Maximum Cliques in Protein Structure Comparison

Computing the similarity between two protein structures is a crucial task in molecular biology, and has been extensively investigated. Many protein structure comparison methods can be modeled as maximum clique problems in specific k-partite graphs, referred here as alignment graphs. In this paper, we propose a new protein structure comparison method based on internal distances (DAST) which is posed as a maximum clique problem in an alignment graph. We also design an algorithm (ACF) for solving such maximum clique problems. ACF is first applied in the context of VAST, a software largely used in the National Center for Biotechnology Information, and then in the context of DAST. The obtained results on real protein alignment instances show that our algorithm is more than 37000 times faster than the original VAST clique solver which is based on Bron & Kerbosch algorithm. We furthermore compare ACF with one of the fastest clique finder, recently conceived by Ostergard. On a popular benchmark (the Skolnick set) we observe that ACF is about 20 times faster in average than the Ostergard's algorithm

Singularly Perturbed Monotone Systems and an Application to Double Phosphorylation Cycles

The theory of monotone dynamical systems has been found very useful in the modeling of some gene, protein, and signaling networks. In monotone systems, every net feedback loop is positive. On the other hand, negative feedback loops are important features of many systems, since they are required for adaptation and precision. This paper shows that, provided that these negative loops act at a comparatively fast time scale, the main dynamical property of (strongly) monotone systems, convergence to steady states, is still valid. An application is worked out to a double-phosphorylation ``futile cycle'' motif which plays a central role in eukaryotic cell signaling.Comment: 21 pages, 3 figures, corrected typos, references remove

A mixed-methods feasibility study of a novel AI-enabled, web-based, clinical decision support system for the treatment of major depression in adults

Background:- The objective of this paper is to discuss perceived clinical utility and impact on physician-patient relationship of a novel, artificial-intelligence (AI) enabled clinical decision support system (CDSS) for use in treating adults with major depression. Methods:- A single arm, naturalistic follow-up study aimed at assessing the acceptability and useability of the software. Patients had a baseline appointment, followed by a minimum of two appointments with the CDSS. Study exit questionnaires and interviews were conducted to assess perceived clinical utility, impact on patient-physician relationship, and understanding and trust. 7 physicians and 17 patients, of which 14 completed, consented to participate. Results:- 86 % of physicians (6/7) felt the information provided by the CDSS provided more comprehensive understanding of patient situations. 71 % (5/7) felt the information was helpful. 86 % of physicians (6/7) reported the AI/predictive model was useful when deciding treatment. 62 % of patients (8/13) reported improved care due to the tool, and 46 %(6/13) reported a significantly or somewhat improved physician-patient relationship 54 % reported no change. 71 % of physicians (5/7) and 62 % of patients (8/13) rated trusting the tool. Limitations:- Small sample size and treatment changes prior to CDSS introduction limits ability to verify impact on outcomes. Conclusions:- Qualitative results from 12 patient exit interviews are analyzed and presented. Findings suggest physicians perceived the tool as useful in conducting appointments and used it while deciding treatment. Physicians and patients generally found the tool trustworthy, and it may have positive effects on physician-patient relationships. (Study identifier: NCT04061642)

Evaluating the clinical feasibility of an artificial intelligence–powered, web-based clinical decision support system for the treatment of depression in adults: longitudinal feasibility study

Background:- Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence–powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows. Objective:- This study aims to examine the feasibility of an artificial intelligence–powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network–based individualized treatment remission prediction. Methods:- Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews. Results:- Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change. Conclusions:- Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Impact of Competition on Quality of Service in Demand Responsive Transit

Demand responsive transportation has the potential to provide efficient public door-to-door transport with a high quality. In currently implemented systems in the Netherlands, however, we observe a decrease in the quality of service (QoS), expressed in longer travel times for the customers. Currently, generally one transport company is responsible for transporting all customers located in a specified geographic zone. In general it is known that when multiple companies compete on costs, the price for customers decreases. In this paper, we investigate whether a similar result can be achieved when competing on quality instead. To arrive at some first conclusions, we set up a multiagent environment to simulate the assignment of rides to companies through an auction on QoS, and the insertion of allocated rides in the companies' schedules using online optimization. Our results reveal that this set-up improves the quality of the service offered to the customers at moderately higher costs.Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

Depression history modulates effects of subthalamic nucleus topography on neuropsychological outcomes of deep brain stimulation for Parkinson’s disease

Patients with psychiatric symptoms, such as depression, anxiety, and visual hallucinations, may be at increased risk for adverse effects following deep brain stimulation of the subthalamic nucleus for Parkinson’s disease, but there have been relatively few studies of associations between locations of chronic stimulation and neuropsychological outcomes. We sought to determine whether psychiatric history modulates associations between stimulation location within the subthalamic nucleus and postoperative affective and cognitive changes. We retrospectively identified 42 patients with Parkinson’s disease who received bilateral subthalamic nucleus deep brain stimulation and who completed both pre- and postoperative neuropsychological testing. Active stimulation contacts were localized in MNI space using Lead-DBS software. Linear discriminant analysis identified vectors maximizing variance in postoperative neuropsychological changes, and Pearson’s correlations were used to assess for linear relationships. Stimulation location was associated with postoperative change for only 3 of the 18 neuropsychological measures. Variation along the superioinferior (z) axis was most influential. Constraining the analysis to patients with a history of depression revealed 10 measures significantly associated with active contact location, primarily related to location along the anterioposterior (y) axis and with worse outcomes associated with more anterior stimulation. Analysis of patients with a history of anxiety revealed 5 measures with location-associated changes without a predominant axis. History of visual hallucinations was not associated with significant findings. Our results suggest that a history of depression may influence the relationship between active contact location and neuropsychological outcomes following subthalamic nucleus deep brain stimulation. These patients may be more sensitive to off-target (nonmotor) stimulation. © 2022, The Author(s).Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]