We perform a geometric analysis for the sky map of the IceCube TeV-PeV neutrino excess and test its compatibility with the sky map of decaying dark matter signals in our galaxy. We have found that a galactic decaying dark matter component in general improve the goodness of the fit of our model, although the pure isotropic hypothesis has a better fit than the pure dark matter one. We also consider several representative decaying dark matter, which can provide a good fit to the observed spectrum at IceCube with a dark matter lifetime of around 12 orders of magnitude longer than the age of the universe.

Satellite data is accumulating that suggests and constrains dark matter physics. We argue there is a very well motivated theoretical preexisting framework consistent with dark matter annihilation being observed by the PAMELA satellite detector. The dark matter is (mainly) the neutral W boson superpartner, the wino with mass below 200 GeV. Using the program GALPROP we study the annihilation products and backgrounds together. Antimatter and gammas from annihilating winos contribute below this energy. We explain why PAMELA data does not imply no antiproton signal was observed by PAMELA or earlier experiments, and explain why the antiproton analysis was misunderstood by earlier papers. Wino annihilation does not describe the Fermi e+ + e- data (except partially below ~ 100 GeV). At higher energies we expect astrophysical mechanisms, and we simply parameterize them so the combination can describe all the data. We emphasize several predictions for satellite data to test the wino interpretation, particularly the turndown of the positron and antiproton spectra above 100 GeV. Most other interpretations require a large rise in the positron rates above 100 GeV. We focus on studying this well-motivated and long predicted wino interpretation, rather than comparisons with other interpretations. We emphasize that interpretations also depend very strongly on assumptions about the cosmological history of the universe, and on propagation of antiprotons and positrons in the galaxy. The winos PAMELA is observing arose from some non-thermal sources such as moduli decay rather than a universe that cooled in thermal equilibrium after the big bang. Then it is appropriate to normalize the wino density to the local relic density, and no "boost factors" are needed to obtain the reported PAMELA rates.

We point out that in theories where the gravitino mass, $M_{3/2}$, is in the range (10–50) TeV, with soft-breaking scalar masses and trilinear couplings of the same order, there exists a robust region of parameter space where the conditions for electroweak symmetry breaking (EWSB) are satisfied without large imposed cancellations. Compactified string/M-theory with stabilized moduli that satisfy cosmological constraints generically require a gravitino mass greater than about 30 TeV and provide the natural explanation for this phenomenon. We find that even though scalar masses and trilinear couplings (and the soft-breaking B parameter) are of order (10–50) TeV, the Higgs vev takes its expected value and the μ parameter is naturally of order a TeV. The mechanism provides a natural solution to the cosmological moduli and gravitino problems with EWSB.

Just as genomes revolutionized molecular genetics, connectomes (maps of neurons and synapses) are transforming neuroscience. To date, the only species with complete connectomes are worms ^1-3 and sea squirts4 (10 ³ -10 ⁴ synapses). By contrast, the fruit fly is more complex (10 ⁸ synaptic connections), with a brain that supports learning and spatial memory ^5,6 and an intricate ventral nerve cord analogous to the vertebrate spinal cord ^7-11 . Here we report the first densely reconstructed adult fly connectome that unites the brain and ventral nerve cord, and we leverage this resource to investigate principles of neural control. We show that effector neurons (motor neurons, endocrine cells and efferent neurons targeting the viscera) are primarily influenced by sensory neurons in the same body part, forming local feedback loops. These local loops are linked by long-range circuits involving ascending and descending neurons organized into behavior-centric modules. Single ascending and descending neurons are often positioned to influence the voluntary movements of multiple body parts, together with the endocrine cells or visceral organs that support those movements. Brain regions involved in learning and navigation supervise these circuits. These results reveal an architecture that is distributed, parallelized and embodied, reminiscent of distributed control architectures in engineered systems ^12,13 .

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (∼1mm ³ ) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE’s data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.