Editorial: CO 2 -based energy systems for cooling, heating, and power

© 2022 Li, Su, Xu, Dai, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/Peer reviewe

Revisiting DETR Pre-training for Object Detection

Motivated by that DETR-based approaches have established new records on COCO detection and segmentation benchmarks, many recent endeavors show increasing interest in how to further improve DETR-based approaches by pre-training the Transformer in a self-supervised manner while keeping the backbone frozen. Some studies already claimed significant improvements in accuracy. In this paper, we take a closer look at their experimental methodology and check if their approaches are still effective on the very recent state-of-the-art such as $\mathcal{H}$-Deformable-DETR. We conduct thorough experiments on COCO object detection tasks to study the influence of the choice of pre-training datasets, localization, and classification target generation schemes. Unfortunately, we find the previous representative self-supervised approach such as DETReg, fails to boost the performance of the strong DETR-based approaches on full data regimes. We further analyze the reasons and find that simply combining a more accurate box predictor and Objects$365$ benchmark can significantly improve the results in follow-up experiments. We demonstrate the effectiveness of our approach by achieving strong object detection results of AP=$59.3\%$ on COCO val set, which surpasses $\mathcal{H}$-Deformable-DETR + Swin-L by +$1.4\%$. Last, we generate a series of synthetic pre-training datasets by combining the very recent image-to-text captioning models (LLaVA) and text-to-image generative models (SDXL). Notably, pre-training on these synthetic datasets leads to notable improvements in object detection performance. Looking ahead, we anticipate substantial advantages through the future expansion of the synthetic pre-training dataset

Rank-DETR for High Quality Object Detection

Modern detection transformers (DETRs) use a set of object queries to predict a list of bounding boxes, sort them by their classification confidence scores, and select the top-ranked predictions as the final detection results for the given input image. A highly performant object detector requires accurate ranking for the bounding box predictions. For DETR-based detectors, the top-ranked bounding boxes suffer from less accurate localization quality due to the misalignment between classification scores and localization accuracy, thus impeding the construction of high-quality detectors. In this work, we introduce a simple and highly performant DETR-based object detector by proposing a series of rank-oriented designs, combinedly called Rank-DETR. Our key contributions include: (i) a rank-oriented architecture design that can prompt positive predictions and suppress the negative ones to ensure lower false positive rates, as well as (ii) a rank-oriented loss function and matching cost design that prioritizes predictions of more accurate localization accuracy during ranking to boost the AP under high IoU thresholds. We apply our method to improve the recent SOTA methods (e.g., H-DETR and DINO-DETR) and report strong COCO object detection results when using different backbones such as ResNet-$50$, Swin-T, and Swin-L, demonstrating the effectiveness of our approach. Code is available at \url{https://github.com/LeapLabTHU/Rank-DETR}.Comment: NeurIPS 202

Chiral Arene Ligand as Stereocontroller for Asymmetric C-H Activation

Development of chiral ligands is the most fundamental task in metal-catalyzed asymmetric synthesis. In the last 60 years, various kinds of ligands have been sophisticatedly developed. However, it remains a long-standing challenge to develop practically useful chiral η6-arene ligands, thereby seriously hampering the asymmetric synthesis promoted by arene-metal catalysts. Herein, we report the design and synthesis of a class of facilely tunable, C2 symmetric chiral arene ligands derived from [2.2]paracyclophane. Its ruthenium(II) complexes have been successfully applied in the enantioselective C-H activation to afford a series of axially chiral biaryl compounds (up to 99% yield and 96% ee). This study not only lays chemists’ longstanding doubts about whether it is possible to use chiral arene ligand to stereocontrol asymmetric C-H activation, but also opens up a new avenue to achieve asymmetric C-H activation

Cross-Linked Versus Conventional Polyethylene for Long-Term Clinical Outcomes After Total Hip Arthroplasty: A Systematic Review and Meta-Analysis

Background: Cross-linked polyethylene (HXLPE) liners have been used for total hip arthroplasty (THA) to address the problem of osteolysis and revision surgery associated with conventional polyethylene (CPE) liners. This systematic review and meta-analysis investigated the long-term efficacy of HXLPE in preventing revision surgery and radiological osteolysis in comparison to CPE. Materials and Methods: A comprehensive search of PubMed, Embase, and the Cochrane Library from their respective inception to September 2018 was conducted to identify potential candidate articles. Data were pooled using Stata software 14.0. The quality of randomized controlled trials (RCTs) and observational studies was assessed by two different authors using the Cochrane risk-of-bias tool and the Newcastle–Ottawa scale (NOS), respectively. Results: Eight RCTs and six observational studies were included in this review. The pooled results significantly favored HXLPE over CPE in terms of total number of revisions and radiological osteolysis, with a risk reduction of 78% (95% confidence interval [CI] 0.13–0.36; p < 0.001) and 80% (95% CI 0.13–0.29; p < 0.001), respectively. Additionally, subgroup analyses of pooled data from RCTs and observational studies both showed the efficacy of HXLPE in the prevention of revision and osteolysis. Polyethylene wear in the HXLPE group was significantly less than that in the CPE group in terms of linear wear rates and head penetration rates (both p < 0.001). No significant differences were observed with regard to functional outcomes. Conclusions: The current evidence shows that HXLPE significantly improved the clinical and radiographic outcomes, but not the functional outcomes, in comparison to CPE in long-term follow-up

Spatial and Temporal Analysis of Lung Cancer in Shenzhen, 2008–2018

Lung cancer is the most commonly diagnosed cancer in China. The incidence trend and geographical distribution of lung cancer in southern China have not been reported. The present study explored the temporal trend and spatial distribution of lung cancer incidence in Shenzhen from 2008 to 2018. The lung cancer incidence data were obtained from the registered population in the Shenzhen Cancer Registry System between 2008 and 2018. The standardized incidence rates of lung cancer were analyzed by using the joinpoint regression model. The Moran&rsquo;s I method was used for spatial autocorrelation analysis and to further draw a spatial cluster map in Shenzhen. From 2008 to 2018, the average crude incidence rate of lung cancer was 27.1 (1/100,000), with an annual percentage change of 2.7% (p &lt; 0.05). The largest average proportion of histological type of lung cancer was determined as adenocarcinoma (69.1%), and an increasing trend was observed in females, with an average annual percentage change of 14.7%. The spatial autocorrelation analysis indicated some sites in Shenzhen as a high incidence rate spatial clustering area. Understanding the incidence patterns of lung cancer is useful for monitoring and prevention

FOXM1/KIF20A axis promotes clear cell renal cell carcinoma progression via regulating EMT signaling and affects immunotherapy response

Background: The correlation between FOXM1 and KIF20A has not been revealed in clear cell renal cell carcinoma (ccRCC). Methods: Public data was downloaded from The Cancer Genome Atlas (TCGA) database. R software was utilized for the execution of bioinformatic analysis. The expression levels of specific molecules (mRNA and protein) were detected using real-time quantitative PCR (qRT-PCR) and Western blot assays. The capacity of cell growth was assessed by employing CCK8 and colony formation assay. Cell invasion and migration ability were assessed using transwell assay. Results: In our study, we illustrated the association between FOXM1 and KIF20A. Our results indicated that both FOXM1 and KIF20A were associated with poor prognosis and clinical performance. The malignant characteristics of ccRCC cells can be significantly suppressed by inhibiting FOXM1 and KIF20A, as demonstrated by in vitro experiments. Moreover, we found that FOXM1 can upregulate KIF20A. Then, EMT signaling was identified as the underlying pathway FOXM1 and KIF20A are involved. WB results indicated that FOXM1/KIF20A axis can activate EMT signaling. Moreover, we noticed that FOXM1 and KIF20A can affect the immunotherapy response and immune microenvironment of ccRCC patients. Conclusions: Our results identified the role of the FOXM1/KIF20A axis in ccRCC progression and immunotherapy, making it the underlying target for ccRCC

Designing data model concept for management and organization at the sphere of logistic system

This thesis is divided into two thematic parts. The first one targets geodatabes design process. The second section of thesis contains application of graph algorithms over the final data model. The goal of the first section is to design a data model. In the first step of data modeling process all the available data information is organized at the conceptual data model. On grounds of conceptual data model is built-up logical data model. Logical scheme is transformed to physical design of personal geodatabase. Final data model design follows all data modeling rules through the data model process. The goal of second section of this thesis is to realize graph algorithms over the data model. The application of graph algorithms over the network contributes to efficiency of organization and management at the sphare of logistic system. These algorithms will apply through ESRI extension Network Analyst. Functionality of data model will be verified on the basis of this part. Keywords: Geodatabase design, Network analyse Supervisor: Mgr. Přemysl Štych, Ph.D

Cytological Observation and RNA-Seq Analyses Reveal <i>miR9564</i> and Its Target Associated with Pollen Sterility in Autotetraploid Rice

Understanding the regulation of autotetraploid sterility is essential for harnessing the strong advantages in genomic buffer capacity, biodiversity, and heterosis of autotetraploid rice. miRNAs play crucial roles in fertility regulation, yet information about their reproductive roles and target genes in tetraploid rice remains limited. Here, we used three tetraploid lines, H1 (fertile), HF (fertile), and LF (sterile), to investigate cytological features and identify factors associated with autotetraploid sterility. LF showed abnormal meiosis, resulting in low pollen fertility and viability, ultimately leading to scarce fertilization and a low-seed setting compared to H1 and HF. RNA-seq revealed 30 miRNA-candidate target pairs related to autotetraploid pollen sterility. These pairs showed opposite expression patterns, with differential expression between fertile lines (H1 and HF) and the sterile line (LF). qRT-PCR confirmed that miR9564, miR528, and miR27874 were highly expressed in the anthers of H1 and HF but not in LF, while opposite results were obtained in their targets (ARPS, M2T, and OsRPC53). Haplotype and expression pattern analyses revealed that ARPS was specifically expressed in lines with the same haplotype of MIR9564 (the precursor of miR9564) as LF. Furthermore, the Dual-GFP assay verified that miR9564 inhibited the fluorescence signal of ARPS-GFP. The over-expression of ARPS significantly decreased the seed setting rate (59.10%) and pollen fertility (50.44%) of neo-tetraploid rice, suggesting that ARPS plays important roles in autotetraploid pollen sterility. This study provides insights into the cytological characteristic and miRNA expression profiles of tetraploid lines with different fertility, shedding light on the role of miRNAs in polyploid rice

Additional file 9: Table S9. of Genome-wide identification and functional prediction of nitrogen-responsive intergenic and intronic long non-coding RNAs in maize (Zea mays L.)

Enriched GO terms of eigengenes of the M1 module. (XLSX 11 kb