Combining tower mixing ratio and community model data to estimate regional-scale net ecosystem carbon exchange by boundary layer inversion over 4 flux towers in the U.S.A.

We evaluated an idealized boundary layer (BL) model with simple parameterizations using vertical transport information from community model outputs (NCAR/NCEP Reanalysis and ECMWF Interim Analysis) to estimate regional-scale net CO2 fluxes from 2002 to 2007 at three forest and one grassland flux sites in the United States. The BL modeling approach builds on a mixed-layer model to infer monthly average net CO2 fluxes using high-precision mixing ratio measurements taken on flux towers. We compared BL model net ecosystem exchange (NEE) with estimates from two independent approaches. First, we compared modeled NEE with tower eddy covariance measurements. The second approach (EC-MOD) was a data-driven method that upscaled EC fluxes from towers to regions using MODIS data streams. Comparisons between modeled CO2 and tower NEE fluxes showed that modeled regional CO2 fluxes displayed interannual and intra-annual variations similar to the tower NEE fluxes at the Rannells Prairie and Wind River Forest sites, but model predictions were frequently different from NEE observations at the Harvard Forest and Howland Forest sites. At the Howland Forest site, modeled CO2 fluxes showed a lag in the onset of growing season uptake by 2 months behind that of tower measurements. At the Harvard Forest site, modeled CO2 fluxes agreed with the timing of growing season uptake but underestimated the magnitude of observed NEE seasonal fluctuation. This modeling inconsistency among sites can be partially attributed to the likely misrepresentation of atmospheric transport and/or CO2gradients between ABL and the free troposphere in the idealized BL model. EC-MOD fluxes showed that spatial heterogeneity in land use and cover very likely explained the majority of the data-model inconsistency. We show a site-dependent atmospheric rectifier effect that appears to have had the largest impact on ABL CO2 inversion in the North American Great Plains. We conclude that a systematic BL modeling approach provided new insights when employed in multiyear, cross-site synthesis studies. These results can be used to develop diagnostic upscaling tools, improving our understanding of the seasonal and interannual variability of surface CO2 fluxes

Computational modelling of solvent effects in a prolific solvatomorphic porous organic cage

Crystal structure prediction methods can enable the in silico design of functional molecular crystals, but solvent effects can have a major influence on relative lattice energies sometimes thwarting predictions. This is particularly true for porous solids, where solvent included in the pores can have an important energetic contribution. Here we present a Monte Carlo solvent insertion procedure for predicting the solvent filling of porous structures from crystal structure prediction landscapes, tested using a highly solvatomorphic porous organic cage molecule, CC1. We use this method to rationalise the fact that the predicted global energy minimum structure for CC1 is never observed from solvent crystallisation. We also explain the formation of three different solvatomorphs of CC1 from three structurally-similar chlorinated solvents. Calculated solvent stabilisation energies are found to correlate with experimental results from thermogravimetric analysis, suggesting a future computational framework for a priori materials design that includes solvation effects

Liposomal Co-Entrapment of CD40mAb Induces Enhanced IgG Responses against Bacterial Polysaccharide and Protein

Background Antibody against CD40 is effective in enhancing immune responses to vaccines when chemically conjugated to the vaccine antigen. Unfortunately the requirement for chemical conjugation presents some difficulties in vaccine production and quality control which are compounded when multivalent vaccines are required. We explore here an alternative to chemical conjugation, involving the co-encapsulation of CD40 antibody and antigens in liposomal vehicles. Methodology/Principal Findings Anti-mouse CD40 mAb or isotype control mAb were co-entrapped individually in cationic liposomal vehicles with pneumococcal polysaccharides or diphtheria and tetanus toxoids. Retention of CD40 binding activity upon liposomal entrapment was assessed by ELISA and flow cytometry. After subcutaneous immunization of BALB/c female mice, anti-polysaccharide and DT/TT responses were measured by ELISA. Simple co-encapsulation of CD40 antibody allowed for the retention of CD40 binding on the liposome surface, and also produced vaccines with enhanced imunogenicity. Antibody responses against both co-entrapped protein in the form of tetanus toxoid, and Streptococcus pneumoniae capsular polysaccharide, were enhanced by co-encapsulation with CD40 antibody. Surprisingly, liposomal encapsulation also appeared to decrease the toxicity of high doses of CD40 antibody as assessed by the degree of splenomegaly induced. Conclusions/Significance Liposomal co-encapsulation with CD40 antibody may represent a practical means of producing more immunogenic multivalent vaccines and inducing IgG responses against polysaccharides without the need for conjugation

3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets

We present 3DMiner -- a pipeline for mining 3D shapes from challenging large-scale unannotated image datasets. Unlike other unsupervised 3D reconstruction methods, we assume that, within a large-enough dataset, there must exist images of objects with similar shapes but varying backgrounds, textures, and viewpoints. Our approach leverages the recent advances in learning self-supervised image representations to cluster images with geometrically similar shapes and find common image correspondences between them. We then exploit these correspondences to obtain rough camera estimates as initialization for bundle-adjustment. Finally, for every image cluster, we apply a progressive bundle-adjusting reconstruction method to learn a neural occupancy field representing the underlying shape. We show that this procedure is robust to several types of errors introduced in previous steps (e.g., wrong camera poses, images containing dissimilar shapes, etc.), allowing us to obtain shape and pose annotations for images in-the-wild. When using images from Pix3D chairs, our method is capable of producing significantly better results than state-of-the-art unsupervised 3D reconstruction techniques, both quantitatively and qualitatively. Furthermore, we show how 3DMiner can be applied to in-the-wild data by reconstructing shapes present in images from the LAION-5B dataset. Project Page: https://ttchengab.github.io/3dminerOfficialComment: In ICCV 202

Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation

A truly generalizable approach to rigid segmentation and motion estimation is fundamental to 3D understanding of articulated objects and moving scenes. In view of the tightly coupled relationship between segmentation and motion estimates, we present an SE(3) equivariant architecture and a training strategy to tackle this task in an unsupervised manner. Our architecture comprises two lightweight and inter-connected heads that predict segmentation masks using point-level invariant features and motion estimates from SE(3) equivariant features without the prerequisites of category information. Our unified training strategy can be performed online while jointly optimizing the two predictions by exploiting the interrelations among scene flow, segmentation mask, and rigid transformations. We show experiments on four datasets as evidence of the superiority of our method both in terms of model performance and computational efficiency with only 0.25M parameters and 0.92G FLOPs. To the best of our knowledge, this is the first work designed for category-agnostic part-level SE(3) equivariance in dynamic point clouds

Locally Reducing KCC2 Activity in the Hippocampus is Sufficient to Induce Temporal Lobe Epilepsy

Mesial temporal lobe epilepsy (mTLE) is the most common form of epilepsy, believed to arise in part from compromised GABAergic inhibition. The neuronal specific K+/Cl− co-transporter 2 (KCC2) is a critical determinant of the efficacy of GABAergic inhibition and deficits in its activity are observed in mTLE patients and animal models of epilepsy. To test if reductions of KCC2 activity directly contribute to the pathophysiology of mTLE, we locally ablated KCC2 expression in a subset of principal neurons within the adult hippocampus. Deletion of KCC2 resulted in compromised GABAergic inhibition and the development of spontaneous, recurrent generalized seizures. Moreover, local ablation of KCC2 activity resulted in hippocampal sclerosis, a key pathological change seen in mTLE. Collectively, our results demonstrate that local deficits in KCC2 activity within the hippocampus are sufficient to precipitate mTLE