Influence of Timed Nutrient Diet on Depression and Light Sensitivity in Seasonal Affective Disorder

Seasonal Affective Disorder (SAD) patients crave and eat more carbohydrates (CHO) in fall‐winter when depressed, especially in the evenings, and feel energetic thereafter. Evening CHO‐rich meals can phase delay circadian rhythms, and glucose increases retinal response to light. We studied timed CHO‐ or protein‐rich (PROT) diet as a putative therapy for SAD. Unmedicated, DSM‐IV‐diagnosed depressed women with SAD (n=22, 19–63 yrs) in the follicular phase of the menstrual cycle (present in 19) were randomized to nine days of eating ～1600 kcal of either CHO before 12:00 h (n=9), CHO after 18:00 h (n=6), or PROT after 18:00 h (n=7); only water was allowed for the rest of the day. Measurements included the depression questionnaire SIGH‐SAD (with 21‐item Hamilton depression subscale), Eating Behavior Questionnaire (DEBQ), percentage fat (by bioimpedancemetry), clinical biochemistry (glucose, cholesterol, triglycerides, TSH, T4, cortisol), and electroretinogram (ERG). No differential effects of diet were found on any of the studied parameters (except DEBQ). Clinically, participants improved slightly; the 21‐HDRS score (mean±SD) decreased from 19.6±6.4 to 14.4±7.4 (p=.004). Percent change correlated significantly with menstrual day at diet onset (mood improved the first week after menstruation onset), change in available sunshine (more sunlight, better mood), and initial percentage fat (fatter patients improved more). Scotopic ERG amplitude was diminished after treatment (p=.025, three groups combined), probably due to greater exposure to sunshine in 14/22 subjects (partial correlation analysis significant). Keeping in mind the limitations of this ambulatory study (i.e., inability to control outdoor light exposure, small number of participants, and briefness of intervention), it is suggested that the 25% clinical improvement (of the order of magnitude of placebo) is not related to nutrient diet or its timing, but rather to natural changes during the menstrual cycle, available sunshine, and ease of dieting for fatter patients.     

TREATMENT-TIME-DEPENDENT DIFFERENCE OF KETAMINE PHARMACOLOGICAL RESPONSE AND TOXICITY IN RATS

In the context of Lewy's phase delay hypothesis, the present study tested whether effective treatment of winter Seasonal Affective Disorder (SAD) is mediated by advancing of circadian phase. Following a baseline week, 78 outpatients with SAD were randomized into 8 weeks of treatment with either fluoxetine and placebo light treatment or light treatment and placebo pill. Depression levels were measured on the Ham₁₇₊₇ and the BDI‐II, and circadian phase was estimated on the basis of daily sleep logs and self‐reported morningness‐eveningness. Among the 61 outpatients with complete data, both treatments were associated with significant antidepressant effect and phase advance. However, pre‐ and post‐treatment comparisons found that the degree of symptom change did not correlate with the degree of phase change associated with treatment. The study therefore provides no evidence that circadian phase advance mediates the therapeutic mechanism in patients with SAD. Findings are discussed in terms of the limitations of the circadian measures employed.     

Spectral turbulence measuring as feature extraction method from EEG on affective computing

In biomedical and psychological applications dealing with EEG, a suitable selection of the most relevant electrodes is useful for lightening the data acquisition and facilitating the signal processing. Therefore, an efficient method for extracting and selecting features from EEG channels is desirable. Classification methods are more and more applied for obtaining important conclusions from diverse psychological processes, and specifically for emotional processing. In this work, an original and straightforward method, inspired by the spectral turbulence (ST) measure from electrocardiogram and the support vector machine-recursive feature elimination (SVM-RFE) algorithm, is proposed for classifying EEG signals. The goal of this study is to introduce the ST concept in applications of artificial intelligence related to cognitive processes and to determine the best EEG channels for distinguishing between two different experimental conditions. By means of this method, the left temporal region of the brain has revealed to be greatly involved in the affective valence processing elicited by visual stimuli.     

Differential dependence on DNA ligase of type II restriction enzymes: a practical way toward ligase-free DNA automaton

DNA computing study is a new paradigm in computer science and biological computing fields. As one of DNA computing approaches, DNA automaton is composed of the hardware, input DNA molecule and state transition molecules. By now restriction enzymes are key hardware for DNA computing automaton. It has been found that DNA computing efficiency may be independent on DNA ligases when type IIS restriction enzymes like FokI are used as hardware. In this study, we compared FokI with four other distinct enzymes HgaI, BsmFI, BbsI, and BseMII, and found their differential independence on T4 DNA ligase when performing automaton reactions. Since DNA automaton is a potential powerful tool to tackle gene relationship in genomic network scale, the feasible ligase-free DNA automaton may set an initial base to develop functional DNA automata for various DNA technology development and implications in genetics study in the near future.     

Multiple evolutionary mechanisms drive papillomavirus diversification

The circular, double-stranded 8-kb DNA genome of papillomaviruses (PVes) consists mainly of 4 large genes, E1, E2, L2, and L1. Approximately 150 papillomavirus genomes have been sequenced to date. We analyzed a representative sample of 53 PVes genomes using maximum likelihood, Bayesian inference, maximum parsimony, and distance-based methods both on nucleotide (nt) and on amino acid (aa) alignments. When the 4 genes were analyzed separately, aa-inferred phylogenies contradicted each other less than nt-inferred trees (judged by partition homogeneity tests). In particular, gene combinations including the L2 gene generated significant incongruence (P < 0.001). Combined analyses of the remaining genes E1-E2-L1 produced a well-supported phylogeny including supertaxon beta + gamma + pi + xi-PVes (infecting Artiodactyla, Carnivora, Primates, and Rodentia) and supertaxon kappa + lambda + mu + nu + sigma-PVes (infecting Carnivora, Lagomorpha, Primates, and Rodentia). Based on the tree topology, host-linked evolution appears plausible at shallow, rather than deeper, taxonomic levels. Diversification within PVes may also involve adaptive radiation establishing different niches (within a single-host species) and recombination events (within single-host cells). Heterogeneous groups of closely related PVes infecting, for example, humans and domestic animals such as hamster, dog, and cattle suggest multiple infections across species borders. Additional evolutionary phenomena such as strong codon usage preferences, and computational biases including reconstruction artifacts and insufficient taxon sampling, may contribute to the incomplete resolution of deep phylogenetic nodes. The molecular data globally supports a complex evolutionary scenario for PVes, which is driven by multiple mechanisms but not exclusively by coevolution with corresponding hosts.     

MOSBIE: a tool for comparison and analysis of rule-based biochemical models

Background

Mechanistic models that describe the dynamical behaviors of biochemical systems are common in computational systems biology, especially in the realm of cellular signaling. The development of families of such models, either by a single research group or by different groups working within the same area, presents significant challenges that range from identifying structural similarities and differences between models to understanding how these differences affect system dynamics.

Results

We present the development and features of an interactive model exploration system, MOSBIE, which provides utilities for identifying similarities and differences between models within a family. Models are clustered using a custom similarity metric, and a visual interface is provided that allows a researcher to interactively compare the structures of pairs of models as well as view simulation results.

Conclusions

We illustrate the usefulness of MOSBIE via two case studies in the cell signaling domain. We also present feedback provided by domain experts and discuss the benefits, as well as the limitations, of the approach.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-316) contains supplementary material, which is available to authorized users.     

MSDA,a proteomics software suite for in‐depth Mass Spectrometry Data Analysis using grid computing

One of the major bottlenecks in the proteomics field today resides in the computational interpretation of the massive data generated by the latest generation of high‐throughput MS instruments. MS/MS datasets are constantly increasing in size and complexity and it becomes challenging to comprehensively process such huge datasets and afterwards deduce most relevant biological information. The Mass Spectrometry Data Analysis (MSDA, https://msda.unistra.fr ) online software suite provides a series of modules for in‐depth MS/MS data analysis. It includes a custom databases generation toolbox, modules for filtering and extracting high‐quality spectra, for running high‐performance database and de novo searches, and for extracting modified peptides spectra and functional annotations. Additionally, MSDA enables running the most computationally intensive steps, namely database and de novo searches, on a computer grid thus providing a net time gain of up to 99% for data processing.     

Demographic histories and genetic diversities of Fennoscandian marine and landlocked ringed seal subspecies

Island populations are on average smaller, genetically less diverse, and at a higher risk to go extinct than mainland populations. Low genetic diversity may elevate extinction probability, but the genetic component of the risk can be affected by the mode of diversity loss, which, in turn, is connected to the demographic history of the population. Here, we examined the history of genetic erosion in three Fennoscandian ringed seal subspecies, of which one inhabits the Baltic Sea ‘mainland’ and two the ‘aquatic islands’ composed of Lake Saimaa in Finland and Lake Ladoga in Russia. Both lakes were colonized by marine seals after their formation c. 9500 years ago, but Lake Ladoga is larger and more contiguous than Lake Saimaa. All three populations suffered dramatic declines during the 20th century, but the bottleneck was particularly severe in Lake Saimaa. Data from 17 microsatellite loci and mitochondrial control‐region sequences show that Saimaa ringed seals have lost most of the genetic diversity present in their Baltic ancestors, while the Ladoga population has experienced only minor reductions. Using Approximate Bayesian computing analyses, we show that the genetic uniformity of the Saimaa subspecies derives from an extended founder event and subsequent slow erosion, rather than from the recent bottleneck. This suggests that the population has persisted for nearly 10,000 years despite having low genetic variation. The relatively high diversity of the Ladoga population appears to result from a high number of initial colonizers and a high post‐colonization population size, but possibly also by a shorter isolation period and/or occasional gene flow from the Baltic Sea.     

Finding Hamiltonian paths in tournaments on clusters

This paper presents a general methodology for the communication-efficient parallelization of graph algorithms using the divide-and-conquer approach and shows that this class of problems can be solved in cluster environments with good communication efficiency. Specifically, the first practical parallel algorithm, based on a general coarse-grained model, for finding Hamiltonian paths in tournaments is presented. On any such parallel machines, this algorithm uses only (3log p+1), where p is the number of processors, communication rounds, which is independent of the tournament size, and can reuse the existing linear-time algorithm in the sequential setting. For theoretical completeness, the algorithm is revised for fine-grained models, where the ratio of computation and communication throughputs is low or the local memory size, , of each individual processor is extremely limited for any , solving the problem with O(log p) communication rounds, while the hidden constant grows with the scalability factor 1/∊. Experiments have been carried out on a Linux cluster of 32 Sun Ultra5 computers and an SGI Origin 2000 with 32 R10000 processors. The algorithm performance on the Linux Cluster reaches 75% of the performance on the SGI Origin 2000 when the tournament size is about one million. Computational resources and technical support are provided by the Center for Computational Research (CCR) at the State University of New York at Buffalo. Chun-Hsi Huang received his Ph.D. degree in Computer Science from the State University of New York at Buffalo in 2001. His is currently an Assistant Professor of Computer Science and Engineering at the University of Connecticut. His interests include High Performance Parallel Computing, Cluster and Grid Computing, Biomedical and Health Informatics, Algorithm Design and Analysis, Experimental Algorithms and Computational Biology. Sanguthevar Rajasekaran received his Ph.D. degree in Computer Science from Harvard University in 1988. Currently he is the UTC Chair Professor of Computer Science and Engineering at the University of Connecticut and the Director of Booth Engineering Center for Advanced Technologies (BECAT). His research interests include Parallel Algorithms, Bioinformatics, Data Mining, Randomized Computing, Computer Simulations, and Combinatorial Optimization. Laurence Tianruo Yang received is Ph.D. degree in Computer Science from the Oxford University. He is currently a professor of Computer Science of the St. Francis Xavier University in Canada. His research interests include high-performance computing, embedded systems, computer archtecture and high-speed networking. Xin He received his Ph.D. degree in Computer Science from the Ohio State University in 1987. He is currently Professor of Computer Science and Engineering at the State University of New York at Buffalo. His research interests include Algorithms, Data Structures, Combinatorics and Computational Geometry.     

Comparative study of the server-initiated lowest algorithm using a load balancing index based on the process behavior for heterogeneous environment

The availability of low cost microcomputers and the evolution of computer networks have increased the development of distributed systems. In order to get a better process allocation on distributed environments, several load balancing algorithms have been proposed. Generally, these algorithms consider as the information policy’s load index the length of the CPU’s process waiting queue. This paper modifies the Server-Initiated Lowest algorithm by using a load index based on the resource occupation. Using this load index the Server-Initiated Lowest algorithm is compared to the Stable symmetrically initiated, which nowadays is defined as the best choice. The comparisons are made by using simulations. The simulations showed that the modified Server-Initiated Lowest algorithm had better results than the Symmetrically Initiated one.