Physicochemical studies on the gelation of hen's egg yolk: Delipidation of yolk plasma by treatment with phospholipase-C and extraction with solvents

A method for the delipidation of egg yolk plasma using phospholipase-C, n-heptane, and 1-butanol has been described. An aggregating protein fraction and a soluble protein fraction were separated by the action of phospholipase-C. The aggregating protein fraction freed of most of the lipids by treatment with n-heptane and 1-butanol was shown to be the apolipoproteins of yolk plasma, whereas the soluble proteins were identified as the livetins. Carbohydrate and the N-terminal amino acid analysis of these protein fractions are reported. A comparison of these protein fractions with the corresponding fractions obtained by formic acid delipidation of yolk plasma has been made. The gelation of yolk plasma by the action of phospholipase-C has been interpreted as an aggregation of lipoproteins caused by ionic interactions. The role of lecithin in maintaining the structural integrity of lipoproteins has been discussed.

Oncolytic Adenoviruses with Radiation Therapy for Treatment of Prostate Cancer

Prostate cancer is the most common cancer in males. Although many patients with localized disease can be cured with surgery and radiotherapy, advanced disease and especially castration resistant metastatic disease remains incurable, with a median life expectancy of less than 18 months. Oncolytic adenoviruses (Ads) are a new promising treatment against cancer due to their innate capacity to kill cancer cells. Viral replication in tumor cells leads to oncolysis and production of a multiplicity of new virions that are capable of further destroying cancerous tissue. Oncolytic Ads can be modified for tumor targeted infection and replication and be armed with therapeutic transgenes to maximize the oncolytic effect. Worldwide, clinical trials with oncolytic Ads have demonstrated good safety while the antitumor efficacy remains to be improved. Importantly, the best responses have been reported when oncolytic adenoviruses have been combined with standard cancer treatments, such as chemotherapy and radiation. Further, a challenge in many virotherapy approaches has been the monitoring of virus replication in vivo. Reporter genes have been extensively used as transgenes to evaluate the biodistribution of the virus and activity of specific promoters. However, these techniques are often limited to preclinical evaluation and not amenable to human use. The aim of the thesis was to find and develop new oncolytic Ads with maximum efficacy against metastatic, castration resistant prostate cancer and study them in vitro and in vivo combined to different forms of radiation therapy. Using combination therapy, we were aiming for better antitumor efficacy with reduced side effects. Capsid modified Ads for enhanced transduction were studied. Serotype 3 targeted chimera, Ad5/3, was found to have enhanced infectivity for prostate cancer and was used for developing new viruses for the study. Correlation between Ad-encoded marker peptide secretion and simultaneous viral replication was evaluated and the effects of radiotherapy on viral replication were studied in detail. We found that the repair of double strand breaks caused by ionizing radiation was inhibited by adenoviral proteins and led to autophagic cell death. Both subcutaneous models and intrapulmonary tumor models mimicking metastatic, aggressive disease were used in vivo. Virus efficacy was evaluated by intratumoral injections. Also, intravenous administration was evaluated to study the effectiveness in metastatic disease. Oncolytic adenovirus treatment led to significant tumor growth control and increased the survival rate of the mice. These results were further improved when oncolytic Ads were combined with radiation therapy. Oncolytic Ads expressing human sodium/iodide transporter (hNIS) as a transgene were evaluated for their oncolytic potency and for the functionality of hNIS in vitro and in vivo. Monitoring of viral replication was also assessed using different imaging modalities relative to clinical use. SPECT imaging of tumor-bearing mice was evaluated and combined with simultaneous CT-scanning to obtain important anatomical information on biodistribution, also in a three-dimensional form. It was shown that hNIS-expressing adenoviruses could harbour a bi-functional transgene allowing for localization and imaging of viral replication. Targeted radiotherapy was applied by systemic radioiodide administration and resulted in iodide accumulation into Ad-infected tumor. The combination treatment showed significantly enhanced antitumor efficacy in mice bearing prostate cancer tumors. In summary, the results presented above aim to provide new treatment modalities for castration resistant prostate cancer. Molecular insights were provided for better understanding of the benefits of combined radiation therapy and oncolytic adenoviruses, which will hopefully facilitate the translation of the approach into clinical use for humans.

A general treatment of two dimensional plane flows, for a micropolar fluid

In this paper we have studied the flow of a micropolar fluid, whose constitutive equations were given by Eringen, in two dimensional plane flow. In two notes, we have discussed the validity of the boundary condition v=a ω and its effect on the entire flow field. We have restricted our study to the case when Stokes' approximation is valid, i. e. slow motion for it is difficult to uncouple the equations in the most general case.

A Geometric Algorithm for Learning Oblique Decision Trees

In this paper we present a novel algorithm for learning oblique decision trees. Most of the current decision tree algorithms rely on impurity measures to assess goodness of hyperplanes at each node. These impurity measures do not properly capture the geometric structures in the data. Motivated by this, our algorithm uses a strategy, based on some recent variants of SVM, to assess the hyperplanes in such a way that the geometric structure in the data is taken into account. We show through empirical studies that our method is effective.

A fast time-domain algorithm for the assessment of tissue blood flow in laser-Doppler flowmetry

In this study, we derive a fast, novel time-domain algorithm to compute the nth-order moment of the power spectral density of the photoelectric current as measured in laser-Doppler flowmetry (LDF). It is well established that in the LDF literature these moments are closely related to fundamental physiological parameters, i.e. concentration of moving erythrocytes and blood flow. In particular, we take advantage of the link between moments in the Fourier domain and fractional derivatives in the temporal domain. Using Parseval's theorem, we establish an exact analytical equivalence between the time-domain expression and the conventional frequency-domain counterpart. Moreover, we demonstrate the appropriateness of estimating the zeroth-, first- and second-order moments using Monte Carlo simulations. Finally, we briefly discuss the feasibility of implementing the proposed algorithm in hardware.

A fast algorithm for macromolecular packing calculation

An algorithm to improve the computation time of packing calculations for macromolecules is presented. This is achieved by reducing the three-dimensional search to a small set of two-dimensional searches.

An approximate algorithm for the minimal vertex nested polygon problem

Given two simple polygons, the Minimal Vertex Nested Polygon Problem is one of finding a polygon nested between the given polygons having the minimum number of vertices. In this paper, we suggest efficient approximate algorithms for interesting special cases of the above using the shortest-path finding graph algorithms.

A reinforcement learning based algorithm for finite horizon Markov decision processes

We develop a simulation based algorithm for finite horizon Markov decision processes with finite state and finite action space. Illustrative numerical experiments with the proposed algorithm are shown for problems in flow control of communication networks and capacity switching in semiconductor fabrication.

A four-timescale algorithm for constrained stochastic optimization of RED

The overall performance of random early detection (RED) routers in the Internet is determined by the settings of their associated parameters. The non-availability of a functional relationship between the RED performance and its parameters makes it difficult to implement optimization techniques directly in order to optimize the RED parameters. In this paper, we formulate a generic optimization framework using a stochastically bounded delay metric to dynamically adapt the RED parameters. The constrained optimization problem thus formulated is solved using traditional nonlinear programming techniques. Here, we implement the barrier and penalty function approaches, respectively. We adopt a second-order nonlinear optimization framework and propose a novel four-timescale stochastic approximation algorithm to estimate the gradient and Hessian of the barrier and penalty objectives and update the RED parameters. A convergence analysis of the proposed algorithm is briefly sketched. We perform simulations to evaluate the performance of our algorithm with both barrier and penalty objectives and compare these with RED and a variant of it in the literature. We observe an improvement in performance using our proposed algorithm over RED, and the above variant of it.

An auction algorithm for procuring wireless channel in a heterogenous wireless network

In this paper, we develop a novel auction algorithm for procuring wireless channel by a wireless node in a heterogeneous wireless network. We assume that the service providers of the heterogeneous wireless network are selfish and non-cooperative in the sense that they are only interested in maximizing their own utilities. The wireless user needs to procure wireless channels to execute multiple tasks. To solve the problem of the wireless user, we propose a reverse optimal (REVOPT) auction and derive an expression for the expected payment by the wireless user. The proposed auction mechanism REVOPT satisfies important game theoretic properties such as Bayesian incentive compatibility and individual rationality.

An efficient and scalable checkpointing and recovery algorithm for distributed systems

In this paper, we describe an efficient coordinated-checkpointing and recovery algorithm which can work even when the channels are assumed to be non-FIFO, and messages may be lost. Nodes are assumed to be autonomous, and they do not block while taking checkpoints. Based on the local conditions, any process can request the previous coordinator for the 'permission' to initiate a new checkpoint. Allowing multiple initiators of checkpoints avoids the bottleneck associated with a single initiator, but the algorithm permits only a single instance of checkpointing process at any given time, thus reducing much of the overhead associated with multiple initiators of distributed algorithms.

Effective treatment of infectious diseases: A nonlinear adaptive control theoretic approach

A nonlinear adaptive system theoretic approach is presented in this paper for effective treatment of infectious diseases that affect various organs of the human body. The generic model used does not represent any specific disease. However, it mimics the generic immunological dynamics of the human body under pathological attack, including the response to external drugs. From a system theoretic point of view, drugs can be interpreted as control inputs. Assuming a set of nominal parameters in the mathematical model, first a nonlinear controller is designed based on the principle of dynamic inversion. This treatment strategy was found to be effective in completely curing "nominal patients". However, in some cases it is ineffective in curing "realistic patients". This leads to serious (sometimes fatal) damage to the affected organ. To make the drug dosage design more effective, a model-following neuro-adaptive control design is carried out using neural networks, which are trained (adapted) online. From simulation studies, this adaptive controller is found to be effective in killing the invading microbes and healing the damaged organ even in the presence of parameter uncertainties and continuing pathogen attack.

Real-time image denoising algorithm in teleradiology systems - art.no.606304

Denoising of medical images in wavelet domain has potential application in transmission technologies such as teleradiology. This technique becomes all the more attractive when we consider the progressive transmission in a teleradiology system. The transmitted images are corrupted mainly due to noisy channels. In this paper, we present a new real time image denoising scheme based on limited restoration of bit-planes of wavelet coefficients. The proposed scheme exploits the fundamental property of wavelet transform - its ability to analyze the image at different resolution levels and the edge information associated with each sub-band. The desired bit-rate control is achieved by applying the restoration on a limited number of bit-planes subject to the optimal smoothing. The proposed method adapts itself to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The proposed scheme relies on the fact that noise commonly manifests itself as a fine-grained structure in image and wavelet transform allows the restoration strategy to adapt itself according to directional features of edges. The proposed approach shows promising results when compared with unrestored case, in context of error reduction. It also has capability to adapt to situations where noise level in the image varies and with the changing requirements of medical-experts. The applicability of the proposed approach has implications in restoration of medical images in teleradiology systems. The proposed scheme is computationally efficient.

A neuro-adaptive augmented optimal dynamic inversion approach for effective and efficient treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an efficient technique is presented for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used for the control (medication) synthesis. First, taking a set of nominal parameters, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat nominal patients (patients having same nominal parameters as used for the control design) effectively. However, since the parameters of an actual patient can be different from that of the ideal patient, to make the treatment strategy more effective and efficient, a model-following neuro-adaptive controller is augmented to the nominal controller. In this approach, a neural network trained online (based on Lyapunov stability theory) facilitates a new adaptive controller, computed online. From the simulation studies, this adaptive control design approach (treatment strategy) is found to be very effective to treat the CML disease for actual patients. Sufficient generality is retained in the theoretical developments in this paper, so that the techniques presented can be applied to other similar problem as well. Note that the technique presented is computationally non-intensive and all computations can be carried out online.