Search

This study explored principals' reactions to findings from a survey regarding their teachers' evaluation beliefs. As participants in a longitudinal study, these principals were invited to focus group meetings to discuss the teacher survey data, which were sent to them for review prior to the meetings. They were asked to consider data that were puzzling, surprising, inconsistent, or consistent with their perceptions of conducting teacher evaluation. The focus group data were analyzed using the Johari Window, consisting of four domains of awareness. Based on the Johari Window analysis, principals shared teachers' awareness of a checklist approach to observations but maintained different beliefs about a checklist's intentions and efficiencies. Principals were unaware of or disagreed with the following teacher beliefs: (a) principal feedback is marginally effective for teachers' instructional improvement, (b) the evaluation ratings are applied unfairly, and (c) too much of principals' time is allotted to the evaluation process. This study illustrates that principals and teachers have contradictory beliefs regarding the practice and value of teacher evaluation. The researchers suggest that principals should consider applying the Johari Window construct to reveal and explore teacher perceptions that could hinder an effective supervision and evaluation process.

Extremely dense breasts can be an independent risk factor for breast cancer. A new FDA rule requires that patients be notified of their breast density and the possible benefits of additional imaging to screen for breast cancer. Clinicians should be cognizant of the data about breast cancer risk, breast density, and recommendations to change screening techniques if patients, particularly premenopausal females, have extremely dense breasts but no other known risk factors. [ABSTRACT FROM AUTHOR]

The utilization of ambient ionization (AI) techniques for mass spectrometry (MS) has significantly grown due to their ability to facilitate rapid and direct sample analysis with minimal sample preparation. This study investigates the performance of various AI techniques, including atmospheric solids analysis probe (ASAP), thermal desorption corona discharge (TDCD), direct analysis in real time (DART), and paper spray coupled to a Waters QDa mass spectrometer. The focus is on evaluating the linearity, repeatability, and limit of detection (LOD) of these techniques across a range of analytes, including amino acids, drugs, and explosives. The results show that each AI technique exhibits distinct advantages and limitations. ASAP and DART cover high concentration ranges, which may make them suitable for semiquantitative analysis. TDCD demonstrates exceptional linearity and repeatability for most analytes, while paper spray offers surprising LODs despite its complex setup (between 80 and 400 pg for most analytes). The comparison with electrospray ionization (ESI) as a standard method shows that ambient ionization techniques can achieve competitive LODs for various compounds such as PETN (80 pg ESI vs 100 pg ASAP), TNT (9 pg ESI vs 4 pg ASAP), and RDX (4 pg ESI vs 10 pg ASAP). This study underscores the importance of selecting the appropriate ambient ionization technique based on the specific analytical requirements. This comprehensive evaluation contributes valuable insights into the selection and optimization of AI techniques for diverse analytical applications. [ABSTRACT FROM AUTHOR]

Abstract: Extremely dense breasts can be an independent risk factor for breast cancer. A new FDA rule requires that patients be notified of their breast density and the possible benefits of additional imaging to screen for breast cancer. Clinicians should be cognizant of the data about breast cancer risk, breast density, and recommendations to change screening techniques if patients, particularly premenopausal females, have extremely dense breasts but no other known risk factors., (Copyright © 2024 American Academy of Physician Associates.)

Ambient ionization (AI) is a rapidly growing field in mass spectrometry (MS). It allows for the direct analysis of samples without any sample preparation, making it a promising technique for the detection of explosives. Previous studies have shown that AI can be used to detect a variety of explosives, but the exact gas-phase reactions that occur during ionization are not fully understood. This is further complicated by differences in mass spectrometers and individual experimental set ups between researchers. This study investigated the gas-phase ion reactions of five different explosives using a variety of AI techniques coupled to a Waters QDa mass spectrometer to identify selective ions for explosive detection and identification based on the applied ambient ionization technique. The results showed that the choice of the ion source can have a significant impact on the number of ions observed. This can affect the sensitivity and selectivity of the data produced. The findings of this study provide new insights into the gas-phase ion reactions of explosives and could lead to the development of more sensitive and selective AI-based methods for their detection.