During the past two decades, it has been amply documented that neuropsychiatric disorders (NPDs) disproportionately account for burden of illness attributable to chronic non-communicable medical disorders globally. It is also likely that human capital costs attributable to NPDs will disproportionately increase as a consequence of population aging and beneficial risk factor modification of other common and chronic medical disorders (e.g., cardiovascular disease). Notwithstanding the availability of multiple modalities of antidepressant treatment, relatively few studies in psychiatry have primarily sought to determine whether improving cognitive function in MDD improves patient reported outcomes (PROs) and/or is cost effective. The mediational relevance of cognition in MDD potentially extrapolates to all NPDs, indicating that screening for, measuring, preventing, and treating cognitive deficits in psychiatry is not only a primary therapeutic target, but also should be conceptualized as a transdiagnostic domain to be considered regardless of patient age and/or differential diagnosis.

Thalénite-(Y), ideally Y3Si3O10F, is a heavy-rare-earth-rich silicate phase occurring in granite pegmatites that may help to illustrate rare-earth element (REE) chemistry and behaviour in natural systems. The crystal structure and mineral chemistry of thalénite-(Y) were analysed by electron microprobe analysis, X-ray diffraction and micro-Raman spectroscopy from a new locality in the peralkaline granite of the Golden Horn batholith, Okanogan County, Washington State, USA, in comparison with new analyses from the White Cloud pegmatite in the Pikes Peak batholith, Colorado, USA. The Golden Horn thalénite-(Y) occurs as late-stage sub-millimetre euhedral bladed transparent crystals in small miarolitic cavities in an arfvedsonite-bearing biotite granite. It exhibits growth zoning with distinct heavy-rare-earth element (HREE) vs. light-rare-earth element (LREE) enriched zones. The White Cloud thalénite-(Y) occurs in two distinct anhedral and botryoidal crystal habits of mostly homogenous composition. In addition, minor secondary thalénite-(Y) is recognized by its distinct Yb-rich composition (up to 0.8 atoms per formula unit (apfu) Yb). Single-crystal X-ray diffraction analysis and structure refinement reveals Y-site ordering with preferential HREE occupation of Y2 vs. Y1 and Y3 REE sites. Chondrite normalization shows continuous enrichment of HREE in White Cloud thalénite-(Y), in contrast to Golden Horn thalénite-(Y) with a slight depletion of the heaviest REE (Tm, Yb and Lu). The results suggest a hydrothermal origin of the Golden Horn miarolitic thalénite-(Y), compared to a combination of both primary magmatic followed by hydrothermal processes responsible for the multiple generations over a range of spatial scales in White Cloud thalénite-(Y).

It has been an underlying assumption in many studies that near-surface layers imaged by ground-penetrating radar (GPR) can be interpreted as depositional markers or isochrones. It has been shown that GPR layers can be approximately reproduced from the measured electrical properties of ice, but these material layers are generally narrower and more closely spaced than can be resolved by typical GPR systems operating in the range 50−400 MHz. Thus GPR layers should be interpreted as interference patterns produced from closely spaced and potentially discontinuous material layers, and should not be assumed to be interpretable as precise markers of isochrones. We present 100 MHz GPR data from Lyddan Ice Rise, Antarctica, in which near-surface (<50 m deep) layers are clearly imaged. The growth of the undulations in these layers with depth is approximately linear, implying that, rather than resulting from a pattern of vertical strain rate, they do correspond to some pattern of snowfall variation. Furthermore, comparison of the GPR layers with snow-stake measurements suggests that around 80% of the rms variability in mean annual accumulation is present in the GPR layers. The observations suggest that, at least in this case, the GPR layers do approximate isochrones, and that patterns of snow accumulation over Lyddan Ice Rise are dominated by extremely persistent spatial variations with only a small residual spatial variability. If this condition is shown to be widely applicable it may reduce the period required for measurements of surface elevation change to be taken as significant indications of mass imbalance.

Crop yield loss–weed density relationships critically influence calculation of economic thresholds and the resulting management recommendations made by a bioeconomic model. To examine site-to-site and year-to-year variation in winter Triticum aestivum L. (winter wheat)–Aegilops cylindrica Host. (jointed goatgrass) interference relationships, the rectangular hyperbolic yield loss function was fit to data sets from multiyear field experiments conducted at Colorado, Idaho, Kansas, Montana, Nebraska, Utah, Washington, and Wyoming. The model was fit to three measures of A. cylindrica density: fall seedling, spring seedling, and reproductive tiller densities. Two parameters: i, the slope of the yield loss curve as A. cylindrica density approaches zero, and a, the maximum percentage yield loss as A. cylindrica density becomes very large, were estimated for each data set using nonlinear regression. Fit of the model to the data was better using spring seedling densities than fall seedling densities, but it was similar for spring seedling and reproductive tiller densities based on the residual mean square (RMS) values. Yield loss functions were less variable among years within a site than among sites for all measures of weed density. For the one site where year-to-year variation was observed (Archer, WY), parameter a varied significantly among years, but parameter i did not. Yield loss functions differed significantly among sites for 7 of 10 comparisons. Site-to-site statistical differences were generally due to variation in estimates of parameter i. Site-to-site and year-to-year variation in winter T. aestivum–A. cylindrica yield loss parameter estimates indicated that management recommendations made by a bioeconomic model cannot be based on a single yield loss function with the same parameter values for the winter T. aestivum-producing region. The predictive ability of a bioeconomic model is likely to be improved when yield loss functions incorporating time of emergence and crop density are built into the model's structure.

Secale cereale is a serious weed problem in winter Triticum aestivum–producing regions. The interference relationships and economic thresholds of S. cereale in winter T. aestivum in Colorado, Kansas, Nebraska, and Wyoming were determined over 4 yr. Winter T. aestivum density was held constant at recommended planting densities for each site. Target S. cereale densities were 0, 5, 10, 25, 50, or 100 plants m−2. Secale cereale–winter T. aestivum interference relationships across locations and years were determined using a negative hyperbolic yield loss function. Two parameters—I, which represents the percent yield loss as S. cereale density approaches zero, and A, the maximum percent yield loss as S. cereale density increases—were estimated for each data set using nonlinear regression. Parameter I was more stable among years within locations than among locations within years, whereas maximum percentage yield loss was more stable across locations and years. Environmental conditions appeared to have a role in the stability of these relationships. Parameter estimates for I and A were incorporated into a second model to determine economic thresholds. On average, threshold values were between 4 and 5 S. cereale plants m−2; however, the large variation in these threshold values signifies considerable risk in making economic weed management decisions based upon these values.

Three models that empirically predict crop yield from crop and weed density were evaluated for their fit to 30 data sets from multistate, multiyear winter wheat–jointed goatgrass interference experiments. The purpose of the evaluation was to identify which model would generally perform best for the prediction of yield (damage function) in a bioeconomic model and which model would best fulfill criteria for hypothesis testing with limited amounts of data. Seven criteria were used to assess the fit of the models to the data. Overall, Model 2 provided the best statistical description of the data. Model 2 regressions were most often statistically significant, as indicated by approximate F tests, explained the largest proportion of total variation about the mean, gave the smallest residual sum of squares, and returned residuals with random distribution more often than Models 1 and 3. Model 2 performed less well based on the remaining criteria. Model 3 outperformed Models 1 and 2 in the number of parameters estimated that were statistically significant. Model 1 outperformed Models 2 and 3 in the proportion of regressions that converged on a solution and more readily exhibited an asymptotic relationship between winter wheat yield and both winter wheat and jointed goatgrass density under the constraint of limited data. In contrast, Model 2 exhibited a relatively linear relationship between yield and crop density and little effect of increasing jointed goatgrass density on yield, thus overpredicting yield at high weed densities when data were scarce. Model 2 had statistical properties that made it superior for hypothesis testing; however, Model 1's properties were determined superior for the damage function in the winter wheat–jointed goatgrass bioeconomic model because it was less likely to cause bias in yield predictions based on data sets of minimum size.