Small Programming Research Articles

Scaffold Hopping and Screening for Potent Small Molecule Agonists for GRP94: Implications to Alleviate ER Stress-Associated Pathogenesis.

Disparity in the activity of Endoplasmic reticulum (ER) leads to degenerative diseases, mainly associated with protein misfolding and aggregation leading to cellular dysfunction and damage, ultimately contributing to ER stress. ER stress activates the complex network of Unfolded Protein Response (UPR) signaling pathways mediated by transmembrane proteins IRE1, ATF6, and PERK. In addition to UPR, many ER chaperones have evolved to optimize the output of properly foldedsecretory and membrane proteins. Glucose-regulated protein 94 (GRP94), an ER chaperone of heat shock protein HSP90 family, directs protein folding through interaction with other components of the ER protein folding machinery and assists in ER-associated degradation (ERAD). Activation of GRP94 would increase the efficacy of protein folding machinery and regulate the UPR pathway toward homeostasis. The present study aims to screen for novel agonists for GRP94 based on Core hopping, pharmacophore hypothesis, 3D-QSAR, and virtual screening with small-molecule compound libraries in order to improve the efficiency of native protein folding by enhancing GRP94 chaperone activity, therefore to reduce protein misfolding and aggregation. In this study, we have employed the strategy of small molecule-dependent ER programming to enhance the chaperone activity of GRP94 through scaffold hopping-based screening approach to identify specific GRP94 agonists. New scaffolds generated by altering the cores of NECA, the known GRP94 agonist, were validated by employing pharmacophore hypothesis testing, 3D-QSAR modeling, and molecular dynamics simulations. This facilitated the identification of small molecules to improve the efficiency of native protein folding by enhancing GRP94 activity. High-throughput virtual screening of the selected pharmacophore hypothesis against Selleckchem and ZINC databases retrieved a total of 2,27,081 compounds. Further analysis on docking and ADMET properties revealed Epimedin A, Narcissoside, Eriocitrin 1,2,3,4,6-O-Pentagalloylglucose, Secoisolariciresinol diglucoside, ZINC92952357, ZINC67650204, and ZINC72457930 as potential lead molecules. The stability and interaction of these small molecules were far better than the known agonist, NECA indicating their efficacy in selectively alleviating ER stress-associated pathogenesis. These results substantiate the fact that small molecule-dependent ER reprogramming would activate the ER chaperones and therefore reduce the protein misfolding as well as aggregation associated with ER stress in order to restore cellular homeostasis.

Scheduling Advertising on Cable Television

Scheduling Advertising on Cable Television Advertisement scheduling is a daily essential operational process in the television business. Efficient distribution of viewers among advertisers allows the television network to satisfy contracts and increase ad sale revenues. Ad scheduling is a challenging multiperiod, mixed-integer programming problem in which the network must create schedules to meet advertisers’ campaign goals and maximize ad revenues. Each campaign must meet a specific target group of viewers and a unique set of constraints. Moreover, the number of viewers is uncertain. To solve this problem, S. Souyris, S. Seshadri, and S. Subramanian develop and implement a practical approach that combines mathematical programming and machine learning to create daily schedules. According to standard business metrics and the small integer programming gap, these schedules are of high quality. Using their methods, leading networks in the United States and India experience a 3% to 5% revenue increase, which translates to about $60 million annually for one prominent user.

Transient stability assessment in bulk power grids using sequential minimal optimization based support vector machine with pinball loss

• A support vector machine with pinball loss is developed for TSA. • Quantile parameter is conducted to improve the robustness of TSA. • Sequential minimal optimization is adopted to accelerate the computational efficiency of TSA. Recent studies show that support vector machine is a promising approach for predicting the transient stability of power systems. However, most of the existing support vector machine methods suffer from excessive training time and low robustness, resulting in inefficiency for implementation. In this paper, a novel TSA method using sequential minimal optimization based support vector machine with pinball loss is developed. In the proposed approach, the system operation indices and projected energy function indices are selected to form the initial TSA feature set. Then, a quantile parameter is introduced to reduce the impact of measurement error by controlling the positions of the boundary samples between stable and unstable classes. Finally, a sequential minimal optimization is applied to decompose the large quadratic programming problem into a series of small quadratic programming problems to accelerate the computational efficiency. The cross-validation and grid search methods are introduced to improve the classification stability and accuracy. The IEEE 50-machine test system and the China Southern Power Grid are used to evaluate the performance of the proposed TSA method. Simulation results demonstrate that the proposed TSA method has high accuracy, robustness, and computational efficiency in bulk power grids.

Research progress on the role of ferroptosis in cardiovascular disease.

The cardiovascular disease pathogenesis is extremely complex and seriously threatens human health. Cardiomyocyte death plays a significant role in cardiovascular disease occurrence and development. In addition to the previously revealed modes of cell death (apoptosis, autophagy, and pyroptosis), ferroptosis is highly related to the development of cardiovascular diseases, including arrhythmia, atherosclerosis, and myocardial ischemia/reperfusion. Ferroptosis is a novel cell death pathway driven by lipid peroxidation and iron overload. Lipid, amino acid, and iron metabolism regulate the ferroptosis pathway. Small molecule compounds (iron chelators, antioxidants, and ferroptosis inhibitors) and genetic programming can alleviate or prevent cardiovascular disease by inhibiting the ferroptosis pathway. Ferroptosis plays a key role in various cardiovascular disease occurrence and development, and inhibiting ferroptosis in cardiomyocytes is expected to become a feasible treatment method. In this mini-review, we systematically summarize the molecular mechanisms of ferroptosis in different cardiovascular diseases, delineate the regulatory network between ferroptosis and cardiovascular diseases, and highlight its potential therapeutic targets.

A New Approach to the Splitting Factor Preconditioner Applied to Linear Programming Problems

In this paper, we present the results of a new approach to the splitting factor preconditioner, which is a preconditioner based on the Incomplete Cholesky factorization and the splitting preconditioner. In previous work for small linear programming problems, the preconditioner was applied in all iterations of the interior point method and compared with the splitting preconditioner also applied in all iterations. In this paper, we will do a hybrid approach, in which in the first iterations the preconditioner is the Incomplete Cholesky Factorization, and in the last iterations, the preconditioner used is the splitting factor preconditioner or the splitting preconditioner. The results obtained show that even in the hybrid approach, the splitting factor preconditioner achieved better performance.

Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application

The main purpose of twin support vector regression (TSVR) is to find linear or nonlinear relationships in sample data, and then predict future data. TSVR is the decomposition of a large convex quadratic programming problem into two small convex quadratic programming problems. Therefore, TSVR not only has the advantages of fast computation and low computational complexity, but also has better regression performance. Classic SVR, TSVR is assuming that accords with mean zero, variance of noise with the variance of the gaussian distribution or not to consider the effects of noise, but in some practical applications, such as wind speed forecasting, noise characteristic is more in line with mean zero, variance for heteroscedasticity of gaussian distribution, therefore, the return of the existing technology is not the best. In this paper, the characteristics of heteroscedasticity Gaussian noise are introduced into the model TSVR, and the twin support vector regression model based on heteroscedasticity Gaussian noise (TSVR-HGN) is constructed. The Lagrange multiplier method is used to solve the problem, and the optimization algorithm is used to find the global optimization. The artificial data set, UCI data set and wind speed data set were selected for experimental comparison. The experimental results show that TSVR-HGN has better prediction accuracy.

Specifying and testing GPU workgroup progress models

As GPU availability has increased and programming support has matured, a wider variety of applications are being ported to these platforms. Many parallel applications contain fine-grained synchronization idioms; as such, their correct execution depends on a degree of relative forward progress between threads (or thread groups). Unfortunately, many GPU programming specifications (e.g. Vulkan and Metal) say almost nothing about relative forward progress guarantees between workgroups. Although prior work has proposed a spectrum of plausible progress models for GPUs, cross-vendor specifications have yet to commit to any model. This work is a collection of tools and experimental data to aid specification designers when considering forward progress guarantees in programming frameworks. As a foundation, we formalize a small parallel programming language that captures the essence of fine-grained synchronization. We then provide a means of formally specifying a progress model, and develop a termination oracle that decides whether a given program is guaranteed to eventually terminate with respect to a given progress model. Next, we formalize a set of constraints that describe concurrent programs that require forward progress to terminate. This allows us to synthesize a large set of 483 progress litmus tests. Combined with the termination oracle, we can determine the expected status of each litmus test -- i.e. whether it is guaranteed to eventually terminate -- under various progress models. We present a large experimental campaign running the litmus tests across 8 GPUs from 5 different vendors. Our results highlight that GPUs have significantly different termination behaviors under our test suite. Most notably, we find that Apple and ARM GPUs do not support the linear occupancy-bound model, as was hypothesized by prior work.

An expected-cost realization-probability optimization approach for the dynamic energy management of microgrid

Dynamic energy management (DEM) is being recognized as an important tool for the economical and efficient operation of microgrid. This paper proposes an expected-cost realization-probability (ECRP) optimization approach for the DEM of microgrid. In the model, the expected cost is taken as an economic goal to find the schedule with the maximum ECRP (i.e., the maximum probability of obtaining an actual operation cost which is lower than this economic goal). It is a stochastic mixed-integer nonlinear programming (MINLP) problem composed of the AC power flow, battery operation characters and logical variables. To solve this NP-hard problem, an ECRP-based approximate dynamic programming is proposed. The original MINLP problem is decomposed into small nonlinear programming subproblems to facilitate the solution. And the ECRP-based value function is formed to describe the relationship among the decisions of subproblems, which helps to obtain the near-optimal decision in a decomposed manner. Simulation examples presented on a real microgrid indicate that the ECRP of the proposed method is higher than those of the other two comparison algorithms.

Double-hyperplane fuzzy classifier design for tendency prediction of silicon content in molten iron

It is very difficult to control a blast furnace efficiently because of the characteristics of the closed smelting and the system's large delay. It is very helpful for blast furnace control to establish a model that can accurately predict the tendency of silicon content in molten iron. In this article, a nonparallel hyperplane–based fuzzy classifier is proposed reconciling the model accuracy with the model interpretability. Each hyperplane is as close as possible to one class of samples and as far as possible from the other class of samples. This leads to two small quadratic programming problems instead of a large one as a support vector machine–based fuzzy classifier. This makes our classifier faster than a support vector machine–based fuzzy classifier. We evaluate the proposed method on blast furnace data and synthetic data, which verifies the effectiveness.

Application of Seq2Seq Models on Code Correction.

We apply various seq2seq models on programming language correction tasks on Juliet Test Suite for C/C++ and Java of Software Assurance Reference Datasets and achieve 75% (for C/C++) and 56% (for Java) repair rates on these tasks. We introduce pyramid encoder in these seq2seq models, which significantly increases the computational efficiency and memory efficiency, while achieving similar repair rate to their nonpyramid counterparts. We successfully carry out error type classification task on ITC benchmark examples (with only 685 code instances) using transfer learning with models pretrained on Juliet Test Suite, pointing out a novel way of processing small programming language datasets.

Smooth twin bounded support vector machine with pinball loss

The twin support vector machine improves the classification performance of the support vector machine by solving two small quadratic programming problems. However, this method has the following defects: (1) For the twin support vector machine and some of its variants, the constructed models use a hinge loss function, which is sensitive to noise and unstable in resampling. (2) The models need to be converted from the original space to the dual space, and their time complexity is high. To further enhance the performance of the twin support vector machine, the pinball loss function is introduced into the twin bounded support vector machine, and the problem of the pinball loss function not being differentiable at zero is solved by constructing a smooth approximation function. Based on this, a smooth twin bounded support vector machine model with pinball loss is obtained. The model is solved iteratively in the original space using the Newton-Armijo method. A smooth twin bounded support vector machine algorithm with pinball loss is proposed, and theoretically the convergence of the iterative algorithm is proven. In the experiments, the proposed algorithm is validated on the UCI datasets and the artificial datasets. Furthermore, the performance of the presented algorithm is compared with those of other representative algorithms, thereby demonstrating the effectiveness of the proposed algorithm.

Two-step generation of mesenchymal stem/stromal cells from human pluripotent stem cells with reinforced efficacy upon osteoarthritis rabbits by HA hydrogel

BackgroundCurrent studies have enlightened the rosy prospects of human pluripotent stem cell (hPSC)-derived mesenchymal stem/stromal cells (MSCs) in regenerative medicine. However, systematic investigation of their signatures and applications with alternative biomaterials in osteoarthritis (OA) remains indistinct.MethodsHerein, we initially took advantage of a small molecule library-mediated programming strategy for hPSC-MSC induction. Then, with the aid of multifaceted analyses such as flow cytometry (FCM), chromosome karyocyte and cell vitality, wound healing and microtubule formation assay and coculturing with T lymphocytes, we systematically evaluated the characterizations of signatures in vitro and the in vivo efficacy of hPSC-MSCs and HA hydrogel composite on rabbit osteoarthritis model.ResultsWe found the combination of LLY-507 and AZD5153 was sufficient for high-efficiency CD73+CD90+CD105+CD31−CD34−CD45−HLA-DR− MSC induction from both hESCs and hiPSCs with stemness (POU5F1/SOX2/NANOG). The programmed hPSC-MSCs revealed conservative transcriptome variations and went through a heterogeneous intermediate-stage with mesenchymal-associated gene expression (NT5E, ENG, VIM and FN1) as well as displayed typical cytomorphology, immunophenotypes and normal karyotyping, multilineage differentiation potential, favorable cell vitality, proangiogenic and immunoregulatory properties in vitro. Meanwhile, the cell population exhibited preferable restorative and ameliorative function on OA rabbits with HA hydrogel in vivo.ConclusionsCollectively, we established a rapid and convenient procedure for hPSC-MSC generation without redundant manipulations. The fundamental and clinical studies upon osteoarthritis (OA) treatment would benefit tremendously from the combination of the inexhaustible hPSC-MSCs and advantageous biomaterials.

MBSVR: Multiple birth support vector regression

Although the learning speed in twin support vector regression (TWSVR) is four times that in support vector regression (SVR), the computing time and fitting precision of TWSVR are limited. This paper develops multiple birth support vector regression (MBSVR), motivated by the multiple birth support vector machine (MBSVM) formulation. MBSVR constructs the final regressor from K hyperplanes, each of which is obtained by solving a small quadratic programming problem (QPP) with the associated constraints, in which all points in each of the corresponding class should be as far away as possible from its corresponding hyperplane. Since MBSVM can be seen as an extension of the twin support vector machine (TWSVM) and its computing time is less than that of TWSVM, the proposed MBSVR is also faster than TWSVR, especially when the number of classes K is large. To verify the performance of the proposed MBSVR, it is compared with TWSVR, TSVR (another form of twin support vector regression) and SVR on several synthetic datasets and UCI datasets.

Development of regional simulation of seismic ground‐motion and induced liquefaction enhanced by GPU computing

AbstractMuch research has been conducted for physics‐based ground‐motion simulation to reproduce seismic response of soil and structures precisely and to mitigate damages caused by earthquakes. We aimed at enabling physics‐based ground‐motion simulations of complex three‐dimensional (3D) models with multiple materials, such as a digital twin (high‐fidelity 3D model of the physical world that is constructed in cyberspace). To perform one case of such simulation requires high computational cost and it is necessary to perform a number of simulations for the estimation of parameters or consideration of the uncertainty of underground soil structure data. To overcome this problem, we proposed a fast simulation method using graphics processing unit computing that enables a simulation with small computational resources. We developed a finite‐element‐based method for large‐scale 3D seismic response analysis with small programming effort and high maintainability by using OpenACC, a directive‐based parallel programming model. A lower precision variable format was introduced to achieve further speeding up of the simulation. For an example usage of the developed method, we applied the developed method to soil liquefaction analysis and conducted two sets of simulations that compared the effect of countermeasures against soil liquefaction: grid‐form ground improvement to strengthen the earthquake resistance of existing houses and replacement of liquefiable backfill soil of river wharves for seismic reinforcement of the wharf structure. The developed method accelerates the simulation and enables us to quantitatively estimate the effect of countermeasures using the high‐fidelity 3D soil‐structure models on a small cluster of computers.

A simple differentiable programming language

Automatic differentiation plays a prominent role in scientific computing and in modern machine learning, often in the context of powerful programming systems. The relation of the various embodiments of automatic differentiation to the mathematical notion of derivative is not always entirely clear---discrepancies can arise, sometimes inadvertently. In order to study automatic differentiation in such programming contexts, we define a small but expressive programming language that includes a construct for reverse-mode differentiation. We give operational and denotational semantics for this language. The operational semantics employs popular implementation techniques, while the denotational semantics employs notions of differentiation familiar from real analysis. We establish that these semantics coincide.

EGOFET Gated by a Molecular Electronic Switch: A Single‐Device Memory Cell

AbstractElectrolyte‐gated organic field‐effect transistors (EGOFETs) exploit the transduction of interfacial phenomena, such as biorecognition or redox processes, into detectable changes of electrical response. Here, it is shown that, beyond sensing applications, EGOFETs may act effectively as memory devices, through the functionalization of the gate electrode with a self‐assembly monolayer comprising a switching molecule undergoing a large and persistent change of dipole moment, upon application of a small (0.6 V) programming potential. This first example of a switchable EGOFET device with memory retention is based on a tetrathiafulvalene derivative self‐assembled on gold and an aqueous buffer as electrolyte in a microfluidic assembly. Changes of the self‐assembled monolayer redox state lead to variations of the gate electrochemical potential and, as a consequence, the EGOFET's threshold voltage undergoes reversible shifts larger than 100 mV. The distinctive electrical readout upon different redox states opens the possibility of writing and erasing information, thus making the transistor behave as a single memory cell.

Autonomous soaring using a simplified MPC approach

ABSTRACTThe need for efficient propulsion systems allied to increasingly more challenging fixed-wing UAV mission requirements has led to recent research on the autonomous thermal soaring field with promising results. As part of that effort, the feasibility and advantages of model predictive control (MPC)-based guidance and control algorithms capable of extracting energy from natural occurring updrafts have already been demonstrated numerically. However, given the nature of the dominant atmospheric phenomena and the amplitude of the required manoeuvres, a non-linear optimal control problem results. Depending on the adopted prediction horizon length, it may be of large order, leading to implementation and real-time operation difficulties. Knowing that, an alternative MPC-based autonomous thermal soaring controller is presented herein. It is designed to yield a simple and small non-linear programming problem to be solved online. In order to accomplish that, linear prediction schemes are employed to impose the differential constraints, thus no extra variables are added to the problem and only linear bound restrictions result. For capturing the governing non-linear effects during the climb phase, a simplified representation of the aircraft kinematics with quasi-steady corrections is used by the controller internal model. Flight simulation results using a 3 degree-of-freedom model subjected to a randomly generated time varying thermal environment show that the aircraft is able to locate and exploit updrafts, suggesting that the proposed algorithm is a feasible MPC strategy to be employed in a practical application.

Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights

Overbooking is one of the most vital revenue management practices that is used in the airline industry. Identification of an overbooking level is a challenging task due to the uncertainties associated with external factors, such as demand for tickets, and inappropriate overbooking levels which may cause revenue losses as well as loss of reputation and customer loyalty. Therefore, the aim of this paper is to propose a fuzzy linear programming model and Genetic Algorithms (GAs) to maximize the overall revenue of a large-scale multi-leg flight network by minimizing the number of empty seats and the number of denied passengers.A fuzzy logic technique is used for modeling the fuzzy demand on overbooking flight tickets and a metaheuristics-based GA technique is adopted to solve large-scale multi-leg flights problem.As part of model verification, the proposed GA is applied to solve a small multi-leg flight linear programming model with a fuzzified demand factor. In addition, experimentation with large-scale problems with different input parameters’ settings such as penalty rate, show-up rate and demand level are also conducted to understand the behavior of the developed model.The validation results show that the proposed GA produces almost identical results to those in a small-scale multi-leg flight problem. In addition, the performance of the large-scale multi-leg flight network represented by a number of KPIs including total booking, denied passengers and net-overbooking profit towards changing these input parameters will also be revealed.

Commenting source code: is it worth it for small programming tasks?

Maintaining a program is a time-consuming and expensive task in software engineering. Consequently, several approaches have been proposed to improve the comprehensibility of source code. One of such approaches are comments in the code that enable developers to explain the program with their own words or predefined tags. Some empirical studies indicate benefits of comments in certain situations, while others find no benefits at all. Thus, the real effect of comments on software development remains uncertain. In this article, we describe an experiment in which 277 participants, mainly professional software developers, performed small programming tasks on differently commented code. Based on quantitative and qualitative feedback, we i) partly replicate previous studies, ii) investigate performances of differently experienced participants when confronted with varying types of comments, and iii) discuss the opinions of developers on comments. Our results indicate that comments seem to be considered more important in previous studies and by our participants than they are for small programming tasks. While other mechanisms, such as proper identifiers, are considered more helpful by our participants, they also emphasize the necessity of comments in certain situations.

Small livestock and aquaculture programming impacts on household livelihood security: a systematic narrative review

ABSTRACTSmall livestock and aquaculture production is thought to positively impact the livelihood security of poor rural households in low-income economies. A systematic narrative review conducted for evidence of this impact found only 29 studies that met the reviews inclusion and classification criteria. These studies indicate that raising small livestock and fish can improve income and nutrition, but results regarding women’s empowerment, disease, and the environment are mixed. More high-quality research is needed to understand the positive and negative impacts of production on human well-being. Future development programming should focus on implementing and evaluating multidimensional programmes and mixed farming systems.