Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks

Predicting SARS-CoV-2 mutations is difficult, but predicting recurrent mutations driven by the host, such as those caused by host deaminases, is feasible. We used machine learning to predict which positions from the SARS-CoV-2 genome will hold a recurrent mutation and which mutations will be the most recurrent. We used data from April 2021 that we separated into three sets: a training set, a validation set, and an independent test set. For the test set, we obtained a specificity value of 0.69, a sensitivity value of 0.79, and an Area Under the Curve (AUC) of 0.8, showing that the prediction of recurrent SARS-CoV-2 mutations is feasible. Subsequently, we compared our predictions with updated data from January 2022, showing that some of the false positives in our prediction model become true positives later on. The most important variables detected by the model’s Shapley Additive exPlanation (SHAP) are the nucleotide that mutates and RNA reactivity. This is consistent with the SARS-CoV-2 mutational bias pattern and the preference of some host deaminases for specific sequences and RNA secondary structures. We extend our investigation by analyzing the mutations from the variants of concern Alpha, Beta, Delta, Gamma, and Omicron. Finally, we analyzed amino acid changes by looking at the predicted recurrent mutations in the M-pro and spike proteins

Prediction of Novel Inhibitors of the Main Protease (M-pro) of SARS-CoV-2 through Consensus Docking and Drug Reposition

Since the outbreak of the COVID-19 pandemic in December 2019 and its rapid spread worldwide, the scientific community has been under pressure to react and make progress in the development of an effective treatment against the virus responsible for the disease. Here, we implement an original virtual screening (VS) protocol for repositioning approved drugs in order to predict which of them could inhibit the main protease of the virus (M-pro), a key target for antiviral drugs given its essential role in the virus’ replication. Two different libraries of approved drugs were docked against the structure of M-pro using Glide, FRED and AutoDock Vina, and only the equivalent high affinity binding modes predicted simultaneously by the three docking programs were considered to correspond to bioactive poses. In this way, we took advantage of the three sampling algorithms to generate hypothetic binding modes without relying on a single scoring function to rank the results. Seven possible SARS-CoV-2 M-pro inhibitors were predicted using this approach: Perampanel, Carprofen, Celecoxib, Alprazolam, Trovafloxacin, Sarafloxacin and ethyl biscoumacetate. Carprofen and Celecoxib have been selected by the COVID Moonshot initiative for in vitro testing; they show 3.97 and 11.90% M-pro inhibition at 50 µM, respectively

Optimal Operation of Isolated Microgrids Considering Frequency Constraints

Isolated microgrids must be able to perform autonomous operation without external grid support. This leads to a challenge when non-dispatchable generators are installed because power imbalances can produce frequency excursions compromising the system operation. This paper addresses the optimal operation of PV–battery–diesel-based microgrids taking into account the frequency constraints. Particularly, a new stochastic optimization method to maximize the PV generation while ensuring the grid frequency limits is proposed. The optimization problem was formulated including a minimum frequency constraint, which was obtained from a dynamic study considering maximum load and photovoltaic power variations. Once the optimization problem was formulated, three complete days were simulated to verify the proper behavior. Finally, the system was validated in a laboratory-scaled microgrid

Optimal operation of isolated microgrids considering frequency constraints

Isolated microgrids must be able to perform autonomous operation without external grid support. This leads to a challenge when non-dispatchable generators are installed because power imbalances can produce frequency excursions compromising the system operation. This paper addresses the optimal operation of PV–battery–diesel-based microgrids taking into account the frequency constraints. Particularly, a new stochastic optimization method to maximize the PV generation while ensuring the grid frequency limits is proposed. The optimization problem was formulated including a minimum frequency constraint, which was obtained from a dynamic study considering maximum load and photovoltaic power variations. Once the optimization problem was formulated, three complete days were simulated to verify the proper behavior. Finally, the system was validated in a laboratory-scaled microgrid.Peer Reviewe

Community asthma outbreaks due to soybean dust inhalation in Barcelona: time cluster study

Since 1981, 26 outbreaks of asthma have been detected in the city of Barcelona. The geographic clustering of cases close to the harbor led us to consider the harbor as the probable source of the outbreaks. We therefore studied the association between the unloading of 26 products from ships in the harbor and outbreaks of asthma in 1985 and 1986. All 13 asthma-epidemic days in these two years coincided with the unloading of soybeans (lower 95 percent confidence limit of the risk ratio, 7.2). Of the remaining 25 products, only the unloading of wheat was related to the epidemics of asthma, although when adjusted for the unloading of soybeans the relation was not statistically significant. High-pressure areas and mild southeasterly to southwesterly winds, which favored the movement of air from the harbor to the city, were registered on all epidemic days. Particles of starch and episperm cells that were recovered from air samplers placed in the city had morphologic characteristics identical to those of soybean particles. Furthermore, the lack of bag filters at the top of one of the harbor silos into which soybeans were unloaded allowed the release of soybean dust into the air. We conclude that these outbreaks of asthma in Barcelona were caused by the inhalation of soybean dust released during the unloading of soybeans at the city harbor.Supported in part by grants (84/1851 and 86/1847) from el Fondo de Investigación de la Seguridad Social and a grant (PA85–0016) from la Comisión Interministerial de Ciencia y TecnologíaPeer reviewe

A rectal cancer organoid platform to study individual responses to chemoradiation

Rectal cancer (RC) is a challenging disease to treat that requires chemotherapy, radiation and surgery to optimize outcomes for individual patients. No accurate model of RC exists to answer fundamental research questions relevant to patients. We established a biorepository of 65 patient-derived RC organoid cultures (tumoroids) from patients with primary, metastatic or recurrent disease. RC tumoroids retained molecular features of the tumors from which they were derived, and their ex vivo responses to clinically relevant chemotherapy and radiation treatment correlated with the clinical responses noted in individual patients' tumors. Upon engraftment into murine rectal mucosa, human RC tumoroids gave rise to invasive RC followed by metastasis to lung and liver. Importantly, engrafted tumors displayed the heterogenous sensitivity to chemotherapy observed clinically. Thus, the biology and drug sensitivity of RC clinical isolates can be efficiently interrogated using an organoid-based, ex vivo platform coupled with in vivo endoluminal propagation in animals