Unilever (Canada)

This study had two primary purposes: to examine the effects of group and individual teaching by nurses in the workplace on 140 female office employees' health knowledge, beliefs, and practices regarding breast self-examination and to identify factors associated with frequency of practice. Skill in technique, confidence in the skill, and frequency of breast self-examination increased significantly with both teaching formats, but there were areas of technique that needed further improvement. Perceived susceptibility to breast cancer and perceived benefits of breast self-examination increased significantly only with individual teaching; knowledge was not increased with either teaching format.

Worldwide, the fat composition of spreads and margarines ("spreads") has significantly changed over the past decades. Data on fat composition of US spreads are limited and outdated. This paper compares the fat composition of spreads sold in 2013 to that sold in 2002 in the USA. The fat composition of 37 spreads representing >80% of the US market sales volume was determined by standard analytical methods. Sales volume weighted averages were calculated. In 2013, a 14 g serving of spread contained on average 7.1 g fat and 0.2 g trans-fatty acids and provided 22% and 15% of the daily amounts recommended for male adults in North America of omega-3 α-linolenic acid and omega-6 linoleic acid, respectively. Our analysis of the ingredient list on the food label showed that 86% of spreads did not contain partially hydrogenated vegetable oils (PHVO) in 2013. From 2002 to 2013, based on a 14 g serving, total fat and trans-fatty acid content of spreads decreased on average by 2.2 g and 1.5 g, respectively. In the same period, the overall fat composition improved as reflected by a decrease of solid fat (from 39% to 30% of total-fatty acids), and an increase of unsaturated fat (from 61% to 70% of total-fatty acids). The majority of US spreads no longer contains PHVO and can contribute to meeting dietary recommendations by providing unsaturated fat.

A Monte Carlo method is described for evaluating quantities such as E = <ψ|H|ψ>/<ψ|ψ> and other expectation values. The method is in principle applicable to a wave function of any number of electrons. As an example, a two-parameter wave function for helium is studied.

Cet article montre qu'aussi bien en contexte d'absence du système ERP que dans le contexte de mise en ?uvre d'un tel système, les mécanismes d'intégration utilisés par les gestionnaires sont à la fois d'ordre structurel, fonctionnel ou social. Toutefois, la contribution de chaque mécanisme d'intégration à l'intégration de l'entreprise n'est pas la même quand on passe du premier contexte au second.

The authors present a steady-state analysis of the dual converter operating in a circulating current mode, with an output low-pass filter, in order to identify the operating conditions of the proposed power supply configuration. In the power supply configuration, interconverter inductors are used to limit the circulating current and an additional low-pass filter is required to reduce output ripple content. The operating conditions of the power supply configuration are identified by closed-form solutions of the instantaneous voltage and current waveforms. The steady-state operation is affected by the low-pass output filter and the effects are examined in terms of the inductance ratio. The effective output filter inductance is the sum of two inductances: one half of the circulating current limiting inductance and the output filter inductance. The Thevenin equivalent circuit of the dual converter with circulating current is identified.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Abstract Background: Enhanced T cell performance and fitness are imperative for the success of adoptive T cell-based therapies. Beyond the types of genetic modifications to CAR/TCR T cells, there is a growing body of literature demonstrating that relatively simple preconditioning protocols can also be used to improve T cell fitness/function. We studied, the impact that preconditioning in elevated concentrations of leucine, glutamine, and arginine has on the killing efficacy and bioenergetics of MART-1-specific TCR T cells. Methods: Using the Agilent xCELLigence RTCA eSight and Seahorse we assessed the killing efficiency and bioenergetics of engineered T-cells after Arginine, Glutamine, and Leucine pre-conditioning using MART-1 specific TCR T cells. CD3+ T-cells (Hemacare, Seattle, WA) were transduced with retrovirus SAMEN-DMF5 with a CD34 marker gene, against MART-1. The T cells were pre-conditioned in a range of concentrations varying between 0-6mM for 7 days, followed by a killing assay using MART-1 expressing melanoma cell line as target cells (624.38) engineered to express a red-fluorescent nuclear protein. The comparison was made with transduced T-cells grown in RPMI (no added amino acid supplementation denoted as RPMI_TCR), RPMI supplemented with Arginine (Arg_TCR) and non-transduced T cells. The T cell killing was measured using impedance/imaging-based assays. CD34 assessment was performed using Novocyte and SRC (spare respiratory capacity) and oxygen consumption rate (OCR) were measured using seahorse assays. Results: Whereas supplementing the growth medium with 6 mM Arginine increased killing efficacy dramatically (up to ∼6-fold), elevated leucine and glutamine concentrations were found to have minimal impact on MART-1 TCR T cell killing of melanoma cells. Arginine (6 mM) supplementation increased basal respiration, ATP linked OCR, and maximal respiration compared to the RPMI control. SRC of Arg_TCR T cells was significantly higher than RPMI preconditioned T cells, a parameter previously correlated with T cell persistence. To check the effect of a shortened pre-conditioning period, 2, and 4 days of pre-conditioning were done along with the 7 days method. After a preconditioning step of only 2 days, Arginine preconditioned T cells acquired a killing efficacy that is &amp;gt;2x higher compared to their counterparts RPMI_TCR T cells. Extending the duration of preconditioning from 2 to 4 days has minimal impact on the RPMI control T cells but more than doubles the killing efficacy of the high Arg grown T cells. Conclusions: In conclusion, Arginine pre-conditioning significantly improved T cell potency and mitochondrial respiration through metabolic rewiring. Citation Format: Rashmi R. Pillai, Xiaoyu Zhang, Yama Abassi, Brandon Lamarche, Mark M. Garner. Arginine pre-conditioning improves T-cell potency and metabolic fitness measured by real-time impedance and seahorse assays [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB094.

&lt;p&gt;Interpretability aims to improve our understanding of the model behavior. Local interpretability methods can explain the specific predictions of a model and create trust between the model and its users, empowering the practitioners with powerful new insights. The temporal nature, and the high dimensionality of the time series data sets unique challenges to interpretability, which are specific to this domain of machine learning. An improved understanding of time series interpretability methods, and availability of suitable evaluation metrics for measuring the accuracy of the explanations can contribute to further progress in time series modeling. This PhD thesis includes four research directions that focus on the local interpretability of time series models. The first research topic that we explore involves introducing two novel evaluation metrics for comparing local interpretability methods on generic time series regression problems. We evaluate the proposed metrics through an extensive numerical study, and find that the SHAP method provides the most accurate explanations among the tested methods. Our second research problem involves a specific application of interpretability in sales forecasting and finance domains. Specifically, we propose a unified framework to predict financial commentaries from the financial data generated by a company. We evaluate multiple time series classification models for the prediction task, and use local interpretability methods to explain the predictions. We find that the proposed framework, supported by the machine learning and local interpretability methods, offers new opportunities to leverage management information systems, providing insights to management on key financial issues, including sales forecasting and inventory management. As the third research problem, we study how local interpretability methods can be used to explain time series clustering models. We provide explanations to the clustering algorithms by using classification models as intermediate models to predict the cluster labels. We perform a detailed numerical study, comparing multiple datasets, clustering models, and classification models. Through a careful analysis of the results, we discuss how and when the proposed methodology can be used to obtain insights on the corresponding model behaviour. Finally, the fourth research problem involves developing a locally interpretable deep neural network model for the time series forecasting problem. We evaluate the model accuracy and explanations, using multiple datasets and methods, and find that it achieves similar performance to those of its non-interpretable counterparts, while remaining interpretable.&lt;/p&gt;

Financial supply chains operate in highly dynamic, interconnected, and risk-intensiveenvironments where traditional rule-based risk management and decision support systems struggle to provide timely and effective responses

Historically vegetable oils spreads and margarines (“spreads”) contained significant amounts of trans fatty acids (TFAs) from partially hydrogenated vegetable oils (PHVOs). Our goal was to survey the US marketplace and determine the amount of TFAs and the use of PHVOs in spreads. We sampled 43 spreads in 2011 and 46 spreads in 2013 and we measured TFAs by capillary gas chromatography. We searched for PHVOs in the ingredient declaration. From 2011 to 2013, the mean TFAs content of spreads per 14g serving decreased from 0.51 g to 0.30 g (p=0.11). As a comparison, it was 1.7 g per 14 g serving in 2002 [1]. TFAs per 14 g serving decreased both in soft spreads (from 0.29 g to 0.13 g; p&lt;0.05) and in stick products (from 1.62 g to 0.98 g; p=0.16). In 2013, 6 out of 11 manufacturers were no longer using PHVOs. Out of the 9 spreads found with PHVOs in 2013, 6 were private label and 4 were stick products. Compared to 2002, the average TFAs content of spreads was markedly reduced by 70% by 2011 and 82% by 2013 to an average of 0.30g per 14g serving. Most manufactures of branded products, notably Unilever and GAF, provide products without PHVOs. Some private label spreads and some stick products still contain significant amounts of PHVOs. At the end of 2013, 80% of the national volume of branded spreads was free of industrial trans fats. [1] Satchithanandam et al. Lipids 2004;39(1):11‐18. Grant Funding Source : Support for the analytical work was provided by Unilever

&lt;p&gt;Interpretability aims to improve our understanding of the model behavior. Local interpretability methods can explain the specific predictions of a model and create trust between the model and its users, empowering the practitioners with powerful new insights. The temporal nature, and the high dimensionality of the time series data sets unique challenges to interpretability, which are specific to this domain of machine learning. An improved understanding of time series interpretability methods, and availability of suitable evaluation metrics for measuring the accuracy of the explanations can contribute to further progress in time series modeling. This PhD thesis includes four research directions that focus on the local interpretability of time series models. The first research topic that we explore involves introducing two novel evaluation metrics for comparing local interpretability methods on generic time series regression problems. We evaluate the proposed metrics through an extensive numerical study, and find that the SHAP method provides the most accurate explanations among the tested methods. Our second research problem involves a specific application of interpretability in sales forecasting and finance domains. Specifically, we propose a unified framework to predict financial commentaries from the financial data generated by a company. We evaluate multiple time series classification models for the prediction task, and use local interpretability methods to explain the predictions. We find that the proposed framework, supported by the machine learning and local interpretability methods, offers new opportunities to leverage management information systems, providing insights to management on key financial issues, including sales forecasting and inventory management. As the third research problem, we study how local interpretability methods can be used to explain time series clustering models. We provide explanations to the clustering algorithms by using classification models as intermediate models to predict the cluster labels. We perform a detailed numerical study, comparing multiple datasets, clustering models, and classification models. Through a careful analysis of the results, we discuss how and when the proposed methodology can be used to obtain insights on the corresponding model behaviour. Finally, the fourth research problem involves developing a locally interpretable deep neural network model for the time series forecasting problem. We evaluate the model accuracy and explanations, using multiple datasets and methods, and find that it achieves similar performance to those of its non-interpretable counterparts, while remaining interpretable.&lt;/p&gt;

Financial supply chains operate in highly dynamic, interconnected, and risk-intensiveenvironments where traditional rule-based risk management and decision support systems struggle to provide timely and effective responses