Photothermal effect by 808-nm laser irradiation of melanin: A proof-of-concept study of photothermal therapy using b16-f10 melanotic melanoma growing in BALB/c mice

The photothermal effect is undergoing great interest due to advances in new photosensitizing materials and better-suited light sources, but studies are frequently hampered by the need to employ exogenous photothermal agents and expensive irradiation devices. Here we present a simple strategy based on direct NIR irradiation of the melanin pigment with a commercial 808-nm laser pointer. Proof-of-concept studies showed efficient photothermal effects on melanin in vitro and in vivo. After NIR irradiation, BALB/c mice bearing B16-F10 melanotic melanoma tumors revealed severe histopathological damage and massive necrosis in melanin-containing tumor tissue, while surrounding healthy tissues showed no damage. Therefore, the feasibility of this approach may allow implementing direct procedures for photothermal therapy of pigmented tumors.Fil: Colombo, Lucas Luis. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Oncología "Ángel H. Roffo"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; ArgentinaFil: Vanzulli, Silvia I.. Comisión Nacional de Energía Atómica; Argentina. Academia Nacional de Medicina de Buenos Aires; ArgentinaFil: Blázquez Castro, Alfonso. Universidad Nacional de Educacion A Distancia. Facultad de Ciencias.; EspañaFil: Terrero, Clara Sanchez. Comisión Nacional de Energía Atómica; ArgentinaFil: Stockert, Juan C.. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Oncología "Ángel H. Roffo"; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Instituto de Investigacion y Tecnología en Reproducción Animal; Argentin

Generating actionable predictions regarding MOOC learners' engagement in peer reviews

Peer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g. improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples

Optimal, Low-Complexity Beamforming for Discrete Phase Reconfigurable Intelligent Surfaces

Reflective reconfigurable intelligent surface (RIS) technology is regarded as an innovative, cost- and power-effective solution that aims at influencing the wireless channel through controlled scattering. The technology can be realized by using metamaterials and/or resonant elements that scatter electromagnetic waves with a configurable phase shift. Most of the previous work on beamforming techniques for RIS assumes ideal hardware and, thus, continuous phase shifts. However, hardware constraints limit the phase shift resolution, manifested into the amount of discrete phase shifts that can be configured into each RIS element. This paper aims to offer a discrete phase shift beamforming algorithm for reflective RISs that targets minimization of the quantization error resulting from discretization of continuous phase shifts. The beamforming solution proves to be optimal under perfect channel knowledge for any discrete set of uniformly distributed phase shifts. The required complexity to find the optimal beamforming vector for our approach is found to be linear with the number of RIS elements, the minimum needed to obtain optimal results. Simulated behavior is validated by measurements, showing robustness against angle misalignments and distance variations

Easing the inferential leap in competency modeling: The effects of task-related information and subject matter expertise

Despite the rising popularity of the practice of competency modeling, research on competency modeling has lagged behind. This study begins to close this practice–science gap through 3 studies (1 lab study and 2 field studies), which employ generalizability analysis to shed light on (a) the quality of inferences made in competency modeling and (b) the effects of incorporating elements of traditional job analysis into compe-tency modeling to raise the quality of competency inferences. Study 1 showed that competency modeling resulted in poor interrater reliabil-ity and poor between-job discriminant validity amongst inexperienced raters. In contrast, Study 2 suggested that the quality of competency inferences was higher among a variety of job experts in a real organiza-tion. Finally, Study 3 showed that blending competency modeling efforts and task-related information increased both interrater reliability among SMEs and their ability to discriminate among jobs. In general, this set of results highlights that the inferences made in competency modelin