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

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

No detrimental effect on renal function during long-term use of fluvastatin in renal transplant recipients in the Assessment of Lescol in Renal Transplantation (ALERT) study

Alert studien er den eneste og den største randomiserte kontrollerte studie hos nyretransplanterte pasienter med nyre og hjertekar hendelser, hvor 2102 pasienter ble fulgt i inntil 8 år. Andelen hjertedød ble redusert med 26% med bruk av det kolesterol senkende middelet fluvastatin. Funnene fra Alert førte til at fluvastatin er blitt rutine behandling hos disse pasienter verden over. Alert materialet er det eneste prospektive materiale som gir mulighet til å vurdere betydningen av potensielle risikofaktorer for disse hendelser. I sin avhandling ”Risikofaktorer for kardiale og renale endepunkter hos nyretransplanterte pasienter” undersøkte Sadollah Abedini og medarbeiderne fire viktige problemstillinger som tidligere ikke er undersøkt hos nyretransplanterte i en prospektiv studie. • Det har vært reist tvil om tryggheten ved statin behandling hos nyre transplanterte mht negativ effekt på transplantat funksjonen. Studien viste at det var trygt å bruke fluvastatin uten negativ virkning på transplantat funksjonen. • Forekomsten og risikofaktorene for slag er også dårlig belyst hos nyretransplanterte. Abedini og medarbeiderne fant at slag forekommer hyppig, og at risikofaktorer varierer i henhold til type slag. • IL-6 og spesielt hsCRP er inflammasjonsmarkører som er etablert risikofaktor for hjertekarsykdom hos ikke-transplanterte, men betydningen hos nyretransplanterte er ukjent. Resultatene fra denne avhandling viser at inflammasjon markørene IL-6 og hsCRP er uavhengig assosiert med hjertekar hendelser og dødelighet. • Asymmetrical DiMethylArginine (ADMA) er en relativ ny aktør som risikofaktor. Studien i denne avhandlingen viser for første gang at ADMA er en uavhengigrisikofaktor for hjertekar hendelser, slag, dødelighet og nedsatt transplantat funksjon hos nyretransplanterte

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)

Beneficial effect of early initiation of lipid-lowering therapy following renal transplantation

Background. Renal transplant recipients have a significantly reduced life expectancy, largely due to premature cardiovascular disease. The aim of the current analysis was to investigate the importance of time of initiation of therapy after transplantation, on the benefits of statin therapy. Methods. 2102 renal transplant recipients with total cholesterol levels of 4.0-9.0 mmol/l were randomly assigned to treatment with fluvastatin (n = 1050) or placebo (n = 1052) and followed for a mean time of 5.1 years. The end-points were major cardiac events. The average median time from transplantation to randomization was 4.5 years (range: 0.5-29 years). Results. In patients starting treatment with fluvastatin 6 years, respectively. The risk reduction for patients initiating therapy with fluvastatin at years 0-2 (compared with >6 years) following transplantation was 59% (RR: 0.41; 95% CI: 0.18-0.92; P = 0.0328). This is also reflected in total time on renal replacement therapy: in patients in the first quartile (120 months) (P = 0.033). Conclusions. Our data support an early introduction of fluvastatin therapy in a population of transplant recipients at high risk of premature coronary heart diseas