Emerging pharmacotherapy for cancer patients with cognitive dysfunction

Advances in the diagnosis and multi-modality treatment of cancer have increased survival rates for many cancer types leading to an increasing load of long-term sequelae of therapy, including that of cognitive dysfunction. The cytotoxic nature of chemotherapeutic agents may also reduce neurogenesis, a key component of the physiology of memory and cognition, with ramifications for the patient's mood and other cognition disorders. Similarly radiotherapy employed as a therapeutic or prophylactic tool in the treatment of primary or metastatic disease may significantly affect cognition. A number of emerging pharmacotherapies are under investigation for the treatment of cognitive dysfunction experienced by cancer patients. Recent data from clinical trials is reviewed involving the stimulants modafinil and methylphenidate, mood stabiliser lithium, anti-Alzheimer's drugs memantine and donepezil, as well as other agents which are currently being explored within dementia, animal, and cell culture models to evaluate their use in treating cognitive dysfunction

Efficient Malware Analysis Using Subspace-Based Methods on Representative Image Patterns

In this paper, we propose a new framework for classifying and visualizing malware files using subspace-based methods. The rise of advanced malware poses a significant threat to internet security, increasing the pressure on traditional cybersecurity measures which may no longer be adequate. As signature-based detection is limited to known threats, sophisticated methods are needed to detect and classify emerging malware that can bypass traditional antivirus software. Using representative image patterns to analyze malware features can provide a more detailed and precise approach by revealing detailed patterns that may be missed otherwise. In our framework, we rely on subspace representation of malware image patterns; a set of malware files belonging to the same class is compactly represented by a low-dimensional subspace in high dimensional vector space. Then, we use Subspace method (SM) and its kernel extension Kernel Subspace method (KSM) to classify a malware file by measuring the angle between the corresponding input vector and each class subspace. Further, we propose a visualization framework based on subspace representation and occlusion sensitivity analysis which enables detection of critical malware features. These visualizations can be used in conjunction with the proposed classification method to aid in interpretation of results and can lead to better understanding of malicious threats. We evaluate our methods on Malimg and Dumpware datasets and demonstrate the advantage of our methods over previous single-image verification methods that are vulnerable to varying conditions. With 98.07% and 97.21% accuracy, our algorithm outperforms other state-of-the-art techniques