How to properly evaluate cardiac vagal tone in oncology studies:a state-of-the-art review

Heart rate variability(HRV)analysis provides an assessment of cardiac vagal tone and consequently global cardiac health as well as systemic condition.In systemic diseases such as cancer and during treatments that affect the whole body,like chemotherapy,the vagus nerve activity is low and deregulated.Some studies focus on using HRV to predict mortality in oncology.However,in cancer patients,systemic alterations substantially increase artifacts during HRV measurement,especially atrial ectopic beats.Moreover,HRV may be altered by various factors(duration and time of measurement,breathing,drugs,and other confounding factors)that alter each metric in different ways.The Standard Deviation of all Normal to Normal intervals(SDNN)is the most commonly used metric to evaluate HRV in oncology,but it does not appear to be specific to the cardiac vagal tone.Thus,cardiac vagal activity diagnosis and vital prognosis of cancer patients can be biased.Our review presents the main HRV metrics that can be currently used in oncology studies and their links with vagus nerve and cancer.We present the influence of external factors and the required duration and time of measurement.Considering all these parameters,this review proposes seven key points for an assessment of HRV and cardiac vagal tone in patients with cancer.

METHODOLOGICAL EVALUATION ON CLINICAL RESEARCH LITERATURE OF ACUPUNCTURE TREATMENT OF FACIAL PARALYSIS

Objective: To try to give an objective evaluation on the clinical research situation about acupuncture treatment of facial paralysis in the past 50 years and try to provide a possible evidence for clinical practice. Methods: All papers are searched and assessed according to the international standards and clinical epidemiology. Results: There is no systematic review (SR) on acupuncture treatment of facial palsy in a total of 1021 articles enlisted in the present paper. Comparing with the quantity of the descriptive studies and expert opinions (constituting 84.84%), that of the randomized controlled trials (RCTs) and clinical controlled trials (CCTs) is smaller (constituting 15.16%), besides, the quality of RCTs and CCTs is unsatisfactory. Conclusion: At present, the quantity and quality of studies with RCTs about acupuncture treatment of facial paralysis can’t meet the need of clinical practice, and in order to improve the therapeutic effect, a higher quality of RCTs and SR is required.

Assessing the Potential Contribution of Fisheries to Economic Development —The Case of Post-Ebola Sierra Leone

This paper examines the potential contribution of the fisheries sector to national economic development in Sierra Leone, following the cessation of the recent Ebola virus disease (EVD). The tragic deaths of some 4000 persons, as a direct result of this epidemic, have also been accompanied by serious economic disruption to one of the world’s poorest countries, amounting to a loss of over USD 1 billion (20 per cent of annual Gross Domestic Product, GDP). Based on recent work undertaken by the New Partnership for Africa’s Development (NEPAD), an assessment was made of the potential contribution of one of the country’s major natural resource sectors—fisheries—to future economic recovery. It was confirmed that the fish resources of Sierra Leone have an estimated capitalised economic value of USD 735 million, and could potentially make an increased contribution to GDP under suitable conditions, over and above the current estimated level of 10 per cent. However, our results also show that with an overall “readiness assessment” score of 41/100, the sector is unlikely to realise its full potential unless a number of specific areas are addressed (based on analysis valid up to January 2016). These include an improvement in fisheries economic assessment, the upgrading of the fisheries governance and management framework, and the establishment of a fisheries development strategy and implementation plan to channel future investments and interventions in an appropriate sequence. Particular attention should be given early on to an increase in the quality and capacity of both public and private sector organisations relevant to roles and functions within a general process of fisheries sector reform. Finally, the fisheries policy evaluation approach used here is novel. While it has been applied to Sierra Leone for the intrinsic interest of that case, this demonstration also shows that it could be applied elsewhere in the future when reviewing fisheries policy.

Optimization and evaluation of fish oil rnicrocapsules

Fish oil microcapsules were prepared using two natural polysaccharides, alginate and chitosan, as the wall materials. A response surface methodology （RSM） was used to optimize the conditions for fish oil encapsulation efficiency （FOEE）. The FOEE was investigated with respect to three key-variables in the RSM： ratio of inner oil phase to aqueous phase （X1 w/w）; concentration of the aqueous phase （X2, wt%）; and ratio of the aqueous phase to outer oil phase （X3, v/v）. The optimal formulation obtained from the RSM model, i.e., 2.7：1 （X1）, 1.6 wt% （X2）, and 11.5：1 （X3）, gave a FOEE of 28%. The model was validated and the fish oil microcapsules prepared under the optimized conditions were characterized in terms of particle size, polydispersity index （PDI）, zeta potential, surface morphology, and in vitro release. The average droplet size, PDI, and zeta potential were 915 nm, 0.038, and ＋5.2 mV, respectively. The fish oil microcapsules were highly uniform microspheres, and had an accumulative release rate of 77.7% in 270 min in a gastrointestinal model, indicating their potential as an alternative carrier for the controlled release of fish oil. In conclusion, formulating optimal microencapsulation conditions by the RSM can be applied to the microencapsulation of various oil-soluble nutrients for food applications.

A methodology for the performance evaluation of low-cost accelerometer and magnetometer sensors in geomatics applications

This paper presents a methodology and its software implementation for the performance evaluation of low-cost accelerometer and magnetometer sensors for use in geomatics applications.A known mathematical calibration model has been adopted.The method was completed with statistical methodologies for adjusting observations and has been extended to calculate accuracies for the attitude,heading,and tilt angles estimation that are of interest to geomatics applications.The evaluation method consists of two stages.First,the evaluation method reviews the total magnitude of acceleration or the strength of the magnetic field.Second,the evaluation is more detailed and concerns the determination of mathematical parameters that describe both accelerometer and magnetometer working model.A software tool that implements the evaluation model has been developed and is applied both in accelerometer and magnetometer measurement data-sets acquired from a low-cost sensor system.

Universal GALS Platform and Evaluation Methodology for Networks-on-Chip

A networks-on-chip （NoC） cost-effective design method was given based on the globallyasynchronous locally-synchronous （GALS） interconnect structure. In this method, the synchronous mode was used to transmit data among routers, network interface （NI）, and intellectual property （IP） via a synchronous circuit. Compared with traditional methods of implementing GALS, this method greatly reduces the transmission latency and is compatible with existing very large scale integration （VLSI） design tools. The platform designed based on the method can support two kinds of packetizing mechanisms, any topology, several kinds of traffic, and many configurable parameters such as the number of virtual channels, thus the platform is universal. An NoC evaluation methodology is given with a case study showing that the platform and evaluation methodology work well.

Evaluating rural health outcomes:A methodological approach using population‐level data

Background:The sustainability of rural surgical and obstetrical facilities depends on their efficacy and quality of care,which are difficult to measure in a rural context.In an evaluation of rural practice,it is often the case that the only comparators are larger referral facilities,for which facility‐level comparisons are difficult due to differences in population demographics,acuity of patients,and services offered.This publication outlines these limitations and highlights a best‐practice approach to making facility‐level comparisons using population‐level data,risk stratification,tests of noninfer-iority,and Firth logistic regression analysis.This includes an investigation of minimum sample‐size requirements through Monte Carlo power analysis in the context of low‐acuity rural surgical care.Methods:Monte Carlo power analysis was used to estimate the minimum sample size required to achieve a power of 0.8 for both logistic regression and Firth logistic regression models that compare the proportion of surgical adverse events against facility type,among other confounders.We provide guidelines for the implementation of a recommended methodology that uses risk stratification,Firth penalized logistic regression,and tests of noninferiority.Results:We illustrate limitations in facility‐level comparison of surgical quality among patients undergoing one of four index procedures including hernia repair,colonoscopy,appendectomy,and cesarean delivery.We identified minimum sample sizes for comparison of each index procedure that fluctuate depending on the level of risk stratification used.Conclusion:The availability of administrative data can provide an adequate sample size to allow for facility‐level comparisons in surgical quality,at the rural level and elsewhere.When they are made appropriately,these comparisons can be used to evaluate the efficacy of general practitioners and nurse practitioners in performing low‐acuity procedures.

Methodological Analysis of Clinical Articles on Therapy Evaluation Published in Chinese Journal of Integra

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Modelling the Dropout Patterns of MOOC Learners

We conduct a survival analysis for the viewing durations of massive open online courses.The hazard function of the empirical duration data presents as a bathtub curve with the Lindy effect in its tail.To understand the evolutionary mechanisms underlying these features,we categorize learners into two classes based on their different distributions of viewing durations,namely lognormal distribution and power law with exponential cutoff.Two random differential equations are provided to describe the growth patterns of viewing durations for the two classes respectively.The expected duration change rate of the learners featured by lognormal distribution is supposed to be dependent on their past duration,and that of the remainder of learners is supposed to be inversely proportional to time.Solutions to the equations predict the features of viewing duration distributions,and those of the hazard function.The equations also reveal the features of memory and memorylessness for the respective viewing behaviors of the two classes.

Generation of meaningful synthetic sensor data—Evaluated with a reliable transferability methodology

As households are equipped with smart meters,supervised Machine Learning(ML)models and especially Non-Intrusive Load Monitoring(NILM)disaggregation algorithms are becoming increasingly important.To be robust,these models require a large amount of data,which is difficult to collect.Consequently,the generation of meaningful synthetic data is becoming more relevant.We use a simulation framework to generate multiple datasets using different techniques and evaluate their quality statistically by measuring the performance of NILM models for transferability.We demonstrate that the method of data generation is crucial to train ML models in a meaningful way.The experiments conducted reveal that adding noise to the synthetic smart meter data is essential to train robust NILM models for transferability.The best results are obtained when this noise is derived from unknown appliances for which no ground truth data is available.Since we observed that NILM models can provide unstable results,we develop a reliable evaluation methodology,based on Cochran’s sample size.Finally,we compare the quality of the generated synthetic data with real data and observe that multiple NILM models trained on synthetic data perform significantly better than those trained on real data.