The mean absolute percentage error (MAPE) is one of the most widely used measures of forecast accuracy, due to its advantages of scale-independency and interpretability. However, MAPE has the ...significant disadvantage that it produces infinite or undefined values for zero or close-to-zero actual values. In order to address this issue in MAPE, we propose a new measure of forecast accuracy called the mean arctangent absolute percentage error (MAAPE). MAAPE has been developed through looking at MAPE from a different angle. In essence, MAAPE is a slope as an angle, while MAPE is a slope as a ratio, considering a triangle with adjacent and opposite sides that are equal to an actual value and the difference between the actual and forecast values, respectively. MAAPE inherently preserves the philosophy of MAPE, overcoming the problem of division by zero by using bounded influences for outliers in a fundamental manner through considering the ratio as an angle instead of a slope. The theoretical properties of MAAPE are investigated, and the practical advantages are demonstrated using both simulated and real-life data.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
In semiconductor manufacturing, a wafer bin map (WBM) represents the results of wafer testing for dies using a binary pass or fail value. For WBMs, defective dies are often clustered into groups of ...local systematic defects. Determining their specific patterns is important, because different patterns are related to different root causes of failure. Recently, because wafer sizes have increased and the process technology has become more complicated, the probability of observing mixed-type defect patterns, i.e., two or more defect patterns in a single wafer, has increased. In this paper, we propose the use of convolutional neural networks (CNNs) to classify mixed-type defect patterns in WBMs in the framework of an individual classification model for each defect pattern. Through simulated and real data examples, we show that the CNN is robust to random noise and performs effectively, even if there are many random defects in WBMs.
The purpose of this study was to identify the most vulnerable group among older Korean adults regarding information literacy. Once that was identified, the study aimed to provide basic data for ...developing strategies to improve information literacy by investigating the factors that influence the ability to utilize the Data Communication Equipment (DCE). The subjects included 10,073 older adults from the 10,299 participants of the 2017 Korean National Survey of Older Adults. The mean age of the older people was 74.06 years from a range of 65 to 106 years old. This study excluded the 216 individuals who did not complete the survey. The data were analyzed using the SPSS 18.0 program. A univariate analysis was performed to identify the most vulnerable group with regard to DCE competence. To investigate the factors that influence DCE competence, a logistic regression analysis was performed on the significant variables in the univariate analysis, while the nominal variables were treated as dummies. Senior citizens in Korea were less able to utilize DCE when they had higher ages, lower education levels, were women, lived alone, lower incomes, decreased sensory function, decreased cognitive function, negative value of learning, no lifelong learning, and smaller social networks. The factors influencing DCE competence in older adults were as follows: age, education level, income level, health status, cognitive function, social networks, lifelong learning, and the value of learning. For DCE competence in older adults to be effectively improved, adequate support must be provided to the vulnerable group. Furthermore, support for personalized DCE utilization seems essential and should consider a person’s age, education level, income, health status, cognitive function, social networks, lifelong learning and the value of learning.
We develop a Bayesian hierarchical log5 model to predict the probability of a particular batter/pitcher matchup event in baseball by extending the log5 model which is widely used for describing ...matchup events. The log5 model is simple and intuitive with fixed coefficients but less flexible than the generalized log5 model that allows the estimation of coefficients using data. Meanwhile, although the generalized log5 model is more flexible, the estimation of coefficients often suffers from a lack of data as a large sample of previous outcomes for a particular batter/pitcher matchup is rarely available in practice. The proposed Bayesian hierarchical log5 model retains the advantages of both models while complementing their disadvantages by estimating the unknown coefficients as in the generalized log5 model, but by using the fixed coefficients of the standard log5 model as prior knowledge. By combining the ideas of the two previous models, the proposed model can estimate the probability of a particular matchup event using a small amount of historical data of the players. Furthermore, we show that the Bayesian hierarchical log5 model achieves better predictive performance than the standard log5 model and the generalized log5 model using a real data example. We further extend the proposed model by including a new variable representing the defensive ability of the pitcher's team and show that the extended model can further improve the predictive performance of the Bayesian hierarchical log5 model.
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Positive-strand RNA viruses replicate in close association with rearranged intracellular membranes. For hepatitis C virus (HCV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), these ...rearrangements comprise endoplasmic reticulum (ER)-derived double membrane vesicles (DMVs) serving as RNA replication sites. Cellular factors involved in DMV biogenesis are poorly defined. Here, we show that despite structural similarity of viral DMVs with autophagosomes, conventional macroautophagy is dispensable for HCV and SARS-CoV-2 replication. However, both viruses exploit factors involved in autophagosome formation, most notably class III phosphatidylinositol 3-kinase (PI3K). As revealed with a biosensor, PI3K is activated in cells infected with either virus to produce phosphatidylinositol 3-phosphate (PI3P) while kinase complex inhibition or depletion profoundly reduces replication and viral DMV formation. The PI3P-binding protein DFCP1, recruited to omegasomes in early steps of autophagosome formation, participates in replication and DMV formation of both viruses. These results indicate that phylogenetically unrelated HCV and SARS-CoV-2 exploit similar components of the autophagy machinery to create their replication organelles.
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•Conventional macroautophagy is dispensable for HCV and SARS-CoV-2 replication•Components of the class III PI3K complex promote HCV and SARS-CoV-2 replication•Class III PI3K and DFCP1 contribute to membranous replication organelle formation
Twu et al. investigate involvement of autophagy machinery components in HCV and SARS-CoV-2 replication. Conventional macroautophagy is dispensable for replication of either virus. However, factors involved in autophagosome biogenesis, including components of the class III PI3K complex, contribute to viral replication. Most likely they promote membranous replication organelle formation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A new ECG monitoring procedure is proposed for diagnosing PVC beats.•The proposed method is based on wavelet-based statistical process control.•Sensitivity and specificity can be easily balanced ...using the significance level.
Automatic detection of premature ventricular contractions (PVCs) is essential to timely diagnosis of dangerous heart conditions. However, accurate detection of PVCs is challenging because of multiform PVCs. In this paper, an electrocardiographic (ECG) monitoring procedure based on wavelet-based statistical process control is proposed for diagnosing PVC beats. After ECG signals are decomposed and denoised via discrete wavelet transforms, significant wavelet coefficients are extracted through a sparse discriminant analysis for constructing a monitoring statistics based on Hotelling's T2 statistics. The proposed monitoring method alarms when the monitoring statistics exceeds the predetermined upper control limit. We demonstrated in this study the effectiveness of the proposed procedure by using the MIT-BIH arrhythmia database: the accuracy, sensitivity, specificity, and positive predictivity were obtained as 0.979, 0.872, 0.988, and 0.846, respectively.
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Previous ordinal classification methods implicitly assume that the class distribution within a dataset is balanced, which is often not the case for real-world datasets. If the dataset is imbalanced, ...the previous methods tend to be biased toward the majority class. The authors propose a new method for ordinal classification that attempts to mitigate the impact of imbalanced datasets. They propose a modified version of the weighted k-nearest neighbors method that determines the class membership using the αth quantile of the estimated class probability distribution and thus mitigates the impact of the class imbalance.
Limited tools exist to predict the risk of chemotherapy toxicity in older adults with early-stage breast cancer.
Patients of age ≥ 65 years with stage I-III breast cancer from 16 institutions treated ...with neoadjuvant or adjuvant chemotherapy were prospectively evaluated for geriatric and clinical features predictive of grade 3-5 chemotherapy toxicity. Logistic regression with best-subsets selection was used to identify and incorporate independent predictors of toxicity into a model with weighted variable scoring. Model performance was evaluated using area under the ROC curve (AUC) and goodness-of-fit statistics. The model was internally and externally validated.
In 473 patients (283 in development and 190 in validation cohort), 46% developed grade 3-5 chemotherapy toxicities. Eight independent predictors were identified (each assigned weighted points): anthracycline use (1 point), stage II or III (3 points), planned treatment duration > 3 months (4 points), abnormal liver function (3 points), low hemoglobin (3 points), falls (4 points), limited walking (3 points), and lack of social support (3 points). We calculated risk scores for each patient and defined three risk groups: low (0-5 points), intermediate (6-11 points), or high (≥ 12 points). In the development cohort, the rates of grade 3-5 chemotherapy toxicity for these three groups were 19%, 54%, and 87%, respectively (
< .01). In the validation cohort, the corresponding toxicity rates were 27%, 45%, and 76%. The AUC was 0.75 (95% CI, 0.70 to 0.81) in the development cohort and 0.69 (95% CI, 0.62 to 0.77) in the validation cohort. Risk groups were also associated with hospitalizations and reduced dose intensity (
< .01).
The Cancer and Aging Research Group-Breast Cancer (CARG-BC) score was developed and validated to predict grade 3-5 chemotherapy toxicity in older adults with early-stage breast cancer.
Seroconversion after COVID‐19 vaccination is impaired in kidney transplant recipients. Emerging variants of concern such as the B.1.617.2 (delta) and the B.1.1.529 (omicron) variants pose an ...increasing threat to these patients. In this observational cohort study, we measured anti‐S1 IgG, surrogate neutralizing, and anti‐receptor‐binding domain antibodies three weeks after a third mRNA vaccine dose in 49 kidney transplant recipients and compared results to 25 age‐matched healthy controls. In addition, vaccine‐induced neutralization of SARS‐CoV‐2 wild‐type, the B.1.617.2 (delta), and the B.1.1.529 (omicron) variants was assessed using a live‐virus assay. After a third vaccine dose, anti‐S1 IgG, surrogate neutralizing, and anti‐receptor‐binding domain antibodies were significantly lower in kidney transplant recipients compared to healthy controls. Only 29/49 (59%) sera of kidney transplant recipients contained neutralizing antibodies against the SARS‐CoV‐2 wild‐type or the B.1.617.2 (delta) variant and neutralization titers were significantly reduced compared to healthy controls (p < 0.001). Vaccine‐induced cross‐neutralization of the B.1.1.529 (omicron) variants was detectable in 15/35 (43%) kidney transplant recipients with seropositivity for anti‐S1 IgG, surrogate neutralizing, and/or anti‐RBD antibodies. Neutralization of the B.1.1.529 (omicron) variants was significantly reduced compared to neutralization of SARS‐CoV‐2 wild‐type or the B.1.617.2 (delta) variant for both, kidney transplant recipients and healthy controls (p < .001 for all).
A third mRNA vaccine dose increases immunogenicity in most kidney transplant recipients but, in comparison to healthy controls, kidney recipients have significantly reduced cross‐neutralizing antibody activity against the immune‐escaping B.1.1.529 (omicron) variant.
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BFBNIB, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
There has been a significant increase in text mining implementation for biomedical literature in recent years. Previous studies introduced the implementation of text mining and literature-based ...discovery to generate hypotheses of potential candidates for drug development. By conducting a hypothesis-generation step and using evidence from published journal articles or proceedings, previous studies have managed to reduce experimental time and costs. First, we applied the closed discovery approach from Swanson's ABC model to collect publications related to 36 Xanthium compounds or diabetes. Second, we extracted biomedical entities and relations using a knowledge extraction engine, the Public Knowledge Discovery Engine for Java or PKDE4J. Third, we built a knowledge graph using the obtained bio entities and relations and then generated paths with Xanthium compounds as source nodes and diabetes as the target node. Lastly, we employed graph embeddings to rank each path and evaluated the results based on domain experts' opinions and literature. Among 36 Xanthium compounds, 35 had direct paths to five diabetes-related nodes. We ranked 2,740,314 paths in total between 35 Xanthium compounds and three diabetes-related phrases: type 1 diabetes, type 2 diabetes, and diabetes mellitus. Based on the top five percentile paths, we concluded that adenosine, choline, beta-sitosterol, rhamnose, and scopoletin were potential candidates for diabetes drug development using natural products. Our framework for hypothesis generation employs a closed discovery from Swanson's ABC model that has proven very helpful in discovering biological linkages between bio entities. The PKDE4J tools we used to capture bio entities from our document collection could label entities into five categories: genes, compounds, phenotypes, biological processes, and molecular functions. Using the BioPREP model, we managed to interpret the semantic relatedness between two nodes and provided paths containing valuable hypotheses. Lastly, using a graph-embedding algorithm in our path-ranking analysis, we exploited the semantic relatedness while preserving the graph structure properties.
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