Comparison of doxycycline and benzathine penicillin G for the treatment of early syphilis

Doxycycline is the preferred recommended second-line treatment for the treatment of early syphilis. Recent reports showed a declining efficacy trend of doxycycline in treatment of early syphilis. The aim of our study was to assess the serological response to the treatment for early syphilis with doxycycline compared with benzathine penicillin G and evaluate whether doxycycline is still an effective agent for the treatment of early syphilis. A record-based retrospective study was conducted. Patients were diagnosed with early syphilis in an sexually transmitted disease (STD) clinic from January 1, 2008 to December 31, 2014. They were treated with a single dose of benzathine penicillin G 2.4MU or oral doxycycline 100 mg twice daily for 14 days. Pearson’s chi-squared test was used for data analysis. 601 cases were included in the final study sample: 105 (17.5%) patients received a 14-day course of doxycycline (doxycycline group), and 496 (82.5%) patients received single-dose benzathine penicillin G (BPG group). The serological responses at 6 months and 12 months after treatment were compared. No statistically significant differences were found between the two groups at 6 months (69.52% vs. 75.00%, P=0.245), and at 12 months (92.38% vs. 96.17%, P=0.115). Doxycycline is still an effective agent for the treatment of early syphilis. </p

DarkneTZ: towards model privacy at the edge using trusted execution environments

We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neural Networks (DNNs). Increasingly, edge devices (smartphones and consumer IoT devices) are equipped with pre-trained DNNs for a variety of applications. This trend comes with privacy risks as models can leak information about their training data through effective membership inference attacks (MIAs). We evaluate the performance of DarkneTZ, including CPU execution time, memory usage, and accurate power consumption, using two small and six large image classification models. Due to the limited memory of the edge device's TEE, we partition model layers into more sensitive layers (to be executed inside the device TEE), and a set of layers to be executed in the untrusted part of the operating system. Our results show that even if a single layer is hidden, we can provide reliable model privacy and defend against state of the art MIAs, with only 3% performance overhead. When fully utilizing the TEE, DarkneTZ provides model protections with up to 10% overhead

Multiple linear regression with compositional response and covariates

<p>The standard regression model designed for real space is not suitable for compositional variables; it should be considered, whether the response and/or covariates are of compositional nature. There are usually three types of multiple regression model with compositional variables: Type 1 refers to the case where all the covariates are compositional data and the response is real; Type 2 is the opposite of Type 1; Type 3 relates to the model with compositional response and covariates. There have been some models for the three types. In this paper, we focus on Type 3 and propose multiple linear regression models including model in the simplex and model in isometric log-ratio (ilr) coordinates. The model in the simplex is based on matrix product, which can project a <math><msub><mi>D</mi><mrow><mn>1</mn></mrow></msub></math>-part composition to another <math><msub><mi>D</mi><mrow><mn>2</mn></mrow></msub></math>-part composition, and can deal with different number of parts of compositional variables. Some theorems are given to point out the relationship of parameters between the proposed models. Moreover, the inference for parameters in proposed models is also given. Real example is studied to verify the validity and usefulness of proposed models.</p

Exploring quantification and analyzing driving force for spatial and temporal differentiation characteristics of vegetation net primary productivity in Shandong Province, China

Exploring the spatiotemporal changes of vegetation net primary productivity (NPP) and its driving mechanism is crucial for green and low-carbon development. Based on the MOD17A3HGF data, this study introduced the gravity center model, wavelet analysis, coefficient of variation, Hurst index, correlation analysis, and Thornthwaite Memorial model to explore the spatiotemporal patterns of NPP and its driving factors in Shandong Province from 2000 to 2019. The following results were attained. (1) Between 2000 and 2019, Shandong’s yearly average NPP showed an upward trend. The first main cycle of the NPP’s interannual cycle lasted 14 years. NPP was predicted to experience a short-term decline in the near future. (2) In Shandong, the NPP displayed a geographical pattern with high values in the east and low values in the west, high values in the south and low values in the north, and progressively declining values from the coast to the interior. The growth rate and increment of NPP in 2000–2019 were the largest in the northeast direction in the Southwest Plain, while those in the Jiaodong Hills, the Central and Southern Mountains, and the Northwest Plain were the largest in the northwest direction. Hurst index analysis showed that NPP would show weak anti-continuity changes in the Central and Southern Mountains and the Jiaodong Hills in the future. (3) Shandong’s NPP exhibited a “three zones and two lines” pattern of the effect of climate change and human activities. This research offers a scientific theoretical foundation for environmental protection and carbon management

Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data

The logratio methodology is not applicable when rounded zeros occur in compositional data. There are many methods to deal with rounded zeros. However, some methods are not suitable for analyzing data sets with high dimensionality. Recently, related methods have been developed, but they cannot balance the calculation time and accuracy. For further improvement, we propose a method based on regression imputation with Q-mode clustering. This method forms the groups of parts and builds partial least squares regression with these groups using centered logratio coordinates. We also prove that using centered logratio coordinates or isometric logratio coordinates in the response of partial least squares regression have the equivalent results for the replacement of rounded zeros. Simulation study and real example are conducted to analyze the performance of the proposed method. The results show that the proposed method can reduce the calculation time in higher dimensions and improve the quality of results

Data-Free Area Detection and Evaluation for Marine Satellite Data Products

The uncertainty verification of satellite ocean color products and the bias analysis of multiple data are both indispensable in the evaluation of ocean color products. Incidentally, ocean color products often have missing information that causes the methods mentioned above to be difficult to evaluate these data effectively. We propose an analysis and evaluation method based on data-free area. The objective of this study is to evaluate the quality of ocean color products with respect to information integrity and continuity. First, we use an improved Spectral Angle Mapper, also called ISAM. It can automatically obtain the optimal threshold value for each class of objects. Then, based on ISAM, we perform spectral information mining on first-level Yellow Sea and Bohai Sea data obtained from the Geostationary Ocean Color Imager (GOCI), Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean and Land Color Instrument (OLCI). In this manner, quantitative results of information related to data-free areas of ocean data products are obtained. The findings indicate that the product data of OLCI are optimal with respect to both completeness and continuity. GOCI and MODIS have striking similarities in their quantitative or visualization results for both evaluation metrics. Moreover, a concomitant phenomenon of ocean-covered objects is apparent in the data-free area with temporal and spatial distribution characteristics. The two characteristics are subsequently explored for further analysis. The evaluation method adopted in this study can help to enrich the content of ocean color product evaluation, facilitate the research of cloud detection algorithms and further understand the composition of the data-free regional information of marine data products. The method proposed in this study has a wide application value