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Amazing applications and perfect programs
Amazing Applications and Perfect Programs helps explain operating systems, computer programs, sorting and storing files, and databases. Learn about the amazing variety of programs that allow users to have fun with words, numbers, pictures, and sounds. Exercises teach key skills such as word processing, creating documents, and using databases, and are not linked to specific software or operating systems.
Book
The Clinicopathological features and survival outcomes of patients with different metastatic sites in stage IV breast cancer
Background
The features and survival of stage IV breast cancer patients with different metastatic sites are poorly understood. This study aims to examine the clinicopathological features and survival of stage IV breast cancer patients according to different metastatic sites.
Methods
Using the Surveillance, Epidemiology, and End Results database, we restricted our study population to stage IV breast cancer patients diagnosed between 2010 to 2015. The clinicopathological features were examined by chi-square tests. Breast cancer-specific survival (BCSS) and overall survival (OS) were compared among patients with different metastatic sites by the Kaplan-Meier method with log-rank test. Univariable and multivariable analyses were also performed using the Cox proportional hazard model to identify statistically significant prognostic factors.
Results
A total of 18,322 patients were identified for survival analysis. Bone-only metastasis accounted for 39.80% of patients, followed by multiple metastasis (33.07%), lung metastasis (10.94%), liver metastasis (7.34%), other metastasis (7.34%), and brain metastasis (1.51%). The Kaplan-Meier plots showed that patients with bone metastasis had the best survival, while patients with brain metastasis had the worst survival in both BCSS and OS (
p
< 0.001, for both). Multivariable analyses showed that age, race, marital status, grade, tumor subtype, tumor size, surgery of primary cancer, and a history of radiotherapy or chemotherapy were independent prognostic factors.
Conclusion
Stage IV breast cancer patients have different clinicopathological characteristics and survival outcomes according to different metastatic sites. Patients with bone metastasis have the best prognosis, and brain metastasis is the most aggressive subgroup.
Journal Article
Surgical trends in breast cancer: a rise in novel operative treatment options over a 12 year analysis
Purpose
Breast cancer surgical techniques are evolving. Few studies have analyzed national trends for the multitude of surgical options that include partial mastectomy (PM), mastectomy without reconstruction (M), mastectomy with reconstruction (M+R), and PM with oncoplastic reconstruction (OS). We hypothesize that the use of M is declining and likely correlates with the rise of surgery with reconstructive options (M+R, OS).
Methods
A retrospective cohort analysis was conducted using the ACS-NSQIP database from 2005 to 2016 and ICD codes for IBC and DCIS. Patients were then grouped together based on current procedural terminology (CPT) codes for PM, M, M+R, and OS. In each group, categories were sorted again based on additional reconstructive procedures. Data analysis was conducted via Pearson’s chi-squared test for demographics, linear regression, and a non-parametric Mann- Kendall test to assess a temporal trend.
Results
The patient cohort consisted of 256,398 patients from the NSQIP data base; 197,387 meet inclusion criteria diagnosed with IBC or DCIS. Annual breast surgery trends changed as follows: PM 46.3–46.1% (
p
= 0.21), M 35.8–26.4% (
p
= 0.001), M+R 15.9–23.0% (
p
= 0.03), and OS 1.8–4.42% (
p
= 0.001). Analyzing the patient cohort who underwent breast conservation, categorical analysis showed a decreased use of PM alone (96–91%) with an increased use of OS (4–9%). For the patient cohort undergoing mastectomy, M alone decreased (69–53%); M+R with muscular flap decreased (9–2%); and M+R with implant placement increased (20–40%)—all three trends
p
< 0.0001.
Conclusion
The modern era of breast surgery is identified by the increasing use of reconstruction for patients undergoing breast conservation (in the form of OS) and mastectomy (in the form of M+R). Our study provides data showing significant trends that will impact the future of both breast cancer surgery and breast training programs.
Journal Article
Monolith to microservices : evolutionary patterns to transform your monolith
\"How do you detangle a monolithic system and migrate it to a microservice architecture? How do you do it while maintaining business-as-usual? As a companion to Sam Newman's extremely popular Building Microservices, this new book details a proven method for transitioning an existing monolithic system to a microservice architecture. With many illustrative examples, insightful migration patterns, and a bevy of practical advice to transition your monolith enterprise into a microservice operation, this practical guide covers multiple scenarios and strategies for a successful migration, from initial planning all the way through application and database decomposition. You'll learn several tried and tested patterns and techniques that you can use as you migrate your existing architecture.\"-- Provided by publisher
Book
EpiCollect: Linking Smartphones to Web Applications for Epidemiology, Ecology and Community Data Collection
Epidemiologists and ecologists often collect data in the field and, on returning to their laboratory, enter their data into a database for further analysis. The recent introduction of mobile phones that utilise the open source Android operating system, and which include (among other features) both GPS and Google Maps, provide new opportunities for developing mobile phone applications, which in conjunction with web applications, allow two-way communication between field workers and their project databases.
Here we describe a generic framework, consisting of mobile phone software, EpiCollect, and a web application located within www.spatialepidemiology.net. Data collected by multiple field workers can be submitted by phone, together with GPS data, to a common web database and can be displayed and analysed, along with previously collected data, using Google Maps (or Google Earth). Similarly, data from the web database can be requested and displayed on the mobile phone, again using Google Maps. Data filtering options allow the display of data submitted by the individual field workers or, for example, those data within certain values of a measured variable or a time period.
Data collection frameworks utilising mobile phones with data submission to and from central databases are widely applicable and can give a field worker similar display and analysis tools on their mobile phone that they would have if viewing the data in their laboratory via the web. We demonstrate their utility for epidemiological data collection and display, and briefly discuss their application in ecological and community data collection. Furthermore, such frameworks offer great potential for recruiting 'citizen scientists' to contribute data easily to central databases through their mobile phone.
Journal Article
A detection method for android application security based on TF-IDF and machine learning
Android is the most widely used mobile operating system (OS). A large number of third-party Android application (app) markets have emerged. The absence of third-party market regulation has prompted research institutions to propose different malware detection techniques. However, due to improvements of malware itself and Android system, it is difficult to design a detection method that can efficiently and effectively detect malicious apps for a long time. Meanwhile, adopting more features will increase the complexity of the model and the computational cost of the system. Permissions play a vital role in the security of the Android apps. Term Frequency-Inverse Document Frequency (TF-IDF) is used to assess the importance of a word for a file set in a corpus. The static analysis method does not need to run the app. It can efficiently and accurately extract the permissions from an app. Based on this cognition and perspective, in this paper, a new static detection method based on TF-IDF and Machine Learning is proposed. The system permissions are extracted in Android application package's (Apk's) manifest file. TF-IDF algorithm is used to calculate the permission value (PV) of each permission and the sensitivity value of apk (SVOA) of each app. The SVOA and the number of the used permissions are learned and tested by machine learning. 6070 benign apps and 9419 malware are used to evaluate the proposed approach. The experiment results show that only use dangerous permissions or the number of used permissions can't accurately distinguish whether an app is malicious or benign. For malware detection, the proposed approach achieve up to 99.5% accuracy and the learning and training time only needs 0.05s. For malware families detection, the accuracy is 99.6%. The results indicate that the method for unknown/new sample's detection accuracy is 92.71%. Compared against other state-of-the-art approaches, the proposed approach is more effective by detecting malware and malware families.
Journal Article
Correction: Effect of anti-epileptic drugs on the survival of patients with glioblastoma multiforme: A retrospective, single-center study
[This corrects the article DOI: 10.1371/journal.pone.0225599.].
Journal Article