Two-photon excited photoluminescence in InGaN multi-quantum-wells structures

In this work, we report on the two-photon absorption induced luminescence of InGaN multiple quantum wells grown on sapphire. When the sample was excited by femtosecond near-infrared laser pulses at room temperature, an intense luminescence signal peaked at ∼415 nm from the sample was observed, which indicates strong nonlinear optical effect in InGaN quantum well structures. The interferometric autocorrelated luminescence traces were recorded to verify the second order nonlinearity of the luminescence. In addition, the strong second harmonic generation signal of the excitation laser was also observed. The mechanism of the two-photon excited photoluminescence in InGaN quantum wells was discussed.published_or_final_versionpublished_or_final_versio

Deterministic Assembly of Arrays of Lithographically Defined WS2 and MoS2 Monolayer Features Directly from Multilayer Sources into Van der Waals Heterostructures

One of the major challenges in the van der Waals (vdW) integration of two-dimensional (2D) materials is achieving high-yield and high-throughput assembly of predefined sequences of monolayers into heterostructure arrays. Mechanical exfoliation has recently been studied as a promising technique to transfer monolayers from a multilayer source synthesized by other techniques, allowing the deposition of a wide variety of 2D materials without exposing the target substrate to harsh synthesis conditions. Although a variety of processes have been developed to exfoliate the 2D materials mechanically from the source and place them deterministically onto a target substrate, they can typically transfer only either a wafer-scale blanket or one small flake at a time with uncontrolled size and shape. Here, we present a method to assemble arrays of lithographically defined monolayer WS2 and MoS2 features from multilayer sources and directly transfer them in a deterministic manner onto target substrates. This exfoliate-align-release process - without the need of an intermediate carrier substrate - is enabled by combining a patterned, gold-mediated exfoliation technique with a new optically transparent, heat-releasable adhesive. WS2/MoS2 vdW heterostructure arrays produced by this method show the expected interlayer exciton between the monolayers. Light-emitting devices using WS2 monolayers were also demonstrated, proving the functionality of the fabricated materials. Our work demonstrates a significant step toward developing mechanical exfoliation as a scalable dry transfer technique for the manufacturing of functional, atomically thin materials

Query Expansion for Survey Question Retrieval in the Social Sciences

In recent years, the importance of research data and the need to archive and to share it in the scientific community have increased enormously. This introduces a whole new set of challenges for digital libraries. In the social sciences typical research data sets consist of surveys and questionnaires. In this paper we focus on the use case of social science survey question reuse and on mechanisms to support users in the query formulation for data sets. We describe and evaluate thesaurus- and co-occurrence-based approaches for query expansion to improve retrieval quality in digital libraries and research data archives. The challenge here is to translate the information need and the underlying sociological phenomena into proper queries. As we can show retrieval quality can be improved by adding related terms to the queries. In a direct comparison automatically expanded queries using extracted co-occurring terms can provide better results than queries manually reformulated by a domain expert and better results than a keyword-based BM25 baseline.Comment: to appear in Proceedings of 19th International Conference on Theory and Practice of Digital Libraries 2015 (TPDL 2015

Foveation for Segmentation of Mega-Pixel Histology Images

Segmenting histology images is challenging because of the sheer size of the images with millions or even billions of pixels. Typical solutions pre-process each histology image by dividing it into patches of fixed size and/or down-sampling to meet memory constraints. Such operations incur information loss in the field-of-view (FoV) (i.e., spatial coverage) and the image resolution. The impact on segmentation performance is, however, as yet understudied. In this work, we first show under typical memory constraints (e.g., 10G GPU memory) that the trade-off between FoV and resolution considerably affects segmentation performance on histology images, and its influence also varies spatially according to local patterns in different areas (see Fig. 1). Based on this insight, we then introduce foveation module, a learnable “dataloader” which, for a given histology image, adaptively chooses the appropriate configuration (FoV/resolution trade-off) of the input patch to feed to the downstream segmentation model at each spatial location (Fig. 1). The foveation module is jointly trained with the segmentation network to maximise the task performance. We demonstrate, on the Gleason2019 challenge dataset for histopathology segmentation, that the foveation module improves segmentation performance over the cases trained with patches of fixed FoV/resolution trade-off. Moreover, our model achieves better segmentation accuracy for the two most clinically important and ambiguous classes (Gleason Grade 3 and 4) than the top performers in the challenge by 13.1% and 7.5%, and improves on the average performance of 6 human experts by 6.5% and 7.5%

Comprehensive Characterization of the Transmitted/Founder env Genes From a Single MSM Cohort in China

Background: The men having sex with men (MSM) population has become one of the major risk groups for HIV-1 infection in China. However, the epidemiological patterns, function of the env genes, and autologous and heterologous neutralization activity in the same MSM population have not been systematically characterized. Methods: The env gene sequences were obtained by the single genome amplification. The time to the most recent common ancestor was estimated for each genotype using the Bayesian Markov Chain Monte Carlo approach. Coreceptor usage was determined in NP-2 cells. Neutralization was analyzed using Env pseudoviruses in TZM-bl cells. Results: We have obtained 547 full-length env gene sequences by single genome amplification from 30 acute/early HIV-1–infected individuals in the Beijing MSM cohort. Three genotypes (subtype B, CRF01_AE, and CRF07_BC) were identified and 20% of the individuals were infected with multiple transmitted/founder (T/F) viruses. The tight clusters of the MSM sequences regardless of geographic origins indicated nearly exclusive transmission within the MSM population and limited number of introductions. The time to the most recent common ancestor for each genotype was 10–15 years after each was first introduced in China. Disparate preferences for coreceptor usages among 3 genotypes might lead to the changes in percentage of different genotypes in the MSM population over time. The genotype-matched and genotype-mismatched neutralization activity varied among the 3 genotypes. Conclusions: The identification of unique characteristics for transmission, coreceptor usage, neutralization profile, and epidemic patterns of HIV-1 is critical for the better understanding of transmission mechanisms, development of preventive strategies, and evaluation of vaccine efficacy in the MSM population in China

Simple and efficient methods for isolation and activity measurement of the recombinant hirudin variant 3 from Bacillus subtilis

A simple purification approach of the recombinant hirudin variant 3 from the Bacillus subtilis was established, by which the hirudin could be purified to the purity of 95% through one-step chromatography with the total recovery rate of 83.9%. A modified Markwardt thrombin titration method for measuring hirudin activity was also set up. Briefly, a series of concentrations of thrombin was prepared and titrated to hirudin sample, respectively and the anti-thrombin activity-range of hirudin was narrowed down by several thrombin solutions at high or low concentration and the optimum group of thrombin concentrations was determined for titration of the hirudin sample. In this modified method, the hirudin activity was determined more accurately, concisely and promptly than the classic Markwardt method.Key words: Hirudin, thrombin titration method, chromatography, purification

Learning to Address Intra-segment Misclassification in Retinal Imaging

Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels in retinal photographs is one such scenario, in which arteries and veins need to be identified and differentiated from each other and from the background. Intra-segment misclassification, i.e. veins classified as arteries or vice versa, frequently occurs when arteries and veins intersect, whereas in binary retinal vessel segmentation, error rates are much lower. We thus propose a new approach that decomposes multi-class segmentation into multiple binary, followed by a binary-to-multi-class fusion network. The network merges representations of artery, vein, and multi-class feature maps, each of which are supervised by expert vessel annotation in adversarial training. A skip-connection based merging process explicitly maintains class-specific gradients to avoid gradient vanishing in deep layers, to favor the discriminative features. The results show that, our model respectively improves F1-score by 4.4%, 5.1%, and 4.2% compared with three state-of-the-art deep learning based methods on DRIVE-AV, LES-AV, and HRF-AV data sets. Code: https://github.com/rmaphoh/Learning-AVSegmentatio

Learning to Address Intra-segment Misclassification in Retinal Imaging

Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels in retinal photographs is one such scenario, in which arteries and veins need to be identified and differentiated from each other and from the background. Intra-segment misclassification, i.e. veins classified as arteries or vice versa, frequently occurs when arteries and veins intersect, whereas in binary retinal vessel segmentation, error rates are much lower. We thus propose a new approach that decomposes multi-class segmentation into multiple binary, followed by a binary-to-multi-class fusion network. The network merges representations of artery, vein, and multi-class feature maps, each of which are supervised by expert vessel annotation in adversarial training. A skip-connection based merging process explicitly maintains class-specific gradients to avoid gradient vanishing in deep layers, to favor the discriminative features. The results show that, our model respectively improves F1-score by 4.4%, 5.1%, and 4.2% compared with three state-of-the-art deep learning based methods on DRIVE-AV, LES-AV, and HRF-AV data sets. Code: https://github.com/rmaphoh/Learning-AVSegmentatio

The role of insulin receptor substrate 2 in hypothalamic and beta cell function

Insulin receptor substrate 2 (Irs2) plays complex roles in energy homeostasis. We generated mice lacking Irs2 in beta cells and a population of hypothalamic neurons (RIPCreIrs2KO), in all neurons (NesCreIrs2KO), and in proopiomelanocortin neurons (POMCCreIrs2KO) to determine the role of Irs2 in the CNS and beta cell. RIPCreIrs2KO mice displayed impaired glucose tolerance and reduced P cell mass. Overt diabetes did not ensue, because beta cells escaping Cre-mediated recombination progressively populated islets. RIPCreIrs2KO and NesCreIrs2KO mice displayed hyperphagia, obesity, and increased body length, which suggests altered melanocortin action. POMCCreIrs2KO mice did not display this phenotype. RIPCreIrs2KO and NesCreIrs2KO mice retained leptin sensitivity, which suggests that CNS Irs2 pathways are not required for leptin action. NesCreIrs2KO and POMCCreIrs2KO mice did not display reduced beta cell mass, but NesCreIrs2KO mice displayed mild abnormalities of glucose homeostasis. RIPCre neurons did not express POMC or neuropeptide Y. Insulin and a melanocortin agonist depolarized RIPCre neurons, whereas leptin was ineffective. Insulin hyperpolarized and leptin depolarized POMC neurons. Our findings demonstrate a critical role for IRS2 in beta cell and hypothalamic function and provide insights into the role of RIPCre neurons, a distinct hypothalamic neuronal population, in growth and energy homeostasis

Disentangling human error from the ground truth in segmentation of medical images

Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depends on the quality of labels. This problem is particularly pertinent in the medical image domain, where both the annotation cost and inter-observer variability are high. In a typical label acquisition process, different human experts provide their estimates of the "true'' segmentation labels under the influence of their own biases and competence levels. Treating these noisy labels blindly as the ground truth limits the performance that automatic segmentation algorithms can achieve. In this work, we present a method for jointly learning, from purely noisy observations alone, the reliability of individual annotators and the true segmentation label distributions, using two coupled CNNs. The separation of the two is achieved by encouraging the estimated annotators to be maximally unreliable while achieving high fidelity with the noisy training data. We first define a toy segmentation dataset based on MNIST and study the properties of the proposed algorithm. We then demonstrate the utility of the method on three public medical imaging segmentation datasets with simulated (when necessary) and real diverse annotations: 1) MSLSC (multiple-sclerosis lesions); 2) BraTS (brain tumours); 3) LIDC-IDRI (lung abnormalities). In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large. The experiments also show strong ability to capture the complex spatial characteristics of annotators' mistakes. Our code is available at \url{https://github.com/moucheng2017/LearnNoisyLabelsMedicalImages}