RS-Net: robust segmentation of green overlapped apples

Fruit detection and segmentation will be essential for future agronomic management, with applications in yield estimation, growth monitoring, intelligent picking, disease detection and etc. In order to more accurately and efficiently realize the recognition and segmentation of apples in natural orchards, a robust segmentation net framework specially developed for fruit production is proposed. This model was improved for the more challenging problem which segments the overlapped apples from the monochromatic background regardless of various corruptions. The method extends Mask R-CNN by embedding an attention mechanism for focusing more on the informative pixels but also suppressing the noise caused by adverse factors (occlusions, overlaps, etc.), which could be more suitable and robust for operating in complex natural environment. Specifically, the Gaussian non-local attention mechanism is transplanted into Mask R-CNN for refining the semantic features generated continuously by residual network and feature pyramid network, then the model forward processing based on the balanced feature levels and finally segments the regions where the apples are located. Experimental results verify the hypothesis of current work and show that the proposed method outperforms other start-of-the-art detection and segmentation models, the AP box and AP mask metric values have reached 85.6% and 86.2% in a reasonable run time, respectively, which can meet the precision and robustness of vision system in agronomic managemen

Association between sleep duration and quality with rapid kidney function decline and development of chronic kidney diseases in adults with normal kidney function: The China health and retirement longitudinal study

Research have shown that sleep is associated with renal function. However, the potential effects of sleep duration or quality on kidney function in middle-aged and older Chinese adults with normal kidney function has rarely been studied. Our study aimed to investigate the association of sleep and kidney function in middle-aged and older Chinese adults. Four thousand and eighty six participants with an eGFR ≥60 ml/min/1.73 m2 at baseline were enrolled between 2011 and 2015 from the China Health and Retirement Longitudinal Study. Survey questionnaire data were collected from conducted interviews in the 2011. The eGFR was estimated from serum creatinine and/or cystatin C using the Chronic Kidney Disease Epidemiology Collaboration equations (CKD-EPI). The primary outcome was defined as rapid kidney function decline. Secondary outcome was defined as rapid kidney function decline with clinical eGFR of <60 ml/min/1.73 m2 at the exit visit. The associations between sleep duration, sleep quality and renal function decline or chronic kidney disease (CKD) were assessed based with logistic regression model. Our results showed that 244 (6.0%) participants developed rapid decline in kidney function, while 102 (2.5%) developed CKD. In addition, participants who had 3–7 days of poor sleep quality per week had higher risks of CKD development (OR 1.86, 95% CI 1.24–2.80). However, compared with those who had 6–8 h of night-time sleep, no significantly higher risks of rapid decline in kidney function was found among those who had <6 h or >8 h of night time sleep after adjustments for demographic, clinical, or psychosocial covariates. Furthermore, daytime nap did not present significant risk in both rapid eGFR decline or CKD development. In conclusion, sleep quality was significantly associated with the development of CKD in middle-aged and older Chinese adults with normal kidney function

Ion Selectivity and Stability Enhancement of SPEEK/Lignin Membrane for Vanadium Redox Flow Battery: The Degree of Sulfonation Effect

A membrane of high ion selectivity, high stability, and low cost is desirable for vanadium redox flow battery (VRB). In this study, a composite membrane is formed by blending the sulfonated poly (ether ether ketone) with lignin (SPEEK/lignin), and optimized by tailoring the degree of sulfonation. The incorporation of lignin into the SPEEK matrix provides more proton transport pathway and meanwhile adjusts the water channel to repulse vanadium ions. The VRB cells assembled with the composite membranes exhibit high coulombic efficiency (~99.27%) and impressive energy efficiency (~82.75%). The cells maintain a discharge capacity of ~95% after 100 cycles and ~85% after 200 cycles at 120 mA cm−2, much higher than the commercial Nafion 212. The SPEEK/lignin composite membranes are promising for application in VRB system

Oncogenic KRAS promotes malignant brain tumors in zebrafish

BACKGROUND: Zebrafish have been used as a vertebrate model to study human cancers such as melanoma, rhabdomyosarcoma, liver cancer, and leukemia as well as for high-throughput screening of small molecules of therapeutic value. However, they are just emerging as a model for human brain tumors, which are among the most devastating and difficult to treat. In this study, we evaluated zebrafish as a brain tumor model by overexpressing a human version of oncogenic KRAS (KRAS[superscript G12V]). METHODS: Using zebrafish cytokeratin 5 (krt5) and glial fibrillary acidic protein (gfap) gene promoters, we activated Ras signaling in the zebrafish central nervous system (CNS) through transient and stable transgenic overexpression. Immunohistochemical analyses were performed to identify activated pathways in the resulting brain tumors. The effects of the MEK inhibitor U0126 on oncogenic KRAS were evaluated. RESULTS: We demonstrated that transient transgenic expression of KRAS[superscript G12V] in putative neural stem and/or progenitor cells induced brain tumorigenesis. When expressed under the control of the krt5 gene promoter, KRAS[superscript G12V] induced brain tumors in ventricular zones (VZ) at low frequency. The majority of other tumors were composed mostly of spindle and epithelioid cells, reminiscent of malignant peripheral nerve sheath tumors (MPNSTs). In contrast, when expressed under the control of the gfap gene promoter, KRAS[superscript G12V] induced brain tumors in both VZs and brain parenchyma at higher frequency. Immunohistochemical analyses indicated prominent activation of the canonical RAS-RAF-ERK pathway, variable activation of the mTOR pathway, but no activation of the PI3K-AKT pathway. In a krt5-derived stable and inducible transgenic line, expression of oncogenic KRAS resulted in skin hyperplasia, and the MEK inhibitor U0126 effectively suppressed this pro-proliferative effects. In a gfap-derived stable and inducible line, expression of oncogenic KRAS led to significantly increased mitotic index in the spinal cord. CONCLUSIONS: Our studies demonstrate that zebrafish could be explored to study cellular origins and molecular mechanisms of brain tumorigenesis and could also be used as a platform for studying human oncogene function and for discovering oncogenic RAS inhibitors.Keywords: gfap, krt5, Oncogenic KRAS (KRAS[superscript G12V]), Zebrafish, Drug screening, Brain tumor

Surgical intervention combined with weight-bearing walking training improves neurological recoveries in 320 patients with clinically complete spinal cord injury: a prospective self-controlled study

Although a large number of trials in the SCI field have been conducted, few proven gains have been realized for patients. In the present study, we determined the efficacy of a novel combination treatment involving surgical intervention and long-term weight-bearing walking training in spinal cord injury (SCI) subjects clinically diagnosed as complete or American Spinal Injury Association Impairment Scale (AIS) Class A (AIS-A). A total of 320 clinically complete SCI subjects (271 male and 49 female), aged 16-60 years, received early (≤ 7 days, n = 201) or delayed (8-30 days, n = 119) surgical interventions to reduce intraspinal or intramedullary pressure. Fifteen days post-surgery, all subjects received a weight-bearing walking training with the "Kunming Locomotion Training Program (KLTP)" for a duration of 6 months. The neurological deficit and recovery were assessed using the AIS scale and a 10-point Kunming Locomotor Scale (KLS). We found that surgical intervention significantly improved AIS scores measured at 15 days post-surgery as compared to the pre-surgery baseline scores. Significant improvement of AIS scores was detected at 3 and 6 months and the KLS further showed significant improvements between all pair-wise comparisons of time points of 15 days, 3 or 6 months indicating continued improvement in walking scores during the 6-month period. In conclusion, combining surgical intervention within 1 month post-injury and weight-bearing locomotor training promoted continued and statistically significant neurological recoveries in subjects with clinically complete SCI, which generally shows little clinical recovery within the first year after injury and most are permanently disabled. This study was approved by the Science and Research Committee of Kunming General Hospital of PLA and Kunming Tongren Hospital, China and registered at ClinicalTrials.gov (Identifier: NCT04034108) on July 26, 2019

Molecular heterogeneity and CXorf67 alterations in posterior fossa group A (PFA) ependymomas

Of nine ependymoma molecular groups detected by DNA methylation profiling, the posterior fossa type A (PFA) is most prevalent. We used DNA methylation profiling to look for further molecular heterogeneity among 675 PFA ependymomas. Two major subgroups, PFA-1 and PFA-2, and nine minor subtypes were discovered. Transcriptome profiling suggested a distinct histogenesis for PFA-1 and PFA-2, but their clinical parameters were similar. In contrast, PFA subtypes differed with respect to age at diagnosis, gender ratio, outcome, and frequencies of genetic alterations. One subtype, PFA-1c, was enriched for 1q gain and had a relatively poor outcome, while patients with PFA-2c ependymomas showed an overall survival at 5 years of > 90%. Unlike other ependymomas, PFA-2c tumors express high levels of OTX2, a potential biomarker for this ependymoma subtype with a good prognosis. We also discovered recurrent mutations among PFA ependymomas. H3 K27M mutations were present in 4.2%, occurring only in PFA-1 tumors, and missense mutations in an uncharacterized gene, CXorf67, were found in 9.4% of PFA ependymomas, but not in other groups. We detected high levels of wildtype or mutant CXorf67 expression in all PFA subtypes except PFA-1f, which is enriched for H3 K27M mutations. PFA ependymomas are characterized by lack of H3 K27 trimethylation (H3 K27-me3), and we tested the hypothesis that CXorf67 binds to PRC2 and can modulate levels of H3 K27-me3. Immunoprecipitation/mass spectrometry detected EZH2, SUZ12, and EED, core components of the PRC2 complex, bound to CXorf67 in the Daoy cell line, which shows high levels of CXorf67 and no expression of H3 K27-me3. Enforced reduction of CXorf67 in Daoy cells restored H3 K27-me3 levels, while enforced expression of CXorf67 in HEK293T and neural stem cells reduced H3 K27-me3 levels. Our data suggest that heterogeneity among PFA ependymomas could have clinicopathologic utility and that CXorf67 may have a functional role in these tumors

Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm

Vast data in the higher education system are used to analyse and evaluate the teaching quality, so that the key factors that affect the quality of teaching can be predicted. Besides, the learner’s personalized behaviour can also become the data source for teaching result prediction. This paper proposes a decision tree model by taking the teaching quality data and the statistical analysis results of the learn-er’s personalized behaviour as inputs. This model was based on the improved C4.5 decision tree algorithm, which used the FAYYAD boundary point decision theorem for effectively reducing the computation time to the most threshold. In this algorithm, the iterative analysis mechanism was introduced in combination with the data change of the learner’s personalized behaviour, so as to dynamically adjust the final teaching evaluation result. Finally, according to the actual statisti-cal data of one academic year, the teaching quality evaluation was effectively completed and the direction of future teaching prediction was proposed

Rapid Non-Destructive Analysis of Food Nutrient Content Using Swin-Nutrition

Food non-destructive detection technology (NDDT) is a powerful impetus to the development of food safety and quality. One of the essential tasks of food quality regulation is the non-destructive detection of the food’s nutrient content. However, existing food nutrient NDDT performs poorly in terms of efficiency and accuracy, which hinders their widespread application in daily meals. Therefore, this paper proposed an end-to-end food nutrition non-destructive detection method, named Swin-Nutrition, which combined deep learning and NDDT to evaluate the nutrient content of food. The method aimed to fully capture the feature information from the food images and thus accurately estimate the nutrient content. Swin-Nutrition resorted to Swin Transformer, the feature fusion module (FFM), and the nutrient prediction module to evaluate nutrient content. In particular, Swin Transformer acted as the backbone network for feature extraction of food images, and FFM was used to obtain the discriminative feature representation to improve the accuracy of prediction. The experimental results on the Nutrition5k dataset demonstrated the effectiveness and efficiency of our proposed method. Specifically, the mean value of the percentage mean absolute error (PMAE) for calories, mass, fat, carbohydrate, and protein were only 15.3%, 12.5%, 22.1%, 20.8%, and 15.4%, respectively. We hope that our simple and effective method will provide a solid foundation for the research of food NDDT

Few‐shot logo detection

Abstract The proliferation of deep learning has driven research into deep learning‐based logo detection, which usually needs a large number of annotated data to train the model. However, due to the occasional appearance of new brands or the high cost of annotation, the number of training data is limited. Against this backdrop, the authors adapt the few‐shot object detection into logo detection, and thus present a cutting‐edge method called Double Classification Head (DCH) for Few‐Shot Logo Detection (DCH‐FSLogo), which aims at detecting the unseen logo classes using few annotated data. Unlike the traditional few‐shot detection, some logo objects are similar to their backgrounds and have diverse shapes as well. For this reason, the authors adopt balanced feature pyramid and deformable Region of Interest pooling in DCH‐FSLogo, this enhances the feature extraction capability and adapts to the different logo shapes. In addition, we introduce the DCH for few‐shot logo detection to detect logo objects using few annotated data. Specifically, we use an extra classification head for the base classes to ease the influence from the novel classes. The experimental results on four datasets, namely: FlickrLogos‐32, FoodLogoDet‐1500‐100, LogoDet‐3K‐100 and QMUL‐OpenLogo‐100, demonstrate that our method achieves better performance