Population Dynamics of Hybrid State during Adaptive Therapy in Cancer

Drug resistance emerges due to drug-induced phenotypic switching of drug-sensitive to drug-resistant subpopulations in cancer during therapy. Existing models indicate the competitive advantage of sensitive over resistant population to regulate tumor and reducing the treatment cost with increased time to progression of tumor ultimately benefiting the patient in a clinical setting. Here, we present a Lotka Volterra (LV) based population dynamics (PD) model of the drug-sensitive, drug-resistant, and transient drug-hybrid state along with phenotypic switching during adaptive therapy based on a simple cancer biomarker (CB) to decide the adaptive therapy dosage to regulate cancer. We identified that the strength of intra-competition along with phenotypic switching parameters is crucial to mediate the effectiveness of adaptive therapy and also investigated the significance of the initial fraction of subpopulations on AT. We hypothesize and predict the dynamics of drug-induced transient hybrid state playing a key role in the cancer cells undergoing metastasis.

Glucoregulatory and Anti-Inflammatory Activities of Peptide Fractions Separated by Electrodialysis with Ultrafiltration Membranes from Salmon Protein Hydrolysate and Identification of Four Novel Glucoregulatory Peptides

Natural bioactive peptides are suitable candidates for preventing the development of Type 2 diabetes (T2D), by reducing the various risk factors. The aim of this study was to concentrate glucoregulatory and anti-inflammatory peptides, from salmon by-products, by electrodialysis with ultrafiltration membrane (EDUF), and to identify peptides responsible for these bioactivities. Two EDUF configurations (1 and 2) were used to concentrate anionic and cationic peptides, respectively. After EDUF separation, two fractions demonstrated interesting properties: the initial fraction of the EDUF configuration 1 and the final fraction of the EDUF configuration 2 both showed biological activities to (1) increase glucose uptake in L6 muscle cells in insulin condition at 1 ng/mL (by 12% and 21%, respectively), (2) decrease hepatic glucose production in hepatic cells at 1 ng/mL in basal (17% and 16%, respectively), and insulin (25% and 34%, respectively) conditions, and (3) decrease LPS-induced inflammation in macrophages at 1 g/mL (45% and 30%, respectively). More impressive, the initial fraction of the EDUF configuration 1 (45% reduction) showed the same effect as the phenformin at 10 μM (40%), a drug used to treat T2D. Thirteen peptides were identified, chemically synthesized, and tested in-vitro for these three bioactivities. Thus, four new bioactive peptides were identified: IPVE increased glucose uptake by muscle cells, IVDI and IEGTL decreased hepatic glucose production (HGP) of insulin, whereas VAPEEHPTL decreased HGP under both basal condition and in the presence of insulin. To the best of our knowledge, this is the first time that (1) bioactive peptide fractions generated after separation by EDUF were demonstrated to be bioactive on three different criteria; all involved in the T2D, and (2) potential sequences involved in the improvement of glucose uptake and/or in the regulation of HGP were identified from a salmon protein hydrolysate.

Application of an Improved DCGAN for Image GenerationApplication of an Improved DCGAN for Image Generation

With the rapid development of deep learning, image generation technology has become one of the current hot research areas. A deep convolutional generative adversarial network (DCGAN) can better adapt to complex image distributions than other methods. In this paper, based on a traditional generative adversarial networks (GANs) image generation model, first, the fully connected layer of the DCGAN is further improved. To solve the problem of gradient disappearance in GANs, the activation functions of all layers of the discriminator are LeakyReLU functions, the output layer of the generator uses the Tanh activation function, and the other layers use ReLU. Second, the improved DCGAN model is verified on the MNIST dataset, and simple initial fraction (ISs) and complex initial fraction (ISc) indexes are established from the two aspects of image quality and image generation diversity, respectively. Finally, through a comparison of the two groups of experiments, it is found that the quality of images generated by the DCGAN model constructed in this paper is 2.02 higher than that of the GANs model, and the diversity of the images generated by the DCGAN is 1.55 higher than that of GANs. The results show that the improved DCGAN model can solve the problem of low-quality images being generated by the GANs and achieve good results.

Electropulse method of selective destruction of abrasive electrocorundum tool

In laboratory conditions the efficiency of electropulse disintegration of scrap abrasive wheels made of electrocorundum has been investigated. It is shown that at the chosen parameters of the process of selective destruction of abrasive scrap, the separation degree of electrocorundum grains, coarseness of which almost corresponds to the initial fraction, is more than 50%.

The approximately universal shapes of epidemic curves in the Susceptible-Exposed-Infectious-Recovered (SEIR) model

Compartmental transmission models have become an invaluable tool to study the dynamics of infectious diseases. The Susceptible-Infectious-Recovered (SIR) model is known to have an exact semi-analytical solution. In the current study, the approach of Harko et al. (Appl. Math. Comput. 236:184–194, 2014) is generalised to obtain an approximate semi-analytical solution of the Susceptible-Exposed-Infectious-Recovered (SEIR) model. The SEIR model curves have nearly the same shapes as the SIR ones, but with a stretch factor applied to them across time that is related to the ratio of the incubation to infectious periods. This finding implies an approximate characteristic timescale, scaled by this stretch factor, that is universal to all SEIR models, which only depends on the basic reproduction number and initial fraction of the population that is infectious.

Quantifying the Performance of Micro-Compartmentalized Directed Evolution Protocols

High-throughput, in vitro approaches for the evolution of enzymes rely on a random micro-encapsulation to link phenotypes to genotypes, followed by screening or selection steps. In order to optimise these approaches, or compare one to another, one needs a measure of their performance at extracting the best variants of a library. Here, we introduce a new metric, the Selection Quality Index (SQI), which can be computed from a simple mock experiment, performed with a known initial fraction of active variants. In contrast to previous approaches, our index integrates the effect of random co-encapsulation, and comes with a straightforward experimental interpretation. We further show how this new metric can be used to extract general protocol efficiency trends or reveal hidden selection mechanisms such as a counterintuitive form of beneficial poisoning in the compartmentalized self-replication protocol.

Effects of convincing power and neutrality on minority opinion spreading

The dynamics evolution of the minority opinion in public debates is studied using a convincing power (CP) model with neutrality. In a given group, an agent with a definite standpoint (yes or no) can be persuaded to be a neutral agent, if its capacity of persuasion is lower than the average CP of its opponents. Besides that a neutral agent will change its state and follow a more persuasive opinion. Starting from two opposite opinions with different rates, repeated local discussions are found to drive the minority reversal. It reveals that in addition to the initial minority, the number of neutral agents is also an important factor to the eventual winners. During the process of consensus, there exists a threshold of initial fraction to guarantee one side win. The results have a guiding significance for designing strategies to win a public debate.