The present study aimed to investigate the relationship between dietary patterns and the risk of type 2 diabetes mellitus (T2DM) among Taiwanese individuals. Data were collected using a nationwide cohort study (2001–15) from the Triple-High Database. Dietary intake was assessed using the twenty-group food frequency questionnaire and used to calculate alternate Mediterranean diet (aMED) and Dietary Approaches to Stop Hypertension (DASH) scores. Principal component analysis (PCA) and partial least-squares (PLS) regression were used to derive dietary patterns, with incident T2DM as the outcome. Multivariable-adjusted hazard ratios and 95 % confidence intervals were calculated using time-dependent Cox proportional hazards (Cox PH) regression analysis, and subgroup analyses were performed. A total of 4705 participants were enrolled in the study, and 995 had newly developed T2DM during the median 5⋅28-year follow-up period (30⋅7 per 1000 person-years). Six dietary patterns were extracted (PCA: Western, prudent, dairy and plant-based; PLS: health-conscious, fish-vegetable and fruit-seafood). The highest aMED score quartile had a 25 % (hazard ratio 0⋅75; 95 % CI 0⋅61, 0⋅92; P = 0⋅039) lower risk of T2DM than the lowest quartile. This association remained significant after adjustment (adjusted hazard ratio 0⋅74; 95 % CI 0⋅60, 0⋅91; P = 0⋅010), and no effect modifier was found for aMED. The DASH scores, PCA and PLS dietary patterns were not significant after adjustment. In conclusion, high adherence to a MED-type dietary pattern by Taiwanese foods was associated with a lower risk of T2DM in the Taiwanese population, regardless of unhealthy lifestyle habits.

In this research communication we compare three different approaches for developing dry matter intake (DMI) prediction models based on milk mid-infrared spectra (MIRS), using data collected from a research herd over five years. In dairy production, knowledge of individual DMI could be important and useful, but DMI can be difficult and expensive to measure on most commercial farms as cows are commonly group-fed. Instead, this parameter is often estimated based on the age, body weight, stage of lactation and body condition score of the cow. Recently, milk MIRS have also been used as a tool to estimate DMI. There are different methods available to create prediction models from large datasets. The main data used were total DMI calculated as a 3-d average, coupled with milk MIRS data available fortnightly. Data on milk yield and lactation stage parameters were also available for each animal. We compared the performance of three prediction approaches: partial least-squares regression, support vector machine regression and random forest regression. The full milk MIRS alone gave low to moderate prediction accuracy (R2 = 0.07–0.40), regardless of prediction modelling approach. Adding more variables to the model improved R2 and decreased the prediction error. Overall, partial least-squares regression proved to be the best method for predicting DMI from milk MIRS data, while MIRS data together with milk yield and concentrate DMI at 3–30 d in milk provided good prediction accuracy (R2 = 0.52–0.65) regardless of the prediction tool used.

Yellow rattle is a hemiparasitic weed that is becoming increasingly problematic in hay fields in the northeastern United States. The biomass of yellow rattle was assessed in two experiments in which commercially available soil amendments were applied to an infested hay field under no-till management. The amendments were applied in the fall of 2010 and 2011 and included wood ash, biochar, hardwood sawdust, lime, and K. Wood ash applied at a rate of 8,967 kg ha−1 (4 tons per acre) had the greatest suppressive effect and reduced yellow rattle biomass the following summer by 86 and 92% relative to the untreated control in Experiments 1 and 2, respectively. Two additional treatments included in Experiment 2, a doubled rate of wood ash and sawdust applied at a rate of 13,450 kg ha−1, also reduced yellow rattle biomass. No other treatments affected yellow rattle biomass. A partial least-squares regression (PLSR) analysis that included aboveground plant community and soil biochemical data explained only 42.4% of the variation in yellow rattle biomass, suggesting that the mechanisms responsible for the suppression of yellow rattle by wood ash and sawdust are complex. Amending hay fields with wood ash or sawdust may be an effective nonherbicide strategy for managing yellow rattle infestations. Additional research will be required to determine the mechanisms and generality of yellow rattle suppression by wood ash and sawdust amendments to the soil.

The use of high-speed detectors made X-ray diffraction (XRD) become an important tool for process control in mining and metal industries. Decreasing ore qualities and increasing prices for raw materials require a better control of processed ore and a more efficient use of energy. Traditionally quality control of iron ore sinter has relied on time-consuming wet chemistry. The mineralogical composition that defines the physical properties such as hardness or reducibility is not monitored. XRD analysis in combination with Rietveld quantification and statistical data evaluation using partial least-squares regression (PLSR) has been successfully established to determine the mineralogical composition and the Fe2+ content of iron ore sinter within an analysis time of less than 10 min per sample. A total of 35 iron ore sinter samples were measured and evaluated using PLSR and the Rietveld method. The results were compared with wet chemistry data. PLSR results show accuracy for the Fe2+ content of ±0.14%. No pure phases, crystal structures, or complex modeling of peak shapes are required. The Rietveld method was used to quantify the total phase composition of the samples. The Fe2+ content could be calculated from all phases present. Both methods take the full XRD pattern into account and can be simultaneously applied on the same measurement. PLSR was found to be the more robust method if only Fe2+ results are required. The Rietveld method helps predict other parameters such as the compressional strength of the sinter by monitoring all existing phases (e.g., larnite, C2S, or silico-ferrite of calcium and aluminum phases).