Star Formation Rates for photometric samples of galaxies using machine learning methods

Star Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based on the photometric estimation of global SFRs for large samples of galaxies, by using methods such as automatic parameter space optimisation, and supervised Machine Learning models. We demonstrate that, with such approach, accurate multi-band photometry allows to estimate reliable SFRs. We also investigate how the use of photometric rather than spectroscopic redshifts, affects the accuracy of derived global SFRs. Finally, we provide a publicly available catalogue of SFRs for more than 27 million galaxies extracted from the Sloan Digital Sky survey Data Release 7. The catalogue is available through the Vizier facility at the following link ftp://cdsarc.u-strasbg.fr/pub/cats/J/MNRAS/486/1377

Photometric SFR using machine learning

Star formation rates (SFRs) are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based on the photometric estimation of global SFRs for large samples of galaxies, by using methods such as automatic parameter space optimisation, and supervised machine learning models. We demonstrate that, with such approach, accurate multiband photometry allows to estimate reliable SFRs. We also investigate how the use of photometric rather than spectroscopic redshifts, affects the accuracy of derived global SFRs. Finally, we provide a publicly available catalogue of SFRs for more than 27 million galaxies extracted from the Sloan Digital Sky Survey Data Release 7. The catalogue will be made available through the Vizier facility

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields

Arterial hypertension and diabetes mellitus in covid-19 patients: What is known by gender differences?

Background. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has infected >160 million people around the world. Hypertension (HT), chronic heart disease (CHD), and diabetes mellitus (DM) increase susceptibility to SARS-CoV-2 infection. Aims. We designed this retrospective study to assess the gender differences in hypertensive diabetic SARS-CoV-2 patients. We reported data, by gender differences, on the inflammatory status, on the hospital stays, intensive care unit (ICU) admission, Rx and CT report, and therapy. Methods. We enrolled 1014 patients with confirmed COVID-19 admitted into different Hospitals of Campania from 26 March to 30 June, 2020. All patients were allocated into two groups: diabetic-hypertensive group (DM-HT group) that includes 556 patients affected by diabetes mellitus and arterial hypertension and the non-diabetic-non-hypertensive group (non-DM, non-HT group) comprising 458 patients. The clinical outcomes (i.e., discharges, mortality, length of stay, therapy, and admission to intensive care) were monitored up to June 30, 2020. Results. We described, in the DM-HT group, higher proportion of cardiopathy ischemic (CHD) (47.5% vs. 14.8%, respectively; p < 0.0001) and lung diseases in females compared to male subjects (34.8% vs. 18.5%, respectively; p < 0.0001). In male subjects, we observed higher proportion of kidney diseases (CKD) (11% vs. 0.01%, respectively; p < 0.0001), a higher hospital stay compared to female subjects (22 days vs. 17 days, respectively, p < 0.0001), a higher admission in ICU (66.9% vs. 12.8%, respectively, p < 0.0001), and higher death rate (17.3% vs. 10.7%, respectively, p < 0.0001). Conclusion. These data confirm that male subjects, compared to female subjects, have a higher hospital stay, a higher admission to ICU, and higher death rate

Chronic Osteomyelitis With Proliferative Periostitis of the Mandible in a Child Report of a Case Managed by Immunosuppressive Treatment:Report of a Case Managed by Immunosuppressive Treatment

Background: Osteomyelitis with proliferative periostitis is a relatively uncommon inflammatory condition of the jaws, mainly characterized by periosteal formation of reactive bone. It primarily affects children and adolescences, also referred to as Garre's osteomyelitis, more frequently involving the molar region of the mandible. Cases lacking an obvious source of infection may have an immunologically mediated etiopathogenesis, falling under the spectrum of primary chronic osteomyelitis or chronic recurrent multifocal osteomyelitis (CRMO). Case report: Herein, we present a case of chronic osteomyelitis in a 6.5-year-old girl, who suffered from recurrent painful episodes of swelling of the mandible for the last 2 years, previously requiring hospitalization and administration of intravenous (IV) antibiotics and NSAIDs with limited responsiveness. The biopsy showed features consistent with osteomyelitis with proliferative periostitis. The patient was initially managed with an IV combination antibiotic regimen with only partial improvement. The possibility of an autoimmune mechanism in the context of primary chronic osteomyelitis or CRMO was considered, and immunosuppressive therapy (TNF inhibitor etanercept along with corticosteroids and methotrexate) was administered, resulting in clinical resolution. Conclusions: Osteomyelitis and its childhood variants are relatively rare and their management presents several challenges. Although typically treated with administration of antibiotics, possibly along with surgical intervention, other treatment modalities may be necessary for resilient and persistent cases. In a subset of cases, especially in the absence of local infectious factors, immunologically mediated mechanisms may play an important role and appropriate immunosuppressive therapy may be effective

ccSOL omics: a webserver for solubility prediction of endogenous and heterologous expression in Escherichia coli

SUMMARY: Here we introduce ccSOL omics, a webserver for large-scale calculations of protein solubility. Our method allows (i) proteome-wide predictions; (ii) identification of soluble fragments within each sequences; (iii) exhaustive single-point mutation analysis.RESULTS: Using coil/disorder, hydrophobicity, hydrophilicity, β-sheet and α-helix propensities, we built a predictor of protein solubility. Our approach shows an accuracy of 79% on the training set (36 990 Target Track entries). Validation on three independent sets indicates that ccSOL omics discriminates soluble and insoluble proteins with an accuracy of 74% on 31 760 proteins sharing <30% sequence similarity.AVAILABILITY AND IMPLEMENTATION: ccSOL omics can be freely accessed on the web at http://s.tartaglialab.com/page/ccsol_group. Documentation and tutorial are available at http://s.tartaglialab.com/static_files/shared/tutorial_ccsol_omics.html.CONTACT: [email protected] INFORMATION: Supplementary data are available at Bioinformatics online

What is the optimal timing for implant placement in oral cancer patients? A scoping literature review:A scoping literature review

Background Oral cancer patients can benefit from dental implant placement. Traditionally, implants are placed after completing oncologic treatment (secondary implant placement). Implant placement during ablative surgery (primary placement) in oral cancer patients seems beneficial in terms of early start of oral rehabilitation and limiting additional surgical interventions. Guidelines on the ideal timing of implant placement in oral cancer patients are missing. Objective To perform a scoping literature review on studies examining the timing of dental implant placement in oral cancer patients and propose a clinical practice recommendations guideline. Methods A literature search for studies dealing with primary and/or secondary implant placement in MEDLINE was conducted (last search December 27, 2019). The primary outcome was 5-year implant survival. Results Sixteen out of 808 studies were considered eligible. Both primary and secondary implant placement showed acceptable overall implant survival ratios with a higher pooled 5-year implant survival rate for primary implant placement 92.8% (95% CI: 87.1%-98.5%) than secondary placed implants (86.4%, 95% CI: 77.0%-95.8%). Primary implant placement is accompanied by earlier prosthetic rehabilitation after tumor surgery. Conclusion Patients with oral cancer greatly benefit from, preferably primary placed, dental implants in their prosthetic rehabilitation. The combination of tumor surgery with implant placement in native mandibular bone should be provided as standard care