Geometric Filterless Photodetectors for Mid-infrared Spin Light

Free-space circularly polarized light (CPL) detection, requiring polarizers and waveplates, has been well established, while such spatial degree of freedom is unfortunately absent in integrated on-chip optoelectronics. So far, those reported filterless CPL photodetectors suffer from the intrinsic small discrimination ratio, vulnerability to the non-CPL field components, and low responsivity. Here, we report a distinct paradigm of geometric photodetectors in mid-infrared exhibiting colossal discrimination ratio, close-to-perfect CPL-specific response, a zero-bias responsivity of 392 V/W at room temperature, and a detectivity of ellipticity down to 0.03$^o$ Hz$^{-1/2}$. Our approach employs plasmonic nanostructures array with judiciously designed symmetry, assisted by graphene ribbons to electrically read their near-field optical information. This geometry-empowered recipe for infrared photodetectors provides a robust, direct, strict, and high-quality solution to on-chip filterless CPL detection and unlocks new opportunities for integrated functional optoelectronic devices

Molecular Docking of Potential Inhibitors for Influenza H7N9

As a new strain of virus emerged in 2013, avian influenza A (H7N9) virus is a threat to the public health, due to its high lethality and pathogenicity. Furthermore, H7N9 has already generated various mutations such as neuraminidase R294K mutation which could make the anti-influenza oseltamivir less effective or ineffective. In this regard, it is urgent to develop new effective anti-H7N9 drug. In this study, we used the general H7N9 neuraminidase and oseltamivir-resistant influenza virus neuraminidase as the acceptors and employed the small molecules including quercetin, chlorogenic acid, baicalein, and oleanolic acid as the donors to perform the molecular docking for exploring the binding abilities between these small molecules and neuraminidase. The results showed that quercetin, chlorogenic acid, oleanolic acid, and baicalein present oseltamivir-comparable high binding potentials with neuraminidase. Further analyses showed that R294K mutation in neuraminidase could remarkably decrease the binding energies for oseltamivir, while other small molecules showed stable binding abilities with mutated neuraminidase. Taken together, the molecular docking studies identified four potential inhibitors for neuraminidase of H7N9, which might be effective for the drug-resistant mutants

Harvey: A Greybox Fuzzer for Smart Contracts

We present Harvey, an industrial greybox fuzzer for smart contracts, which are programs managing accounts on a blockchain. Greybox fuzzing is a lightweight test-generation approach that effectively detects bugs and security vulnerabilities. However, greybox fuzzers randomly mutate program inputs to exercise new paths; this makes it challenging to cover code that is guarded by narrow checks, which are satisfied by no more than a few input values. Moreover, most real-world smart contracts transition through many different states during their lifetime, e.g., for every bid in an auction. To explore these states and thereby detect deep vulnerabilities, a greybox fuzzer would need to generate sequences of contract transactions, e.g., by creating bids from multiple users, while at the same time keeping the search space and test suite tractable. In this experience paper, we explain how Harvey alleviates both challenges with two key fuzzing techniques and distill the main lessons learned. First, Harvey extends standard greybox fuzzing with a method for predicting new inputs that are more likely to cover new paths or reveal vulnerabilities in smart contracts. Second, it fuzzes transaction sequences in a targeted and demand-driven way. We have evaluated our approach on 27 real-world contracts. Our experiments show that the underlying techniques significantly increase Harvey's effectiveness in achieving high coverage and detecting vulnerabilities, in most cases orders-of-magnitude faster; they also reveal new insights about contract code.Comment: arXiv admin note: substantial text overlap with arXiv:1807.0787

See you at heritage: A new urban regeneration approach based on heritage revitalization for South Luogu Lane area, Beijing

The story of this project starts with the bad relationship between resident, tourists and the heritage sites. Firstly, local residents’ existence is not being acknowledged as part of the heritage. Secondly, tourists cannot find open heritage site easily because of excessive commercialization and unreadable space. Thirdly, there is a bad social interaction between the social group because tourists see residents’ living space as tourism spot. Based on this, the research question was put forward: “How to maximize the potential of heritage in South Luogu lane area through the participation of residents, the design of legible urban space and active social interaction?”The final output of this research design in an new urban regeneration approach that can balance the relationship between residents, tourists and heritage, forming a harmonious coexistence for this old district under today’s new context. The specific guideline that this approach put forward is not universal for all historical district. But the main idea of visiting route, which is separation & controlled mixture provides a new way of thinking for the coexistence of tourist and resident in over-developed tourism city in China.Architecture, Urbanism and Building Science

Does Online Credit Scoring Matter: An Empirical Analysis of the Effect of Zhima Credit on Short-Term Rental

In the recent decade, the short-term rental market is growing at a rapid rate. But some negative issues have followed. Trust has been regarded as one of the main impediments to the development of the short-term rental industry. In 2016, Xiaozhu.com encouraged hosts to present their Zhima Credit-a most popular third party online credit scoring in China- as a way to earn trust. We collect data of hosts in Shanghai and Beijing before and after they post Zhima Credit and conduct a difference-in-difference analysis to figure out whether reservations would be influenced by this action. According to our study, the entry of Zhima Credit does have a positive impact on the hosts’ reservations on Xiaozhu.com and the effect will last for quite a long time

Prenatal and early-life exposure to traffic-related air pollution and allergic rhinitis in children: A systematic literature review.

BackgroundTraffic-related air pollution (TRAP) is hypothesised to play a role in the development of allergic rhinitis (AR). Prenatal and early-life exposure to traffic-related air pollution is considered critical for later respiratory health. However, we could not find any articles systematically reviewing the risk of prenatal and early-life exposure to traffic-related air pollution for allergic rhinitis in children.MethodsA systematic literature search of PubMed, Web of Science and Medline was conducted to identify studies focused on the association between prenatal and early-life exposure to TRAP and AR in children. Other inclusion criteria were: 1) original articles; 2) based upon prospective or retrospective studies or case-control studies; and 3) publications were restricted to English. Literature quality assessment was processed using the Newcastle-Ottawa Scale (NOS) evaluation scale. This systematic literature review has been registered on the prospero (crd.york.ac.uk/prospero) with the following registry number: CRD42022361179.ResultsOnly eight studies met the inclusion criteria. The exposure assessment indicators included PM2.5, PM2.5 absorbance, PM10, NOx, CO, and black carbon. On the whole, exposure to TRAP during pregnancy and the first year of life were positively associated with the development of AR in children.ConclusionsThis systematic review presents supportive evidence about prenatal and early-life exposure to TRAP and the risk of AR in children

Plasma Proteomic Analysis Based on 4D-DIA Evaluates the Clinical Response to Imrecoxib in the Early Treatment of Osteoarthritis

Abstract Introduction Nonsteroidal anti-inflammatory drugs (NSAIDs) are the primary treatment for osteoarthritis (OA), but prolonged use has adverse effects and varying efficacy. Among NSAIDs, imrecoxib, a selective cyclooxygenase-2 (COX-2) inhibitor, reduces side effects yet remains ineffective for half of the patient population. This study aims to identify biomarkers for early evaluation of imrecoxib efficacy in OA for personalized therapy optimization. Methods From September 2021 to January 2022, imrecoxib was administered to patients with OA at Nanjing Drum Tower Hospital. Plasma samples from these patients underwent proteomic analysis through the four-dimensional data-independent acquisition (4D-DIA) method, followed by bioinformatics analysis. Potential differentially expressed proteins (DEPs) were validated using enzyme-linked immunosorbent assays (ELISA). Results Sixty-six patients with knee OA were included and divided into responders (n = 35) and non-responders (n = 31). Proteomic analysis was conducted on 15 patients from each group, with ELISA validation for every patient. We found 140 DEPs between the two groups after imrecoxib treatment, characterized by 29 proteins showing upregulation and 111 displaying downregulation (P   ± 1.2). Galectin-1 (LGALS1), galectin-3 (LGALS3), and cluster of differentiation 44 (CD44) were identified as potential markers for evaluating clinical response to imrecoxib in OA following ELISA validation. Conclusion This study successfully identified biomarkers for evaluating imrecoxib’s clinical response in patients with OA using 4D-DIA technology. These biomarkers may play a vital role in future personalized OA treatment strategies, pending further confirmation

MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images

Monocular 3D object detection is very challenging in autonomous driving due to the lack of depth information. This paper proposes a one-stage monocular 3D object detection network (MDS Net), which uses the anchor-free method to detect 3D objects in a per-pixel prediction. Firstly, a novel depth-based stratification structure is developed to improve the network’s ability of depth prediction, which exploits the mathematical relationship between the size and the depth in the image of an object based on the pinhole model. Secondly, a new angle loss function is developed to further improve both the accuracy of the angle prediction and the convergence speed of training. An optimized Soft-NMS is finally applied in the post-processing stage to adjust the confidence score of the candidate boxes. Experiment results on the KITTI benchmark demonstrate that the proposed MDS-Net outperforms the existing monocular 3D detection methods in both tasks of 3D detection and BEV detection while fulfilling real-time requirements