Assessment of a Teaching Module for Cardiac Auscultation of Horses by Veterinary Students

Auscultation of heart sounds is an important veterinary skill requiring an understanding of anatomy, physiology, pathophysiology and pattern recognition. This cross-sectional study was developed to evaluate a targeted, audio-visual training resource for veterinary students to improve their understanding and auscultation of common heart conditions in horses. Fourth- and fifth-year 2021 and 2022 Bachelor of Veterinary Science students at the University of Queensland (UQ) were provided the learning resource and surveyed via online pre- and post-intervention surveys. Results were quantitatively analyzed using descriptive statistics and Mann-Whitney U tests. Open-ended survey questions were qualitatively analyzed by thematic analysis and Leximancer™ Version 4 program software analysis. Over the two-year period, 231 fourth-year and 222 fifth-year veterinary students had access to the resource; 89 completed the pre-intervention survey and 57 completed the post-intervention survey. Quantitative results showed the resource helped students prepare for practicals and their perception of competency and confidence when auscultating equine cardiac sounds improved (p < 0.05). Compared to fifth-year students, fourth-year students felt less competent at identifying murmurs and arrythmias prior to accessing the learning resource (p < 0.05). Fourth-year and fifth-year students' familiarity with detection of murmurs improved after completing the learning resource (p < 0.001). Qualitative analysis demonstrated a limited number of opportunities to practice equine cardiac auscultation throughout the veterinary degree, especially during the COVID-19 pandemic, and that integrated audio-visual resources are an effective means of teaching auscultation.

Design and Implementation of a Career Development Program for Physician-Scientists: Lessons Learned

IMPORTANCE

Although skills in health services research and data science have great potential to advance the field of urogynecology, few clinical researchers obtain such training.

OBJECTIVES

The aim of the R25 UrogynCREST Program is to prepare the next generation of physician-scientists for a successful career in urogynecologic health services research through skilled mentoring and advanced training. The purpose of this report is to describe program implementation and lessons learned.

STUDY DESIGN

Administered through the program institution and in partnership with the American Urogynecologic Society, this program provided junior faculty with advanced online training and, through a core facility, access to health care databases for research projects. Participants received individualized mentoring and biostatistical support. Anonymous surveys captured actionable, real-time feedback from participants as they moved through the program.

RESULTS

Despite a limited budget, UrogynCREST maintained a core of excellent faculty, high-quality biostatistical support, and engaged, knowledgeable advisors and mentors. This allowed for similar experiences across cohorts while permitting program improvements between cohorts in faculty-participant interactions, team dynamics, and data and regulatory support. Administrative management by a single institution facilitated responses to fiscal and regulatory changes. Asynchronized learning and partnering with a society attracted a diverse group of physician-scientists.

CONCLUSIONS

Career development programs that incorporate online education, mentoring, database access, and biostatistical support must be prepared for midprogram changes. Regular communication among stakeholders was vital. Working with a core facility provided efficient database access, but evolving regulatory and administrative processes and costs presented challenges. Our experiences implementing this program can benefit similar programs that train early-career physician-scientists.

MONETA: A Processing-In-Memory-Based Hardware Platform for the Hybrid Convolutional Spiking Neural Network With Online Learning

We present a processing-in-memory (PIM)-based hardware platform, referred to as MONETA, for on-chip acceleration of inference and learning in hybrid convolutional spiking neural network. MONETAuses 8T static random-access memory (SRAM)-based PIM cores for vector matrix multiplication (VMM) augmented with spike-time-dependent-plasticity (STDP) based weight update. The spiking neural network (SNN)-focused data flow is presented to minimize data movement in MONETAwhile ensuring learning accuracy. MONETAsupports on-line and on-chip training on PIM architecture. The STDP-trained convolutional neural network within SNN (ConvSNN) with the proposed data flow, 4-bit input precision, and 8-bit weight precision shows only 1.63% lower accuracy in CIFAR-10 compared to the STDP accuracy implemented by the software. Further, the proposed architecture is used to accelerate a hybrid SNN architecture that couples off-chip supervised (back propagation through time) and on-chip unsupervised (STDP) training. We also evaluate the hybrid network architecture with the proposed data flow. The accuracy of this hybrid network is 10.84% higher than STDP trained accuracy result and 1.4% higher compared to the backpropagated training-based ConvSNN result with the CIFAR-10 dataset. Physical design of MONETAin 65 nm complementary metal-oxide-semiconductor (CMOS) shows 18.69 tera operation per second (TOPS)/W, 7.25 TOPS/W and 10.41 TOPS/W power efficiencies for the inference mode, learning mode, and hybrid learning mode, respectively.

Academic clinical learning environment in obstetrics and gynecology during the COVID-19 pandemic: responses and lessons learned

COVID-19 pandemic is changing profoundly the obstetrics and gynecology (OB/GYN) academic clinical learning environment in many different ways. Rapid developments affecting our learners, patients, faculty and staff require unprecedented collaboration and quick, deeply consequential readjustments, almost on a daily basis. We summarized here our experiences, opportunities, challenges and lessons learned and outline how to move forward. The COVID-19 pandemic taught us there is a clear need for collaboration in implementing the most current evidence-based medicine, rapidly assess and improve the everchanging healthcare environment by problem solving and "how to" instead of "should we" approach. In addition, as a community with very limited resources we have to rely heavily on internal expertise, ingenuity and innovation. The key points to succeed are efficient and timely communication, transparency in decision making and reengagement. As time continues to pass, it is certain that more lessons will emerge.

Immunology Education Without Borders

One of the mandates of the International Union of Immunological Societies (IUIS) is to promote immunological education to young scientists across the globe, including a large focus on those from low and low-to-middle income countries (LIC and LMIC). It strives to achieve this goal through the Education Committee (EDU), which is one of ten committees of the IUIS. To this end, EDU organizes three to four one-week courses per year in close cooperation with regional immunological societies and local organizers. Initially, the focus has been on Africa, addressing the most relevant topics and health issues facing specific countries or regions in the continent. The idea was then extended to Latin America and now also includes courses in Asia. The faculty of all courses is a blend of international and local/regional experts also known for their teaching expertise. The courses are highly interactive, and include “meet-the-speakers” sessions, poster walks, and sessions on grant or PhD project writing, and on practical aspects of becoming a successful scientist. Importantly, all the IUIS-EDU courses use a combination of pre- and during-course on-line learning followed by consolidation of knowledge in a collegial setting. This “flipped” classroom approach ensures that participants have acquired the basic knowledge needed to optimize their participation in the course. Immunopaedia is the IUIS-endorsed immunology learning site used for this purpose. All faculty members are requested to contribute material related to their specific topic while students must learn the on-line material before coming in person to the course. All course participants have free access to all Immunopaedia material indefinitely. The implementation of regional immunology courses targeted to local health issues in areas of the world where PhD students, post-doctoral, and early career scientists often do not have access to open on-line resources and contact with renowned experts in the field has proven to be highly successful. The long-term impact of this structured educational program is already visible through the large number of young scientists who are now connected via Immunopaedia and who are forming networks in regions where there had been very little contact before and building new Immunological Societies.

National Society for Histotechnology and Digital Pathology Association Online Self-Paced Digital Pathology Certificate of Completion Program

The field of digital pathology has rapidly expanded within the last few years with increasing adoption and growth in popularity. As digital pathology matures, it is apparent that we need well-trained individuals to manage our whole-slide imaging systems. This editorial introduces the joint National Society for Histotechnology and Digital Pathology Association online self-paced digital pathology certificate program which was launched in May 2018 that was established to meet this demand. An overview of how this program was developed, the content of the educational modules, and the way that this program is being offered is discussed.

Statistical Mechanics of On-Line Learning Under Concept Drift

We introduce a modeling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of a classification scheme from clustered data by means of prototype-based Learning Vector Quantization (LVQ). In the second, we study the training of layered neural networks with sigmoidal activations for the purpose of regression. In both cases, the target, i.e., the classification or regression scheme, is considered to change continuously while the system is trained from a stream of labeled data. We extend and apply methods borrowed from statistical physics which have been used frequently for the exact description of training dynamics in stationary environments. Extensions of the approach allow for the computation of typical learning curves in the presence of concept drift in a variety of model situations. First results are presented and discussed for stochastic drift processes in classification and regression problems. They indicate that LVQ is capable of tracking a classification scheme under drift to a non-trivial extent. Furthermore, we show that concept drift can cause the persistence of sub-optimal plateau states in gradient based training of layered neural networks for regression.

Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning

Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware. However, neuromorphic implementations of embedded learning at large scales that are both flexible and efficient have been hindered by a lack of a suitable algorithmic framework. As a result, most neuromorphic hardware are trained off-line on large clusters of dedicated processors or GPUs and transferred post hoc to the device. We address this by introducing the neural and synaptic array transceiver (NSAT), a neuromorphic computational framework facilitating flexible and efficient embedded learning by matching algorithmic requirements and neural and synaptic dynamics. NSAT supports event-driven supervised, unsupervised and reinforcement learning algorithms including deep learning. We demonstrate the NSAT in a wide range of tasks, including the simulation of Mihalas-Niebur neuron, dynamic neural fields, event-driven random back-propagation for event-based deep learning, event-based contrastive divergence for unsupervised learning, and voltage-based learning rules for sequence learning. We anticipate that this contribution will establish the foundation for a new generation of devices enabling adaptive mobile systems, wearable devices, and robots with data-driven autonomy.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

Design for learning - a case study of blended learning in a science unit

Making material available through learning management systems is standard practice in most universities, but this is generally seen as an adjunct to the 'real' teaching, that takes place in face-to-face classes. Lecture attendance is poor, and it is becoming increasingly difficult to engage students, both in the material being taught and campus life. This paper describes the redevelopment of a large course in scientific practice and communication that is compulsory for all science students studying at our Melbourne and Malaysian campuses, or by distance education. Working with an educational designer, a blended learning methodology was developed, converting the environment provided by the learning management system into a teaching space, rather than a filing system. To ensure focus, topics are clustered into themes with a 'question of the week', a pre-class stimulus and follow up activities. The content of the course did not change, but by restructuring the delivery using educationally relevant design techniques, the content was contextualised resulting in an integrated learning experience. Students are more engaged intellectually, and lecture attendance has improved. The approach we describe here is a simple and effective approach to bringing this university's teaching and learning into the 21 (st) century.

On-line inverse multiple instance boosting for classifier grids

Classifier grids have shown to be a considerable choice for object detection from static cameras. By applying a single classifier per image location the classifier's complexity can be reduced and more specific and thus more accurate classifiers can be estimated. In addition, by using an on-line learner a highly adaptive but stable detection system can be obtained. Even though long-term stability has been demonstrated such systems still suffer from short-term drifting if an object is not moving over a long period of time. The goal of this work is to overcome this problem and thus to increase the recall while preserving the accuracy. In particular, we adapt ideas from multiple instance learning (MIL) for on-line boosting. In contrast to standard MIL approaches, which assume an ambiguity on the positive samples, we apply this concept to the negative samples: inverse multiple instance learning. By introducing temporal bags consisting of background images operating on different time scales, we can ensure that each bag contains at least one sample having a negative label, providing the theoretical requirements. The experimental results demonstrate superior classification results in presence of non-moving objects.

An e-learning reproductive health module to support improved student learning and interaction: a prospective interventional study at a medical school in Egypt

BACKGROUND

The Public Health (PH) course at the medical college of Cairo University is based on traditional lectures. Large enrollment limits students' discussions and interactions with instructors.

AIM

Evaluate students' learning outcomes as measured by improved knowledge acquisition and opinions of redesigning the Reproductive Health (RH) section of the PH course into e-learning and assessing e-course utilization.

METHODS

This prospective interventional study started with development of an e-learning course covering the RH section, with visual and interactive emphasis, to satisfy students' diverse learning styles. Two student groups participated in this study. The first group received traditional lecturing, while the second volunteered to enroll in the e-learning course, taking online course quizzes. Both groups answered knowledge and course evaluation questionnaires and were invited to group discussions. Additionally, the first group answered another questionnaire about reasons for non-participation.

RESULTS

Students participating in the e-learning course showed significantly better results, than those receiving traditional tutoring. Students who originally shunned the e-course expressed eagerness to access the course before the end of the academic year. Overall, students using the redesigned e-course reported better learning experiences.

CONCLUSIONS

An online course with interactivities and interaction, can overcome many educational drawbacks of large enrolment classes, enhance student's learning and complement pit-falls of large enrollment traditional tutoring.