ChatGPT: Literate or intelligent about UN sustainable development goals?

Generative AI tools, such as ChatGPT, are progressively transforming numerous sectors, demonstrating a capacity to impact human life dramatically. This research seeks to evaluate the UN Sustainable Development Goals (SDGs) literacy of ChatGPT, which is crucial for diverse stakeholders involved in SDG-related policies. Experimental outcomes from two widely used Sustainability Assessment tests-the UN SDG Fitness Test and Sustainability Literacy Test (SULITEST) - suggest that ChatGPT exhibits high SDG literacy, yet its comprehensive SDG intelligence needs further exploration. The Fitness Test gauges eight vital competencies across introductory, intermediate, and advanced levels. Accurate mapping of these to the test questions is essential for partial evaluation of SDG intelligence. To assess SDG intelligence, the questions from both tests were mapped to 17 SDGs and eight cross-cutting SDG core competencies, but both test questionnaires were found to be insufficient. SULITEST could satisfactorily map only 5 out of 8 competencies, whereas the Fitness Test managed to map 6 out of 8. Regarding the coverage of the Fitness Test and SULITEST, their mapping to the 17 SDGs, both tests fell short. Most SDGs were underrepresented in both instruments, with certain SDGs not represented at all. Consequently, both tools proved ineffective in assessing SDG intelligence through SDG coverage. The study recommends future versions of ChatGPT to enhance competencies such as collaboration, critical thinking, systems thinking, and others to achieve the SDGs. It concludes that while AI models like ChatGPT hold considerable potential in sustainable development, their usage must be approached carefully, considering current limitations and ethical implications.

Resolving the polycistronic aftermath: Essential role of topoisomerase IA in preventing R-loops in Leishmania.

Kinetoplastid parasites are "living bridges" in the evolution from prokaryotes to higher eukaryotes. The near-intronless genome of the kinetoplastid Leishmania exhibits polycistronic transcription which can facilitate R-loop formation. Therefore, to prevent such DNA-RNA hybrids, Leishmania has retained prokaryotic-like DNA Topoisomerase IA (LdTOPIA) in the course of evolution. LdTOPIA is an essential enzyme that is expressed ubiquitously and is adapted for the compartmentalized eukaryotic form in harboring functional bipartite nuclear localization signals. Although exhibiting greater homology to mycobacterial TOPIA, LdTOPIA could functionally complement the growth lethality of Escherichia coli TOPIA null GyrB ts strain at non-permissive temperatures. Purified LdTOPIA exhibits Mg2+-dependent relaxation of only negatively supercoiled DNA and preference towards single-stranded DNA substrates. LdTOPIA prevents nuclear R-loops as conditional LdTOPIA downregulated parasites exhibit R-loop formation and thereby parasite killing. The clinically used tricyclic antidepressant, norclomipramine could specifically inhibit LdTOPIA and lead to R-loop formation and parasite elimination. This comprehensive study therefore paves an avenue for drug repurposing against Leishmania.

Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units.

Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the development of machine-learning (ML) models that make these predictions more efficiently. Training graph neural networks over large atomistic databases introduces unique computational challenges, such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs, such as social networks. We demonstrate a novel hardware-software codesign approach to scale up the training of atomistic graph neural networks (GNN) for structure and property prediction. First, to eliminate redundant computation and memory associated with alternative padding techniques and to improve throughput via minimizing communication, we formulate the effective coalescing of the batches of variable-size atomistic graphs as the bin packing problem and introduce a hardware-agnostic algorithm to pack these batches. In addition, we propose hardware-specific optimizations, including a planner and vectorization for the gather-scatter operations targeted for Graphcore's Intelligence Processing Unit (IPU), as well as model-specific optimizations such as merged communication collectives and optimized softplus. Putting these all together, we demonstrate the effectiveness of the proposed codesign approach by providing an implementation of a well-established atomistic GNN on the Graphcore IPUs. We evaluate the training performance on multiple atomistic graph databases with varying degrees of graph counts, sizes, and sparsity. We demonstrate that such a codesign approach can reduce the training time of atomistic GNNs and can improve their performance by up to 1.5× compared to the baseline implementation of the model on the IPUs. Additionally, we compare our IPU implementation with a Nvidia GPU-based implementation and show that our atomistic GNN implementation on the IPUs can run 1.8× faster on average compared to the execution time on the GPUs.

Accelerating drug target inhibitor discovery with a deep generative foundation model.

Inhibitor discovery for emerging drug-target proteins is challenging, especially when target structure or active molecules are unknown. Here, we experimentally validate the broad utility of a deep generative framework trained at-scale on protein sequences, small molecules, and their mutual interactions-unbiased toward any specific target. We performed a protein sequence-conditioned sampling on the generative foundation model to design small-molecule inhibitors for two dissimilar targets: the spike protein receptor-binding domain (RBD) and the main protease from SARS-CoV-2. Despite using only the target sequence information during the model inference, micromolar-level inhibition was observed in vitro for two candidates out of four synthesized for each target. The most potent spike RBD inhibitor exhibited activity against several variants in live virus neutralization assays. These results establish that a single, broadly deployable generative foundation model for accelerated inhibitor discovery is effective and efficient, even in the absence of target structure or binder information.

An end-to-end deep learning framework for translating mass spectra to de-novo molecules.

Elucidating the structure of a chemical compound is a fundamental task in chemistry with applications in multiple domains including drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a compound is to obtain a mass spectrum and subsequently retrieve its structure from spectral databases. However, these methods fail for novel molecules that are not present in the reference database. We propose Spec2Mol, a deep learning architecture for molecular structure recommendation given mass spectra alone. Spec2Mol is inspired by the Speech2Text deep learning architectures for translating audio signals into text. Our approach is based on an encoder-decoder architecture. The encoder learns the spectra embeddings, while the decoder, pre-trained on a massive dataset of chemical structures for translating between different molecular representations, reconstructs SMILES sequences of the recommended chemical structures. We have evaluated Spec2Mol by assessing the molecular similarity between the recommended structures and the original structure. Our analysis showed that Spec2Mol is able to identify the presence of key molecular substructures from its mass spectrum, and shows on par performance, when compared to existing fragmentation tree methods particularly when test structure information is not available during training or present in the reference database.

A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening.

Over the past few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules. However, many scoring functions make predictions based on data set biases rather than an understanding of the physics of binding. These scoring functions perform well when tested on similar targets to those in the training set but fail to generalize to dissimilar targets. To test what a machine learning-based scoring function has learned, input attribution, a technique for learning which features are important to a model when making a prediction on a particular data point, can be applied. If a model successfully learns something beyond data set biases, attribution should give insight into the important binding interactions that are taking place. We built a machine learning-based scoring function that aimed to avoid the influence of bias via thorough train and test data set filtering and show that it achieves comparable performance on the Comparative Assessment of Scoring Functions, 2016 (CASF-2016) benchmark to other leading methods. We then use the CASF-2016 test set to perform attribution and find that the bonds identified as important by PointVS, unlike those extracted from other scoring functions, have a high correlation with those found by a distance-based interaction profiler. We then show that attribution can be used to extract important binding pharmacophores from a given protein target when supplied with a number of bound structures. We use this information to perform fragment elaboration and see improvements in docking scores compared to using structural information from a traditional, data-based approach. This not only provides definitive proof that the scoring function has learned to identify some important binding interactions but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design.

Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations.

Explainable machine learning for molecular toxicity prediction is a promising approach for efficient drug development and chemical safety. A predictive ML model of toxicity can reduce experimental cost and time while mitigating ethical concerns by significantly reducing animal and clinical testing. Herein, we use a deep learning framework for simultaneously modeling in vitro, in vivo, and clinical toxicity data. Two different molecular input representations are used; Morgan fingerprints and pre-trained SMILES embeddings. A multi-task deep learning model accurately predicts toxicity for all endpoints, including clinical, as indicated by the area under the Receiver Operator Characteristic curve and balanced accuracy. In particular, pre-trained molecular SMILES embeddings as input to the multi-task model improved clinical toxicity predictions compared to existing models in MoleculeNet benchmark. Additionally, our multitask approach is comprehensive in the sense that it is comparable to state-of-the-art approaches for specific endpoints in in vitro, in vivo and clinical platforms. Through both the multi-task model and transfer learning, we were able to indicate the minimal need of in vivo data for clinical toxicity predictions. To provide confidence and explain the model's predictions, we adapt a post-hoc contrastive explanation method that returns pertinent positive and negative features, which correspond well to known mutagenic and reactive toxicophores, such as unsubstituted bonded heteroatoms, aromatic amines, and Michael receptors. Furthermore, toxicophore recovery by pertinent feature analysis captures more of the in vitro (53%) and in vivo (56%), rather than of the clinical (8%), endpoints, and indeed uncovers a preference in known toxicophore data towards in vitro and in vivo experimental data. To our knowledge, this is the first contrastive explanation, using both present and absent substructures, for predictions of clinical and in vivo molecular toxicity.

Ancestral SARS-CoV-2-Driven Antibody Repertoire Diversity in an Unvaccinated Individual Correlates with Expanded Neutralization Breadth.

Understanding the quality of immune repertoire triggered during natural infection can provide vital clues that form the basis for development of a humoral immune response in some individuals capable of broadly neutralizing pan-SARS-CoV-2 variants. In the present study, we report variations in neutralization potential against Omicron variants of two novel neutralizing monoclonal antibodies (MAbs), THSC20.HVTR11 and THSC20.HVTR55, isolated from an unvaccinated convalescent individual that represent distinct B cell lineage origins and epitope specificity compared to five MAbs we previously reported that were isolated from the same individual. In addition, we observed neutralization of Omicron variants by plasma antibodies obtained from this particular individual postvaccination with increased magnitude. Interestingly, this observation was found to be comparable with six additional individuals who initially were also infected with ancestral SARS-CoV-2 and then received vaccines, indicating that hybrid immunity can provide robust humoral immunity likely by antibody affinity maturation. Development of a distinct antigen-specific B cell repertoire capable of producing polyclonal antibodies with distinct affinity and specificities offers the highest probability of protecting against evolving SARS-CoV-2 variants. IMPORTANCE Development of robust neutralizing antibodies in SARS-CoV-2 convalescent individuals is known; however, it varies at the population level. We isolated monoclonal antibodies from an individual infected with ancestral SARS-CoV-2 in early 2020 that not only varied in their B cell lineage origin but also varied in their capability and potency to neutralize all the known variants of concern (VOCs) and currently circulating Omicron variants. This indicated establishment of unique lineages that contributed in forming a B cell repertoire in this particular individual immediately following infection, giving rise to diverse antibody responses that could complement each other in providing a broadly neutralizing polyclonal antibody response. Individuals who were able to produce polyclonal antibody responses with higher magnitude have a higher chance of being protected from evolving SARS-CoV-2 variants.

IL-13 and the hydroperoxy fatty acid 13(S)HpODE play crucial role in inducing an apoptotic pathway in cancer cells involving MAO-A/ROS/p53/p21 signaling axis.

This study depicted the effect of IL-13 and 13(S)HpODE (the endogenous product during IL-13 activation) in the process of cancer cell apoptosis. We examined the role of both IL-13 and 13(S)HpODE in mediating apoptotic pathway in three different in vitro cellular models namely A549 lung cancer, HCT116 colorectal cancer and CCF52 GBM cells. Our data showed that IL-13 promotes apoptosis of A549 lung carcinoma cells through the involvement of 15-LO, PPARγ and MAO-A. Our observations demonstrated that IL-13/13(S)HpODE stimulate MAO-A-mediated intracellular ROS production and p53 as well as p21 induction which play a crucial role in IL-13-stimulated A549 cell apoptosis. We further showed that 13(S)HpODE promotes apoptosis of HCT116 and CCF52 cells through the up-regulation of p53 and p21 expression. Our data delineated that IL-13 stimulates p53 and p21 induction which is mediated through 15-LO and MAO-A in A549 cells. In addition, we observed that PPARγ plays a vital role in apoptosis as well as in p53 and p21 expression in A549 cells in the presence of IL-13. We validated our observations in case of an in vivo colon cancer tumorigenic study using syngeneic mice model and demonstrated that 13(S)HpODE significantly reduces solid tumor growth through the activation of apoptosis. These data thus confirmed that IL-13 > 15-LO>13(S)HpODE > PPARγ>MAO-A > ROS > p53>p21 axis has a major contribution in regulating cancer cell apoptosis and further identified 13(S)HpODE as a potential chemo-preventive agent which can improve the efficacy of cancer treatment as a combination compound.

Epidemiological trend and clinical profile of COVID-19 patients: Experience from a designated COVID-19 center in Delhi.

Objective: To study the epidemiological characteristics of the pandemic by describing the clinical profile of the COVID-19 patients presenting to a super specialty hospital. Methods: This was a descriptive study using medical records of patients who tested positive for SARS-CoV-2 RNA using reverse transcription-polymerase chain reaction between 17th March and 15th January 2021 while maintaining confidentiality. The clinical and demographic data of all the patients were entered in a Microsoft Excel and statistical analysis was done using SPSS 21 software. Regression analysis was performed and a P value < 0.05 was considered to be statistically significant. Results: A total of 3534 patients were enrolled in this study aged 9-96 years. Among patients with symptoms, fever and cough were the most common presenting symptoms, while 5.6% of the patients were asymptomatic. Hypertension was the most common comorbidity (37%), while no comorbidities were present in 43.0% of the participants and this was statistically significant for age (P = 0.000). Among patient outcomes, >50% of patients were in home isolation, while 11% of patients had a fatal outcome. Elder age group had a higher proportion of expiry among outcomes (P <= 0.001). Most patients had a hospital stay of 9-11 days. A total of 63 health workers were included with male: female ratio being 3.5:1. Conclusion: Our study reflects that majority of the positive cases that presented to the hospital had mild/moderate symptoms. We believe that appropriate triaging of patients followed by early institution of medicine and good critical care services may help to control this epidemic.

A combination of potently neutralizing monoclonal antibodies isolated from an Indian convalescent donor protects against the SARS-CoV-2 Delta variant.

Although efficacious vaccines have significantly reduced the morbidity and mortality of COVID-19, there remains an unmet medical need for treatment options, which monoclonal antibodies (mAbs) can potentially fill. This unmet need is exacerbated by the emergence and spread of SARS-CoV-2 variants of concern (VOCs) that have shown some resistance to vaccine responses. Here we report the isolation of five neutralizing mAbs from an Indian convalescent donor, out of which two (THSC20.HVTR04 and THSC20.HVTR26) showed potent neutralization of SARS-CoV-2 VOCs at picomolar concentrations, including the Delta variant (B.1.617.2). One of these (THSC20.HVTR26) also retained activity against the Omicron variant. These two mAbs target non-overlapping epitopes on the receptor-binding domain (RBD) of the spike protein and prevent virus attachment to its host receptor, human angiotensin converting enzyme-2 (hACE2). Furthermore, the mAb cocktail demonstrated protection against the Delta variant at low antibody doses when passively administered in the K18 hACE2 transgenic mice model, highlighting their potential as a cocktail for prophylactic and therapeutic applications. Developing the capacity to rapidly discover and develop mAbs effective against highly transmissible pathogens like coronaviruses at a local level, especially in a low- and middle-income country (LMIC) such as India, will enable prompt responses to future pandemics as an important component of global pandemic preparedness.

Present and prospective research themes for tourism and hospitality education post-COVID19: A bibliometric analysis.

Academic research in tourism and hospitality sector adds value directly to the way the industry grows and develops. Scholars in this area struggle with the pressures to publish in high ranking journals. The present study attempts to help doctoral students and tourism educators in identifying emerging themes in the tourism and hospitality arising out after COVID-19 pandemic. Using bibliometric analysis, five broad areas of emerging research themes are identified. Such research would further help managers, tourism related state administrators, and firm owners to recover from the devastating impact of COVID-19 on the industry across the world.

Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design.

Designing novel protein sequences for a desired 3D topological fold is a fundamental yet nontrivial task in protein engineering. Challenges exist due to the complex sequence-fold relationship, as well as the difficulties to capture the diversity of the sequences (therefore structures and functions) within a fold. To overcome these challenges, we propose Fold2Seq, a novel transformer-based generative framework for designing protein sequences conditioned on a specific target fold. To model the complex sequence-structure relationship, Fold2Seq jointly learns a sequence embedding using a transformer and a fold embedding from the density of secondary structural elements in 3D voxels. On test sets with single, high-resolution and complete structure inputs for individual folds, our experiments demonstrate improved or comparable performance of Fold2Seq in terms of speed, coverage, and reliability for sequence design, when compared to existing state-of-the-art methods that include data-driven deep generative models and physics-based RosettaDesign. The unique advantages of fold-based Fold2Seq, in comparison to a structure-based deep model and RosettaDesign, become more evident on three additional real-world challenges originating from low-quality, incomplete, or ambiguous input structures. Source code and data are available at https://github.com/IBM/fold2seq.

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency against diverse Gram-positive and Gram-negative pathogens (including multidrug-resistant Klebsiella pneumoniae) and a low propensity to induce drug resistance in Escherichia coli. Both peptides have low toxicity, as validated in vitro and in mice. We also show using live-cell confocal imaging that the bactericidal mode of action of the peptides involves the formation of membrane pores. The combination of deep learning and molecular dynamics may accelerate the discovery of potent and selective broad-spectrum antimicrobials.

The ultimate fate determinants of drug induced cell-death mechanisms in Trypanosomatids.

Chemotherapy constitutes a major part of modern-day therapy for infectious and chronic diseases. A drug is said to be effective if it can inhibit its target, induce stress, and thereby trigger an array of cell death pathways in the form of programmed cell death, autophagy, necrosis, etc. Chemotherapy is the only treatment choice against trypanosomatid diseases like Leishmaniasis, Chagas disease, and sleeping sickness. Anti-trypanosomatid drugs can induce various cell death phenotypes depending upon the drug dose and growth stage of the parasites. The mechanisms and pathways triggering cell death in Trypanosomatids serve to help identify potential targets for the development of effective anti-trypanosomatids. Studies show that the key proteins involved in cell death of trypanosomatids are metacaspases, Endonuclease G, Apoptosis-Inducing Factor, cysteine proteases, serine proteases, antioxidant systems, etc. Unlike higher eukaryotes, these organisms either lack the complete set of effectors involved in cell death pathways, or are yet to be deciphered. A detailed summary of the existing knowledge of different drug-induced cell death pathways would help identify the lacuna in each of these pathways and therefore open new avenues for research and thereby new therapeutic targets to explore. The cell death pathway associated complexities in metazoans are absent in trypanosomatids; hence this summary can also help understand the trigger points as well as cross-talk between these pathways. Here we provide an in-depth overview of the existing knowledge of these drug-induced trypanosomatid cell death pathways, describe their associated physiological changes, and suggest potential interconnections amongst them.

"It Takes Two to Tango": Role of Neglected Macrophage Manipulators Coronin 1 and Protein Kinase G in Mycobacterial Pathogenesis.

Macrophages being the connecting link between innate and adaptive immune system plays a crucial role in microbial antigen presentation and orchestrates the subsequent clearance of microorganisms. Microbial invasion of macrophages trigger a plethora of signaling cascades, which interact among them to generate a dynamically altered hostile environment, that ultimately leads to disruption of microbial pathogenesis. Paradoxically, Mycobacterium sp. exploits macrophage proteins such as Coronin 1, Calcineurin, LRG47, SOCS1, CISH, Gbp5 etc. and secretes virulence proteins such as PknG, PtpA, SapM, Eis etc. to hijack these intra-macrophage, signaling cascades and thereby develop its own niche. Coronin 1, being a cortical protein is transiently recruited to all mycobacteria containing phagosomes, but only pathogenic mycobacteria can retain it on the phagosome, to hinder its maturation. Additionally, mycobacterial infection linked secretion of virulence factor Protein Kinase G through its phosphorylation, manipulates several macrophage signaling pathways and thus promotes pathogenesis at various stages, form early infection to latency to granuloma formation. Here we discuss the present status of mycobacteria engaged Coronin 1-dependent signaling cascades and secreted PknG related sequence of events promoting mycobacterial pathogenesis. Current knowledge about these two proteins in context of macrophage signaling manipulation encompassing diverse mechanisms like calcium-calcineurin signaling, reduced proinflamtory cytokine secretion, cytoskeletal changes, and adaptation in acidic environment, which ultimately converge toward mycobacterial survival inside the macrophages has been discussed.

ESX secretion system: The gatekeepers of mycobacterial survivability and pathogenesis.

Mycobacterium tuberculosis, the causative agent of Tuberculosis has plagued humankind for ages and has surfaced stronger than ever with the advent of drug resistance. Mycobacteria are adept at evading the host immune system and establishing infection by engaging host factors and secreting several virulence factors. Hence these secretion systems play a key role in mycobacterial pathogenesis. The type VII secretion system or ESX (early secretory antigenic target (ESAT6) secretion) system is one such crucial system that comprises five different pathways having distinct roles in mycobacterial proliferation, pathogenesis, cytosolic escape within macrophages, regulation of macrophage apoptosis, metal ion homeostasis, etc. ESX 1-5 systems are implicated in the secretion of a plethora of proteins, of which only a few are functionally characterized. Here we summarize the current knowledge of ESX secretion systems of mycobacteria with a special focus on ESX-1 and ESX-5 systems that subvert macrophage defenses and help mycobacteria to establish their niche within the macrophage.

Nanoscale Interplay of Membrane Composition and Amyloid Self-Assembly.

Cell membranes are complex assemblies of lipids and proteins exhibiting lipid compositional heterogeneity between the inner and outer leaflets of the bilayer. Aberrant protein aggregation, implicated in a number of neurodegenerative diseases including Alzheimer's, is known to result in both extracellular and intracellular deposits with divergent pathophysiological effects. Mounting evidence substantiates membrane-mediated amyloid effects and indicates membrane composition, particularly gangliosides, as a plausible factor influencing the fibrillation process. By employing exhaustive molecular dynamics simulations using a coarse-grained model, we probed the assembly behavior of amyloidogenic Aß(12-28) peptides on the chemically heterogeneous extracellular (outer) and cytosolic (inner) leaflets of a mammalian plasma membrane. Our results indicate that the compositional nature of the membrane has a crucial impact on the peptide self-assembly. Peptide oligomerization is hindered on the outer leaflet relative to the inner leaflet due to a competition between interpeptide and peptide-membrane interactions, resulting in higher population of smaller oligomers. The weaker associations among peptides on the outer membrane can be attributed to the favorable interactions of the peptides with gangliosides (GM) that characterize the extracellular membrane. At a higher peptide:GM ratio, we observe enhanced nanoclustering of GM lipids mediated by preferential GM-Aß binding. Interaction between peptide and GM further impacts local membrane curvature; there is a concomitant loss in membrane concavity due to looser GM packing. Our simulations provide molecular insights into the role of membrane composition on Aß aggregation and lend credence to earlier reports of ganglioside-mediated Aß aggregation in the outer membrane. We also demonstrate the effects of local peptide assemblies on the membrane structure and dynamics.