Synapsin selectively controls the mobility of resting pool vesicles at hippocampal terminals

Presynaptic terminals are specialized sites for information transmission where vesicles fuse with the plasma membrane and are locally recycled. Recent work has extended this classical view, with the observation that a subset of functional vesicles is dynamically shared between adjacent terminals by lateral axonal transport. Conceptually, such transport would be expected to disrupt vesicle retention around the active zone, yet terminals are characterized by a high-density vesicle cluster, suggesting that counteracting stabilizing mechanisms must operate against this tendency. The synapsins are a family of proteins that associate with synaptic vesicles and determine vesicle numbers at the terminal, but their specific function remains controversial. Here, using multiple quantitative fluorescence-based approaches and electron microscopy, we show that synapsin is instrumental for resisting vesicle dispersion and serves as a regulatory element for controlling lateral vesicle sharing between synapses. Deleting synapsin disrupts the organization of presynaptic vesicle clusters, making their boundaries hard to define. Concurrently, the fraction of vesicles amenable to transport is increased, and more vesicles are translocated to the axon. Importantly, in neurons from synapsin knock-out mice the resting and recycling pools are equally mobile. Synapsin, when present, specifically restricts the mobility of resting pool vesicles without affecting the division of vesicles between these pools. Specific expression of synapsin IIa, the sole isoform affecting synaptic depression, rescues the knock-out phenotype. Together, our results show that synapsin is pivotal for maintaining synaptic vesicle cluster integrity and that it contributes to the regulated sharing of vesicles between terminals

Matrix metalloproteinase-2 is elevated in midtrimester amniotic fluid prior to the development of preeclampsia

<p>Abstract</p> <p>Objective</p> <p>To evaluate levels of matrix metalloproteinases (MMP) and their inhibitors (TIMP) in second trimester amniotic fluid of women with hypertensive disorders compared to normotensive women.</p> <p>Study Design</p> <p>Amniotic fluid was obtained from 133 women undergoing genetic second trimester amniocentesis. Zymography was performed for MMP characterization and an MMP-2 ELISA kit was used to determine MMP-2 levels. TIMP-2 expression was evaluated using western blot.</p> <p>Results</p> <p>Mean amniotic fluid MMP-2 and TIMP-2 levels were significantly higher in women who developed a hypertensive disorder compared to normotensive women (P < 0.0004 and P < 0.01, respectively). When subdivided into subgroups, amniotic fluid from women who eventually developed preeclampsia or superimposed preeclampsia showed significantly higher MMP-2 levels than normotensive women (P < 0.05). However, no statistical difference in MMP-2 levels was found between patients with gestational hypertension and normotensive patients.</p> <p>Conclusion</p> <p>Higher amniotic fluid MMP-2 and TIMP-2 levels are found in women who eventually develop preeclampsia.</p

Generalization Error in Deep Learning

Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. Thus, an important question is what makes deep neural networks able to generalize well from the training set to new data. In this article, we provide an overview of the existing theory and bounds for the characterization of the generalization error of deep neural networks, combining both classical and more recent theoretical and empirical results

Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

Smart premise selection is essential when using automated reasoning as a tool for large-theory formal proof development. A good method for premise selection in complex mathematical libraries is the application of machine learning to large corpora of proofs. This work develops learning-based premise selection in two ways. First, a newly available minimal dependency analysis of existing high-level formal mathematical proofs is used to build a large knowledge base of proof dependencies, providing precise data for ATP-based re-verification and for training premise selection algorithms. Second, a new machine learning algorithm for premise selection based on kernel methods is proposed and implemented. To evaluate the impact of both techniques, a benchmark consisting of 2078 large-theory mathematical problems is constructed,extending the older MPTP Challenge benchmark. The combined effect of the techniques results in a 50% improvement on the benchmark over the Vampire/SInE state-of-the-art system for automated reasoning in large theories.Comment: 26 page

Aggregate Plaque Volume by Coronary Computed Tomography Angiography Is Superior and Incremental to Luminal Narrowing for Diagnosis of Ischemic Lesions of Intermediate Stenosis Severity

ObjectivesThis study examined the performance of percent aggregate plaque volume (%APV), which represents cumulative plaque volume as a function of total vessel volume, by coronary computed tomography angiography (CTA) for identification of ischemic lesions of intermediate stenosis severity.BackgroundCoronary lesions of intermediate stenosis demonstrate significant rates of ischemia. Coronary CTA enables quantification of luminal narrowing and %APV.MethodsWe identified 58 patients with intermediate lesions (30% to 69% diameter stenosis) who underwent invasive angiography and fractional flow reserve. Coronary CTA measures included diameter stenosis, area stenosis, minimal lumen diameter (MLD), minimal lumen area (MLA) and %APV. %APV was defined as the sum of plaque volume divided by the sum of vessel volume from the ostium to the distal portion of the lesion. Fractional flow reserve ≤0.80 was considered diagnostic of lesion-specific ischemia. Area under the receiver operating characteristic curve and net reclassification improvement (NRI) were also evaluated.ResultsTwenty-two of 58 lesions (38%) caused ischemia. Compared with nonischemic lesions, ischemic lesions had smaller MLD (1.3 vs. 1.7 mm, p = 0.01), smaller MLA (2.5 vs. 3.8 mm2, p = 0.01), and greater %APV (48.9% vs. 39.3%, p < 0.0001). Area under the receiver operating characteristic curve was highest for %APV (0.85) compared with diameter stenosis (0.68), area stenosis (0.66), MLD (0.75), or MLA (0.78). Addition of %APV to other measures showed significant reclassification over diameter stenosis (NRI 0.77, p < 0.001), area stenosis (NRI 0.63, p = 0.002), MLD (NRI 0.62, p = 0.001), and MLA (NRI 0.43, p = 0.01).ConclusionsCompared with diameter stenosis, area stenosis, MLD, and MLA, %APV by coronary CTA improves identification, discrimination, and reclassification of ischemic lesions of intermediate stenosis severity