Miracle-dz/UniFAR-FTUs huggingface dataset:
Drug Transport at the Brain and Endothelial Dysfunction in Preeclampsia: Implications and Perspectives

Transport of drugs across biological barriers has been a subject of study for decades. The discovery and characterization of proteins that confer the barrier properties of endothelia and epithelia, including tight junction proteins and membrane transporters belonging to the ATP-binding cassette (ABC) and Solute Carrier (SLC) families, represented a significant step forward into understanding the mechanisms that govern drug disposition. Subsequently, numerous studies, including both pre-clinical approaches and clinical investigations, have been carried out to determine the influence of physiological and pathological states on drug disposition. Importantly, there has been increasing interest in gaining a better understanding of drug disposition during pregnancy, since epidemiological and clinical studies have demonstrated that the use of medications by pregnant women is significant and this condition embodies a series of significant anatomical and physiological modifications, particularly at excretory organs and barrier sites (e.g., placenta, breast) expressing transporter proteins which influence pharmacokinetics. Currently, most of the research in this field has focused on the expression profiling of transporter proteins in trophoblasts and endothelial cells of the placenta, regulation of drug-resistance mechanisms in disease states and pharmacokinetic studies. However, little attention has been placed on the influence that the cerebrovascular dysfunction present in pregnancy-related disorders, such as preeclampsia, might exert on drug disposition in the mother’s brain. This issue is particularly important since recent findings have demonstrated that preeclamptic women suffer from long-term alterations in the integrity of the blood-brain barrier (BBB). In this review we aim to analyze the available evidence regarding the influence of pregnancy on the expression of transporters and TJ proteins in brain endothelial cells, as well the mechanisms that govern the pathophysiological alterations in the BBB of women who experience preeclampsia. Future research efforts should be focused not only on achieving a better understanding of the influence of preeclampsia-associated endothelial dysfunction on drug disposition, but also in optimizing the pharmacological treatments of women suffering pregnancy-related disorders, its comorbidities and to develop new therapies aiming to restore the integrity of the BBB.
Improvement in Analytical Methods for Determination of Sugars in Fermented Alcoholic Beverages

The main objective of this study was to improve the performance of analytical methods for the determination of sugars in fermented alcoholic beverages based on mid-infrared-partial least squares (MIR-PLS), high-performance liquid chromatography with the ultraviolet detector (HPLC-UV), high-performance liquid chromatography with the refractive index detector (HPLC-RI), and sulfuric acid methods. The MIR-PLS method was found to give good prediction of individual sugars: glucose, fructose, sucrose, and maltose in the alcoholic beverages with less than 4% error. The HPLC-UV method can be used for the determination of glucose in alcoholic beverages after derivatization with p-aminobenzoic acid ethyl ester. The HPLC-RI method was found to be applicable for the determination of individual sugars: glucose, fructose, sucrose, and maltose in the alcoholic beverages. The limit of detection (%, w/w) and recovery (%) of the individual sugars by the HPLC-RI method were fructose 0.001, 89.4–106; glucose 0.002, 92.4–109; and sucrose 0.002, 94.2–95.1. The sulfuric acid method was found to be useful for the determination of total sugar in the alcoholic beverages. The limit of detection (%, w/w) and recovery (%) of the total sugar by the sulfuric acid method were found to be 0.009, 98.2–109. The HPLC-RI method was applied to determine the level of individual sugars, while the sulfuric acid method was used to determine total sugar in Ethiopian traditional fermented alcoholic beverages: Tella, Netch Tella, Filter Tella, Borde, Tej, Korefe, Keribo, and Birz. The sugar contents in the real samples were found in the ranges (%): glucose 0.07–5.60, fructose 0.09–8.50, sucrose and maltose 0.08–3.00, and total sugar 12.0–64.5. The levels of sugars in Ethiopian traditional fermented alcoholic beverages were found to be comparable with literature data.
Building a Winning Self-Driving Car in Six Months

The SAE AutoDrive Challenge is a three-year competition to develop a Level 4 autonomous vehicle by 2020. The first set of challenges were held in April of 2018 in Yuma, Arizona. Our team (aUToronto/Zeus) placed first. In this paper, we describe Zeus' complete system architecture and specialized algorithms that enabled us to win. We show that it is possible to develop a vehicle with basic autonomy features in just six months relying on simple, robust algorithms. We do not make use of a prior map. Instead, we have developed a multi-sensor visual localization solution. All the algorithms in the paper run in real-time using CPUs only. We also highlight the closed-loop performance of the system in detail in several experiments.
Pathway-Directed Screen for Inhibitors of the Bacterial Cell Elongation Machinery

New antibiotics are needed to combat the growing problem of resistant bacterial infections. An attractive avenue toward the discovery of such next-generation therapies is to identify novel inhibitors of clinically validated targets, like cell wall biogenesis. ABSTRACT New antibiotics are needed to combat the growing problem of resistant bacterial infections. An attractive avenue toward the discovery of such next-generation therapies is to identify novel inhibitors of clinically validated targets, like cell wall biogenesis. We have therefore developed a pathway-directed whole-cell screen for small molecules that block the activity of the Rod system of Escherichia coli. This conserved multiprotein complex is required for cell elongation and the morphogenesis of rod-shaped bacteria. It is composed of cell wall synthases and membrane proteins of unknown function that are organized by filaments of the actin-like MreB protein. Our screen takes advantage of the conditional essentiality of the Rod system and the ability of the beta-lactam mecillinam (also known as amdinocillin) to cause a toxic malfunctioning of the machinery. Rod system inhibitors can therefore be identified as molecules that promote growth in the presence of mecillinam under conditions permissive for the growth of Rod– cells. A screen of ∼690,000 compounds identified 1,300 compounds that were active against E. coli. Pathway-directed screening of a majority of this subset of compounds for Rod inhibitors successfully identified eight analogs of the MreB antagonist A22. Further characterization of the A22 analogs identified showed that their antibiotic activity under conditions where the Rod system is essential was strongly correlated with their ability to suppress mecillinam toxicity. This result combined with those from additional biological studies reinforce the notion that A22-like molecules are relatively specific for MreB and suggest that the lipoprotein transport factor LolA is unlikely to be a physiologically relevant target as previously proposed.
Unsupervised RGBD Video Object Segmentation Using GANs

Video object segmentation is a fundamental step in many advanced vision applications. Most existing algorithms are based on handcrafted features such as HOG, super-pixel segmentation or texture-based techniques, while recently deep features have been found to be more efficient. Existing algorithms observe performance degradation in the presence of challenges such as illumination variations, shadows, and color camouflage. To handle these challenges we propose a fusion based moving object segmentation algorithm which exploits color as well as depth information using GAN to achieve more accuracy. Our goal is to segment moving objects in the presence of challenging background scenes, in real environments. To address this problem, GAN is trained in an unsupervised manner on color and depth information independently with challenging video sequences. During testing, the trained GAN generates backgrounds similar to that in the test sample. The generated background samples are then compared with the test sample to segment moving objects. The final result is computed by fusion of object boundaries in both modalities, RGB and the depth. The comparison of our proposed algorithm with five state-of-the-art methods on publicly available dataset has shown the strength of our algorithm for moving object segmentation in videos in the presence of challenging real scenarios.
A Batched Scalable Multi-Objective Bayesian Optimization Algorithm

The surrogate-assisted optimization algorithm is a promising approach for solving expensive multi-objective optimization problems. However, most existing surrogate-assisted multi-objective optimization algorithms have three main drawbacks: 1) cannot scale well for solving problems with high dimensional decision space, 2) cannot incorporate available gradient information, and 3) do not support batch optimization. These drawbacks prevent their use for solving many real-world large scale optimization problems. This paper proposes a batched scalable multi-objective Bayesian optimization algorithm to tackle these issues. The proposed algorithm uses the Bayesian neural network as the scalable surrogate model. Powered with Monte Carlo dropout and Sobolov training, the model can be easily trained and can incorporate available gradient information. We also propose a novel batch hypervolume upper confidence bound acquisition function to support batch optimization. Experimental results on various benchmark problems and a real-world application demonstrate the efficiency of the proposed algorithm.
RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, few studies investigate 3D networks for liver tumor segmentation. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, i.e., RA-UNet, to precisely extract the liver region and segment tumors from the liver. The proposed network has a basic architecture as U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention residual modules are integrated so that the attention-aware features change adaptively. This is the first work that an attention residual mechanism is used to segment tumors from 3D medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and tested the generalization on the 3DIRCADb dataset. The experiments show that our architecture obtains competitive results.
Rethinking floating point for deep learning

Reducing hardware overhead of neural networks for faster or lower power inference and training is an active area of research. Uniform quantization using integer multiply-add has been thoroughly investigated, which requires learning many quantization parameters, fine-tuning training or other prerequisites. Little effort is made to improve floating point relative to this baseline; it remains energy inefficient, and word size reduction yields drastic loss in needed dynamic range. We improve floating point to be more energy efficient than equivalent bit width integer hardware on a 28 nm ASIC process while retaining accuracy in 8 bits with a novel hybrid log multiply/linear add, Kulisch accumulation and tapered encodings from Gustafson's posit format. With no network retraining, and drop-in replacement of all math and float32 parameters via round-to-nearest-even only, this open-sourced 8-bit log float is within 0.9% top-1 and 0.2% top-5 accuracy of the original float32 ResNet-50 CNN model on ImageNet. Unlike int8 quantization, it is still a general purpose floating point arithmetic, interpretable out-of-the-box. Our 8/38-bit log float multiply-add is synthesized and power profiled at 28 nm at 0.96x the power and 1.12x the area of 8/32-bit integer multiply-add. In 16 bits, our log float multiply-add is 0.59x the power and 0.68x the area of IEEE 754 float16 fused multiply-add, maintaining the same signficand precision and dynamic range, proving useful for training ASICs as well.
Suppression, disaggregation, and modulation of γ‐Synuclein fibrillation pathway by green tea polyphenol EGCG

Oligomerization of γ‐Synuclein is known to have implications for both neurodegeneration and cancer. Although it is known to co‐exist with the fibrillar deposits of α‐Synuclein (Lewy bodies), a hallmark in Parkinson's disease (PD), the effect of potential therapeutic modulators on the fibrillation pathway of γ‐Syn remains unexplored. By a combined use of various biophysical tools and cytotoxicity assays we demonstrate that the flavonoid epigallocatechin‐3‐gallate (EGCG) significantly suppresses γ‐Syn fibrillation by affecting its nucleation and binds with the unstructured, nucleus forming oligomers of γ‐Syn to modulate the pathway to form α‐helical containing higher‐order oligomers (~158 kDa and ~ 670 kDa) that are SDS‐resistant and conformationally restrained in nature. Seeding studies reveal that these oligomers although “on‐pathway” in nature, are kinetically retarded and rate‐limiting species that slows down fibril elongation. We observe that EGCG also disaggregates the protofibrils and mature γ‐Syn fibrils into similar SDS‐resistant oligomers. Steady‐state and time‐resolved fluorescence spectroscopy and isothermal titration calorimetry (ITC) reveal a weak non‐covalent interaction between EGCG and γ‐Syn with the dissociation constant in the mM range (Kd ~ 2–10 mM). Interestingly, while EGCG‐generated oligomers completely rescue the breast cancer (MCF‐7) cells from γ‐Syn toxicity, it reduces the viability of neuroblastoma (SH‐SY5Y) cells. However, the disaggregated oligomers of γ‐Syn are more toxic than the disaggregated fibrils for MCF‐7cells. These findings throw light on EGCG‐mediated modulation of γ‐Syn fibrillation and suggest that investigation on the effects of such modulators on γ‐Syn fibrillation is critical in identifying effective therapeutic strategies using small molecule modulators of synucleopathies.
grippeNET App: Enhancing Participatory Influenza Monitoring Through Mobile Phone Sensors

We describe the vision, implementation and initial deployment experience with an App based crowd sourcing system for the analysis and prediction of the influence of aggregated individual behavior patterns on the spread of infectious diseases. The App builds on InfluenzaNet [19], a well established European participatory network of self reporting web platforms for monitoring Influeza-Like-Ilnesses (ILI) and goes starting from the current practice of explicit self reporting towards the use of sensor derived behavior information. The App has been cleared with the Regional Research Ethics Committee (CCER) in Geneva, Switzerland and was test deployed through the Google Play Store in Switzerland during last year's influenza season. It is currently being prepared for a broader European deployment.
Learning to Defense by Learning to Attack

Adversarial training is a principled approach for training robust neural networks. From an optimization perspective, adversarial training is solving a bilevel optimization problem (a general form of minimax approaches): The leader problem targets on learning a robust classifier; The follower problem tries to generate adversarial samples. Unfortunately, such a bilevel problem is very challenging to solve due to its highly complicated structure. This work proposes a new adversarial training method based on a generic learning-to-learn (L2L) framework. Specifically, instead of applying hand-designed algorithms for the follower problem, we learn an optimizer, which is parametrized by a convolutional neural network. Meanwhile, a robust classifier is learned to defense the adversarial attacks generated by the learned optimizer. Our experiments over CIFAR datasets demonstrate that L2L improves upon existing methods in both robust accuracy and computational efficiency. Moreover, the L2L framework can be extended to other popular bilevel problems in machine learning.
New Perspectives on Emotional Processing in People with Symptomatic Huntington's Disease: Impaired Emotion Regulation and Recognition of Emotional Body Language.

Objective Emotion regulation and emotional body language (EBL) recognition represent two fundamental components of emotional processing that have recently seen a considerable surge in research interest, in part due to the role they play in optimizing mental health. This appears to be particularly true for clinical conditions that can profoundly affect emotional functioning. Among these is Huntington's disease (HD), a neurodegenerative disorder that is associated with several psychological difficulties and cognitive impairments, including well-established deficits in facial emotion recognition. However, although the theoretical case for impairments is strong, the current evidence in HD on other components such as emotion regulation and EBL recognition is sparse. Method In this study, it was hypothesized that emotion regulation and recognition of EBL are impaired in people with symptomatic HD, and that these impairments significantly and positively correlate with each other. A between-subjects design was adopted to compare 13 people with symptomatic HD with 12 non-affected controls matched for age and education. Results The results showed that emotion regulation and EBL recognition were significantly impaired in individuals with HD. Moreover, a significant positive correlation was observed between facial and EBL recognition impairments, whereas EBL performance was negatively related to the disease stage. However, emotion regulation and recognition performances were not significantly correlated. Conclusions This investigation represents the first evidence of a deficit of emotion regulation and EBL recognition in individuals with HD. The clinical implications of these findings are explored, and indications for future research are proposed.
Painful Understanding of VEGF

Our nervous system is capable of detecting a wide range of stimuli which can evoke pain. These can generate a short-term sensations (acute pain) which usually resolves. However, sometimes this pain becomes persistent. Constant stimulation provokes alterations in nociceptive transmission, enhancing pain signals and increasing sensitivity. If this state persists for more than 3 months, it is defined as chronic pain. It affects over one-quarter of people worldwide and is more prevalent in women than in men.1 The mechanisms that sustain and drive chronic pain have been comprehensively reviewed elsewhere (Woolf and Salter, 2000; Basbaum et al., 2009; vonHehn et al., 2012). The current analgesic treatments (opioids or NSAIDs) do not meet patients needs or are inefficient. In addition, their side effects limit their use. Therefore, the development of new drugs is urgently required. Preclinical research studies have identified an array of molecular targets that are involved in the establishment and maintenance of chronic pain (see references above) and may represent interesting targets for pharmacological intervention. Among these mediators, Vascular Endotelial Growth Factor (VEGF) has been postulated as a key factor. Alterations in the VEGF system, characterized by changes in the expression of its components, have been related to a plethora of diseases. Some of these diseases can occur concomitantly with pain, such as cancer, rheumathoid arthritis or diabetic complications (Ferrara, 2004; Maharaj and D’Amore, 2007; Rosenstein et al., 2010). VEGF is a potent pro-angiogenic factor and a key mediator of neovascularisation, a process which is involved in the pathogenesis of cancerous tumors. For this reason, there are currently several anti-VEGF-related drugs used in clinical settings for the cancer treatment in combination with chemotherapy. Anti-VEGF drugs are also used to attenuate neovascularisation in age-related macular degeneration and diabetic macular oedema (Ferrara, 2004; Kim and D’Amore, 2012). However, in recent years, the role of the VEGF family in neuroprotection and nociception has received increased attention (Beazley-Long et al., 2013, 2018; Hulse et al., 2014, 2015, 2016; Selvaraj et al., 2015; Hulse, 2017; Lai et al., 2017). The involvement of VEGF in the pathophysiology of pain is not fully understood, however, the association between this growth factor and some of the main hallmarks of painful diseases warrants the investigation of VEGF as a therapeutic target for pain treatment. Considering the aforementioned, the potential of VEGF as druggable target for the treatment of different types of pain will be discussed. The VEGF family consists of five members. The most widely studied of them are VEGF-A and, to a lesser extent, VEGF-B. VEGF-A and VEGF-B are known to bind to two different tyrosine kinases receptors (VEGFR1 and VEGFR2) that are both expressed in nociceptors (Hulse et al., 2014; Selvaraj et al., 2015; Hamilton et al., 2016), and two neuropilin co-receptors (NRP-1 and NRP-2) that enhance the affinity of the ligands for the receptors. Additionally
Neural Task Representations as Weak Supervision for Model Agnostic Cross-Lingual Transfer

Natural language processing is heavily Anglo-centric, while the demand for models that work in languages other than English is greater than ever. Yet, the task of transferring a model from one language to another can be expensive in terms of annotation costs, engineering time and effort. In this paper, we present a general framework for easily and effectively transferring neural models from English to other languages. The framework, which relies on task representations as a form of weak supervision, is model and task agnostic, meaning that many existing neural architectures can be ported to other languages with minimal effort. The only requirement is unlabeled parallel data, and a loss defined over task representations. We evaluate our framework by transferring an English sentiment classifier to three different languages. On a battery of tests, we show that our models outperform a number of strong baselines and rival state-of-the-art results, which rely on more complex approaches and significantly more resources and data. Additionally, we find that the framework proposed in this paper is able to capture semantically rich and meaningful representations across languages, despite the lack of direct supervision.
A novel mechanism of gland formation in zebrafish involving transdifferentiation of renal epithelial cells and live cell extrusion

Transdifferentiation is the poorly understood phenomenon whereby a terminally differentiated cell acquires a completely new identity. Here, we describe a rare example of a naturally occurring transdifferentiation in zebrafish in which kidney distal tubule epithelial cells are converted into an endocrine gland known as the Corpuscles of Stannius (CS). We find that this process requires Notch signalling and is associated with the cytoplasmic sequestration of the Hnf1b transcription factor, a master-regulator of renal tubule fate. A deficiency in the Irx3b transcription factor results in ectopic transdifferentiation of distal tubule cells to a CS identity but in a Notch-dependent fashion. Using live-cell imaging we show that CS cells undergo apical constriction en masse and are then extruded from the tubule to form a distinct organ. This system provides a valuable new model to understand the molecular and morphological basis of transdifferentiation and will advance efforts to exploit this rare phenomenon therapeutically.