Problem Based Learning and STEM Model design in a Secondary Biology Curriculum

Education in the field of science is designed to prepare students to achieve success and understanding of the science that surrounds them and hopefully train them for a continued education and understanding of the scientific field. When educational content is demonstrated rather than discovered it inhibits a student’s ability to become a lifelong learner and explorer of the sciences. Learning through demonstration also prevents students from experiencing the collaboration between science, technology, engineering and math (STEM). The following education plan models education that is designed around discovering how real world body systems function and collaborating to apply knowledge rather than repeat it. The literature review following will explain further benefits of real world discovery based education. Further review explains how problem based learning drives students to approach real world scientific problems in local communities with a scientific and systematic belief that their knowledge is applicable in a real world setting

Lake Pollution

This lesson discusses chemistry as it relates to real life environmental problems by stressing how concentration and rate of diffusion are key factors of pollution in bodies of water. This lesson allows students to gain familiarity with computational thinking. Students who have experience with this simulation will have a simple unintimidating introduction to computational science. Math skills will be assessed and strengthened through calculations and experience with the excel program. The primary file is a lesson plan, accompanied by supplemental files. In the supplemental zipped files, you will find: Student worksheets Lesson plan Powerpoint presentation

Innovations in Metastatic Brain Tumor Treatment

Metastatic brain tumors (MBTs) are the most common intracranial tumor and occur in up to 40% of patients with certain cancer diagnoses. The most common and frequent primary locations are cancers originating from the lung, breast, kidney, gastrointestinal tract or skin, and also may arising from any part of the body. Treatment for brain metastasis management includes surgery, whole brain radiotherapy (WBRT), stereotactic radiosurgery (SRS), and chemotherapy. Standard treatment for MBTs includes surgery and SRS which offer the best outcomes, while the WBRT is still an important treatment option for patients who cannot tolerate surgery and SRS or patients with multiple brain metastases. Newer approaches such as immunotherapy and molecularly targeted therapy (e.g., small molecules and monoclonal antibodies) are currently being evaluated for the treatment of MBTs. In this chapter, we will review current available treatments for MBTs and discuss treatments that are undergoing active investigation

Designing an ideal 3D-bioprint conduit for axonal repair and regeneration: a neurosurgical perspective

Peripheral nerve injuries occur through three mechanisms, specifically, crush, compression or transection. Disruption of communication between the peripheral and central nervous system follows and leads to motor and sensory deficits. Peripheral nerves in humans have a limited capacity to self-regenerate following injury, which makes nerve transfer the current gold-standard for treatment. Functional nerve regeneration is contingent on several factors ranging from span of injury and the age of the patient. Bioprinted nerve guidance conduits are an emerging candidate for treating peripheral nerve injuries. To optimize the performance of nerve guidance conduits, a firm understanding of neurobiology and the pathophysiology following injury is necessary. This article provides an overview of nerve regeneration and the desirable features when designing a nerve conduit from a neurosurgical perspective

Machine intelligence for nerve conduit design and production

Nerve guidance conduits (NGCs) have emerged from recent advances within tissue engineering as a promising alternative to autografts for peripheral nerve repair. NGCs are tubular structures with engineered biomaterials, which guide axonal regeneration from the injured proximal nerve to the distal stump. NGC design can synergistically combine multiple properties to enhance proliferation of stem and neuronal cells, improve nerve migration, attenuate inflammation and reduce scar tissue formation. The aim of most laboratories fabricating NGCs is the development of an automated process that incorporates patient-specific features and complex tissue blueprints (e.g. neurovascular conduit) that serve as the basis for more complicated muscular and skin grafts. One of the major limitations for tissue engineering is lack of guidance for generating tissue blueprints and the absence of streamlined manufacturing processes. With the rapid expansion of machine intelligence, high dimensional image analysis, and computational scaffold design, optimized tissue templates for 3D bioprinting (3DBP) are feasible. In this review, we examine the translational challenges to peripheral nerve regeneration and where machine intelligence can innovate bottlenecks in neural tissue engineering

Dental Professionals’ Perspective on Direct-To-Consumer Clear Aligners

Background: Technology continues to drastically change the practice of orthodontics. One recent change includes direct-to-consumer (DTC) clear aligners, a model that omits a clinical exam by a licensed dentist and radiographic evaluation prior to initiating treatment. The purpose of this study was to collect quantitative data about dental professionals’ perspectives of the DTC treatment model. Materials and Methods: The Qualtrics-based survey was disseminated to dental professionals using several email lists. The survey included 26 questions, containing four domains: basic demographic information, perceptions of the direct-to-consumer clear aligner model, standards of orthodontic care, and patient experiences. Responses were summarized with descriptive statistics. Associations between respondent demographics and their perceptions about DTC clear aligner treatment and standards of orthodontic care were evaluated using Mantel- Haenszel Chi-squared tests. Results: There were 334 completed surveys, with 155 orthodontists (46.4%), 154 general dentists (46.1%), and 25 other dental specialties (7.5%) participants. More than 95% of respondents had a generally negative view of the DTC treatment model, with most respondents citing “suboptimal orthodontic care” and “misleading the public about orthodontic treatment” as the biggest influence in their view. Over 94% of respondents agreed that it is not within the standard of care to initiate orthodontic treatment without an in-person clinical exam or radiographs. Conclusion: Results suggest that dental professionals regard treatment rendered by DTC modalities not in the best interest of the public. Practical Implications: Dentists should be more active with educating patients about the impact of different dental treatment modalities.Indiana University School of Dentistr

SSL4EO-L: Datasets and Foundation Models for Landsat Imagery

The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites. The multispectral imagery captured by sensors onboard these satellites is critical for a wide range of scientific fields. Despite the increasing popularity of deep learning and remote sensing, the majority of researchers still use decision trees and random forests for Landsat image analysis due to the prevalence of small labeled datasets and lack of foundation models. In this paper, we introduce SSL4EO-L, the first ever dataset designed for Self-Supervised Learning for Earth Observation for the Landsat family of satellites (including 3 sensors and 2 product levels) and the largest Landsat dataset in history (5M image patches). Additionally, we modernize and re-release the L7 Irish and L8 Biome cloud detection datasets, and introduce the first ML benchmark datasets for Landsats 4-5 TM and Landsat 7 ETM+ SR. Finally, we pre-train the first foundation models for Landsat imagery using SSL4EO-L and evaluate their performance on multiple semantic segmentation tasks. All datasets and model weights are available via the TorchGeo (https://github.com/microsoft/torchgeo) library, making reproducibility and experimentation easy, and enabling scientific advancements in the burgeoning field of remote sensing for a multitude of downstream applications

Does inter-border conflict influence the views of task sharing among community health volunteers in Nigeria? A qualitative study

Background: Volunteer community health workers are increasingly being engaged in Nigeria, through the World Health Organization’s task sharing strategy. This strategy aims to address gaps in human resources for health, including inequitable distribution of health workers. Recent conflicts in rural and fragile border communities in northcentral Nigeria create challenges for volunteer community health workers to meet their community's increasing health needs. This study aimed to explore the perception of volunteers involved in task sharing to understand factors affecting performance and delivery in such contexts. Methods: This was a qualitative study conducted in fragile border communities in north central Nigeria. Eighteen audio recorded, semi-structured interviews with volunteers and supervisors were performed. Their perceptions on how task sharing and allocation of tasks affect performance and delivery were elucidated. The transactional social framework was applied during the thematic analysis process to generate an explanatory account of the research data, which was analysed using NVivo software. Results: Promotive and preventive tasks were shared among the predominantly agrarian respondents. There was a structured task allocation process that linked the community with the health system and mainly cordial relationships were in place. However, there were barriers related to ethnoreligious crises and current conflict, timing of task allocations, gender inequities in volunteerism, shortage of commodities, inadequate incentives, dwindling community support and negative attitudes of some volunteers. Conclusion: The perception of task sharing was mainly positive, despite the challenges, especially the current conflict. In this fragile context, reconsideration of non-seasonal task allocations within improved community-driven selection and security systems should be encouraged. Supportive supervision and providing adequate and timely renumeration will also be beneficial in this fragile setting

SSL4EO-L: Datasets and Foundation Models for Landsat Imagery

The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites. The multispectral imagery captured by sensors onboard these satellites is critical for a wide range of scientific fields. Despite the increasing popularity of deep learning and remote sensing, the majority of researchers still use decision trees and random forests for Landsat image analysis due to the prevalence of small labeled datasets and lack of foundation models. In this paper, we introduce SSL4EO-L, the first ever dataset designed for Self-Supervised Learning for Earth Observation for the Landsat family of satellites (including 3 sensors and 2 product levels) and the largest Landsat dataset in history (5M image patches). Additionally, we modernize and re-release the L7 Irish and L8 Biome cloud detection datasets, and introduce the first ML benchmark datasets for Landsats 4–5 TM and Landsat 7 ETM+ SR. Finally, we pre-train the first foundation models for Landsat imagery using SSL4EO-L and evaluate their performance on multiple semantic segmentation tasks. All datasets and model weights are available via the TorchGeo library, making reproducibility and experimentation easy, and enabling scientific advancements in the burgeoning field of remote sensing for a multitude of downstream applications