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The increasing prevalence of surveillance cameras in smart cities, coupled with the surge of online video applications, has heightened concerns regarding public security and privacy protection, which propelled automated Video Anomaly Detection (VAD) into a fundamental research task within the Artificial Intelligence (AI) community. With the advancements in deep learning and edge computing, VAD has made significant progress and advances synergized with emerging applications in smart cities and video internet, which has moved beyond the conventional research scope of algorithm engineering to deployable Networking Systems for VAD (NSVAD), a practical hotspot for intersection exploration in the AI, IoVT, and computing fields. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes, offering an exhaustive tutorial for novices in NSVAD. This article elucidates core concepts by reviewing recent advances and typical solutions, and aggregating available research resources (e.g., literatures, code, tools, and workshops) accessible at https://github.com/fdjingliu/NSVAD. Additionally, we showcase our latest NSVAD research in industrial IoT and smart cities, along with an end-cloud collaborative architecture for deployable NSVAD to further elucidate its potential scope of research and application. Lastly, this article projects future development trends and discusses how the integration of AI and computing technologies can address existing research challenges and promote open opportunities, serving as an insightful guide for prospective researchers and engineers., Comment: Submitted to ACM Computing Surveys, under review,for more information and supplementary material, please see https://github.com/fdjingliu/NSVAD

Wan-Ling Liu,1– 4,* Yong-Qu Zhang,1– 5,* Xiang-Jie Luo,6 Yuan-Yuan Zhu,1– 4 Liang Song,7 Zi-He Ming,1– 4 Li-Xin Zhang,1– 4 Meng-Jun Li,1– 4 Rui-Chan Lv,8 Guo-Jun Zhang,1– 4,9,* Min Chen2– 4,* 1Department of Breast-Thyroid-Surgery and Cancer Center, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, People’s Republic of China; 2Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang’an Hospital of Xiamen University, Xiamen, People’s Republic of China; 3Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, People’s Republic of China; 4Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, Xiamen, People’s Republic of China; 5Department of Breast Center, Cancer Hospital of Shantou University Medical College, Shantou, People’s Republic of China; 6College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, People’s Republic of China; 7Xiamen Institute of Rare Earth Materials, Haixi Institute, Chinese Academy of Sciences and Technology University, Xiamen, People’s Republic of China; 8Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shanxi, People’s Republic of China; 9Cancer Research Center, School of Medicine, Xiamen University, Xiamen, People’s Republic of China*These authors contributed equally to this workCorrespondence: Min Chen; Guo-Jun Zhang, Email mchen@xah.xmu.edu.cn; gjzhang@xah.xmu.edu.cnBackground: Durable responses to immune-checkpoint blocking therapy (ICT) targeting programmed cell death protein-1/ligand-1 (PD-1/PD-L1) have improved outcomes for patients with triple negative breast cancer (TNBC). Unfortunately, only 19– 23% of patients benefit from ICT. Hence, non-invasive strategies evaluating responses to therapy and selecting patients who will benefit from

With the rapid development of intelligent transportation system applications, a tremendous amount of multi-view video data has emerged to enhance vehicle perception. However, performing video analytics efficiently by exploiting the spatial-temporal redundancy from video data remains challenging. Accordingly, we propose a novel traffic-related framework named CEVAS to achieve efficient object detection using multi-view video data. Briefly, a fine-grained input filtering policy is introduced to produce a reasonable region of interest from the captured images. Also, we design a sharing object manager to manage the information of objects with spatial redundancy and share their results with other vehicles. We further derive a content-aware model selection policy to select detection methods adaptively. Experimental results show that our framework significantly reduces response latency while achieving the same detection accuracy as the state-of-the-art methods., Comment: Accepted by ICASSP 2023

Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within videos. While existing reviews predominantly concentrate on conventional unsupervised methods, they often overlook the emergence of weakly-supervised and fully-unsupervised approaches. To address this gap, this survey extends the conventional scope of VAD beyond unsupervised methods, encompassing a broader spectrum termed Generalized Video Anomaly Event Detection (GVAED). By skillfully incorporating recent advancements rooted in diverse assumptions and learning frameworks, this survey introduces an intuitive taxonomy that seamlessly navigates through unsupervised, weakly-supervised, supervised and fully-unsupervised VAD methodologies, elucidating the distinctions and interconnections within these research trajectories. In addition, this survey facilitates prospective researchers by assembling a compilation of research resources, including public datasets, available codebases, programming tools, and pertinent literature. Furthermore, this survey quantitatively assesses model performance, delves into research challenges and directions, and outlines potential avenues for future exploration., Comment: Accepted by ACM Computing Surveys. For more information, please see our project page: https://github.com/fudanyliu/GVAED

Lan Zhong,1,2 Rutie Yin,1,2 Liang Song1,2 1Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China; 2Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, 610041, People’s Republic of ChinaCorrespondence: Liang Song, Department of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University, No. 20 The Third Section of South Renmin Road, Chengdu, Sichuan, 610041, People’s Republic of China, Tel +86-19828966121, Email dr_songliang@163.comAbstract: PARP inhibitors (PARPi) are the maintenance therapy after first line platinum-based chemotherapy for patients with advanced epithelial ovarian cancer (EOC) with germline and pathogenic somatic BRCA1/2 mutations. However, as with chemotherapy, patients can develop resistance to PARPi. The selective pressure generated by heterogeneous somatic BRCA mutations may give rise to chemotherapy or PARPi resistant tumors. Here, we present the case of a patient harboring a pathogenic p.Glu143* (c.427G>T) somatic BRCA1 mutation conferring resistance to olaparib following cytoreductive surgery and platinum-based chemotherapy. We ordered a plasma ctDNA analysis (tissue biopsy of recurrent lesions was contraindicated due to their anatomical location) to figure out the possible resistance mechanism. Analysis of ctDNA did not detect the pathogenic somatic BRCA1 p.Glu143* (c.427G>T) mutation seen before. The tumor cells harboring the pathogenic BRCA1 mutation were probably eliminated by the platinum-based chemotherapy, leaving only those without BRCA mutations to proliferate.Keywords: PARPi resistance, BRCA1 mutation, ovarian carcinoma, circulating tumor DNA, sequencing

We propose SNI-SLAM, a semantic SLAM system utilizing neural implicit representation, that simultaneously performs accurate semantic mapping, high-quality surface reconstruction, and robust camera tracking. In this system, we introduce hierarchical semantic representation to allow multi-level semantic comprehension for top-down structured semantic mapping of the scene. In addition, to fully utilize the correlation between multiple attributes of the environment, we integrate appearance, geometry and semantic features through cross-attention for feature collaboration. This strategy enables a more multifaceted understanding of the environment, thereby allowing SNI-SLAM to remain robust even when single attribute is defective. Then, we design an internal fusion-based decoder to obtain semantic, RGB, Truncated Signed Distance Field (TSDF) values from multi-level features for accurate decoding. Furthermore, we propose a feature loss to update the scene representation at the feature level. Compared with low-level losses such as RGB loss and depth loss, our feature loss is capable of guiding the network optimization on a higher-level. Our SNI-SLAM method demonstrates superior performance over all recent NeRF-based SLAM methods in terms of mapping and tracking accuracy on Replica and ScanNet datasets, while also showing excellent capabilities in accurate semantic segmentation and real-time semantic mapping., Comment: Accepted to CVPR 2024

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2., Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan2

In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor scenes. In this work, we extend the generalization task to outdoor scenes, trained only on object-level datasets. This approach presents two challenges. Firstly, the significant distributional shift between training and testing scenes leads to black artifacts in rendering results. Secondly, viewpoint changes in outdoor scenes cause ghosting or missing regions in rendered images. To address these challenges, we propose a geometric correction module and an appearance correction module based on multi-head attention mechanisms. We normalize rendered depth and combine it with light direction as query in the attention mechanism. Our network effectively corrects varying scene structures and geometric features in outdoor scenes, generalizing well from object-level to unseen outdoor scenes. Additionally, we use appearance correction module to correct appearance features, preventing rendering artifacts like blank borders and ghosting due to viewpoint changes. By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results. When evaluated on four datasets (Blender, DTU, LLFF, Spaces), our network outperforms previous methods. Notably, compared to MVSNeRF, our network improves average PSNR from 19.369 to 25.989, SSIM from 0.838 to 0.889, and reduces LPIPS from 0.265 to 0.224 on Spaces outdoor scenes.

Video anomaly detection is an essential yet challenging task in the multimedia community, with promising applications in smart cities and secure communities. Existing methods attempt to learn abstract representations of regular events with statistical dependence to model the endogenous normality, which discriminates anomalies by measuring the deviations to the learned distribution. However, conventional representation learning is only a crude description of video normality and lacks an exploration of its underlying causality. The learned statistical dependence is unreliable for diverse regular events in the real world and may cause high false alarms due to overgeneralization. Inspired by causal representation learning, we think that there exists a causal variable capable of adequately representing the general patterns of regular events in which anomalies will present significant variations. Therefore, we design a causality-inspired representation consistency (CRC) framework to implicitly learn the unobservable causal variables of normality directly from available normal videos and detect abnormal events with the learned representation consistency. Extensive experiments show that the causality-inspired normality is robust to regular events with label-independent shifts, and the proposed CRC framework can quickly and accurately detect various complicated anomalies from real-world surveillance videos., Comment: Accepted to ACM MM 2023

Multi-agent collaborative perception as a potential application for vehicle-to-everything communication could significantly improve the perception performance of autonomous vehicles over single-agent perception. However, several challenges remain in achieving pragmatic information sharing in this emerging research. In this paper, we propose SCOPE, a novel collaborative perception framework that aggregates the spatio-temporal awareness characteristics across on-road agents in an end-to-end manner. Specifically, SCOPE has three distinct strengths: i) it considers effective semantic cues of the temporal context to enhance current representations of the target agent; ii) it aggregates perceptually critical spatial information from heterogeneous agents and overcomes localization errors via multi-scale feature interactions; iii) it integrates multi-source representations of the target agent based on their complementary contributions by an adaptive fusion paradigm. To thoroughly evaluate SCOPE, we consider both real-world and simulated scenarios of collaborative 3D object detection tasks on three datasets. Extensive experiments demonstrate the superiority of our approach and the necessity of the proposed components., Comment: Accepted by ICCV 2023

Video Anomaly Detection (VAD) is an essential yet challenging task in signal processing. Since certain anomalies cannot be detected by isolated analysis of either temporal or spatial information, the interaction between these two types of data is considered crucial for VAD. However, current dual-stream architectures either confine this integral interaction to the bottleneck of the autoencoder or introduce anomaly-irrelevant background pixels into the interactive process, hindering the accuracy of VAD. To address these deficiencies, we propose a Multi-scale Spatial-Temporal Interaction Network (MSTI-Net) for VAD. First, to prioritize the detection of moving objects in the scene and harmonize the substantial semantic discrepancies between the two types of data, we propose an Attention-based Spatial-Temporal Fusion Module (ASTFM) as a substitute for the conventional direct fusion. Furthermore, we inject multi-ASTFM-based connections that bridge the appearance and motion streams of the dual-stream network, thus fostering multi-scale spatial-temporal interaction. Finally, to bolster the delineation between normal and abnormal activities, our system records the regular information in a memory module. Experimental results on three benchmark datasets validate the effectiveness of our approach, which achieves AUCs of 96.8%, 87.6%, and 73.9% on the UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets, respectively.

Epiphytic communities offer an original framework to disentangle the contributions of environmental filters, biotic interactions and dispersal limitations to community structure at fine spatial scales. We determine here whether variations in light, microclimatic conditions and host tree size affect the variation in species composition and phylogenetic structure of epiphytic bryophyte communities, and hence, assess the contribution of environmental filtering, phylogenetic constraints and competition to community assembly. A canopy crane giving access to 1.1 ha of tropical rainforest in Yunnan (China) was employed to record hourly light and microclimatic conditions from 54 dataloggers and epiphytic bryophyte communities from 408 plots. Generalized Dissimilarity Modelling was implemented to analyse the relationship between taxonomic and phylogenetic turnover among epiphytic communities, host-tree characteristics and microclimatic variation. Within-tree vertical turnover of bryophyte communities was significantly about 30% higher than horizontal turnover among-trees. Thus, the sharp vertical variations in microclimatic conditions from tree base to canopy are more important than differences in age, reflecting the likelihood of colonization, area, and habitat conditions between young and old trees, in shaping the composition of epiphytic bryophyte communities. Our models, to which microclimatic factors contributed most (83–98%), accounted for 33% and 18% of the variation in vertical turnover in mosses and liverworts, respectively. Phylogenetic turnover shifted from significantly negative or non-significant within communities to significantly positive among communities, and was slightly, but significantly, correlated with microclimatic variation. These patterns highlight the crucial role of microclimates in determining the composition and phylogenetic structure of epiphytic communities. Synthesis. The mostly non-significant phylogenetic turnover observed within communities, SCOPUS: ar.j, info:eu-repo/semantics/published

Video anomaly detection is a challenging task in the computer vision community. Most single task-based methods do not consider the independence of unique spatial and temporal patterns, while two-stream structures lack the exploration of the correlations. In this paper, we propose spatial-temporal memories augmented two-stream auto-encoder framework, which learns the appearance normality and motion normality independently and explores the correlations via adversarial learning. Specifically, we first design two proxy tasks to train the two-stream structure to extract appearance and motion features in isolation. Then, the prototypical features are recorded in the corresponding spatial and temporal memory pools. Finally, the encoding-decoding network performs adversarial learning with the discriminator to explore the correlations between spatial and temporal patterns. Experimental results show that our framework outperforms the state-of-the-art methods, achieving AUCs of 98.1% and 89.8% on UCSD Ped2 and CUHK Avenue datasets., Comment: Accepted by ICME 2022

Adversarial attacks are often considered as threats to the robustness of Deep Neural Networks (DNNs). Various defending techniques have been developed to mitigate the potential negative impact of adversarial attacks against task predictions. This work analyzes adversarial attacks from a different perspective. Namely, adversarial examples contain implicit information that is useful to the predictions i.e., image classification, and treat the adversarial attacks against DNNs for data self-expression as extracted abstract representations that are capable of facilitating specific learning tasks. We propose an algorithmic framework that leverages the advantages of the DNNs for data self-expression and task-specific predictions, to improve image classification. The framework jointly learns a DNN for attacking Variational Autoencoder (VAE) networks and a DNN for classification, coined as Attacking VAE for Improve Classification (AVIC). The experiment results show that AVIC can achieve higher accuracy on standard datasets compared to the training with clean examples and the traditional adversarial training.

Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically investigate how to explain the EEG-based deep learning models. We conduct a review to summarize the existing works explaining the EEG-based deep learning model. Unfortunately, we find that there is no appropriate method to explain them. Based on the characteristic of EEG data, we suggest a context-aware perturbation method to generate a saliency map from the perspective of the raw EEG signal. Moreover, we also justify that the context information can be used to suppress the artifacts in the EEG-based deep learning model. In practice, some users might want a simple version of the explanation, which only indicates a few features as salient points. To this end, we propose an optional area limitation strategy to restrict the highlighted region. To validate our idea and make a comparison with the other methods, we select three representative EEG-based models to implement experiments on the emotional EEG dataset DEAP. The results of the experiments support the advantages of our method.

A 2D imine-linked COF with a hitherto unreported htb type topology was synthesized from a linear diamine linker and a judiciously designed tetra-aldehyde building block. This work opens the door to the development of COFs with unprecedented topologies and may broaden the scope of COF functional materials by pore size and pore surface engineering.

Liang Li,1 Xue Yu,2 Guanqiang Ma,1 Zhiqi Ji,1 Shihao Bao,1 Xiaopeng He,1,3 Liang Song,1,3 Yang Yu,1,3 Mo Shi,1,3 Xiangyan Liu1,3 1Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People’s Republic of China; 2Department of Pediatrics, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 420100, People’s Republic of China; 3Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People’s Republic of ChinaCorrespondence: Mo Shi; Xiangyan LiuDepartment of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People’s Republic of ChinaEmail legendsm01@163.com; liuxiangyan1@163.comBackground: Early-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets.Methods: Gene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and d

The interconnectedness of all things is continuously expanding which has allowed every individual to increase their level of interaction with their surroundings. Internet of Things (IoT) devices are used in a plethora of context-aware application such as Proximity-Based Services (PBS), and Location-Based Services (LBS). For these systems to perform, it is essential to have reliable hardware and predict a user's position in the area with high accuracy in order to differentiate between individuals in a small area. A variety of wireless solutions that utilize Received Signal Strength Indicators (RSSI) have been proposed to provide PBS and LBS for indoor environments, though each solution presents its own drawbacks. In this work, Bluetooth Low Energy (BLE) beacons are examined in terms of their accuracy in proximity estimation. Specifically, a mobile application is developed along with three Bayesian filtering techniques to improve the BLE beacon proximity estimation accuracy. This includes a Kalman filter, a particle filter, and a Non-parametric Information (NI) filter. Since the RSSI is heavily influenced by the environment, experiments were conducted to examine the performance of beacons from three popular vendors in two different environments. The error is compared in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). According to the experimental results, Bayesian filters can improve proximity estimation accuracy up to 30 % in comparison with traditional filtering, when the beacon and the receiver are within 3 m.

Few-shot segmentation is a challenging task that aims to segment objects of new classes given scarce support images. In the inductive setting, existing prototype-based methods focus on extracting prototypes from the support images; however, they fail to utilize semantic information of the query images. In this paper, we propose Bi-level Optimization (BiOpt), which succeeds to compute class prototypes from the query images under inductive setting. The learning procedure of BiOpt is decomposed into two nested loops: inner and outer loop. On each task, the inner loop aims to learn optimized prototypes from the query images. An init step is conducted to fully exploit knowledge from both support and query features, so as to give reasonable initialized prototypes into the inner loop. The outer loop aims to learn a discriminative embedding space across different tasks. Extensive experiments on two benchmarks verify the superiority of our proposed BiOpt algorithm. In particular, we consistently achieve the state-of-the-art performance on 5-shot PASCAL-$5^i$ and 1-shot COCO-$20^i$.

Liang Song,1,* Xiang-Xin Li,2,* Xiang-Yan Liu,1 Zhou Wang,1 Yang Yu,1 Mo Shi,1 Bin Jiang,1 Xiao-Peng He1 1Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, People’s Republic of China; 2Department of Hematology, Qilu Hospital of Shandong University, Jinan, Shandong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiao-Peng HeDepartment of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong University, 324 Jing Wu Road, Jinan, Shandong Province 250021, People’s Republic of ChinaTel/Fax +86-531-6877-7884Email hexiaopeng7715@126.comBackground: Epidermal growth factor-containing fibulin-like extracellular matrix protein 2 (EFEMP2), also known as fibulin-4, MBP1 and UPH1, is an extracellular matrix protein associated with a variety of tumors. The purpose of this study was to investigate the prognostic value and the function of EFEMP2 in lung cancer.Methods: The mRNA and protein expression of EFEMP2 in lung normal and cancer tissues, lung cancer cell lines (A549, H460, H1299 and H1650) and normal epithelial cell line BEAS-2B were evaluated by immunohistochemistry, RT-qPCR and Western blotting. The Public databases (Oncomine and Kaplan-Meier plotter) were used to investigate the prognostic value of EFEMP2 in lung cancer. RNA interference (RNAi) and overexpression transfection were performed to detect the effects of EFEMP2 up- or down-regulation on lung normal and cancer cell proliferation, invasion and metastasis in vitro and in vivo.Results: EFEMP2 was lowly expressed in lung cancer tissues and cells, and its low expression was associated with malignant phenotype and poor prognosis of lung cancer. The same conclusion had been drawn from the Public databases. EFEMP2 overexpression significantly inhibited the invasion of lung cancer cells, hampered the process of EMT, and decreased the expression and activity of MMP2 and MMP9

A detailed understanding of abiotic stress tolerance in plants is essential to provide food security in the face of increasingly harsh climatic conditions. Glucosinolates (GLSs) are secondary metabolites found in the Brassicaceae that protect plants from herbivory and pathogen attack. Here we report that in Arabidopsis, aliphatic GLS levels are regulated by the auxin-sensitive Aux/IAA repressors IAA5, IAA6, and IAA19. These proteins act in a transcriptional cascade that maintains expression of GLS levels when plants are exposed to drought conditions. Loss of IAA5/6/19 results in reduced GLS levels and decreased drought tolerance. Further, we show that this phenotype is associated with a defect in stomatal regulation. Application of GLS to the iaa5,6,19 mutants restores stomatal regulation and normal drought tolerance. GLS action is dependent on the receptor kinase GHR1, suggesting that GLS may signal via reactive oxygen species. These results provide a novel connection between auxin signaling, GLS levels and drought response.

Two stable crystalline hydrazone-linked covalent organic frameworks (COFs) (Bth-Dha and Bth-Dma) containing functional O,N,O'-chelating sites have been designed and successfully synthesized by the Schiff-base condensation reactions between benzene-1,3,5-tricarbohydrazide (Bth) and 2,5-dihydroxyterephthalaldehyde (Dha) or 2,5-dimethoxyterephthal-aldehyde (Dma), respectively. Bth-Dma exhibits strong fluorescence in the solid state and in an aqueous dispersion, while no fluorescence can be observed for Bth-Dha. Interestingly, the as-synthesized Bth-Dma can be used as a turn-off fluorescence sensor for the Fe(III) ion in aqueous solution with outstanding selectivity and sensitivity. The recognition process can be attributed to the coordination interaction between Fe(III) ion and the O,N,O'-chelating sites in the pore wall of Bth-Dma COF, as verified by X-ray photoelectron spectroscopy and 1H NMR spectroscopy. To the best of our knowledge, this is the first report on the rational design of luminescent COF with predesigned O,N,O'-chelating sites as a fluorescence sensor for highly selective and sensitive metal ion detection. This work may pave the way for designing luminescent COF sensors with functional binding sites for detecting specific metal ions.

Few-shot learning requires to recognize novel classes with scarce labeled data. Prototypical network is useful in existing researches, however, training on narrow-size distribution of scarce data usually tends to get biased prototypes. In this paper, we figure out two key influencing factors of the process: the intra-class bias and the cross-class bias. We then propose a simple yet effective approach for prototype rectification in transductive setting. The approach utilizes label propagation to diminish the intra-class bias and feature shifting to diminish the cross-class bias. We also conduct theoretical analysis to derive its rationality as well as the lower bound of the performance. Effectiveness is shown on three few-shot benchmarks. Notably, our approach achieves state-of-the-art performance on both miniImageNet (70.31% on 1-shot and 81.89% on 5-shot) and tieredImageNet (78.74% on 1-shot and 86.92% on 5-shot)., Comment: ECCV 2020 Oral

Many Few-Shot Learning research works have two stages: pre-training base model and adapting to novel model. In this paper, we propose to use closed-form base learner, which constrains the adapting stage with pre-trained base model to get better generalized novel model. Following theoretical analysis proves its rationality as well as indication of how to train a well-generalized base model. We then conduct experiments on four benchmarks and achieve state-of-the-art performance in all cases. Notably, we achieve the accuracy of 87.75% on 5-shot miniImageNet which approximately outperforms existing methods by 10%., Comment: We found a bug in the script in reexamining some of this work. We decide to withdraw for further modification

We present both an innovative theoretical model and an experimental validation of a molecular gas optically pumped far-infrared (OPFIR) laser at 0.25 THz that exhibits 10× greater efficiency (39% of the Manley–Rowe limit) and 1,000× smaller volume than comparable commercial lasers. Unlike previous OPFIR-laser models involving only a few energy levels that failed even qualitatively to match experiments at high pressures, our ab initio theory matches experiments quantitatively, within experimental uncertainties with no free parameters, by accurately capturing the interplay of millions of degrees of freedom in the laser. We show that previous OPFIR lasers were inefficient simply by being too large and that high powers favor high pressures and small cavities. We believe that these results will revive interest in OPFIR laser as a powerful and compact source of terahertz radiation. Keywords: optically pumped far-infrared laser; terahertz source; continuous wave gas laser; laser modeling; rotational population inversion, United States. Army Research Office (Contract W911NF-18-2-0048), United States. Army Research Office (Contract W911NF-13-D-0001), National Science Foundation (U.S.) (Award DMR-1419807)

We report the design and synthesis of an imine-based two-dimensional covalent organic framework (2D COF) with a novel brick-wall topology by judiciously choosing a tritopic T-shaped building block and a ditopic linear linker. Unlike the main body of COF frameworks reported to-date, which consists of higher-symmetry 2D topologies, the unconventional layered brick-wall topology have only been proposed but never been realized experimentally. The brick-wall structure was characterized by powder X-ray diffraction analysis, FT-IR, solid state 13C NMR spectroscopy, nitrogen, and carbon oxide adsorption-desorption measurements as well as theoretical simulations. Our present work opens the door to the design of novel 2D COFs and will broaden the scope of emerging COF materials.

The nature of light interaction with matter can be dramatically altered in optical cavities, often inducing nonclassical behavior. In solid-state systems, excitons need to be spatially incorporated within nanostructured cavities to achieve such behavior. Although fascinating phenomena have been observed with inorganic nanostructures, the incorporation of organic molecules into the typically inorganic cavity is more challenging. Here,we present a unique optofluidic platform comprising organic molecules in solution suspended on a photonic crystal surface, which supports macroscopic Fano resonances and allows strong and tunable interactions with the molecules anywhere along the surface. We develop a theoretical framework of this system and present a rigorous comparison with experimental measurements, showing dramatic spectral and angular enhancement of emission. We then demonstrate tha t these enhancement mechanisms enable lasing of only a 100-nm thin layer of diluted solution of organic molecules with substantially reduced threshold intensity, which has important implications for organic light-emitting devices and molecular sensing. Keywords: flourescence enhancement; enhanced light-matter interaction, United States. Department of Energy. Office of Basic Energy Sciences (Grant DE-SC0001299), United States. Army Research Office (Contract W911NF-07-D0004), National Science Foundation (U.S.) (Award DMR-0819762), United States. Defense Advanced Research Projects Agency (Contract N66001-09-1-2070-DOD)

Zhongwei Xin,* Gongsun Xin,* Mo Shi, Liang Song, Qiang Wang, Bin Jiang, Xiangyan Liu Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, People’s Republic of China *These authors contributed equally to this work Background: The mucin 1 (MUC1) heterodimeric protein (N-terminal subunit and C-terminal subunit) is aberrantly overexpressed in esophageal squamous cell carcinoma (ESCC) and has been linked to poor outcomes in this disease. The detailed mechanism(s), however, remains unclear. In this article, we investigate the effects of the MUC1 C-terminal transmembrane subunit (MUC1-C) through the inhibitor GO-201, which inhibits MUC1-C targeting to nuclear. Patients and methods: The expression of MUC1-C and MYC in the ESCC samples and cell lines was detected by immunohistochemistry, immunofluorescence and western blotting. MYC mRNA level was determined by using quantitative real-time polymerase chain reaction. In addition, Cell Counting Kit-8, clonogenic assay, transwell assay and tumor xenograft in nude mice assay were utilized to determine the role of MUC1-C in proliferation, invasion and migration of ESCC cells. Results: The level of MUC1-C in nuclear and MYC in whole cells in the ESCC tissue is significantly higher than that in the noncancerous tissue. Treatment of MUC1-C-overexpressing ESCC cells with GO-201 was associated with downregulation of MYC expression and induction of apoptosis. Besides, in vitro and in vivo assays have both shown that inhibiting MUC1-C targeting to the nucleus by the GO-201 significantly decreased the abilities of proliferation, invasion and migration in ESCC cells. Conclusion: Our findings suggest that MUC1-C targeting to the nucleus plays an important role in suppressing the malignant growth of ESCC and indicate that MUC1-C is a potential target for the treatment of ESCC. Keywords: MUC1 C-terminal subunit (MUC1-C), MYC, esophageal squamous cell carcinoma (ESCC), GO-201, maligna

The Aux/IAA proteins are auxin-sensitive repressors that mediate diverse physiological and developmental processes in plants [1, 2]. There are 29 Aux/IAA genes in Arabidopsis that exhibit unique but partially overlapping patterns of expression [3]. Although some studies have suggested that individual Aux/IAA genes have specialized function, genetic analyses of the family have been limited by the scarcity of loss-of-function phenotypes [4]. Furthermore, with a few exceptions, our knowledge of the factors that regulate Aux/IAA expression is limited [1, 5]. We hypothesize that transcriptional control of Aux/IAA genes plays a central role in the establishment of the auxin-signaling pathways that regulate organogenesis, growth, and environmental response. Here, we describe a screen for transcription factors (TFs) that regulate the Aux/IAA genes. We identify TFs from 38 families, including 26 members of the DREB/CBF family. Several DREB/CBF TFs directly promote transcription of the IAA5 and IAA19 genes in response to abiotic stress. Recessive mutations in these IAA genes result in decreased tolerance to stress conditions, demonstrating a role for auxin in abiotic stress. Our results demonstrate that stress pathways interact with the auxin gene regulatory network (GRN) through transcription of the Aux/IAA genes. We propose that the Aux/IAA genes function as hubs that integrate genetic and environmental information to achieve the appropriate developmental or physiological outcome.

Forest canopies are dynamic interfaces between organisms and atmosphere, providing buffered microclimates and complex microhabitats. Canopies form vertically stratified ecosystems interconnected with other strata. Some forest biodiversity patterns and food webs have been documented and measurements of ecophysiology and biogeochemical cycling have allowed analyses of large-scale transfer of CO2, water, and trace gases between forests and the atmosphere. However, many knowledge gaps remain. With global research networks and databases, and new technologies and infrastructure, we envisage rapid advances in our understanding of the mechanisms that drive the spatial and temporal dynamics of forests and their canopies. Such understanding is vital for the successful management and conservation of global forests and the ecosystem services they provide to the world.

Organic dye molecules offer significant potential as gain media in the emerging field of optical amplification and lasing at subwavelength scales. Here, we investigate the laser dynamics in systems comprising subwavelength-structured cavities that incorporate organic dyes. To this end, we have developed a comprehensive theoretical framework able to accurately describe the interaction of organic molecules with any arbitrary photonic structure to produce single-mode lasing. The model provides explicit analytic expressions of the threshold and slope efficiency that characterize this class of lasers, and also the duration over which lasing action can be sustained before the dye photobleaches. Both the physical properties of the dyes and the optical properties of the cavities are considered. We also systematically studied the feasibility of achieving lasing action under continuous-wave excitation in optically pumped monolithic organic dye lasers. This study suggests routes to realize an organic laser that can potentially lase with a threshold of only a few W cm⁻². Our work puts forward a theoretical formalism that could enable the advancement of nanostructured organic-based light emitting and sensing devices., National Science Foundation (U.S.) (DMR-0819762), Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies (W911NF-13-D-0001), Solid-State Solar-Thermal Energy Conversion Center (DE-SC0001299)

The Dirac cone underlies many unique electronic properties of graphene1 and topological insulators, and its band structure—two conical bands touching at a single point—has also been realized for photons in waveguide arrays, atoms in optical lattices, and through accidental degeneracy. Deformation of the Dirac cone often reveals intriguing properties; an example is the quantum Hall effect, where a constant magnetic field breaks the Dirac cone into isolated Landau levels. A seemingly unrelated phenomenon is the exceptional point also known as the parity–time symmetry breaking point where two resonances coincide in both their positions and widths. Exceptional points lead to counter-intuitive phenomena such as loss-induced transparency unidirectional transmission or reflection and lasers with reversed pump dependence or single-mode operation. Dirac cones and exceptional points are connected: it was theoretically suggested that certain non-Hermitian perturbations can deform a Dirac cone and spawn a ring of exceptional points. Here we experimentally demonstrate such an ‘exceptional ring’ in a photonic crystal slab. Angle-resolved reflection measurements of the photonic crystal slab reveal that the peaks of reflectivity follow the conical band structure of a Dirac cone resulting from accidental degeneracy, whereas the complex eigenvalues of the system are deformed into a two-dimensional flat band enclosed by an exceptional ring. This deformation arises from the dissimilar radiation rates of dipole and quadrupole resonances, which play a role analogous to the loss and gain in parity–time symmetric systems. Our results indicate that the radiation existing in any open system can fundamentally alter its physical properties in ways previously expected only in the presence of material loss and gain., United States. Army Research Office (W911NF-07-D0004), United States. Army Research Office (W911NF-13-D-0001), Solid-State Solar-Thermal Energy Conversion Center, National Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (DMR-1419807)

The ability to confine light is important both scientifically and technologically. Many light confinement methods exist, but they all achieve confinement with materials or systems that forbid outgoing waves. These systems can be implemented by metallic mirrors, by photonic band-gap materials, by highly disordered media (Anderson localization) and, for a subset of outgoing waves, by translational symmetry (total internal reflection) or by rotational or reflection symmetry. Exceptions to these examples exist only in theoretical proposals. Here we predict and show experimentally that light can be perfectly confined in a patterned dielectric slab, even though outgoing waves are allowed in the surrounding medium. Technically, this is an observation of an ‘embedded eigenvalue’—namely, a bound state in a continuum of radiation modes—that is not due to symmetry incompatibility. Such a bound state can exist stably in a general class of geometries in which all of its radiation amplitudes vanish simultaneously as a result of destructive interference. This method to trap electromagnetic waves is also applicable to electronic and mechanical waves., United States. Army Research Office (Institute for Soldier Nanotechnologies under contract no. W911NF-07-D0004), United States. Department of Energy (grant no. DE-SC0001299), National Science Foundation (U.S.) (NSF grant no. DMR-0819762)

We investigate laser emission from optically-pumped rotationally excited molecular gases confined in a metallic cavity. To this end, we have developed a theoretical framework able to accurately describe, both in the spatial and temporal domains, the molecular collisional and diffusion processes characterizing the operation of this class of lasers. The effect on the main lasing features of the spatial variation of the electric field intensity and the ohmic losses associated to each cavity mode are also included in our analysis. Our simulations show that, for the exemplary case of methyl fluoride gas confined in a cylindrical copper cavity, the region of maximum population inversion is located near the cavity walls. Based on this fact, our calculations show that the lowest lasing threshold intensity corresponds to the cavity mode that, while maximizing the spatial overlap between the corresponding population inversion and electric-field intensity distributions, simultaneously minimizes the absorption losses occurring at the cavity walls. The dependence of the lasing threshold intensity on both the gas pressure and the cavity radius is also analyzed and compared with experiment. We find that as the cavity size is varied, the interplay between the overall gain of the system and the corresponding ohmic losses allows for the existence of an optimal cavity radius which minimizes the intensity threshold for a large range of gas pressures. The theoretical analysis presented in this work expands the current understanding of lasing action in optically-pumped far-infrared lasers and, thus, could contribute to the development of a new class of compact far-infrared and terahertz sources able to operate efficiently at room temperature., National Science Foundation (U.S.) (Award DMR-0819762), United States. Army Research Office. (Contract W911NF-07-D0004)

By using animal models of acute pancreatitis (AP) and sterile cholangitis (Mdr2−/−) in Bcl-3 deficient (Bcl-3−/−) mice, we analyzed the contribution of Bcl-3 to the control and resolution of sterile inflammation in the pancreas and biliary system. Genetic inhibition of Bcl-3 resulted in more severe forms of AP and cholangitis, accompanied by increased infiltration of immune cells. Using various genetic tools we showed that Bcl-3 inhibits ubiquitination and proteasomal mediated degradation of p50 homodimers, thus blocking prolonged binding of nuclear factor κB (NF-κB) heterodimers to the DNA., Anhand von Tiermodellen der akuten Pankreatitis (AP) und sterilen Cholangitis (Mdr2−/−) in Bcl-3 defizienten (Bcl-3−/−) Mäusen wurde die Rolle von Bcl-3 in der Steuerung und Auflösung der sterilen Bauchspeicheldrüsen- und Gallenwegsentzündung untersucht. Genetische Inaktivierung von Bcl-3 führte zu schwereren Formen der AP und Cholangitis, begleitet von einer vermehrten Infiltration von Immunzellen. Darüber hinaus belegen unsere Daten, dass Bcl-3 die Ubiquitinierung und den Proteasom-vermittelten Abbau von p50-Homodimeren hemmt und so die prolongierte Bindung von nuklear Faktor κB (NF-κB)-Heterodimeren an die DNA blockiert.

A chelator-free doping method is developed for constructing a Ni-integrated CuS nanostructure as a novel PA/MRI contrast agent. It exhibits tunable near-infrared absorption. Moreover, the hybrid nanostructure has demonstrated a dramatically enhanced T1 relaxivity compared with Ni ions. Due to these unique properties, chelator-free nanoparticles have been successfully applied for in vivo PA/MRI dual-modal imaging.

Mobility data records the change of location and time about the crowd activities, reflecting a large amount of semantic knowledge about human mobility and hot regions. From the perspective of regional semantic knowledge, mining anomalous regions of overcrowded area is essential for disaster-aware resilience system scheme. This paper studies how to discover anomalous regions of moving crowds over the mobility data. From the perspective of spatial information analysis about the location sequence of moving crowds, the paper introduces grid structure to index activity space and proposes a density calculation method of grid cells based on kernel function. By adopting Top-k sorting method, the algorithm determines the density thresholds to detect the anomalous regions. Finally, experimental results validate the feasibility and effectiveness of the above method on practical data sets.

From detailed numerical calculations, we demonstrate that in simple photonic crystal structures, a discrete number of Bloch surface-localized eigenstates can exist inside the continuum of free-space modes. Coupling to the free space causes the surface modes to leak, but the forward and back-reflected leakage may interfere destructively to create a perfectly bound surface state with zero leakage. We perform analytical temporal coupled-mode theory analysis to show the generality of such phenomenon and its robustness from variations of system parameters. Periodicity, time-reversal invariance, two-fold rotational symmetry and a perfectly reflecting boundary are necessary for these unique states., United States. Dept. of Energy. Office of Science (Solid-State Solar-Thermal Energy Conversion Center Grant DE-SC0001299), National Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (Award DMR-0819762), Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies (Contract W911NF-07-D0004)

Traditional diagnostic imaging technologies—including ultrasound, x-ray computed tomography, and magnetic resonance imaging—have dramatically improved patient care in the clinic. However, these technologies still have limited ability to detect small lesions. In most cases, the detectable lesions are ~1 cm in dimeter, corresponding to ~10^9 cells (including circulating and microscopic metastatic deposits) and representing ~2/3 of the natural history of a cancer [1]. Consequently, the window of opportunity for treatment before a tumor reaches its lethal state is narrow. Hence, it is imperative to develop novel technologies capable of detecting neoplasia early. Non ionizing biomedical optical imaging is highly desirable because of its rich contrast. In particular, optical absorption—to which photoacoustic tomography is sensitive—can be used to obtain physiological parameters such as the total concentration and oxygenation of hemoglobin is related to hyper-metabolism. Since both angiogenesis and hypermetabolism are hallmarks of cancer, photoacoustic tomography can potentially provide early-cancer detection.

By altering the lattice geometry of the photonic crystal (PhC) surface-emitting lasers (PCSELs), we tune the regular lasing band edges of quadratic dispersions to form a single accidental Dirac point of linear dispersion at the Brillouin zone center. This not only increases the mode spacing by orders of magnitude but also eliminates the distributed in-plane feedback to enable single-mode PCSELs of substantially larger area and thus substantially higher output power. The advantages of using accidental Dirac cones are systematically evaluated through two-dimensional in-plane calculations and confirmed by three-dimensional simulations of PhC slab devices., National Science Foundation (U.S.) (MRSEC Program, award no. DMR-0819762), United States. Army Research Office. Institute for Soldier Nanotechnologies (Contract W911NF-13-D-0001), United States. Dept. of Energy (MIT S3TEC Energy Research Frontier Center, Grant No. DE-SC0001299), Spain. Ministerio de Ciencia e Innovación (Ramon-y-Cajal program, Grant No. RyC-2009-05489)

Photonic crystals provide superb opportunities for tailoring of the photonic density of states. This ability can in turn be explored to control radiation into far-field, enhance fluorescent light emission, as well as optimize laser emission. In order to make these phenomena useful for large macroscopic devices, large-area nano-fabrication techniques have to be successfully implemented. In this talk, I will present some of our recent theoretical and experimental progress in exploring these opportunities.

Jin-dan Luo, Ben Liu, Ping Wang, Feng Zhou, Xiang-lai Xu, Shi-qi Li, Song-liang Cai, Yi-min Wang Department of Urology, the First Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang, People's Republic of China Abstract: Iliac vein-ureteral fistula is a rare cause of hematuria. The diagnosis of an iliac vein-ureteral fistula can be elusive even with the use of multiple methods. With regards to the treatment, there appears to be a shift in management from primarily open surgical to primarily angiographic management. We present a unique case of an external iliac vein – transplant ureteral fistula. A 48 year-old female complained of recurrent gross hematuria. She underwent transplant nephrectomy and radical left nephrectomy because of rejection of transplanted kidney and cystic renal cell carcinoma when the hematuria arose for the first time. Ten months later, the hematuria recurred again, and cystoscopy showed bleeding from the right transplant ureteral orifice. Open exploration confirmed the diagnosis of external iliac vein – transplant ureteral fistula. Diagnostic difficulties and treatment dilemma of such a rare cause of hematuria are also discussed. Keywords: iliac vein, ureteral fistula, renal transplantation, hematuria

Acute lung injury (ALI) is an inflammatory disease with a high mortality rate. Although typically seen in individuals with sepsis, ALI is also a major complication in severe acute pancreatitis (SAP). The pathophysiology of SAP-associated ALI is poorly understood, but elevated serum levels of IL-6 is a reliable marker for disease severity. Here, we used a mouse model of acute pancreatitis–associated (AP-associated) ALI to determine the role of IL-6 in ALI lethality. Il6-deficient mice had a lower death rate compared with wild-type mice with AP, while mice injected with IL-6 were more likely to develop lethal ALI. We found that inflammation-associated NF-κB induced myeloid cell secretion of IL-6, and the effects of secreted IL-6 were mediated by complexation with soluble IL-6 receptor, a process known as trans-signaling. IL-6 trans-signaling stimulated phosphorylation of STAT3 and production of the neutrophil attractant CXCL1 in pancreatic acinar cells. Examination of human samples revealed expression of IL-6 in combination with soluble IL-6 receptor was a reliable predictor of ALI in SAP. These results demonstrate that IL-6 trans-signaling is an essential mediator of ALI in SAP across species and suggest that therapeutic inhibition of IL-6 may prevent SAP-associated ALI.

As business process management technology matures, organisations acquire more and more business process models. The management of the resulting collections of process models poses real challenges. One of these challenges concerns model retrieval where support should be provided for the formulation and efficient execution of business process model queries. As queries based on only structural information cannot deal with all querying requirements in practice, there should be support for queries that require knowledge of process model semantics. In this paper we formally define a process model query language that is based on semantic relationships between tasks in process models and is independent of any particular process modelling notation.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013., Cataloged from PDF version of thesis., Includes bibliographical references (p. 133-142)., The laser has long been established as the best possible optical source for fundamental studies and applications requiring high field intensity, single mode operation, a high degree of coherence, a narrow linewidth and short pulses. There are many applications that require lasers of varying frequencies, powers, and far-field properties. In science, the laser is a useful source in spectroscopy and microscopy, for investigating nonlinear optics phenomena and nuclear fusion. More commonly, we find them in barcode readers, laser pointers, and printers. They are also widely used for military, medical, and industrial applications. This thesis is focused on achieving new understanding of the principles and phenomena involved in the interaction of light with a variety of material systems, which will in turn guide the designs of compact lasers with feedback structures having features at the subwavelength-scale. The thesis begins by describing the interaction of light with an arbitrary complex material system, and implementing them into the electromagnetic model using two different theoretical techniques suitable for analyzing microstructured lasers: exact finite-difference time-domain calculations and a semi-analytic coupled-mode formalism. These methods are first applied to analyze lasing action in the photonic crystal (PhC) slabs. This class of lasers, commonly referred to as the photonic crystal surface-emitting lasers (PCSELs), can be integrated on-chip and is essentially the two-dimensional (2D) versions of the second-order distributed feedback lasers, where the higher quality factor lasing mode (dark Fano resonance) is selected through the symmetry mismatch to the free-space modes. The PCSELs have not only achieved the highest surface-emitting single-mode power but also the ability to control the shapes, polarizations and directions of their far-fields. However, as in all laser cavities, the lasing areas of PCSELs are limited by two fundamental constraints; a large are, by Song-Liang Chua., Ph.D.

The separation and reconstructions of charged hadron and neutral hadron from their overlapped showers in electromagnetic calorimeter is very important for the reconstructions of some particles with hadronic decays, for example the tau reconstruction in the searches for the Standard Model and supersymmetric Higgs bosons at the LHC. In this paper, a method combining the shower cluster in electromagnetic calorimeter and the parametric formula for hadron showers, was developed to separate the overlapped showers between charged hadron and neutral hadron. Taking the hadronic decay containing one charged pion and one neutral pion in the final status of tau for example, satisfied results of the separation of the overlapped showers, the reconstructions of the energy and positions of the hadrons were obtained. An improved result for the tau reconstruction with this decay model can be also achieved after the application of the proposed method., Comment: 7 pages

We present a theoretical analysis of lasing action in photonic crystal surface-emitting lasers (PCSELs). The semiclassical laser equations for such structures are simulated with three different theoretical techniques: exact finite-difference time-domain calculations, an steady-state ab-initio laser theory and a semi-analytical coupled-mode formalism. Our simulations show that, for an exemplary four-level gain model, the excitation of dark Fano resonances featuring arbitrarily large quality factors can lead to a significant reduction of the lasing threshold of PCSELs with respect to conventional vertical-cavity surface-emitting lasers. Our calculations also suggest that at the onset of lasing action, most of the laser power generated by finite-size PCSELs is emitted in the photonic crystal plane rather than the vertical direction. In addition to their fundamental interest, these findings may affect further engineering of active devices based on photonic crystal slabs., National Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (Award DMR-0819762), United States. Dept. of Energy. Office of Science (Grant DE-SC0001299), Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies (Contract W911NF-07-D-0004)

Malaria is re-emerging in Anhui Province, China after a decade long’ low level of endemicity. The number of human cases has increased rapidly since 2000 and reached its peak in 2006. That year, the malaria cases accounted for 54.5% of total cases in mainland China. However, the spatial and temporal patterns of human cases and factors underlying the re-emergence remain unclear. We established a database containing 20 years’ (1990–2009) records of monthly reported malaria cases and meteorological parameters. Spearman correlations were used to assess the crude association between malaria incidence and meteorological variables, and a polynomial distributed lag (PDL) time-series regression was performed to examine contribution of meteorological factors to malaria transmission in three geographic regions (northern, mid and southern Anhui Province), respectively. Then, a two-year (2008–2009) prediction was performed to validate the PDL model that was created by using the data collected from 1990 to 2007. We found that malaria incidence decreased in Anhui Province in 1990s. However, the incidence has dramatically increased in the north since 2000, while the transmission has remained at a relatively low level in the mid and south. Spearman correlation analyses showed that the monthly incidences of malaria were significantly associated with temperature, rainfall, relative humidity, and the multivariate El Niño/Southern Oscillation index with lags of 0–2 months in all three regions. The PDL model revealed that only rainfall with a 1–2 month lag was significantly associated with malaria incidence in all three regions. The model validation showed a high accuracy for the prediction of monthly incidence over a 2-year predictive period. Malaria epidemics showed a high spatial heterogeneity in Anhui Province during the 1990–2009 study periods. The change in rainfall drives the reemergence of malaria in the northern Anhui Province.