Asking the Wrong Questions About American Science Education: Insights from a Longitudinal Study of High School Biotechnology Lab Instruction

The discussion of American science education is often framed by the questions: Why do American precollege students do poorly on international science assessments and what we are doing wrong? Rather we need to ask: Why do so many international students come to US universities for science, what are we doing right in science, and how do we stay ahead in science education? Poor scores on international assessments belie the fact that the U.S. has the best science education system in the world. Our study of 6,200 high school teachers in 1998 and 2018 documented striking success in retooling classrooms for lab-based instruction in biotechnology and provided a pre-COVID-19 snapshot of what is right with American biology education. However, it also highlights the need revitalize our precollege teaching resource with a renewed National Science Foundation commitment to in-service training.

Why Imaginary Worlds?

Imaginary worlds are extremely successful. The most popular fictions produced in the last decades contain such a fictional world. They can be found in all fictional media, from novels (e.g., Lord of The Ring, Harry Potter) to films (e.g., Star Wars, Avatar), video games (e.g., The Legend of Zelda, Final Fantasy), graphic novels (e.g., One piece, Naruto) and TV series (e.g., Star Trek, Game of Thrones), and they date as far back as ancient literature (e.g., the Cyclops Islands in The Odyssey, 850 BCE). Why such a success? Why so much attention devoted to nonexistent worlds? In this article, we propose that imaginary worlds co-opt our preferences for exploration, which have evolved in humans and non-human animals alike, to propel individuals toward new environments and new sources of reward. Humans would find imaginary worlds very attractive for the very same reasons, and under the same circumstances, as they are lured by unfamiliar environments in real life. After reviewing research on exploratory preferences in behavioral ecology, environmental aesthetics, neuroscience, and evolutionary and developmental psychology, we focus on the sources of their variability across time and space, which we argue can account for the variability of the cultural preference for imaginary worlds. This hypothesis can therefore explain the way imaginary worlds evolved culturally, their shape and content, their recent striking success, and their distribution across time and populations.

A Brief Review of Implicit Regularization and Its Connection with the BPHZ Theorem

Quantum Field Theory, as the keystone of particle physics, has offered great insights into deciphering the core of Nature. Despite its striking success, by adhering to local interactions, Quantum Field Theory suffers from the appearance of divergent quantities in intermediary steps of the calculation, which encompasses the need for some regularization/renormalization prescription. As an alternative to traditional methods, based on the analytic extension of space–time dimension, frameworks that stay in the physical dimension have emerged; Implicit Regularization is one among them. We briefly review the method, aiming to illustrate how Implicit Regularization complies with the BPHZ theorem, which implies that it respects unitarity and locality to arbitrary loop order. We also pedagogically discuss how the method complies with gauge symmetry using one- and two-loop examples in QED and QCD.

Taiwan

iTaiwan’s automotive development strongly supports the proposition that capable institutions are crucial to helping firms in developing countries undertake industrial upgrading. Research institutes, testing and certification centers, training programs, industry associations, and government-supported corporate alliances have flourished for decades. Though modest in size and little known abroad, Taiwan’s leading auto companies export high-quality cars, design and engineer their own models, invest abroad, and export a wide variety of auto parts and car electronics. The success of Taiwanese firms is all the more striking in light of Taiwan’s small and stagnant domestic market. The government embarked on a course of gradual liberalization in the 1980s. Yet it never relinquished the goal of fostering domestically -owned companies capable of making their own vehicles, and simultaneously supported the activities of small and medium-sized firms that have achieved striking success in exporting automotive parts, especially bumpers, body panels, and other accessories for the after-sales market.

Pan-ERBB kinase inhibition augments CDK4/6 inhibitor efficacy in oesophageal squamous cell carcinoma

ObjectiveOesophageal squamous cell carcinoma (OSCC), like other squamous carcinomas, harbour highly recurrent cell cycle pathway alterations, especially hyperactivation of the CCND1/CDK4/6 axis, raising the potential for use of existing CDK4/6 inhibitors in these cancers. Although CDK4/6 inhibition has shown striking success when combined with endocrine therapy in oestrogen receptor positive breast cancer, CDK4/6 inhibitor palbociclib monotherapy has not revealed evidence of efficacy to date in OSCC clinical studies. Herein, we sought to elucidate the identification of key dependencies in OSCC as a foundation for the selection of targets whose blockade could be combined with CDK4/6 inhibition.DesignWe combined large-scale genomic dependency and pharmaceutical screening datasets with preclinical cell line models, to identified potential combination therapies in squamous cell cancer.ResultsWe identified sensitivity to inhibitors to the ERBB family of receptor kinases, results clearly extending beyond the previously described minority of tumours with EGFR amplification/dependence, specifically finding a subset of OSCCs with dual dependence on ERBB3 and ERBB2. Subsequently. we demonstrated marked efficacy of combined pan-ERBB and CDK4/6 inhibition in vitro and in vivo. Furthermore, we demonstrated that squamous lineage transcription factor KLF5 facilitated activation of ERBBs in OSCC.ConclusionThese results provide clear rationale for development of combined ERBB and CDK4/6 inhibition in these cancers and raises the potential for KLF5 expression as a candidate biomarker to guide the use of these agents. These data suggested that by combining existing Food and Drug Administration (FDA)-approved agents, we have the capacity to improve therapy for OSCC and other squamous cancer.

Role of the Retail Sector in Taiwanese Economy

The government-guided establishment and incubation of the retail sector in Taiwan has achieved striking success and generated high consumption. This success was even more striking because when the increase of consumption began, the country had not enough retail shopping centers and it was to accept some foreign retail investments. Originally there were no Taiwanese retail firms with deep technological roots and managerial skills. Yet government decision-makers recognized the challenges of upgrading the nation’s economy basing and formulating a strategy that entailed the creation of high-level economic growth which serve as vehicles to stimulate the consumption. Beyond a comprehensive review on Taiwan’s retail sector, the main objective is to analyse the role and the influence of the sector in the Taiwanese macroeconomy. The first issue is getting a picture in Asia (special in Asia – Pacific region) of retail trends. To examine this issue, the authors have done some comparisons between Asia and Europe and apart from the obvious similarities, number of significant differences have been found. The research objective is to provide an effective introduction to the business context of consumption in Taiwan.

High Dimensional Hyperbolic Geometry of Complex Networks

High dimensional embeddings of graph data into hyperbolic space have recently been shown to have great value in encoding hierarchical structures, especially in the area of natural language processing, named entity recognition, and machine generation of ontologies. Given the striking success of these approaches, we extend the famous hyperbolic geometric random graph models of Krioukov et al. to arbitrary dimension, providing a detailed analysis of the degree distribution behavior of the model in an expanded portion of the parameter space, considering several regimes which have yet to be considered. Our analysis includes a study of the asymptotic correlations of degree in the network, revealing a non-trivial dependence on the dimension and power law exponent. These results pave the way to using hyperbolic geometric random graph models in high dimensional contexts, which may provide a new window into the internal states of network nodes, manifested only by their external interconnectivity.

Automatic diagnosis of the 12-lead ECG using a deep neural network

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.

Statistical Mechanics of Deep Learning

The recent striking success of deep neural networks in machine learning raises profound questions about the theoretical principles underlying their success. For example, what can such deep networks compute? How can we train them? How does information propagate through them? Why can they generalize? And how can we teach them to imagine? We review recent work in which methods of physical analysis rooted in statistical mechanics have begun to provide conceptual insights into these questions. These insights yield connections between deep learning and diverse physical and mathematical topics, including random landscapes, spin glasses, jamming, dynamical phase transitions, chaos, Riemannian geometry, random matrix theory, free probability, and nonequilibrium statistical mechanics. Indeed, the fields of statistical mechanics and machine learning have long enjoyed a rich history of strongly coupled interactions, and recent advances at the intersection of statistical mechanics and deep learning suggest these interactions will only deepen going forward.

The Long Negotiation

This chapter explains how both the IRA and the DUP were fully incorporated into the 1998 Agreement, even if the former saw it as transitional, whereas the latter pretended it was not working within its provisions. The striking success of simply having a sustained period of cooperative power-sharing is celebrated, and a full comparison is made with the Sunningdale Agreement of 1973–4. The Rise and Fall of the House of Paisley is considered. Sinn Féin’s Martin McGuinness’s interactions with his DUP opposite numbers Peter Robinson and Arlene Foster are assessed. The turbulent implementation of the agreement between 2003 and 2015 is considered. Unresolved controversies over languages and flags and over assessing the conflict are evaluated.