Mental Health and School Functioning for Girls in the Child Welfare System : the Mediating Role of Future Orientation and School Engagement

This study investigated the association between mental health problems and academic and behavioral school functioning for adolescent girls in the child welfare system and determined whether school engagement and future orientation meditated the relationship. Participants were 231 girls aged between 12 and 19 who had been involved with the child welfare system. Results indicated that 39% of girls reported depressive symptoms in the clinical range and 54% reported posttraumatic symptoms in the clinical range. The most common school functioning problems reported were failing a class (41%) and physical fights with other students (35%). Participants reported a mean number of 1.7 school functioning problems. Higher levels of depression and PTSD were significantly associated with more school functioning problems. School engagement fully mediated the relationship between depression and school functioning and between PTSD and school functioning, both models controlling for age, race, and placement stability. Future orientation was not significantly associated with school functioning problems at the bivariate level. Findings suggest that school engagement is a potentially modifiable target for interventions aiming to ameliorate the negative influence of mental health problems on school functioning for adolescent girls with histories of abuse or neglect

Development and Applications of Fluorogen/Light-Up RNA Aptamer Pairs for RNA Detection and More.

The central role of RNA in living systems made it highly desirable to have noninvasive and sensitive technologies allowing for imaging the synthesis and the location of these molecules in living cells. This need motivated the development of small pro-fluorescent molecules called "fluorogens" that become fluorescent upon binding to genetically encodable RNAs called "light-up aptamers." Yet, the development of these fluorogen/light-up RNA pairs is a long and thorough process starting with the careful design of the fluorogen and pursued by the selection of a specific and efficient synthetic aptamer. This chapter summarizes the main design and the selection strategies used up to now prior to introducing the main pairs. Then, the vast application potential of these molecules for live-cell RNA imaging and other applications is presented and discussed.journal article2020importe

Design of machine learning models with domain experts for automated sensor selection for energy fault detection

Data-driven techniques that extract insights from sensor data reduce the cost of improving system energy performance through fault detection and system health monitoring. To lower cost barriers to widespread deployment, a methodology is proposed that takes advantage of existing sensor data, encodes expert knowledge about the application system to create ‘virtual sensors’ and applies statistical and mathematical methods to reduce the time required for manual configurations. The approach combines sensor data points with encoded expert knowledge that is generic to the application system but independent of a particular deployment, thereby reducing the need to tailor to individual deployments. This paper not only presents a method that detects faults from measured energy data, but also (1) describes an engagement method with experts in the energy system domain to identify data, (2) integrates domain knowledge with the data, (3) automatically selects from among the large pool of potential input data, and (4) uses machine learning to automatically build a data-driven fault detection model. Demonstration on a commercial building chiller plant shows that only a small number of virtual sensors is necessary for fault detection with high accuracy rates. This corresponds to the use of only five out of 52 original sensor data points features. With as few as four features, classification F1 scores exceed 90% on the training set and 80% on the testing set. The results are implementable and realizable using off-the-shelf tools. The goal is to design with domain experts an energy monitoring system that can be configured once and then widely deployed with little additional cost or effort

Design of machine learning models with domain experts for automated sensor selection for energy fault detection

Data-driven techniques that extract insights from sensor data reduce the cost of improving system energy performance through fault detection and system health monitoring. To lower cost barriers to widespread deployment, a methodology is proposed that takes advantage of existing sensor data, encodes expert knowledge about the application system to create ‘virtual sensors’ and applies statistical and mathematical methods to reduce the time required for manual configurations. The approach combines sensor data points with encoded expert knowledge that is generic to the application system but independent of a particular deployment, thereby reducing the need to tailor to individual deployments. This paper not only presents a method that detects faults from measured energy data, but also (1) describes an engagement method with experts in the energy system domain to identify data, (2) integrates domain knowledge with the data, (3) automatically selects from among the large pool of potential input data, and (4) uses machine learning to automatically build a data-driven fault detection model. Demonstration on a commercial building chiller plant shows that only a small number of virtual sensors is necessary for fault detection with high accuracy rates. This corresponds to the use of only five out of 52 original sensor data points features. With as few as four features, classification F1 scores exceed 90% on the training set and 80% on the testing set. The results are implementable and realizable using off-the-shelf tools. The goal is to design with domain experts an energy monitoring system that can be configured once and then widely deployed with little additional cost or effort

Abnormal EEG slow activity in left temporal areas in senile dementia of the Alzheimer type.

Resting 32-channel topographical measures of EEG slow activity were compared in 12 elderly controls and 12 patients with senile dementia of the Alzheimer type. The patients had higher amplitude delta and theta than controls, especially in the left temporal regions. This greater amount of low frequency EEG activity in the left temporal area is consistent with recent EEG, neuropsychological assessment, and positron emission tomography findings in SDAT patients. Five patients with mild-to-moderate dementia (as determined by the Folstein Mini-Mental State scale) primarily exhibited focal, abnormal slow activity in the left temporal regions. Seven patients with severe dementia exhibited increased slow activity across the head, which was still most abnormal in the left temporal regions

Abnormal EEG slow activity in left temporal areas in senile dementia of the Alzheimer type.

Resting 32-channel topographical measures of EEG slow activity were compared in 12 elderly controls and 12 patients with senile dementia of the Alzheimer type. The patients had higher amplitude delta and theta than controls, especially in the left temporal regions. This greater amount of low frequency EEG activity in the left temporal area is consistent with recent EEG, neuropsychological assessment, and positron emission tomography findings in SDAT patients. Five patients with mild-to-moderate dementia (as determined by the Folstein Mini-Mental State scale) primarily exhibited focal, abnormal slow activity in the left temporal regions. Seven patients with severe dementia exhibited increased slow activity across the head, which was still most abnormal in the left temporal regions

Nicholson's blowflies revisited

A simple time-delay model of laboratory insect populations which postulates a ‘humped’ relationship between future adult recruitment and current adult population gives good quantitative agreement with Nicholson's classic blowfly data and explains the appearance of narrow ‘discrete’ generations in cycling populations