जर्नल ऑफ़ कंप्यूटर साइंस एंड सिस्टम्स बायोलॉजी

Introduction: Many patients state that they frequently make the wrong choices when it comes to their healthcare treatment. These patients reported poor knowledge about the medical alternatives, physician biases, and lack of consideration of their goals and concerns in the treatment alternatives available led to these poor choices. However, current studies suggest that if patients are given aids that improve their knowledge and address their goals and concerns, they are able to make choices that are medically recommended as well as being right for them. We hypothesized that a computer model designed to improve knowledge and take into account patients’ concerns and goals will be able to aid patients in making such decisions. Methods: Using the Expert Choice Comparion system, we designed a program to assist morbidly obese patients in deciding which treatment options will be best suited for them. This system incorporated treatment objectives, treatment alternatives, pros and cons of each alternative, utility curves, and dynamic and performance sensitivity graphs to reach treatment recommendations. Patients were surveyed about their choices. Results: 8 patients from a convenience sample participated in decision analysis. Most chose reduction of co-morbidities, followed by treatment safety, followed by weight loss as their primary objectives. All patients were satisfied with their choice, all 8 felt their concerns were addressed and 7 of 8 were likely to follow recommendations. The program provided them with choices that meet national guidelines. Five of 8 patients described the ease of use of the program as moderate, 2 described it as excellent, and 1 described it as poor. Conclusions: Patients can use computer modeling to assist in making health choices for themselves.

Macroscopic quantum systems (MQS) are macroscopic systems driven by quantum rather than classical mechanics, a long studied area with minimal success till recently. Harnessing the benefits of quantum mechanics on a macroscopic level would revolutionize fields ranging from telecommunication to biology, the latter focused on here for reasons discussed. Contrary to misconceptions, there are no known physical laws that prevent the development of MQS. Instead, they are generally believed universally lost in complex systems from environmental entanglements (decoherence). But we argue success is achievable MQS with decoherence compensation developed, naturally or artificially, from top-down rather current reductionist approaches. This paper advances the MQS field by a complex systems approach to decoherence. First, why complex system decoherence approaches (top-down) are needed is discussed. Specifically, complex adaptive systems (CAS) are not amenable to reductionist models (and their master equations) because of emergent behaviour, approximation failures, not accounting for quantum compensatory mechanisms, ignoring path integrals, and the subentity problem. In addition, since MQS must exist within the context of the classical world, where rapid decoherence and prolonged coherence are both needed. Nature has already demonstrated this for quantum subsystems such as photosynthesis and magnetoreception. Second, we perform a preliminary study that illustrates a top-down approach to potential MQS. In summary, reductionist arguments against MQS are not justifiable. It is more likely they are not easily detectable in large intact classical systems or have been destroyed by reductionist experimental set-ups. This complex systems decoherence approach, using top down investigations, is critical to paradigm shifts in MQS research both in biological and non-biological systems.

The problem of locating sensors in telecommunication networks is a distance geometry problem (DGP). In such a case, the positions of some sensors are known (which are called anchors) and some of the distances between sensors (which can or cannot be anchors) are known. The DGP is to locate the positions of all the sensors. Molecular DGP (MDGP) looks sensors as atoms and their telecommunication network as a molecule for the determination of its three-dimensional (3D) structure. This Chapter defines some sensor networks for determining molecular structures of PrP(113-120) AGAAAAGA amyloid fibrils, which are unstable, noncrystalline, insoluble and hard to be determined in NMR or X-ray experimental laboratories. The amyloid fibril structure is the common structure associated with some 20 neurodegenerative amyloid diseases (including Parkinson’s, Alzheimer’s, Huntington’s, and Prions’), and other diseases such as Type II diabetes, etc. The sensor networks established in this Chapter will benefit the study of 3D molecular structures of all these diseases and will be useful in the research areas such as structural materials, computer-aided or structure-based drug design, and the computational theory of molecular dynamics, and quantum mechanics/molecular mechanics.

Cluster analysis method is one of the main analytical methods in data mining; this method of clustering algorithm will influence the clustering results directly. This paper proposes an Advanced Clustering Algorithm in order to solve the question of high dimensionality and large data set. The Advanced Clustering Algorithm method avoids computing the distance of each data object to the cluster centers again and again and save the running time. ACA requires a simple data structure to store information in every iteration, which is to be used in the next iteration. Experimental results show that the Advanced Clustering Algorithm method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the traditional algorithms (K-Means, SOM and HAC). This paper includes Advanced Clustering Algorithm (ACA) and its experimental results through experimenting with academic data sets.

The growth of wireless technology and tablet PC in this era is creating a great impact on our lives. Some early efforts have been made to combine and utilize both of these technologies in advancement of hospitality industry. Nowadays, the majority of restaurants are still operating in an old fashioned way, by using pen and paper to register the orders of customers. The problem using “traditional menu” is probability of paper lost high and misinterprets the handwriting of order. Ours is designed to overcome this problem. By using “tablet PC”, the customer can send orders to the cooking room and cashier in a fast and easy way. TSIR can also give customer feedback to restaurant staff. The methodology that has been used in this paper is based on wireless communication (Wi-Fi). This system is developed by using (visual basic 6.0 and SQL server 2000). This system makes the food ordering process easier. This system, implements wireless data access to SQL server. The Windows 7 application on customer tablet pc will have all the menu details. The restaurant manager can manage the menu modifications easily, via adding and removing items.

Brassinosteroids (BRs) are a family of steroid hormones present ubiquitously in plant kingdoms and regulate wide range of physiological and developmental processes. BRs signal to regulate brassinosteroid transcription factors (BRTF’s), which regulate target genes for various BR responses. However, the role of BRs in developing fiber and during drought stress which is major limiting factor in its development is still limited. Our in silico modelling using Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) reveals that BRTF’s form a gene regulatory network (GRN) during cotton fiber development. Hierarchical clustering highlighted gene expression during acute dehydration response from early, peak and late elongation stage. SEA (Singular Enrichment Analysis) based gene ontology analysis demonstrate synergistic effect of BRs with other hormones like auxin. Our results provide a strong footprint of BRs based gene regulation in cotton fiber growth and drought stress.