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In the centrosymmetric title complexes, di-μ-acetato-bis({N,N-dimethyl-2-[phenyl(pyridin-2-yl)methylidene]hydrazine-1-carbothioamidato}zinc(II)), [Zn₂(C₁₅H₁₅N₄S)₂(C₂H₃O₂)₂] (I), and di-μ-acetato-bis({N-ethyl-2-[phenyl(pyridin-2-yl)methylidene]hydrazine-1-carbothioamidato}zinc(II)), [Zn₂(C₁₆H₁₇N₄S)₂(C₂H₃O₂)₂] (II), the zinc ions are chelated by theN,N,S-tridentate ligands and bridged by pairs of acetate ions. The acetate ion in (I) is disordered over two orientations in a 0.756 (6):0.244 (6) ratio, leading to different zinc coordination modes for the major (5-coordinate) and minor (6-coordinate) disorder components. Geometrical indices [τ₅= 0.32 and 0.30 for (I) (major component) and (II), respectively] suggest the zinc coordination in these phases to be distorted square pyramidal. This study forms part of our aim to discern the mechanism of metal binding in these chelators, their specificity and selectivity, and to gain insight into the role of cellular zinc in physiological processes such as infection, immunity and cancer. 

Disrupted iron balance causes anemia and iron overload leading to hypoxia and systemic oxidative stress. Iron overload may arise from red blood cell disorders such as sickle cell disease, thalassemia major and primary hemochromatosis, or from treatment with multiple transfusions. These hematological disorders are characterized by constant red blood cell hemolysis and the release of iron. Hemolysis is a continuous source of reactive oxygen species whose accumulation changes the redox potential in the erythrocyte, the endothelium and other tissue causing damage to organ systems. Iron overload and its consequences can be treated with iron chelating therapy. We have carried out structural studies of small molecule ligands that were previously reported for their iron chelating ability. The chelators were analyzed using mass spectrometry, proton nuclear magnetic resonance and infrared spectroscopy. The iron chelators, 2-benzoylpyridine-4,4-dimethyl-3-thiosemicarbazone, 3-ethyl-1-{[2-phenyl-1-(pyridin-2-yl)ethylidene]amino}thiourea and 1-{[2-phenyl-1-(pyridin-2-yl)ethylidene]amino}-3-(prop‑2-en-1-yl)thiourea in their unbound conformation were crystallized and their structures were determined. This work addresses the evolution of a thiosemicarbazone class of iron chelators by analyzing and comparing the structure and properties of a series of closely related molecules, relating these to their in vitro activity thus providing valuable update to the search for newer, better and more effective iron chelators and metal-based therapeutics. 

Wasserstein distances form a family of metrics on spaces of probability measures that have recently seen many applications. However, statistical analysis in these spaces is complex due to the nonlinearity of Wasserstein spaces. One potential solution to this problem is Linear Optimal Transport (LOT). This method allows one to find a Euclidean embedding, called {\it LOT embedding}, of measures in some Wasserstein spaces, but some information is lost in this embedding. So, to understand whether statistical analysis relying on LOT embeddings can make valid inferences about original data, it is helpful to quantify how well these embeddings describe that data. To answer this question, we present a decomposition of the {\it Fr\'echet variance} of a set of measures in the 2-Wasserstein space, which allows one to compute the percentage of variance explained by LOT embeddings of those measures. We then extend this decomposition to the Fused Gromov-Wasserstein setting. We also present several experiments that explore the relationship between the dimension of the LOT embedding, the percentage of variance explained by the embedding, and the classification accuracy of machine learning classifiers built on the embedded data. We use the MNIST handwritten digits dataset, IMDB-50000 dataset, and Diffusion Tensor MRI images for these experiments. Our results illustrate the effectiveness of low dimensional LOT embeddings in terms of the percentage of variance explained and the classification accuracy of models built on the embedded data. 

Abstract Language models are typically evaluated on their success at predicting the distribution of specific words in specific contexts. Yet linguistic knowledge also encodes relationships between contexts, allowing inferences between word distributions. We investigate the degree to which pre-trained transformer-based large language models (LLMs) represent such relationships, focusing on the domain of argument structure. We find that LLMs perform well in generalizing the distribution of a novel noun argument between related contexts that were seen during pre-training (e.g., the active object and passive subject of the verb spray), succeeding by making use of the semantically organized structure of the embedding space for word embeddings. However, LLMs fail at generalizations between related contexts that have not been observed during pre-training, but which instantiate more abstract, but well-attested structural generalizations (e.g., between the active object and passive subject of an arbitrary verb). Instead, in this case, LLMs show a bias to generalize based on linear order. This finding points to a limitation with current models and points to a reason for which their training is data-intensive.1 

Climate change has led to a variety of disasters that have caused damage to infrastructure and the economy with societal impacts to human living. Understanding people’s emotions and stressors during disaster times will enable preparation strategies for mitigating further consequences. In this paper, we mine emotions and stressors encountered by people and shared on Twitter during Hurricane Harvey in 2017 as a showcase. In this work, we acquired a dataset of tweets from Twitter on Hurricane Harvey from 20 August 2017 to 30 August 2017. The dataset consists of around 400,000 tweets and is available on Kaggle. Next, a BERT-based model is employed to predict emotions associated with tweets posted by users. Then, natural language processing (NLP) techniques are utilized on negative-emotion tweets to explore the trends and prevalence of the topics discussed during the disaster event. Using Latent Dirichlet Allocation (LDA) topic modeling, we identified themes, enabling us to manually extract stressors termed as climate-change-related stressors. Results show that 20 climate-change-related stressors were extracted and that emotions peaked during the deadliest phase of the disaster. This indicates that tracking emotions may be a useful approach for studying environmentally determined well-being outcomes in light of understanding climate change impacts.