SignificanceReprogramming the human genome toward any desirable state is within reach; application of select transcription factors drives cell types toward different lineages in many settings. We introduce the concept of data-guided control in building a universal algorithm for directly reprogramming any human cell type into any other type. Our algorithm is based on time series genome transcription and architecture data and known regulatory activities of transcription factors, with natural dimension reduction using genome architectural features. Our algorithm predicts known reprogramming factors, top candidates for new settings, and ideal timing for application of transcription factors. This framework can be used to develop strategies for tissue regeneration, cancer cell reprogramming, and control of dynamical systems beyond cell biology.

Polymer blends with synergetic performance play an integral part in modern society. The discovery of compatible polymer systems often relies on strong chemical interactions. By contrast, the role of entropy in polymers is often neglected. In this work, we show that entropy effect could control the phase structure and mechanical behaviors of polymer blends. For weakly interacting polymer pairs, the seemingly small mixing entropy favors the formation of nanoscale cocontinuous structures. The abundant nanointerfaces could initiate large plastic deformations by crazing or shear, thus, transforming brittle polymers (elongation < 9%) into superductile materials (elongation ∼ 146%). The resultant polymer blends display high transparency, strength (∼70 MPa), and toughness (∼60 MJ/m 3 ) beyond most engineering plastics. The principle of entropy-driven blends may also be applied in other polymer systems, offering a strategy to develop mechanically robust bulk polymeric materials for emerging applications such as biomedicine and electronics.

The day we understand the time evolution of subcellular elements at a level of detail comparable to physical systems governed by Newton's laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology, providing data-guided frameworks that allow us to develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. In this paper, we describe an approach to optimizing the use of transcription factors (TFs) in the context of cellular reprogramming. We construct an approximate model for the natural evolution of a cell cycle synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points along the cell cycle. In order to arrive at a model of moderate complexity, we cluster gene expression based on the division of the genome into topologically associating domains (TADs) and then model the dynamics of the TAD expression levels. Based on this dynamical model and known bioinformatics, such as transcription factor binding sites (TFBS) and functions, we develop a methodology for identifying the top transcription factor candidates for a specific cellular reprogramming task. The approach used is based on a device commonly used in optimal control. Our data-guided methodology identifies a number of transcription factors previously validated for reprogramming and/or natural differentiation. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes. Significance StatementReprogramming the human genome toward any desirable state is within reach; application of select transcription factors drives cell types toward different lineages in many settings. We introduce the concept of data-guided control in building a universal algorithm for directly reprogramming any human cell type into any other type. Our algorithm is based on time series genome transcription and architecture data and known regulatory activities of transcription factors, with natural dimension reduction using genome architectural features. Our algorithm predicts known reprogramming factors, top candidates for new settings, and ideal timing for application of transcription factors. This framework can be used to develop strategies for tissue regeneration, cancer cell reprogramming, and control of dynamical systems beyond cell biology.

No abstract

This work is aimed to study the possibility of recycling plastic waste (polypropylene (PP)) as aggregate instead of sand in the manufacturing of mortar or concrete. For this, an experimental study was carried out to evaluate the influence of nano-SiO2 and recycled PP plastic particles' content on physical, mechanical, and shrinkage properties and microstructure of the mortars with recycled PP plastic particles. The sand is substituted with the recycled PP plastic particles at dosages (0%, 20%, 40%, and 60% by volume of the sand). The nano-SiO2 content is 5% by weight of cement. The physical (porosity, water absorption, and density), mechanical (compressive and flexural strength) and shrinkage properties of the mortars were evaluated, and a complementary study on microstructure of the interface between cementitious matrix and PP plastic particles was made. The measurements of physical and mechanical properties showed that PP-filled mortar had lower density and better toughness (higher ratio of flexural strength to compressive strength). However, the compressive strength and flexural strength of PP-filled mortar is reduced, and the porosity, water absorption, autogenous shrinkage, and dry shrinkage increased as compared to normal cement mortar. The addition of nano-SiO2 reduced the porosity, water absorption, and drying shrinkage of PP-filled mortar and effectively improved the mechanical properties, but increased its autogenous shrinkage. A microscopic study of the interfacial zone (plastic-binder) has shown that there is poor adhesion between PP plastic particles and cement paste. From this work, it is found that recycled PP plastic waste has a great potential to be a construction material. It can be used as partial replacement of natural aggregates instead.

Purpose This paper aims to explore the impact of corporate social responsibility and hypocrisy on the relationship among psychological contract violation, trust and perceived betrayal. Design/methodology/approach This study used purposive sampling and selected students in Taiwan as the research participants. The theory of psychological contract violation and consumer awareness process in violation hypocrisy on psychological contract violation were used to investigate the effect of its impact on trust and perceived betrayal. Then, the moderating effect of social responsibility and hypocritical on trust, and the mediating effect of trust between psychological contract violation and perceived betrayal were analyzed. Findings The results indicated that hypocrisy had a significant and negative impact on psychological contract violation toward trust; hypocrisy had a significantly positive impact on psychological contract violation toward perceived betrayal; trust had a significantly negative impact on perceived betrayal; perceived betrayal had a significantly positive impact on both direct and indirect revenges; trust had a mediating effect between hypocrisy toward psychological contract violation and perceived betrayal; and higher hypocrisy would produce a stronger effect through trust on the relationships between hypocrisy toward psychological contract violation and perceived betrayal. Originality/value Perception of consumers would differ whenever there were failures of service recovery occurred; especially, stronger betrayal feeling would be perceived with the companies who emphasized social responsibility and did not carry out what they should do. Research results could be references for companies whom advertising and praising social responsibility.

In IoT (Internet of Things) edge computing, task offloading can lead to additional transmission delays and transmission energy consumption. To reduce the cost of resources required for task offloading and improve the utilization of server resources, in this paper, we model the task offloading problem as a joint decision making problem for cost minimization, which integrates the processing latency, processing energy consumption, and the task throw rate of latency-sensitive tasks. The Online Predictive Offloading (OPO) algorithm based on Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) networks is proposed to solve the above task offloading decision problem. In the training phase of the model, this algorithm predicts the load of the edge server in real-time with the LSTM algorithm, which effectively improves the convergence accuracy and convergence speed of the DRL algorithm in the offloading process. In the testing phase, the LSTM network is used to predict the characteristics of the next task, and then the computational resources are allocated for the task in advance by the DRL decision model, thus further reducing the response delay of the task and enhancing the offloading performance of the system. The experimental evaluation shows that this algorithm can effectively reduce the average latency by 6.25%, the offloading cost by 25.6%, and the task throw rate by 31.7%.

No abstract