Classification and recognition of milk somatic cell images based on PolyLoss and PCAM-Reset50

Somatic cell count (SCC) is a fundamental approach for determining the quality of cattle and bovine milk. So far, different classification and recognition methods have been proposed, all with certain limitations. In this study, we introduced a new deep learning tool, i.e., an improved ResNet50 model constructed based on the residual network and fused with the position attention module and channel attention module to extract the feature information more effectively. In this paper, macrophages, lymphocytes, epithelial cells, and neutrophils were assessed. An image dataset for milk somatic cells was constructed by preprocessing to increase the diversity of samples. PolyLoss was selected as the loss function to solve the unbalanced category samples and difficult sample mining. The Adam optimization algorithm was used to update the gradient, while Warm-up was used to warm up the learning rate to alleviate the overfitting caused by small sample data sets and improve the model's generalization ability. The experimental results showed that the classification accuracy, precision rate, recall rate, and comprehensive evaluation index F value of the proposed model reached 97%, 94.5%, 90.75%, and 92.25%, respectively, indicating that the proposed model could effectively classify the milk somatic cell images, showing a better classification performance than five previous models (i.e., ResNet50, ResNet18, ResNet34, AlexNet andMobileNetv2). The accuracies of the ResNet18, ResNet34, ResNet50, AlexNet, MobileNetv2, and the new model were 95%, 93%, 93%, 56%, 37%, and 97%, respectively. In addition, the comprehensive evaluation index F1 showed the best effect, fully verifying the effectiveness of the proposed method in this paper. The proposed method overcame the limitations of image preprocessing and manual feature extraction by traditional machine learning methods and the limitations of manual feature selection, improving the classification accuracy and showing a strong generalization ability

Accuracies of field CO2–H2O data from open-path eddy-covariance flux systems: assessment based on atmospheric physics and biological environment

Ecosystem CO2–H2O data measured by infrared gas analyzers in open-path eddy-covariance (OPEC) systems have numerous applications, such as estimations of CO2 and H2O fluxes in the atmospheric boundary layer. To assess the applicability of the data for these estimations, data uncertainties from analyzer measurements are needed. The uncertainties are sourced from the analyzers in zero drift, gain drift, cross-sensitivity, and precision variability. These four uncertainty sources are individually specified for analyzer performance, but so far no methodology exists yet to combine these individual sources into a composite uncertainty for the specification of an overall accuracy, which is ultimately needed. Using the methodology for closed-path eddy-covariance systems, this overall accuracy for OPEC systems is determined from all individual uncertainties via an accuracy model and further formulated into CO2 and H2O accuracy equations. Based on atmospheric physics and the biological environment, for EC150 infrared CO2–H2O analyzers, these equations are used to evaluate CO2 accuracy (±1.22 mgCO2 m−3, relatively ±0.19 %) and H2O accuracy (±0.10 gH2O m−3, relatively ±0.18 % in saturated air at 35 ∘C and 101.325 kPa). Both accuracies are applied to conceptual models addressing their roles in uncertainty analyses for CO2 and H2O fluxes. For the high-frequency air temperature derived from H2O density along with sonic temperature and atmospheric pressure, the role of H2O accuracy in its uncertainty is similarly addressed. Among the four uncertainty sources, cross-sensitivity and precision variability are minor, although unavoidable, uncertainties, whereas zero drift and gain drift are major uncertainties but are minimizable via corresponding zero and span procedures during field maintenance. The accuracy equations provide rationales to assess and guide the procedures. For the atmospheric background CO2 concentration, CO2 zero and CO2 span procedures can narrow the CO2 accuracy range by 40 %, from ±1.22 to ±0.72 mgCO2 m−3. In hot and humid weather, H2O gain drift potentially adds more to the H2O measurement uncertainty, which requires more attention. If H2O zero and H2O span procedures can be performed practically from 5 to 35 ∘C, the H2O accuracy can be improved by at least 30 %: from ±0.10 to ±0.07 gH2O m−3. Under freezing conditions, the H2O span procedure is impractical but can be neglected because of its trivial contributions to the overall uncertainty. However, the zero procedure for H2O, along with CO2, is imperative as an operational and efficient option under these conditions to minimize H2O measurement uncertainty.</p

Correlation of pain with substance P and neurokinin-1 receptor in the L5–S2 spinal cord in rats with chronic nonbacterial prostatitis

The incidence of prostate pain is 90%–95% in prostatitis. The symptoms are persistent, which is prone to relapse and difficult to be cured. It seriously affects the survival and quality of life of patients. This study analyzed the correlation between pain and substance P (SP) and neurokinin-1 receptors (NK-1R) in the L5–S2 spinal cord of chronic nonbacterial prostatitis (CNP) rats, which may give a new way to explore the pathogenesis and treatment of pain in prostatitis. We randomly divided the rats into control group, 45 d group, 60 d group, and 90 d group. After making a rat model with autoimmune method, the paw withdrawal threshold (PWT) was measured, the histomorphological changes in the prostate was observed by transmission electron microscopy and light microscopy. The expression of SP and NK-1R was measured by immunohistochemistry, and the concentrations of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-2 (IL-2), and interleukin-10 (IL-10) were measured by enzyme linked immunosorbent assay (ELISA). Compared with the control group, the PWT was decreased by 34.21%, 41.90% and 64.79%, TNF-α was increased by 74.19%, 89.45% and 132.15%, IL-1β was increased by 148.88%, 181.95% and 250.74%, IL-2 was increased by 75.97%, 82.15% and 128.57% and IL-10 was increased by 31.04%, 63.28% and 212.99% in the 45 d group, 60 d group and 90 d group respectively. Microscope observation showed the structure of prostate tissue in control group was normal. However, the prostate tissue had obvious inflammatory response with the model extension. The expressions of SP and NK-1R in each model group were significantly higher than the control group. There was a significant correlation between pain and SP in L5–S2 spinal cord in CNP rats. These findings are indicative of a correlation between pain and the expression levels of SP and NK-1R in the L5–S2 spinal cord of CNP rats

Modeling Rett Syndrome Using TALEN-Edited MECP2 Mutant Cynomolgus Monkeys

Gene-editing technologies have made it feasible to create nonhuman primate models for human genetic disorders. Here, we report detailed genotypes and phenotypes of TALEN-edited MECP2 mutant cynomolgus monkeys serving as a model for a neurodevelopmental disorder, Rett syndrome (RTT), which is caused by loss-of-function mutations in the human MECP2 gene. Male mutant monkeys were embryonic lethal, reiterating that RTT is a disease of females. Through a battery of behavioral analyses, including primate-unique eye-tracking tests, in combination with brain imaging via MRI, we found a series of physiological, behavioral, and structural abnormalities resembling clinical manifestations of RTT. Moreover, blood transcriptome profiling revealed that mutant monkeys resembled RTT patients in immune gene dysregulation. Taken together, the stark similarity in phenotype and/or endophenotype between monkeys and patients suggested that gene-edited RTT founder monkeys would be of value for disease mechanistic studies as well as development of potential therapeutic interventions for RTT

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure