Chinese worker, 26, making Apple iPhones died after enduring 12 hour shifts, seven days a week, family claim

This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.CLW_2015_Report_China_chinese_worker.pdf: 233 downloads, before Oct. 1, 2020

RNA aptamers generated against oligomeric Abeta40 recognize common amyloid aptatopes with low specificity but high sensitivity.

Aptamers are useful molecular recognition tools in research, diagnostics, and therapy. Despite promising results in other fields, aptamer use has remained scarce in amyloid research, including Alzheimer's disease (AD). AD is a progressive neurodegenerative disease believed to be caused by neurotoxic amyloid beta-protein (Abeta) oligomers. Abeta oligomers therefore are an attractive target for development of diagnostic and therapeutic reagents. We used covalently-stabilized oligomers of the 40-residue form of Abeta (Abeta40) for aptamer selection. Despite gradually increasing the stringency of selection conditions, the selected aptamers did not recognize Abeta40 oligomers but reacted with fibrils of Abeta40, Abeta42, and several other amyloidogenic proteins. Aptamer reactivity with amyloid fibrils showed some degree of protein-sequence dependency. Significant fibril binding also was found for the naïve library and could not be eliminated by counter-selection using Abeta40 fibrils, suggesting that aptamer binding to amyloid fibrils was RNA-sequence-independent. Aptamer binding depended on fibrillogenesis and showed a lag phase. Interestingly, aptamers detected fibril formation with > or =15-fold higher sensitivity than thioflavin T (ThT), revealing substantial beta-sheet and fibril formation undetected by ThT. The data suggest that under physiologic conditions, aptamers for oligomeric forms of amyloidogenic proteins cannot be selected due to high, non-specific affinity of oligonucleotides for amyloid fibrils. Nevertheless, the high sensitivity, whereby aptamers detect beta-sheet formation, suggests that they can serve as superior amyloid recognition tools

Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes

Due to mutual interference between users, power allocation problems in wireless networks are often non-convex and computationally challenging. Graph neural networks (GNNs) have recently emerged as a promising approach to tackling these problems and an approach that exploits the underlying topology of wireless networks. In this paper, we propose a novel graph representation method for wireless networks that include full-duplex (FD) nodes. We then design a corresponding FD Graph Neural Network (F-GNN) with the aim of allocating transmit powers to maximise the network throughput. Our results show that our F-GNN achieves state-of-art performance with significantly less computation time. Besides, F-GNN offers an excellent trade-off between performance and complexity compared to classical approaches. We further refine this trade-off by introducing a distance-based threshold for inclusion or exclusion of edges in the network. We show that an appropriately chosen threshold reduces required training time by roughly 20% with a relatively minor loss in performance

Consumer privacy: what information do web sites collect?

This article discusses the results of an updated consumer privacy survey based on the original version of the FTC survey. Its main objective was to find out what kind of personal information web sites are collecting from consumers today, and which web sites offer privacy notices regarding the handling and collection of personal information on their web site

Modelling supplier selection and material purchasing for the construction supply chain in a fuzzy scenario-based environment

Mathematical relations between supplier capacities, the resulting material supply shortages, together with the impact of material delays on construction projects are not well defined. In response to this, this paper presents a novel multi-objective mixed integer linear programming model that considers the selection of suitable suppliers, inventory management practices, order quantities and the possibility of splitting a material order as integrated decisions to be optimised. The trade-off between the overall procurement cost and the weighted lateness, a measure of material delay impacts, is optimised. Material prices, supplier capacities, and resulting delays are treated as fuzzy scenario-based parameters. The proposed model is tested on a numerical example and computation experiments validate the model performance. An extensive sensitivity analysis is carried out and results suggest that by considering high variations in uncertain supplier capacities, the model would generate lower procurement cost and show less significant delay impacts. Whereas greater variations in uncertain material prices cause the total procurement cost to grow 55%; greater variations in uncertain delay durations also drastically increase the weighted lateness by over 70%. This highlights the importance of having high quality estimates for uncertain parameters. Additionally, the analysis also indicates that a minimum overall satisfaction level of 0.9338 can be achieved depending on the model user's strategies, and the proposed scenario-adjusted problem outperforms problems modelled under deterministic market conditions. The major contribution of this paper lies in the development of a fuzzy scenario-based model to solve the supplier selection and material purchasing problem in construction supply chains

The Ketogenic Diet in the Treatment of Post-concussion Syndrome—A Feasibility Study

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Concussion is the most common form of mild traumatic brain injury (mTBI). Although most patients' symptoms resolve within a month, patients with post-concussion syndrome (PCS) may continue to experience symptoms for years and have limited treatment options. This pilot study assessed the feasibility and symptom-related effects of a ketogenic diet (KD) in patients with PCS symptoms. The Ketogenic Diet in Post-Concussion Syndrome (KD-PCS) was a single-arm trial of a 2-month KD high in non-starchy vegetables and supplemented with medium-chain triglyceride (MCT) oil. Macronutrient targets were ≥70% fat, ≤10% carbohydrate, and the remainder as protein as energy. We assessed feasibility by daily self-reported measure of urine acetoacetate and collection of 3-day food records and serum beta-hydroxybutyrate at multiple timepoints. We assessed symptoms by administering the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) and Modified Balance Error Scoring System (M-BESS) at baseline and month 2 and the Post-Concussion Symptom Scale (PCSS) at baseline, month 1, and month 2. Fourteen participants enrolled in the KD-PCS. Twelve participants completed the study and 11 implemented the KD (73% fat, 9% carbohydrate, and 18% protein) and achieved ketosis. One participant complained of MCT-related diarrhea that resolved and another reported nausea and fatigue that resulted in withdrawal from the study. Among compliant participants, the visual memory domain of the ImPACT improved by 12 points (p = 0.02) and PCSS scores improved by 9 points, although not statistically significant. This pilot trial suggests that the KD is a feasible experimental treatment for PCS and justifies further study of its efficacy