Meanwhile, the PCA-derived MA component is employed to identify and exclude the MA-contaminated segments. To evaluate the new algorithm, we performed a comparative experiment (N = 22) with a cuffless MWPPG measurement device and used double-tube auscultatory BP measurement as a reference. The results demonstrate clearly the accuracy improvement enabled by the PCA-based operations on MWPPG signals, yielding errors of 1.44 ± 6.89 mmHg for systolic blood pressure and -1.00 ± 6.71 mm Hg for diastolic blood pressure. In conclusion, the proposed PCA-based method can improve the performance of MWPPG in wearable medical devices for cuffless BP measurement.Perturbation in the normal function of the cell signaling pathways often leads to diseases. One of the factors that help understand the mechanism of diseases is the precise identification and investigation of perturbed signaling pathways. MEK inhibitor review Pathway analysis methods have been developed as their purpose is to identify perturbed signaling pathways in given conditions. Among these methods, some consider the pathways topologies in their analysis, which are referred to as topology-based methods. Most of the topology-based methods used simple graph-based models to incorporate topology in their analysis, which have some limitations. We describe a new Pathway Analysis method using PETri net (PAPET) that uses the Petri net to model the signaling pathways and then propose an algorithm to measure the perturbation on a given pathway under a given condition. Modeling with Petri net has some advantages and could overcome the shortcomings of the simple graph-based models. We illustrate the capabilities of the proposed method using sensitivity, prioritization, mean reciprocal rank, and false-positive rate metrics on 36 real datasets from various diseases. The results of comparing PAPET with five pathway analysis methods FoPA, PADOG, GSEA, CePa and SPIA show that PAPET is the best one that provides a good compromise between all metrics. In addition, the results of applying methods to gene expression profiles in normal and Pancreatic Ductal Adenocarcinoma cancer (PDAC) samples show that the PAPET method achieves the best rank among others in finding the pathways that have been previously reported for PDAC. The PAPET method is available at https//github.com/fmansoori/PAPET. Cervical spinal cord injury (cSCI) can impair motor function in the upper limbs. Video from wearable cameras (egocentric video) has the potential to provide monitoring of rehabilitation outcomes at home, but methods for automated analysis of this data are needed. Wrist flexion/extension is an essential element to track grasping strategies after cSCI, as it may reflect the use of the tenodesis grasp, a common compensatory strategy. However, there is no established method to evaluate wrist flexion/extension from egocentric video. We propose a machine learning-based approach consisting of three steps-hand detection, pose estimation, and arm orientation estimation-to estimate wrist angle data, leading to the detection of tenodesis grasp. The hand detection in conjunction with the pose estimation algorithm correctly located wrist and index finger metacarpophalangeal coordinates in 63% and 76% of 15,319 annotated frames, respectively, extracted from egocentric videos of individuals with cSCI performing activities of daily living in a home simulation laboratory. The arm orientation algorithm had a mean absolute error of 2.76 +/- 0.39 degrees in 12,863 labeled frames. Using these estimates, the presence of a tenodesis grasp was correctly detected in 72% +/- 11% of frames in videos of 6 activities. The results provided a clear indication of which participants relied on tenodesis grasp and which did not. This paradigm provides the first method that can enable clinicians and researchers to monitor the use of the tenodesis grasp by individuals with cSCI at home, with implications for remote therapeutic guidance.This paradigm provides the first method that can enable clinicians and researchers to monitor the use of the tenodesis grasp by individuals with cSCI at home, with implications for remote therapeutic guidance.There is a need for a reliable and reproducible quantification of the immune infiltrate within the heterogeneous microenvironment of tumors in order to support therapy selection in oncology. Here we present an automated, modular method for whole-slide image analysis of the spatial distribution of tumor-infiltrating CD8-positive lymphocytes. The method uses a deep learning tissue-type classification algorithm on the hematoxylin eosin (HE) stained tissue section to identify the central tumor (CT) and invasive margin (IM) of the tumor. A CD8-positive cell detection algorithm using a deep learning-based nucleus detection is applied to a sequential immunohistochemistry (IHC)-stained tissue section. Image registration then allows obtaining IHC-derived CD8 scores for the HE-derived CT and the IM, respectively. Both, the mean and the standard deviation of the spatial CD8-positive density distributions were determined for the CT and IM in a cohort of post-menopausal, estrogen receptor-positive invasive breast cancer patients who received adjuvant tamoxifen therapy. Spatial density distributions were found to be highly heterogeneous. In contrast to previous studies, CD8 density in the IM and CT correlated positively with clinical outcome. However, statistical significance was only achieved for the standard deviation of the CD8 density distribution. We hypothesize that this is due to the positive contribution of local high-density areas. The IM/CT density ratio did not correlate with outcome. In view of the clinical relevance of our finding, we would like to encourage a study with a larger cohort. Our modular pipeline approach allows a robust and objective scoring of CD8 infiltrate based on routine pathology staining and should contribute to clinical adoption of computational pathology.False positives (FPs) reduction is indispensable for clustered microcalcifications (MCs) detection in digital breast tomosynthesis (DBT), since there might be excessive false candidates in the detection stage. Considering that DBT volume has an anisotropic resolution, we proposed a novel 3D context-aware convolutional neural network (CNN) to reduce FPs, which consists of a 2D intra-slices feature extraction branch and a 3D inter-slice features fusion branch. In particular, 3D anisotropic convolutions were designed to learn representations from DBT volumes and inter-slice information fusion is only performed on the feature map level, which could avoid the influence of anisotropic resolution of DBT volume. The proposed method was evaluated on a large-scale Chinese women population of 877 cases with 1754 DBT volumes and compared with 8 related methods. Experimental results show that the proposed network achieved the best performance with an accuracy of 92.68% for FPs reduction with an AUC of 97.65%, and the FPs are 0.