Synaptic partner prediction from point annotations in insect brains

High-throughput electron microscopy allows recording of lar- ge stacks of neural tissue with sufficient resolution to extract the wiring diagram of the underlying neural network. Current efforts to automate this process focus mainly on the segmentation of neurons. However, in order to recover a wiring diagram, synaptic partners need to be identi- fied as well. This is especially challenging in insect brains like Drosophila melanogaster, where one presynaptic site is associated with multiple post- synaptic elements. Here we propose a 3D U-Net architecture to directly identify pairs of voxels that are pre- and postsynaptic to each other. To that end, we formulate the problem of synaptic partner identification as a classification problem on long-range edges between voxels to encode both the presence of a synaptic pair and its direction. This formulation allows us to directly learn from synaptic point annotations instead of more ex- pensive voxel-based synaptic cleft or vesicle annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and improve over the current state of the art, producing 3% fewer errors than the next best method

Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available

Clinically stable very low birthweight infants are at risk for recurrent tissue glucose fluctuations even after fully established enteral nutrition

Objective: In previous cases, we have observed occasional hypoglycaemic episodes in preterm infants after initial intensive care. In this prospective study, we determined the frequency and severity of abnormal tissue glucose (TG) in clinically stable preterm infants on full enteral nutrition. Methods: Preterm infants born at <1000 g (n=23; G1) and birth weight 1000–1500 g (n=18; G2) were studied at a postmenstrual age of 32±2 weeks (G1) and 33±2 weeks (G2). Infants were fed two or three hourly, according to a standard bolus-nutrition protocol, and continuous subcutaneous glucose measurements were performed for 72 h. Normal glucose values were assumed at ≥2.5 mmol/L (45 mg/dL) and ≤8.3 mmol/L (150 mg/dL). Frequency, severity and duration of glucose values beyond normal values were determined. Results: We observed asymptomatic low TG values in 39% of infants in G1 and in 44% in G2. High TG values were detected in 83% in G1 and 61% in G2. Infants in G1 experienced prolonged and more severe low TG episodes, and also more frequent and severe high TG episodes. In G1 and G2, 87% and 67% of the infants, respectively, showed glucose fluctuations characterised by rapid glucose increase followed by a rapid glucose drop after feeds. In more mature infants, glucose fluctuations were less pronounced and less dependent on enteral feeds. Conclusions: Clinically stable well-developing preterm infants beyond their initial period of intensive care show interstitial glucose instabilities exceeding values as low as 2.5 mmol/L and as high as 8.3 mmol/L. This novel observation may play an important role for the susceptibility of these high-risk infants for the development of the metabolic syndrome

Impact of Po Valley emissions on the highest glacier of the Eastern European Alps

Abstract. In June 2009, we conducted the first extensive glaciological survey of Alto dell'Ortles, the uppermost glacier of Mt. Ortles (3905 m a.s.l.), the highest summit of the Eastern European Alps. This section of the Alps is located in a rain shadow and is characterized by the lowest precipitation rate in the entire Alpine arc. Mt. Ortles offers a unique opportunity to test deposition mechanisms of chemical species that until now were studied only in the climatically-different western sector. We analyzed snow samples collected on Alto dell'Ortles from a 4.5 m snow-pit at 3830 m a.s.l., and we determined a large suite of trace elements and ionic compounds that comprise the atmospheric deposition over the past two years. Trace element concentrations measured in snow samples are extremely low with mean concentrations at pg g−1 levels. Only Al and Fe present median values of 1.8 and 3.3 ng g−1, with maximum concentrations of 21 and 25 ng g−1. The median crustal enrichment factor (EFc) values for Be, Rb, Sr, Ba, U, Li, Al, Ca, Cr, Mn, Fe, Co, Ga and V are lower than 10 suggesting that these elements originated mainly from soil and mineral aerosol. EFc higher than 100 are reported for Zn (118), Ag (135), Bi (185), Sb (401) and Cd (514), demonstrating the predominance of non-crustal depositions and suggesting an anthropogenic origin. Our data show that the physical stratigraphy and the chemical signals of several species were well preserved in the uppermost snow of the Alto dell'Ortles glacier. A clear seasonality emerges from the data as the summer snow is more affected by anthropogenic and marine contributions while the winter aerosol flux is dominated by crustal sources. For trace elements, the largest mean EFc seasonal variations are displayed by V (with a factor of 3.8), Sb (3.3), Cu (3.3), Pb (2.9), Bi (2.8), Cd (2.1), Zn (1.9), Ni (1.8), Ag (1.8), As (1.7) and Co (1.6). When trace species ratios in local and Po Valley emissions are compared with those in Alto dell'Ortles snow, the deposition on Mt. Ortles is clearly linked with Po Valley summer emissions. Despite climatic differences between the Eastern and Western Alps, trace element ratios from Alto dell'Ortles are comparable with those obtained from high-altitude glaciers in the Western Alps, suggesting similar sources and transport processes at seasonal time scales in these two distinct areas. In particular, the large changes in trace element concentrations both in the Eastern and Western Alps appear to be more related to the regional vertical structure of the troposphere rather than the synoptic weather patterns

Sustainable agriculture: Recognizing the potential of conflict as a positive driver for transformative change

Transformative changes in agriculture at multiple scales are needed to ensure sustainability, i.e. achieving food security while fostering social justice and environmental integrity. These transformations go beyond technological fixes and require fundamental changes in cognitive, relational, structural and functional aspects of agricultural systems. However, research on agricultural transformations fails to engage deeply with underlying social aspects such as differing perceptions of sustainability, uncertainties and ambiguities, politics of knowledge, power imbalances and deficits in democracy. In this paper, we suggest that conflict is one manifestation of such underlying social aspects. We present an original conceptualization and analytical framework, wherein conflict is recognized as an important motor for redistribution of power and leverage for social learning that—if addressed through a conflict transformation process—could potentially create a step-change in agricultural transformation towards greater sustainability. Our analysis, building on an extensive literature review and empirical case studies from around the world, suggests a novel approach to guide future transdisciplinary research that can support agricultural transformations towards sustainability

Big data in nanoscale connectomicx, and the greed for training labels

The neurosciences have developed methods that outpace most other biomedical fields in terms of acquired bytes. We review how the information content and analysis challenge of such data indicates that electron microscopy (EM)-based connectomics is an especially hard problem. Here, as in many other current machine learing applications, the need for excessive amounts of labelled data while utilizing only a small fraction of available raw image data for algorithm training illustrates the still fundamental gap between artificial and biological intelligence. Substantial improvements of label and energy efficiency in machine learning may be required to address the formidable challenge of acquiring the nanoscale connectome of a human brain

Synaptic Partner Prediction from Point Annotations in Insect Brains

High-throughput electron microscopy allows recording of large stacks of neural tissue with sufficient resolution to extract the wiring diagram of the underlying neural network. Current efforts to automate this process focus mainly on the segmentation of neurons. However, in order to recover a wiring diagram, synaptic partners need to be identified as well. This is especially challenging in insect brains like Drosophila melanogaster, where one presynaptic site is associated with multiple postsynaptic elements. Here we propose a 3D U-Net architecture to directly identify pairs of voxels that are pre- and postsynaptic to each other. To that end, we formulate the problem of synaptic partner identification as a classification problem on long-range edges between voxels to encode both the presence of a synaptic pair and its direction. This formulation allows us to directly learn from synaptic point annotations instead of more expensive voxel-based synaptic cleft or vesicle annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and improve over the current state of the art, producing 3% fewer errors than the next best method (Code at: https://github.com/juliabuhmann/syntist)