Machine Partitioning Research Articles

Prediction of Drug-Induced Liver Injury: From Molecular Physicochemical Properties and Scaffold Architectures to Machine Learning Approaches.

The process of developing new drugs is widely acknowledged as being time-intensive and requiring substantial financial investment. Despite ongoing efforts to reduce time and expenses in drug development, ensuring medication safety remains an urgent problem. One of the major problems involved in drug development is hepatotoxicity, specifically known as drug-induced liver injury (DILI). The popularity of new drugs often poses a significant barrier during development and frequently leads to their recall after launch. In silico methods have many advantages compared with traditional invivo and invitro assays. To establish a more precise and reliable prediction model, it is necessary to utilize an extensive and high-quality database consisting of information on drug molecule properties and structural patterns. In addition, we should also carefully select appropriate molecular descriptors that can be used to accurately depict compound characteristics. The aim of this study was to conduct a comprehensive investigation into the prediction of DILI. First, we conducted a comparative analysis of the physicochemical properties of extensively well-prepared DILI-positive and DILI-negative compounds. Then, we used classic substructure dissection methods to identify structural pattern differences between these two different types of chemical molecules. These findings indicate that it is not feasible to establish property or substructure-based rules for distinguishing between DILI-positive and DILI-negative compounds. Finally, we developed quantitative classification models for predicting DILI using the naïve Bayes classifier (NBC) and recursive partitioning (RP) machine learning techniques. The optimal DILI prediction model was obtained using NBC, which combines 21 physicochemical properties, the VolSurf descriptors and the LCFP_10 fingerprint set. This model achieved a global accuracy (GA) of 0.855 and an area under the curve (AUC) of 0.704 for the training set, while the corresponding values were 0.619 and 0.674 for the test set, respectively. Moreover, indicative substructural fragments favorable or unfavorable for DILI were identified from the best naïve Bayesian classification model. These findings may help prioritize lead compounds in the early stage of drug development pipelines.

Development of novel ALOX15 inhibitors combining dual machine learning filtering and fragment substitution optimisation approaches, molecular docking and dynamic simulation methods

The oxidation of unsaturated lipids, facilitated by the enzyme Arachidonic acid 15-lipoxygenase (ALOX15), is an essential element in the development of ferroptosis. This study combined a dual-score exclusion strategy with high-throughput virtual screening, naive Bayesian and recursive partitioning machine learning models, the already established ALOX15 inhibitor i472, and a docking-based fragment substitution optimisation approach to identify potential ALOX15 inhibitors, ultimately leading to the discovery of three FDA-approved drugs that demonstrate optimal inhibitory potential against ALOX15. Through fragment substitution-based optimisation, seven new inhibitor structures have been developed. To evaluate their practicality, ADMET predictions and molecular dynamics simulations were performed. In conclusion, the compounds found in this study provide a novel approach to combat conditions related to ferroptosis-related injury by inhibiting ALOX15.

State Minimization of General Finite Fuzzy Automata

The minimization of automaton is important to reduce space and computational time. Reduction in number of states and transitions leads to equivalent automaton with less number of states and transitions. In this paper, state minimization of General Finite Fuzzy Automata (GFFA) is discussed. To obtain minimal equivalent GFFA we have removed redundant states and transitions using substitution property (SP) partition and quotient machine. The algorithm to find membership values of states of the GFFA is described and algorithm to associate states with quotient machine to obtain minimal machine with less number of states is discussed.

Salvage High-Dose-Rate Brachytherapy for Recurrent Prostate Cancer After Definitive Radiation

PurposeSalvage high-dose-rate brachytherapy (sHDRBT) for locally recurrent prostate cancer after definitive radiation is associated with biochemical control in approximately half of patients at 3 to 5 years. Given potential toxicity, patient selection is critical. We present our institutional experience with sHDRBT and validate a recursive partitioning machines model for biochemical control. Materials and MethodsWe performed a retrospective analysis of 129 patients who underwent whole-gland sHDRBT between 1998 and 2016. We evaluated clinical factors associated with biochemical control as well as toxicity. ResultsAt diagnosis the median prostate-specific antigen (PSA) was 7.77 ng/mL. A majority of patients had T1-2 (73%) and Gleason 6-7 (82%) disease; 71% received external beam radiation therapy (RT) alone, and 22% received permanent prostate implants. The median disease-free interval (DFI) was 56 months, and median presalvage PSA was 4.95 ng/mL. At sHDRBT, 46% had T3 disease and 51% had Gleason 8 to 10 disease.At a median of 68 months after sHDRBT, 3- and 5-year disease-free survival were 85% (95% CI, 79-91) and 71% (95% CI, 62-79), respectively. Median PSA nadir was 0.18 ng/mL, achieved a median of 10 months after sHDRBT. Patients with ≥35%+ cores and a DFI <4.1 years had worse biochemical control (19% vs 50%, P = .02). Local failure (with or without regional/distant failure) was seen in 11% of patients (14/129), and 14 patients (11%) developed acute urinary obstruction requiring Foley placement and 19 patients (15%) developed strictures requiring dilation. ConclusionssHDRBT is a reasonable option for patients with locally recurrent prostate cancer after definitive RT. Those with <35%+ cores or an initial DFI of ≥4.1 years may be more likely to achieve long-term disease control after sHDRBT.

PARTITIONING SIGNAL CLASSES USING TRANSPORT TRANSFORMS FOR DATA ANALYSIS AND MACHINE LEARNING.

A relatively new set of transport-based transforms (CDT, R-CDT, LOT) have shown their strength and great potential in various image and data processing tasks such as parametric signal estimation, classification, cancer detection among many others. It is hence worthwhile to elucidate some of the mathematical properties that explain the successes of these transforms when they are used as tools in data analysis, signal processing or data classification. In particular, we give conditions under which classes of signals that are created by algebraic generative models are transformed into convex sets by the transport transforms. Such convexification of the classes simplify the classification and other data analysis and processing problems when viewed in the transform domain. More specifically, we study the extent and limitation of the convexification ability of these transforms under an algebraic generative modeling framework. We hope that this paper will serve as an introduction to these transforms and will encourage mathematicians and other researchers to further explore the theoretical underpinnings and algorithmic tools that will help understand the successes of these transforms and lay the groundwork for further successful applications.

Pengembangan Media Pembelajaran Mesin Menggunakan Teknologi Augmented Reality

Learning media are all things that can be used to channel messages and stimulate the learning process for students. Learning machine of two-wheeled vehicles in SMK YPT 2 Purbalingga is still using Ms. Powerpoint as a learning medium. In one of the subjects that discussed the partitioning machine parts, how to work a motorcycle engine and troubleshooting the machine parts as well as how to repair or replace spare parts, students felt the use of Ms. media. Powerpoint is less interesting and difficult to understand the material. Educators also see students tend to get bored easily when learning so that they need other alternatives as a learning medium. For that reason, the writer applies a markerless augmented reality technology as a medium for learning two-wheeled vehicles to help as well as learning media for students, the method used is MDLC (Multimedia Development Life Cycle). This research produces an augmented reality application that helps visualize motorcycle engines in the form of 3D models for vocational students.

Salvage High Dose Rate Brachytherapy for Recurrent Prostate Cancer after Definitive Radiation

PURPOSE Salvage high-dose-rate brachytherapy (sHDRBT) for locally recurrent prostate cancer after definitive radiation is associated with biochemical control in approximately half of patients at 3-5 years. Given potential toxicity, patient selection is critical. We present our institutional experience with sHDRBT and validate a recursive partitioning machines model for biochemical control. MATERIALS AND METHODS We performed a retrospective analysis of 129 patients who underwent whole-gland sHDRBT between 1998-2016. We evaluated clinical factors associated with biochemical control as well as toxicity. RESULTS At diagnosis the median PSA was 7.77 ng/mL. Majority of patients had T1-2 (73%) and Gleason 6-7 (82%) disease. 71% received external beam RT alone, while 22% received permanent prostate implants. The median disease free interval (DFI) was 56 months, and median pre-salvage PSA was 4.95ng/mL. At sHDRBT, 46% had T3 disease and 51% had Gleason 8-10 disease. At a median of 68 months following sHDRBT, 3 and 5-year disease free survival were 85% (95% CI 79-91%) and 71% (95% CI 62-79%), respectively. Median PSA nadir was 0.18 ng/mL, achieved a median of 10 months after sHDRBT. Patients with ≥35%+ cores and a DFI <4.1 years had worse biochemical control (19% vs. 50%, p = 0.02). Local failure (with or without regional/distant failure) was seen in 11% of patients (14/129). 14 patients (11%) developed acute urinary obstruction requiring Foley placement and 19 patients (15%) developed strictures requiring dilation. CONCLUSIONS sHDRBT is a reasonable option for patients with locally recurrent prostate cancer following definitive RT. Those with <35%+ cores or an initial DFI of ≥4.1 years may be more likely to achieve long-term disease control following sHDRBT.

Enhancing indoor positioning based on filter partitioning cascade machine learning models

This paper proposes a method, called the filter partitioning machine learning classification (FPMLC). It can enhance the accuracy of indoor positioning based on fingerprinting using machine learning algorithms and prominent access points (APs). FPMLC selects limited information from groups of signal strengths and combines a clustering task and a classification task. There are three processes in FPMLC, i.e., feature selection to choose prominent APs, clustering to determine approximated positions, and classification to determine fine positions. This work demonstrates the procedure for FPMLC creation. The results of FPMLC are compared with those of a primitive method using measured data. FPMLC is compared with well-known machine learning classifiers, i.e., Decision Tree, Naive Bayes, and Artificial Neural Networks. The performance comparison is done in terms of accuracy and error distance between classified positions and actual positions. The appropriate number of selected prominent APs and the number of clusters are assigned in the clustering process. The result of this study shows that FPMLC can increase performance for indoor positioning of all classifiers. Additionally, FPMLC is the most optimized model using a Decision Tree as its classifier.

A PSO-based approach to cell formation problems with alternative process routings

Group technology (GT) has been extensively applied to cellular manufacturing system (CMS) design for decades due to many benefits such as decreased number of part movements among cells and increased machine utilisation in cells. This paper considers cell formation problems with alternative process routings and proposes a discrete particle swarm optimisation (PSO) approach to minimise the number of exceptional parts outside machine cells. The approach contains two main steps: machine partition and part-routing assignment. Through inheritance and random search, the proposed algorithm can effectively partition machines into different cells with consideration of multiple part process routings. The computational results are compared with those obtained by using simulated annealing (SA)-based and tabu search (TS)-based algorithms. Experimental results demonstrate that the proposed algorithm can find equal or fewer exceptional elements than existing algorithms for most of the test problems selected from the literature. Moreover, the proposed algorithm is further tailed to incorporate various production factors in order to extend its applicability. Four sample cases are tested and the results suggest that the algorithm is capable of solving more practical cell formation problems.

Chromosome segregation in Archaea mediated by a hybrid DNA partition machine

Eukarya and, more recently, some bacteria have been shown to rely on a cytoskeleton-based apparatus to drive chromosome segregation. In contrast, the factors and mechanisms underpinning this fundamental process are underexplored in archaea, the third domain of life. Here we establish that the archaeon Sulfolobus solfataricus harbors a hybrid segrosome consisting of two interacting proteins, SegA and SegB, that play a key role in genome segregation in this organism. SegA is an ortholog of bacterial, Walker-type ParA proteins, whereas SegB is an archaea-specific factor lacking sequence identity to either eukaryotic or bacterial proteins, but sharing homology with a cluster of uncharacterized factors conserved in both crenarchaea and euryarchaea, the two major archaeal sub-phyla. We show that SegA is an ATPase that polymerizes in vitro and that SegB is a site-specific DNA-binding protein contacting palindromic sequences located upstream of the segAB cassette. SegB interacts with SegA in the presence of nucleotides and dramatically affects its polymerization dynamics. Our data demonstrate that SegB strongly stimulates SegA polymerization, possibly by promoting SegA nucleation and accelerating polymer growth. Increased expression levels of segAB resulted in severe growth and chromosome segregation defects, including formation of anucleate cells, compact nucleoids confined to one half of the cell compartment and fragmented nucleoids. The overall picture emerging from our findings indicates that the SegAB complex fulfills a crucial function in chromosome segregation and is the prototype of a DNA partition machine widespread across archaea.

PBSVM: Partitioning and biased support vector machine for vocal fold pathology assessment using labeled and unlabeled data sets

Most of the existing classification methods, used for voice pathology assessment, are built based on labeled pathological and normal voice signals. This paper studies the problem of building a classifier using labeled and unlabeled data. We propose a novel learning technique, called Partitioning and Biased Support Vector Machine Classification (PBSVM), which tries to utilize all the available data in two steps: (1) a new heuristically partition-based algorithm, which extracts high quality pathological and normal samples from an unlabeled set, and (2) a more principle approach based on biased formulation of support vector machine, which is fairly robust to mislabeling and unbalance data problem. Experiments with wavelet-based energy features extracted from sustained vowels show that the new recognition scheme is highly feasible and significantly outperform the baseline classical SVM classifier, especially in the situation where the labeled training data is small.

Partitioning machines in tandem AGV systems based on “balanced flow strategy” by simulated annealing

A tandem automated guided vehicle (AGV) system deals with grouping workstations into some non-overlapping areas and assigning to each area exactly one AGV. This paper presents a new non-linear integer mathematical model to group n machines into N loops to minimize both inter-loop and intra-loop flow simultaneously based on balanced-loops strategy and inter-machine flows taken from ideas of cellular manufacturing systems. Due to computational difficulties of exact methods in solving the proposed model, a simulated annealing (SA) algorithm is proposed. A number of test problems are generated at random and solved by the proposed SA in order to show the efficiency of the algorithm. Finally, the results are reported by both the Lingo software and the proposed SA algorithm.

Bacterial Mitosis: Actin in a New Role at the Origin

MreB is a prokaryotic homolog of actin involved in cellular organization and chromosome segregation. Recent results suggest that MreB is part of a kinetochore-like complex that specifically segregates the replication origin region of the bacterial chromosome.

Diagnostic usefulness of PCR profiling of the differentially expressed marker genes in thyroid papillary carcinomas

The study was set out to determine whether characteristic changes in the gene expression profile in papillary thyroid carcinoma (PTC) discovered by microarray assays can be used for conventional molecular diagnosis. Expression levels of five reported to be overexpressed and three underexpressed genes were examined in PTC and normal human tissues by real-time PCR and semi-quantitative duplex PCR. Stepwise logistic regression analysis, duplex PCR data evaluation with recursive partition machine algorithm and hierarchical cluster analysis identified SFTPB (upregulated) and TFF3 (downregulated) gene combination as most favorable for differential molecular diagnosis of PTC. Sensitivity, specificity and accuracy obtained in a series of histologically characterized thyroid tumor and normal tissue samples were 88.9, 96.7 and 94.9%, respectively. Applicability of the method to fine needle aspiration biopsy (FNAB) samples was demonstrated using a collection of needle washouts. In spite individual thyroid tumor and normal tissues as well as FNAB samples displayed a substantial degree of variability in the expression levels of analyzed genes, simultaneous molecular analysis of a panel of optimal markers allows making a high probability predictive estimate and may be considered as an informative method of preoperative PTC diagnosis.

Hierarchically accelerated dynamic programming for finite-state machines

A procedure called hierarchically accelerated dynamic programming (HADP) is presented which, at the cost of a degree of suboptimality, can significantly accelerate dynamic programming algorithms for discrete event systems modeled by finite-state machines (FSMs). The methodology is based. upon the (possibly iterated) dynamical abstraction of a given FSM by state aggregation in order to generate a so-called partition machine hierarchy. Necessary and sufficient conditions for the HADP procedure to generate globally optimal solutions are given as well as bounds on the degree of suboptimality of the method. A group of examples called the Broken Manhattan Grid problems is used to illustrate an implementation of HADP with two and three level hierarchies. A set of open problems is described concerning the construction and selection of the partition machine abstractions and the improvement of the estimation of HADP suboptimality.

Scheduling in Robotic Cells: Heuristics and Cell Design

This paper considers scheduling problems arising in robot-served manufacturing cells in which the machines are configured in a flowshop that repetitively produces a family of similar parts. We study the problem of determining the robot move cycle and the part sequence that jointly minimize the average steady-state cycle time required for the repetitive production of a minimal part set, or equivalently maximize the long-run throughput rate. Three earlier related papers provide algorithms, or proofs of intractability, for a variety of cell configurations. We use the intuition developed there to design and test simple heuristic procedures for the part sequencing problem under different robot move cycles in three-machine cells where that problem is intractable. This enables us to develop a heuristic procedure for a general three-machine cell. A methodology for extending this heuristic to four-machine cells is described and tested. Ideas for extension to even larger cells are also discussed. We also describe and test two heuristics for a cell design problem that involves partitioning machines into cells as well as determining the sequence of robot moves and parts. Finally, we provide a list of open research problems.

Hierarchical hybrid systems: Geometry, controllability and applications to air traffic control *

In this paper the notions of finite analytic partition, dynamical consistency, and partition machines (originally developed in (Wei, 1995; Caines and Wei, 1998) for dynamical systems on differentiate manifolds) is generalized to hybrid systems. The theory is illustrated via a highly simplified air traffic management example which employs new conditions for the global controllability of nonlinear systems (Caines and Lemon, 1998; Lemch and Caines, 1998).

Hierarchically accelerated dynamic programming with applications to transportation networks

In this paper, we propose a scheme called Hierarchically Accelerated Dynamic Programming (HADP) which gives significant speed up of Dynamic Programming (up to several orders of magnitude) for Discrete Event Systems (DES) modelled by finite state machines: this is at the cost of a degree of sub-optimality. This methodology is based upon a dynamical abstraction of the given DES by state aggregation which generates a so-called partition machine (Caines and Wei, 1995, 1998). We discuss necessary and sufficient conditions for the HADP procedure to generate global optimal solutions and. further, give bounds on the degree of sub-optimality which can occur. Finally, we use an example called the Broken Manhattan Grid to illustrate our software implementation of HADP with a three level hierarchy.

On the existence of hybrid models for finite state machines

Within the framework of the hierarchical hybrid control theory presented in (Caines and Wei, IEEE Trans. Automat. Control 43 (4) (1988) 501–508), one may pose the following hybrid modelling problem: for a given finite state machine M, find a state space D, a state space partition π, and a controlled ODE system S on D such that the associated hierarchical hybrid partition machine M π is a hybrid in-block controllable machine which is isomorphic to M. In this paper a constructive solution is given to this problem for all finite machines M.

Hierarchical hybrid control systems: a lattice theoretic formulation

The notion of dynamical consistency is extended to hybrid systems so as to define the set of dynamically consistent hybrid partition machines associated with a continuous system S. Following the formulation of the notions of between-block and in-block controllable hybrid partition machines, the lattice HIBC(S) of hybrid in-block controllable partition machines is defined and investigated. Based on these constructions, the definition and properties of hierarchical-hybrid control systems are presented together with an example.