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Background We evaluated whether outbreaks of Zika virus (ZIKV) infection, newborn microcephaly, and Guillain-Barré syndrome (GBS) in Latin America may be detected through current surveillance systems, and how cases detected through surveillance may increase health care burden. Methods We estimated the sensitivity and specificity of surveillance case definitions using published data. We assumed a 10% ZIKV infection risk during a non-outbreak period and hypothetical increases in risk during an outbreak period. We used sensitivity and specificity estimates to correct for non-differential misclassification, and calculated a misclassification-corrected relative risk comparing both periods. To identify the smallest hypothetical increase in risk resulting in a detectable outbreak we compared the misclassification-corrected relative risk to the relative risk corresponding to the upper limit of the endemic channel (mean + 2 SD). We also estimated the proportion of false positive cases detected during the outbreak. We followed the same approach for microcephaly and GBS, but assumed the risk of ZIKV infection doubled during the outbreak, and ZIKV infection increased the risk of both diseases. Results ZIKV infection outbreaks were not detectable through non-serological surveillance. Outbreaks were detectable through serologic surveillance if infection risk increased by at least 10%, but more than 50% of all cases were false positive. Outbreaks of severe microcephaly were detected if ZIKV infection increased prevalence of this condition by at least 24.0 times. When ZIKV infection did not increase the prevalence of severe microcephaly, 34.7 to 82.5% of all cases were false positive, depending on diagnostic accuracy. GBS outbreaks were detected if ZIKV infection increased the GBS risk by at least seven times. For optimal GBS diagnosis accuracy, the proportion of false positive cases ranged from 29 to 54% and from 45 to 56% depending on the incidence of GBS mimics. Conclusions Current surveillance systems have a low probability of detecting outbreaks of ZIKV infection, severe microcephaly, and GBS, and could result in significant increases in health care burden, due to the detection of large numbers of false positive cases. In view of these limitations, Latin American countries should consider alternative options for surveillance.

PUFA might modulate inflammatory responses involved in the development of severe dengue. We aimed to examine whether serum PUFA concentrations in patients diagnosed with dengue fever (DF) were related to the risk of progression to dengue haemorrhagic fever/dengue shock syndrome (DHF/DSS). A secondary aim was to assess correlations between fatty acids (FA) and inflammatory biomarkers in patients with DF. We conducted a prospective case–control study nested within a cohort of patients who were diagnosed with DF and followed during the acute episode. We compared the distribution of individual FA (% of total FA) at onset of fever between 109 cases who progressed to DHF/DSS and 235 DF non-progressing controls using unconditional logistic regression. We estimated correlations between baseline FA and cytokine concentrations and compared FA concentrations between the acute episode and >1 year post-convalescence in a subgroup. DHA was positively related to progression to DHF/DSS (multivariable adjusted OR (AOR) for DHA in quintile 5 v. 1=5·34, 95 % CI 2·03, 14·1; P trend=0·007). Dihomo-γ-linolenic acid (DGLA) was inversely associated with progression (AOR for quintile 5 v. 1=0·30, 95 % CI 0·13, 0·69; P trend=0·007). Pentadecanoic acid concentrations were inversely related to DHF/DSS. Correlations of PUFA with cytokines at baseline were low. PUFA were lower during the acute episode than in a disease-free period. In conclusion, serum DHA in patients with DF predicts higher odds of progression to DHF/DSS whereas DGLA and pentadecanoic acid predict lower odds.

Covariance analysis from wavelet data in electroencephalographic records (EEG) was, for the first time, applied in this study to unravel information contained in the standard EEG, which was previously not taken into consideration due to the mathematical models used. The methodology discussed here could be applied to any neurological condition, including the important early stages of neurodegenerative diseases. In this study, we analyzed EEG from control (CL) participants and participants with diagnosed Parkinson’s disease (PD), who were age-matched women in an eyes-closed resting state, to test the model. PD is predicted to rise over the next decades as the population ages. Furthermore, women are more likely to undergo PD-related complications and worse disability than men. Two groups based on age were considered: under and over 60 years (PD patients <60 and >60; CL <60 and >60). Continuous Wavelet Transform and Cross Wavelet Transform were applied to determine patterns of global wavelet curves, main frequencies, and power analyses. Our results indicate that both CL age groups and PD patients <60 share a main α brainwave and PD patients >60 showed a main δ brainwave. Interestingly, power anomalies analyses show a decreasing anteroposterior gradient in CL, whereas it is increasing in PD patients, which was not previously observed. The brainwave power in PD patients <60 was higher in θ, α and β waves and in >60 group, the δ, θ and β brainwaves were predominant. This methodology offers a tool to reveal abnormal electrical brain activity unseen by a regular EEG analysis. The advent of new models that process EEG, such as the model proposed in this study, promotes renewed interest in electrophysiology of the brain to study the early stages of PD and improve understanding of the origin and progress of the disease.

In recent decades, efforts to investigate atmospheric circulation patterns have predominantly relied on either semi-empirical datasets (i.e., reanalyses) or modeled output (i.e., global climate models, GCMs). While both approaches can provide important insights, there is a need for more empirical data to supplement these approaches. In this paper, we demonstrate how the application of relatively simple calculations to the basic measurements from a standard weather balloon radiosonde can provide a vertical profile of the horizontal atmospheric mass fluxes. These mass fluxes can be resolved into their meridional (north/south) and zonal (east/west) components. This provides a new useful empirical tool for analyzing atmospheric circulations. As a case study, we analyze the results for a selected five stations along a fairly constant meridian in the North Atlantic sector from 2015–2019. For each station, we find the atmospheric mass flux profiles from the lower troposphere to mid-stratosphere are surprisingly coherent, suggesting stronger interconnection between the troposphere and stratosphere than previously thought. Although our five stations span a region nominally covered by the classical polar, Ferrel and Hadley meridional circulation cells, the results are inconsistent with those expected for polar and Ferrel cells and only partially consistent with that of a Hadley cell. However, the region is marked by very strong prevailing westerly (west to east) mass fluxes for most of the atmosphere except for the equatorial surface easterlies (“trade winds”). We suggest that the extension of the techniques of this case study to other stations and time periods could improve our understanding of atmospheric circulation patterns and their time variations.

Spent coffee is rich in bioactive compounds, including chlorogenic acid, caffeic acid, and caffeine, which offer health benefits. However, digestive processes can degrade these compounds; therefore, microencapsulation within Saccharomyces cerevisiae yeast cells offers a novel method to stabilize these bioactive compounds during digestion. In fact, it is important to mention that this technique of microencapsulation in Saccharomyces cerevisiae has not been previously applied to spent coffee extract. As a result, in this study, spent coffee extract was microencapsulated in non-plasmolyzed (NPCs) and plasmolyzed (PCs) yeast cells using the spray drying method. The physicochemical properties of the extract and the microencapsulates were characterized, and the bioaccessibility of the bioactive compounds was evaluated with digestion in vitro. Encapsulation efficiency (EE) was 38.62% for NPCs and 55.78% for PCs, with loading capacities (LCs) of 126.36 and 242 g/kg, respectively (according to Equations (1) and (2)). The presence of antioxidant compounds, identified by HPLC in spent coffee, was confirmed in the microencapsulates using FTIR. In vitro digestion assays revealed higher bioaccessibility of bioactive compounds in the intestinal phase, greater than 90%, and increased antioxidant activity in beer made with plasmolyzed microcapsules (BPM). These results suggest that yeast microencapsulation effectively stabilizes the bioactive compounds of spent coffee extract, releasing them throughout the gastrointestinal tract in vitro, mainly in the intestinal phase. Thus, microencapsulated compounds could serve as functional additives with a good percentage of intestinal bioaccessibility.

In this paper, we comprehensively explain how we built a novel implementation of the Random Forest algorithm on the High Performance Computing Cluster (HPCC) Systems Platform from LexisNexis. The algorithm was previously unavailable on that platform. Random Forest’s learning process is based on the principle of recursive partitioning and although recursion per se is not allowed in ECL (HPCC’s programming language), we were able to implement the recursive partition algorithm as an iterative split/partition process. In addition, we analyze the flaws found in our initial implementation and we thoroughly describe all the modifications required to overcome the bottleneck within the iterative split/partition process, i.e., the optimization of the data gathering of selected independent variables which are used for the node’s best-split analysis. Essentially, we describe how our initial Random Forest implementation has been optimized and has become an efficient distributed machine learning implementation for Big Data. By taking full advantage of the HPCC Systems Platform’s Big Data processing and analytics capabilities, we succeed in enhancing the data gathering method from an inefficient Pass them All and Filter approach into an effective and completely parallelized Fetching on Demand approach. Finally, based upon the results of our learning process runtime comparison between these two approaches, we confirm the speed up of our optimized Random Forest implementation.

It is uncertain whether hydrocolloid dressings, a more costly intervention than offering standard care with petrolatum, is superior to prevent pressure ulcers among hospitalized high-risk adults. Randomized, parallel-group, open-label, superiority trial with an active control group, blinded for investigators, event validators, and analysts (December 1, 2015 to December 12, 2017). Eligible patients were ≥ 18 years of age with intact skin judged as high-risk for skin ulcers (Braden scale), admitted to surgical or medical wards of two tertiary-level hospitals. Participants were randomized (1:1) to protection with hydrocolloid dressings or petrolatum. The primary outcome was the first occurrence of pressure ulcers (with post-injury photographs adjudicated by three judges) under intention-to-treat analysis. Based on prior cost analysis, and the available resources (assumed incidence of 6 ulcers/1000 patient-days in controls), inclusion of up to 1500 participants allowed to surpass a one-sided superiority threshold > 5% based on a target efficacy > 40% for dressings. We planned an economic analysis using a decision tree model based on the effectiveness of the study results from a perspective of the third payer of health care. After inclusion of 689 patients (69 events), the trial was stopped for futility after a planned interim analysis (conditional power < 0.1 for all scenarios if the trial was completed). Pressure ulcers had occurred in 34 (10.2%) patients in the intervention group [9.6 per 1000 patient-days] and 35 (9.9%) participants in the control group [7.9 per 1000 patient-days], HR = 1.07 [95% CI 0.67 to 1.71]. The estimated incremental cost for dressings (a dominated strategy) was USD 52.11 per patient. Using hydrocolloid dressings was found similar to petrolatum for preventing pressure ulcers among hospitalized high-risk patients. As it conveys additional costs, and in this study was unlikely to demonstrate enough superiority, this strategy did not overcome conventional skin care. Trial registration: ClinicalTrials.gov identifier (NCT number): NCT02565745 registered on December 1, 2015.

Limited knowledge on the prevalence and distribution of risk factors impairs the planning and implementation of cardiovascular prevention programs in the Latin American and Caribbean (LAC) region. Prevalence of hypertension, diabetes mellitus, abnormal lipoprotein levels, obesity, and smoking were estimated from individual-level patient data pooled from population-based surveys (1998-2007, n=31,009) from eight LAC countries and from a national survey of the United States (US) population (1999-2004) Age and gender specific prevalence were estimated and age-gender adjusted comparisons between both populations were conducted. Prevalence of diabetes mellitus, hypertension, and low high-density lipoprotein (HDL)-cholesterol in LAC were 5% (95% confidence interval [95% CI]: 3.4, 7.9), 20.2% (95% CI: 12.5, 31), and 53.3% (95% CI: 47, 63.4), respectively. Compared to LAC region's average, the prevalence of each risk factor tended to be lower in Peru and higher in Chile. LAC women had higher prevalence of obesity and low HDL-cholesterol than men. Obesity, hypercholesterolemia, and hypertriglyceridemia were more prevalent in the US population than in LAC population (31 vs. 16.1%, 16.8 vs. 8.9%, and 36.2 vs. 26.5%, respectively). However, the prevalence of low HDL-cholesterol was higher in LAC than in the US (53.3 vs. 33.7%). Major cardiovascular risk factors are highly prevalent in LAC region, in particular low HDL-cholesterol. In addition, marked differences do exist in this prevalence profile between LAC and the US. The observed patterns of obesity-related risk factors and their current and future impact on the burden of cardiovascular diseases remain to be explained.

Background Dengue has been prevalent in Colombia with high risk of outbreaks in various locations. While the prediction of dengue epidemics will bring significant benefits to the society, accurate forecasts have been a challenge. Given competing health demands in Colombia, it is critical to consider the effective use of the limited healthcare resources by identifying high risk areas for dengue fever. Methods The Climate Risk Factor (CRF) index was constructed based upon temperature, precipitation, and humidity. Considering the conditions necessary for vector survival and transmission behavior, elevation and population density were taken into account. An Early Warning Signal (EWS) model was developed by estimating the elasticity of the climate risk factor function to detect dengue epidemics. The climate risk factor index was further estimated at the smaller geographical unit (5 km by 5 km resolution) to identify populations at high risk. Results From January 2007 to December 2015, the Early Warning Signal model successfully detected 75% of the total number of outbreaks 1 ~ 5 months ahead of time, 12.5% in the same month, and missed 12.5% of all outbreaks. The climate risk factors showed that populations at high risk are concentrated in the Western part of Colombia where more suitable climate conditions for vector mosquitoes and the high population level were observed compared to the East. Conclusions This study concludes that it is possible to detect dengue outbreaks ahead of time and identify populations at high risk for various disease prevention activities based upon observed climate and non-climate information. The study outcomes can be used to minimize potential societal losses by prioritizing limited healthcare services and resources, as well as by conducting vector control activities prior to experiencing epidemics.

Observed changes in Northern Hemisphere snow cover from satellite records were compared to those predicted by all available Coupled Model Intercomparison Project Phase 5 (“CMIP5”) climate models over the duration of the satellite’s records, i.e., 1967–2018. A total of 196 climate model runs were analyzed (taken from 24 climate models). Separate analyses were conducted for the annual averages and for each of the seasons (winter, spring, summer, and autumn/fall). A longer record (1922–2018) for the spring season which combines ground-based measurements with satellite measurements was also compared to the model outputs. The climate models were found to poorly explain the observed trends. While the models suggest snow cover should have steadily decreased for all four seasons, only spring and summer exhibited a long-term decrease, and the pattern of the observed decreases for these seasons was quite different from the modelled predictions. Moreover, the observed trends for autumn and winter suggest a long-term increase, although these trends were not statistically significant. Possible explanations for the poor performance of the climate models are discussed.