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Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RSCB PDB) provides a wide range of digital data regarding biology and biomedicine. This huge internet resource involves a wide range of important biological data, obtained from experiments around the globe by different scientists. The Worldwide Protein Data Bank (wwPDB) represents a brilliant collection of 3D structure data associated with important and vital biomolecules including nucleic acids (RNAs and DNAs) and proteins. Moreover, this database accumulates knowledge regarding function and evolution of biomacromolecules which supports different disciplines such as biotechnology. 3D structure, functional characteristics and phylogenetic properties of biomacromolecules give a deep understanding of the biomolecules’ characteristics. An important advantage of the wwPDB database is the data updating time, which is done every week. This updating process helps users to have the newest data and information for their projects. The data and information in wwPDB can be a great support to have an accurate imagination and illustrations of the biomacromolecules in biotechnology. As demonstrated by the SARS-CoV-2 pandemic, rapidly reliable and accessible biological data for microbiology, immunology, vaccinology, and drug development are critical to address many healthcare-related challenges that are facing humanity. The aim of this paper is to introduce the readers to wwPDB, and to highlight the importance of this database in biotechnology, with the expectation that the number of scientists interested in the utilization of Protein Data Bank’s resources will increase substantially in the coming years.


SLEEP ◽  
2021 ◽  
Author(s):  
Emanuela Postiglione ◽  
Lucie Barateau ◽  
Fabio Pizza ◽  
Régis Lopez ◽  
Elena Antelmi ◽  
...  

Abstract Study objectives To describe the phenotype of narcolepsy with intermediate cerebrospinal hypocretin-1 levels (CSF hcrt-1). Methods From 1600 consecutive patients with narcolepsy from Bologna and Montpellier sleep centers we selected patients with intermediate CSF hcrt-1 levels (110-200 pg/ml). Clinical, neurophysiological and biological data were contrasted for the presence of cataplexy, HLA-DQB1*06:02, and median CSF hcrt-1 levels (149.34 pg/mL). Results Forty-five (55% males, aged 35 ± 17 years) patients (2.8% of all cases) were included. Thirty-three (73%) were HLA-DQB1*06:02, 29 (64%) reported cataplexy (21, 72.4% with typical features), and 5 (11%) had presumed secondary etiology. Cataplexy was associated with other core narcolepsy symptoms, increased sleep onset REM periods, and nocturnal sleep disruption. Cataplexy and irrepressible daytime sleep were more frequent in HLA DQB1*06:02 positive patients. Lower CSF hcrt-1 levels were associated with hallucinations. Conclusion Narcolepsy with intermediate CSF hcrt-1 level is a rare condition with heterogeneous phenotype. HLA DQB1*06:02 and lower CSF hcrt-1 were associated with typical narcolepsy features, calling for future research to distinguish incomplete from secondary narcolepsy forms.


Zootaxa ◽  
2021 ◽  
Vol 5081 (2) ◽  
pp. 203-222
Author(s):  
ENRIQUE MEDIANERO ◽  
JAMES A. NICHOLLS ◽  
GRAHAM N. STONE ◽  
JOSÉ LUIS NIEVES-ALDREY

A new genus, Prokius Nieves Aldrey, Medianero & Nicholls, gen. nov., and two new species of oak gall wasps (Hymenoptera: Cynipidae: Cynipini), Prokius cambrai Medianero & Nieves-Aldrey sp. nov. and Prokius lisethiae Medianero & Nieves-Aldrey sp. nov., are described from adults reared from galls on Quercus bumelioides Liebm (Fagaceae, sect. Quercus, white oaks) collected in Panama. The new genus is phylogenetically and morphologically close to Dros Kinsey and forms part of a large clade that includes species from several other genera that appear to require revision, including Andricus Hartig and Phylloteras Ashmead. Molecular and morphological data, diagnostic characters, gall descriptions, distribution and biological data of the new genus and the new species are given. This new genus represents the fourth recently described genus of Cynipidae endemic to the Neotropical region.  


Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3175
Author(s):  
Nathalie Verdière ◽  
Oscar Navarro ◽  
Aude Naud ◽  
Alexandre Berred ◽  
Damienne Provitolo

In this paper, we investigate the calibration of a mathematical model describing different behaviors occurring during a natural, a societal, or a technological catastrophe. This model was developed in collaboration with geographers and psychologists. To collect information on the level of stress, psychologists of the LPPL laboratory of Nantes (France) led virtual reality experiments. These experiments consisted in immersing individuals in a situation of catastrophe and measuring their electrocardiogram. From the physical and biological data collected, we present the methodology to calibrate the behavioral model. First, a theoretical analysis is carried out to determine (i) if the parameters can be uniquely estimated, (ii) the minimal number of discrete measurements required for the estimation. Then, from these analyses, an estimation procedure is performed to calibrate the mathematical model or at least to have an order magnitude of the model parameters. Through this work, we will show from simulations that the proposed system makes it possible to apprehend non observable human processes.


Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6210
Author(s):  
Sébastien Benzekry ◽  
Mathieu Grangeon ◽  
Mélanie Karlsen ◽  
Maria Alexa ◽  
Isabella Bicalho-Frazeto ◽  
...  

Background: Immune checkpoint inhibitors (ICIs) are now a therapeutic standard in advanced non-small cell lung cancer (NSCLC), but strong predictive markers for ICIs efficacy are still lacking. We evaluated machine learning models built on simple clinical and biological data to individually predict response to ICIs. Methods: Patients with metastatic NSCLC who received ICI in second line or later were included. We collected clinical and hematological data and studied the association of this data with disease control rate (DCR), progression free survival (PFS) and overall survival (OS). Multiple machine learning (ML) algorithms were assessed for their ability to predict response. Results: Overall, 298 patients were enrolled. The overall response rate and DCR were 15.3% and 53%, respectively. Median PFS and OS were 3.3 and 11.4 months, respectively. In multivariable analysis, DCR was significantly associated with performance status (PS) and hemoglobin level (OR 0.58, p < 0.0001; OR 1.8, p < 0.001). These variables were also associated with PFS and OS and ranked top in random forest-based feature importance. Neutrophil-to-lymphocyte ratio was also associated with DCR, PFS and OS. The best ML algorithm was a random forest. It could predict DCR with satisfactory efficacy based on these three variables. Ten-fold cross-validated performances were: accuracy 0.68 ± 0.04, sensitivity 0.58 ± 0.08; specificity 0.78 ± 0.06; positive predictive value 0.70 ± 0.08; negative predictive value 0.68 ± 0.06; AUC 0.74 ± 0.03. Conclusion: Combination of simple clinical and biological data could accurately predict disease control rate at the individual level.


2021 ◽  
Author(s):  
Sebastien Benzekry ◽  
Mathieu Grangeon ◽  
Melanie Karlsen ◽  
Maria Alexa ◽  
Isabella Bicalho-Frazeto ◽  
...  

Background Immune checkpoint inhibitors (ICIs) are now a therapeutic standard in advanced non- small cell lung cancer (NSCLC), but strong predictive markers for ICIs efficacy are still lacking. We evaluated machine learning models built on simple clinical and biological data to individually predict response to ICIs. Methods Patients with metastatic NSCLC who received ICI in second line or later were included. We collected clinical and hematological data and studied the association of this data with disease control rate (DCR), progression free survival (PFS) and overall survival (OS). Multiple machine learning (ML) algorithms were assessed for their ability to predict response. Results Overall, 298 patients were enrolled. The overall response rate and DCR were 15.3 % and 53%, respectively. Median PFS and OS were 3.3 and 11.4 months, respectively. In multivariable analysis, DCR was significantly associated with performance status (PS) and hemoglobin level (OR 0.58, p<0.0001; OR 1.8, p<0.001). These variables were also associated with PFS and OS and ranked top in random forest-based feature importance. Neutrophils-to-lymphocytes ratio was also associated with DCR, PFS and OS. The best ML algorithm was a random forest. It could predict DCR with satisfactory efficacy based on these three variables. Ten-fold cross-validated performances were: accuracy 0.68 +/- 0.04, sensitivity 0.58 +/- 0.08; specificity 0.78 +/- 0.06; positive predictive value 0.70 +/- 0.08; negative predictive value 0.68 +/- 0.06; AUC 0.74 +/- 0.03. Conclusion Combination of simple clinical and biological data could accurately predict disease control rate at the individual level.


2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 676-676
Author(s):  
Samuel Beck ◽  
Jun-Yeong Lee ◽  
Jarod Rollins

Abstract In this era of Big Data, the volume of biological data is growing exponentially. Systematic profiling and analysis of these data will provide a new insight into biology and human health. Among diverse types of biological data, gene expression data closely mirror both the static phenotypes and the dynamic changes in biological systems. Drug-to-drug or drug-to-disease comparison of gene expression signature allows repurposing/repositioning of existing pharmaceutics to treat additional diseases that, in turn, provides a rapid and cost-effective approach for drug discovery. Thanks to technological advances, gene expression profiling by mRNA-seq became a routine tool to address all aspects of the problem in modern biological research. Here, we present how drug repositioning using published mRNA-seq data can provide unbiased and applicable pharmaco-chemical intervention strategies to human diseases and aging. In specifics, we profiled over a half-million gene expression profiling data generated from various contexts, and using this, we screened conditions that can suppress age-associated gene expression changes. As a result, our analysis identified various previously validated aging intervention strategies as positive hits. Furthermore, our analysis also predicted a novel group of chemicals that has not been studied from an aging context, and this indeed significantly extended the life span in model animals. Taken together, our data demonstrate that our community knowledge-guided in silico drug-discovery pipeline provides a useful and effective tool to identify the novel aging intervention strategy.


2021 ◽  
Vol 15 (4) ◽  
Author(s):  
Roberta De Vito ◽  
Ruggero Bellio ◽  
Lorenzo Trippa ◽  
Giovanni Parmigiani

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