Study of Traffic Forecast for Intelligent Transportation System

The number of cities, particularly those with advanced infrastructure, is increasing rapidly. There has been a steady increase in the number of automobiles on the road, which has led to severe congestion and wasted time and money. Increasing the number of roads or lanes available is a costly solution to traffic congestion. The primary objective of this research was to examine the traffic pattern using machine learning technologies, which is the optimal method in such situations. The primary objective was to compare the LSTM and ARIMA algorithms across 15-minute intervals, which is confirmed by calculating the observed error. The data obtained was then normalized and filtered to meet the requirements of this study, and machine learning methods are used to make predictions about traffic volume and average speed. Predictions from regression models can be utilized for decision-making. A prediction is a statement about how a variable will change or stay the same. A decision, on the other hand, is what to do in response to a prediction. The LSTM model has less error from the start of the project, while the ARIMA model performance improves with time or at the latter stage. The percentage error of the LSTM model is about 15% less than that of the ARIMA model, hence it can conclude that the LSTM model will perform better than the ARIMA model

Using mass spectrometry imaging to map fluxes quantitatively in the tumor ecosystem

Tumors are comprised of a multitude of cell types spanning different microenvironments. Mass spectrometry imaging (MSI) has the potential to identify metabolic patterns within the tumor ecosystem and surrounding tissues, but conventional workflows have not yet fully integrated the breadth of experimental techniques in metabolomics. Here, we combine MSI, stable isotope labeling, and a spatial variant of Isotopologue Spectral Analysis to map distributions of metabolite abundances, nutrient contributions, and metabolic turnover fluxes across the brains of mice harboring GL261 glioma, a widely used model for glioblastoma. When integrated with MSI, the combination of ion mobility, desorption electrospray ionization, and matrix assisted laser desorption ionization reveals alterations in multiple anabolic pathways. De novo fatty acid synthesis flux is increased by approximately 3-fold in glioma relative to surrounding healthy tissue. Fatty acid elongation flux is elevated even higher at 8-fold relative to surrounding healthy tissue and highlights the importance of elongase activity in glioma

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determines disease severity. Through analysis of longitudinal samples, we confirm that most of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19

GLACIOLOGY AN INTERESTING AND ADVENTUROUS CAREER

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Double doping: a method to decrease dislocation densities in LEC InP crystals

In this paper we shall report on the characteristics of LEC InP double doped with Cd and Te. Unlike (Cd,S)-doped material, (Cd, Te)-doped InP is p-type, with low-carrier concentration. In the following, the results of structural and electrical analysis carried out on several LEC InP crystals co-doped with Cd and Te are presented. The role of multiple doping as a strategy for dislocation reduction will also be discussed

Gemcitabine (A Chemotherapy Medication) Vs. Phytochemicals Against Non-Small Cell Lung Cancer – A Computational Approach

Non-small cell lung cancer is a type of lung cancer, where healthy cells in the lungs grow out of control forming a tumor or a nodule. Although small cell lung cancer and non-small cell lung cancer share few common symptoms and causes, their rate of spread or metastasis differs significantly. Targeted gene therapies involving uses of targeted drugs against specific gene or protein are increasingly being used for the treatment of lung cancer. This study aims at understanding the efficacy of the bioactive compounds over the already existing chemotherapy drug- Gemcitabine. For this study, the target receptor proteins of Non-Small Cell Lung Cancer (NSCLC) were retrieved from Protein Data Bank (PDB) and the ligand compounds were retrieved using PubChem NCBI. Based on various research studies, four target proteins from NSCLC and sixteen bioactive compounds of Curculigoorchioides (Black musale) were selected. And their effect of bioactive compounds has been studied by means of in-silico approach and further the identified potential active compounds have been compared with control. This comparative in-silico study has predicted that the bioactive principle of Curculigoorchioides has better efficacy against cancer receptors and can considered as an effective alternative drug for cancer treatment. Concluding, the present study will be useful in future for designing novel therapeutic plant-based drug with higher efficacy for the treatment of lung cancer

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determines disease severity. Through analysis of longitudinal samples, we confirm that most of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19