Information content of colored motifs in complex networks

We study complex networks in which the nodes of the network are tagged with different colors depending on the functionality of the nodes (colored graphs), using information theory applied to the distribution of motifs in such networks. We find that colored motifs can be viewed as the building blocks of the networks (much more so than the uncolored structural motifs can be) and that the relative frequency with which these motifs appear in the network can be used to define the information content of the network. This information is defined in such a way that a network with random coloration (but keeping the relative number of nodes with different colors the same) has zero color information content. Thus, colored motif information captures the exceptionality of coloring in the motifs that is maintained via selection. We study the motif information content of the C. elegans brain as well as the evolution of colored motif information in networks that reflect the interaction between instructions in genomes of digital life organisms. While we find that colored motif information appears to capture essential functionality in the C. elegans brain (where the color assignment of nodes is straightforward) it is not obvious whether the colored motif information content always increases during evolution, as would be expected from a measure that captures network complexity. For a single choice of color assignment of instructions in the digital life form Avida, we find rather that colored motif information content increases or decreases during evolution, depending on how the genomes are organized, and therefore could be an interesting tool to dissect genomic rearrangements.Comment: 21 pages, 8 figures, to appear in Artificial Lif

Recent Advances in Biomarkers for Parkinson’s Disease

Parkinson’s disease (PD) is one of the common progressive neurodegenerative disorders with several motor and non-motor symptoms. Most of the motor symptoms may appear at a late stage where most of the dopaminergic neurons have been already damaged. In order to provide better clinical intervention and treatment at the onset of disease, it is imperative to find accurate biomarkers for early diagnosis, including prodromal diagnosis and preclinical diagnosis. At the same time, these reliable biomarkers can also be utilized to monitor the progress of the disease. In this review article, we will discuss recent advances in the development of PD biomarkers from different aspects, including clinical, biochemical, neuroimaging and genetic aspects. Although various biomarkers for PD have been developed so far, their specificity and sensitivity are not ideal when applied individually. So, the combination of multimodal biomarkers will greatly improve the diagnostic accuracy and facilitate the implementation of personalized medicine

Factors Associated With Dyskinesia in Parkinson's Disease in Mainland China

Background and Objectives: Studies examining the risk factors for dyskinesia in Parkinson's disease (PD) have been inconsistent, and racial differences exist. Since there have been no systematic studies of the characteristics of dyskinesia in the Mainland Chinese population, we sought to elucidate the risk factors for dyskinesia.Methods: A total of 1974 PD patients from Mainland China were systematically investigated by univariable and multivariable analyses. PD patients with and without dyskinesia were stratified into 4 groups according to levodopa equivalent daily dose (LEDD) and analyzed by a Cox proportional hazards model. A longitudinal study of 87 patients with dyskinesia was classified into 3 groups according to the duration from onset of PD to the initiation of levodopa, and comparisons among groups were analyzed by the Mann-Whitney test.Results: Early age of onset, long disease duration, being female, high LEDD, low UPDRS III scores (ON-state) and high Hoehn-Yahr stage (ON-state) were predictors of dyskinesia. Dyskinesia was levodopa dosage-dependent, and the incidence increased remarkably when LEDD exceeded 300 mg/d (p < 0.05). The emergence of dyskinesia had no association with the initiation time of levodopa, and if the latter was more than 4 years, the duration of time on chronic levodopa free of motor complications was significantly shortened.Conclusions: We found risk factors for the prediction of dyskinesia. Our data shows that physicians should be cautious if the LEDD exceeds 300 mg/d. The development of dyskinesia was not correlated with the time of levodopa initiation

Constructing prediction models for excessive daytime sleepiness by nomogram and machine learning: A large Chinese multicenter cohort study

ObjectiveAlthough risk factors for excessive daytime sleepiness (EDS) have been reported, there are still few cohort-based predictive models for EDS in Parkinson’s disease (PD). This 1-year longitudinal study aimed to develop a predictive model of EDS in patients with PD using a nomogram and machine learning (ML).Materials and methodsA total of 995 patients with PD without EDS were included, and clinical data during the baseline period were recorded, which included basic information as well as motor and non-motor symptoms. One year later, the presence of EDS in this population was re-evaluated. First, the baseline characteristics of patients with PD with or without EDS were analyzed. Furthermore, a Cox proportional risk regression model and XGBoost ML were used to construct a prediction model of EDS in PD.ResultsAt the 1-year follow-up, EDS occurred in 260 of 995 patients with PD (26.13%). Baseline features analysis showed that EDS correlated significantly with age, age of onset (AOO), hypertension, freezing of gait (FOG). In the Cox proportional risk regression model, we included high body mass index (BMI), late AOO, low motor score on the 39-item Parkinson’s Disease Questionnaire (PDQ-39), low orientation score on the Mini-Mental State Examination (MMSE), and absence of FOG. Kaplan–Meier survival curves showed that the survival prognosis of patients with PD in the high-risk group was significantly worse than that in the low-risk group. XGBoost demonstrated that BMI, AOO, PDQ-39 motor score, MMSE orientation score, and FOG contributed to the model to different degrees, in decreasing order of importance, and the overall accuracy of the model was 71.86% after testing.ConclusionIn this study, we showed that risk factors for EDS in patients with PD include high BMI, late AOO, a low motor score of PDQ-39, low orientation score of MMSE, and lack of FOG, and their importance decreased in turn. Our model can predict EDS in PD with relative effectivity and accuracy

Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain

Background: Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network. Methodology/Principal Findings: Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm's locomotion. Conclusions/Significance: The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available

Identification of <em>CHIP</em> as a novel causative gene for autosomal recessive cerebellar ataxia

Autosomal recessive cerebellar ataxias are a group of neurodegenerative disorders that are characterized by complex clinical and genetic heterogeneity. Although more than 20 disease-causing genes have been identified, many patients are still currently without a molecular diagnosis. In a two-generation autosomal recessive cerebellar ataxia family, we mapped a linkage to a minimal candidate region on chromosome 16p13.3 flanked by single-nucleotide polymorphism markers rs11248850 and rs1218762. By combining the defined linkage region with the whole-exome sequencing results, we identified a homozygous mutation (c.493CT) in CHIP (NM_005861) in this family. Using Sanger sequencing, we also identified two compound heterozygous mutations (c.389AT/c.441GT; c.621C>G/c.707GC) in CHIP gene in two additional kindreds. These mutations co-segregated exactly with the disease in these families and were not observed in 500 control subjects with matched ancestry. CHIP colocalized with NR2A, a subunit of the N-methyl-D-aspartate receptor, in the cerebellum, pons, medulla oblongata, hippocampus and cerebral cortex. Wild-type, but not disease-associated mutant CHIPs promoted the degradation of NR2A, which may underlie the pathogenesis of ataxia. In conclusion, using a combination of whole-exome sequencing and linkage analysis, we identified CHIP, encoding a U-box containing ubiquitin E3 ligase, as a novel causative gene for autosomal recessive cerebellar ataxia

The Nearest Neutron Star Candidate in a Binary Revealed by Optical Time-domain Surveys

Recent studies have revealed the global deposition on Earth of radioactive elements (e.g., $^{60}$Fe) resulting from the metal-enriched ejecta of nearby (within $\sim 100$ pc) supernova explosions. The majority of neutron stars in our Solar neighborhood remain to be discovered. Here we report the discovery of the nearest ($127.7 \pm 0.3$ pc) neutron star candidate in the single-lined spectroscopic binary LAMOST J235456.76+335625.7 (hereafter J2354). Utilizing the multi-epoch spectra and high-cadence periodic light curves, we measure the mass of the visible star ($M_{\rm vis}=0.70\pm 0.05\ M_{\odot}$) and determine the mass function of the invisible object $f(M)=0.525 \pm 0.004\ M_{\odot}$, i.e., the mass of the unseen compact object is $M_{\rm inv} \geq 1.26 \pm 0.03\ M_{\odot}$. The excess UV emission due to a hot supramassive white dwarf is absent. Hence, it is likely that J2354 harbors a neutron star. J2354 is X-ray dim (the$0.1$--$2.4$keV luminosity$<10^{30}\ {\rm erg\ s^{-1}}$) since it is not detected in the ROSAT all-sky surveys in X-ray. One-hour exceptionally sensitive radio follow-up observations with FAST, the largest single-dish radio telescope, failed to reveal any radio pulsating signals (the potential pulse power at$1.4$GHz is$<6.8\times 10^{23}\ {\rm erg\ s^{-1}}$). Hence, the neutron star candidate in J2354 can only be discovered via our time-resolved observations. The alternative scenario involving a nearby supramassive cold white dwarf cannot be fully excluded. Our discovery demonstrates a promising way to unveil the missing population of backyard inactive neutron stars or supramassive cold white dwarfs in binaries by exploring the optical time domain, thereby facilitating understanding of the supernovae explosion and metal-enrichment history in our Solar neighborhood.Comment: 35 pages, 8 figures, to be submitte

Sequence dependence of isothermal DNA amplification via EXPAR

Isothermal nucleic acid amplification is becoming increasingly important for molecular diagnostics. Therefore, new computational tools are needed to facilitate assay design. In the isothermal EXPonential Amplification Reaction (EXPAR), template sequences with similar thermodynamic characteristics perform very differently. To understand what causes this variability, we characterized the performance of 384 template sequences, and used this data to develop two computational methods to predict EXPAR template performance based on sequence: a position weight matrix approach with support vector machine classifier, and RELIEF attribute evaluation with Naïve Bayes classification. The methods identified well and poorly performing EXPAR templates with 67–70% sensitivity and 77–80% specificity. We combined these methods into a computational tool that can accelerate new assay design by ruling out likely poor performers. Furthermore, our data suggest that variability in template performance is linked to specific sequence motifs. Cytidine, a pyrimidine base, is over-represented in certain positions of well-performing templates. Guanosine and adenosine, both purine bases, are over-represented in similar regions of poorly performing templates, frequently as GA or AG dimers. Since polymerases have a higher affinity for purine oligonucleotides, polymerase binding to GA-rich regions of a single-stranded DNA template may promote non-specific amplification in EXPAR and other nucleic acid amplification reactions