Development and clinical application of a new testicular prosthesis

A new type of testicular prosthesis made of silastic with an elliptical shape to mimic a normal testis was developed by our team and submitted for patenting in China. The prosthesis was produced in different sizes to imitate the normal testis of the patient. To investigate the effects and safety of the testicular prosthesis, 20 patients receiving testicular prosthesis implantation were recruited for this study. Follow-up after 6 months revealed no complications in the patients. All the patients answered that they were satisfied with their body image and the position of the implants, 19 patients were satisfied with the size and 16 patients were satisfied with the weight. These results show that the testicular prosthesis used in this study can meet patient's expectations. Patients undergoing orchiectomy should be offered the option to receive a testicular prosthesis implantation. The dimensions and weight of the available prosthetic implants should be further addressed to improve patient satisfaction

Study on the Conventional Performance and Microscopic Properties of PPA/SBS-Modified Bio-Mixed Asphalt

To promote the construction of environmentally friendly, sustainable pavements and solve the impact of the scarcity of asphalt resources on highway development, bio-mixed asphalt (BMA) modified by SBS and polyphosphoric acid (PPA) was prepared, and the influence of the ratio of bio-asphalt (BA) replacing petroleum asphalt on different PPA/SBS blending schemes was explored through conventional property tests. According to each PPA/SBS blending scheme, the optimal replacement ratio of bio-asphalt was optimized, and the microstructure and distribution morphology of different PPA/SBS-modified BMA were evaluated. Conventional property test results show that with the same PPA/SBS content, the replacement ratio of bio-asphalt has a significant impact on the conventional performance of composite-modified asphalt, but the appropriate replacement ratio of bio-asphalt can improve the storage stability and conventional performance of composite-modified asphalt; in micromorphological analysis, it was found that the number of bee-like structures on the surface of the modified BMA decreased significantly, which indicated that the molecular heterogeneity of various components in the asphalt was reduced. In addition, bio-asphalt changed the particle morphology and improved the dispersity of SBS in asphalt. The composite-modified BMA had a lower SBS content, but its conventional performance was still excellent—so it has significant application prospects in road engineering

Recent Advances of Biosensors for Detection of Multiple Antibiotics

The abuse of antibiotics has caused a serious threat to human life and health. It is urgent to develop sensors that can detect multiple antibiotics quickly and efficiently. Biosensors are widely used in the field of antibiotic detection because of their high specificity. Advanced artificial intelligence/machine learning algorithms have allowed for remarkable achievements in image analysis and face recognition, but have not yet been widely used in the field of biosensors. Herein, this paper reviews the biosensors that have been widely used in the simultaneous detection of multiple antibiotics based on different detection mechanisms and biorecognition elements in recent years, and compares and analyzes their characteristics and specific applications. In particular, this review summarizes some AI/ML algorithms with excellent performance in the field of antibiotic detection, and which provide a platform for the intelligence of sensors and terminal apps portability. Furthermore, this review gives a short review of biosensors for the detection of multiple antibiotics

Mechanical damage characteristics of CR/PPA composite modified asphalt pavement under multi-factor coupling effect in the seasonally frozen region

Asphalt pavements are inevitably affected by coupled effects of dry-wet/freeze-thaw cycles, UV radiation, temperature, and loading during service, resulting in degradation of their road performance and reduced service life. Therefore, this study used crumb rubber (CR) and polyphosphoric acid (PPA) as modifiers to prepare CR/PPA composite modified asphalt mixtures for testing. To investigate the mechanical properties of the composite modified asphalt mixtures under the action of multi-factor coupling. Meanwhile, the performance analysis of matrix asphalt and SBS modified asphalt mixtures were carried out as a comparison. Density and water penetration tests, splitting tests, beam bending tests, and four-point bending fatigue tests reveal the evolution of asphalt mixture road performance. Finally, regression fitting was performed on the test data to establish the mechanical damage model of each type of asphalt mixture. The results show that with the increase of wet-dry/freeze-thaw cycles and the increase of UV radiation time, the void ratio and water permeability coefficient increase and the splitting strength, bending and tensile strain, and fatigue life decrease for all types of asphalt mixtures. When the number of wet-dry/freeze-thaw cycles reached 8, the mechanical properties of the asphalt mixture decreased significantly, while the changes leveled off after the number of cycles reached 15. When the UV radiation time is less than 4 months, the mechanical properties of the asphalt mixture decay more slowly. However, with a further increase in radiation time, the mechanical properties decay rapidly. The water-saturated specimens have a more significant effect on the mechanical damage of each type of asphalt mixture. Compared with the other two mixtures, the composite modified asphalt mixtures have superior mechanical properties and fatigue life. The mixture performance damage model based on the least squares Levenberg-Marquardt method (L-M method) was able to simulate the experimental data well, with a minimum correlation coefficient R2 of 0.9285. In summary, the road performance of all types of asphalt mixtures under the coupling of multiple factors has deteriorated, while the composite modified asphalt mixtures have better anti-damage characteristics, with good prospects for promotion

YOLO Algorithm for Long-Term Tracking and Detection of Escherichia Coli at Different Depths of Microchannels Based on Microsphere Positioning Assistance

The effect evaluation of the antibiotic susceptibility test based on bacterial solution is of great significance for clinical diagnosis and prevention of antibiotic abuse. Applying a microfluidic chip as the detection platform, the detection method of using microscopic images to observe bacteria under antibiotic can greatly speed up the detection time, which is more suitable for high-throughput detection. However, due to the influence of the depth of the microchannel, there are multiple layers of bacteria under the focal depth of the microscope, which greatly affects the counting and recognition accuracy and increases the difficulty of relocation of the target bacteria, as well as extracting the characteristics of bacterial liquid changes under the action of antibiotics. After the focal depth of the target bacteria is determined, although the z-axis can be controlled with the help of a three-dimensional micro-operator, the equipment is difficult to operate and the long-term changes of the target bacteria cannot be tracked quickly and accurately. In this paper, the YOLOv5 algorithm is adopted to accurately identify bacteria with different focusing states of multi-layer bacteria at the z-axis with any focal depth. In the meantime, a certain amount of microspheres were mixed into bacteria to assist in locating bacteria, which was convenient for tracking the growth state of bacteria over a long period, and the recognition rates of both bacteria and microspheres were high. The recognition accuracy and counting accuracy of bacteria are 0.734 and 0.714, and the two recognition rates of microspheres are 0.910 and 0.927, respectively, which are much higher than the counting accuracy of 0.142 for bacteria and 0.781 for microspheres with the method of enhanced depth of field (EDF method). Moreover, during long-term bacterial tracking and detection, target bacteria at multiple z-axis focal depth positions can be recorded by the aid of microspheres as a positioning aid for 3D reconstruction, and the focal depth positions can be repositioned within 3–10 h. The structural similarity (SSIM) of microscopic image structure differences at the same focal depth fluctuates between 0.960 and 0.975 at different times, and the root-mean-square error (RMSE) fluctuates between 8 and 12, which indicates that the method also has good relocation accuracy. Thus, this method provides the basis for rapid, high-throughput, and long-term analysis of microscopic changes (e.g., morphology, size) of bacteria detection under the addition of antibiotics with different concentrations based on microfluidic channels in the future

Clinical Outcomes and Prognosis Analysis of Younger Bladder Cancer Patients

Background: Generally, little is known about prognostic factors in bladder cancer patients under 40 years of age. We therefore performed a retrospective study to identify prognostic factors in these younger bladder cancer patients. Methods: We collected clinicopathological data on bladder cancer patients ≤40 years old diagnosed between 1975 and 2018 from the Surveillance, Epidemiology, and End Results (SEER) database. Survival curves were generated using the Kaplan–Meier method, and the differences between groups were analyzed using the log-rank test. Univariate and multivariate Cox hazards regression analyses were performed to define hazard ratios (HRs) for cancer-specific survival (CSS). Results: There were statistical differences in race, histological type, cancer stage, tumor size, and surgery treatment groups between overall survival and CSS. Only tumor size and cancer stage were significant independent prognostic risk factors in younger bladder cancer patients for the prediction of CSS. Conclusion: Tumors greater than 30 mm in size and a more advanced stage of bladder cancer were indicative of a poor prognosis in bladder cancer patients ≤40 years old, and long-term follow-up is suggested

Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey

Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. Meanwhile, the performance of remote sensing imaging hardware has also evolved significantly. The detection technology used with high-resolution RSIs has been pushed to unprecedented heights, making important contributions in practical applications such as urban detection, building planning, and disaster prediction. However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. In this paper, we first briefly review the recent history of remote sensing object detection (RSOD) techniques, including traditional methods as well as DL-based methods. Then, we systematically summarize the procedures used in DL-based detection algorithms. Most importantly, starting from the problems of complex object features, complex background information, tedious sample annotation that will be faced by high-resolution RSI object detection, we introduce a taxonomy based on various detection methods, which focuses on summarizing and classifying the existing attention mechanisms, multi-scale feature fusion, super-resolution and other major improvement strategies. We also introduce recognized open-source remote sensing detection benchmarks and evaluation metrics. Finally, based on the current state of the technology, we conclude by discussing the challenges and potential trends in the field of RSOD in order to provide a reference for researchers who have just entered the field

Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey

Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. Meanwhile, the performance of remote sensing imaging hardware has also evolved significantly. The detection technology used with high-resolution RSIs has been pushed to unprecedented heights, making important contributions in practical applications such as urban detection, building planning, and disaster prediction. However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. In this paper, we first briefly review the recent history of remote sensing object detection (RSOD) techniques, including traditional methods as well as DL-based methods. Then, we systematically summarize the procedures used in DL-based detection algorithms. Most importantly, starting from the problems of complex object features, complex background information, tedious sample annotation that will be faced by high-resolution RSI object detection, we introduce a taxonomy based on various detection methods, which focuses on summarizing and classifying the existing attention mechanisms, multi-scale feature fusion, super-resolution and other major improvement strategies. We also introduce recognized open-source remote sensing detection benchmarks and evaluation metrics. Finally, based on the current state of the technology, we conclude by discussing the challenges and potential trends in the field of RSOD in order to provide a reference for researchers who have just entered the field

The complete chloroplast genome of Camellia vietnamensis, an economic shrub producing edible seed oil

Camellia vietnamensis is an economic woody plant producing high-value edible oils, which is commonly found and cultivated in south areas of China. To provide genetic information for future genetic research, we have sequenced and assembled the complete chloroplast (cp) genome of C. vietnamensis based on the Illumina Hiseq platform. The total genome size is 161,958 bp in length with 37% GC, which contains a large single copy (LSC, 86,657 bp) region, a small single copy (SSC, 13,347 bp) region, and a pair of inverted repeat (IRs, 30,977 bp) regions. It is comprised of 81 protein-coding genes, 44 transfer RNAs and 4 ribosomal RNAs. To obtain the phylogeny relationship, the cp genome of C. vietnamensis has been compared with other Camellia species; the results indicate that C. vietnamensis is closely related to C. taliensis. This study provides fundamental information of C. vietnamensis cp genome, and it is valuable to the molecular phylogenetic and genetic diversity analyses in future

Orientation-First Strategy With Angle Attention Module for Rotated Object Detection in Remote Sensing Images

Recently, object detection in remote sensing images (RSIs) have received extensive attention and made significant progress. Nonetheless, the arbitrary orientations of objects in RSIs make their detection a challenging task. Most of the existing detection methods are difficult to extract the orientation features of objects due to the lack of directionality of conventional convolutions. In addition, the boundary discontinuity in angle regression affects the detection of object orientations. In response to these problems, this article proposes an orientation-first refinement detector (OFRDet), which is based on a strategy that enables the detector to detect the angle of an object ahead of others and presets oriented anchors. In OFRDet, we propose an angle encoding regression module (AERM) and an angle channel attention module (ACAM). AERM transforms angle detection into multiparameter regression, which eliminates boundary discontinuities. ACAM uses convolution kernels with different angles to extract directional features purposefully according to the preset oriented anchors. After these two modules, more accurate bounding boxes are generated and sent to the refined stage to obtain the final detection results. We evaluate our method and demonstrate the effectiveness of it by conducting experiments on two challenging and credible datasets, DOTA, HRSC2016. OFRDet achieves competitive results 79.56%, 96.29% mAP on the two datasets, respectively