Independent Validation Sample Research Articles

Genetically Determined Levels of Circulating Cytokines and Risk of Stroke.

Cytokines and growth factors have been implicated in the initiation and propagation of vascular disease. Observational studies have shown associations of their circulating levels with stroke. Our objective was to explore whether genetically determined circulating levels of cytokines and growth factors are associated with stroke and its etiologic subtypes by conducting a 2-sample Mendelian randomization (MR) study. Genetic instruments for 41 cytokines and growth factors were obtained from a genome-wide association study of 8293 healthy adults. Their associations with stroke and stroke subtypes were evaluated in the MEGASTROKE genome-wide association study data set (67 162 cases; 454 450 controls) applying inverse variance-weighted meta-analysis, weighted-median analysis, Mendelian randomization-Egger regression, and multivariable Mendelian randomization. The UK Biobank cohort was used as an independent validation sample (4985 cases; 364 434 controls). Genetic instruments for monocyte chemoattractant protein-1 (MCP-1/CCL2) were further tested for association with etiologically related vascular traits by using publicly available genome-wide association study data. Genetic predisposition to higher MCP-1 levels was associated with higher risk of any stroke (odds ratio [OR] per 1 SD increase, 1.06; 95% CI, 1.02-1.09; P=0.0009), any ischemic stroke (OR, 1.06; 95% CI, 1.02-1.10; P=0.002), large-artery stroke (OR, 1.19; 95% CI, 1.09-1.30; P=0.0002), and cardioembolic stroke (OR, 1.14; 95% CI, 1.06-1.23; P=0.0004), but not with small-vessel stroke or intracerebral hemorrhage. The results were stable in sensitivity analyses and remained significant after adjustment for cardiovascular risk factors. Analyses in the UK Biobank showed similar associations for available phenotypes (any stroke: OR, 1.08; 95% CI, 0.99-1.17; P=0.09; any ischemic stroke: OR, 1.07; 95% CI, 0.97-1.18; P=0.17). Genetically determined higher MCP-1 levels were further associated with coronary artery disease (OR, 1.04; 95% CI, 1.00-1.08; P=0.04) and myocardial infarction (OR, 1.05; 95% CI, 1.01-1.09; P=0.02), but not with atrial fibrillation. A meta-analysis of observational studies showed higher circulating MCP-1 levels in patients with stroke in comparison with controls. Genetic predisposition to elevated circulating levels of MCP-1 is associated with higher risk of stroke, in particular with large-artery stroke and cardioembolic stroke. Whether targeting MCP-1 or its receptors can lower stroke incidence requires further study.

Performance comparison between a miniaturized and a conventional near infrared reflectance (NIR) spectrometer for characterizing soil carbon and nitrogen

Miniaturized near infrared spectrometers are now available, at more affordable prices than conventional spectrometers, but their performances have been poorly studied to date. This paper aimed at comparing the performances of the JDSU MicroNIR 2200 spectrophotometer (weight < 0.1 kg) with those of a conventional bench-top instrument for predicting carbon and nitrogen contents in laboratory conditions, on a range of representative Malagasy soils.Though its noticeably narrower and less resolved spectra (1151–2186 nm at 8.15 nm step vs. 1100–2498 nm at 2 nm step), the microspectrometer yielded predictions in independent validation that were almost as accurate as those of the conventional instrument (standard errors of prediction were 4.6 vs. 3.4 gC kg−1, but 3.9 vs. 3.4 gC kg−1 after bias correction, and 0.36 vs. 0.35 gN kg−1, respectively). Due to noisy features, the MicroNIR spectra needed mathematical pretreatment (e.g. standard normal variate SNV), and bias correction for C, for providing accurate predictions, while the raw absorbance spectra from the conventional instrument did not. Furthermore, building multivariate models with MicroNIR spectra required less latent variables than with their conventional counterparts, and these models were less prone to performance degradation when applied to independent validation samples. Fitting the spectra of the conventional instrument to those of the MicroNIR (1150–2182 nm at 2 or 8 nm step) showed that (moderately) less accurate MicroNIR predictions could be firstly attributed to narrower spectral range rather than to poorer resolution.Considering their performances, such microspectrometers could thus represent a cost-effective alternative to conventional spectrometers. They have now to be tested in field conditions.

Anthropometric prediction of DXA-measured body composition in female team handball players.

BackgroundThe relevance of body composition (BC) to performance in sport has long been appreciated with special concern on the total and regional proportion of fat and muscle. Dual-energy X-ray absorptiometry (DXA) is able to accurately measure BC, but it may not be easily available in practice; anthropometry has long been used as a simple and inexpensive field method to objectively assess BC. The aim of this study was twofold: first, to develop and validate a sport-specific anthropometric predictive equation for total body fat mass (FM) and lean mass components in female handball players to be used in the sport setting; second, to cross-validate in female team handball players several independently developed, predictive equations for BC in female athletes.MethodsA total of 85 female team handball players (30 wings, 31 backs, 14 pivots, 10 goalkeepers) of different competitive levels underwent anthropometry and a whole-body DXA scan. Multiple linear regression analysis was used to develop predictive equations in a derivation sample (n = 60) of randomly selected players using demographic and anthropometric variables. The developed equations were used to predict DXA outcomes in an independent validation sample (n = 25).ResultsStatistically significant (P < 0.001) models were developed for total body FM (adjusted R2 = 0.943, standard error of the estimate, SEE = 1,379 g), percentage FM (adjusted R2 = 0.877, SEE = 2.00%), fat-free soft tissue mass (FFSTM) (adjusted R2 = 0.834, SEE = 2,412 g), fat-free mass (FFSTM + bone mineral content; adjusted R2 = 0.829, SEE = 2,579 g). All models were robust to collinearity. Each developed equation was successfully validated in the remaining 25 players using correlation analysis, mean signed difference, t-test, and Bland–Altman plot. The whole dataset of team handball players (n = 85) was used to cross-validate several predictive equations independently developed by others in female athletes. Equations significantly (P < 0.001 for all; t-test) over- or underestimated the corresponding DXA measurements.DiscussionIt is concluded that in team female handball players the anthropometric equations presented herein are able to estimate body fat and FFSTM with accuracy. Several BC predictive anthropometric equations developed in different female athletic populations revealed inaccurate when tested in team handball players. These results should be of use for coaches, physical trainers, and nutritionists when evaluating the physical status of female team handball players.

External Validation of a Deep Learning-Based Auto-Segmentation Method for Radiation Therapy

Automated delineation of the organs-at-risk (OARs) has high application value for treatment planning in radiotherapy due to the efficiency and consistency. We previously reported a Deep Dilated Convolutional Neural Networks (DDCNN)-based auto-segmentation method for radiotherapy of rectal cancer. The DDCNN model was constructed and implemented with data from a single institution. In this study, we investigated the reproducibility of DDCNN using independent validation samples from a public dataset for external validation. The DDCNN was trained using data of 218 patients from institution A. We performed model testing with 60 patients from institution A (internal validation dataset), and 18 patients with expert consensus delineations from a public dataset (external validation dataset). All patients from institution A were set up with the prone position. While the patients from the external validation dataset were set up in the supine position. The images were processed geometrically to be consistent with the positioning required by the model (prone vs. supine). A histogram normalization operation was applied to ensure the consistency of the imaging parameters. The performance of DDCNN was evaluated on segmentation of the bladder, left femoral head, and right femoral head. The segmentation accuracy was consistent for both internal and external cases. The Dice Similarity Coefficient (DSC) values were 0.93±0.03 vs. 0.89±0.07 for the bladder, 0.92±0.04 vs. 0.90±0.05 for the left femur, and 0.92±0.03 vs. 0.91±0.05 for the right femur, respectively. For the bladder, the accuracy of segmentation from external validation was significantly worse than that from internal validation (p<0.05). However, the lower dice value was due to the different filling levels of the bladder and the contrast agents remaining in the bladder in some cases of external validation set. We selected the cases that had high consistency with the training set through visual inspection and evaluated the segmentation accuracy. The DSC value was 0.92±0.03, which was comparable with that of the internal validation. Despite different patient positioning, patient sizes, and imaging parameters, DDCNN showed overall comparable agreements on segmentation of the OARs with clear boundaries between the internal and external validation sets. The external validation demonstrates that the deep learning method has the potential for general application in radiotherapy, even though its performance in segmentation of organs with considerable inter- and intra-observer variability needs further investigation.

Outcome after stroke attributable to baseline factors—The PROSpective Cohort with Incident Stroke (PROSCIS)

BackgroundThe impact of risk factors on poor outcome after ischemic stroke is well known, but estimating the amount of poor outcome attributable to single factors is challenging in presence of multimorbidity. We aim to compare population attributable risk estimates obtained from different statistical approaches regarding their consistency. We use a real-life data set from the PROSCIS study to identify predictors for mortality and functional impairment one year after first-ever ischemic stroke and quantify their contribution to poor outcome using population attributable risks.MethodsThe PROSpective Cohort with Incident Stroke (PROSCIS) is a prospective observational hospital-based cohort study of patients after first-ever stroke conducted independently in Berlin (PROSCIS-B) and Munich (PROSCIS-M). The association of baseline factors with poor outcome one year after stroke in PROSCIS-B was analysed using multiple logistic regression analysis and population attributable risks were calculated, which were estimated using sequential population attributable risk based on a multiple generalized additive regression model, doubly robust estimation, as well as using average sequential population attributable risk. Findings were reproduced in an independent validation sample from PROSCIS-M.ResultsOut of 507 patients with available outcome information after 12 months in PROSCIS-B, 20.5% suffered from poor outcome. Factors associated with poor outcome were age, pre-stroke physical disability, stroke severity (NIHSS), education, and diabetes mellitus. The order of risk factors ranked by magnitudes of population attributable risk was almost similar for all methods, but population attributable risk estimates varied markedly between the methods. In PROSCIS-M, incidence of poor outcome and distribution of baseline parameters were comparable. The multiple logistic regression model could be reproduced for all predictors, except pre-stroke physical disability. Similar to PROSCIS-B, the order of risk factors ranked by magnitudes of population attributable risk was almost similar for all methods, but magnitudes of population attributable risk differed markedly between the methods.ConclusionsRanking of risk factors by population impact is not affected by the different statistical approaches. Thus, for a rational decision on which risk factor to target in disease interventions, population attributable risk is a supportive tool. However, population attributable risk estimates are difficult to interpret and are not comparable when they origin from studies applying different methodology. The predictors for poor outcome identified in PROSCIS-B have a relevant impact on mortality and functional impairment one year after first-ever ischemic stroke.

Prediction of psychosis in prodrome: development and validation of a simple, personalized risk calculator.

This study aim to derive and validate a simple and well-performing risk calculator (RC) for predicting psychosis in individual patients at clinical high risk (CHR). From the ongoing ShangHai-At-Risk-for-Psychosis (SHARP) program, 417 CHR cases were identified based on the Structured Interview for Prodromal Symptoms (SIPS), of whom 349 had at least 1-year follow-up assessment. Of these 349 cases, 83 converted to psychosis. Logistic regression was used to build a multivariate model to predict conversion. The area under the receiver operating characteristic (ROC) curve (AUC) was used to test the effectiveness of the SIPS-RC. Second, an independent sample of 100 CHR subjects was recruited based on an identical baseline and follow-up procedures to validate the performance of the SIPS-RC. Four predictors (each based on a subset of SIPS-based items) were used to construct the SIPS-RC: (1) functional decline; (2) positive symptoms (unusual thoughts, suspiciousness); (3) negative symptoms (social anhedonia, expression of emotion, ideational richness); and (4) general symptoms (dysphoric mood). The SIPS-RC showed moderate discrimination of subsequent transition to psychosis with an AUC of 0.744 (p < 0.001). A risk estimate of 25% or higher had around 75% accuracy for predicting psychosis. The personalized risk generated by the SIPS-RC provided a solid estimate of conversion outcomes in the independent validation sample, with an AUC of 0.804 [95% confidence interval (CI) 0.662-0.951]. The SIPS-RC, which is simple and easy to use, can perform in the same manner as the NAPLS-2 RC in the Chinese clinical population. Such a tool may be used by clinicians to counsel appropriately their patients about clinical monitor v. potential treatment options.

RAE1 is a novel biomarker to improve clinical prediction in colorectal cancer liver metastasis: integrating bioinformatics analysis

Background: Liver metastasis is common in patients with colorectal cancer (CRC), and is correlated with poor outcome. It is crucial to target the key genes controlling pathogenesis in CRC liver metastasis. Methods: In this study, we performed an integrated bioinformatics analysis of four CRC liver metastasis mRNAs expression datasets from Gene Expression Ominus (GEO). The expression of RAE1 was validated in the independent validation sample cohort. We knocked down the expression of RAE1 in Colon cancer cells expressing high level RAE1, after which cell proliferation, migration, invasion were assayed. Xenograft mouse models were used to determine the role of RAE1 in CRC tumorigenicity in vivo. Results: RAE1 was demonstrated to be potentially useful diagnostic markers in the clinical setting. CRC liver metastasis with a high level of RAE1 in tumor tissues had significantly shortened survival as revealed by our CRC liver metastasis cohort. RAE1 knockdown in DLD1 and HCT116 cells repressed cell growth, invasion, and migration. Conclusions: We discovered a CRC liver metastasis-specific gene-RAE1,through intensive validation. RAE1 may function as a prognostic factor in CRC patients with liver metastasis. Keywords: RAE1, colorectal cancer (CRC), Prognosis, Biomarker, Liver metastasis

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282.

Mapping annual urban dynamics (1985–2015) using time series of Landsat data

The information of urban dynamics at fine spatiotemporal resolutions is crucial to urban growth modeling and sustainable urban development. However, there are still challenges in deriving the change information of urbanization in timing and location over a long period. In this study, we developed a framework to map urban expansion at an annual interval from 1985 to 2015 by using the time series of Landsat data. First, the time series of Landsat data (1985–2015) were grouped into three periods, i.e., 1985–2001, 2001–2011, and 2011–2015, according to the available National Land Cover Database (NLCD). Then, a temporal segmentation approach was implemented for each period using three indicators representing changes from vegetation, water, and bare land to urban. Turning years of the start and end of change were identified. Three temporal segments representing phases of prior change, change, and post change, were generated accordingly. Thereafter, urban extents before 2001 and after 2011 were classified using a change vector analysis (CVA) based approach aided by the NLCD and identified temporal segments. Finally, urbanized pixels in each period were determined according to the identified turning years. Our approach of temporal segmentation is reliable for detecting changes caused by urban growth, with an overall accuracy of 90% in identifying turning years (±1 year). Using an independent validation sample set, the CVA based approach reaches an overall accuracy of 87%. The derived product of urban dynamics shows a relatively stable increment of urban growth in Des Moines and Ames, Iowa, US, and most urbanized areas were converted from vegetated lands within 2–3 years. The proposed framework is capable of mapping long-term dynamics of urban extents at an annual interval and the outcome is useful in effectively updating current products of urban extents and improving urban growth modeling.

Optimizing prediction of human assessments of dairy odors using input variable selection

Use of instruments instead of human panels to assess odors can make the collection and measurement process more efficient and reliable. Odor-emitting samples from dairy farms, including manure, feed, and bedding materials, were collected and assessed by an electronic nose and a human panel. Artificial neural networks based on the Levenberg-Marquardt Back-propagation algorithm were used to build prediction models to predict human response to odor pleasantness. Feature selection methods, including Forward Selection (FS), Gamma Test (GT), and Principal Component Analysis (PCA), were applied to reduce the dimensionality of the measurements, potentially eliminating noise. Out of the 28 variable candidates (eNose sensors), 10 variables were selected when PCA was applied, and 16 variables were selected when either FS or GT approaches were applied. The model developed using GT provided the lowest mean square error of 0.56 (2.5%) hedonic scale units for separate validation. The GT-based model was able to predict the human assessments within 10% of the target for 81% of the independent validation samples and within 5% of the target for 63% of the independent validation samples.

Determination of acid end group in Polybutylene Terepthalate using Fourier Transform near-infrared (FT-NIR) spectroscopy and chemometrics

A robust quantitative method for the determination of acid end group (AEG) in Polybutylene Terepthalate (PBT) pellets using near-infrared (NIR) diffuse reflectance spectroscopy and chemometrics is presented in this paper. A set of 66 calibration samples were used to develop a multivariate calibration model based on partial least-squares (PLS) regression. The AEG in the calibration samples varied from normalized concentration of 20–90 meq/kg. To minimize non-chemical spectral variations, the acquired NIR spectra were preprocessed using standard normal variate (SNV). Spectral range selection was explored in order to identify optimal spectral regions that were required to generate a robust PLS regression model for AEG. The performance of the optimized PLS model was evaluated on a set of 219 independent validation samples. The optimized calibration models exhibited RMSEP of 2.87 meq/kg.

MiR-760 suppresses human colorectal cancer growth by targeting BATF3/AP-1/cyclinD1 signaling

BackgroundRecent studies have reported that microRNAs (miRNAs) often function as negative post-transcriptional regulators with altered expression levels found in colorectal cancer (CRC). There have been few studies on miRNAs that regulate the oncogenic alterations in CRC. Here, we aim to explore the anti-cancer miRNA and the potential mechanisms by which miRNAs modulate CRC progression.MethodsWe performed an integrated analysis of CRC miRNA expression datasets in The Cancer Genome Atlas (TCGA). The miRNA with the lowest expression, miR-760, was validated in an independent validation sample cohort of 76 CRC tissues. Functional assays, such as CCK-8 assay, colony formation assay, and CFSE staining, were used to determine the oncogenic role of miR-760 in human CRC progression. Furthermore, western blotting and dual-luciferase reporter assay were used to determine the mechanism by which miR-760 promotes proliferation of CRC cells. Xenograft nude mouse models were used to determine the role of miR-760 in CRC tumorigenicity in vivo. Immunohistochemical assays were conducted to study the relationship between miR-760 expression and basic leucine zipper transcriptional factor ATF-like 3 (BATF3) expression in human CRC samples.ResultsmiR-760 was markedly downregulated in CRC tissues, and low miR-760 expression was associated with poor prognosis among CRC patients. Upregulation of miR-760 suppressed CRC cell proliferation, whereas downregulation of miR-760 promoted CRC proliferation in vitro. Additionally, we identified BATF3 as a direct target of miR-760, and that the essential biological function of miR-760 during CRC progression both in vitro and in vivo is to suppress the expression of BATF3 and downstream cyclinD1 via AP-1 transcription factor. Finally, we showed a significant correlation between miR-760 and BATF3 expression in CRC tissues.ConclusionsmiR-760 inhibited CRC growth by downregulating BATF3/AP-1/ cyclinD1 signaling.

Calibration of the global physical activity questionnaire to Accelerometry measured physical activity and sedentary behavior

BackgroundSelf-report questionnaires are a valuable method of physical activity measurement in public health research; however, accuracy is often lacking. The purpose of this study is to improve the validity of the Global Physical Activity Questionnaire by calibrating it to 7 days of accelerometer measured physical activity and sedentary behavior.MethodsParticipants (n = 108) wore an ActiGraph GT9X Link on their non-dominant wrist for 7 days. Following the accelerometer wear period, participants completed a telephone Global Physical Activity Questionnaire with a research assistant. Data were split into training and testing samples, and multivariable linear regression models built using functions of the GPAQ self-report data to predict ActiGraph measured physical activity and sedentary behavior. Models were evaluated with the testing sample and an independent validation sample (n = 120) using Mean Squared Prediction Errors.ResultsThe prediction models utilized sedentary behavior, and moderate- and vigorous-intensity physical activity self-reported scores from the questionnaire, and participant age. Transformations of each variable, as well as break point analysis were considered. Prediction errors were reduced by 77.7–80.6% for sedentary behavior and 61.3–98.6% for physical activity by using the multivariable linear regression models over raw questionnaire scores.ConclusionsThis research demonstrates the utility of calibrating self-report questionnaire data to objective measures to improve estimates of physical activity and sedentary behavior. It provides an understanding of the divide between objective and subjective measures, and provides a means to utilize the two methods as a unified measure.

Susceptibility of brain atrophy to TRIB3 in Alzheimer's disease, evidence from functional prioritization in imaging genetics.

The joint modeling of brain imaging information and genetic data is a promising research avenue to highlight the functional role of genes in determining the pathophysiological mechanisms of Alzheimer's disease (AD). However, since genome-wide association (GWA) studies are essentially limited to the exploration of statistical correlations between genetic variants and phenotype, the validation and interpretation of the findings are usually nontrivial and prone to false positives. To address this issue, in this work, we investigate the functional genetic mechanisms underlying brain atrophy in AD by studying the involvement of candidate variants in known genetic regulatory functions. This approach, here termed functional prioritization, aims at testing the sets of gene variants identified by high-dimensional multivariate statistical modeling with respect to known biological processes to introduce a biology-driven validation scheme. When applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, the functional prioritization allowed for identifying a link between tribbles pseudokinase 3 (TRIB3) and the stereotypical pattern of gray matter loss in AD, which was confirmed in an independent validation sample, and that provides evidence about the relation between this gene and known mechanisms of neurodegeneration.

Monitoring Liquid-Liquid Mixtures Using Fractional Calculus and Image Analysis

A fractional-calculus-based model is used to analyze the data obtained from the image analysis of mixtures of olive and soybean oil, which were quantified with the RGB color system. The model consists in a linear fractional differential equation, containing one fractional derivative of order α and an additional term multiplied by a parameter k. Using a hybrid parameter estimation scheme (genetic algorithm and a simplex-based algorithm), the model parameters were estimated as k = 3.42 ± 0.12 and α = 1.196 ± 0.027, while a correlation coefficient value of 0.997 was obtained. For the sake of comparison, parameter α was set equal to 1 and an integer order model was also studied, resulting in a one-parameter model with k = 3.11 ± 0.28. Joint confidence regions are calculated for the fractional order model, showing that the derivative order is statistically different from 1. Finally, an independent validation sample of color component B equal to 96 obtained from a sample with olive oil mass fraction equal to 0.25 is used for prediction purposes. The fractional model predicted the color B value equal to 93.1 ± 6.6.

A pathway-based association analysis identified FMNL1-MAP3K14 as susceptibility genes for leprosy.

The nuclear transcription factor-κB (NF-κB) plays a pivotal role in controlling both innate and adaptive immunity and regulates the expressions of many immunological mediators. Abundant evidences have showed the importance of NF-κB pathway in the host immune responses against Mycobacterium leprae in the development of leprosy. However, no particular association study between leprosy and NF-κB pathway-related gene polymorphisms was reported. Here, we performed a large-scale and two-stage candidate association study to investigate the association between 94 NF-κB pathway-related genes and leprosy. Our results showed that rs58744688 was significantly associated with leprosy (P=7.57×10-7 , OR=1.12) by combining the previous genomewide association data sets and four independent validation sample series, consisting of a total of 4631 leprosy cases and 6413 healthy controls. This founding implicated that MAP3K14 and FMNL1 were susceptibility genes for leprosy, which suggested the involvement of macrophage targeting and NF-κB pathway in the development of leprosy.

The effect of white matter hyperintensities on verbal memory: Mediation by temporal lobe atrophy.

To determine the relationship between white matter hyperintensities (WMH) presumed to indicate disease of the cerebral small vessels, temporal lobe atrophy, and verbal memory deficits in Alzheimer disease (AD) and other dementias. We recruited groups of participants with and without AD, including strata with extensive WMH and minimal WMH, into a cross-sectional proof-of-principle study (n = 118). A consecutive case series from a memory clinic was used as an independent validation sample (n = 702; Sunnybrook Dementia Study; NCT01800214). We assessed WMH volume and left temporal lobe atrophy (measured as the brain parenchymal fraction) using structural MRI and verbal memory using the California Verbal Learning Test. Using path modeling with an inferential bootstrapping procedure, we tested an indirect effect of WMH on verbal recall that depends sequentially on temporal lobe atrophy and verbal learning. In both samples, WMH predicted poorer verbal recall, specifically due to temporal lobe atrophy and poorer verbal learning (proof-of-principle -1.53, 95% bootstrap confidence interval [CI] -2.45 to -0.88; and confirmation -0.66, 95% CI [-0.95 to -0.41] words). This pathway was significant in subgroups with (-0.20, 95% CI [-0.38 to -0.07] words, n = 363) and without (-0.71, 95% CI [-1.12 to -0.37] words, n = 339) AD. Via the identical pathway, WMH contributed to deficits in recognition memory (-1.82%, 95% CI [-2.64% to -1.11%]), a sensitive and specific sign of AD. Across dementia syndromes, WMH contribute indirectly to verbal memory deficits considered pathognomonic of Alzheimer disease, specifically by contributing to temporal lobe atrophy.

Implementation of NIR technology for at-line rapid detection of sunflower oil adulterated with mineral oil

Three experimental setups, based on near infrared technology (NIR), were tested for rapid “at-line” assessment of sunflower oil adulteration by mineral oil. Experimental setups included a commercial portable NIR, coupled to both reflexion (S1) and immersion probes (S2), and a prototype of a multichannel Quasi Imaging Visible NIR spectrometer (QIVN) coupled to an immersion probe (S3). Independent calibration and validation samples sets were prepared with mineral oils (MOs) content up to 10% (w/w), and calibrations were developed using partial least square (PLS) regressions. Root mean square error of prediction (RMSEP) ranges from 0.23 to 1.26% (w/w) MOs, depending on the NIR setup.The best performances were obtained with S1, which provides satisfactory calibrations, and low number of false positives starting from levels of mineral oil around 1%. S3 still provides acceptable calibrations, and could be practically used to detect mineral oil at concentrations higher than 2.5% (w/w) MOs.

Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions from at-line laboratory to in-line industrial scale

Calibration transfer or standardisation aims at creating a uniform spectral response on different spectroscopic instruments or under varying conditions, without requiring a full recalibration for each situation. In the current study, this strategy is applied to construct at-line multivariate calibration models and consequently employ them in-line in a continuous industrial production line, using the same spectrometer.Firstly, quantitative multivariate models are constructed at-line at laboratory scale for predicting the concentration of two main ingredients in hard surface cleaners. By regressing the Raman spectra of a set of small-scale calibration samples against their reference concentration values, partial least squares (PLS) models are developed to quantify the surfactant levels in the liquid detergent compositions under investigation. After evaluating the models performance with a set of independent validation samples, a univariate slope/bias correction is applied in view of transporting these at-line calibration models to an in-line manufacturing set-up. This standardisation technique allows a fast and easy transfer of the PLS regression models, by simply correcting the model predictions on the in-line set-up, without adjusting anything to the original multivariate calibration models.An extensive statistical analysis is performed in order to assess the predictive quality of the transferred regression models. Before and after transfer, the R2 and RMSEP of both models is compared for evaluating if their magnitude is similar. T-tests are then performed to investigate whether the slope and intercept of the transferred regression line are not statistically different from 1 and 0, respectively. Furthermore, it is inspected whether no significant bias can be noted. F-tests are executed as well, for assessing the linearity of the transfer regression line and for investigating the statistical coincidence of the transfer and validation regression line. Finally, a paired t-test is performed to compare the original at-line model to the slope/bias corrected in-line model, using interval hypotheses.It is shown that the calibration models of Surfactant 1 and Surfactant 2 yield satisfactory in-line predictions after slope/bias correction. While Surfactant 1 passes seven out of eight statistical tests, the recommended validation parameters are 100% successful for Surfactant 2. It is hence concluded that the proposed strategy for transferring at-line calibration models to an in-line industrial environment via a univariate slope/bias correction of the predicted values offers a successful standardisation approach.

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January–June 2016 and period 2: July–December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail.