Quantitative Biology

Biological systems are often modeled as a system of ordinary differential equations (ODEs) with time-invariant parameters. However, cell signaling events or pharmacological interventions may alter the cellular state and induce multi-mode dynamics of the system. Such systems are naturally modeled as hybrid automata, which possess multiple operational modes with specific nonlinear dynamics in each mode. In this paper we introduce a model checking-enabled framework than can model and analyze both single- and multi-mode biological systems. We tackle the central problem in systems biology--identify parameter values such that a model satisfies desired behaviors--using bounded model checking. We resort to the delta-decision procedures to solve satisfiability modulo theories (SMT) problems and sidestep undecidability of reachability problems. Our framework enables several analysis tasks including model calibration and falsification, therapeutic strategy identification, and Lyapunov stability analysis. We demonstrate the applicablitliy of these methods using case studies of prostate cancer progression, cardiac cell action potential and radiation diseases.

We have established a novel mathematical model that considers various aspects of the spreading of the virus, including, the transmission based on being in the latent period, environment to human transmission, governmental decisions, and control measures. To accomplish this, a compartmental model with eight batches (sub-population groups) has been proposed and the simulation of the set of differential equations has been conducted to show the effects of the various involved parameters. Also, to achieve more accurate results and closer to reality, the coefficients of a system of differential equations containing transmission rates, death rates, recovery rates and etc. have been proposed by some new step-functions viewpoint. Results: First of all, the efficiency of the proposed model has been shown for Iran and Italy, which completely denoted the flexibility of our model for predicting the epidemic progress and its moment behavior. The model has shown that the reopening plans and governmental measures directly affect the number of active cases of the disease. Also, it has specified that even releasing a small portion of the population (about 2-3 percent) can lead to a severe increase in active patients and consequently multiple waves in the disease progress. The effects of the healthcare capacities of the country have been obtained (quantitatively), which clearly specify the importance of this context. Control strategies including strict implementation of mitigation (reducing the transmission rates) and re-quarantine of some portion of population have been investigated and their efficiency has been shown.

Motivation: Drug combination is a sensible strategy for disease treatment by improving the efficacy and reducing concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Currently, a plenty of studies have focused on predicting potential drug combinations. However, these methods are not entirely satisfactory in performance and scalability. Results: In this paper, we proposed a Network Embedding framework in Multiplex Networks (NEMN) to predict synthetic drug combinations. Based on a multiplex drug similarity network, we offered alternative methods to integrate useful information from different aspects and to decide quantitative importance of each network. To explain the feasibility of NEMN, we applied our framework to the data of drug-drug interactions, on which it showed better performance in terms of AUPR and ROC. For Drug combination prediction, we found seven novel drug combinations which have been validated by external sources among the top-ranked predictions of our model.

BACKGROUND: Augmented reality (AR) is gaining popularity in varying field such as computer gaming and medical education fields. However, still few of applications in real surgeries. Orthopedic surgical applications are currently limited and underdeveloped. - METHODS: The clinic validation was prepared with the currently available AR equipment and software. A total of 1 Vertebroplasty, 2 ORIF Pelvis fracture, 1 ORIF with PFN for Proximal Femoral Fracture, 1 CRIF for distal radius fracture and 2 ORIF for Tibia Fracture cases were performed with fluoroscopy combined with AR smart surgical glasses system. - RESULTS: A total of 1 Vertebroplasty, 2 ORIF Pelvis fracture, 1 ORIF with PFN for Proximal Femoral Fracture, 1 CRIF for distal radius fracture and 2 ORIF for Tibia Fracture cases are performed to evaluate the benefits of AR surgery. Among the AR surgeries, surgeons wear the smart surgical are lot reduce of eyes of turns to focus on the monitors. This paper shows the potential ability of augmented reality technology for trauma surgery.

Microarray technology is known as one of the most important tools for collecting DNA expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with challenges such as small sample size, a significant number of genes, imbalanced data, etc. that make classification models inefficient. Thus, a new hybrid solution based on multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, to reduce the dataset's dimensions, a multi-filter model uses a combination of five filter methods to remove redundant and irrelevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, and hypervolume indicator criterion with five hybrid multi-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior performance compared to other multi-objective methods. Furthermore, the proposed Ensemble Classifier model could provide better classification accuracy and generalizability in microarray data compared to conventional ensemble methods.

In this paper we propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a set up under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate are the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly simpler and more tractable than semi-Markov models with a similar level of generality. Based on the notion of stochastic stability, we derive a sufficient condition for a shrinking epidemic in terms of the queuing system's occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of recent data on the COVID-19 epidemic and assess the effect of different stabilising strategies. The results suggest that it is possible to curb the epidemic with various occupation rate levels, as long as the mitigation is not excessively procrastinated.

Pancreatic cancer (PC) is the fourth leading cause of cancer death in the United States due to its five-year survival rate of 10%. Late diagnosis, affiliated with the asymptomatic nature in early stages and the location of the cancer with respect to the pancreas, makes current widely-accepted screening methods unavailable. Prior studies have achieved low (70-75%) diagnostic accuracy, possibly because 80% of PC cases are associated with diabetes, leading to misdiagnosis. To address the problems of frequent late diagnosis and misdiagnosis, we developed an accessible, accurate and affordable diagnostic tool for PC, by analyzing the expression of nineteen genes in PC and diabetes. First, machine learning algorithms were trained on four groups of subjects, depending on the occurrence of PC and Diabetes. The models were analyzed with 400 PC subjects at varying stages to ensure validity. Naive Bayes, Neural Network and K-Nearest Neighbors models achieved the highest testing accuracy of around 92.6%. Second, the biological implication of the nineteen genes was investigated using bioinformatics tools. It was found that these genes were significantly involved in regulating the cytoplasm, cytoskeleton and nuclear receptor activity in the pancreas, specifically in acinar and ductal cells. Our novel tool is the first in the literature that achieves a PC diagnostic accuracy of above 90%, having the potential to significantly improve the detection of PC in the background of diabetes and increase the five-year survival rate.

Radio-frequency identification (RFID) is an increasingly popular wireless technology that allows researchers to monitor wild bird populations from fixed locations in the field. Our lab has developed an RFID-equipped birdfeeder based on the Raspberry Pi Zero W, a low-cost single-board computer, that collects continuous visitation data from birds tagged with passive integrated transponder (PIT) tags. Each birdfeeder has a perch antenna connected to an RFID reader board on a Raspberry Pi powered by a portable battery. When a tagged bird lands on the perch to eat from the feeder, its unique code is stored with the date and time on the Raspberry Pi. These birdfeeders require only basic soldering and coding skills to assemble, and can be easily outfitted with additional hardware like video cameras and microphones. We outline the process of assembling the hardware and setting up the operating system for the birdfeeders. Then, we describe an example implementation of the birdfeeders to track house finches (Haemorhous mexicanus) on the campus of Queens College in New York City.

Hooge et al. asked the question: "Is human classification by experienced untrained observers a gold standard in fixation detection?" They conclude the answer is no. If they had entitled their paper: "Is human classification by experienced untrained observers a gold standard in fixation detection when data quality is very poor, data are error-filled, data presentation was not optimal, and the analysis was seriously flawed?", I would have no case to make. In the present report, I will present evidence to support my view that this latter title is justified. The low quality data assessment is based on using a relatively imprecise eye-tracker, the absence of head restraint for any subjects, and the use of infants as the majority of subjects (60 of 70 subjects). Allowing subjects with more than 50% missing data (as much as 95%) is also evidence of low quality data. The error-filled assessment is based on evidence that a number of the "fixations" classified by "experts" have obvious saccades within them, and that, apparently, a number of fixations were classified on the basis of no signal at all. The evidence for non-optimal data presentation stems from the fact that, in a number of cases, perfectly good data was not presented to the coders. The flaws in the analysis are evidenced by the fact that entire stretches of missing data were considered classified, and that the measurement of saccade amplitude was based on many cases in which there was no saccade at all. Without general evidence to the contrary, it is correct to assume that some human classifiers under some conditions may meet the criteria for a gold standard, and classifiers under other conditions may not. This conditionality is not recognized by Hooge et al. A fair assessment would conclude that whether or not humans can be considered a gold standard is still very much an open question.

The coronary artery disease (CAD) involves narrowing and damaging the major blood vessels has become the most life threating disease in the world especially in south Asian reason. Although outstanding medical facilities are available in Singapore and India for CAD patients, early detection of CAD stages are necessary to minimize the patients' sufferings and expenses. It is really challenging for doctors to incorporate numerous factors for details analysis and CAD detections are expensive as it needs expensive medical facilities. Clinical Decision Support Systems (CDSS) may assist to analyze numerous factors for patients. In this paper, a Rule Based Expert System (RBES) is proposed which can predict five different stages of CAD. RBES contains five different Belief Rule Based (BRB) systems and the final output is produced by combining all BRBs using the Evidential Reasoning (ER). Success, Error, Failure, False Omission rates are calculated to measures the performance of the RBES. The Success Rate and False Omission Rate show better performance comparing to existing CDSS.