PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantumclassical computations. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware.We provide plugins for Strawberry Fields, Rigetti Forest, Qiskit, and ProjectQ, allowing PennyLane optimizations to be run on publicly accessible quantum devices provided by Rigetti and IBM Q. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, and autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.

No abstract

As the network based applications are growing rapidly, the network security mechanisms require more attention to improve speed and precision. The ever evolving new intrusion types pose a serious threat to network security. Although numerous network security tools have been developed, yet the fast growth of intrusive activities is still a serious issue. Intrusion detection systems (IDSs) are used to detect intrusive activities on the network. Machine learning and classification algorithms help to design "Intrusion Detection Models" which can classify the network traffic into intrusive or normal traffic. In this paper we present the comparative performance of NSL-KDD based data set compatible classification algorithms. These classifiers have been evaluated in WEKA (Waikato Environment for Knowledge Analysis) environment using 41 attributes. Around 94,000 instances from complete KDD dataset have been included in the training data set and over 48,000 instances have been included in the testing data set. Garrett's Ranking Technique has been applied to rank different classifiers according to their performance. Rotation Forest classification approach outperformed the rest.

The present study evaluates the prophylactic efficacy of α-tocopherol (α-TOH), resveratrol (RES), and coenzyme Q10 (CoQ10) co-loaded self-nanoemulsifying drug delivery system (α-TOH-RES-CoQ10 SNEDDS) in 7,12-Dimethylbenz[a]anthracene (DMBA) induced breast cancer model. SNEDDS formulation components were rationally selected and optimized for maximum drug loading by applying the design of experiments and further evaluated for stability in simulated gastrointestinal fluids, functional stability of antioxidants, in vitro release, Caco-2 cell uptake, oral bioavailability and prophylactic anticancer activity. The SNEDDS demonstrated excellent stability in stimulated gastrointestinal fluids. The functional activity of antioxidants was confirmed by 2,2-diphenylpicrylhydrazyl (DPPH) scavenging assay wherein significantly (p > .05) higher antioxidant activity was observed in case of SNEDDS as compared with free antioxidants. Coumarin 6 (C-6)-loaded SNEDDS formulation demonstrated remarkably higher Caco-2 cell uptake in comparison with free C-6, indicative of efficient internalization of sub-micron SNEDDS droplets by Caco-2 cells. In line with Caco-2 cell uptake observations, α-TOH-RES-CoQ10-SNEDDS showed ∼2.30- and ∼3.64-fold increase in the AUC values of RES and CoQ10 in comparison with free antioxidants. Significantly lower (p < .001) tumor volume (∼327 mm) was found in case of animals treated with α-TOH-RES-CoQ10-SNEDDS in comparison with free antioxidant combination (∼1070 mm) and DMBA control (∼1540 mm) groups. Conclusively, the proposed strategy posed great potential in improving the prophylactic activity of antioxidants and hold promise for further exploration.

KCa3.1 protein is part of a heterotetrameric voltage-independent potassium channel, the activity of which depends on the intracellular calcium binding to calmodulin. KCa3.1 is immensely significant in regulating immune responses and primarily expressed in cells of hematopoietic lineage. It is one of the attractive pharmacological targets that are known to inhibit neuroinflammation. KCa3.1 blockers mediate neuroprotection through multiple mechanisms, such as by targeting microglia-mediated neuronal killing. KCa3.1 modulators may provide alternative treatment options for neurological disorders like ischemic stroke, Alzheimer disease, glioblastoma multiforme, multiple sclerosis and spinal cord injury. This review is an attempt to draw attention towards KCa3.1 channel, which was never exploited to its full potential as a viable therapeutic candidate against various neurological disorders.