KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations

While recent works have been considerably improving the quality of the natural language explanations (NLEs) generated by a model to justify its predictions, there is very limited research in detecting and alleviating inconsistencies among generated NLEs. In this work, we leverage external knowledge bases to significantly improve on an existing adversarial attack for detecting inconsistent NLEs. We apply our attack to high-performing NLE models and show that models with higher NLE quality do not necessarily generate fewer inconsistencies. Moreover, we propose an off-the-shelf mitigation method to alleviate inconsistencies by grounding the model into external background knowledge. Our method decreases the inconsistencies of previous high-performing NLE models as detected by our attack

Comparative genomics of <em>Fusarium oxysporum</em> f. sp. <em>melonis</em> reveals the secreted protein recognized by the <em>Fom-2</em> resistance gene in melon

Development of resistant crops is the most effective way to control plant diseases to safeguard food and feed production. Disease resistance is commonly based on resistance genes, which generally mediate the recognition of small proteins secreted by invading pathogens. These proteins secreted by pathogens are called 'avirulence' proteins. Their identification is important for being able to assess the usefulness and durability of resistance genes in agricultural settings. We have used genome sequencing of a set of strains of the melon wilt fungus Fusarium oxysporum f. sp. melonis (Fom), bioinformatics-based genome comparison and genetic transformation of the fungus to identify AVRFOM2, the gene that encodes the avirulence protein recognized by the melon Fom-2 gene. Both an unbiased and a candidate gene approach identified a single candidate for the AVRFOM2 gene. Genetic complementation of AVRFOM2 in three different race 2 isolates resulted in resistance of Fom-2-harbouring melon cultivars. AvrFom2 is a small, secreted protein with two cysteine residues and weak similarity to secreted proteins of other fungi. The identification of AVRFOM2 will not only be helpful to select melon cultivars to avoid melon Fusarium wilt, but also to monitor how quickly a Fom population can adapt to deployment of Fom-2-containing cultivars in the field

In Vivo Tracking and 1H/19F Magnetic Resonance Imaging of Biodegradable Polyhydroxyalkanoate / Polycaprolactone Blend Scaffolds Seeded with Labeled Cardiac Stem Cells

Medium-chain length Polyhydroxyalkanoates (MCL-PHAs) have demonstrated exceptional properties for cardiac tissue engineering (CTE) applications. Despite prior work on MCL-PHA/Polycaprolactone (PCL) blends, optimal scaffold production and use as an alternative delivery route for controlled release of seeded cardiac progenitor cells (CPCs) in CTE applications in vivo has been lacking, We present herein applicability of MCL-PHA/PCL (95/5 wt%) blends fabricated as thin films with an improved performance compared to the neat MCL-PHA aiming to a) benefit from the material properties of natural and synthetic polymers, b) achieve controlled delivery and increase retention of delivered cells to the murine myocardium, c) extend the temporal window over which the release of labeled CPCs occurs compared to traditional direct injection techniques, and d) use 19F MRI/MRS to noninvasively detect, and longitudinally monitor the seeded scaffolds. Polymer characterization confirmed the chemical structure and composition of the synthesized scaffolds, while thermal, wettability, and mechanical properties were also investigated and compared in neat and porous counterparts. In vitro cytocompatibility studies were performed using perfluorocrown-ether (PFCE)-nanoparticle-labeled murine cardiac progenitor cells (CPC), and studied using confocal microscopy and 19F MRS/MRI. Seeded scaffolds were implanted and studied in the post-mortem murine heart in situ, and in two additional C57BL/6 mice in vivo (using single-layered and double-layered scaffolds) and imaged immediately after and at 7 days post-implantation. Superior MCL-PHA/PCL scaffold performance has been demonstrated compared to MCL-PHA through experimental comparisons of a) morphological data using scanning electron microscopy and b) contact angle measurements attesting to improved CPC adhesion, c) in vitro confocal microscopy showing increased SC proliferative capacity, d) mechanical testing that elicited good overall responses. In vitro MRI results justify the increased seeding density, increased in vitro MRI signal, and improved MRI visibility in vivo, in the double-layered compared to the single-layered scaffolds. Histological evaluations (bright-field, cytoplasmic (Atto647) and nuclear (DAPI) stains) performed in conjunction with confocal microscopy imaging attest to CPC binding within the scaffold, subsequent release and migration to the neighboring myocardium, and to increased retention in the murine myocardium in the case of the double-layered scaffold. Thus MCL-PHA/PCL blends possess tremendous potential for controlled delivery of CPCs and to maximize possible regeneration in myocardial infarction

Antimicrobial materials with lime oil and a poly(3-hydroxyalkanoate) produced via valorisation of sugar cane molasses

A medium chain-length polyhydroxyalkanoate (PHA) was produced by Pseudomonas mendocina CH50 using a cheap carbon substrate, sugarcane molasses. A PHA yield of 14.2% dry cell weight was achieved. Chemical analysis confirmed that the polymer produced was a medium chain-length PHA, a copolymer of 3-hydroxyoctanoate and 3-hydroxydecanoate, P(3HO-co-3HD). Lime oil, an essential oil with known antimicrobial activity, was used as an additive to P(3HO-co-3HD) to confer antibacterial properties to this biodegradable polymer. The incorporation of lime oil induced a slight decrease in crystallinity of P(3HO-co-3HD) films. The antibacterial properties of lime oil were investigated using ISO 20776 against Staphylococcus aureus 6538P and Escherichia coli 8739, showing a higher activity against the Gram-positive bacteria. The higher activity of the oil against S. aureus 6538P defined the higher efficiency of loaded polymer films against this strain. The effect of storage on the antimicrobial properties of the loaded films was investigated. After one-year storage, the content of lime oil in the films decreased, causing a reduction of the antimicrobial activity of the materials produced. However, the films still possessed antibacterial activity against S. aureus 6538P

Multimodality in Pervasive Environment

Future pervasive environments are expected to immerse users in a consistent world of probes, sensors and actuators. Multimodal interfaces combined with social computing interactions and high-performance networking can foster a new generation of pervasive environments. However, much work is still needed to harness the full potential of multimodal interaction. In this paper we discuss some short-term research goals, including advanced techniques for joining and correlating multiple data flows, each with its own approximations and uncertainty models. Also, we discuss some longer term objectives, like providing users with a mental model of their own multimodal "aura", enabling them to collaborate with the network infrastructure toward inter-modal correlation of multimodal inputs, much in the same way as the human brain extracts a single self-conscious experience from multiple sensorial data flows

Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records

\ua9 2022 IEEE. Electronic health records (EHR) represent a holistic overview of patients\u27 trajectories. Their increasing availability has fueled new hopes to leverage them and develop accurate risk prediction models for a wide range of diseases. Given the complex interrelationships of medical records and patient outcomes, deep learning models have shown clear merits in achieving this goal. However, a key limitation of current study remains their capacity in processing long sequences, and long sequence modelling and its application in the context of healthcare and EHR remains unexplored. Capturing the whole history of medical encounters is expected to lead to more accurate predictions, but the inclusion of records collected for decades and from multiple resources can inevitably exceed the receptive field of the most existing deep learning architectures. This can result in missing crucial, long-term dependencies. To address this gap, we present Hi-BEHRT, a hierarchical Transformer-based model that can significantly expand the receptive field of Transformers and extract associations from much longer sequences. Using a multimodal large-scale linked longitudinal EHR, the Hi-BEHRT exceeds the state-of-the-art deep learning models 1% to 5% for area under the receiver operating characteristic (AUROC) curve and 1% to 8% for area under the precision recall (AUPRC) curve on average, and 2% to 8% (AUROC) and 2% to 11% (AUPRC) for patients with long medical history for 5-year heart failure, diabetes, chronic kidney disease, and stroke risk prediction. Additionally, because pretraining for hierarchical Transformer is not well-established, we provide an effective end-to-end contrastive pre-training strategy for Hi-BEHRT using EHR, improving its transferability on predicting clinical events with relatively small training dataset