Alcoholism Identification Based on an AlexNet Transfer Learning Model

Aim: This paper proposes a novel alcoholism identification approach that can assist radiologists in patient diagnosis.Method: AlexNet was used as the basic transfer learning model. The global learning rate was small, at 10−4, and the iteration epoch number was at 10. The learning rate factor of replaced layers was 10 times larger than that of the transferred layers. We tested five different replacement configurations of transfer learning.Results: The experiment shows that the best performance was achieved by replacing the final fully connected layer. Our method yielded a sensitivity of 97.44%± 1.15%, a specificity of 97.41 ± 1.51%, a precision of 97.34 ± 1.49%, an accuracy of 97.42 ± 0.95%, and an F1 score of 97.37 ± 0.97% on the test set.Conclusion: This method can assist radiologists in their routine alcoholism screening of brain magnetic resonance images

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients

Application of Signal Processing and Soft Computing To Genomics

A major challenge for genomic research is to establish a relationship among sequences,structures and function of genes. In addition processing and analyzing this information are of prime importance. Basically genes are repositories for protein coding information and proteins in turn are responsible for most of the important biological functions in all cells. These in turn gives rise to analysis of DNA sequences in proteins, designing of various drugs for genetic diseases. This thesis deals with the applications of signal processing and soft computing algorithms to the field of genomics and proteinomics. Diseases like SARS and Migraine have been modeled using these tools and potential druggable compounds have been proposed which are better than the previous available drugs. Protein structural classes have been identified more accurately based on Genetic Algorithm and Particle Swarm Optimization.Better and efficient methods like Sliding-DFT and Adaptive AR Modeling were proposed to identify Protein coding regions in genes. The proposed methods showed better results as compared to existing methods

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Standard methods for molecular research in Apis mellifera

From studies of behaviour, chemical communication, genomics and developmental biology, among many others, honey bees have long been a key organism for fundamental breakthroughs in biology. With a genome sequence in hand, and much improved genetic tools, honey bees are now an even more appealing target for answering the major questions of evolutionary biology, population structure, and social organization. At the same time, agricultural incentives to understand how honey bees fall prey to disease, or evade and survive their many pests and pathogens, have pushed for a genetic understanding of individual and social immunity in this species. Below we describe and reference tools for using modern molecular-biology techniques to understand bee behaviour, health, and other aspects of their biology. We focus on DNA and RNA techniques, largely because techniques for assessing bee proteins are covered in detail in Hartfelder et al. (2013). We cover practical needs for bee sampling, transport, and storage, and then discuss a range of current techniques for genetic analysis. We then provide a roadmap for genomic resources and methods for studying bees, followed by specific statistical protocols for population genetics, quantitative genetics, and phylogenetics. Finally, we end with three important tools for predicting gene regulation and function in honey bees: Fluorescence in situ hybridization (FISH), RNA interference (RNAi), and the estimation of chromosomal methylation and its role in epigenetic gene regulation.Fundação para a Ciência e Tecnologi

2017 - The Twenty-second Annual Symposium of Student Scholars

The full program book from the Twenty-second Annual Symposium of Student Scholars, held on April 20, 2017. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1019/thumbnail.jp

Defensive aggregation to predatory threat in the laboratory rat: behavioural, neural, pharmacological and epigenetic correlates

Abstract: Defensive aggregation is the tight clumping together of conspecifics observed in response to predatory threat across many species. While much field research has explored this social response to threat, in particular the important survival advantages it affords, it has received little examination in the laboratory. Chapter 2 of this thesis presents the first laboratory rodent model of defensive aggregation, demonstrating that it can be readily elicited in groups of four rats presented with an unconditioned stressor (cat fur or bright light). This provides a novel opportunity to explore the more subtle benefits accrued from defensive aggregation as well as its underlying neurobiology and pharmacology. Chapter 3 illustrates that defensive aggregation has a hitherto unknown social buffering effect that reduces neural and behavioural stress responsivity and facilitates reengagement in important non-threat-related behaviours. It also demonstrates that stable active and passive stress coping rats exist amongst populations that are group exposed to predator threat. Chapter 4 demonstrates that the neuropeptide oxytocin acts at vasopressin V1ARs to selectively promote social responding to threat without increasing anxiety-like behaviour. This suggests that developing novel pharmacotherapies that target V1ARs may prove useful for the treatment of chronic social withdrawal in the face of stress, which occurs in numerous psychiatric disorders. Finally, Chapter 5 provides the first report of striking epigenetic differences in the medial amygdala AVP system between active and passive coping rats, providing a potential mechanism through which the proactive response style seen in some animals confronted with threat might be maintained. It is hoped that the work presented in this thesis has served as a foundation for the future investigation of the neurobiological mechanisms driving, and adaptive benefits underlying, the social response to threat and an active stress coping strategy

Social Preference in Juvenile Zebrafish

Social behaviours are essential for the survival and reproduction of many species, including our own. A fundamental feature of all social behaviour is social preference, which is an individual’s propensity to interact with members of their species (termed conspecifics). In an average population, various social preference behaviours are readily observed, ranging from uninterested (not engaging with conspecifics) to very social (engaging with conspecifics). Individuals expressing these behaviours are typically labelled as having an asocial or prosocial, respectively. Little is known about how the underlying social circuitry gives rise to such distinct social behaviours in the population. It is well established that adverse social experiences can impact social behaviour, including isolation during early development. Undesired social isolation (loneliness) alters behavioural patterns, neuroanatomy (e.g., brain volume) and neurochemistry in ways that resemble developmental neuropsychiatric disorders, including autism and schizophrenia. However, few studies have investigated the impact of early life isolation on social circuitry, and how this results in dysfunctional social behaviour commonly associated with these and other disorders. In this thesis, juvenile zebrafish was used to model social preference behaviour, as it is an excellent translational model for human developmental and behavioural disorders. Population-level analysis revealed that several features of social preference behaviour could be summarised via Visual Preference Index (VPI) scores representing sociality. Using multiple behavioural parameters, comprehensive investigations of asocial and prosocial fish identified via VPIs revealed distinct responses towards conspecifics between the two phenotypes. These initial results served as a baseline for facilitating the identification of atypical social behaviour following periods of social isolation. The impact of isolation on social preference was assessed by applying either the full isolation over the initial three weeks of development or partial isolation, 48 hours or 24 hours, before testing. Following periods of social isolation, juvenile zebrafish displayed anxiety-like behaviours. Furthermore, full and partial isolation of 48 hours, but not 24 hours, altered responses to conspecifics. To assess the impact of social isolation on the social circuitry, the brain activities of fish were analysed and compared between different rearing conditions using high-resolution two-photon imaging. Whole-brain functional maps of isolated social phenotypes were distinct from those in the average population. Isolation-induced activity changes were found mainly in brain regions linked to social behaviour, social cue processing, and anxiety/stress (e.g., the caudal hypothalamus and preoptic area). Since some of these affected regions are modulated by serotonin, the reversibility of the adverse effects of social isolation on preference behaviour was investigated by using pharmacological manipulation of the monoaminergic system. The administration of an anxiolytic the drug buspirone demonstrated that altered social preference behaviour in isolated fish could be rescued by acutely reducing serotonin levels. By investigating social preference at the behavioural and functional level in wild-type juvenile zebrafish, this work contributes to our understanding of how the social brain circuity produces diverse social preferences. Furthermore, it provides important information on how early-life environmental adversity gives rise to atypical social behaviour and the neurotransmitters modulating the circuit, offering new opportunities for effective intervention