“…METHODS: Twelve populations of A. aurantiaca were sampled in large (4), medium (3), and small (5) forest fragments in the lowland tropical rainforest at Los Tuxtlas region. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Being male (adjusted OR 2.0; 95% CI 1.2-3.4); being a young adult <30 years (aOR 2.1; 95% CI 1.3–3.4); recent forest travel (aOR 2.8; 95% CI 1.6–4.8); and, a history of malaria (aOR 5.2; 95% CI 2.5–10.7) were independent risk factors for parasitaemia. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Similar decreases in the main malaria indicators were observed in the three epidemiological strata (coastal, forest, savannah). All-cause admissions increased significantly in patients covered by the National Health Insurance Scheme (NHIS) compared to the non-insured. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…RESULTS: Some of the identified strengths of ebola virus include: Ebola virus is an RNA virus with inherent capability to mutate, reassort and recombine to generate mutant or reassortant virulent strains; Ebola virus has a broad cellular tropism; Natural Reservoir of ebola virus is unconfirmed but fruit bats, arthropods, and plants are hypothesized; Ebola virus primarily targets and selectively destroys the immune system; Ebola viruses possess accessory proteins that inhibits the host’ immune responses; Secreted glycoprotein (sGP), a truncated soluble protein that triggers immune activation and increased vascular permeability is uniquely associated with Ebola virus only; Ability to effectively cross the species barrier and establish productive infection in humans, non human primates, and other mammals; Ebola virus attacks every part of the human body; The Weaknesses include: Ebola virus transmission and persistence is severely limited by its virulence; Ebola virus essentially requires host encoded protein Niemann–Pick C1 (NPC1) for host’s cell’ entry; Ebola virus essentially requires host encoded proteins (TIM-1) for cell’ entry; Relative abundance of Ebolavirus Nucleoprotein than the other virion components; The Opportunities harnessed by ebola virus include: Lack of infection control practices in African health-care facilities and paucity of health infrastructures, especially in the endemic zones; Permissiveness of circulating Monocytes, Macrophages and dendritic cells in virus mobilization and dissemination; Collection, consumption and trade of wild games (bushmeats); Pertubation and drastic changes in forest ecosystems present opportunities for Ebola virus; Use of dogs in hunting predisposes man and animals to inter-species contact; Poverty, malnutrition, crowding, social disorder, mobility and political instability; Ease of travel and aviation as potentials for global spread; Possible mechanical transmission by arthropod vectors; No vaccines or therapeutics are yet approved for human treatment; The Threats to ebola virus include: Avoidance of direct contact with infected blood and other bodily fluids of infected patient; Appropriate and correct burial practices; Adoption of barrier Nursing; Improved surveillance to prevent potential spread of epidemic; Making Available Rapid laboratory equipment and procedures for prompt detection (ELISA, Western Blot, PCR); Sterilization or disinfection of equipment and safe disposal of instrument; Prompt hospitalization, isolation and quarantine of infected individual; Active contact tracing and monitoring, among others. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…METHODS: Beginning in 2001, a total of 5145 overweight or obese people with type 2 diabetes, aged 45–76 years, participating in the multicentre Look AHEAD (Action for Health in Diabetes) study were randomised to ILI (n = 2570) or to a diabetes support and education (DSE) control group (n = 2575) using a web-based management system at the study coordinating centre at Wake Forest School of Medicine (Winston-Salem, NC, USA). …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Statistical heterogeneity was assessed by visual inspection of forest plots and by calculating inconsistency (I(2)) for traditional meta-analyses and diversity (D(2)) for TSA. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The CNN models performed better on all classification tasks than k-nearest neighbors, naïve Bayes, support vector machines, or random forest. Detailed comparison between support vector machines and the CNN models showed that the major contributor to the overall superiority of the CNN models is the improvement on recall, especially for classes with low occurrence. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The internal binary classifier was a random forest model within a 3×10–fold cross-validated recursive feature elimination (RFE) resampling which selected the final predictor subset that best differentiated adherers versus nonadherers. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Analysis was performed using Random Survival Forests (RSF)/ multivariable Cox Proportional-Hazards models. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Multivariate logistic regression (MLR), least absolute shrinkage and selection operator–type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Machine learning was used to generate a model of participants who would complete the intervention. Random forest models were trained at days 1, 3, and 7 of the intervention, and the generalizability of the models was assessed using leave-one-out cross-validation. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted trees, and random forest models). RESULTS: On the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

Enlace del recurso
Enlace del recurso
Enlace del recurso

“…J.E.G. reports grants from the National Center for Responsible Gaming, Forest Pharmaceuticals, Takeda, Brainsway, and Roche and others from Oxford Press, Norton, McGraw-Hill and American Psychiatric Publishing outside of the submitted work.…”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Strains isolated from conventional horticulture (CH) soil composed three bacterial genera, suggesting a lower diversity for the diazotrophs/N scavenger bacterial community than that observed for soils under organic management (ORG) or secondary forest coverture (SF). Conversely, diazotrophs/N scavenger strains from tomato plants grown in CH soil comprised a higher number of bacterial genera than did strains isolated from tomato plants grown in ORG or SF soils. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…MAIN OUTCOMES AND MEASURES: All-cause mortality, HF hospitalization, top cost decile, and home days loss greater than 25% were modeled using logistic regression, least absolute shrinkage and selection operation regression, classification and regression trees, random forests, and gradient-boosted modeling (GBM). All models were trained using data from network 1 and tested in network 2. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…We did sparse random forest classifier analyses on the bacterial data, viral data, metadata, and the combination of all three datasets to distinguish cases from controls. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Statistical analysis was based on a random effects model from which forest plots were generated. Effect sizes were reported as the standardized mean difference (SMD) with 95% confidence intervals (CI). …”
Enlace del recurso
Enlace del recurso

“…In the next step, Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Gaussian Naïve Bayes (GNB), and Random Forest (RF) classifiers with 10-fold cross validation were then trained on those features with GAF category at follow-up used as the binary label column. …”
Enlace del recurso
Enlace del recurso

“…Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso