Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining

We present a strong object detector with encoder-decoder pretraining and finetuning. Our method, called Group DETR v2, is built upon a vision transformer encoder ViT-Huge~\cite{dosovitskiy2020image}, a DETR variant DINO~\cite{zhang2022dino}, and an efficient DETR training method Group DETR~\cite{chen2022group}. The training process consists of self-supervised pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the detector on Object365, and finally finetuning it on COCO. Group DETR v2 achieves $\textbf{64.5}$ mAP on COCO test-dev, and establishes a new SoTA on the COCO leaderboard https://paperswithcode.com/sota/object-detection-on-cocoComment: Tech report, 3 pages. We establishes a new SoTA (64.5 mAP) on the COCO test-de

Epidemiological and genomic characteristics of global mcr-positive Escherichia coli isolates

ObjectiveThe worldwide dissemination of colistin-resistant Escherichia coli (E. coli) endangers public health. This study aimed to better understand the global genomic epidemiology of E. coli isolates carrying mobilized colistin resistance (mcr) genes, providing information to assist in infection and prevention.MethodsEscherichia coli genomes were downloaded from NCBI, and mcr was detected using BLASTP. Per software was used to extract information on hosts, resources, collection data, and countries of origin from GenBank. Sequence types (STs), prevalence of plasmids, antimicrobial resistance genes (ARG), and virulence factors (VF) in these genomes were analyzed. Statistical analyses were performed to assess the relationships between mcr, ARGs, plasmids, and STs.ResultsIn total, 778 mcr-positive isolates were identified. Four mcr variants were detected, with mcr-1 (86.1%) being the most widespread, followed by mcr-9 (5.7%), mcr-5 (4.4%), and mcr-3 (3.0%). Multiple ARGs were identified, with blaCTX–M (53.3%), fosA (28.8%), qnr (26.1%), blaNDM (19.8%), and aac (6’)-Ib-cr (14.5%) being the most common. Overall, 239 distinct STs were identified, of which ST10 (13.8%) was the most prevalent. A total of 113 different VFs were found, terC (99.9%) and gad (83.0%) were most frequently detected. Twenty types of plasmids were identified; IncFIB (64.1%), IncX (42.3%), and IncX (42.3%) were the most common replicons. IncI2 and IncX4 were frequently detected in mcr-1-positive isolates, whereas IncFII, IncI1-I, and IncHI2 were dominant plasmids in mcr-3, mcr-5, and mcr-9-positive isolates, respectively. A higher frequency of ARGs and VFs was observed among ST156 and ST131 isolates.ConclusionOur data indicated that more than half of the mcr-positive E. coli strains carried endemic ARGs and VFs. ST10 and ST156 isolates deserved further attention, given the rapid transmission of ST10 and the convergence of ARGs and VFs in ST156

Crystal structure of (4aS,5S,6aS,6a1S, 10aS)-4a,5,6a,6a1,9,10-hexahydro-7H-4,5-methanocyclobuta[4,5]naphtho[8a,1-b]pyran-6(2H)-one, C15H16O2

C15H16O2, triclinic, P1̄ (no. 2), a = 5.6034(7) Å, b = 8.7105(11) Å, c = 12.5661(16) Å, α = 92.790(7)°, β = 100.516(7)°, γ = 106.105(6)°, V = 576.15(13) Å3, Z = 2, Rgt(F) = 0.0476, wRref(F2) = 0.1348, T = 296.15 K

Crystal structure of (6aR,6a1S,10aS)-2,4a,6a,6a1,9,10-hexahydro-7H-4,5-methanocyclobuta[4,5]naphtho[8a,1-b]pyran, C15H16O

C15H16O, monoclinic, P21/c (no. 14), a = 5.4371(7) Å, b = 17.567(2) Å, c = 11.8840(18) Å, β = 101.043(9)°, V = 1114.1(3) Å3, Z = 4, Rgt(F) = 0.0422, wRref(F2) = 0.1155, T = 296(2) K

Crystal structure of (1aS,1a1S,2S)-4a-butoxy-1a,1a1,2,4a,5,6-hexahydro-1H-cyclobuta[de]naphthalen-2-yl-4-nitrobenzoate, C22H25NO5

C22H25NO5, monoclinic, P21/c (no. 14), a = 15.7241(13) Å, b = 7.0223(5) Å, c = 18.4613(14) Å, β = 100.329(4)°, V = 2005.4(3) Å3, Z = 4, Rgt(F) = 0.0495, wRref(F2) = 0.1564, T = 296(2) K

Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search

The goal of person search is to localize and match query persons from scene images. For high efficiency, one-step methods have been developed to jointly handle the pedestrian detection and identification sub-tasks using a single network. There are two major challenges in the current one-step approaches. One is the mutual interference between the optimization objectives of multiple sub-tasks. The other is the sub-optimal identification feature learning caused by small batch size when end-to-end training. To overcome these problems, we propose a decoupled and memory-reinforced network (DMRNet). Specifically, to reconcile the conflicts of multiple objectives, we simplify the standard tightly coupled pipelines and establish a deeply decoupled multi-task learning framework. Further, we build a memory-reinforced mechanism to boost the identification feature learning. By queuing the identification features of recently accessed instances into a memory bank, the mechanism augments the similarity pair construction for pairwise metric learning. For better encoding consistency of the stored features, a slow-moving average of the network is applied for extracting these features. In this way, the dual networks reinforce each other and converge to robust solution states. Experimentally, the proposed method obtains 93.2% and 46.9% mAP on CUHK-SYSU and PRW datasets, which exceeds all the existing one-step methods.Comment: 8 pages, 6 figures. Accepted by AAAI 202

Surface and Interface Engineering for Nanocellulosic Advanced Materials

How do trees support their upright massive bodies? The support comes from the incredibly strong and stiff, and highly crystalline nanoscale fibrils of extended cellulose chains, called cellulose nanofibers. Cellulose nanofibers and their crystalline parts-cellulose nanocrystals, collectively nanocelluloses, are therefore the recent hot materials to incorporate in man-made sustainable, environmentally sound, and mechanically strong materials. Nanocelluloses are generally obtained through a top-down process, during or after which the original surface chemistry and interface interactions can be dramatically changed. Therefore, surface and interface engineering are extremely important when nanocellulosic materials with a bottom-up process are fabricated. Herein, the main focus is on promising chemical modification and nonmodification approaches, aiming to prospect this hot topic from novel aspects, including nanocellulose-, chemistry-, and process-oriented surface and interface engineering for advanced nanocellulosic materials. The reinforcement of nanocelluloses in some functional materials, such as structural materials, films, filaments, aerogels, and foams, is discussed, relating to tailored surface and/or interface engineering. Although some of the nanocellulosic products have already reached the industrial arena, it is hoped that more and more nanocellulose-based products will become available in everyday life in the next few years