The present study is mainly aimed at delivering a drug into the brain via the intranasal route using a liposomal formulation. For this purpose, rivastigmine, which is used in the management of Alzheimer's disease, was selected as a model drug. Conventional liposomes were formulated by the lipid layer hydration method using cholesterol and soya lecithin as lipid components. The concentration of rivastigmine in brain and plasma after intranasal liposomes, free drug and per oral administration was studied in rat models. A significantly higher level of drug was found in the brain with intranasal liposomes of rivastigmine compared to the intranasal free drug and the oral route. Intranasal liposomes had a longer half-life in the brain than intranasally or orally administered free drug. Delivering rivastigmine liposomes through the intranasal route for the treatment of Alzheimer's disease might be a new approach to the management of this condition.

Brain cancer is one of the cell synthesis diseases. Brain cancer cells are analyzed for patient diagnosis. Due to this composite cell, the conceptual classifications differ from each and every brain cancer investigation. In the gene test, patient prognosis is identified based on individual biocell appearance. Classification of advanced artificial neural network subtypes attains improved performance compared to previous enhanced artificial neural network (EANN) biocell subtype investigation. In this research, the proposed features are selected based on improved gene expression programming (IGEP) with modified brute force algorithm. Then, the maximum and minimum term survivals are classified by using PCA with enhanced artificial neural network (EANN). In this, the improved gene expression programming (IGEP) effectual features are selected by using remainder performance to improve the prognosis efficiency. This system is estimated by using the Cancer Genome Atlas (CGA) dataset. Simulation outputs present improved gene expression programming (IGEP) with modified brute force algorithm which achieves accurate efficiency of 96.37%, specificity of 96.37%, sensitivity of 98.37%, precision of 78.78%, F -measure of 80.22%, and recall of 64.29% when compared to generalized regression neural network (GRNN), improved extreme learning machine (IELM) with minimum redundancy maximum relevance (MRMR) method, and support vector machine (SVM).

2-Isopropyl-3-methoxypyrazine (IPMP) is a grape-derived component of wine flavor in some wine varieties as well as the causal compound of the off-flavor known as ladybug taint (LBT), which occurs when Harmonia axyridis beetles are incorporated with the grapes during juice and wine processing. The main objective of this study was to obtain robust estimates of the orthonasal (ON) and retronasal (RN) detection thresholds (DTs) for IPMP in wines of differing styles. The ASTM E679 ascending forced choice method of limits was used to determine DTs for 47 individuals in 3 different wines--Chardonnay, Gewürztraminer, and a red wine blend of Baco Noir and Marechel Foch. The group best estimate thresholds (BETs) obtained for IPMP (ng/L) were Chardonnay, ON: 0.32; Gewürztraminer, ON: 1.56, RN: 1.15, and red wine blend, ON: 1.03, RN: 2.29. A large variation in individual DTs was observed. Familiarity with LBT was inversely correlated with DTs for Gewürztraminer, and no difference in thresholds was observed between winemakers and nonwinemakers. We conclude that the human DT for IPMP is extremely low and influenced significantly by wine style and evaluation mode. We recommend against the reporting of single-threshold values for wine flavor compounds, and encourage the determination of consumer rejection thresholds for IPMP in wine.

a b s t r a c tWe consider a system consisting of N parallel servers, where jobs with different resource requirements arrive and are assigned to the servers for processing. Each server has a finite resource capacity and therefore can serve only a finite number of jobs at a time. We assume that different servers have different resource capacities. A job is accepted for processing only if the resource requested by the job is available at the server to which it is assigned. Otherwise, the job is discarded or blocked. We consider randomized schemes to assign jobs to servers with the aim of reducing the average blocking probability of jobs in the system. In particular, we consider a scheme that assigns an incoming job to the server having maximum available vacancy or unused resource among d randomly sampled servers. We consider the system in the limit where both the number of servers and the arrival rates of jobs are scaled by a large factor. This gives rise to a mean field analysis. We show that in the limiting system the servers behave independently-a property termed as propagation of chaos. Stationary tail probabilities of server occupancies are obtained from the stationary solution of the mean field which is shown to be unique and globally attractive. We further characterize the rate of decay of the stationary tail probabilities. Numerical results suggest that the proposed scheme significantly reduces the average blocking probability of jobs as compared to static schemes that probabilistically route jobs to servers independently of their states.

We consider the job assignment problem in a multi-server system consisting of N parallel processor sharing servers, categorized into M (≪ N ) different types according to their processing capacity or speed. Jobs of random sizes arrive at the system according to a Poisson process with rate N λ. Upon each arrival, a small number of servers from each type is sampled uniformly at random. The job is then assigned to one of the sampled servers based on a selection rule. We propose two schemes, each corresponding to a specific selection rule that aims at reducing the mean sojourn time of jobs in the system.We first show that both methods achieve the maximal stability region. We then analyze the system operating under the proposed schemes as N → ∞ which corresponds to the mean field. Our results show that asymptotic independence among servers holds even when M is finite and exchangeability holds only within servers of the same type. We further establish the existence and uniqueness of stationary solution of the mean field and show that the tail distribution of server occupancy decays doubly exponentially for each server type. When the estimates of arrival rates are not available, the proposed schemes offer simpler alternatives to achieving lower mean sojourn time of jobs, as shown by our numerical studies.