motivating further investigation. DESIGN An observational design with repeated measures. METHODS General and orthopaedic patients answered a questionnaire (October 2012-January 2015) about nausea and impact on recovery on postoperative days 1 (N = 479) and 2 (N = 441). Questions about average nausea intensity at rest and during activity were answered based on the Numeric Rating Scale (NRS) (0-10). Impact on recovery was evaluated using three dimensions from the postoperative recovery profile tool. RESULTS About one-fifth of the patients reported nausea intensity as moderate to severe on days 1 and 2. Nausea intensity was associated with eight of nine aspects of recovery on postoperative day 1. Nausea intensity on day 1 also reflected four of nine aspects of recovery on day 2. About reflecting physical recovery, the association was strongest between nausea intensity and appetite changes. CONCLUSIONS As postoperative nausea is common, regular assessments by healthcare professionals are needed. Assessment of nausea is of importance since it reflects physical recovery. This also shows the importance of treating nausea without delay. Using the NRS to measure nausea intensity is a simple method that is easy to use in clinic.
Encoded Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery

Fully Convolutional Network (FCN) has been widely used in recent work for semantic segmentation of high resolution aerial imagery. However, FCN is poor at extracting multi-scale features and exploiting contextual information. In this paper, we explore stacked encoder-decoder structure which enables repeated bottom-up, top-down inference across various scales and consolidates global and local information of the image. Moreover, we utilize the Context Encoding Module to capture the global contextual semantics of scenes and selectively emphasize or de-emphasize class-dependent featuremaps. Our approach is further enhanced by intermediate supervision on the predictions of multiple decoders and has achieved 87.01% pixel accuracy and 69.78% mIoU on Potsdam test set, which surpasses various baseline models.
The autophagy inhibitor spautin-1, either alone or combined with doxorubicin, decreases cell survival and colony formation in canine appendicular osteosarcoma cells

Dogs diagnosed with appendicular osteosarcoma typically succumb to metastatic disease within a year of diagnosis. The current standard of care for curative intent, amputation followed by adjuvant chemotherapy, increases survival time but chemoresistance is a major contributor to mortality. Unfortunately, the mechanisms driving the progression of metastatic disease and the development of chemoresistance are unknown. One theory is that autophagy may contribute to chemoresistance by providing neoplastic cells with a mechanism to survive chemotherapy treatment. Our objective was to evaluate the effect of combining an autophagy inhibitor with a standard chemotherapeutic drug on response to chemotherapy in canine appendicular osteosarcoma cells. We hypothesized that combining the autophagy inhibitor spautin-1 with doxorubicin treatment would enhance chemoresponsiveness. Using commercial (D17) and primary cell lines derived from 1° and 2° sites of osteosarcoma, we showed that this combination treatment enhances cell killing and inhibits colony formation. Our findings support the theory that autophagy contributes to chemoresistance in canine appendicular osteosarcoma and indicate that adding an autophagy inhibitor to the standard of care has the potential to improve outcome.