Recognizing multimodal entailment

How information is created, shared and consumed has changed rapidly in recent decades, in part thanks to new social platforms and technologies on the web. With ever-larger amounts of unstructured and limited labels, organizing and reconciling information from different sources and modalities is a central challenge in machine learning. This cutting-edge tutorial aims to introduce the multimodal entailment task, which can be useful for detecting semantic alignments when a single modality alone does not suffice for a whole content understanding. Starting with a brief overview of natural language processing, computer vision, structured data and neural graph learning, we lay the foundations for the multimodal sections to follow. We then discuss recent multimodal learning literature covering visual, audio and language streams, and explore case studies focusing on tasks which require fine-grained understanding of visual and linguistic semantics question answering, veracity and hatred classification. Finally, we introduce a new dataset for recognizing multimodal entailment, exploring it in a hands-on collaborative section. Overall, this tutorial gives an overview of multimodal learning, introduces a multimodal entailment dataset, and encourages future research in the topic

Breast cancer management pathways during the COVID-19 pandemic: outcomes from the UK ‘Alert Level 4’ phase of the B-MaP-C study

Abstract: Background: The B-MaP-C study aimed to determine alterations to breast cancer (BC) management during the peak transmission period of the UK COVID-19 pandemic and the potential impact of these treatment decisions. Methods: This was a national cohort study of patients with early BC undergoing multidisciplinary team (MDT)-guided treatment recommendations during the pandemic, designated ‘standard’ or ‘COVID-altered’, in the preoperative, operative and post-operative setting. Findings: Of 3776 patients (from 64 UK units) in the study, 2246 (59%) had ‘COVID-altered’ management. ‘Bridging’ endocrine therapy was used (n = 951) where theatre capacity was reduced. There was increasing access to COVID-19 low-risk theatres during the study period (59%). In line with national guidance, immediate breast reconstruction was avoided (n = 299). Where adjuvant chemotherapy was omitted (n = 81), the median benefit was only 3% (IQR 2–9%) using ‘NHS Predict’. There was the rapid adoption of new evidence-based hypofractionated radiotherapy (n = 781, from 46 units). Only 14 patients (1%) tested positive for SARS-CoV-2 during their treatment journey. Conclusions: The majority of ‘COVID-altered’ management decisions were largely in line with pre-COVID evidence-based guidelines, implying that breast cancer survival outcomes are unlikely to be negatively impacted by the pandemic. However, in this study, the potential impact of delays to BC presentation or diagnosis remains unknown

Improved Bounds for Bipartite Matching on Surfaces

We exhibit the following new upper bounds on the space complexity and the parallel complexity of the Bipartite Perfect Matching (BPM) problem for graphs of small genus: (1) BPM in planar graphs is in UL (improves upon the SPL bound from Datta et. al. [7]); (2) BPM in constant genus graphs is in NL (orthogonal to the SPL bound from Datta et. al. [8]); (3) BPM in poly-logarithmic genus graphs is in NC; (extends the NC bound for O(log n) genus graphs from Mahajan and Varadarajan [22], and Kulkarni et. al. [19]. For Part (1) we combine the flow technique of Miller and Naor [23] with the double counting technique of Reinhardt and Allender [27]. For Part (2) and (3) we extend [23] to higher genus surfaces in the spirit of Chambers, Erickson and Nayyeri [4]

Cytotoxic Potential of Bioactive Compounds from Aspergillus flavus, an Endophytic Fungus Isolated from Cynodon dactylon, against Breast Cancer: Experimental and Computational Approach

Endophytic fungi are a diverse group of microorganisms that colonize the inter- or intracellular spaces of plants and exhibit mutual benefits. Their interactions with the host plant and other microbiomes are multidimensional and play a crucial role in the production of secondary metabolites. We screened bioactive compounds present in the extracts of Aspergillus flavus, an endophytic fungus isolated from the roots of the medicinal grass Cynodon dactylon, for its anticancer potential. An in vitro analysis of the Ethyl acetate extract from A. flavus showed significant cytostatic effects (IC50: 16.25 μg/mL) against breast cancer cells (MCF-7). A morphological analysis of the cells and a flow cytometry of the cells with annexin V/Propidium Iodide suggested that the extract induced apoptosis in the MCF-7 cells. The extract of A. flavus increased reactive oxygen species (ROS) generation and caused a loss of mitochondrial membrane potential in MCF-7 cells. To identify the metabolites that might be responsible for the anticancer effect, the extract was subjected to a gas chromatography-mass spectrometry (GC-MS) analysis. Interestingly, nine phytochemicals that induced cytotoxicity in the breast cancer cell line were found in the extract. The in silico molecular docking and molecular dynamics simulation studies revealed that two compounds, 2,4,7-trinitrofluorenone and 3α, 5 α-cyclo-ergosta-7,9(11), 22t-triene-6beta-ol exhibited significant binding affinities (−9.20, and −9.50 Kcal/mol, respectively) against Bcl-2, along with binding stability and intermolecular interactions of its ligand-Bcl-2 complexes. Overall, the study found that the endophytic A. flavus from C. dactylon contains plant-like bioactive compounds that have a promising effect in breast cancer

A Pan-Cancer Analysis Reveals High-Frequency Genetic Alterations in Mediators of Signaling by the TGF-β Superfamily

We present an integromic analysis of gene alterations that modulate transforming growth factor β (TGF-β)-Smad-mediated signaling in 9,125 tumor samples across 33 cancer types in The Cancer Genome Atlas (TCGA). Focusing on genes that encode mediators and regulators of TGF-β signaling, we found at least one genomic alteration (mutation, homozygous deletion, or amplification) in 39% of samples, with highest frequencies in gastrointestinal cancers. We identified mutation hotspots in genes that encode TGF-β ligands (BMP5), receptors (TGFBR2, AVCR2A, and BMPR2), and Smads (SMAD2 and SMAD4). Alterations in the TGF-β superfamily correlated positively with expression of metastasis-associated genes and with decreased survival. Correlation analyses showed the contributions of mutation, amplification, deletion, DNA methylation, and miRNA expression to transcriptional activity of TGF-β signaling in each cancer type. This study provides a broad molecular perspective relevant for future functional and therapeutic studies of the diverse cancer pathways mediated by the TGF-β superfamily. To date, there are no studies of the TGF-β superfamily of signaling pathways across multiple cancers. This study represents a key starting point for unraveling the role of this complex superfamily in 33 divergent cancer types from over 9,000 patients