Black Phosphorus Degradation during Intercalation and Alloying in Batteries

Numerous layered materials are being recognized as promising candidates for high-performance alkali-ion battery anodes, but black phosphorus (BP) has received particular attention. This is due to its high specific capacity, due to a mixed alkali-ion storage mechanism (intercalation-alloying), and fast alkali-ion transport within its layers. Unfortunately, BP based batteries are also commonly associated with serious irreversible losses and poor cycling stability. This is known to be linked to alloying, but there is little experimental evidence of the morphological, mechanical, or chemical changes that BP undergoes in operational cells and thus little understanding of the factors that must be mitigated to optimize performance. Here the degradation mechanisms of BP alkali-ion battery anodes are revealed through operando electrochemical atomic force microscopy (EC-AFM) and ex situ spectroscopy. Among other phenomena, BP is observed to wrinkle and deform during intercalation but suffers from complete structural breakdown upon alloying. The solid electrolyte interphase (SEI) is also found to be unstable, nucleating at defects before spreading across the basal planes but then disintegrating upon desodiation, even above alloying potentials. By directly linking these localized phenomena with the whole-cell performance, we can now engineer stabilizing protocols for next-generation high-capacity alkali-ion batteries

Prevalencia de depresión y ansiedad y variables asociadas en gestantes de Bucaramanga y Floridablanca (Santander, Colombia)

Abstract Introduction. Depression and anxiety are frequent conditions in women of childbearing age and are associated with adverse perinatal outcomes. The prevalence in Colombian population of low obstetric risk is unknown. Objective. Establish the prevalence of depression and gestational anxiety, and the associated demographic, psychosocial and clinical variables, in women attending prenatal care in Bucaramanga and Floridablanca, Santander. Methodology. A cross-sectional, descriptive study applying a survey and Edinburgh Postnatal Depression Scales, Zung Anxiety Self-Assessment, family apgar and perceived social support questionnaire. The prevalence ratios were established with 95% confidence intervals. Results. A total of 244 pregnant women were studied, with an average of 24.8 years. The prevalence of depression was 24.6%, 95% CI (19.1-30.0) and anxiety was 25.8%, 95% CI (20.3-31.3). Depression is associated with a family history of depression in first or second degree, prevalence ratio: 2.0, 95% CI (1.1-3.7); presence of anxiety, prevalence ratio: 22.5, 95% CI (9.4-53.7); and alcohol consumption, prevalence ratio: 2.9, 95% CI (1.1-8.2). A protective factor was found to have two sources of income (couple and family), prevalence ratio: 0.6, 95% CI (0.4-0.8). Additionally, anxiety was associated with the presence of depression, prevalence ratio: 13.3, 95% CI (6.3-28.1); presence of psychological violence, prevalence ratio: 2.3, 95% CI (1.1-4.8) and trusting the couple, prevalence ratio: 3.4, 95% CI (1.5-8.2). Conclusion. There is a strong association between anxiety and depression so it should be screened during pregnancy. Keywords: Pregnancy; Depression; Anxiety; Prevalence; Risk factors.Introducción. La depresión y la ansiedad son condiciones frecuentes en la mujer en edad fértil y están asociadas a desenlaces perinatales adversos. Se desconoce la prevalencia en población colombiana de bajo riesgo obstétrico. Objetivo. Determinar la prevalencia de depresión y ansiedad gestacional, y las variables demográficas, psicosociales y clínicas asociadas, en mujeres consultantes a control prenatal en Bucaramanga y Floridablanca, Santander. Metodología. Estudio descriptivo, transversal aplicando una encuesta y las escalas de Depresión Posnatal de Edimburgo, autoevaluación de ansiedad de Zung, apgar familiar y cuestionario de apoyo social percibido. Se establecieron las razones de prevalencia con intervalos de confianza del 95%. Resultados. Se estudiaron 244 gestantes, con un promedio de 24.8 años. La prevalencia de depresión fue de 24.6%, IC 95% (19.1-30.0) y ansiedad fue de 25.8%, IC 95% (20.3-31.3). La depresión está asociada con antecedente familiar de depresión en primer o segundo grado, razón de prevalencia: 2.0, IC 95% (1.1-3.7); presencia de ansiedad, razón de prevalencia: 22.5, IC 95% (9.4-53.7); y consumo de alcohol, razón de prevalencia: 2.9, IC 95% (1.1-8.2). Como factor protector se encontró tener dos fuentes de ingresos (pareja y familia), razón de prevalencia: 0.6, IC 95% (0.4-0.8). Adicionalmente, la ansiedad se asoció a presencia de depresión, razón de prevalencia: 13.3, IC 95% (6.3-28.1); presencia de violencia psicológica, razón de prevalencia: 2.3, IC 95% (1.1-4.8) y tener confianza en la pareja, razón de prevalencia: 3.4, IC 95% (1.5-8.2). Conclusión. Existe una fuerte asociación entre ansiedad y depresión por lo que debe ser tamizada durante la gestación. &nbsp

Bridging Time Scales in Cellular Decision Making with a Stochastic Bistable Switch

Cellular transformations which involve a significant phenotypical change of the cell's state use bistable biochemical switches as underlying decision systems. In this work, we aim at linking cellular decisions taking place on a time scale of years to decades with the biochemical dynamics in signal transduction and gene regulation, occuring on a time scale of minutes to hours. We show that a stochastic bistable switch forms a viable biochemical mechanism to implement decision processes on long time scales. As a case study, the mechanism is applied to model the initiation of follicle growth in mammalian ovaries, where the physiological time scale of follicle pool depletion is on the order of the organism's lifespan. We construct a simple mathematical model for this process based on experimental evidence for the involved genetic mechanisms. Despite the underlying stochasticity, the proposed mechanism turns out to yield reliable behavior in large populations of cells subject to the considered decision process. Our model explains how the physiological time constant may emerge from the intrinsic stochasticity of the underlying gene regulatory network. Apart from ovarian follicles, the proposed mechanism may also be of relevance for other physiological systems where cells take binary decisions over a long time scale.Comment: 14 pages, 4 figure

The BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data

<p>Abstract</p> <p>Background</p> <p>Neuroimaging researchers have developed rigorous community data and metadata standards that encourage meta-analysis as a method for establishing robust and meaningful convergence of knowledge of human brain structure and function. Capitalizing on these standards, the BrainMap project offers databases, software applications, and other associated tools for supporting and promoting quantitative coordinate-based meta-analysis of the structural and functional neuroimaging literature.</p> <p>Findings</p> <p>In this report, we describe recent technical updates to the project and provide an educational description for performing meta-analyses in the BrainMap environment.</p> <p>Conclusions</p> <p>The BrainMap project will continue to evolve in response to the meta-analytic needs of biomedical researchers in the structural and functional neuroimaging communities. Future work on the BrainMap project regarding software and hardware advances are also discussed.</p

Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data

Clustering is a fundamental data processing technique. While clustering of static (vector based) data and of fixed window size time series have been well explored, dynamic clustering of spatiotemporal data has been little researched if at all. Especially when patterns of changes (events) in the data across space and time have to be captured and understood. The paper presents novel methods for clustering of spatiotemporal data using the NeuCube spiking neural network (SNN) architecture. Clusters of spatiotemporal data were created and modified on-line in a continuous, incremental way, where spatiotemporal relationships of changes in variables are incrementally learned in a 3D SNN model and the model connectivity and spiking activity are incrementally clustered. Two clustering methods were proposed for SNN, one performed during unsupervised and one—during supervised learning models. Before submitted to the models, the data is encoded as spike trains, a spike representing a change in the variable value (an event). During the unsupervised learning, the cluster centres were predefined by the spatial locations of the input data variables in a 3D SNN model. Then clusters are evolving during the learning, i.e. they are adapted continuously over time reflecting the dynamics of the changes in the data. In the supervised learning, clusters represent the dynamic sequence of neuron spiking activities in a trained SNN model, specific for a particular class of data or for an individual instance. We illustrate the proposed clustering method on a real case study of spatiotemporal EEG data, recorded from three groups of subjects during a cognitive task. The clusters were referred back to the brain data for a better understanding of the data and the processes that generated it. The cluster analysis allowed to discover and understand differences on temporal sequences and spatial involvement of brain regions in response to a cognitive task

Anatomical Global Spatial Normalization

Anatomical global spatial normalization (aGSN) is presented as a method to scale high-resolution brain images to control for variability in brain size without altering the mean size of other brain structures. Two types of mean preserving scaling methods were investigated, “shape preserving” and “shape standardizing”. aGSN was tested by examining 56 brain structures from an adult brain atlas of 40 individuals (LPBA40) before and after normalization, with detailed analyses of cerebral hemispheres, all gyri collectively, cerebellum, brainstem, and left and right caudate, putamen, and hippocampus. Mean sizes of brain structures as measured by volume, distance, and area were preserved and variance reduced for both types of scale factors. An interesting finding was that scale factors derived from each of the ten brain structures were also mean preserving. However, variance was best reduced using whole brain hemispheres as the reference structure, and this reduction was related to its high average correlation with other brain structures. The fractional reduction in variance of structure volumes was directly related to ρ2, the square of the reference-to-structure correlation coefficient. The average reduction in variance in volumes by aGSN with whole brain hemispheres as the reference structure was approximately 32%. An analytical method was provided to directly convert between conventional and aGSN scale factors to support adaptation of aGSN to popular spatial normalization software packages

Spatially anisotropic S=1 square-lattice antiferromagnet with single-ion anisotropy realized in a Ni(II) pyrazine- n,n′ -dioxide coordination polymer

The Ni(NCS)2(pyzdo)2 coordination polymer is found to be an S=1 spatially anisotropic square lattice with easy-axis single-ion anisotropy. This conclusion is based upon considering in concert the experimental probes x-ray diffraction, magnetic susceptibility, magnetic-field-dependent heat capacity, muon-spin relaxation, neutron diffraction, neutron spectroscopy, and pulsed-field magnetization. Long-range antiferromagnetic (AFM) order develops at TN=18.5K. Although the samples are polycrystalline, there is an observable spin-flop transition and saturation of the magnetization at ≈80T. Linear spin-wave theory yields spatially anisotropic exchanges within an AFM square lattice, Jx=0.235meV, Jy=2.014meV, and an easy-axis single-ion anisotropy D=-1.622meV (after renormalization). The anisotropy of the exchanges is supported by density functional theory

Primary cilia elongation in response to interleukin-1 mediates the inflammatory response

Primary cilia are singular, cytoskeletal organelles present in the majority of mammalian cell types where they function as coordinating centres for mechanotransduction, Wnt and hedgehog signalling. The length of the primary cilium is proposed to modulate cilia function, governed in part by the activity of intraflagellar transport (IFT). In articular cartilage, primary cilia length is increased and hedgehog signaling activated in osteoarthritis (OA). Here, we examine primary cilia length with exposure to the quintessential inflammatory cytokine interleukin-1 (IL-1), which is up-regulated in OA. We then test the hypothesis that the cilium is involved in mediating the downstream inflammatory response. Primary chondrocytes treated with IL-1 exhibited a 50 % increase in cilia length after 3 h exposure. IL-1-induced cilia elongation was also observed in human fibroblasts. In chondrocytes, this elongation occurred via a protein kinase A (PKA)-dependent mechanism. G-protein coupled adenylate cyclase also regulated the length of chondrocyte primary cilia but not downstream of IL-1. Chondrocytes treated with IL-1 exhibit a characteristic increase in the release of the inflammatory chemokines, nitric oxide and prostaglandin E2. However, in cells with a mutation in IFT88 whereby the cilia structure is lost, this response to IL-1 was significantly attenuated and, in the case of nitric oxide, completely abolished. Inhibition of IL-1-induced cilia elongation by PKA inhibition also attenuated the chemokine response. These results suggest that cilia assembly regulates the response to inflammatory cytokines. Therefore, the cilia proteome may provide a novel therapeutic target for the treatment of inflammatory pathologies, including OA

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample