Promoting a Syndemic Approach for Cardiometabolic Disease Management During COVID-19: The CAPISCO International Expert Panel

Efforts in the fight against COVID-19 are achieving success in many parts of the world, although progress remains slow in other regions. We believe that a syndemic approach needs to be adopted to address this pandemic given the strong apparent interplay between COVID-19, its related complications, and the socio-structural environment. We have assembled an international, multidisciplinary group of researchers and clinical practitioners to promote a novel syndemic approach to COVID-19: the CArdiometabolic Panel of International experts on Syndemic COvid-19 (CAPISCO). This geographically diverse group aims to facilitate collaborative-networking and scientific exchanges between researchers and clinicians facing a multitude of challenges on different continents during the pandemic. In the present article we present our “manifesto”, with the intent to provide evidence-based guidance to the global medical and scientific community for better management of patients both during and after the current pandemic.

Recellularization of decellularized porcine caval veins

Decellularized material has been reported to be more suitable for cells to grow when compared to synthetic materials because repopulating cells are provided with the structural environment of native tissue. The aim of this study is to prepare and to test porcine caval veins through their decellularization followed by repopulation with human endothelial cells.

Design and kinematic modeling of an origami-inspired cable-driven flexible arm

The cable-driven flexible arm based on the principle of origami is a new type of non-articulated compliant actuator with high integration, high environmental adaptability, large workspace/large deployment ratio. The forward/reverse kinematic models of joint space, operation space and driving space along with trajectory error model of the cable-driven flexible arm were proposed in this paper. The prototype of the flexible arm was developed capable of realizing bending, torsion and expansion/contraction. The simulation and experiment results of the cable-driven foldable flexible arm verified feasibility of the kinematic models and driving method proposed in this paper. Above research achievements lay necessary foundation for the next step to realize the key service functions of grasping/manipulation, three-dimensional precise movement, non-structural environment interaction/adaptation of the flexible arm with variable stiffness, variable configuration and variable size.

A holistic seismotectonic model of Delhi region

Delhi region in northern India experiences frequent shaking due to both far-field and near-field earthquakes from the Himalayan and local sources, respectively. The recent M3.5 and M3.4 earthquakes of 12th April 2020 and 10th May 2020 respectively in northeast Delhi and M4.4 earthquake of 29th May 2020 near Rohtak (~ 50 km west of Delhi), followed by more than a dozen aftershocks, created panic in this densely populated habitat. The past seismic history and the current activity emphasize the need to revisit the subsurface structural setting and its association with the seismicity of the region. Fault plane solutions are determined using data collected from a dense network in Delhi region. The strain energy released in the last two decades is also estimated to understand the subsurface structural environment. Based on fault plane solutions, together with information obtained from strain energy estimates and the available geophysical and geological studies, it is inferred that the Delhi region is sitting on two contrasting structural environments: reverse faulting in the west and normal faulting in the east, separated by the NE-SW trending Delhi Hardwar Ridge/Mahendragarh-Dehradun Fault (DHR-MDF). The WNW-ESE trending Delhi Sargoda Ridge (DSR), which intersects DHR-MDF in the west, is inferred as a thrust fault. The transfer of stress from the interaction zone of DHR-MDF and DSR to nearby smaller faults could further contribute to the scattered shallow seismicity in Delhi region.

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

<div>Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency, and a new machine learning based method, first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that estimates a residue's contributions towards protein stability dG in its local structural environment. The difference between independently predicted contributions of the reference and mutant residues in a missense mutation is reported as dG. We show that this self-consistent machine learning architecture is immune to many common biases in datasets, relies less on data than existing methods, and is robust to overfitting.</div><div><br></div>

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

<div>Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency, and a new machine learning based method, first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that estimates a residue's contributions towards protein stability dG in its local structural environment. The difference between independently predicted contributions of the reference and mutant residues in a missense mutation is reported as dG. We show that this self-consistent machine learning architecture is immune to many common biases in datasets, relies less on data than existing methods, and is robust to overfitting.</div><div><br></div>

A Deep Molecular Generative Model Based on Multi-Resolution Graph Variational Autoencoders

Deep generative models have recently emerged as encouraging tools for the de novo molecular structure generation. Even though considerable advances have been achieved in recent years, the field of generative molecular design is still in its infancy. One potential solution may be to integrate domain knowledge of structural or medicinal chemistry into the data-driven machine learning process to address specific deep molecule generation goals. This manuscript proposes a new graph-based hierarchical variational autoencoder (VAE) model for molecular generation. Training molecules are first decomposed into small molecular fragments. Unlike other motif-based molecular graph generative models, we further group decomposed fragments into different interchangeable fragment clusters according to their local structural environment around the attachment points where the bond-breaking occurs. In this way, each chemical structure can be transformed into a three-layer graph, in which individual atoms, decomposed fragments, or obtained fragment clusters act as graph nodes at each corresponding layer, respectively. We construct a hierarchical VAE model to learn such three-layer hierarchical graph representations of chemical structures in a fine-to-coarse order, in which atoms, decomposed fragments, and related fragment clusters act as graph nodes at each corresponding graph layer. The decoder component is designed to iteratively select a fragment out of a predicted fragment cluster vocabulary and then attach it to the preceding substructure. The newly introduced third graph layer will allow us to incorporate specific chemical structural knowledge, e.g., interchangeable fragments sharing similar local chemical environments or bioisosteres derived from matched molecular pair analysis information, into the molecular generation process. It will increase the odds of assembling new chemical moieties absent in the original training set and enhance structural diversity/novelty scores of generated structures. Our proposed approach demonstrates comparatively good performance in terms of model efficiency and other molecular evaluation metrics when compared with several other graph- and SMILES-based generative molecular models. We also analyze how our generative models' performance varies when choosing different fragment sampling techniques and radius parameters that determine the local structural environment of interchangeable fragment clusters. Hopefully, our multi-level hierarchical VAE prototyping model might promote more sophisticated works of knowledge-augmented deep molecular generation in the future.

A Deep Molecular Generative Model Based on Multi-Resolution Graph Variational Autoencoders

Deep generative models have recently emerged as encouraging tools for the de novo molecular structure generation. Even though considerable advances have been achieved in recent years, the field of generative molecular design is still in its infancy. One potential solution may be to integrate domain knowledge of structural or medicinal chemistry into the data-driven machine learning process to address specific deep molecule generation goals. This manuscript proposes a new graph-based hierarchical variational autoencoder (VAE) model for molecular generation. Training molecules are first decomposed into small molecular fragments. Unlike other motif-based molecular graph generative models, we further group decomposed fragments into different interchangeable fragment clusters according to their local structural environment around the attachment points where the bond-breaking occurs. In this way, each chemical structure can be transformed into a three-layer graph, in which individual atoms, decomposed fragments, or obtained fragment clusters act as graph nodes at each corresponding layer, respectively. We construct a hierarchical VAE model to learn such three-layer hierarchical graph representations of chemical structures in a fine-to-coarse order, in which atoms, decomposed fragments, and related fragment clusters act as graph nodes at each corresponding graph layer. The decoder component is designed to iteratively select a fragment out of a predicted fragment cluster vocabulary and then attach it to the preceding substructure. The newly introduced third graph layer will allow us to incorporate specific chemical structural knowledge, e.g., interchangeable fragments sharing similar local chemical environments or bioisosteres derived from matched molecular pair analysis information, into the molecular generation process. It will increase the odds of assembling new chemical moieties absent in the original training set and enhance structural diversity/novelty scores of generated structures. Our proposed approach demonstrates comparatively good performance in terms of model efficiency and other molecular evaluation metrics when compared with several other graph- and SMILES-based generative molecular models. We also analyze how our generative models' performance varies when choosing different fragment sampling techniques and radius parameters that determine the local structural environment of interchangeable fragment clusters. Hopefully, our multi-level hierarchical VAE prototyping model might promote more sophisticated works of knowledge-augmented deep molecular generation in the future.

Class Resentment and Leadership: Manipulation of Negative Emotions in Political Leadership Practices

The aim of this paper is to discuss the emotional plane in the context of the formation of leadership asymmetry, especially the instrumental use of grudge and resentment by a leader in the process of political leadership. The starting point to explaining the instrumentalisation of the affective sphere in politics, interpreted as a source of political leadership, will be the critical argumentation in relation to the assumptions of the theory of rational choice about the stability and predictability of political process. This article attempts a critical look at the rationality of leadership practices in politics, where it is emphasized that the individual and collective structures – including collective identities – of beliefs on which the leadership process is formed, consolidated and based, are not permanent, but increasingly dependent on irrational manipulative strategies. Hence, this paper will analyse the mechanism of creating or reactivating negative emotions by the leader in a given cultural and structural environment as the reasons for the emergence and legitimacy of supra-individual resentment or the functioning of class resentment, which translates into the escalation of hostility in a stratified society, as well as into followers’ receptivity; more broadly – into the form and shape of effective political leadership.