The Use of the Gaps-In-Noise Test as an Index of the Enhanced Left Temporal Cortical Thinning Associated with the Transition between Mild Cognitive Impairment and Alzheimer's Disease

Background: The known link between auditory perception and cognition is often overlooked when testing for cognition. Purpose: To evaluate auditory perception in a group of older adults diagnosed with mild cognitive impairment (MCI). Research Design: A cross-sectional study of auditory perception. Study Sample: Adults with MCI and adults with no documented cognitive issues and matched hearing sensitivity and age. Data collection: Auditory perception was evaluated in both groups, assessing for hearing sensitivity, speech in babble (SinB), and temporal resolution. Results: Mann‐Whitney test revealed significantly poorer scores for SinB and temporal resolution abilities of MCIs versus normal controls for both ears. The right-ear gap detection thresholds on the Gaps-In-Noise (GIN) Test clearly differentiated between the two groups (p < 0.001), with no overlap of values. The left ear results also differentiated the two groups (p < 0.01); however, there was a small degree of overlap ∼8-msec threshold values. With the exception of the left-ear inattentiveness index, which showed a similar distribution between groups, both impulsivity and inattentiveness indexes were higher for the MCIs compared to the control group. Conclusions: The results support central auditory processing evaluation in the elderly population as a promising tool to achieve earlier diagnosis of dementia, while identifying central auditory processing deficits that can contribute to communication deficits in the MCI patient population. A measure of temporal resolution (GIN) may offer an early, albeit indirect, measure reflecting left temporal cortical thinning associated with the transition between MCI and Alzheimer’s disease

GIBA: a clustering tool for detecting protein complexes

Background: During the last years, high throughput experimental methods have been developed which generate large datasets of protein - protein interactions (PPIs). However, due to the experimental methodologies these datasets contain errors mainly in terms of false positive data sets and reducing therefore the quality of any derived information. Typically these datasets can be modeled as graphs, where vertices represent proteins and edges the pairwise PPIs, making it easy to apply automated clustering methods to detect protein complexes or other biological significant functional groupings. Methods: In this paper, a clustering tool, called GIBA (named by the first characters of its developers' nicknames), is presented. GIBA implements a two step procedure to a given dataset of protein-protein interaction data. First, a clustering algorithm is applied to the interaction data, which is then followed by a filtering step to generate the final candidate list of predicted complexes. Results: The efficiency of GIBA is demonstrated through the analysis of 6 different yeast protein interaction datasets in comparison to four other available algorithms. We compared the results of the different methods by applying five different performance measurement metrices. Moreover, the parameters of the methods that constitute the filter have been checked on how they affect the final results. Conclusion: GIBA is an effective and easy to use tool for the detection of protein complexes out of experimentally measured protein - protein interaction networks. The results show that GIBA has superior prediction accuracy than previously published methods

Which clustering algorithm is better for predicting protein complexes?

<p>Abstract</p> <p>Background</p> <p>Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.</p> <p>Results</p> <p>In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.</p> <p>Conclusions</p> <p>While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: <url>http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm</url></p

Effect of Neutralizing Monoclonal Antibody Treatment on Early Trajectories of Virologic and Immunologic Biomarkers in Patients Hospitalized With COVID-19

BACKGROUND: Neutralizing monoclonal antibodies (nmAbs) failed to show clear benefit for hospitalized patients with coronavirus disease 2019 (COVID-19). Dynamics of virologic and immunologic biomarkers remain poorly understood. METHODS: Participants enrolled in the Therapeutics for Inpatients with COVID-19 trials were randomized to nmAb versus placebo. Longitudinal differences between treatment and placebo groups in levels of plasma nucleocapsid antigen (N-Ag), anti-nucleocapsid antibody, C-reactive protein, interleukin-6, and D-dimer at enrollment, day 1, 3, and 5 were estimated using linear mixed models. A 7-point pulmonary ordinal scale assessed at day 5 was compared using proportional odds models. RESULTS: Analysis included 2149 participants enrolled between August 2020 and September 2021. Treatment resulted in 20% lower levels of plasma N-Ag compared with placebo (95% confidence interval, 12%-27%; P \u3c .001), and a steeper rate of decline through the first 5 days (P \u3c .001). The treatment difference did not vary between subgroups, and no difference was observed in trajectories of other biomarkers or the day 5 pulmonary ordinal scale. CONCLUSIONS: Our study suggests that nmAb has an antiviral effect assessed by plasma N-Ag among hospitalized patients with COVID-19, with no blunting of the endogenous anti-nucleocapsid antibody response. No effect on systemic inflammation or day 5 clinical status was observed. CLINICAL TRIALS REGISTRATION: NCT04501978

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system

A multivariate beta model

A multivariate beta model is introduced by combining univariate real beta variables and uniform random variables. Various properties of this model are studied, including product moments, conditional moments, exact density, Chebyshev's type inequalities and estimation of parameters

Journal Bearing Performance Prediction Using Machine Learning and Octave-Band Signal Analysis of Sound and Vibration Measurements

Journal and thrust bearings utilise hydrodynamic lubrication to reduce friction and wear between the shaft and the bearing. The process to determine the lubricant film thickness or the actual applied load is vital to ensure proper and trouble-free operation. However, taking accurate measurements of the oil film thickness or load in bearings of operating engines is very difficult and requires specialised equipment and extensive experience. In the present work, the performance parameters of journal bearings of the same principal dimensions are measured experimentally, aiming at training a Machine Learning (ML) algorithm capable of predicting the loading condition of any similar bearing. To this end, an experimental procedure using the Bently Nevada Rotor Kit 4 is set up, combined with sound and vibration measurements in the vicinity of the journal bearing structure. First, sound and acceleration measurements for different values of bearing load and rotational speed are collected and post-processed utilising 1/3 octave band analysis techniques, for parametrisation of the input datasets of the ML algorithms. Next, several ML algorithms are trained and tested. Comparison of the results produced by each algorithm determines the fittest one for each application. The results of this work demonstrate that, in a laboratory environment, the operational parameters of journal bearings can be efficiently identified utilising non-intrusive sound and vibration measurements. The presented approach may substantially improve bearing condition identification and monitoring, which is an imperative step to prevent journal bearing failures and conduct condition-based maintenance. © 2021 Marios Moschopoulos et al., published by Sciendo