This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of wholescene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB images are themselves calculated numerically using the ground-truth HS images and supplied spectral sensitivity functions (ii) a "Real World" track, simulating capture by an uncalibrated and unknown camera, where the HS images are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever, natural hyperspectral image data set is presented, containing a total of 510 HS images. The Clean and Real World tracks had 103 and 78 registered participants respectively, with 14 teams competing in the final testing phase. A description of the proposed methods, alongside their challenge scores and an extensive evaluation of top performing methods is also provided. They gauge the state-of-the-art in spectral reconstruction from an RGB image. arXiv:2005.03412v1 [eess.IV] 7 May 2020

Multiciliated cells of the airways, brain ventricles, and female reproductive tract provide the motive force for mucociliary clearance, cerebrospinal fluid circulation, and ovum transport. Despite their clear importance to human biology and health, the molecular mechanisms underlying multiciliated cell differentiation are poorly understood. Prior studies implicate the distal appendage/transition fiber protein CEP164 as a central regulator of primary ciliogenesis; however, its role in multiciliogenesis remains unknown. In this study, we have generated a novel conditional mouse model that lacks CEP164 in multiciliated tissues and the testis. These mice show a profound loss of airway, ependymal, and oviduct multicilia and develop hydrocephalus and male infertility. Using primary cultures of tracheal multiciliated cells as a model system, we found that CEP164 is critical for multiciliogenesis, at least in part, via its regulation of small vesicle recruitment, ciliary vesicle formation, and basal body docking. In addition, CEP164 is necessary for the proper recruitment of another distal appendage/transition fiber protein Chibby1 (Cby1) and its binding partners FAM92A and FAM92B to the ciliary base in multiciliated cells. In contrast to primary ciliogenesis, CEP164 is dispensable for the recruitment of intraflagellar transport (IFT) components to multicilia. Finally, we provide evidence that CEP164 differentially controls the ciliary targeting of membrane-associated proteins, including the small GTPases Rab8, Rab11, and Arl13b, in multiciliated cells. Altogether, our studies unravel unique requirements for CEP164 in primary versus multiciliogenesis and suggest that CEP164 modulates the selective transport of membrane vesicles and their cargoes into the ciliary compartment in multiciliated cells. Furthermore, our mouse model provides a useful tool to gain physiological insight into diseases associated with defective multicilia.

While the extracellular matrix (ECM) is known to regulate neural stem cell quiescence in the adult subventricular zone (SVZ), the function of ECM in the developing SVZ remains unknown. Here, we report that the ECM receptor dystroglycan regulates a unique developmental restructuring of ECM in the early postnatal SVZ. Dystroglycan is furthermore required for ependymal cell differentiation and assembly of niche pinwheel structures, at least in part by suppressing Notch activation in radial glial cells, which leads to the increased expression of MCI, Myb, and FoxJ1, transcriptional regulators necessary for acquisition of the multiciliated phenotype. Dystroglycan also regulates perinatal radial glial cell proliferation and transition into intermediate gliogenic progenitors, such that either acute or constitutive loss of function in dystroglycan results in increased oligodendrogenesis. These findings reveal a role for dystroglycan in orchestrating both the assembly and function of the SVZ neural stem cell niche.

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Positive-unlabeled learning is often studied under the assumption that the labeled positive sample is drawn randomly from the true distribution of positives. In many application domains, however, certain regions in the support of the positive class-conditional distribution are over-represented while others are under-represented in the positive sample. Although this introduces problems in all aspects of positive-unlabeled learning, we begin to address this challenge by focusing on the estimation of class priors, quantities central to the estimation of posterior probabilities and the recovery of true classification performance. We start by making a set of assumptions to model the sampling bias. We then extend the identifiability theory of class priors from the unbiased to the biased setting. Finally, we derive an algorithm for estimating the class priors that relies on clustering to decompose the original problem into subproblems of unbiased positive-unlabeled learning. Our empirical investigation suggests feasibility of the correction strategy and overall good performance.