MOREMATA: Stata module (Mata) to provide various functions

This package includes various Mata functions. kern(): various kernel functions; kint(): kernel integral functions; kdel0(): canonical bandwidth of kernel; quantile(): quantile function; median(): median; iqrange(): inter-quartile range; ecdf(): cumulative distribution function; relrank(): grade transformation; ranks(): ranks/cumulative frequencies; freq(): compute frequency counts; histogram(): produce histogram data; mgof(): multinomial goodness-of-fit tests; collapse(): summary statistics by subgroups; _collapse(): summary statistics by subgroups; gini(): Gini coefficient; sample(): draw random sample; srswr(): SRS with replacement; srswor(): SRS without replacement; upswr(): UPS with replacement; upswor(): UPS without replacement; bs(): bootstrap estimation; bs2(): bootstrap estimation; bs_report(): report bootstrap results; jk(): jackknife estimation; jk_report(): report jackknife results; subset(): obtain subsets, one at a time; composition(): obtain compositions, one by one; ncompositions(): determine number of compositions; partition(): obtain partitions, one at a time; npartitionss(): determine number of partitions; rsubset(): draw random subset; rcomposition(): draw random composition; colvar(): variance, by column; meancolvar(): mean and variance, by column; variance0(): population variance; meanvariance0(): mean and population variance; mse(): mean squared error; colmse(): mean squared error, by column; sse(): sum of squared errors; colsse(): sum of squared errors, by column; benford(): Benford distribution; cauchy(): cumulative Cauchy-Lorentz dist.; cauchyden(): Cauchy-Lorentz density; cauchytail(): reverse cumulative Cauchy-Lorentz; invcauchy(): inverse cumulative Cauchy-Lorentz; rbinomial(): generate binomial random numbers; cebinomial(): cond. expect. of binomial r.v.; root(): Brent's univariate zero finder; nrroot(): Newton-Raphson zero finder; finvert(): univariate function inverter; integrate_sr(): univariate function integration (Simpson's rule); integrate_38(): univariate function integration (Simpson's 3/8 rule); ipolate(): linear interpolation; polint(): polynomial inter-/extrapolation; plot(): Draw twoway plot; _plot(): Draw twoway plot; panels(): identify nested panel structure; _panels(): identify panel sizes; npanels(): identify number of panels; nunique(): count number of distinct values; nuniqrows(): count number of unique rows; isconstant(): whether matrix is constant; nobs(): number of observations; colrunsum(): running sum of each column; linbin(): linear binning; fastlinbin(): fast linear binning; exactbin(): exact binning; makegrid(): equally spaced grid points; cut(): categorize data vector; posof(): find element in vector; which(): positions of nonzero elements; locate(): search an ordered vector; hunt(): consecutive search; cond(): matrix conditional operator; expand(): duplicate single rows/columns; _expand(): duplicate rows/columns in place; repeat(): duplicate contents as a whole; _repeat(): duplicate contents in place; unorder2(): stable version of unorder(); jumble2(): stable version of jumble(); _jumble2(): stable version of _jumble(); pieces(): break string into pieces; npieces(): count number of pieces; _npieces(): count number of pieces; invtokens(): reverse of tokens(); realofstr(): convert string into real; strexpand(): expand string argument; matlist(): display a (real) matrix; insheet(): read spreadsheet file; infile(): read free-format file; outsheet(): write spreadsheet file; callf(): pass optional args to function; callf_setup(): setup for mm_callf().

Long-term Responses of Mountain Ecosystems to Environmental Changes: Resilience, Adjustment, and Vulnerability

The steep environmental gradients of mountain ecosystems over short distances reflect large gradients of several climatic parameters and hence provide excellent possibilities for ecological research on the effects of environmental change. To gain a better understanding of the dynamics of abiotic and biotic parameters of mountain ecosystems, long-term records are required since permanent plots in mountain regions cover in the best case about 50 - 70 years. In order to extend investigations of ecological dynamics beyond these temporal limitations of permanent plots, paleoecological approaches can be used if the sampling resolution can be adapted to ecological research questions, e.g. a sample every 10 years. Paleoecological studies in mountain ecosystems can provide new ecological insights through the combination of different spatial and temporal scales. [f we thus improve our understanding of processes across both steep environmental gradients and different time scales, we may be able to better estimate ecosystem responses to current and future environmental change (Ammann et al. 1993; Lotter et al. 1997). The complexity of ecological interactions in mountain regions forces us to concentrate on a number of sub-systems - without losing sight of the wider context. Here, we summarize a few case studies on the effects of Holocene climate change and disturbance on the vegetation of the Western Alps. To categorize the main response modes of vegetation to climatic change and disturbance in the Alps we use three classes of ecological behaviour: "resilience", "adjustment", and "vulnerability", We assume a resilient (or elastic) behaviour if vegetation is able to recover to its former state, regaining important ecosystem characteristics, such as floristic composition, biodiversity, species abundances, and biomass (e.g. Küttel 1990; Aber and Melillo 199 1). Conversely, vegetation displacements may occur in response to climatic change and/or disturbance. In some cases, this may culminate in irreversible large-scale processes such as species and/or community extinctions. Such drastic developments indicate high ecosystem vulnerability (or inelasticity or instability, for detailed definitions see Küttel 1990; Aber and Melillo 199 1) to climatic change and/or disturbance. In this sense, the "vulnerability" (or instability) of an ecosystem is expressed by the degree of failure to recover to the original state before disturbance and/or climatic change. Between these two extremes (resilience vs. vulnerability), ecosystem adjustments to climatic change and/or disturbance may occur, including the appearance of new and/or the disappearance of old species. The term "adjustment" is hence used to indicate the response of vegetational communities, which adapted to new environmental conditions without losing their main character. For forest ecosystems, we assume vegetational adjustments (rather than vulnerability) if the dominant (or co-dominant) tree species are not outnumbered or replaced by formerly unimportant plant species or new invaders. Adaptation as a genetic process is not discussed here and will require additional pbylogeographical studies (that incorporate the analysis of ancient DNA) in order to fully understand the distributions of ecotypes.

Prospective longitudinal voxel-based morphometry study of major depressive disorder in young individuals at high familial risk

BACKGROUND Previous neuroimaging studies indicate abnormalities in cortico-limbic circuitry in mood disorder. Here we employ prospective longitudinal voxel-based morphometry to examine the trajectory of these abnormalities during early stages of illness development. METHOD Unaffected individuals (16-25 years) at high and low familial risk of mood disorder underwent structural brain imaging on two occasions 2 years apart. Further clinical assessment was conducted 2 years after the second scan (time 3). Clinical outcome data at time 3 was used to categorize individuals: (i) healthy controls ('low risk', n = 48); (ii) high-risk individuals who remained well (HR well, n = 53); and (iii) high-risk individuals who developed a major depressive disorder (HR MDD, n = 30). Groups were compared using longitudinal voxel-based morphometry. We also examined whether progress to illness was associated with changes in other potential risk markers (personality traits, symptoms scores and baseline measures of childhood trauma), and whether any changes in brain structure could be indexed using these measures. RESULTS Significant decreases in right amygdala grey matter were found in HR MDD v. controls (p = 0.001) and v. HR well (p = 0.005). This structural change was not related to measures of childhood trauma, symptom severity or measures of sub-diagnostic anxiety, neuroticism or extraversion, although cross-sectionally these measures significantly differentiated the groups at baseline. CONCLUSIONS These longitudinal findings implicate structural amygdala changes in the neurobiology of mood disorder. They also provide a potential biomarker for risk stratification capturing additional information beyond clinically ascertained measures.