scholarly journals Assessment of presentation patterns of repetitive behaviors in Autism: a cross-sectional video-recording study. Preliminary report

2021 ◽  
Author(s):  
Enzo Grossi ◽  
Elisa Caminada ◽  
Beatrice Vescovo ◽  
Tristana Castrignano ◽  
Daniele Piscitelli ◽  
...  
Keyword(s):  
Expression Patterns ◽  
Video Recording ◽  
Repetitive Behaviors ◽  
Data Driven ◽  
Body Parts ◽  
Autism Severity ◽  
Cross Sectional ◽  
Pattern Complexity ◽  
Data Driven Approach

AbstractTwenty expert caregivers wearing a body cam recorded 1868 videoclips in 67 autistic subjects along a 3 months close follow-up. A team consisting of a senior child neuro-psychiatrist and a senior psychologist selected 780 of them as expressing repetitive behaviors (RB) and made an empirical classification according to components, complexity, body parts and sensory channels involved, with the aim to understand better the pattern complexity and correlate with autism severity. The RB spectrum for each subject ranged from 1 to 33 different patterns (average= 11.6; S.D.= 6.82). Forty subjects expressed prevalent simple pattern and 27 prevalent complex patterns. No significant differences are found between the two groups according to ADOS score severity. This study represents a first attempt to systematically document expression patterns of RB with a data driven approach. This may provide a better understanding of the pathophysiology, diagnosis, and treatment of RB.

A data driven approach to neuropsychological features in isolated rem behavior disorder: A follow-up investigation

2021 ◽  
Vol 429 ◽  
pp. 118662
Author(s):  
Samantha Mombelli ◽  
Caterina Leitner ◽  
Marco Sforza ◽  
Andrea Galbiati ◽  
Giada D'Este ◽  
...  
Keyword(s):  
Behavior Disorder ◽  
Data Driven ◽  
Rem Behavior Disorder ◽  
Data Driven Approach

scholarly journals Quantifying Diet Intake and Its Association with Cardiometabolic Risk in the UK Airwave Health Monitoring Study: A Data-Driven Approach

Nutrients ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1170 ◽  
Author(s):  
Larissa C. Hunt ◽  
Hassan S. Dashti ◽  
Queenie Chan ◽  
Rachel Gibson ◽  
Céline Vetter
Keyword(s):  
Health Monitoring ◽  
Cardiometabolic Risk ◽  
Carbohydrate Intake ◽  
Data Driven ◽  
Nutritional Epidemiology ◽  
Central Adiposity ◽  
Cross Sectional ◽  
Lower Likelihood ◽  
Monitoring Study ◽  
Data Driven Approach

We used data-driven approaches to identify independent diet exposures among 45 candidate variables, for which we then probed cross-sectional associations with cardiometabolic risk (CMR). We derived average daily caloric intake and macronutrient composition, daily meal frequencies, and irregularity of energy and macronutrient intake from 7-day food diaries in the Airwave Health Monitoring Study participants (N = 8090). We used K-means and hierarchical clustering to identify non-redundant diet exposures with representative exposures for each cluster chosen by silhouette value. We then used multi-variable adjusted logistic regression to estimate prevalence ratios (PR) and 95% confidence intervals (95%CI) for CMR (≥3 criteria: dyslipidemia, hypertension, central adiposity, inflammation and impaired glucose control) across diet exposure quartiles. We identified four clusters: i) fat intake, ii) carbohydrate intake, iii) protein intake and intake regularity, and iv) meal frequencies and energy intake. Of these clusters, higher carbohydrate intake was associated with lower likelihood of CMR (PR = 0.89, 95%CI = 0.81–0.98; ptrend = 0.02), as was higher fiber intake (PR = 0.76, 95%CI = 0.68–0.85; ptrend < 0.001). Higher meal frequency was also associated with lower likelihood of CMR (PR = 0.76, 95%CI = 0.68–0.85; ptrend < 0.001). Our results highlight a novel, data-driven approach to select non-redundant, minimally collinear, primary exposures across a host of potentially relevant exposures (including diet composition, temporal distribution, and regularity), as often encountered in nutritional epidemiology.

scholarly journals Condition-adaptive fused graphical lasso (CFGL): an adaptive procedure for inferring condition-specific gene co-expression network

2018 ◽  
Author(s):  
Yafei Lyu ◽  
Lingzhou Xue ◽  
Feipeng Zhang ◽  
Hillary Koch ◽  
Laura Saba ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Expression Patterns ◽  
Joint Estimation ◽  
Data Driven ◽  
Specific Gene ◽  
Breast Cancer Dataset ◽  
Biological Processes ◽  
Graphical Lasso ◽  
Data Driven Approach ◽  
Multiple Conditions

AbstractCo-expression network analysis provides useful information for studying gene regulation in biological processes. Examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. One challenge in this type of analysis is that the sample sizes in each condition are usually small, making the statistical inference of co-expression patterns highly underpowered. A joint network construction that borrows information from related structures across conditions has the potential to improve the power of the analysis.One possible approach to constructing the co-expression network is to use the Gaussian graphical model. Though several methods are available for joint estimation of multiple graphical models, they do not fully account for the heterogeneity between samples and between co-expression patterns introduced by condition specificity. Here we develop the condition-adaptive fused graphical lasso (CFGL), a data-driven approach to incorporate condition specificity in the estimation of co-expression networks. We show that this method improves the accuracy with which networks are learned. The application of this method on a rat multi-tissue dataset and The Cancer Genome Atlas (TCGA) breast cancer dataset provides interesting biological insights. In both analyses, we identify numerous modules enriched for Gene Ontology functions and observe that the modules that are upregulated in a particular condition are often involved in condition-specific activities. Interestingly, we observe that the genes strongly associated with survival time in the TCGA dataset are less likely to be network hubs, suggesting that genes associated with cancer progression are likely to govern specific functions, rather than regulating a large number of biological processes. Additionally, we observed that the tumor-specific hub genes tend to have few shared edges with normal tissue, revealing tumor-specific regulatory mechanism.Author summaryGene co-expression networks provide insights into the mechanism of cellular activity and gene regulation. Condition-specific mechanisms may be identified by constructing and comparing co-expression networks of multiple conditions. We propose a novel statistical method to jointly construct co-expression networks for gene expression profiles from multiple conditions. By using a data-driven approach to capture condition-specific co-expression patterns, this method is effective in identifying both co-expression patterns that are specific to a condition and that are common across conditions. The application of this method on real datasets reveals interesting biological insights.

scholarly journals Modulating Cancer Progression from Leukoplakia via Bayesian Gene Networks

2021 ◽  
Author(s):  
Alessandro Villa DDS ◽  
Amin Zollanvari
Keyword(s):  
Oral Cancer ◽  
Malignant Transformation ◽  
Cancer Progression ◽  
Gene Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Data Driven ◽  
Data Driven Approach ◽  
Potentially Malignant

Oral squamous cell carcinoma often arises from an oral potentially malignant disorder called oral leukoplakia (OL). With this work we aimed to develop a novel data-driven predictive model based on gene expression profiles to distinguish OL patients who underwent malignant transformation from those who did not. We used the Tree Augmented Naïve (TAN) Bayes classifier to predict the posterior probability of having oral cancer given the data. 86 patients were included with a median follow-up of 7.11 years. Fifty-one patients (51/86; 59%) underwent malignant transformation. We found that 16 genes were predictors of oral cancer in patients with OL and these included SLC7A11, SPINK6, SERPINA12, VIT, ATP1B3, CST6, FLRT2, ELMOD1, AZGP1, RNASE13, DIO2, ECM1, CYP4F11, SYTL4, AKR1C1, and AKR1C3. In conclusion, we showed that Bayesian gene networks are a data-driven approach which could be used also in other predictor models in oncology.

Ascertaining cancer survivors in Ontario using the Ontario Cancer Registry and administrative data.

2018 ◽  
Vol 36 (7_suppl) ◽  
pp. 34-34
Author(s):  
Munaza Chaudhry ◽  
Catherine Chan ◽  
Sue Su-Myat ◽  
Stefanie De Rossi ◽  
Victoria Zwicker ◽  
...  
Keyword(s):  
Cancer Survivors ◽  
Administrative Data ◽  
Cancer Registry ◽  
Stage Iv ◽  
Cross Sectional ◽  
Lack Of Information ◽  
Ontario Cancer Registry ◽  
Follow Up Care ◽  
Data Driven Approach

34 Background: The number of cancer survivors in Ontario has grown rapidly due to increasing incidence and advances in screening, diagnostic technologies and treatment. However, there is a lack of information to plan, monitor and improve follow-up care. Using the Ontario Cancer Registry (OCR) and health services administrative data, we developed a cohort of cancer survivors from which we could determine demographic characteristics, where follow-up care was received, and concordance with guideline-recommended surveillance testing. Methods: Individuals were included in the cumulative survivor cohort if they had at least one diagnosed incident malignant cancer from 1964 to 2017 in the OCR. Patients were considered survivors upon completion of treatment (surgery, chemotherapy, radiation therapy). Treatment was ascertained from clinical and administrative data using a data-driven approach combined with clinical expert input. In the absence of recurrence data, a treatment-based proxy was developed. Stage IV and complex malignant haematology cancer patients were excluded. We did a cross-sectional analysis of survivors in the cohort in 2016. We produced descriptive statistics and also determined the year of survivorship. For those who were in their first to fifth year of survival, we calculated the proportion who saw a medical or radiation oncologist (MO/RO) in 2016 stratified by year of survivorship. Results: As of December 31, 2016, there were 414,134 cancer survivors in the cohort, roughly 3% of the Ontario population. Ninety-three percent of survivors had a single primary cancer diagnosis, 66% were aged 65 or older, and slightly more were female (55%). Also, 22% had been diagnosed with breast cancer, 22% with prostate, and 12% with colorectal cancer. For those in their first year of survivorship, roughly 50% saw a MO/RO; whereas, for those in their fifth year of survival, 36% had seen a MO and 27% had seen an RO. Conclusions: The development of a cancer survivor cohort has enabled us to produce timely data on a previously unidentified patient population. Linking this cohort with existing administrative data will enable further examination of visit trajectories as well as cancer and non-cancer health outcomes.

State-of-the-art aortic imaging: Part II - applications in transcatheter aortic valve replacement and endovascular aortic aneurysm repair

VASA ◽  
2014 ◽  
Vol 43 (1) ◽  
pp. 6-26 ◽  
Author(s):  
Fabian Rengier ◽  
Philipp Geisbüsch ◽  
Paul Schoenhagen ◽  
Matthias Müller-Eschner ◽  
Rolf Vosshenrich ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Transcatheter Aortic Valve Replacement ◽  
Surgical Repair ◽  
Imaging Modality ◽  
Open Surgical Repair ◽  
Cross Sectional ◽  
Transcatheter Aortic Valve ◽  
Cross Sectional Imaging ◽  
Sectional Imaging

Transcatheter aortic valve replacement (TAVR) as well as thoracic and abdominal endovascular aortic repair (TEVAR and EVAR) rely on accurate pre- and postprocedural imaging. This review article discusses the application of imaging, including preprocedural assessment and measurements as well as postprocedural imaging of complications. Furthermore, the exciting perspective of computational fluid dynamics (CFD) based on cross-sectional imaging is presented. TAVR is a minimally invasive alternative for treatment of aortic valve stenosis in patients with high age and multiple comorbidities who cannot undergo traditional open surgical repair. Given the lack of direct visualization during the procedure, pre- and peri-procedural imaging forms an essential part of the intervention. Computed tomography angiography (CTA) is the imaging modality of choice for preprocedural planning. Routine postprocedural follow-up is performed by echocardiography to confirm treatment success and detect complications. EVAR and TEVAR are minimally invasive alternatives to open surgical repair of aortic pathologies. CTA constitutes the preferred imaging modality for both preoperative planning and postoperative follow-up including detection of endoleaks. Magnetic resonance imaging is an excellent alternative to CT for postoperative follow-up, and is especially beneficial for younger patients given the lack of radiation. Ultrasound is applied in screening and postoperative follow-up of abdominal aortic aneurysms, but cross-sectional imaging is required once abnormalities are detected. Contrast-enhanced ultrasound may be as sensitive as CTA in detecting endoleaks.

Psychological Resilience Provides No Independent Protection From Suicidal Risk

Crisis ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 130-139 ◽  
Author(s):  
Danica W. Y. Liu ◽  
A. Kate Fairweather-Schmidt ◽  
Richard Burns ◽  
Rachel M. Roberts ◽  
Kaarin J. Anstey
Keyword(s):  
Risk Factors ◽  
Well Being ◽  
Current Status ◽  
Psychological Resilience ◽  
Cross Sectional ◽  
Life Project ◽  
Resilience Factors ◽  
First Time

Abstract. Background: Little is known about the role of resilience in the likelihood of suicidal ideation (SI) over time. Aims: We examined the association between resilience and SI in a young-adult cohort over 4 years. Our objectives were to determine whether resilience was associated with SI at follow-up or, conversely, whether SI was associated with lowered resilience at follow-up. Method: Participants were selected from the Personality and Total Health (PATH) Through Life Project from Canberra and Queanbeyan, Australia, aged 28–32 years at the first time point and 32–36 at the second. Multinomial, linear, and binary regression analyses explored the association between resilience and SI over two time points. Models were adjusted for suicidality risk factors. Results: While unadjusted analyses identified associations between resilience and SI, these effects were fully explained by the inclusion of other suicidality risk factors. Conclusion: Despite strong cross-sectional associations, resilience and SI appear to be unrelated in a longitudinal context, once risk/resilience factors are controlled for. As independent indicators of psychological well-being, suicidality and resilience are essential if current status is to be captured. However, the addition of other factors (e.g., support, mastery) makes this association tenuous. Consequently, resilience per se may not be protective of SI.

Theoretical and Methodological Problems of a 10-Year Follow-Up Program Evaluation Study

2002 ◽  
Vol 18 (3) ◽  
pp. 229-241 ◽  
Author(s):  
Kurt A. Heller ◽  
Ralph Reimann
Keyword(s):  
Program Evaluation ◽  
Evaluation Studies ◽  
Evaluation Study ◽  
Control Groups ◽  
Cross Sectional ◽  
Long Term Study ◽  
Partial Inclusion ◽  
Methodological Problems

Summary In this paper, conceptual and methodological problems of school program evaluation are discussed. The data were collected in conjunction with a 10 year cross-sectional/longitudinal investigation with partial inclusion of control groups. The experiences and conclusions resulting from this long-term study are revealing not only from the vantage point of the scientific evaluation of new scholastic models, but are also valuable for program evaluation studies in general, particularly in the field of gifted education.

Hearing Outcome of Stapes Surgery in NIENT, Bangladesh

2020 ◽  
Vol 26 (1) ◽  
pp. 31-36
Author(s):  
Md Zakaria Sarkar ◽  
AHM Ferdows Nur ◽  
Utpal Kumar Dutta ◽  
Muhammad Rafiqul Islam ◽  
Debabrota Roy ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Cross Sectional Study ◽  
Surgical Trauma ◽  
Group 14 ◽  
Progressive Hearing Loss ◽  
Cross Sectional ◽  
Hearing Gain ◽  
Hearing Outcome

Objective: The aim of this study was to evaluate hearing outcome after stapedotomy in patients with Otosclerosis. Methods: This cross sectional study was carried out from July 2017 to January 2019 in National Institute of ENT, Unit V. About 22 patients with Otosclerosis were included in this study. Diagnosis of Otosclerosis was based on the history, medical status with Otoscopy, Tuning fork tests and Audiometric tests. We compiled data on the pre and post operative air-bone gap (ABG) at 0.5, 1, 2 KHZ. The ABG was Calculated using AC and BC thresholds on the same audiogram. Post operative hearing gain was then Calculated from the ABG before the operation minus the ABG of the last follow up examination Results: In this study most of the cases were age group 14-30 years (72.7%), female (54.5%). Most common symptoms was progressive hearing loss, tinnitus (77.8%).The average preoperative hearing loss in this study was (AC) was 48.31±7.68. The average post opt. hearing (AC) at follow up was 28.95±10.30 with an average hearing gain of 15.40±8.53 dB which was significant. The average pre-operative ABG was 28.99 dB ± 8.10. The average post opt. ABG was analyzed at 1 follow up showed ABG 13.18±8.09 dB which was found to be significant. Conclusion: Stapedotomy is an effective surgical procedure for the treatment of otosclerosis which leads to improvement in patient’s quality of life. A favorable hearing outcome can be obtained by the combination of experienced hands with minimal surgical trauma and appropriate surgical technique. Bangladesh J Otorhinolaryngol; April 2020; 26(1): 31-36

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