An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths

Barriers and facilitators to using a web-based tool for diagnosis and monitoring of patients with depression: a qualitative study among Danish general practitioners

Abstract Background Depression constitutes a significant part of the global burden of diseases. General practice plays a central role in diagnosing and monitoring depression. A telemedicine solution comprising a web-based psychometric tool may reduce number of visits to general practice and increase patient empowerment. However, the current use of telemedicine solutions in the field of general practice is limited. This study aims to explore barriers and facilitators to using a web-based version of the Major Depression Inventory (eMDI) for psychometric testing of potentially depressive patients in general practice. Methods Semi-structured individual interviews were conducted with nine general practitioners (GPs) from eight general practices in the Central Denmark Region. All interviewees had previous experience in using the eMDI in general practice. Determinants for using the eMDI were identified in relation to the GPs’ capability, opportunity and motivation to change clinical behaviour (the COM-B system). Results Our results indicate that the main barriers for using the eMDI are related to limitations in the GPs’ opportunity in regards to having the time it takes to introduce change. Further, the use of the eMDI seems to be hampered by the time-consuming login process. Facilitating factors included behavioural aspects of capability, opportunity and motivation. The implementation of the eMDI was facilitated by the interviewees’ previous familiarity with the paper-based version of the tool. Continued use of the eMDI was facilitated by a time-saving documentation process and motivational factors associated with clinical core values. These factors included perceptions of improved consultation quality and services for patients, improved possibilities for GPs to prioritise their patients and improved possibilities for disease monitoring. Furthermore, the flexible nature of the eMDI allowed the GPs to use the paper-based MDI for patients whom the eMDI was not considered appropriate. Conclusions Implementation of a telemedicine intervention in general practice can be facilitated by resemblance between the intervention and already existing tools as well as the perception among GPs that the intervention is time-saving and improves quality of care for the patients

The STAR Heavy Flavor Tracker (HFT)

The heavy quark hadrons are suggested as a clean probe for studying the early dynamic evolution of the dense and hot medium created in high-energy nuclear collisions. The Heavy Flavor Tracker (HFT) of the STAR experiment, designed to improve the vertex resolution and extend the measurement capabilities in the heavy flavor domain, was installed for the 2014 heavy ion run of RHIC. It is composed of three different silicon detectors arranged in four concentric cylinders close to the STAR interaction point. The two innermost layers are based on CMOS monolithic active pixels (MAPS), featured for the first time in a collider experiment, and the two outer layers are based on pads and strips. The two innermost HFT layers are placed at a radius of 2.8 and 8~cm from the beam line and accommodate 400 ultra-thin ($50 \mu m$) high resolution MAPS sensors arranged in 10-sensor ladders to cover a total silicon area of $0.16m^{2}$. Each sensor includes a pixel array of 928 rows and 960 columns with a $20.7\mu m$ pixel pitch, providing a sensitive area of $\sim 3.8 cm^{2}$. The sensor features $185.6 \mu s$ readout time and $170 mW/cm^{2}$ power dissipation, allowing it to be air cooled, which results in a global material budget of only 0.5% radiation length per layer in the run 14 detector. A novel mechanical approach to detector insertion enables effective installation and integration of the pixel layers within a 12 hour shift during the on-going STAR Run. After a detailed description of the design specifications and the technology implementation, the detector status and operations during the 200 GeV Au+Au RHIC run of 2014 will be presented in this paper. A preliminary estimation of the detector performance meeting the design requirements will be reported

Association between left ventricular diastolic function and right ventricular function and morphology in asymptomatic aortic stenosis.

BackgroundAortic stenosis (AS) is a progressive disease in which left ventricular (LV) diastolic dysfunction is common. However, the association between diastolic dysfunction and right ventricular (RV) loading conditions and function has not been investigated in asymptomatic AS patients.Methods and findingsA total of 41 patients underwent right heart catheterization and simultaneous echocardiography at rest and during maximal supine exercise, stratified according to resting diastolic function. Cardiac chamber size and morphology was assessed using cardiac magnetic resonance imaging (cMRI). RV stroke work index, pulmonary artery (PA) compliance, PA elastance, PA pulsatility index, and right atrial pressure (RAP) were calculated at rest and maximal exercise. Ten patients (24%) had normal LV filling pattern, 20 patients (49%) had grade 1, and 11 patients (27%) had grade 2 diastolic dysfunction. Compared to patients with normal diastolic filling pattern, patients with diastolic dysfunction had lower RV end-diastolic volume (66 ± 11 ml/m2 vs. 79 ± 15 ml/m2, p = 0.02) and end-systolic volume (25 ± 7 ml/m2 vs. 32 ± 9 ml/m2, p = 0.04). An increase in mean RAP to ≥15 mmHg following exercise was not seen in patients with normal LV filling, compared to 4 patients (20%) with mild and 7 patients (63%) with moderate diastolic dysfunction (p = 0.003). PA pressure and PA elastance was increased in grade 2 diastolic dysfunction and correlated with RV volume and maximal oxygen consumption (r = -0.71, p ConclusionsModerate diastolic dysfunction is associated with increased RV afterload (elastance), which is compensated at rest, but is associated with increased RAP and inversely related to maximal oxygen consumption during maximal exercise