Reversible Data Hiding for DNA Sequence Using Multilevel Histogram Shifting

A large number of studies have examined DNA storage to achieve information hiding in DNA sequences with DNA computing technology. However, most data hiding methods are irreversible in that the original DNA sequence cannot be recovered from the watermarked DNA sequence. This study presents reversible data hiding methods based on multilevel histogram shifting to prevent biological mutations, preserve sequence length, increase watermark capacity, and facilitate blind detection/recovery. The main features of our method are as follows. First, we encode a sequence of nucleotide bases with four-character symbols into integer values using the numeric order. Second, we embed multiple bits in each integer value by multilevel histogram shifting of noncircular type (NHS) and circular type (CHS). Third, we prevent the generation of false start/stop codons by verifying whether a start/stop codon is included in an integer value or between adjacent integer values. The results of our experiments confirmed that the NHS- and CHS-based methods have higher watermark capacities than conventional methods in terms of supplementary data used for decoding. Moreover, unlike conventional methods, our methods do not generate false start/stop codons

Quantitative analysis with machine learning models for multi-parametric brain imaging data

Gliomas are considered to be the most common primary adult malignant brain tumor. With the dramatic increases in computational power and improvements in image analysis algorithms, computer-aided medical image analysis has been introduced into clinical applications. Precision tumor grading and genotyping play an indispensable role in clinical diagnosis, treatment and prognosis. Gliomas diagnostic procedures include histopathological imaging tests, molecular imaging scans and tumor grading. Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human study has limitations that can result in low reproducibility and inter-observer agreement. Compared with histopathological images, Magnetic resonance (MR) imaging present the different structure and functional features, which might serve as noninvasive surrogates for tumor genotypes. Therefore, computer-aided image analysis has been adopted in clinical application, which might partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure multilevel features on multi-parametric medical information. Imaging features obtained from a single modal image do not fully represent the disease, so quantitative imaging features, including morphological, structural, cellular and molecular level features, derived from multi-modality medical images should be integrated into computer-aided medical image analysis. The image quality differentiation between multi-modality images is a challenge in the field of computer-aided medical image analysis. In this thesis, we aim to integrate the quantitative imaging data obtained from multiple modalities into mathematical models of tumor prediction response to achieve additional insights into practical predictive value. Our major contributions in this thesis are: 1. Firstly, to resolve the imaging quality difference and observer-dependent in histological image diagnosis, we proposed an automated machine-learning brain tumor-grading platform to investigate contributions of multi-parameters from multimodal data including imaging parameters or features from Whole Slide Images (WSI) and the proliferation marker KI-67. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. A quantitative interpretable machine learning approach (Local Interpretable Model-Agnostic Explanations) was followed to measure the contribution of features for single case. Most grading systems based on machine learning models are considered “black boxes,” whereas with this system the clinically trusted reasoning could be revealed. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. 2. Based on the automated brain tumor-grading platform we propose, multimodal Magnetic Resonance Images (MRIs) have been introduced in our research. A new imaging–tissue correlation based approach called RA-PA-Thomics was proposed to predict the IDH genotype. Inspired by the concept of image fusion, we integrate multimodal MRIs and the scans of histopathological images for indirect, fast, and cost saving IDH genotyping. The proposed model has been verified by multiple evaluation criteria for the integrated data set and compared to the results in the prior art. The experimental data set includes public data sets and image information from two hospitals. Experimental results indicate that the model provided improves the accuracy of glioma grading and genotyping

Plant Science

The book "Plant Science" consists of 12 chapters divided into three sections authored by many researchers from different parts of the Globe. Section-I: Plant and Environment, describes the relationship between plants and environment, particularly enumerating species-environment relationship and response of plants to different environmental stress conditions. Section-II: Plant-Microbe relation, embodies broadly on both positive and negative aspects of microbes on plants. Section-III: Plant Biotechnology, shed light on current biotechnological research to develop modern technology for producing biologicals and also increasing plant immunity in present environmental conditions. The book "Plant Science" will be helpful to a wide group peoples; readers, scientists, researchers and allied professionals. We recommend it to you; enjoy reading it, save the plant and save life

Detecting and tracking populations at-risk of Alzheimer’s disease: studying asymptomatic cognitive profiles and symptomatic clinical presentations

Familial Alzheimer’s disease (FAD) is a penetrant autosomal dominantly inherited condition. Due to its clinical and neurophysiological similarities with sporadic AD features, it represents an important clinical group in its own right but also offers a potential model for AD. This thesis is largely based on the longitudinal FAD study but also includes data from ‘Insight 46’ in an attempt to broaden the scope of these investigations to other ‘at-risk’ cohorts. The overarching aim of the thesis is to study the early subtle cognitive changes (with a particular focus on visual short-term memory but also subjective cognitive decline) and the symptomatic presentations (both cognitive and clinical) that accompany disease progression in AD. The key findings were that over time, presymptomatic mutation carriers (PMCs) had a faster rate of decline in visual short-term memory (VSTM) function, specifically in the ability to remember the location and the target identity. This relational binding deficit was strongest in the most challenging task condition: 3-items, 4s delay (high load, longest delay), and is clinically relevant as it shows sensitivity in tracking individuals during preclinical AD stages. Consequent eye movement investigations of VSTM function, revealed a stronger cognitive effort for PMCs compared to controls during encoding, a finding which may increase the diagnostic value of relational binding tasks. Other important findings were: the higher incidence of subjective cognitive decline symptoms in two otherwise different populations “at-risk” of AD: PMCs carriers and amyloid-positive ~70-year-old participants and the ineffective VSTM function and much smaller influence of mutation specificity on survival time variance in comparison to variance in age at onset for symptomatic FAD individuals. Together, this work has implications for the interpretation of cognitive and clinical data, the understanding of heterogeneity in FAD and may help detect and track subtle cognitive decline of potential value to clinical practice

Oxidative stress and cancer heterogeneity orchestrate NRF2 roles relevant for therapy response

Oxidative stress and its end-products, such as 4-hydroxynonenal (HNE), initiate activation of the Nuclear Factor Erythroid 2-Related Factor 2 (NRF2)/Kelch Like ECH Associated Protein 1 (KEAP1) signaling pathway that plays a crucial role in the maintenance of cellular redox homeostasis. However, an involvement of 4-HNE and NRF2 in processes associated with the initiation of cancer, its progression, and response to therapy includes numerous, highly complex events. They occur through interactions between cancer and stromal cells. These events are dependent on many cell-type specific features. They start with the extent of NRF2 binding to its cytoplasmic repressor, KEAP1, and extend to the permissiveness of chromatin for transcription of Antioxidant Response Element (ARE)-containing genes that are NRF2 targets. This review will explore epigenetic molecular mechanisms of NRF2 transcription through the specific molecular anatomy of its promoter. It will explain the role of NRF2 in cancer stem cells, with respect to cancer therapy resistance. Additionally, it also discusses NRF2 involvement at the cross-roads of communication between tumor associated inflammatory and stromal cells, which is also an important factor involved in the response to therapy

Biophysical Chemistry

Biophysical chemistry is one of the most interesting interdisciplinary research fields. Some of its different subjects have been intensively studied for decades. Now the field attracts not only scientists from chemistry, physics, and biology backgrounds but also those from medicine, pharmacy, and other sciences. We aimed to start this version of the book Biophysical Chemistry from advanced principles, as we include some of the most advanced subject matter, such as advanced topics in catalysis applications (first section) and therapeutic applications (second section). This led us to limit our selection to only chapters with high standards, therefore there are only six chapters, divided into two sections. We have assumed that the interested readers are familiar with the fundamentals of some advanced topics in mathematics such as integration, differentiation, and calculus and have some knowledge of organic and physical chemistry, biology, and pharmacy. We hope that the book will be valuable to graduate and postdoctoral students with the requisite background, and by some advanced researchers active in chemistry, biology, biochemistry, medicine, pharmacy, and other sciences

Translational studies on bipolar disorder and anorexia nervosa

Translational medicine aims at closing the gap between basic and clinical sciences in an integrative way. Psychiatry is one of the few medical specialties in which diagnosis is primarily based on clinical observation and all mental disorders are defined by abnormal behaviors and cognitions. The lack of biomarkers supporting diagnostic and therapeutic procedures has been a challenge in psychiatry. A better biological understanding is needed to move the field forward, it will enhance diagnostics and treatment, while reducing the stigma that surrounds mental disorders that are so poorly understood. Over the last years, advances in fundamental sciences like genetics and neuroscience have made it clear that there is shared biology between many psychiatric disorders and that integration of methods might lead to new understandings. The studies presented in this thesis focus on bipolar disorder (BD) and anorexia nervosa (AN), both severe mental disorders with high suicide rates, high heritability, and both lacking in biological understanding. BD, formerly known as manic-depressive disorder, is a mood disorder, characterized by manic or hypomanic episodes, often in combination with depressive episodes. AN is an eating disorder characterized by severe weight loss together with pathological behaviors. This thesis includes five main studies on the biology underlying these disorders, based on large, well characterized cohorts, covering several methods, including genetic, imaging and protein markers, as well as preliminary data on the establishment of in vitro models. Specifically, in study I, we attempted to replicate previously published findings on the association between subphenotypes of bipolar disorder and genetic variations in the AKT1 gene. Using frequentist and Bayesian approaches, as well as publicly available results from genome-wide association studies (GWAS), we were able to reject previously proposed associations. In study II, we explored the effects of genetic variations in genes involved in glutamate regulation on glutamate levels in two brain regions and their associations with other phenotypes. We found that the minor allele of rs3812778/rs3829280 in the 5’-untranslated region of SLC1A2, coding for a glutamate transporter, is associated (1) with increased glutamate levels in the anterior cingulate cortex, (2) with increased expression levels, in several brain regions, of the transmembrane receptor gene CD44, which is implicated in inflammation and brain development, as well as (3) with an increased risk for rapid-cycling in bipolar disorder, potentially linking CD44/SLC1A2 to a more severe phenotype of BD. In study III, we investigated the effects of clinical and genetic parameters on lithium pharmacokinetics in order to better understand lithium biology and improve lithium dose prediction models for bipolar patients, using the ratio between serum lithium and daily lithium intake, as outcome. We were able to confirm the association of several clinical predictors. Although no genome-wide significant locus was found, we report that genetic variation is important and might influence the outcome. Finally, based on the results obtained in the study, we developed a prediction algorithm that can be tested in the clinic. In study IV, we investigated the involvement of neuronal degeneration in AN by studying neurofilament light chain (NfL), a known marker of neurodegeneration, in a case-control setting and found increased levels of NfL in patients with active AN in two different cohorts. In study V, we studied the involvement of inflammation in AN, using a panel of 92 inflammatory markers in a case-control setting and report an aberrant inflammatory profile in patients with active AN, but not in patients that have recovered from AN. These studies exemplify possible approaches that can be taken in translational psychiatry. The integration of clinical, technical and analytical approaches illustrates important learning outcomes for an aspiring clinical scientist in psychiatr