Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-accidental injury for medical students – is case-based e-learning effective?

Background: Non-accidental injury (NAI) is a significant paediatric issue that can have many damaging physical and psychological consequences. Early identification has proven to be key in preventing these, however cases continue to be missed regularly. This is often due to inadequate training and therefore improving undergraduate NAI teaching is essential to tackling this. E-learning is a well-established educational method effective in many areas but there has been little investigation of this in the field of NAI. Methodology: An interactive, case-based e-learning module was designed for medical students, focusing on recognition and management of NAI in different clinical scenarios. It includes case-based scenarios, formative assessment and images of various injuries. In order to measure its efficacy, students were required to complete the module as well as preand post-module tests with questions incorporating injury knowledge and recognition and management of NAI. Scores were analysed using t-testing and multivariate analyses. Results: There was a significant increase in mean score between the pre(23.37/32) and post-module (25.55/32) tests. Mean recognition (77.63% to 85.84%) and management scores (54.07% to 92.83%) also improved. Interestingly, injury knowledge score decreased (79.91% to 60.37%). Previous child protection training and year of study were found to be predictive of both pre-module score and change in score after module completion. Discussion: Interactive, case-based e-learning improves medical students’ ability to recognise and manage NAI. It appears to be more useful in areas of clinical decision-making than theoretical knowledge. It is particularly beneficial for students in their initial years of study, who are likely to have less prior knowledge than in later years. Surprisingly, the module benefits students with previous child protection training more than others. Further research is needed to examine how e-learning in this area can be adjusted to suit students with different requirements and better teach Ryan N, Sadler D MedEdPublish https://doi.org/10.15694/mep.2020. 000147.1 Page | 2 knowledge of injuries.
Uncertainty-aware RAN Slicing via Machine Learning Predictions in Next-Generation Networks

Network slicing enables 5G network operators to offer diverse services in the form of end-to-end isolated slices, over shared physical infrastructure. Wireless service providers are facing the need to plan and rapidly evolve their slices configuration to meet the varied tenants’ demand. Network slicing unfolds a new market dimension to the infrastructure providers as well as to the tenants, who may acquire a network slice from the infrastructure provider to deliver a specific service to their respective subscribers. In this new context, there is a growing need for new network resource allocation algorithms to capture such proposition. This paper addresses this problem by introducing a family of online algorithms with the aim to (i) minimize tenants spectrum allocation costs, (ii) maximize radio resource utilization and (iii) ensure that the service level agreements (SLAs) provided to tenants are satisfied. We focus on improving the performance of prediction-based decisions that are made by a tenant when prediction models lack the desired accuracy. Our evaluations show that the proposed probabilistic approach can automatically adapt to prediction error variance, while largely improving network slice acquisition cost and resource utilization.
Handbook of Layered Materials

CLAY MINERALS Introduction: Clay Structure, Surface Acidity, and Catalysis, K.A. Carrado Molecular Modeling of Clay Mineral Structure and Surface Chemistry, S. -H. Park and G. Sposito Clay-Organic Interactions: Organoclay Complexes and Polymer-Clay Nanocomposites, E. Ruiz-Hitzky, P. Aranda, and J.M. Serratosa Sorption of Nitroaromatic Compounds on Clay Surfaces, C.T. Johnston, S.A. Boyd, B.J. Teppen, and G. Sheng Photoprocesses in Clay-Organic Complexes, M. Ogawa Pillared Clays and Porous Clay Heterostructures, P. Cool and E.F. Vansant OTHER LAYERED MATERIALS Layered alpha-Zirconium Phosphates and Phosphonates, C.V. Kumar, A. Bhambhani, and N. Hnatiuk Layered Double Hydroxides (LDHs), P.S. Braterman, Z.P. Xu, and F. Yarberry Layered Manganese Oxides: Synthesis, Properties, and Applications, J. Liu, J.P. Durand, L. Espinal, L. -J. Garces, S. Gomez, Y. -C. Son, J. Villegas, and S.L. Suib Layered Metal Chalcogenides, C.O. Oriakhi and M.M. Lerner Alkali Silicates and Crystalline Silicic Acids, W. Schweiger and G. Lagaly
Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid

Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced signal processing for a radial distribution grid. The three-phase current signals, one cycle before and one cycle after the inception of the fault are measured at the sending end of the grid. A discrete wavelet transform (DWT) is employed to extract useful features from the three-phase current signal. Standard statistical techniques are then applied onto DWT coefficients to extract the useful features. Among many features, mean, standard deviation (SD), energy, skewness, kurtosis, and entropy are evaluated and fed into the artificial neural network (ANN), Multilayer perceptron (MLP), and extreme learning machine (ELM), to identify the fault type and its location. During the training process, all types of faults with variations in the loading and fault resistance are considered. The performance of the proposed fault locating methods is evaluated in terms of root mean absolute percentage error (MAPE), root mean squared error (RMSE), Willmott’s index of agreement (WIA), coefficient of determination ( R 2 ), and Nash-Sutcliffe model efficiency coefficient (NSEC). The time it takes for training and testing are also considered. The proposed method that discrete wavelet transforms with machine learning is a very accurate and reliable method for fault classifying and locating in both a balanced and unbalanced radial system. 100% fault detection accuracy is achieved for all types of faults. Except for the slight confusion of three line to ground (3LG) and three line (3L) faults, 100% classification accuracy is also achieved. The performance measures show that both MLP and ELM are very accurate and comparative in locating faults. The method can be further applied for meshed networks with multiple distributed generators. Renewable generations in the form of distributed generation units can also be studied.
A Política do Novo Consenso Macroeconômico: Fundamentos e Revisão Pós Crise Financeira de 2007-2008

Este trabalho tem como objetivo resgatar os fundamentos e retratar a revisão da política monetária americana após a crise financeira internacional de 2007-2008, na medida em que essa crise evidenciou que importantes postulados do novo consenso macroeconômico foram postos em xeque. Através de um corte histórico, o presente trabalho busca discutir em que medida o mainstream revisa esse consenso e quais as reflexões e alternativas discutidas sobre a política de estabilização no contexto atual. Verificou-se que as revisões apontadas não abalaram a estrutura teórica deste consenso e que a utilização de instrumentos de política não convencionais foi justificada pelo impacto nefasto sobre a economia. A essência de suas hipóteses permanece, na medida em que o regime de metas de inflação continua sendo a referência para condução da política monetária americana e base guia para as demais políticas.
Active learning effectively identifies a minimal set of maximally informative and asymptotically performant cytotoxic structure-activity patterns in NCI-60 cell lines.

The NCI-60 cancer cell line screening panel has provided insights for development of subtype-specific chemical therapies and repurposing. By extracting chemical structure and cytotoxicity patterns, virtual screening potentially complements the availability of high-throughput assay platforms and improves bioactive compound discovery rates by computational prefiltering of candidate compound libraries. Many groups report high prediction performances in computational models of NCI-60 data when using cross-validation or similar techniques, yet prospective therapy development in novel cancers may have little to no such data and further may not have the resources to perform hit identification using large compound libraries. In contrast to bulk screening and analysis, the active learning methodology has demonstrated how to identify compounds for screening in small batches and update computational models iteratively, leading to predictive models with a minimum number of compounds, and importantly clarifying data volumes at which limits in predictive ability are achieved. Here, in replicate per-cell line experiments using 50% of data (∼20 000 compounds) as the external prediction target, predictive limits are reproducibly demonstrated at the stage of systematic selection of 10-30% of the incorporable half. The pattern was consistent across all 60 cell lines. Limits of predictability are found to be correlated to the doubling times of cell lines and the number of cellular response discontinuities (activity cliffs) present per cell line. Organization into chemical scaffolds delineated degrees of predictive challenge. These results provide key insights for strategies in developing new inhibitors in existing cell lines or for future automated therapy selection in personalized oncotherapy.
Identification and characterization of DNA sequence variants associated with multiple myeloma

Multiple myeloma (MM) is the second most common hematologic malignancy. The disease is characterized by an uncontrolled growth of malignant plasma cells in the bone marrow, producing a monoclonal immunoglobulin (“M protein”) that can be detected in peripheral blood. Clinically, MM is characterized by bone marrow failure, lytic bone lesions, hypercalcemia, and kidney failure. It is preceded by monoclonal gammopathy of unknown significance (MGUS), a common condition defined as a clonal growth of plasma cells that does not yet satisfy the criteria for MM, but progresses to MM at a rate of 1% per year. Increasing evidence supports that the biology of MM is influenced by inborn genetic variation and first degree relatives of patients with MM and MGUS seem to have higher risk for MM, and a higher risk of certain other malignancies. In this Ph.D. project, we aim to find DNA sequence variants that predispose for MM, and understand how these variants contribute to MM development.Case-control genome-wide association study (GWAS) is our approach for finding variants. Previous GWASes have identified eight genetic variants that associate with MM development. As a first step in this study, we also carried out a GWAS on a case-control data set from Sweden-Norway and Iceland (Paper I). Following statistical analysis we could identify one novel MM risk locus related to the ELL2 (Elongation Factor for RNA Polymerase II 2) gene and a promising association with the TOM1-HMGXB4 locus at 22q13 and we could confirm the previously reported loci. In the second part of the project, we carried out a meta-analysis of six genome-wide association studies together with the United Kingdom, Germany, Netherlands, the United States and Iceland. Previously reported loci were confirmed and eight new loci were discovered (Paper II). Understanding how the identified risk variants contribute to MM development is the next challenge. In the third part of the project we focused on the ELL2 gene located on the locus 5q15 and tried to understand its mechanism of action in MM (Paper III). Finally, we retrieved clinical information for 871 patients diagnosed with MM from the Swedish Multiple Myeloma Registry. We tested for association between sequence variants and MM overall survival (Paper IV). Our findings provide further insights into the genetic and biological basis of MM predisposition. (Less)
Application of bagasse extract in economic Nannochloropsisoculata mass production

Algae were considered as an alternative source of food and a lot of biologically active ingredient. Beside, algae nutrition seems to be the most limiting factor concerning proper growth and economy cost. The main figure in this respect is carbon nutrition. The aim of the current work was to use bagasse for carbon nutrition of Nannochloropsis alga beside the selection of the proper concentration. Growth was performed using F2 growth medium for inoculum preparation and sub-culturing, while artificial growth medium was made and used to be mass culture of alga. The investigated parameters were dry weight, total chlorophyll and carotenoids. Results showed that a high nutritional composition of bagasse extract as an alternative source of organic carbon (89.9% of total cell carbon)and other nutrients0.035 N, 7.0 P and 51K . (ppm)especially when integrated with original growth medium (F2)and bagasse extract led to extra growth enhancement. Low and medium concentrations of bagasse in cultures free growth medium led to decrease the chlorophyll content and the maximum chlorophyll content (0.54g.l-1) was obtained by cultures grown with full F2 growth medium enriched by 10% of bagasse extract. By such ctreatment, carotene possessed the lowest (0.14g.l-1)outdoor zigzag shape photobioreactor led to the highest volumetric algal biomass production compare to indoor systems . Unit specification markedly affected growth characteristics and yield both of Nannochloropsisoculata in dry weight and oil content.Chemical composition reveledthe relatively high content of carbohydrates (26.6%) and oils (11.9%) on the expense of protein content (32.8%) and the maximum figure of ash content (2%) goes back to sodium ions.
Integrating direct employee voice within the framework of worker representation: The role of an Italian trade union in organising disintermediation

Facing the spread of management-led direct employee voice, trade unions are stuck with a theoretical and practical dilemma: organising against the disintermediation threats or accepting the risk of marginalisation and partnering with management to at least secure mutual gains. This article casts light on organising disintermediation as a chance for trade unions to overcome the ‘organising against vs surrendering to disintermediation’ trade-off, by taking the lead in the promotion, regulation and implementation of direct employee voice. The analysis of FIM-CISL Brescia shows that far from exclusively being a link to organisational performance and a challenge to union intermediation, direct employee voice may become a trade union goal, being the expression of employee self-determination and human dignity. Pursuing this goal translates into a multifaceted change for the trade union, regarding both its internal (organising) and external (partnership) dimension.
Oxidative stress during insulin resistance in prediabetes : a review on role of antioxidants

Oxidative stress is one of the major causal factors behind insulin resistance during prediabetes. It can impair insulin signalling by triggering stress activated signaling pathways and can also directly oxidize and damage the proteins of these pathways. Although cells have impressive stock of antioxidant enzymes and minor antioxidant moieties, these agents may not be adequate to maintain the redox balance during oxidative stress. The important functional antioxidant vitamins for tissueprotection fromexcess free radicals comprises vitamin C, E and A. Vitamin C can improve insulin resistance by altering endothelial function and reducing oxidative stress. Vitamin E is an excellent trap for peroxyl radicals which suppresses ROS production in the pancreas, preserves pancreatic β cell function and maintains structural integrity of pancreatic islet cells. Vitamin A prevents tissue oxidative damage as well as exhibits a regenerative role in the pancreas. Like vitamins, minerals play a crucial role during oxidative stress and insulin resistance. Zinc is crucial for insulin actions and carbohydrate metabolism. Chromium activates insulin receptors through the oligopeptidechromudulin, which occurs in the insulin sensitive cells that binds to the insulin receptor, thereby increasing insulin signal transduction and sensitivity. Selenium up regulates glutathione peroxidase activity can inhibit NF-kappa B activation. The risk of diabetes and its complicationswill bemorewith elevating oxidative stress. Interventionswith these antioxidants have proven bene icial role in reducing oxidative stress which can be utilized for reduction of insulin resistance in prediabetes.
Acuity of goal-directed arm movements and movement control; evaluation of differences between patients with persistent neck/shoulder pain and healthy controls

Abstract Background The main aim was to examine whether patients with persistent upper quadrant pain have higher end-point variability in goal directed pointing movements than pain-free controls when the pointing task is performed in total darkness and under full vision. An additional aim was to study associations between the magnitude of end-point variability and a clinical movement control test battery and self-rated functioning among patients. Methods Seventeen patients and 17 age- and gender-matched pain-free controls performed a pointing task that evaluated end-point variability of repetitive shoulder movements in horizontal adduction and abduction with full vision, and abduction with no visual information, completed a movement control test battery of neck and shoulder control tests and answered questionnaires. Results Patients had higher end point variability for horizontal abduction when performed with no visual information. For horizontal adduction the variability was higher, but only when it was controlled for movement time. No significant correlations were found between end-point variability and self-rated functioning, nor between end-point variability and neuromuscular control of the glenohumeral joint. Conclusions This study provides preliminary evidence that patients with persistent neck/shoulder pain can partly compensate proprioceptive deficits in goal-directed arm movement when visual feedback is present.
Benchmark Performance of Emergency Medicine Residents in Pediatric Resuscitation: Are We Optimizing Pediatric Education for Emergency Medicine Trainees?

The majority of children in the United States seek emergency care at community‐based general emergency departments (GEDs); however, the quality of GED pediatric emergency care varies widely. This may be explained by a number of factors, including residency training environments and postgraduate knowledge decay. Emergency medicine (EM) residents train in academic pediatric EDs, but didactic and clinical experience vary widely between programs, and little is known about the pediatric skills of these EM residents. This study aimed to assess the performance of senior EM residents in treating simulated pediatric patients at the end of their training.
Topics in Extremal and Random Discrete Structures

We study several problems in extremal combinatorics, random graphs, and asymptotic convex geometry. Extremal combinatorics is the area of mathematics studying the behavior of the \best" discrete structure among a family of structures with respect to certain parameter, whereas probabilistic combinatorics often study the \average"behavior shown by the most of the structures. Random graphs is one of the biggest fi? elds in probabilistic combinatorics. Asymptotic convex geometry, on the other hand, deals with geomteric problems on convex sets in \large" dimensional Euclidean space. In Chapter 2, we systematically study a natural problem in extremal graph theory, to minimize the number of edges in a graph with a ? fixed number of vertices, subject to a certain local condition: each vertex must be in a copy of a ? fixed graph H. We completely solve this problem when H is a clique, as well as more generally when H is any regular graph with degree at least about half its number of vertices. We alsocharacterize the extremal graphs when H is an Erdos-Renyi random graph. In Chapter 3, we consider two important questions in the well-studied theory of graphs that are F-saturated. We ? first resolve the most fundamental question ofminimizing the number of cliques of size r in a Ks-saturated graph for all sufficiently large numbers of vertices, con? rming a conjecture of Kritschgau, Methuku, Tait, and Timmons. We then make partial progress in the negative direction on a central and longstanding conjecture in graph saturation made by Tuza. In Chapter 4, we determine the maximum number of cliques of a fi? xed order in a graph with ? fixed number of edges and bounded maximum degree with complete characterization of extremal graphs, resolving a conjecture by Kirsch and Radcliffe. In Chapter 5, we generalize the notion of game chromatic number of a graphto hypergraphs. We analyze the game chromatic number of a random k-uniform hypergraph and prove upper and lower bounds that hold w.h. p. In Chapter 6, we study a question posed by Frieze in [67] related to the existenceof a patterned perfect matching in a randomly colored graph.In Chapter 7, we study the isomorphism problem for random hypergraphs. We show that it is polynomially time solvable for the binomial random k-uniform hypergraph Hn;p;k, for a wide range of p, and for random r-regular, k-uniform hypergraphs. Finally in Chapter 8, we study the expected volume of random polytopes generated by taking the convex hull of independent identically distributed points from a givendistribution.
Recommended Vaccination During Pregnancy: A Short Commentary

Vaccination is the process of stimulating a protective adaptive immune response against microbes by exposure to non-pathogenic forms or microbial components. Live vaccines killed vaccines and pathogen derived puriﬁed macromolecules are the three types of vaccines. Maternal vaccination provide protection to both the mother and fetus from the morbidity of certain serious infectious diseases which can otherwise be prevented by vaccination. Influenza and Pertussis vaccines are routinely recommended during pregnancy. Vaccines can also result in adverse reactions both in the mother and child. This review focuses on guidelines and recommendations for vaccination during pregnancy.