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Preparation of single atom catalysts (SACs) is of broad interest to materials scientists and chemists but remains a formidable challenge. Herein, we develop an efficient approach to synthesize SACs via a precursor-dilution strategy, in which metalloporphyrin (MTPP) with target metals are co-polymerized with diluents (tetraphenylporphyrin, TPP), followed by pyrolysis to N-doped porous carbon supported SACs (M 1 /N-C). Twenty-four different SACs, including noble metals and non-noble metals, are successfully prepared. In addition, the synthesis of a series of catalysts with different surface atom densities, bi-metallic sites, and metal aggregation states are achieved. This approach shows remarkable adjustability and generality, providing sufficient freedom to design catalysts at atomic-scale and explore the unique catalytic properties of SACs. As an example, we show that the prepared Pt 1 /N-C exhibits superior chemoselectivity and regioselectivity in hydrogenation. It only converts terminal alkynes to alkenes while keeping other reducible functional groups such as alkenyl, nitro group, and even internal alkyne intact. The general synthesis of single atom catalysts (SACs) is of broad interest to chemists but remains a formidable challenge. Here, with the precursor-dilution strategy, the authors successfully prepare 24 different SACs and the Pt SACs exhibit superior chemo- and regio-selectivity in hydrogenation.

The organization of the human genome in space and time is critical for transcriptional regulation and cell fate determination. However, robust methods for tracking genome organization or genomic interactions over time in living cells are lacking. Here, we developed a multicolor DNA labeling system, ParSite, to simultaneously track triple genomic loci in the U2OS cells. The tricolor ParSite system is derived from the T . thermophilus ParB/ParSc ( Tt ParB/ParSc) system by rational design. We mutated the interface between Tt ParB and ParSc and generated a new pair of Tt ParBm and ParSm for genomic DNA labeling. The insertions of 16 base-pair palindromic ParSc and ParSm into genomic loci allow dual-color DNA imaging in living cells. A pair of genomic loci labeled by ParSite could be colocalized with p53-binding protein 1 (53BP1) in response to CRISPR/Cas9-mediated double-strand breaks (DSBs). The ParSite permits tracking promoter and terminator dynamics of the APP gene, which spans 290 kilobases in length. Intriguingly, the hybrid ParS (ParSh) of half-ParSc and half-ParSm enables for the visualization of a third locus independent of ParSc or ParSm. We simultaneously labeled 3 loci with a genomic distance of 36, 89, and 352 kilobases downstream the C3 repeat locus, respectively. In sum, the ParSite is a robust DNA labeling system for tracking multiple genomic loci in space and time in living cells.

Point of interest (POI) recommendation is a popular personalized location-based service. This paper proposes a Geographic Personal Matrix Factorization (GPMF) model that makes effective use of geographic information from the perspective of the relationship between POIs and users. This model considers the role of geographic information from multiple perspectives based on the locational relationship among users, the distributional relationship between users and POIs, and the proximity and clustering relationship among POIs. The GPMF mines the influence of geographic information on different objects and carries out unique modeling through cosine similarity, non-linear function, and k nearest neighbor (KNN). This study explored the influence of geographic information on POI recommendation through extensive experiments with data from Foursquare. The result shows that GPMF performs better than the commonly used POI recommendation algorithm in terms of both precision and recall. Geographic information through proximity relations effectively improves the recommendation algorithm.

The addition of camrelizumab to gemcitabine and cisplatin showed promising activity as first-line therapy in patients with recurrent or metastatic nasopharyngeal carcinoma in a phase 1 trial. We therefore compared camrelizumab plus gemcitabine and cisplatin with placebo plus gemcitabine and cisplatin in a randomised phase 3 trial. In this randomised, double-blind, phase 3 trial done at 28 hospitals in China, patients were eligible if they were aged 18–75 years, had an Eastern Cooperative Oncology Group (ECOG) performance status of 0–1, and had previously untreated recurrent or metastatic nasopharyngeal carcinoma. Patients were randomly assigned (1:1; using an interactive web-response system with a block size of four) to receive either camrelizumab (200 mg on day 1) or matching placebo intravenously, plus gemcitabine and cisplatin (gemcitabine 1000 mg/m2 on days 1 and 8; cisplatin 80 mg/m2 on day 1) intravenously every 3 weeks for four to six cycles, followed by maintenance therapy with camrelizumab or placebo, until radiographic progression, unacceptable toxicity, start of new anticancer treatment, investigator decision, or withdrawal of consent. Stratification factors used in randomisation were liver metastases, previous radical concurrent chemoradiotherapy, and ECOG performance status. The allocation sequence was generated by an independent randomisation group. The primary endpoint was progression-free survival per independent review committee. The significance threshold for independent review committee-assessed progression-free survival was p=0·0086 (one-sided) at the interim analysis. Efficacy and safety analyses included all patients who received at least one dose of study drug. This trial is registered with ClinicalTrials.gov, NCT03707509, and is closed for enrolment but is ongoing. Between Nov 13, 2018, and Nov 29, 2019, 343 patients were screened and 263 were eligible and were randomly assigned to the camrelizumab group (n=134) or placebo group (n=129). At the prespecified interim analysis (June 15, 2020), independent review committee-assessed progression-free survival was significantly longer in the camrelizumab group (median 9·7 months [95% CI 8·3–11·4]) than in the placebo group (median 6·9 months [5·9–7·3]; hazard ratio 0·54 [95% CI 0·39–0·76]; one-sided p=0·0002). As of Dec 31, 2020, the most common grade 3 or worse adverse events of any cause were decreased white blood cell count (89 [66%] of 134 patients in the camrelizumab group vs 90 [70%] of 129 patients in the placebo group), decreased neutrophil count (86 [64%] vs 85 [66%]), anaemia (53 [40%] vs 57 [44%]), and decreased platelet count (53 [40%] vs 52 [40%]). Serious adverse events were reported in 59 (44%) of 134 patients in the camrelizumab group and 48 (37%) of 129 patients in the placebo group. Treatment-related deaths occurred in five (4%) patients in the camrelizumab group (two unknown cause of death, one multiple organ dysfunction syndrome, one pharyngeal haemorrhage, and one arrhythmia) and one (<1%) patient in the placebo group (unknown cause of death). Our findings suggest that camrelizumab plus gemcitabine and cisplatin could be a new standard of care for patients with recurrent or metastatic nasopharyngeal carcinoma in the first-line setting. Longer follow-up is needed to confirm this conclusion. Jiangsu Hengrui Pharmaceuticals (formerly Jiangsu Hengrui Medicine). For the Chinese translation of the abstract see Supplementary Materials section.

Owing to the development of computerized vision technology, object detection based on convolutional neural networks is being widely used in the field of bridge crack detection. However, these networks have limited utility in bridge crack detection because of low precision and poor real-time performance. In this study, an improved single-shot multi-box detector (SSD) called ISSD is proposed, which seamlessly combines the depth separable deformation convolution module (DSDCM), inception module (IM), and feature recalibration module (FRM) in a tightly coupled manner to tackle the challenges of bridge crack detection. Specifically, DSDCM was utilized for extracting the characteristic information of irregularly shaped bridge cracks. IM was designed to expand the width of the network, reduce network calculations, and improve network computing speed. The FRM was employed to determine the importance of each feature channel through learning, enhance the useful features according to their importance, and suppress the features that are insignificant for bridge crack detection. The experimental results demonstrated that ISSD is effective in bridge crack detection tasks and offers competitive performance compared to state-of-the-art networks.

BackgroundThis study aimed to explore the efficacy and safety of pembrolizumab combined with chemotherapy as neoadjuvant therapy in patients with resectable locally advanced head and neck squamous cell carcinomas (LA-HNSCCs).MethodsIn this prospective, single-arm, single-centre clinical trial, patients meeting the inclusion criteria were treated with preoperative neoadjuvant therapy with 200 mg pembrolizumab combined with 75 mg/m2 cisplatin and 175 mg/m2 paclitaxel. This was followed by surgery and postoperative adjuvant therapy. The primary endpoint was the postoperative pathological complete response (pCR) rate. All statistical analyses were performed using SPSS 26.ResultsA total of 22 patients were enrolled. The location of primary lesion showed: hypopharynx were 15 (68.2%), oropharynx were 6 (27.3%) and oral cavity was 1 (4.5%). The postoperative pCR rate, was 36.4% (8/22), and there was no delay to surgery due to adverse drug reactions. The rate of laryngeal function preservation was 90.9% (20/22). Delayed wound healing was the main surgical complication, with an incidence of 22.7% (5/22). The median follow-up time was 9.5 months, and only 1 patient (4.55%) suffered a regional recurrence.ConclusionPreoperative treatment with pembrolizumab and chemotherapy in resectable LA-HNSCC has a high pCR rate with no significant impact on surgical safety. This treatment was found to increase the rate of laryngeal function preservation. However, the effects of neoadjuvant immunotherapy on long-term prognosis in LA-HNSCCs require further study.

Having the excellent catalytic performance, single atom catalysts (SACs) arouse extensive research interest. However, the application of SACs is hindered by the lack of versatile and scalable preparation approaches. Here, we show a precursor-atomization strategy to produce SACs, involving the spray of droplets of solutions containing metal precursors onto support surface through ultrasonic atomization and the subsequent calcination. This approach is versatile to successful synthesis of a series of catalysts, including 19 SACs with different metal sites and supports and 3 derivatives of SACs (single atom alloys, double atom catalysts and bi-metallic SACs). Furthermore, it can be scaled up by a homemade production line with productivity over 1 kg day −1 , and the well-controlled catalyst uniformity is evidenced by the identical characterization results and catalytic properties in Suzuki-Miyaura cross-coupling. This strategy lays a foundation for further investigation and may accelerate the trend from basic research to industrial applications of SACs. The application of single atom catalysts (SACs) is hindered by the lack of versatile and scalable preparation approaches. Here the authors report a precursor-atomization strategy that can be scaled up with productivity over 1 kg/day for the synthesis of a series of SACs and derivatives.

Accurate and fast path calculation is essential for applications such as vehicle navigation systems and transportation network routing. Although many shortest path algorithms for restricted search areas have been developed in the past ten years to speed up the efficiency of path query, the performance including the practicability still needs to be improved. To settle this problem, this paper proposes a new method of calculating statistical parameters based on a unidirectional road network model that is more in line with the real world and a path planning algorithm for dynamically restricted search areas that constructs virtual boundaries at a lower confidence level. We conducted a detailed experiment on the proposed algorithm with the real road network in Zhengzhou. As the experiment shows, compared with the existing algorithms, the proposed algorithm improves the search performance significantly in the condition of optimal path under the premise of ensuring the optimal path solution.

The Pawnee M5.8 earthquake is the largest event in Oklahoma instrument recorded history. It occurred near the edge of active seismic zones, similar to other M5+ earthquakes since 2011. It ruptured a previously unmapped fault and triggered aftershocks along a complex conjugate fault system. With a high-resolution earthquake catalog, we observe propagating foreshocks leading to the mainshock within 0.5 km distance, suggesting existence of precursory aseismic slip. At approximately 100 days before the mainshock, two M ≥ 3.5 earthquakes occurred along a mapped fault that is conjugate to the mainshock fault. At about 40 days before, two earthquakes clusters started, with one M3 earthquake occurred two days before the mainshock. The three M ≥ 3 foreshocks all produced positive Coulomb stress at the mainshock hypocenter. These foreshock activities within the conjugate fault system are near-instantaneously responding to variations in injection rates at 95% confidence. The short time delay between injection and seismicity differs from both the hypothetical expected time scale of diffusion process and the long time delay observed in this region prior to 2016, suggesting a possible role of elastic stress transfer and critical stress state of the fault. Our results suggest that the Pawnee earthquake is a result of interplay among injection, tectonic faults, and foreshocks.

With the exploration and development of marine resources, deep learning is more and more widely used in underwater image processing. However, the quality of the original underwater images is so low that traditional semantic segmentation methods obtain poor segmentation results, such as blurred target edges, insufficient segmentation accuracy, and poor regional boundary segmentation effects. To solve these problems, this paper proposes a semantic segmentation method for underwater images. Firstly, the image enhancement based on multi-spatial transformation is performed to improve the quality of the original images, which is not common in other advanced semantic segmentation methods. Then, the densely connected hybrid atrous convolution effectively expands the receptive field and slows down the speed of resolution reduction. Next, the cascaded atrous convolutional spatial pyramid pooling module integrates boundary features of different scales to enrich target details. Finally, the context information aggregation decoder fuses the features of the shallow network and the deep network to extract rich contextual information, which greatly reduces information loss. The proposed method was evaluated on RUIE, HabCam UID, and UIEBD. Compared with the state-of-the-art semantic segmentation algorithms, the proposed method has advantages in segmentation integrity, location accuracy, boundary clarity, and detail in subjective perception. On the objective data, the proposed method achieves the highest MIOU of 68.3 and OA of 79.4, and it has a low resource consumption. Besides, the ablation experiment also verifies the effectiveness of our method.