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Abstract Background Tuberculosis is a chronic infectious disease caused by mycobacterium tuberculosis (MTB) and is the ninth leading cause of death worldwide. It is still difficult to distinguish active TB from latent TB,but it is very important for individualized management and treatment to distinguish whether patients are active or latent tuberculosis infection. Methods A total of 220 subjects, including active TB patients (ATB, n = 97) and latent TB patients (LTB, n = 113), were recruited in this study .46 features about blood routine indicators and the VCS parameters (volume, conductivity, light scatter) of neutrophils(NE), monocytes(MO), and lymphocytes(LY) were collected and was constructed classification model by four machine learning algorithms(logistic regression(LR), random forest(RF), support vector machine(SVM) and k-nearest neighbor(KNN)). And the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) to estimate of the model’s predictive performance for dentifying active and latent tuberculosis infection. Results After verification,among the four classifications, LR and RF had the best performance (AUROC = 1, AUPRC = 1), followed by SVM (AUROC = 0.967, AUPRC = 0.971), KNN (AUROC = 0.943, AUPRC = 0.959) in the training set. And LR had the best performance (AUROC = 0.977, AUPRC = 0.957), followed by SVM (AUROC = 0.962, AUPRC = 0.949), RF (AUROC = 0.903, AUPRC = 0.922),KNN(AUROC = 0.883, AUPRC = 0.901) in the testing set. Conclusions The machine learning algorithm classifier based on leukocyte VCS parameters is of great value in identifying active and latent tuberculosis infection.

ObjectiveOur study aimed to assess the predictive value of the preoperative neutrophil-to-lymphocyte ratio(NLR) in distinguishing sarcomatoid renal cell carcinoma (SRCC) from clear cell renal cell carcinoma(CCRCC) and to developing a nomogram based on the preoperative NLR and other factors to distinguish SRCC from CCRCC.Materials and methodsThe database involved 280 patients, including 46 SRCC and 234 CCRCC. logistic analysis was conducted to select the variables associated with identifying SRCC preoperatively, and subgroup analysis was used to further validate the ability of NLR with preoperative identification of SRCC.In addition, The data were randomly separated into a training cohort(n=195) and a validation cohort(n=85). And an NLR-based nomogram was plotted based on the logistic analysis results. The nomogram was evaluated according to its discrimination, consistency, and clinical benefits.ResultsMultivariate analysis indicated that NLR, flank pain, tumor size, and total cholesterol(TC) were independent risk factors for identifying SRCC. The results of subgroup analysis showed that higher NLR was associated with a higher probability of SRCC in most subgroups. The area under the curve(AUC) of the training and validation cohorts were 0.801 and 0.738, respectively. The results of the calibration curve show high consistency between predicted and actual results. Decision Curve Analysis(DCA) showed clinical intervention based on the model was beneficial over most of the threshold risk range.ConclusionNLR is a potential indicator for preoperative differentiation of SRCC and CCRCC, and the predictive model constructed based on NLR has a good predictive ability. The new model could provide suggestions for the early identification of SRCC.

ObjectiveThymoid carcinoma of the thyroid gland is a rare thyroid tumor, which is often presented in case reports.MethodsThe clinical data of two patients with thymic carcinoma of the thyroid gland were retrospectively reviewed.ResultsCase 1: a middle-aged woman who was admitted to the hospital because of “progressive enlargement of the anterior cervical mass for 8 months.” Color Doppler ultrasound and CT showed malignant tumor with high possibility of bilateral cervical lymph node metastasis. Total thyroidectomy and bilateral central cervical lymph node dissection were performed. Lymph node biopsy showed the metastasis of small cell undifferentiated thyroid carcinoma. Because the biopsy pathological result was not consistent with the pathology of the primary lesion, immunohistochemistry was performed again, and the final diagnosis was thymic carcinoma in the thyroid gland. Case 2: the patient was an elderly man who was admitted to the hospital due to hoarseness for half a month. During the operation, the tumor invaded the trachea, esophagus, internal jugular vein, common carotid artery, and surrounding tissues. Palliative resection of the tumor was performed. The tumor postoperative pathology suggested thymoid carcinoma of the thyroid gland. It recurred and compressed the trachea 4 months after the operation, resulting in dyspnea of the patient, and finally tracheotomy was performed to alleviate the symptoms.ConclusionCase 1 showed multiple differences in pathological diagnosis, suggesting that the lack of specific imaging and clinical manifestations of thymoid-differentiated thyroid carcinoma made the diagnosis so difficult. Case 2 progressed rapidly, suggesting that thymoid-differentiated thyroid carcinoma was not always inert, and the treatment and follow-up should follow the principle of individualization.

The differential diagnosis of renal tumors and abscesses is crucial owing to their different treatments. Although antibacterial administration and radiological examination are excellent means for distinction, misdiagnosis is common and may lead to severe consequences, such as the need for nephrectomy. Here, we report a case involving a 52-year-old Asian woman with a renal mass for which a differential diagnosis was challenging. The mass persisted after administration of intravenous antibiotic therapy for 1 month. A computed tomography scan indicated an inflammatory lesion, whereas magnetic resonance imaging suggested a diagnosis of a tumor. Despite these indications, a right renal abscess was suspected during robot-assisted laparoscopic surgery, and nephron-sparing surgery was performed, which allowed confirmation of the final pathological result by biopsy specimen. Postoperatively, the mass gradually decreased in size after antibiotic therapy for a further month. This case, in which a renal abscess mimicked a tumor and the patient almost underwent a nephrectomy, highlights the need for caution in establishing therapeutic schedules for patients with inaccurate diagnoses. The management strategies for such patients must be reviewed and improved.

Distribution and sharing are social preference behaviors supported and shaped by selection pressures, which express individuals’ concern for the welfare of others. Distributive behavior results in distributive justice, which is at the core of moral justice. Sharing is a feature of the prosocial realm. The connotations of distribution and sharing are different, so the principles, research paradigms, and social functions of the two are also different. Three potential causes of confusion between the two in the current research on distribution and sharing are discussed. First, they share common factors in terms of individual cognition, situation, and social factors. Second, although they are conceptually different, prosocial sharing and distribution fairness sensitivity are mutually predictive in individual infants. Similarly, neural differences in preschoolers’ perception of distribution fairness predict their subsequent sharing generosity. Finally, similar activation regions are relevant to distribution and sharing situations that need behavioral control on a neural basis.

BackgroundThere remains a demand for a practical method of identifying lipid-poor adrenal lesions.PurposeTo explore the predictive value of computed tomography (CT) features combined with demographic characteristics for lipid-poor adrenal adenomas and nonadenomas.Materials and MethodsWe retrospectively recruited patients with lipid-poor adrenal lesions between January 2015 and August 2021 from two independent institutions as follows: Institution 1 for the training set and the internal validation set and Institution 2 for the external validation set. Two radiologists reviewed CT images for the three sets. We performed a least absolute shrinkage and selection operator (LASSO) algorithm to select variables; subsequently, multivariate analysis was used to develop a generalized linear model. The probability threshold of the model was set to 0.5 in the external validation set. We calculated the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for the model and radiologists. The model was validated and tested in the internal validation and external validation sets; moreover, the accuracy between the model and both radiologists were compared using the McNemar test in the external validation set.ResultsIn total, 253 patients (median age, 55 years [interquartile range, 47–64 years]; 135 men) with 121 lipid-poor adrenal adenomas and 132 nonadenomas were included in Institution 1, whereas another 55 patients were included in Institution 2. The multivariable analysis showed that age, male, lesion size, necrosis, unenhanced attenuation, and portal venous phase attenuation were independently associated with adrenal adenomas. The clinical-image model showed AUCs of 0.96 (95% confidence interval [CI]: 0.91, 0.98), 0.93 (95% CI: 0.84, 0.97), and 0.86 (95% CI: 0.74, 0.94) in the training set, internal validation set, and external validation set, respectively. In the external validation set, the model showed a significantly and non-significantly higher accuracy than reader 1 (84% vs. 65%, P = 0.031) and reader 2 (84% vs. 69%, P = 0.057), respectively.ConclusionsOur clinical-image model displayed good utility in differentiating lipid-poor adrenal adenomas. Further, it showed better diagnostic ability than experienced radiologists in the external validation set.

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region’s boundary in a manner that is more substantially consistent with the true color image. Using the PM2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM2.5 ≥ 35 μg/cm3 is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM2.5 ≥ 50 μg/cm3 is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite.

Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy.Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study.Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature “age at seizure onset” with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD.Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes.

Abstract. In the nature, difficulties with distinguishing of closely related species like Stone marten (Martes foina) and Pine marten (Martes martes) appear. Based on 280 photos a non-invasive method for determining the Stone marten and the Pine marten by camera trapping was performed. Sloping trees (similar to Leaning-Pole or Running-Pole) were used as a route for animals to pass with a purpose to be photographed. Honey and distal parts of bird wings were used as a lure. The camera was set to capture the climbing animal from the side. The coat coloration and scent marking patterns - proper species indicators - were clearly visible on daily photos. The night photos showed the differences in body proportion but not these in throat patch coloration. Profile photographing also allowed an individual determination based on throat patch shape and area.

From the perspective of philosophical classification of information forms, calligraphy appreciation can be divided into the stages self-contained information of objective and indirect existence, self-contained information of a subject’s intuitive recognition, and regenerative information of subjective initiative creation. In the self-information stage of the indirect existence of an objective view, the calligraphy works themselves have not been recognized and grasped during appreciation and the calligraphy works are only the objective form externalizing the subjective spirit of the creative subject. In the self-information stage of a subject’s intuitive recognition, the spatiotemporal information, the semantic information, technical information, and aesthetic information condensed in the works are directly recognized by the appreciator. In the regenerated information stage of subjective initiative creation, the appreciator subjectively reprocesses and recreates on the basis of the overall grasp of space–time information, semantic information, technical information, and aesthetic information. It includes two forms, the analogy of “skeuomorphism” and the extension of “potential”.

There is a requirement to develop an novel vaccine to distinguish between vaccinated and infected animals after vaccination. Two mutants of Brucella melitensis M5-90Δbp261 and M5-90Δbp262, knockout of the fragments amino acids 55-152 and 22-185 respectively, were primarily generated. Then the two mutants were inoculated 7 weeks old BALB/c mice with 3.0 × 106 CFU/0.2 mL parent or mutant strains, Serum samples were evaluated by Rose Bengal plate tests, serum agglutination tests, colloidal gold immunochromatographic strips (ICS) coated with VirB5 or BP26 and indirect enzyme-linked immunosorbent assays. Spleen tissue samples were used to determine B. melitensis abundance at 10, 20, 30, and 40 days post-immunization (dpi). The serological results showed that the parent and mutant strains elicited a weak immunological reaction at 10–30 dpi. Fewer colony forming units (CFUs) were recovered from spleens in mutant strains group than the parent strain group during 20-40 post-immunization (dpi). The ICS were able to distinguish between the sera from mice immunized with either a parent or mutant strain of B. melitensis. These results indicate that the two mutants strain of B. melitensis have potentially perspectives in distinguishing infected animals from vaccination combined with ICS in this study.

Natural image matting is an important image processing task. How to leverage the advantages of both sampling matting and propagation matting is a challenge issue. In this paper, we propose a novel sampling-propagation matting method. First, in an overall framework, we propose a three-stage method for sampling-propagation matting, in which the sampling matting stage and propagation matting stage are bridged by a new stage (stage 2). Second, in the sampling matting stage (stage 1), a new gradient sampling matting is presented to cover more diversified samples, and a new equation is proposed to calculate the impact of sample-pair overlap. Third, in bridge stage (stage 2), we propose a judgment criterion to distinguish each pixel of matte after stage 1. For pixels that fail to meet the criterion, we propose an automatic labeling method. Fourth, in the propagation stage (stage 3), we discriminatingly process non-labeled pixels and labeled pixels with separate weight coefficients. The non-labeled pixels are smoothed by propagation, and the labeled pixels are solved by propagation matting. Finally, the proposed method is compared with other methods on public available benchmark. The results show that our method outperforms many other methods and achieves a good ranking.

An approach to distinguish eight kinds of different human cells by Raman spectroscopy was proposed and demonstrated in this paper. Original spectra of suspension cells in the frequency range of 623~1783 cm−1 were acquired and pre-processed by baseline calibration, and principal component analysis (PCA) was employed to extract the useful spectral information. To develop a robust discrimination model, a linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were attempted comparatively in the work. The results showed that the QDA model is better than the LDA model. The optimal QDA model was generated with 12 principal components. The classification rates are 100% in the calibration and prediction set, respectively. From the experimental results, it is concluded that Raman spectroscopy combined with appropriate discriminant analysis methods has significant potential in human cell detection.