Kyzylkumite, Ti2V3+O5(OH): new structure type, modularity and revised formula

The crystal structure of kyzylkumite, ideally Ti2V3+O5(OH), from the Sludyanka complex in South Baikal, Russia was solved and refined (including the hydrogen atom position) to an agreement index, R1, of 2.34 using X-ray diffraction data collected on a twinned crystal. Kyzylkumite crystallizes in space group P21/c, with a = 8.4787(1), b = 4.5624(1), c = 10.0330(1) Å, β = 93.174(1)°, V = 387.51(1) Å3 and Z = 4. Tivanite, TiV3+O3OH, and kyzylkumite have modular structures based on hexagonal close packing of oxygen, which are made up of rutile TiO2 and montroseite V3+O(OH) slices. In tivanite the rutile:montroseite ratio is 1:1, in kyzylkumite the ratio is 2:1. The montroseite module may be replaced by the isotypic paramontroseite V4+O2 module, which produces a phase with the formula Ti2V4+O6. In the metamorphic rocks of the Sludyanka complex, vanadium can be present as V4+ and V3+ within the same mineral (e.g. in batisivite, schreyerite and berdesinskiite). Kyzylkumite has a flexible composition with respect to the M4+/M3+ ratio. The relationship between kyzylkumite and a closely related Be-bearing kyzylkumite-like mineral with an orthorhombic norbergite-type structure from Byrud mine, Norway is discussed. Both minerals have similar X-ray powder diffraction patterns.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.

Atlas-Based Segmentation of Brain Tumor Images Using a Markov Random Field-Based Tumor Growth Model and Non-Rigid Registration

We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

The course of chronic and recurrent low back pain in the general population

Using latent class analysis (LCA), a previous study on patients attending primary care identified four courses of low back pain (LBP) over the subsequent 6 months. To date, no studies have used longitudinal pain recordings to examine the "natural" course of recurrent and chronic LBP in a population-based sample of individuals. This study examines the course of LBP in the general population and elaborates on the stability and criterion-related validity of the clusters derived. A random sample of 400 individuals reporting LBP in a population-based study was asked to complete a comprehensive questionnaire at the start and end of the year's survey, and 52 weekly pain diaries in between. The latter were analyzed using LCA. 305 individuals returned more than 50% of the diaries. Four clusters were identified (severe persistent, moderate persistent, mild persistent, and fluctuating). The clusters differed significantly with regards to pain and disability. Assessment of cluster stability showed that a considerable proportion of patients in the "fluctuating" group changed their classification over time. Three of the four clusters describing the typical course of pain matched the clusters described previously for patients in primary care. Due to the population-based design, this study achieves, for the first time, a close insight into the "natural" course of chronic and recurrent low back pain, including individuals that did not necessarily visit the general practitioner. The findings will help to understand better the nature of this pain in the general population.

Efficacy of experimental treatments compared with standard treatments in non-inferiority trials: a meta-analysis of randomized controlled trials

Background There is concern that non-inferiority trials might be deliberately designed to conceal that a new treatment is less effective than a standard treatment. In order to test this hypothesis we performed a meta-analysis of non-inferiority trials to assess the average effect of experimental treatments compared with standard treatments. Methods One hundred and seventy non-inferiority treatment trials published in 121 core clinical journals were included. The trials were identified through a search of PubMed (1991 to 20 February 2009). Combined relative risk (RR) from meta-analysis comparing experimental with standard treatments was the main outcome measure. Results The 170 trials contributed a total of 175 independent comparisons of experimental with standard treatments. The combined RR for all 175 comparisons was 0.994 [95% confidence interval (CI) 0.978–1.010] using a random-effects model and 1.002 (95% CI 0.996–1.008) using a fixed-effects model. Of the 175 comparisons, experimental treatment was considered to be non-inferior in 130 (74%). The combined RR for these 130 comparisons was 0.995 (95% CI 0.983–1.006) and the point estimate favoured the experimental treatment in 58% (n = 76) and standard treatment in 42% (n = 54). The median non-inferiority margin (RR) pre-specified by trialists was 1.31 [inter-quartile range (IQR) 1.18–1.59]. Conclusion In this meta-analysis of non-inferiority trials the average RR comparing experimental with standard treatments was close to 1. The experimental treatments that gain a verdict of non-inferiority in published trials do not appear to be systematically less effective than the standard treatments. Importantly, publication bias and bias in the design and reporting of the studies cannot be ruled out and may have skewed the study results in favour of the experimental treatments. Further studies are required to examine the importance of such bias.

A residue close to ?1 loop F disrupts modulation of GABAA receptors by benzodiazepines while their binding is maintained

Benzodiazepines act at the major isoforms of GABA type A receptors where they potentiate the current evoked by the agonist GABA. The underlying mechanism of this potentiation is poorly understood, but hypothesized to be related to the mechanism that links agonist binding to channel opening in these ligand activated ion channels. The loop F of the ?(1) and the ?(2) subunit have been implicated in channel gating, and loop F of the ?(2) subunit in the modulation by benzodiazepines. We have identified the conservative point mutation Y168F located N-terminally of loop F in the ?(1) subunit that fails to affect agonist properties. Interestingly, it disrupts modulation by benzodiazepines, but leaves high affinity binding to the benzodiazepine binding site intact. Modulation by barbiturates and neurosteroids is also unaffected. Residue ?(1) Y168 is not located either near the binding pockets for GABA, or for benzodiazepines, or close to the loop F of the ?(2) subunit. Our results support the fact, that broader regions of ligand gated receptors are conformationally affected by the binding of benzodiazepines. We infer that also broader regions could contribute to signaling from GABA agonist binding to channel opening.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

Vulnerability to risk among small farmers in Tajikistan: results of a 2011 survey

Tajikistan is judged to be highly vulnerable to risk, including food insecurity risks and climate change risks. By some vulnerability measures it is the most vulnerable among all 28 countries in the World Bank’s Europe and Central Asia Region – ECA (World Bank 2009). The rural population, with its relatively high incidence of poverty, is particularly vulnerable. The Pilot Program for Climate Resilience (PPCR) in Tajikistan (2011) provided an opportunity to conduct a farm-level survey with the objective of assessing various dimensions of rural population’s vulnerability to risk and their perception of constraints to farming operations and livelihoods. The survey should be accordingly referred to as the 2011 PPCR survey. The rural population in Tajikistan is highly agrarian, with about 50% of family income deriving from agriculture (see Figure 4.1; also LSMS 2007 – own calculations). Tajikistan’s agriculture basically consists of two groups of producers: small household plots – the successors of Soviet “private agriculture” – and dehkan (or “peasant”) farms – new family farming structures that began to be created under relevant legislation passed after 1992 (Lerman and Sedik, 2008). The household plots manage 20% of arable land and produce 65% of gross agricultural output (GAO). Dehkan farms manage 65% of arable land and produce close to 30% of GAO. The remaining 15% of arable land is held in agricultural enterprises – the rapidly shrinking sector of corporate farms that succeeded the Soviet kolkhozes and sovkhozes and today produces less than 10% of GAO (TajStat 2011) The survey conducted in May 2011 focused on dehkan farms, as budgetary constraints precluded the inclusion of household plots. A total of 142 dehkan farms were surveyed in face-to-face interviews. They were sampled from 17 districts across all four regions – Sughd, Khatlon, RRP, and GBAO. The districts were selected so as to represent different agro-climatic zones, different vulnerability zones (based on the World Bank (2011) vulnerability assessment), and different food-insecurity zones (based on WFP/IPC assessments). Within each district, 3-4 jamoats were chosen at random and 2-3 farms were selected in each jamoat from lists provided by jamoat administration so as to maximize the variability by farm characteristics. The sample design by region/district is presented in Table A, which also shows the agro-climatic zone and the food security phase for each district. The sample districts are superimposed on a map of food security phases based on IPC April 2011.

Dimethyloxalylglycine stabilizes HIF-1α in cultured human endothelial cells and increases random-pattern skin flap survival in vivo

The goal of this study was to evaluate in vitro and in vivo the effects of up-regulation of the proangiogenic hypoxia inducible factor (HIF)-1α induced by dimethyloxalylglycine on endothelial cell cultures and on skin flap survival.