scholarly journals A full sample-type magneto-impedance (MI) magnetometer with programmable circuit for high compatibility

AIP Advances ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 015026
Author(s):  
Ke Shi ◽  
Tsuyoshi Uchiyama
Keyword(s):  
Sample Type ◽  
Full Sample ◽  
Programmable Circuit

Rumination in Borderline Personality Disorder: Meta-Analysis

2018 ◽  
Author(s):  
Mara J. Richman ◽  
Zsolt Unoka ◽  
Robert Dudas ◽  
Zsolt Demetrovics
Keyword(s):  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Emotion Dysregulation ◽  
Meta Analysis ◽  
Borderline Personality ◽  
Therapeutic Interventions ◽  
Sample Type ◽  
Moderator Variables ◽  
Analytic Review ◽  
Self Harm

Borderline personality disorder (BPD) is characterized by deficits in emotion regulation and affective liability. Of this domain, ruminative behaviors have been considered a core feature of emotion dysregulation difficulties. Despite this, inconsistencies have existed in the literature regarding which rumination type is most prominent in those with BPD symptoms. Moreover, no meta-analytic review has been performed to date on rumination in BPD. Taking this into consideration, a meta-analysis was performed to assess how BPD symptoms correlate with rumination, while also considering clinical moderator variables (i.e., BPD symptom domain, co-morbidities, GAF score) and demographic moderator variables (i.e., age, gender, sample type, and education level). Analysis of correlation across rumination domains for the entire sample revealed a medium overall correlation between BPD symptoms and rumination. When assessing types of rumination, the largest correlation was among pain rumination followed by anger, depressive, and anxious rumination. Among BPD symptom domain, affective instability had the strongest correlation with increased rumination, followed by unstable relationships, identity disturbance, and self-harm/ impulsivity, respectively. Demographic variables showed no significance. Clinical implications are considered and further therapeutic interventions are discussed in the context of rumination.

Team Learning Behaviors and Performance: A Meta-Analysis of Direct Effects and Moderators

2021 ◽  
pp. 105960112110169
Author(s):  
Christopher W. Wiese ◽  
C. Shawn Burke ◽  
Yichen Tang ◽  
Claudia Hernandez ◽  
Ryan Howell
Keyword(s):  
Team Performance ◽  
Team Learning ◽  
Task Complexity ◽  
Meta Analysis ◽  
Sample Type ◽  
Learning Behaviors ◽  
Learning Teams ◽  
And Performance ◽  
The Relationship ◽  
Team Familiarity

Under what conditions do team learning behaviors best predict team performance? The current meta-analytic efforts synthesize results from 113 effect sizes and 7758 teams to investigate how different conceptualizations (fundamental, intrateam, and interteam), team characteristics (team size and team familiarity), task characteristics (interdependence, complexity, and type), and methodological characteristics (students vs. nonstudents and measurement choice) affect the relationship between team learning behaviors and team performance. Our results suggest that while different conceptualizations of team learning behaviors independently predict performance, only intrateam learning behaviors uniquely predict performance. A more in-depth investigation into the moderating conditions contradicts the familiar adage of “it depends.” The strength of the relationship between intrateam learning behaviors and team performance did not depend on team familiarity, task complexity, or sample type. However, our results suggested this relationship was stronger in larger teams, teams with moderate task interdependence, teams performing project/action tasks, and studies that use measures that capture a wider breadth of the team learning behavior construct space. These efforts suggest that common boundary conditions do not moderate this relationship. Scholars can leverage these results to develop more comprehensive theories addressing the different conceptualizations of team learning behaviors as well as providing clarity on the scenarios where team learning behaviors are most needed. Further, practitioners can use our results to develop more guided team-based policies that can overcome some of the challenges of forming and developing learning teams.

scholarly journals Explainable AI enables clinical trial patient selection to retrospectively improve treatment effects in schizophrenia

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Monika S. Mellem ◽  
Matt Kollada ◽  
Jane Tiller ◽  
Thomas Lauritzen
Keyword(s):  
Clinical Trial ◽  
Patient Selection ◽  
Treatment Effect ◽  
Controlled Clinical Trial ◽  
Learning Approaches ◽  
Full Sample ◽  
Cohen’S D ◽  
Explainable Ai ◽  
Cohen's D ◽  
Analytical Approaches

Abstract Background Heterogeneity among patients’ responses to treatment is prevalent in psychiatric disorders. Personalized medicine approaches—which involve parsing patients into subgroups better indicated for a particular treatment—could therefore improve patient outcomes and serve as a powerful tool in patient selection within clinical trials. Machine learning approaches can identify patient subgroups but are often not “explainable” due to the use of complex algorithms that do not mirror clinicians’ natural decision-making processes. Methods Here we combine two analytical approaches—Personalized Advantage Index and Bayesian Rule Lists—to identify paliperidone-indicated schizophrenia patients in a way that emphasizes model explainability. We apply these approaches retrospectively to randomized, placebo-controlled clinical trial data to identify a paliperidone-indicated subgroup of schizophrenia patients who demonstrate a larger treatment effect (outcome on treatment superior than on placebo) than that of the full randomized sample as assessed with Cohen’s d. For this study, the outcome corresponded to a reduction in the Positive and Negative Syndrome Scale (PANSS) total score which measures positive (e.g., hallucinations, delusions), negative (e.g., blunted affect, emotional withdrawal), and general psychopathological (e.g., disturbance of volition, uncooperativeness) symptoms in schizophrenia. Results Using our combined explainable AI approach to identify a subgroup more responsive to paliperidone than placebo, the treatment effect increased significantly over that of the full sample (p < 0.0001 for a one-sample t-test comparing the full sample Cohen’s d = 0.82 and a generated distribution of subgroup Cohen’s d’s with mean d = 1.22, std d = 0.09). In addition, our modeling approach produces simple logical statements (if–then-else), termed a “rule list”, to ease interpretability for clinicians. A majority of the rule lists generated from cross-validation found two general psychopathology symptoms, disturbance of volition and uncooperativeness, to predict membership in the paliperidone-indicated subgroup. Conclusions These results help to technically validate our explainable AI approach to patient selection for a clinical trial by identifying a subgroup with an improved treatment effect. With these data, the explainable rule lists also suggest that paliperidone may provide an improved therapeutic benefit for the treatment of schizophrenia patients with either of the symptoms of high disturbance of volition or high uncooperativeness. Trial Registration: clincialtrials.gov identifier: NCT 00,083,668; prospectively registered May 28, 2004

scholarly journals PERFORMANCE CHARACTERISTICS OF A FRET-BASED IMMUNOASSAY FOR QUANTITATION OF INFLIXIMAB ON A POINT-OF-CARE INSTRUMENT SYSTEM

2021 ◽  
Vol 27 (Supplement_1) ◽  
pp. S57-S57
Author(s):  
Edgar Ong ◽  
Ruo Huang ◽  
Richard Kirkland ◽  
Michael Hale ◽  
Larry Mimms
Keyword(s):  
Whole Blood ◽  
Limit Of Detection ◽  
Point Of Care ◽  
Quantitative Detection ◽  
Therapeutic Drug ◽  
Analytical Sensitivity ◽  
Sample Type ◽  
Hook Effect ◽  
Limit Of Quantitation ◽  
Assay Precision

Abstract Introduction A fast (&lt;5 min), time-resolved fluorescence resonance energy transfer (FRET)-based immunoassay was developed for the quantitative detection of infliximab (IFX) and biosimilars for use in therapeutic drug monitoring using only 20 µL of fingerstick whole blood or serum at the point-of-care. The Procise IFX assay and ProciseDx analyzer are CE-marked. Studies were performed to characterize analytical performance of the Procise IFX assay on the ProciseDx analyzer. Methods Analytical testing was performed by spiking known amounts of IFX into negative serum and whole blood specimens. Analytical sensitivity was determined using limiting concentrations of IFX. Linearity was determined by testing IFX across the assay range. Hook effect was assessed at IFX concentrations beyond levels expected to be found within a patient. Testing of assay precision, cross-reactivity and potential interfering substances, and biosimilars was performed. The Procise IFX assay was also compared head-to-head with another CE-marked assay: LISA-TRACKER infliximab ELISA test (Theradiag, France). The accuracy of the Procise IFX assay is established through calibrators and controls traceable to the WHO 1st International Standard for Infliximab (NIBSC code: 16/170). Results The Procise IFX assay shows a Limit of Blank, Limit of Detection, and Lower Limit of Quantitation (LLoQ) of 0.1, 0.2, and 1.1 µg/mL in serum and 0.6, 1.1, and 1.7 µg/mL in whole blood, respectively. The linear assay range was determined to be 1.7 to 77.2 µg/mL in serum and whole blood. No hook effect was observed at an IFX concentration of 200 µg/mL as the value reported as “&gt;ULoQ”. Assay precision testing across 20 days with multiple runs and reagent lots showed an intra-assay coefficient of variation (CV) of 2.7%, an inter-assay CV of &lt;2%, and a total CV of 3.4%. The presence of potentially interfering/cross-reacting substances showed minimal impact on assay specificity with %bias within ±8% of control. Testing of biosimilars (infliximab-dyyb and infliximab-abda) showed good recovery. A good correlation to the Theradiag infliximab ELISA was obtained for both serum (slope=1.01; r=0.99) and whole blood (slope=1.01; r=0.98) samples (Figure 1). Conclusion Results indicate that the Procise IFX assay is sensitive, specific, and precise yielding results within 5 minutes from both whole blood and serum without the operator needing to specify sample type. Additionally, it shows very good correlation to a comparator assay that takes several hours and sample manipulation to yield results. This makes the Procise IFX assay ideal for obtaining fast and accurate IFX quantitation, thus allowing for immediate drug level dosing decisions to be made by the physician during patient treatment.

The DIVING3D Project: Analysis of the nuclear region of Early-type Galaxies

2020 ◽  
Vol 15 (S359) ◽  
pp. 454-456
Author(s):  
T. V. Ricci ◽  
J. E. Steiner ◽  
R. B. Menezes
Keyword(s):  
Emission Lines ◽  
H Ii Regions ◽  
Sample Type ◽  
Early Type ◽  
Integral Field Spectroscopy ◽  
Project Analysis ◽  
Line Region ◽  
Field Unit ◽  
Integral Field

AbstractIn this work, we present preliminary results regarding the nuclear emission lines of a statistically complete sample of 56 early-type galaxies that are part of the Deep Integral Field Spectroscopy View of Nuclei of Galaxies (DIVING3D) Project. All early type galaxies (ETGs) were observed with the Gemini Multi-Object Spectrograph Integral Field Unit (GMOS-IFU) installed on the Gemini South Telescope. We detected emission lines in 93% of the sample, mostly low-ionization nuclear emission-line region galaxies (LINERs). We did not find Transition Objects nor H II regions in the sample. Type 1 objects are seen in ∼23% of the galaxies.

scholarly journals Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2195
Author(s):  
Lucas de Paula Corrêdo ◽  
Leonardo Felipe Maldaner ◽  
Helizani Couto Bazame ◽  
José Paulo Molin
Keyword(s):  
Sample Preparation ◽  
Prediction Models ◽  
Spectral Response ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Partial Least Square ◽  
Least Square ◽  
Sample Type ◽  
Sampling Strategies ◽  
Significant Difference

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

scholarly journals Intelligent Recognition Method of Low-Altitude Squint Optical Ship Target Fused with Simulation Samples

2021 ◽  
Vol 13 (14) ◽  
pp. 2697
Author(s):  
Bo Liu ◽  
Qi Xiao ◽  
Yuhao Zhang ◽  
Wei Ni ◽  
Zhen Yang ◽  
...  
Keyword(s):  
Feature Recognition ◽  
Spectral Characteristics ◽  
Transmission Model ◽  
Sample Type ◽  
Recognition Method ◽  
Atmospheric Transmission ◽  
K Nearest Neighbors ◽  
Imaging Simulation ◽  
Low Altitude ◽  
Intelligent Recognition

To address the problem of intelligent recognition of optical ship targets under low-altitude squint detection, we propose an intelligent recognition method based on simulation samples. This method comprehensively considers geometric and spectral characteristics of ship targets and ocean background and performs full link modeling combined with the squint detection atmospheric transmission model. It also generates and expands squint multi-angle imaging simulation samples of ship targets in the visible light band using the expanded sample type to perform feature analysis and modification on SqueezeNet. Shallow and deeper features are combined to improve the accuracy of feature recognition. The experimental results demonstrate that using simulation samples to expand the training set can improve the performance of the traditional k-nearest neighbors algorithm and modified SqueezeNet. For the classification of specific ship target types, a mixed-scene dataset expanded with simulation samples was used for training. The classification accuracy of the modified SqueezeNet was 91.85%. These results verify the effectiveness of the proposed method.

scholarly journals Does seeing a psychologist reduce sickness absence due to mental disorders? A quasi-experiment

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Lahti ◽  
J Harkko ◽  
H Sumanen ◽  
K Piha ◽  
O Pietiläinen ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Sickness Absence ◽  
Work Ability ◽  
Early Stage ◽  
Matched Sample ◽  
Full Sample ◽  
Common Support ◽  
The City ◽  
Quasi Experiment

Abstract Background Mental ill-health in young adults is a major public health and work-life problem. We examined in a quasi-experimental design whether occupational psychologist appointment can reduce subsequent sickness absence due to mental disorders among young Finnish employees. Methods The present study was conducted among 18-39-year-old employees of the City of Helsinki using register data from the City of Helsinki and the Social lnsurance Institution of Finland. We used Wald test to compare the differences in sickness absence days due to mental disorders (ICD-10, F-diagnosed) between those treated (occupational psychologist appointment for work ability support) and the non-treated (no psychologist appointment) during a one year follow-up. The full sample (n = 2156, 84% women) consisted of employees with mental disorder diagnosed sickness absence during 2009-2014. To account for the systematic differences between the treated and non-treated, the participants were matched according to their characteristics (age, sex, occupational class, education, previous sickness absence and psychotropic medication). The matched sample included 886 participants. We excluded those with treatment before the treatment screening time (± 3 months to the end of sickness absence period), non-treated with treatment during the follow-up and those that could not be matched (lack of common support). Results In the full sample, the mean of sickness absence days due to mental disorders was 17.7 (95% CI, 11.4, 24.1) days for those treated (n = 240) and 23.2 (95% CI, 20.5, 25.9) days for non-treated (n = 1916), difference being non-significant. The corresponding figures in the matched sample were (16.8, 95% CI, 9.5-24.1) for those treated (n = 195) and (27.8, 95% CI, 22.6-32.9) for non-treated (n = 691), difference being statistically significant (p = 0.02). Conclusions This quasi-experiment suggests that seeing an occupational psychologist to support work ability may be reduce mental health related sickness absence. Key messages We showed that supporting work ability at an early stage may prevent sickness absence due to mental disorders. More efforts to provide early stage support for maintaining work ability may prove useful in reducing sickness absence rates in younger employees.

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