Incidence and spontaneous clearance of gonorrhea and chlamydia infections among men who have sex with men: a prospective cohort study in Zhuhai, China.

Background: Men who have sex with men (MSM) face significant risks of Chlamydia trachomatis (CT) and/or Neisseria gonorrhoeae (NG) infection. Nevertheless, only limited studies have looked into the site-specific infection and clearance of CT/NG. In order to prevent transmission, it is essential to understand the underlying factors that drive infection and spontaneous clearance. Methods: A 12-week cohort study examined the association between CT/NG infection, self-clearance, and sexual behaviors among MSM. The Willingness Service recruited participants who completed weekly questionnaires and provided urine, throat, and rectal swab samples. Results: The study involved 151 men, in which 51 (33.8%) were diagnosed with CT/NG infection during the study period. HIV (OR = 11.31), kissing (OR = 1.59), receptive oral sex (OR = 36.64), and insertive anal sex (OR = 19.73) constituted significant risk factors. 100% condom use (OR = 5.78) and antibiotic (OR = 7.53) were more likely to cause spontaneous clearance. Discussion: MSM may engage in riskier sexual behaviors due to insufficient knowledge and awareness of STI prevention, leading to increased susceptibility to NG/CT. It is crucial to concentrate on enhancing health education for MSM. Conclusion: This study found that the rectum was the most prevalent site of CT/NG and sexual behavior can influence the infection. Additionally, the appropriate use of antibiotics and consistent condom use may contribute to clear spontaneously.

Rainfall during the night can trigger non-migratory take-off behavior of the white-backed planthopper, Sogatella furcifera.

Take-off behavior is crucial to the overall success of insect migration. Although most high-altitude migratory flights commence with mass take-offs around dusk and dawn, little is known about nighttime take-off behavior. The take-off behavior of migratory Sogatella furcifera was investigated in field cages from 2017 to 2019. The species showed a bimodal take-off pattern at dusk and dawn on rainless nights, with mass flight at dusk more intense than dawn flight. However, a higher frequency of take-offs during the nighttime was observed on rainy nights, resulting in the absence of dawn take-offs. Most migratory take-off individuals at dusk and dawn landed on the cage top or the walls above 150 cm, while non-migratory individuals that took off during the nighttime due to rainfall mainly landed on the cage walls below 150 cm. Furthermore, it has been observed that migratory take-off individuals possess stronger sustained flight capabilities and exhibit more immature ovaries compared with non-migratory take-offs. These findings advance our understanding of the take-off behavior of S. furcifera and thus provide a basis for the accurate prediction and management of the migratory dynamics of this pest.

Unmixing biological fluorescence image data with sparse and low-rank Poisson regression.

MOTIVATION: Multispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (i) as the number of fluorophores used in any experiment increases and (ii) as the signal-to-noise ratio in the recorded images decreases. Further, the availability of prior knowledge regarding the expected spatial distributions of fluorophores in images of labeled cells provides an opportunity to improve the accuracy of fluorophore identification and abundance. RESULTS: We propose a regularized sparse and low-rank Poisson regression unmixing approach (SL-PRU) to deconvolve spectral images labeled with highly overlapping fluorophores which are recorded in low signal-to-noise regimes. First, SL-PRU implements multipenalty terms when pursuing sparseness and spatial correlation of the resulting abundances in small neighborhoods simultaneously. Second, SL-PRU makes use of Poisson regression for unmixing instead of least squares regression to better estimate photon abundance. Third, we propose a method to tune the SL-PRU parameters involved in the unmixing procedure in the absence of knowledge of the ground truth abundance information in a recorded image. By validating on simulated and real-world images, we show that our proposed method leads to improved accuracy in unmixing fluorophores with highly overlapping spectra. AVAILABILITY AND IMPLEMENTATION: The source code used for this article was written in MATLAB and is available with the test data at https://github.com/WANGRUOGU/SL-PRU.

Unmixing Biological Fluorescence Image Data with Sparse and Low-Rank Poisson Regression.

Multispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (1) as the number of fluorophores used in any experiment increases and (2) as the signal-to-noise ratio in the recorded images decreases. Further, the availability of prior knowledge regarding the expected spatial distributions of fluorophores in images of labeled cells provides an opportunity to improve the accuracy of fluorophore identification and abundance. We propose a regularized sparse and low-rank Poisson unmixing approach (SL-PRU) to deconvolve spectral images labeled with highly overlapping fluorophores which are recorded in low signal-to-noise regimes. Firstly, SL-PRU implements multi-penalty terms when pursuing sparseness and spatial correlation of the resulting abundances in small neighborhoods simultaneously. Secondly, SL-PRU makes use of Poisson regression for unmixing instead of least squares regression to better estimate photon abundance. Thirdly, we propose a method to tune the SL-PRU parameters involved in the unmixing procedure in the absence of knowledge of the ground truth abundance information in a recorded image. By validating on simulated and real-world images, we show that our proposed method leads to improved accuracy in unmixing fluorophores with highly overlapping spectra.

Factors Associated with First-Time HIV Testing Among MSM via Secondary Distribution of HIV Self-test Kits in Zhuhai, China.

HIV self-testing (HIVST) is an effective method to expand HIV testing coverage worldwide. We analyze the results of HIVST and sexual behaviors of first-time testers among Men who have sex with men (MSM) who participated in a secondary distribution of HIVST kits. A total of 589 participants were recruited, including 173 first-time testers and 416 non-first-time testers. The first-time testers were mainly of Han ethnicity (aOR 1.88, 95% CI 1.10, 3.24), more likely to be HIV positive (aOR 7.18, 95% CI 2.37, 21.72), and had higher income (aOR 2.01, 95% CI 1.10, 3.69). Both groups were less likely to have anal sex with male partners (χ2: 146.24, P < 0.01), (χ2: 582.72, P < 0.01) or have sex with female partners (χ2: 19.01, P < 0.01), (χ2: 35.74, P < 0.01) after HIVST. We should expand HIVST among MSM and other key populations to identify first-time testers.

Comparison between HIV self-testing and facility-based HIV testing approach on HIV early detection among men who have sex with men: A cross-sectional study.

Background: To assess whether HIV self-testing (HIVST) has a better performance in identifying HIV-infected cases than the facility-based HIV testing (HIVFBT) approach. Methods: A cross-sectional study was conducted among men who have sex with men (MSM) by using an online questionnaire (including information on sociodemographic, sexual biography, and HIV testing history) and blood samples (for limiting antigen avidity enzyme immunoassay, gene subtype testing, and taking confirmed HIV test). MSM who were firstly identified as HIV positive through HIVST and HIVFBT were compared. Chi-square or Fisher's exact test was used to explore any association between both groups and their subgroups. Results: In total, 124 MSM HIV cases were identified from 2017 to 2021 in Zhuhai, China, including 60 identified through HIVST and 64 through HIVFBT. Participants in the HIVST group were younger (≤30 years, 76.7% vs. 46.9%), were better educated (>high school, 61.7% vs. 39.1%), and had higher viral load (≥1,000 copies/ml, 71.7% vs. 50.0%) than MSM cases identified through HIVFBT. The proportion of early HIV infection in the HIVST group was higher than in the HIVFBT group, identified using four recent infection testing algorithms (RITAs) (RITA 1, 46.7% vs. 25.0%; RITA 2, 43.3% vs. 20.3%; RITA 3, 30.0% vs. 14.1%; RITA 4, 26.7% vs. 10.9%; all p < 0.05). Conclusions: The study showed that HIVST has better HIV early detection among MSM and that recent HIV infection cases mainly occur in younger and better-educated MSM. Compared with HIVFBT, HIVST is more accessible to the most at-risk population on time and tends to identify the case early. Further implementation studies are needed to fill the knowledge gap on this medical service model among MSM and other target populations.

Raman microspectroscopy fingerprinting of organoid differentiation state.

BACKGROUND: Organoids, which are organs grown in a dish from stem or progenitor cells, model the structure and function of organs and can be used to define molecular events during organ formation, model human disease, assess drug responses, and perform grafting in vivo for regenerative medicine approaches. For therapeutic applications, there is a need for nondestructive methods to identify the differentiation state of unlabeled organoids in response to treatment with growth factors or pharmacologicals. METHODS: Using complex 3D submandibular salivary gland organoids developed from embryonic progenitor cells, which respond to EGF by proliferating and FGF2 by undergoing branching morphogenesis and proacinar differentiation, we developed Raman confocal microspectroscopy methods to define Raman signatures for each of these organoid states using both fixed and live organoids. RESULTS: Three separate quantitative comparisons, Raman spectral features, multivariate analysis, and machine learning, classified distinct organoid differentiation signatures and revealed that the Raman spectral signatures were predictive of organoid phenotype. CONCLUSIONS: As the organoids were unlabeled, intact, and hydrated at the time of imaging, Raman spectral fingerprints can be used to noninvasively distinguish between different organoid phenotypes for future applications in disease modeling, drug screening, and regenerative medicine.

Fast Rates of Gaussian Empirical Gain Maximization With Heavy-Tailed Noise.

In a regression setup, we study in this brief the performance of Gaussian empirical gain maximization (EGM), which includes a broad variety of well-established robust estimation approaches. In particular, we conduct a refined learning theory analysis for Gaussian EGM, investigate its regression calibration properties, and develop improved convergence rates in the presence of heavy-tailed noise. To achieve these purposes, we first introduce a new weak moment condition that could accommodate the cases where the noise distribution may be heavy-tailed. Based on the moment condition, we then develop a novel comparison theorem that can be used to characterize the regression calibration properties of Gaussian EGM. It also plays an essential role in deriving improved convergence rates. Therefore, the present study broadens our theoretical understanding of Gaussian EGM.

A Framework of Learning Through Empirical Gain Maximization.

We develop in this letter a framework of empirical gain maximization (EGM) to address the robust regression problem where heavy-tailed noise or outliers may be present in the response variable. The idea of EGM is to approximate the density function of the noise distribution instead of approximating the truth function directly as usual. Unlike the classical maximum likelihood estimation that encourages equal importance of all observations and could be problematic in the presence of abnormal observations, EGM schemes can be interpreted from a minimum distance estimation viewpoint and allow the ignorance of those observations. Furthermore, we show that several well-known robust nonconvex regression paradigms, such as Tukey regression and truncated least square regression, can be reformulated into this new framework. We then develop a learning theory for EGM by means of which a unified analysis can be conducted for these well-established but not fully understood regression approaches. This new framework leads to a novel interpretation of existing bounded nonconvex loss functions. Within this new framework, the two seemingly irrelevant terminologies, the well-known Tukey's biweight loss for robust regression and the triweight kernel for nonparametric smoothing, are closely related. More precisely, we show that Tukey's biweight loss can be derived from the triweight kernel. Other frequently employed bounded nonconvex loss functions in machine learning, such as the truncated square loss, the Geman-McClure loss, and the exponential squared loss, can also be reformulated from certain smoothing kernels in statistics. In addition, the new framework enables us to devise new bounded nonconvex loss functions for robust learning.

New Insights Into Learning With Correntropy-Based Regression.

Stemming from information-theoretic learning, the correntropy criterion and its applications to machine learning tasks have been extensively studied and explored. Its application to regression problems leads to the robustness-enhanced regression paradigm: correntropy-based regression. Having drawn a great variety of successful real-world applications, its theoretical properties have also been investigated recently in a series of studies from a statistical learning viewpoint. The resulting big picture is that correntropy-based regression regresses toward the conditional mode function or the conditional mean function robustly under certain conditions. Continuing this trend and going further, in this study, we report some new insights into this problem. First, we show that under the additive noise regression model, such a regression paradigm can be deduced from minimum distance estimation, implying that the resulting estimator is essentially a minimum distance estimator and thus possesses robustness properties. Second, we show that the regression paradigm in fact provides a unified approach to regression problems in that it approaches the conditional mean, the conditional mode, and the conditional median functions under certain conditions. Third, we present some new results when it is used to learn the conditional mean function by developing its error bounds and exponential convergence rates under conditional (1+ε)-moment assumptions. The saturation effect on the established convergence rates, which was observed under (1+ε)-moment assumptions, still occurs, indicating the inherent bias of the regression estimator. These novel insights deepen our understanding of correntropy-based regression, help cement the theoretic correntropy framework, and enable us to investigate learning schemes induced by general bounded nonconvex loss functions.

An automatic evaluation method for retinal image registration based on similar vessel structure matching.

Registration of retinal images is significant for clinical diagnosis. Numerous methods have been proposed to evaluate registration performance. The available evaluation methods can work well in normal image pairs, but fair evaluation cannot be obtained for image pairs with anatomical changes. We propose an automatic method to quantitatively assess the registration of retinal images based on the extraction of similar vessel structures and modified Hausdorff distance. Firstly, vessel detection and skeletonization are performed to detect the vascular centerline. Secondly, the vessel segments having similar structures in the image pair are selected for assessment of registration. The bifurcation and terminal points are determined from the vascular centerline. Then, the Hungarian matching algorithm with a pruning process is employed to match the bifurcation and terminal points to detect similar vessel segments. Finally, a modified Hausdorff distance is employed to evaluate the performance of registration. Our experimental results show that the Pearson product-moment correlation coefficient can reach 0.76 and 0.63 in test set of normal image pairs and image pairs with anomalies respectively, which outperforms other methods. An accurate evaluation can not only compare the performance of different registration methods but also can facilitate the clinical diagnosis by screening out the inaccurate registration. Graphical abstract .

Automatic detection of parapapillary atrophy and its association with children myopia.

BACKGROUND AND OBJECTIVE: To develop an automatic parapapillary atrophy (PPA) detection algorithm in retinal fundus images and discuss the association between PPA and myopia to facilitate diagnosis and prediction of children myopia. METHODS: The proposed algorithm consists of PPA identification and segmentation, which are evaluated by comparing with ophthalmologist's annotation. The association between PPA parameters and myopia is analyzed via Spearman correlation. RESULTS: The accuracy of PPA identification reaches 90.78%. The F1-score of PPA segmentation is 0.67, and the Pearson correlation between the automatic measurement and ground truths for the area of PPA (APPA), the ratio (µ) of APPA to the area of optic disc (OD) and the maximal width of PPA (W) are 0.74, 0.60, and 0.69 (all p < 0.001). All these parameter changes are significantly correlated with the change of ratio of axial length to corneal curvature (ΔALCC), spherical equivalent (ΔSE), and axial length (ΔAL) (all p < 0.01), in which the highest association is 0.75 between ΔW (the change of W) and ΔALCC. CONCLUSIONS: The proposed algorithm can provide accurate PPA measurement. Strong association between the changes of PPA and the progress of children myopia are observed and the width of PPA has the best association among three PPA parameters.

Study on the Selective Laser Melting of CuSn10 Powder.

The selective laser melting of tin bronze (CuSn10) powder was performed with a laser energy density intensity level at 210, 220, and 230 J/mm². The composition was homogeneous with almost all tin dissolved into the matrix. The grain size of the obtained alpha copper phase was around 5 μm. The best properties were achieved at 220 J/mm² laser energy density with a density of 8.82 g/cm³, hardness of 78.2 HRB (Rockwell Hardness measured on the B scale), yield strength of 399 MPa, tensile strength of 490 MPa, and an elongation that reached 19%. “Balling effect” appeared and resulted into a decrease of properties when the laser energy density increased to 230 J/mm².

Correntropy Based Matrix Completion.

This paper studies the matrix completion problems when the entries are contaminated by non-Gaussian noise or outliers. The proposed approach employs a nonconvex loss function induced by the maximum correntropy criterion. With the help of this loss function, we develop a rank constrained, as well as a nuclear norm regularized model, which is resistant to non-Gaussian noise and outliers. However, its non-convexity also leads to certain difficulties. To tackle this problem, we use the simple iterative soft and hard thresholding strategies. We show that when extending to the general affine rank minimization problems, under proper conditions, certain recoverability results can be obtained for the proposed algorithms. Numerical experiments indicate the improved performance of our proposed approach.

An aminopyrimidine-functionalized cage-based metal-organic framework exhibiting highly selective adsorption of C2H2 and CO2 over CH4.

There has been considerable interest in adsorptive separation of C2H2/CH4 and CO2/CH4 gas mixtures due to its industrial significance and scientific challenge. In this work, we have designed and synthesized a bent diisophthalate ligand functionalized with aminopyrimidine groups, and constructed via a solvothermal reaction, a porous copper-based framework. Single-crystal X-ray diffraction studies show that the framework is a three-dimensional network containing three different types of polyhedral nanocages, which are stacked together to form two distinct types of one-dimensional channels along the crystallographic c axis. The compound after activation shows exceptionally high C2H2 and CO2 uptakes of 211 and 120 cm(3) (STP) g(-1) at 295 K and 1 atm, as well as impressive adsorption selectivities towards C2H2 and CO2 over CH4. High C2H2 and CO2 uptake capacities as well as significant adsorption selectivities of C2H2 and CO2 over CH4 imply potential applications in the adsorptive separation and purification of C2H2/CH4 and CO2/CH4 gas mixtures, which have been verified by column breakthrough experiments. Several important binding sites for C2H2 and CO2 in ZJNU-54 were revealed by quantum chemical calculations, demonstrating that the organic linkers in ZJNU-54 form unique structures that facilitate the adsorption of C2H2, while the amine groups and the Lewis basic pyrimidine-ring nitrogen sites in the organic linker improve the adsorption energies for CO2, finally leading to the increase of adsorption capacities for these two gas molecules. This work provides an efficient strategy for incorporating specific functional groups into cage-based MOFs for generating new adsorbents for highly selective gas storage and separation.

Learning Theory Estimates with Observations from General Stationary Stochastic Processes.

This letter investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by general, we mean that many stationary stochastic processes can be included. We show that when the stochastic processes satisfy a generalized Bernstein-type inequality, a unified treatment on analyzing the learning schemes with various mixing processes can be conducted and a sharp oracle inequality for generic regularized empirical risk minimization schemes can be established. The obtained oracle inequality is then applied to derive convergence rates for several learning schemes such as empirical risk minimization (ERM), least squares support vector machines (LS-SVMs) using given generic kernels, and SVMs using gaussian kernels for both least squares and quantile regression. It turns out that for independent and identically distributed (i.i.d.) processes, our learning rates for ERM recover the optimal rates. For non-i.i.d. processes, including geometrically [Formula: see text]-mixing Markov processes, geometrically [Formula: see text]-mixing processes with restricted decay, [Formula: see text]-mixing processes, and (time-reversed) geometrically [Formula: see text]-mixing processes, our learning rates for SVMs with gaussian kernels match, up to some arbitrarily small extra term in the exponent, the optimal rates. For the remaining cases, our rates are at least close to the optimal rates. As a by-product, the assumed generalized Bernstein-type inequality also provides an interpretation of the so-called effective number of observations for various mixing processes.

Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses.

This letter addresses the robustness problem when learning a large margin classifier in the presence of label noise. In our study, we achieve this purpose by proposing robustified large margin support vector machines. The robustness of the proposed robust support vector classifiers (RSVC), which is interpreted from a weighted viewpoint in this work, is due to the use of nonconvex classification losses. Besides the robustness, we also show that the proposed RSCV is simultaneously smooth, which again benefits from using smooth classification losses. The idea of proposing RSVC comes from M-estimation in statistics since the proposed robust and smooth classification losses can be taken as one-sided cost functions in robust statistics. Its Fisher consistency property and generalization ability are also investigated. Besides the robustness and smoothness, another nice property of RSVC lies in the fact that its solution can be obtained by solving weighted squared hinge loss-based support vector machine problems iteratively. We further show that in each iteration, it is a quadratic programming problem in its dual space and can be solved by using state-of-the-art methods. We thus propose an iteratively reweighted type algorithm and provide a constructive proof of its convergence to a stationary point. Effectiveness of the proposed classifiers is verified on both artificial and real data sets.

Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery.

Kernelized elastic net regularization (KENReg) is a kernelization of the well-known elastic net regularization (Zou & Hastie, 2005). The kernel in KENReg is not required to be a Mercer kernel since it learns from a kernelized dictionary in the coefficient space. Feng, Yang, Zhao, Lv, and Suykens (2014) showed that KENReg has some nice properties including stability, sparseness, and generalization. In this letter, we continue our study on KENReg by conducting a refined learning theory analysis. This letter makes the following three main contributions. First, we present refined error analysis on the generalization performance of KENReg. The main difficulty of analyzing the generalization error of KENReg lies in characterizing the population version of its empirical target function. We overcome this by introducing a weighted Banach space associated with the elastic net regularization. We are then able to conduct elaborated learning theory analysis and obtain fast convergence rates under proper complexity and regularity assumptions. Second, we study the sparse recovery problem in KENReg with fixed design and show that the kernelization may improve the sparse recovery ability compared to the classical elastic net regularization. Finally, we discuss the interplay among different properties of KENReg that include sparseness, stability, and generalization. We show that the stability of KENReg leads to generalization, and its sparseness confidence can be derived from generalization. Moreover, KENReg is stable and can be simultaneously sparse, which makes it attractive theoretically and practically.

Robust Low-Rank Tensor Recovery With Regularized Redescending M-Estimator.

This paper addresses the robust low-rank tensor recovery problems. Tensor recovery aims at reconstructing a low-rank tensor from some linear measurements, which finds applications in image processing, pattern recognition, multitask learning, and so on. In real-world applications, data might be contaminated by sparse gross errors. However, the existing approaches may not be very robust to outliers. To resolve this problem, this paper proposes approaches based on the regularized redescending M-estimators, which have been introduced in robust statistics. The robustness of the proposed approaches is achieved by the regularized redescending M-estimators. However, the nonconvexity also leads to a computational difficulty. To handle this problem, we develop algorithms based on proximal and linearized block coordinate descent methods. By explicitly deriving the Lipschitz constant of the gradient of the data-fitting risk, the descent property of the algorithms is present. Moreover, we verify that the objective functions of the proposed approaches satisfy the Kurdyka-Lojasiewicz property, which establishes the global convergence of the algorithms. The numerical experiments on synthetic data as well as real data verify that our approaches are robust in the presence of outliers and still effective in the absence of outliers.

Robust Gradient Learning With Applications.

This paper addresses the robust gradient learning (RGL) problem. Gradient learning models aim at learning the gradient vector of some target functions in supervised learning problems, which can be further used to applications, such as variable selection, coordinate covariance estimation, and supervised dimension reduction. However, existing GL models are not robust to outliers or heavy-tailed noise. This paper provides an RGL framework to address this problem in both regression and classification. This is achieved by introducing a robust regression loss function and proposing a robust classification loss. Moreover, our RGL algorithm works in an instance-based kernelized dictionary instead of some fixed reproducing kernel Hilbert space, which may provide more flexibility. To solve the proposed nonconvex model, a simple computational algorithm based on gradient descent is provided and the convergence of the proposed method is also analyzed. We then apply the proposed RGL model to applications, such as nonlinear variable selection and coordinate covariance estimation. The efficiency of our proposed model is verified on both synthetic and real data sets.