Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging Noise

Accurate analysis of microscopy images is hindered by the presence of noise. This noise is usually signal-dependent and often additionally correlated along rows or columns of pixels. Current self- and unsupervised denoisers can address signal-dependent noise, but none can reliably remove noise that is also row- or column-correlated. Here, we present the first fully unsupervised deep learning-based denoiser capable of handling imaging noise that is row-correlated as well as signal-dependent. Our approach uses a Variational Autoencoder (VAE) with a specially designed autoregressive decoder. This decoder is capable of modeling row-correlated and signal-dependent noise but is incapable of independently modeling underlying clean signal. The VAE therefore produces latent variables containing only clean signal information, and these are mapped back into image space using a proposed second decoder network. Our method does not require a pre-trained noise model and can be trained from scratch using unpaired noisy data. We show that our approach achieves competitive results when applied to a range of different sensor types and imaging modalities

Direct Unsupervised Denoising

Traditional supervised denoisers are trained using pairs of noisy input and clean target images. They learn to predict a central tendency of the posterior distribution over possible clean images. When, e.g., trained with the popular quadratic loss function, the network's output will correspond to the minimum mean square error (MMSE) estimate. Unsupervised denoisers based on Variational AutoEncoders (VAEs) have succeeded in achieving state-of-the-art results while requiring only unpaired noisy data as training input. In contrast to the traditional supervised approach, unsupervised denoisers do not directly produce a single prediction, such as the MMSE estimate, but allow us to draw samples from the posterior distribution of clean solutions corresponding to the noisy input. To approximate the MMSE estimate during inference, unsupervised methods have to create and draw a large number of samples - a computationally expensive process - rendering the approach inapplicable in many situations. Here, we present an alternative approach that trains a deterministic network alongside the VAE to directly predict a central tendency. Our method achieves results that surpass the results achieved by the unsupervised method at a fraction of the computational cost

Direct Unsupervised Denoising

Traditional supervised denoisers are trained using pairs of noisy input and clean target images. They learn to predict a central tendency of the posterior distribution over possible clean images. When, e.g., trained with the popular quadratic loss function, the network's output will correspond to the minimum mean square error (MMSE) estimate. Unsupervised denoisers based on Variational AutoEncoders (VAEs) have succeeded in achieving state-of-the-art results while requiring only unpaired noisy data as training input. In contrast to the traditional supervised approach, unsupervised denoisers do not directly produce a single prediction, such as the MMSE estimate, but allow us to draw samples from the posterior distribution of clean solutions corresponding to the noisy input. To approximate the MMSE estimate during inference, unsupervised methods have to create and draw a large number of samples - a computationally expensive process - rendering the approach inapplicable in many situations. Here, we present an alternative approach that trains a deterministic network alongside the VAE to directly predict a central tendency. Our method achieves results that surpass the results achieved by the unsupervised method at a fraction of the computational cost

Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging Noise

Accurate analysis of microscopy images is hindered by the presence of noise. This noise is usually signal-dependent and often additionally correlated along rows or columns of pixels. Current self- and unsupervised denoisers can address signal-dependent noise, but none can reliably remove noise that is also row- or column-correlated. Here, we present the first fully unsupervised deep learning-based denoiser capable of handling imaging noise that is row-correlated as well as signal-dependent. Our approach uses a Variational Autoencoder (VAE) with a specially designed autoregressive decoder. This decoder is capable of modeling row-correlated and signal-dependent noise but is incapable of independently modeling underlying clean signal. The VAE therefore produces latent variables containing only clean signal information, and these are mapped back into image space using a proposed second decoder network. Our method does not require a pre-trained noise model and can be trained from scratch using unpaired noisy data. We show that our approach achieves competitive results when applied to a range of different sensor types and imaging modalities

Improve learning combining crowdsourced labels by weighting Areas Under the Margin

In supervised learning -- for instance in image classification -- modern massive datasets are commonly labeled by a crowd of workers. The obtained labels in this crowdsourcing setting are then aggregated for training. The aggregation step generally leverages a per worker trust score. Yet, such worker-centric approaches discard each task ambiguity. Some intrinsically ambiguous tasks might even fool expert workers, which could eventually be harmful for the learning step. In a standard supervised learning setting -- with one label per task and balanced classes -- the Area Under the Margin (AUM) statistic is tailored to identify mislabeled data. We adapt the AUM to identify ambiguous tasks in crowdsourced learning scenarios, introducing the Weighted AUM (WAUM). The WAUM is an average of AUMs weighted by worker and task dependent scores. We show that the WAUM can help discarding ambiguous tasks from the training set, leading to better generalization or calibration performance. We report improvements with respect to feature-blind aggregation strategies both for simulated settings and for the CIFAR-10H crowdsourced dataset

Collective diffusion coefficient of a charged colloidal dispersion: interferometric measurements in a drying drop

In the present work, we use Mach-Zehnder interferometry to thoroughly investigate the drying dynamics of a 2D confined drop of a charged colloidal dispersion. This technique makes it possible to measure the colloid concentration field during the drying of the drop at a high accuracy (about 0.5%) and with a high temporal and spatial resolution (about 1 frame/s and 5 $\mu$m/pixel). These features allow us to probe mass transport of the charged dispersion in this out-of-equilibrium situation. In particular, our experiments provide the evidence that mass transport within the drop can be described by a purely diffusive process for some range of parameters for which the buoyancy-driven convection is negligible. We are then able to extract from these experiments the collective diffusion coefficient of the dispersion $D(\varphi)$ over a wide concentration range $\varphi=0.24$-$0.5$, i.e. from the liquid dispersed state to the solid glass regime, with a high accuracy. The measured values of $D(\varphi)\simeq 5$-$12 D_0$ are significantly larger than the simple estimate $D_0$ given by the Stokes-Einstein relation, thus highlighting the important role played by the colloidal interactions in such dispersions

Feedback from activity trackers improves daily step count after knee and hip arthroplasty: A randomized controlled trial

Background: Commercial wrist-worn activity monitors have the potential to accurately assess activity levels and are being increasingly adopted in the general population. The aim of this study was to determine if feedback from a commercial activity monitor improves activity levels over the first 6 weeks after total hip arthroplasty (THA) or total knee arthroplasty (TKA). Methods: One hundred sixty-three consecutive subjects undergoing primary TKA or THAwere randomized into 2 groups. Subjects received an activity tracker with the step display obscured 2 weeks before surgery and completed patient-reported outcome measures (PROMs). On day 1 after surgery, participants were randomized to either the “feedback (FB) group” or the “no feedback (NFB) group.” The FB group was able to view their daily step count and was given a daily step goal. Participants in the NFB group wore the device with the display obscured for 2 weeks after surgery, after which time they were also able to see their daily step count but did not receive a formal step goal. The mean daily steps at 1, 2, 6 weeks, and 6 months were monitored. At 6 months after surgery, subjects repeated PROMs and daily step count collection. Results: Of the 163 subjects, 95 underwent THA and 68 underwent TKA. FB subjects had a significantly higher (P \u3c .03) mean daily step count by 43% in week 1, 33% in week 2, 21% in week 6, and 17% at 6 months, compared with NFB. The FB subjects were 1.7 times more likely to achieve a mean 7000 steps per day than the NFB subjects at 6 weeks after surgery (P ¼ .02). There was no significant difference between the groups in PROMs at 6 months. Ninety percent of FB and 83% of NFB participants reported that they were satisfied with the results of the surgery (P ¼ .08). At 6 months after surgery, 70% of subjects had a greater mean daily step count compared with their preoperative level. Conclusion: Subjects who received feedback from a commercial activity tracker with a daily step goal had significantly higher activity levels after hip and knee arthroplasty over 6 weeks and 6 months, compared with subjects who did not receive feedback in a randomized controlled trial. Commercial activity trackers may be a useful and effective adjunct after arthroplasty

Influence du stockage des boues de STEP sur les émissions de NH3 et de COV durant leur séchage

Le séchage constitue une étape importante en aval de la déshydratation mécanique en vue de la valorisation agricole ou énergétique des boues de station d’épuration. La teneur en eau peut être réduite à moins de 5%, diminuant ainsi la masse et le volume des boues et, par conséquent, le coût pour le stockage, la manutention et le transport. L'élimination de l'eau augmente considérablement le pouvoir calorifique inférieur, transformant les boues en un combustible convenable. En outre, les boues séchées peuvent être stabilisées et exemptes d'agents pathogènes en fonction de la température et de la durée de traitement. Les technologies convectives sont largement utilisées pour le séchage des boues. Le principal avantage est la simplicité de la technologie et l’inconvénient majeur résulte de la grande quantité d'air à épurer et désodoriser. Le but des travaux menés par l'Université de Liège et VEOLIA Environnement est d'effectuer une caractérisation en laboratoire des émissions gazeuses en fonction des conditions de séchage. Pour ce faire, il est primordial de garantir une qualité constante de l'échantillon initial tout au long des mesures. En effet, même si elles sont conservées à basse température, les boues peuvent être le siège de dégradations biologiques et les propriétés de séchage peuvent être modifiées. Ainsi, la première partie de ce travail est consacrée à l’étude de l'influence de la durée de stockage des boues à 4°C sur les émissions gazeuses produites au cours de leur séchage convectif. Deux types de boues, l’une ayant subi une digestion et l’autre pas, sont étudiés. L’échantillonnage est effectué après la déshydratation mécanique dans deux stations de traitement des eaux usées situées à proximité de l'Université de Liège. Les échantillons sont stockés dans le laboratoire à 4°C dans un récipient hermétique. Pour effectuer les essais, 300 g de boue sont déposés dans le sécheur sous la forme d’un lit d'extrudés de 6 mm de diamètre. La masse de boue, la concentration en ammoniac et la concentration en composés organiques volatils sont mesurées en ligne respectivement par une balance, un analyseur infrarouge et un détecteur à ionisation de flamme. Des thermocouples permettent le suivi de la température en amont, au sein et en aval du lit de boue. Des essais de séchage sont effectués au jour 0 (= jour du prélèvement), et après 1, 2, 4, 10, 17 et 20 jours sous les conditions suivantes : température de l'air = 140°C; vitesse superficielle de l'air = 1 m/s; humidité absolue = 0,005 kgeau/kgair sec. La seconde partie du travail a été réalisée sur un échantillon de boue non digérée conservé à 12°C pour simuler des conditions réelles de stockage. Les essais de séchage ont été menés le jour de prélèvement et après 4, 10 et 20 jours, avec des conditions opératoires similaires. L’étude réalisée avec un stockage à 4°C montre que les émissions gazeuses sont maximales le jour du prélèvement, diminuent fortement durant les deux premiers jours de stockage pour atteindre un niveau constant durant deux semaines avant d’augmenter. Lors du stockage à 12°C, les émissions d’ammoniac et de COV sont multipliées respectivement par un facteur 40 et 4 entre le jour 0 et le jour 20. Ces résultats mettent en évidence l’impact des conditions et de la durée de stockage sur les émissions lors du séchage des boues et montrent l’importance de sécher les boues le plus rapidement possible pour limiter les nuisances

How the kinetochore couples microtubule force and centromere stretch to move chromosomes

The Ndc80 complex (Ndc80, Nuf2, Spc24, Spc25) is a highly conserved kinetochore protein essential for end-on anchorage to spindle microtubule plus-ends and for force generation coupled to plus-end polymerization and depolymerization. Spc24/Spc25 at one end of the Ndc80 complex binds the kinetochore. The N-terminal tail and CH domains of Ndc80 bind microtubules, and an internal domain binds microtubule-associated proteins (MAPs) such as the Dam1 complex. To determine how the microtubule and MAP binding domains of Ndc80 contribute to force production at the kinetochore in budding yeast, we have inserted a FRET tension sensor into the Ndc80 protein about halfway between its microtubule binding and internal loop domains. The data support a mechanical model of force generation at metaphase where the position of the kinetochore relative to the microtubule plus-end reflects the relative strengths of microtubule depolymerization, centromere stretch and microtubule binding interactions with Ndc80 and Dam1 complexes

A quantitative description of Ndc80 complex linkage to human kinetochores

The Ndc80 complex, which mediates end-on attachment of spindle microtubules, is linked to centromeric chromatin in human cells by two inner kinetochore proteins, CENP-T and CENP-C. Here to quantify their relative contributions to Ndc80 recruitment, we combine measurements of kinetochore protein copy number with selective protein depletion assays. This approach reveals about 244 Ndc80 complexes per human kinetochore (∼14 per kinetochore microtubule), 215 CENP-C, 72 CENP-T and only 151 Ndc80s as part of the KMN protein network (1:1:1 Knl1, Mis12 and Ndc80 complexes). Each CENP-T molecule recruits ∼2 Ndc80 complexes; one as part of a KMN network. In contrast, ∼40% of CENP-C recruits only a KMN network. Replacing the CENP-C domain that binds KMN with the CENP-T domain that recruits both an Ndc80 complex and KMN network yielded functional kinetochores. These results provide a quantitative picture of the linkages between centromeric chromatin and the microtubule-binding Ndc80 complex at the human kinetochore