Long-term prognosis of patients with alcohol-related liver disease or non-alcoholic fatty liver disease according to metabolic syndrome or alcohol use.

BACKGROUND & AIMS: The boundary between non-alcoholic (NAFLD) and alcohol-related liver disease (ALD) is based on alcohol consumption. However, metabolic syndrome and alcohol use frequently co-exist. The aim of this study was to determine prognostic factors of long-term morbidity and mortality in patients with NAFLD or ALD. METHODS: From 2003 to 2016, all consecutive NAFLD or ALD patients were prospectively included in this cohort study. We evaluated overall survival, specific cause of mortality and occurrence of any complication. The primary endpoint was analysed by the Kaplan Meier method, secondary endpoints were estimated by Gray test method or logistic regressions. Factors independently associated with overall mortality and morbidity were identified by a multivariate Cox model. RESULTS: A total of 3365 patients (1667 with ALD and 1698 with NAFLD) were included. Median follow-up was 54 months (range: 30-86) and 563 subjects died. In the overall population, overall mortality was higher in patients with ALD (HR: 10.1 [7.57-13.3]), and with weekly alcohol consumption >7 units (HR:1.66 [1.41-1.96]). Liver-related mortality was higher in patients with ALD (HR: 11 [7.27-16.5]). In the NAFLD group, weekly alcohol consumption >1 unit was associated with higher overall mortality (HR: 1.9 [1.1-3.4]), and weekly alcohol consumption >7 units was associated with higher overall morbidity (OR: 1.89 [1.61-2.21]). In the ALD group, the presence of metabolic syndrome was associated with higher overall (HR:1.27 [1.02-1.57]), and liver (HR: 1.47 [1.1-1.96]) mortalities, and overall (OR: 1.46 [1.14-1.88]), liver (OR: 1.46 [1.14-1.88]) morbidities. CONCLUSION: In fatty liver diseases, light alcohol consumption and metabolic syndrome are prognosis cofactors.

BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification.

Research on brain-computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals.

Leaf-rolling in maize crops: from leaf scoring to canopy-level measurements for phenotyping.

Leaf rolling in maize crops is one of the main plant reactions to water stress that can be visually scored in the field. However, leaf-scoring techniques do not meet the high-throughput requirements needed by breeders for efficient phenotyping. Consequently, this study investigated the relationship between leaf-rolling scores and changes in canopy structure that can be determined by high-throughput remote-sensing techniques. Experiments were conducted in 2015 and 2016 on maize genotypes subjected to water stress. Leaf-rolling was scored visually over the whole day around the flowering stage. Concurrent digital hemispherical photographs were taken to evaluate the impact of leaf-rolling on canopy structure using the computed fraction of intercepted diffuse photosynthetically active radiation, FIPARdif. The results showed that leaves started to roll due to water stress around 09:00 h and leaf-rolling reached its maximum around 15:00 h (the photoperiod was about 05:00-20:00 h). In contrast, plants maintained under well-watered conditions did not show any significant rolling during the same day. A canopy-level index of rolling (CLIR) is proposed to quantify the diurnal changes in canopy structure induced by leaf-rolling. It normalizes for the differences in FIPARdif between genotypes observed in the early morning when leaves are unrolled, as well as for yearly effects linked to environmental conditions. Leaf-level rolling score was very strongly correlated with changes in canopy structure as described by the CLIR (r2=0.86, n=370). The daily time course of rolling was characterized using the amplitude of variation, and the rate and the timing of development computed at both the leaf and canopy levels. Results obtained from eight genotypes common between the two years of experiments showed that the amplitude of variation of the CLIR was the more repeatable trait (Spearman coefficient ρ=0.62) as compared to the rate (ρ=0.29) and the timing of development (ρ=0.33). The potential of these findings for the development of a high-throughput method for determining leaf-rolling based on aerial drone observations are considered.

Long-term prognosis of patients with metabolic (dysfunction)-associated fatty liver disease by non-invasive methods.

BACKGROUND: Non-invasive assessment of fibrosis is predictive of the prognosis of non-alcoholic and alcoholic fatty liver disease but this has not been demonstrated in metabolic (dysfunction)-associated fatty liver disease (MAFLD). AIMS: We assessed the prognosis of non-invasive methods in patients with MAFLD. METHODS: All consecutive patients with MAFLD, with liver stiffness measurements, FIB-4 (Fibrosis-4), and LIVERFASt were included in this cohort study. The primary endpoint was analysed by the Kaplan-Meier method and secondary endpoints were estimated by Gray test or logistic regression. Factors independently associated with overall mortality and morbidity were identified by a multivariate Cox model. The prognostic performance of non-invasive methods for prediction of mortality was evaluated by Harrell's C-index and for morbidity by area under the receiver operating characteristics curve (AUC). RESULTS: A total of 1239 patients with MAFLD were analysed (median age 56 years, male 56.5%, median body mass index 31 kg/m2 and obesity 59%). The median follow-up was 62 months [42-91 months] and 73 (5.8%) subjects died. Baseline results of non-invasive methods were correlated with overall and liver-related mortalities (P < 0.001), and with all-cause and liver-related outcomes (P < 0.001). A predictive model (composed of clinical parameters and liver stiffness measurement, FIB-4 or LIVERFASt) was an excellent predictor of overall and liver-related mortalities (C-index 0.8-0.9), and a good predictor of overall and liver-related morbidities (AUC 0.72-0.74). CONCLUSION: Baseline liver stiffness measurement, FIB-4 and LIVERFASt can predict global and liver-related mortality and morbidity in patients with MAFLD and could be prognosis endpoints in clinical trials.

High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass.

Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes. Two experiments were carried out in two different sites where several genotypes were grown under contrasted irrigation and nitrogen treatments. A high spatial resolution RGB camera was used to capture the residual stems standing straight after the cutting by the combine machine during harvest. It provided a ground spatial resolution better than 0.2 mm. A Faster Regional Convolutional Neural Network (Faster-RCNN) deep-learning model was first trained to identify the stems cross section. Results showed that the identification provided precision and recall close to 95%. Further, the balance between precision and recall allowed getting accurate estimates of the stem density with a relative RMSE close to 7% and robustness across the two experimental sites. The estimated stem density was also compared with the ear density measured in the field with traditional methods. A very high correlation was found with almost no bias, indicating that the stem density could be a good proxy of the ear density. The heritability/repeatability evaluated over 16 genotypes in one of the two experiments was slightly higher (80%) than that of the ear density (78%). The diameter of each stem was computed from the profile of gray values in the extracts of the stem cross section. Results show that the stem diameters follow a gamma distribution over each microplot with an average diameter close to 2.0 mm. Finally, the biovolume computed as the product of the average stem diameter, the stem density, and plant height is closely related to the above-ground biomass at harvest with a relative RMSE of 6%. Possible limitations of the findings and future applications are finally discussed.

A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery.

The dynamics of the Green Leaf Area Index (GLAI) is of great interest for numerous applications such as yield prediction and plant breeding. We present a high-throughput model-assisted method for characterizing GLAI dynamics in maize (Zea mays subsp. mays) using multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV). Two trials were conducted with a high diversity panel of 400 lines under well-watered and water-deficient treatments in 2016 and 2017. For each UAV flight, we first derived GLAI estimates from empirical relationships between the multispectral reflectance and ground level measurements of GLAI achieved over a small sample of microplots. We then fitted a simple but physiologically sound GLAI dynamics model over the GLAI values estimated previously. Results show that GLAI dynamics was estimated accurately throughout the cycle (R2 > 0.9). Two parameters of the model, biggest leaf area and leaf longevity, were also estimated successfully. We showed that GLAI dynamics and the parameters of the fitted model are highly heritable (0.65 ≤ H2 ≤ 0.98), responsive to environmental conditions, and linked to yield and drought tolerance. This method, combining growth modeling, UAV imagery and simple non-destructive field measurements, provides new high-throughput tools for understanding the adaptation of GLAI dynamics and its interaction with the environment. GLAI dynamics is also a promising trait for crop breeding, and paves the way for future genetic studies.

High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates.

The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phénomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the plant height can be estimated. Plant height first defined as the z-value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of plant height (RMSE = 3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion plant height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values (H2> 0.90) were found for both techniques when lodging was not present. The dynamics of plant height shows that it carries pertinent information regarding the period and magnitude of the plant stress. Further, the date when the maximum plant height is reached was found to be very heritable (H2> 0.88) and a good proxy of the flowering stage. Finally, the capacity of plant height as a proxy for total above ground biomass and yield is discussed.