Computer-Aided Drug Design of Pyranopyrazoles and Related Compounds for Checkpoint Kinase-1

Checkpoint-1 kinase plays an important role in the G(2)M cell cycle control, therefore its inhibition by small molecules is of great therapeutic interest in oncology. In this paper, we have reported the virtual screening of an in-house library of 2499 pyranopyrazole derivatives against the ATP-binding site of Chk1 kinase using Glide 5.0 program, which resulted in six hits. All these ligands were docked into the site forming most crucial interactions with Cys87, Glu91 and Leu15 residues. From the observed results these ligands are suggested to be potent inhibitors of Chk1 kinase with sufficient scope for further elaboration.

Project Energise: Using participatory approaches and real time computer prompts to reduce occupational sitting and increase work time physical activity in office workers

Objectives This efficacy study assessed the added impact real time computer prompts had on a participatory approach to reduce occupational sedentary exposure and increase physical activity. Design Quasi-experimental. Methods 57 Australian office workers (mean [SD]; age = 47 [11] years; BMI = 28 [5] kg/m2; 46 men) generated a menu of 20 occupational ‘sit less and move more’ strategies through participatory workshops, and were then tasked with implementing strategies for five months (July–November 2014). During implementation, a sub-sample of workers (n = 24) used a chair sensor/software package (Sitting Pad) that gave real time prompts to interrupt desk sitting. Baseline and intervention sedentary behaviour and physical activity (GENEActiv accelerometer; mean work time percentages), and minutes spent sitting at desks (Sitting Pad; mean total time and longest bout) were compared between non-prompt and prompt workers using a two-way ANOVA. Results Workers spent close to three quarters of their work time sedentary, mostly sitting at desks (mean [SD]; total desk sitting time = 371 [71] min/day; longest bout spent desk sitting = 104 [43] min/day). Intervention effects were four times greater in workers who used real time computer prompts (8% decrease in work time sedentary behaviour and increase in light intensity physical activity; p < 0.01). Respective mean differences between baseline and intervention total time spent sitting at desks, and the longest bout spent desk sitting, were 23 and 32 min/day lower in prompt than in non-prompt workers (p < 0.01). Conclusions In this sample of office workers, real time computer prompts facilitated the impact of a participatory approach on reductions in occupational sedentary exposure, and increases in physical activity.

Automated feature engineering for HTTP tunnel detection

Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.

NUPARM and NUCGEN: software for analysis and generation of sequence dependent nucleic acid structures

Software packages NUPARM and NUCGEN, are described, which can be used to understand sequence directed structural variations in nucleic acids, by analysis and generation of non-uniform structures. A set of local inter basepair parameters (viz. tilt, roll, twist, shift, slide and rise) have been defined, which use geometry and coordinates of two successive basepairs only and can be used to generate polymeric structures with varying geometries for each of the 16 possible dinucleotide steps. Intra basepair parameters, propeller, buckle, opening and the C6...C8 distance can also be varied, if required, while the sugar phosphate backbone atoms are fixed in some standard conformation ill each of the nucleotides. NUPARM can be used to analyse both DNA and RNA structures, with single as well as double stranded helices. The NUCGEN software generates double helical models with the backbone fixed in B-form DNA, but with appropriate modifications in the input data, it can also generate A-form DNA ar rd RNA duplex structures.

Semi-active vibration isolation using a near-zero-spring-rate device:steady state analysis of a single-degree-of-freedom model

A vibration isolator is described which incorporates a near-zero-spring-rate device within its operating range. The device is an assembly of a vertical spring in parallel with two inclined springs. A low spring rate is achieved by combining the equivalent stiffness in the vertical direction of the inclined springs with the stiffness of the vertical central spring. It is shown that there is a relation between the geometry and the stiffness of the individual springs that results in a low spring rate. Computer simulation studies of a single-degree-of-freedom model for harmonic base input show that the performance of the proposed scheme is superior to that of the passive schemes with linear springs and skyhook damping configuration. The response curves show that, for small to large amplitudes of base disturbance, the system goes into resonance at low frequencies of excitation. Thus, it is possible to achieve very good isolation over a wide low-frequency band. Also, the damper force requirements for the proposed scheme are much lower than for the damper force of a skyhook configuration or a conventional linear spring with a semi-active damper.

Two-level k-means clustering algorithm for k-tau relationship establishment and linear-time classification

Partitional clustering algorithms, which partition the dataset into a pre-defined number of clusters, can be broadly classified into two types: algorithms which explicitly take the number of clusters as input and algorithms that take the expected size of a cluster as input. In this paper, we propose a variant of the k-means algorithm and prove that it is more efficient than standard k-means algorithms. An important contribution of this paper is the establishment of a relation between the number of clusters and the size of the clusters in a dataset through the analysis of our algorithm. We also demonstrate that the integration of this algorithm as a pre-processing step in classification algorithms reduces their running-time complexity.

Adsorption of Nitrogen on Activated Carbon-Refit of Experimental Data and Derivation of Properties Required for Design of Equipment

Volumetric method based adsorption measurements of nitrogen on two specimens of activated carbon (Fluka and Sarabhai) reported by us are refitted to two popular isotherms, namely, Dubunin−Astakhov (D−A) and Toth, in light of improved fitting methods derived recently. Those isotherms have been used to derive other data of relevance in design of engineering equipment such as the concentration dependence of heat of adsorption and Henry’s law coefficients. The present fits provide a better representation of experimental measurements than before because the temperature dependence of adsorbed phase volume and structural heterogeneity of micropore distribution have been accounted for in the D−A equation. A new correlation to the Toth equation is a further contribution. The heat of adsorption in the limiting uptake condition is correlated with the Henry’s law coefficients at the near zero uptake condition.

Conflict-Tolerant Features

We consider systems composed of a base system with multiple “features” or “controllers”, each of which independently advise the system on how to react to input events so as to conform to their individual specifications. We propose a methodology for developing such systems in a way that guarantees the “maximal” use of each feature. The methodology is based on the notion of “conflict-tolerant” features that are designed to continue offering advice even when their advice has been overridden in the past. We give a simple priority-based composition scheme for such features, which ensures that each feature is maximally utilized. We also provide a formal framework for specifying, verifying, and synthesizing such features. In particular we obtain a compositional technique for verifying systems developed in this framework.

Conflict-Tolerant Real-Time Features

This paper addresses the problem of detecting and resolving conflicts due to timing constraints imposed by features in real-time systems. We consider systems composed of a base system with multiple features or controllers, each of which independently advise the system on how to react to input events so as to conform to their individual specifications. We propose a methodology for developing such systems in a modular manner based on the notion of conflict tolerant features that are designed to continue offering advice even when their advice has been overridden in the past. We give a simple priority based scheme for composing such features. This guarantees the maximal use of each feature. We provide a formal framework for specifying such features, and a compositional technique for verifying systems developed in this framework.

Design of multiunit electronic exchanges through decomposition

In this paper, we exploit the idea of decomposition to match buyers and sellers in an electronic exchange for trading large volumes of homogeneous goods, where the buyers and sellers specify marginal-decreasing piecewise constant price curves to capture volume discounts. Such exchanges are relevant for automated trading in many e-business applications. The problem of determining winners and Vickrey prices in such exchanges is known to have a worst-case complexity equal to that of as many as (1 + m + n) NP-hard problems, where m is the number of buyers and n is the number of sellers. Our method proposes the overall exchange problem to be solved as two separate and simpler problems: 1) forward auction and 2) reverse auction, which turns out to be generalized knapsack problems. In the proposed approach, we first determine the quantity of units to be traded between the sellers and the buyers using fast heuristics developed by us. Next, we solve a forward auction and a reverse auction using fully polynomial time approximation schemes available in the literature. The proposed approach has worst-case polynomial time complexity. and our experimentation shows that the approach produces good quality solutions to the problem. Note to Practitioners- In recent times, electronic marketplaces have provided an efficient way for businesses and consumers to trade goods and services. The use of innovative mechanisms and algorithms has made it possible to improve the efficiency of electronic marketplaces by enabling optimization of revenues for the marketplace and of utilities for the buyers and sellers. In this paper, we look at single-item, multiunit electronic exchanges. These are electronic marketplaces where buyers submit bids and sellers ask for multiple units of a single item. We allow buyers and sellers to specify volume discounts using suitable functions. Such exchanges are relevant for high-volume business-to-business trading of standard products, such as silicon wafers, very large-scale integrated chips, desktops, telecommunications equipment, commoditized goods, etc. The problem of determining winners and prices in such exchanges is known to involve solving many NP-hard problems. Our paper exploits the familiar idea of decomposition, uses certain algorithms from the literature, and develops two fast heuristics to solve the problem in a near optimal way in worst-case polynomial time.

Early and late electrophysiological effects of distractor frequency in picture naming: Reconciling input and output accounts

The “distractor-frequency effect” refers to the finding that high-frequency (HF) distractor words slow picture naming less than low-frequency distractors in the picture–word interference paradigm. Rival input and output accounts of this effect have been proposed. The former attributes the effect to attentional selection mechanisms operating during distractor recognition, whereas the latter attributes it to monitoring/decision mechanisms operating on distractor and target responses in an articulatory buffer. Using high-density (128-channel) EEG, we tested hypotheses from these rival accounts. In addition to conducting stimulus- and response-locked whole-brain corrected analyses, we investigated the correct-related negativity, an ERP observed on correct trials at fronto-central electrodes proposed to reflect the involvement of domain general monitoring. The wholebrain ERP analysis revealed a significant effect of distractor frequency at inferior right frontal and temporal sites between 100 and 300-msec post-stimulus onset, during which lexical access is thought to occur. Response-locked, region of interest (ROI) analyses of fronto-central electrodes revealed a correct-related negativity starting 121 msec before and peaking 125 msec after vocal onset on the grand averages. Slope analysis of this component revealed a significant difference between HF and lowfrequency distractor words, with the former associated with a steeper slope on the time windowspanning from100 msec before to 100 msec after vocal onset. The finding of ERP effects in time windows and components corresponding to both lexical processing and monitoring suggests the distractor frequency effect is most likely associated with more than one physiological mechanism.

Genetic programming metamodel for rotating beams

This paper investigates the use of Genetic Programming (GP) to create an approximate model for the non-linear relationship between flexural stiffness, length, mass per unit length and rotation speed associated with rotating beams and their natural frequencies. GP, a relatively new form of artificial intelligence, is derived from the Darwinian concept of evolution and genetics and it creates computer programs to solve problems by manipulating their tree structures. GP predicts the size and structural complexity of the empirical model by minimizing the mean square error at the specified points of input-output relationship dataset. This dataset is generated using a finite element model. The validity of the GP-generated model is tested by comparing the natural frequencies at training and at additional input data points. It is found that by using a non-dimensional stiffness, it is possible to get simple and accurate function approximation for the natural frequency. This function approximation model is then used to study the relationships between natural frequency and various influencing parameters for uniform and tapered beams. The relations obtained with GP model agree well with FEM results and can be used for preliminary design and structural optimization studies.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.