Nonhypothesis Analysis of a Mutagenic Soybean (Glycine max [L.]) Population for Protein and Fatty‐Acid Composition

Soybean is a major source of oil for food, feed, and biofuel production. Mutagenesis is a tool for creating unique traits useful in breeding programs. The aim of this study is to use nonhypothesis statistical testing methods to make decisions about a mutagenic population. To this end, a total of 1037 mutation lines and 28 wild‐type lines were analyzed for fatty‐acid composition and protein content. Principal component analysis (PCA) was used to analyze the fatty acid profile, multivariate analysis of variance (MANOVA) to build a selection model for seed weight per plant and weight per 10 seeds, and clustering in conjunction with power analysis to determine the minimum number of individuals needed to create a MANOVA selection model for the oil to protein content. Five of the 35 possible entries were identified by PCA analysis for stearic acid and four of 16 possible entries for oleic acid. Interestingly, most of the selected mutants were validated genetically. In fact, selected mutants with high seed stearic acid or high seed oleic acid contents were verified to carry mutations on GmFAD2‐1A, GmFAD2‐1B, and GmSACPD‐C genes. This shows a promising method of identifying smaller portion of the population to screen for desired mutations.     

Comparative assessment of statistical and machine learning techniques towards estimating the risk of developing type 2 diabetes and cardiovascular complications

The aim of the present study is to comparatively assess the performance of different machine learning and statistical techniques with regard to their ability to estimate the risk of developing type 2 diabetes mellitus (Case 1) and cardiovascular disease complications (Case 2). This is the first work investigating the application of ensembles of artificial neural networks (EANN) towards producing the 5‐year risk of developing type 2 diabetes mellitus and cardiovascular disease as a long‐term diabetes complication. The performance of the proposed models has been comparatively assessed with the performance obtained by applying logistic regression, Bayesian‐based approaches, and decision trees. The models' discrimination and calibration have been evaluated using the classification accuracy (ACC), the area under the curve (AUC) criterion, and the Hosmer–Lemeshow goodness of fit test. The obtained results demonstrate the superiority of the proposed models (EANN) over the other models. In Case 1, EANN with different topologies has achieved high discrimination and good calibration performance (ACC = 80.20%, AUC = 0.849, p value = .886). In Case 2, EANN based on bagging has resulted in good discrimination and calibration performance (ACC = 92.86%, AUC = 0.739, p value = .755).     

Comparison of cleaning of toothpaste from surfaces and pilot scale pipework

Experiments have been conducted to study the cleaning of toothpaste at two length scales. Laboratory scale cleaning studies have been conducted on fouled coupons in a horizontal flow cell. At pilot scale, the cleaning of fully fouled pipes has been studied and monitored by temperature, conductivity and turbidity probes. Removal from the pipe occurs in two steps: core removal, leaving a thin wall layer that is then removed by thin film removal that takes the majority of the cleaning time. At both scales, cleaning time is influenced by temperature and velocity of the cleaning fluid. The pipe length range studied, 0.3–2 m, does not appear to have a significant impact on cleaning time. Cleaning time correlates well with (shear stress)^?1. A dimensionless cleaning time is defined and a correlation between cleaning time and Reynolds number developed.     

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.     

Polyurethane Foam Rafts Supported In Vitro Cultures of Rindera graeca Roots for Enhanced Production of Rinderol,Potent Proapoptotic Naphthoquinone Compound

Unique phytochemical profile of plants belonging to Boraginaceae family provides a prolific resource of lipophilic pigments from the group of naphthoquinone derivatives. To overcome low compound content, the major obstacle of plant-based production, immobilization of Rindera graeca roots in in vitro cultures was implemented for efficient production of rinderol, novel furanonaphthoquinone derivative with anticancer properties. Chromatographic procedures revealed rinderol presence in extracts of all investigated root lines, derived both from root biomass and post-culture medium. Unexpectedly, in the second stage of the experiment, rinderol production was ceased in control, unmodified culture systems. On the contrary, roots immobilized on PUF rafts uniformly and stably produced rinderol, and its highest amount was noted for transformed root lines after 42 days of cultivation (222.98 ± 10.47 µg/flask). PUF occurred to be the main place of compound accumulation. Moreover, investigation of rinderol biological activity revealed its fast-acting cell death induction in HeLa cervical cancer cells at relatively low concentrations. Presented results revealed successful application of R. graeca roots immobilization on PUF rafts for production and in situ product removal of rinderol, novel lipophilic furanonaphthoquinone with suggested proapoptotic activity.     

Comparison of standard forearm prosthetic loop grafts to composite semiloop forearm grafts (“semi‐grafts”) in hemodialysis patients: A prospective study

Introduction: To prospectively assess the performance of composite semiloop antebrachial grafts (“semi‐grafts,” SGs) in hemodialysis patients. Methods: Eighty‐five patients who received 67 loop antebrachial grafts (LG‐group) and 25 antebrachial semigrafts (SG‐group) were enrolled. SGs were defined as those originating from the brachial artery and anastomosed with the proximal mature mid‐antebrachial cephalic vein. Cephalic vein length should be at least 10 cm in length and of ≥5 mm in diameter for inclusion in the SG‐group. LG‐group included all possible outflow vein options of minimum diameter 3 mm. Kaplan‐Meier statistics was used for comparison of groups. Findings: Main indication for a SG was a failing radiocephalic fistula with extensive distal cephalic vein stenosis not amenable to correction or failed after endovascular repair or requiring long interposition grafting. The mean follow‐up period was 20.16 ± 22.6 and 29.6 ± 36.7 months for the LG‐ and SG‐group, respectively (P = 0.14). Forty‐two patients died during the follow‐up. Primary patency (up to first intervention or failure) at 6 and 12 months for LG‐ vs. SG‐group was 93.9% vs. 83.7% and 47% vs. 55.8% (P = 0.08). Secondary patency (up to abandonment) was 58.2% vs. 61.1% and 36% vs. 45.8% at 12 and 24 months (P = 0.18). Mortality at 48 months was 22.4% (LG‐group) and 24% (SG‐group) (P = 0.9). Discussion: There was a trend toward better primary and secondary patency rates for the SGs especially in the long‐term. This is a valuable option in selected patients that access surgeons and nephrologists should be aware of.     

Stereo System for Remote Monitoring of River Flows

In this article we present a video-based method for river flow monitoring. The proposed method aims at deriving efficient approximations of the river velocity using natural formations on the river surface. In order to overcome peculiarities of the flow, we propose to uniformly exploit all such structures that appear locally with short temporal duration. Towards this direction we explore the expanded capabilities of a stereoscopic camera layout with the dual observation fields and the potential of reverting projective deformations. By mapping to world coordinates, all spatial locations in the video reflect velocity as a uniform field, except for local flow variations. The velocity estimation is performed by computing the optical flow using a series of video frames, combining the information of the views of both cameras. The novelty of the proposed river flow estimation scheme lies on the fact that the accuracy of motion estimation is increased due to the use of the complementary views, which also enables the transition from a 2-Dimensional image-based velocity estimate to 3-Dimensional estimates. The estimated optical velocity is back-projected to the real world coordinates using the parameters extracted using the stereoscopic layout. The results on simulated and real conditions demonstrate that the proposed method is efficient in the estimation of the surface velocity and robust against locally disappearing formations, since it can compensate for a loss with other formations active in the field of view.     

Inuit food security in canada: arctic marine ethnoecology

Quantitative Seascape Ethnoecology practice is considered through a community-based food security lens. Fish and marine mammal records obtained from a wide range of Inuit co-management agreements are, for the first time combined to calculate an Inuit-culture marine ecological footprint, targeted on calendar year 2008. A reflexive, participatory and inclusive education/governance strategy for Arctic coastal food security is presented, particularly in Nunavut; considering caloric marine catch may average as high as 40% of human basal metabolic requirements for some communities. The current work provides a foundation to mitigate food insecurity for the Inuit in what may be the most insecure indigenous food setting in any country that is generally considered as developed. Four large marine ecosystems are considered within Food and Agricultural Organization (FAO) areas 18 and 21. Initial fish catch reconstructions for 1950–2001 are combined with mammal harvest records. Food system changes and planning are examined from a community health and management perspective. The relationship between food security, gender, livelihoods and ecosystem capacity are discussed within current management challenges and the related mandates for Canadian government agencies, based in part upon the 2007, United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP). A process to combine academic ecological knowledge with traditional ecological knowledge (TEK) and an Inuit Qaujimajatuqangit (IQ) is suggested within a beneficiary-led marine protected area network. A re-evaluation of Canada’s northern (Nunavut) transfer and land claim agreements are suggested, with a goal to provide redress for UNDRIP compliance. Therein, consideration should be given to dedicated funds for reflexive and participatory development of university-level Ethnoecology programming and community-based offices for Nunavut food security science.     

Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

This paper proposes a novel forecasting method that combines the deep learning method – long short-term memory (LSTM) networks and random forest (RF). The proposed method can model complex relationships of both temporal and regression type which gives it an edge in accuracy over other forecasting methods. We evaluated the new method on a real-world multivariate dataset from a multi-channel retailer. We benchmark the forecasting performance of the new proposition against neural networks, multiple regression, ARIMAX, LSTM networks, and RF. We employed forecasting performance metrics to measure bias, accuracy, and variance, and the empirical evidence suggests that the new proposition is (statistically) significantly better. Furthermore, our method ranks the explanatory variables in terms of their relative importance. The empirical evaluations are replicated for longer forecasting horizons, and online and offline channels and the same conclusions hold; thus, advocating for the robustness of our forecasting proposition as well as the suitability in multi-channel retail demand forecasting.