Non-parametric statistical thresholding for sparse magnetoencephalography source reconstructions.

Uncovering brain activity from magnetoencephalography (MEG) data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources. Sparse reconstruction algorithms, such as Champagne, show great promise in that they provide focal brain activations robust to these confounds. In this paper, we address the technical considerations of statistically thresholding brain images obtained from sparse reconstruction algorithms. The source power distribution of sparse algorithms makes this class of algorithms ill-suited to "conventional" techniques. We propose two non-parametric resampling methods hypothesized to be compatible with sparse algorithms. The first adapts the maximal statistic procedure to sparse reconstruction results and the second departs from the maximal statistic, putting forth a less stringent procedure that protects against spurious peaks. Simulated MEG data and three real data sets are utilized to demonstrate the efficacy of the proposed methods. Two sparse algorithms, Champagne and generalized minimum-current estimation (G-MCE), are compared to two non-sparse algorithms, a variant of minimum-norm estimation, sLORETA, and an adaptive beamformer. The results, in general, demonstrate that the already sparse images obtained from Champagne and G-MCE are further thresholded by both proposed statistical thresholding procedures. While non-sparse algorithms are thresholded by the maximal statistic procedure, they are not made sparse. The work presented here is one of the first attempts to address the problem of statistically thresholding sparse reconstructions, and aims to improve upon this already advantageous and powerful class of algorithm

White matter connectome correlates of auditory over-responsivity: edge density imaging and machine-learning classifiers

Sensory over-responsivity (SOR) commonly involves auditory and/or tactile domains, and can affect children with or without additional neurodevelopmental challenges. In this study, we examined white matter microstructural and connectome correlates of auditory over-responsivity (AOR), analyzing prospectively collected data from 39 boys, aged 8–12 years. In addition to conventional diffusion tensor imaging (DTI) maps – including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD); we used DTI and high-resolution T1 scans to develop connectome Edge Density (ED) maps. The tract-based spatial statistics was used for voxel-wise comparison of diffusion and ED maps. Then, stepwise penalized logistic regression was applied to identify independent variable (s) predicting AOR, as potential imaging biomarker (s) for AOR. Finally, we compared different combinations of machine learning algorithms (i.e., naïve Bayes, random forest, and support vector machine (SVM) and tract-based DTI/connectome metrics for classification of children with AOR. In direct sensory phenotype assessment, 15 (out of 39) boys exhibited AOR (with or without neurodevelopmental concerns). Voxel-wise analysis demonstrates extensive impairment of white matter microstructural integrity in children with AOR on DTI maps – evidenced by lower FA and higher MD and RD; moreover, there was lower connectome ED in anterior-superior corona radiata, genu and body of corpus callosum. In stepwise logistic regression, the average FA of left superior longitudinal fasciculus (SLF) was the single independent variable distinguishing children with AOR (p = 0.007). Subsequently, the left SLF average FA yielded an area under the curve of 0.756 in receiver operating characteristic analysis for prediction of AOR (p = 0.008) as a region-of-interest (ROI)-based imaging biomarker. In comparative study of different combinations of machine-learning models and DTI/ED metrics, random forest algorithms using ED had higher accuracy for AOR classification. Our results demonstrate extensive white matter microstructural impairment in children with AOR, with specifically lower connectomic ED in anterior-superior tracts and associated commissural pathways. Also, average FA of left SLF can be applied as ROI-based imaging biomarker for prediction of SOR. Finally, machine-learning models can provide accurate and objective image-based classifiers for identification of children with AOR based on white matter tracts connectome ED

Refocusing multiple stressor research around the targets and scales of ecological impacts

Ecological communities face a variety of environmental and anthropogenic stressors acting simultaneously. Stressor impacts can combine additively or can interact, causing synergistic or antagonistic effects. Our knowledge of when and how interactions arise is limited, as most models and experiments only consider the effect of a small number of non-interacting stressors at one or few scales of ecological organization. This is concerning because it could lead to significant underestimations or overestimations of threats to biodiversity. Furthermore, stressors have been largely classified by their source rather than by the mechanisms and ecological scales at which they act (the target). Here, we argue, first, that a more nuanced classification of stressors by target and ecological scale can generate valuable new insights and hypotheses about stressor interactions. Second, that the predictability of multiple stressor effects, and consistent patterns in their impacts, can be evaluated by examining the distribution of stressor effects across targets and ecological scales. Third, that a variety of existing mechanistic and statistical modelling tools can play an important role in our framework and advance multiple stressor research

Suppression of mutant Kirsten-RAS (KRASG12D)-driven pancreatic carcinogenesis by dual-specificity MAP kinase phosphatases 5 and 6

The cytoplasmic phosphatase DUSP6 and its nuclear counterpart DUSP5 are negative regulators of RAS/ERK signalling. Here we use deletion of either Dusp5 or Dusp6 to explore the roles of these phosphatases in a murine model of KRASG12D-driven pancreatic cancer. By 56-days, loss of either DUSP5 or DUSP6 causes a significant increase in KRASG12D-driven pancreatic hyperplasia. This is accompanied by increased pancreatic acinar to ductal metaplasia (ADM) and the development of pre-neoplastic pancreatic intraepithelial neoplasia (PanINs). In contrast, by 100-days, pancreatic hyperplasia is reversed with significant atrophy of pancreatic tissue and weight loss observed in animals lacking either DUSP5 or DUSP6. On further ageing, Dusp6−/− mice display accelerated development of metastatic pancreatic ductal adenocarcinoma (PDAC), while in Dusp5−/− animals, although PDAC development is increased this process is attenuated by atrophy of pancreatic acinar tissue and severe weight loss in some animals before cancer could progress. Our data suggest that despite a common target in the ERK MAP kinase, DUSP5 and DUSP6 play partially non-redundant roles in suppressing oncogenic KRASG12D signalling, thus retarding both tumour initiation and progression. Our data suggest that loss of either DUSP5 or DUSP6, as observed in certain human tumours, including the pancreas, could promote carcinogenesis

Ibrutinib Unmasks Critical Role of Bruton Tyrosine Kinase in Primary CNS Lymphoma.

Bruton tyrosine kinase (BTK) links the B-cell antigen receptor (BCR) and Toll-like receptors with NF-κB. The role of BTK in primary central nervous system (CNS) lymphoma (PCNSL) is unknown. We performed a phase I clinical trial with ibrutinib, the first-in-class BTK inhibitor, for patients with relapsed or refractory CNS lymphoma. Clinical responses to ibrutinib occurred in 10 of 13 (77%) patients with PCNSL, including five complete responses. The only PCNSL with complete ibrutinib resistance harbored a mutation within the coiled-coil domain of CARD11, a known ibrutinib resistance mechanism. Incomplete tumor responses were associated with mutations in the B-cell antigen receptor-associated protein CD79B

White Matter Microstructure Associations of Cognitive and Visuomotor Control in Children: A Sensory Processing Perspective

Objective: Recent evidence suggests that co-occurring deficits in cognitive control and visuomotor control are common to many neurodevelopmental disorders. Specifically, children with sensory processing dysfunction (SPD), a condition characterized by sensory hyper/hypo-sensitivity, show varying degrees of overlapping attention and visuomotor challenges. In this study, we assess associations between cognitive and visuomotor control abilities among children with and without SPD. In this same context, we also examined the common and unique diffusion tensor imaging (DTI) tracts that may support the overlap of cognitive control and visuomotor control.Method: We collected cognitive control and visuomotor control behavioral measures as well as DTI data in 37 children with SPD and 25 typically developing controls (TDCs). We constructed regressions to assess for associations between behavioral performance and mean fractional anisotropy (FA) in selected regions of interest (ROIs).Results: We observed an association between behavioral performance on cognitive control and visuomotor control. Further, our findings indicated that FA in the anterior limb of the internal capsule (ALIC), the anterior thalamic radiation (ATR), and the superior longitudinal fasciculus (SLF) are associated with both cognitive control and visuomotor control, while FA in the superior corona radiata (SCR) uniquely correlate with cognitive control performance and FA in the posterior limb of the internal capsule (PLIC) and the cerebral peduncle (CP) tract uniquely correlate with visuomotor control performance.Conclusions: These findings suggest that children who demonstrate lower cognitive control are also more likely to demonstrate lower visuomotor control, and vice-versa, regardless of clinical cohort assignment. The overlapping neural tracts, which correlate with both cognitive and visuomotor control suggest a possible common neural mechanism supporting both control-based processes

Defining a sustainable development target space for 2030 and 2050

With the establishment of the sustainable development goals (SDGs), countries worldwide agreed to a prosperous, socially inclusive, and environmentally sustainable future for all. This ambition, however, exposes a critical gap in science-based insights, namely on how to achieve the 17 SDGs simultaneously. Quantitative goal-seeking scenario studies could help explore the needed systems' transformations. This requires a clear definition of the "target space." The 169 targets and 232 indicators used for monitoring SDG implementation cannot be used for this; they are too many, too broad, unstructured, and sometimes not formulated quantitatively. Here, we propose a streamlined set of science-based indicators and associated target values that are quantifiable and actionable to make scenario analysis meaningful, relevant, and simple enough to be transparent and communicable. The 36 targets are based on the SDGs, existing multilateral agreements, literature, and expert assessment. They include 2050 as a longer-term reference point. This target space can guide researchers in developing new sustainable development pathways

MicroRNA-135b promotes cancer progression by acting as a downstream effector of oncogenic pathways in colon cancer

MicroRNA deregulation is frequent in human colorectal cancers (CRCs), but little is known as to whether it represents a bystander event or actually drives tumor progression in vivo. We show that miR-135b overexpression is triggered in mice and humans by APC loss, PTEN/PI3K pathway deregulation, and SRC overexpression and promotes tumor transformation and progression. We show that miR-135b upregulation is common in sporadic and inflammatory bowel disease-associated human CRCs and correlates with tumor stage and poor clinical outcome. Inhibition of miR-135b in CRC mouse models reduces tumor growth by controlling genes involved in proliferation, invasion, and apoptosis. We identify miR-135b as a key downsteam effector of oncogenic pathways and a potential target for CRC treatment

ADAM17-dependent proteolysis of L-selectin promotes early clonal expansion of cytotoxic T cells

L-selectin on T-cells is best known as an adhesion molecule that supports recruitment of blood-borne naïve and central memory cells into lymph nodes. Proteolytic shedding of the ectodomain is thought to redirect activated T-cells from lymph nodes to sites of infection. However, we have shown that activated T-cells re-express L-selectin before lymph node egress and use L-selectin to locate to virus-infected tissues. Therefore, we considered other roles for L-selectin proteolysis during T cell activation. In this study, we used T cells expressing cleavable or non-cleavable L-selectin and determined the impact of L-selectin proteolysis on T cell activation in virus-infected mice. We confirm an essential and non-redundant role for ADAM17 in TCR-induced proteolysis of L-selectin in mouse and human T cells and show that L-selectin cleavage does not regulate T cell activation measured by CD69 or TCR internalisation. Following virus infection of mice, L-selectin proteolysis promoted early clonal expansion of cytotoxic T cells resulting in an 8-fold increase over T cells unable to cleave L-selectin. T cells unable to cleave L-selectin showed delayed proliferation in vitro which correlated with lower CD25 expression. Based on these results, we propose that ADAM17-dependent proteolysis of L-selectin should be considered a regulator of T-cell activation at sites of immune activity