This research reveals that from among the total 37 epidemiological weeks, the maximum impact was observed between weeks 22 and 27. The geographical flow and hotspots associated with dengue have been shown through thematic maps. A positive correlation between the risk for dengue and age was observed. The findings of this research can help health officials and decision-makers alert the public about future outbreaks and take preventive measures to considerably reduce the mortality and morbidity associated with the disease.

Background Alzheimer’s disease (AD) is a neurodegenerative brain pathology formed due to piling up of amyloid proteins, development of plaques and disappearance of neurons. Another common subtype of dementia like AD, Parkinson’s disease (PD) is determined by the disappearance of dopaminergic neurons in the region known as substantia nigra pars compacta located in the midbrain. Both AD and PD target aged population worldwide forming a major chunk of healthcare costs. Hence, there is a need for methods that help in the early diagnosis of these diseases. PD subjects especially those who have confirmed postmortem plaque are a strong candidate for a second AD diagnosis. Modalities such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) can be combined with deep learning methods to diagnose these two diseases for the benefit of clinicians. Result In this work, we deployed a 3D Convolutional Neural Network (CNN) to extract features for multiclass classification of both AD and PD in the frequency and spatial domains using PET and SPECT neuroimaging modalities to differentiate between AD, PD and Normal Control (NC) classes. Discrete Cosine Transform has been deployed as a frequency domain learning method along with random weak Gaussian blurring and random zooming in/out augmentation methods in both frequency and spatial domains. To select the hyperparameters of the 3D-CNN model, we deployed both 5- and 10-fold cross-validation (CV) approaches. The best performing model was found to be AD/NC(SPECT)/PD classification with random weak Gaussian blurred augmentation in the spatial domain using fivefold CV approach while the worst performing model happens to be AD/NC(PET)/PD classification without augmentation in the frequency domain using tenfold CV approach. We also found that spatial domain methods tend to perform better than their frequency domain counterparts. Conclusion The proposed model provides a good performance in discriminating AD and PD subjects due to minimal correlation between these two dementia types on the clinicopathological continuum between AD and PD subjects from a neuroimaging perspective.

An incident, in the perception of information technology, is an event that is not part of a normal process and disrupts operational procedure. This research work particularly focuses on software failure incidents. In any operational environment, software failure can put the quality and performance of services at risk. Many efforts are made to overcome this incident of software failure and to restore normal service as soon as possible. The main contribution of this study is software failure incidents classification and prediction using machine learning. In this study, an active learning approach is used to selectively label those data which is considered to be more informative to build models. Firstly, the sample with the highest randomness (entropy) is selected for labeling. Secondly, to classify the labeled observation into either failure or no failure classes, a binary classifier is used that predicts the target class label as failure or not. For classification, Support Vector Machine is used as a main classifier to classify the data. We derived our prediction models from the failure log files collected from the ECLIPSE software repository.

PurposeThis retrospective study presents a comparative analysis of the overall survival and toxicities, as side effects, of docetaxel plus cyclophosphamide (TC) and doxorubicin plus cyclophosphamide (AC). The study measured their efficacies during adjuvant chemotherapy, treating Pakistani breast cancer patients by validating the results obtained, with the published analysis of the same treatment given to US patients.Patients and methodsBetween June 2015 and September 2017, for four chemotherapy cycles, 189 patients out of 358 received TC (75 mg/m2 of docetaxel, 600 mg/m2 of cyclophosphamide) and 169 were treated with AC (60 mg/m2 of doxorubicin, 600 mg/m2 of cyclophosphamide). On the basis of using pathological markers to assess patients, toxicities, as side effects, (due to docetaxel, doxorubicin, and cyclophosphamide) were listed in the database of this study. Common factors with respect to common terminology criteria for adverse events version 5.0 and side effects listed in MedlinePlus, NIH US database, and from the database of this study were then separated to be included in comparison for this study. Statistically, chi-squared test was used at α=0.05.ResultsThere was no statistically significant difference between the proportions of patients with vomiting, extreme tiredness, diarrhea, mild anemia, stability, and overall survival because P-value >0.05. However, AC remained less toxic (P-value <0.05) by 22.6%, 25.7%, 25.3%, 12.4%, 20.8%, and 16.4% compared to TC for changes in taste, muscle pain, burning hands, change in hemoglobin level, moderate anemia, and needing blood transfusion respectively, whereas TC remained less toxic by 52.9%, 32.5%, and 26.3% for dizziness, weight loss, and sores in throat and mouth, respectively.ConclusionAt 27 months, TC was more toxic than AC, whereas both combinations had the same overall survival rate.

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