An Effective Method for Milling Tool Condition Monitoring Using On-machine Measurement

Smart machining technology is now under intensive research worldwide. As a kernel technique of smart machining, on-machine measurement (OMM) technology can automatically measure tool diameter and length with laser tool setters on machine so that the machine controller compensates for the tool wear while machining, which can improve part accuracy without manual tool measurement on tool pre-setters. However, there is a dilemma that the OMM technique cannot predict tool failure so that the operator can replace tools before it fails. To address this problem, this research proposes a new approach to predict failure of round-insert face mill in rough and finish machining to automatically change tools right before their failure. First, the geometric equation of flank wear land width of round-insert face mill is formulated; second, after a tool diameter is measured, the flank wear width is calculated. Third, an experimental method is proposed to determine the tool radius reduction threshold and the tool location of measurement, and then the tool failure can be predicted in rough machining. Then, a new method is established to determine the criteria of tool radius reduction in finish machining according to the machined surface roughness. Finally, several experiments are conducted to verify this approach, and it is applied to a practical example. This approach can be directly applied in industry, and it can advance the smart machining technology

The Minimization of Piecewise Functions: Pseudo Stationarity

There are many significant applied contexts that require the solution of discontinuous optimization problems in finite dimensions. Yet these problems are very difficult, both computationally and analytically. With the functions being discontinuous and a minimizer (local or global) of the problems, even if it exists, being impossible to verifiably compute, a foremost question is what kind of ''stationary solutions'' one can expect to obtain; these solutions provide promising candidates for minimizers; i.e., their defining conditions are necessary for optimality. Motivated by recent results on sparse optimization, we introduce in this paper such a kind of solution, termed ''pseudo B- (for Bouligand) stationary solution'', for a broad class of discontinuous piecewise continuous optimization problems with objective and constraint defined by indicator functions of the positive real axis composite with functions that are possibly nonsmooth. We present two approaches for computing such a solution. One approach is based on lifting the problem to a higher dimension via the epigraphical formulation of the indicator functions; this requires the addition of some auxiliary variables. The other approach is based on certain continuous (albeit not necessarily differentiable) piecewise approximations of the indicator functions and the convergence to a pseudo B-stationary solution of the original problem is established. The conditions for convergence are discussed and illustrated by an example

UniMOS: A Universal Framework For Multi-Organ Segmentation Over Label-Constrained Datasets

Machine learning models for medical images can help physicians diagnose and manage diseases. However, due to the fact that medical image annotation requires a great deal of manpower and expertise, as well as the fact that clinical departments perform image annotation based on task orientation, there is the problem of having fewer medical image annotation data with more unlabeled data and having many datasets that annotate only a single organ. In this paper, we present UniMOS, the first universal framework for achieving the utilization of fully and partially labeled images as well as unlabeled images. Specifically, we construct a Multi-Organ Segmentation (MOS) module over fully/partially labeled data as the basenet and designed a new target adaptive loss. Furthermore, we incorporate a semi-supervised training module that combines consistent regularization and pseudolabeling techniques on unlabeled data, which significantly improves the segmentation of unlabeled data. Experiments show that the framework exhibits excellent performance in several medical image segmentation tasks compared to other advanced methods, and also significantly improves data utilization and reduces annotation cost. Code and models are available at: https://github.com/lw8807001/UniMOS.Comment: Accepted by BIBM202

Functional mapping of genotype-environment interactions for soybean growth by a semiparametric approach

<p>Abstract</p> <p>Background</p> <p>Functional mapping is a powerful approach for mapping quantitative trait loci (QTLs) that control biological processes. Functional mapping incorporates mathematical aspects of growth and development into a general QTL mapping framework and has been recently integrated with composite interval mapping to build up a so-called composite functional mapping model, aimed to separate multiple linked QTLs on the same chromosomal region.</p> <p>Results</p> <p>This article reports the principle of using composite functional mapping to estimate the effects of QTL-environment interactions on growth trajectories by parametrically modeling the tested QTL in a marker interval and nonparametrically modeling the markers outside the interval as co-factors. With this new model, we can characterize the dynamic patterns of the genetic effects of QTLs governing growth trajectories, estimate the global effects of the underlying QTLs during the course of growth and development, and test the differentiation in the shapes of QTL genotype-specific growth curves between different environments. By analyzing a real example from a soybean genome project, our model detects several QTLs that cause significant genotype-environment interactions for plant height growth processes.</p> <p>Conclusions</p> <p>The model provides a basis for deciphering the genetic architecture of trait expression adjusted to different biotic and abiotic environments for any organism.</p

Prognostic value of various immune cells and Immunoscore in triple-negative breast cancer

BackgroundThis study aimed to evaluate the expression status and prognostic role of various immunoregulatory cells and test in triple-negative breast cancer (TNBC).MethodsThe expression of five markers (CD3/CD4/CD8/CD19/CD163) of tumor immune cells was evaluated retrospectively in tumor sections from 68 consecutive cases of TNBC by immunohistochemistry. Computational image analysis was used to quantify the density and distribution of each immune marker within the tumor region, tumor invasive margin, and expression hotspots. Immunoscores were calculated using an automated approach. Other clinical characteristics were also analyzed.ResultsFor all patients, Kaplan–Meier survival analysis showed that high CD3+ signals in the tumor region (disease-free survival (DFS), P=0.0014; overall survival (OS), P=0.0031) and total region (DFS, P=0.0014; OS, P=0.0031) were significantly associated with better survival. High CD4+ levels in the tumor region and total regions were significantly associated with better survival (P&lt;0.05). For Hotspot analysis, CD3+ was associated with significantly better survival for all Top1, Top2, and Top3 densities (DFS and OS, P&lt;0.05). High CD4+ levels were significantly associated with better prognosis for Top1 and Top3 densities (DFS and OS, P&lt;0.05). For stage IIB and IIIC patients, CD3+ in the tumor region and all Top hotspots was found to be significantly correlated with survival (DFS and OS, P&lt;0.05). CD4+ cells were significantly associated with survival in the tumor region, total region, and Top3 density (DFS, P=0.0213; OS, P=0.0728). CD8+ cells were significantly associated with survival in the invasive margin, Top2 density, and Top3 density. Spatial parameter analysis showed that high colocalization of tumor cells and immune cells (CD3+, CD4+, or CD8+) was significantly associated with patient survival.ConclusionComputational image analysis is a reliable tool for evaluating the density and distribution of immune regulatory cells and for calculating the Immunoscore in TNBC. The Immunoscore retains its prognostic significance in TNBC later than IIB stage breast cancer. Future studies are required to confirm its potential to predict tumor responses to chemotherapy and immune therapy

Expression of neuroendocrine markers predicts increased survival in triple-negative breast cancer patients

BackgroundThe significance of neuroendocrine (NE) markers in triple-negative breast cancer (TNBC) patients has not been investigated. This study aims to clarify the incidence and prognostic significance of NE marker expression in TNBC, determine its association with other clinicopathological parameters, and further explore the pathological features and potential treatment options for TNBC patients expressing NE markers.MethodsClinicopathological data were collected from 396 TNBC patients undergoing radical breast cancer surgery at Peking Union Medical College Hospital from January 2002 to December 2014, with a final follow-up in July 2019. Immunohistochemistry (IHC) staining was performed for NE markers including chromogranin A (CgA) and synaptophysin (Syn). For TNBC patients with positive NE marker expression, IHC staining was then performed for alpha-thalassemia/mental retardation X-linked (ATRX), O(6)-methylguanine-methyltransferase (MGMT), somatostatin receptor 2 (SSTR2), and programmed death receptor-ligand 1 (PD-L1). The chi-square or Fisher exact test was used to evaluate the correlations between NE marker expression and other parameters. Survival curves were plotted using the Kaplan-Meier (K-M) method to assess the prognostic significance of NE markers in TNBC.ResultsNE marker-positive staining was observed in 7.6% (30/396) of all TNBC cases. Only 0.5% (2/396) cases had ≥ 90% neoplastic cells expressing NE markers. Positive NE marker expression was associated with negative basal-like marker expression. K-M survival analysis showed that the NE marker-positive TNBC patients had higher disease-free survival (DFS) rates than the NE marker-negative patients at the same stage. Among the 30 NE marker-positive TNBC cases, 13.3% and 26.7% showed negative IHC staining for ATRX and MGMT, respectively, while 13.3% had a 3+ score for SSTR2 IHC staining. For PD-L1 IHC staining, 13.3% of the 30 TNBC cases were higher than 10 scores in Combined Positive Score (CPS), and 10.0% were higher than 10% in Tumor Cell Proportion Score (TPS).ConclusionThere was a small proportion of TNBC patients expressing NE markers. TNBC patients with positive NE marker expression had a better prognosis than the negative group at the same stage. TNBC cases with positive NE marker expression may potentially benefit from immunotherapy or somatostatin analogue treatment

Feed nutritional composition affects the intestinal microbiota and digestive enzyme activity of black soldier fly larvae

IntroductionUsing black soldier fly larvae (BSFLs) to treat food waste is one of the most promising environmental protection technologies.MethodsWe used high-throughput sequencing to study the effects of different nutritional compositions on the intestinal microbiota and digestive enzymes of BSF.ResultsCompared with standard feed (CK), high-protein feed (CAS), high-fat feed (OIL) and high-starch feed (STA) had different effects on the BSF intestinal microbiota. CAS significantly reduced the bacterial and fungal diversity in the BSF intestinal tract. At the genus level, CAS, OIL and STA decreased the Enterococcus abundance compared with CK, CAS increased the Lysinibacillus abundance, and OIL increased the Klebsiella, Acinetobacter and Bacillus abundances. Diutina, Issatchenkia and Candida were the dominant fungal genera in the BSFL gut. The relative abundance of Diutina in the CAS group was the highest, and that of Issatchenkia and Candida in the OIL group increased, while STA decreased the abundance of Diutina and increased that of Issatchenkia. The digestive enzyme activities differed among the four groups. The α-amylase, pepsin and lipase activities in the CK group were the highest, and those in the CAS group were the lowest or the second lowest. Correlation analysis of environmental factors showed a significant correlation between the intestinal microbiota composition and digestive enzyme activity, especially α-amylase activity, which was highly correlated with bacteria and fungi with high relative abundances. Moreover, the mortality rate of the CAS group was the highest, and that of the OIL group was the lowest.DiscussionIn summary, different nutritional compositions significantly affected the community structure of bacteria and fungi in the BSFL intestinal tract, affected digestive enzyme activity, and ultimately affected larval mortality. The high oil diet gave the best results in terms of growth, survival and intestinal microbiota diversity, although the digestive enzymes activities were not the highest

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data

This study was supported by the NSF China Programs (Grant No. 31300539 and 31570629) and the Public Welfare Technology Application Research Program of Zhejiang province (Grant No. 2015C31004).Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.Yeshttp://www.plosone.org/static/editorial#pee

THE IMPACT OF INSTITUTIONAL INVESTORS ON CROSS BORDER LISTINGS

Bachelor'sBACHELOR OF BUSINESS ADMINISTRATION WITH HONOUR