Folding mechanisms steer the amyloid fibril formation propensity of highly homologous proteins

Significant advances in the understanding of the molecular determinants of fibrillogenesis can be expected from comparative studies of the aggregation propensities of proteins with highly homologous structures but different folding pathways. Here, we fully characterize, by means of stopped-flow, T-jump, CD and DSC experiments, the unfolding mechanisms of three highly homologous proteins, zinc binding Ros87 and Ml153-149 and zinc-lacking Ml452-151. The results indicate that the three proteins significantly differ in terms of stability and (un)folding mechanisms. Particularly, Ros87 and Ml153-149 appear to be much more stable to guanidine denaturation and are characterized by folding mechanisms including the presence of an intermediate. On the other hand, metal lacking Ml452-151 folds according to a classic two-state model. Successively, we have monitored the capabilities of Ros87, Ml452-151 and Ml153-149 to form amyloid fibrils under native conditions. Particularly, we show, by CD, fluorescence, DLS, TEM and SEM experiments, that after 168 hours, amyloid formation of Ros87 has started, while Ml153-149 has formed only amorphous aggregates and Ml452-151 is still monomeric in solution. This study shows how metal binding can influence protein folding pathways and thereby control conformational accessibility to aggregation-prone states, which in turn changes aggregation kinetics, shedding light on the role of metal ions in the development of protein deposition diseases

Calibrating Water Depths of Ordovician Communities: Lithological and Ecological Controls on Depositional Gradients in Upper Ordovician Strata of Southern Ohio and North-Central Kentucky, USA

Limestone and shale facies of the Upper Ordovician Grant Lake Formation (Katian: Cincinnatian, Maysvillian) are well exposed in the Cincinnati Arch region of southern Ohio and north-central Kentucky, USA. These rocks record a gradual change in lithofacies and biofacies along a gently northward-sloping ramp. This gradient spans very shallow, olive-gray, platy, laminated dolostones with sparse ostracodes in the south to offshore, nodular, phosphatic, brachiopod-rich limestones and marls in the north. This study uses facies analysis in outcrop to determine paleoenvironmental parameters, particularly those related to water depth (e.g., position of the photic zone and shoreline, relative degree of environmental energy). Within a tightly correlated stratigraphic interval (the Mount Auburn and Straight Creek members of the Grant Lake Formation and the Terrill Member of the Ashlock Formation), we document the occurrence of paleoenvironmental indicators, including desiccation cracks and light-depth indicators, such as red and green algal fossils and oncolites. This permitted recognition of a ramp with an average gradient of 10.20 cm water depth per horizontal kilometer. Thus, shallow subtidal (.lagoonal.) deposits in the upramp portion fall within the 1.5.6 m depth range, cross-bedded grainstones representing shoal-type environments fall within the 6.18 m depth range and subtidal, shell-rich deposits in the downramp portion fall within the 20.30 m depth range. These estimates match interpretations of depth independently derived from faunal and sedimentologic evidence that previously suggested a gentle ramp gradient and contribute to ongoing and future high-resolution paleontologic and stratigraphic studies of the Cincinnati Arch region

A perturbed MicroRNA expression pattern characterizes embryonic neural stem cells derived from a severe mouse model of spinal muscular atrophy (SMA)

Spinal muscular atrophy (SMA) is an inherited neuromuscular disorder and the leading genetic cause of death in infants. Despite the disease-causing gene, survival motor neuron (SMN1), encodes a ubiquitous protein, SMN1 deficiency preferentially affects spinal motor neurons (MNs), leaving the basis of this selective cell damage still unexplained. As neural stem cells (NSCs) are multipotent self-renewing cells that can differentiate into neurons, they represent an in vitro model for elucidating the pathogenetic mechanism of neurodegenerative diseases such as SMA. Here we characterize for the first time neural stem cells (NSCs) derived from embryonic spinal cords of a severe SMNΔ7 SMA mouse model. SMNΔ7 NSCs behave as their wild type (WT) counterparts, when we consider neurosphere formation ability and the expression levels of specific regional and self-renewal markers. However, they show a perturbed cell cycle phase distribution and an increased proliferation rate compared to wild type cells. Moreover, SMNΔ7 NSCs are characterized by the differential expression of a limited number of miRNAs, among which miR-335-5p and miR-100-5p, reduced in SMNΔ7 NSCs compared to WT cells. We suggest that such miRNAs may be related to the proliferation differences characterizing SMNΔ7 NSCs, and may be potentially involved in the molecular mechanisms of SMA

Modeling and Representing Conceptual Change in the Learning of Successive Theories: The Case of the Classical-Quantum Transition

Most educational literature on conceptual change concerns the process by which introductory students acquire scientific knowledge. However, with modern developments in science and technology, the social significance of learning successive theories is steadily increasing, thus opening new areas of interest to discipline-based education research, e.g., quantum logic, quantum information and communication. Here we present an initial proposal for modeling the transition from the understanding of a theory to the understanding of its successor and explore its generative potential by applying it to a concrete case: the classical-quantum transition in physics. In pursue of such task, we make coordinated use of contributions not only from research on conceptual change in education, but also on the history and philosophy of science, on the teaching and learning of quantum mechanics, on mathematics education. By means of analytical instruments developed for characterizing conceptual trajectories at different representational levels, we review empirical literature in the search for the connections between theory change and cognitive demands. The analysis shows a rich landscape of changes and new challenges that are absent in the traditionally considered cases of conceptual change. In order to fully disclose the educational potential of the analysis, we visualize categorical changes by means of dynamic frames, identifying recognizable patterns that answer to students' need of comparability between the older and the new paradigm. Finally, we show how the frame representation can be used to suggest pattern-dependent strategies to promote the understanding of the new content, and may work as a guide to curricular design.Comment: Submitted to Science & Educatio

Suggestions on the teaching of atmospheric pressure at university and secondary school levels

The distinction between pressure in a liquid and in a gas is often treated in a cursory way, or not treated at all, even in university level textbooks. Most texts fail to point out the relation between pressure and density in a gas as compared to pressure in a—virtually incompressible—liquid. In many instances this also results in a dismissive treatment of atmospheric pressure. In this paper we suggest that in the physics curriculum of university and secondary school students, kinetic theory of gases be treated before fluid mechanics and thermodynamics. In this way, the definitions of pressure P and absolute temperature T in a gas can be derived consistently, with the remarkable advantage that the links between the macroscopic parameters P and T and the velocity of molecules—a microscopic parameter—are made clear at an early stage, as well as the relation between P and density ρ

Algorithmic Impact Assessments Under the GDPR: Producing Multi-Layered Explanations

Policy-makers, scholars, and commentators are increasingly concerned with the risks of using profiling algorithms and automated decision-making. The EU’s General Data Protection Regulation (GDPR) has tried to address these concerns through an array of regulatory tools. As one of us has argued, the GDPR combines individual rights with systemic governance, towards algorithmic accountability. The individual tools are largely geared towards individual “legibility”: making the decision-making system understandable to an individual invoking her rights. The systemic governance tools, instead, focus on bringing expertise and oversight into the system as a whole, and rely on the tactics of “collaborative governance,” that is, use public-private partnerships towards these goals. How these two approaches to transparency and accountability interact remains a largely unexplored question, with much of the legal literature focusing instead on whether there is an individual right to explanation.The GDPR contains an array of systemic accountability tools. Of these tools, impact assessments (Art. 35) have recently received particular attention on both sides of the Atlantic, as a means of implementing algorithmic accountability at early stages of design, development, and training. The aim of this paper is to address how a Data Protection Impact Assessment (DPIA) links the two faces of the GDPR’s approach to algorithmic accountability: individual rights and systemic collaborative governance. We address the relationship between DPIAs and individual transparency rights. We propose, too, that impact assessments link the GDPR’s two methods of governing algorithmic decision-making by both providing systemic governance and serving as an important “suitable safeguard” (Art. 22) of individual rights.After noting the potential shortcomings of DPIAs, this paper closes with a call — and some suggestions — for a Model Algorithmic Impact Assessment in the context of the GDPR. Our examination of DPIAs suggests that the current focus on the right to explanation is too narrow. We call, instead, for data controllers to consciously use the required DPIA process to produce what we call “multi-layered explanations” of algorithmic systems. This concept of multi-layered explanations not only more accurately describes what the GDPR is attempting to do, but also normatively better fills potential gaps between the GDPR’s two approaches to algorithmic accountability

Algorithmic Impact Assessments Under the GDPR: Producing Multi-Layered Explanations

Policy-makers, scholars, and commentators are increasingly concerned with the risks of using profiling algorithms and automated decision-making. The EU’s General Data Protection Regulation (GDPR) has tried to address these concerns through an array of regulatory tools. As one of us has argued, the GDPR combines individual rights with systemic governance, towards algorithmic accountability. The individual tools are largely geared towards individual “legibility”: making the decision-making system understandable to an individual invoking her rights. The systemic governance tools, instead, focus on bringing expertise and oversight into the system as a whole, and rely on the tactics of “collaborative governance,” that is, use public-private partnerships towards these goals. How these two approaches to transparency and accountability interact remains a largely unexplored question, with much of the legal literature focusing instead on whether there is an individual right to explanation.The GDPR contains an array of systemic accountability tools. Of these tools, impact assessments (Art. 35) have recently received particular attention on both sides of the Atlantic, as a means of implementing algorithmic accountability at early stages of design, development, and training. The aim of this paper is to address how a Data Protection Impact Assessment (DPIA) links the two faces of the GDPR’s approach to algorithmic accountability: individual rights and systemic collaborative governance. We address the relationship between DPIAs and individual transparency rights. We propose, too, that impact assessments link the GDPR’s two methods of governing algorithmic decision-making by both providing systemic governance and serving as an important “suitable safeguard” (Art. 22) of individual rights.After noting the potential shortcomings of DPIAs, this paper closes with a call — and some suggestions — for a Model Algorithmic Impact Assessment in the context of the GDPR. Our examination of DPIAs suggests that the current focus on the right to explanation is too narrow. We call, instead, for data controllers to consciously use the required DPIA process to produce what we call “multi-layered explanations” of algorithmic systems. This concept of multi-layered explanations not only more accurately describes what the GDPR is attempting to do, but also normatively better fills potential gaps between the GDPR’s two approaches to algorithmic accountability

Metabolic and anthropometric changes in early breast cancer patients receiving adjuvant therapy

Weight gain and metabolic changes have been related to survival of early breast cancer patients (EBC). ''However, factors influencing metabolism post-diagnosis are not fully understood. We measured anthropometric [body mass index (BMI), body weight, waist and hip circumferences, and waist-to-hip ratio] and metabolic (levels of insulin, glucose, H1Ac, total, HDL, and LDL cholesterol, triglycerides, and the homeostasis model assessment score [HOMA]) parameters in 433 pre- and post-menopausal women with EBC at diagnosis and 3, 6, 9, 12, and 24 months thereafter. At diagnosis, compared with post-menopausal women, pre-menopausal patients were more likely to be leaner and to have a lower BMI, smaller waist and hip circumferences, and waist-to-hip ratio. They had also lower glucose, HbA1c, and triglyceride levels and a lower HOMA score. Furthermore, they were more likely to have an estrogen- and/or progesterone-positive tumor and a higher proliferating breast cancer. During the first two post-diagnosis years, all women showed a significant increase of weight (+0.72 kg/year, P < 0.001), waist circumference (+1.53 cm/year, P < 0.001), and plasma levels of LDL cholesterol (+5.4 mg/dl per year, P = 0.045) and triglycerides (+10.73 mg/dl per year, P = 0.017). In patients receiving chemotherapy only, there was a significant increase in hip circumference (+3.16 cm/year, P < 0.001) and plasma cholesterol levels (+21.26 mg/dl per year, P < 0.001). We showed that weight, body fat distribution, and lipid profile changed in EBC patients receiving adjuvant therapy. These changes occurred during the first 2 years after diagnosis and were not specifically related to chemotherapy, menopausal status, or initial body weight