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Anaemia is a global public health problem affecting women worldwide, and reproductive-age women are at increased risk. We conducted a population-based cross-sectional study analysing the prevalence of overall anaemia and anaemia according to severity in Chinese pre-pregnant women to update current knowledge on anaemia epidemiology. Based on the National Free Preconception Check-up Projects supported by the Chinese government, 5 679 782 women participating in this project in 2017 were included in the present study. The cyanmethemoglobin method was applied to assess Hb concentrations. Univariate and multivariate logistic regressions were applied for associated factors. The prevalence of anaemia among Chinese pre-pregnant women was 21·64 % (mild: 14·10 %, moderate: 7·17 % and severe : 0·37 %). The prevalence of overall and severe anaemia was the highest in Tibet and the lowest in Beijing among thirty-one provinces. Women’s age, region, ethnic origin, educational level, occupation and pregnancy history were all correlated with anaemia. Women with B blood type (adjusted OR (aOR) = 0·89), higher BMI (overweight: aOR = 0·84; obesity: aOR = 0·70) and alcohol consumption (aOR = 0·69) were less likely to have anaemia, while those with rhesus negative blood type (aOR = 1·10), history of anaemia (aOR = 2·60), older age at menarche (aOR = 1·19), heavy menstrual blood loss (aOR = 1·39), longer menstrual period (aOR = 1·09) and shorter menstrual cycle (aOR = 1·08) were more likely to suffer from anaemia. Meat or egg eaters were not significantly associated with severe anaemia. Anaemia is of moderate public health significance among Chinese pre-pregnant women. Interventions should be considered to prevent anaemia to the greatest extent possible to avoid potential harm in this population.
The tobacco cutworm Spodoptera litura (Lepidoptera: Noctuidae) is a polyphagous pest with a highly selective and sensitive chemosensory system involved in complex physiological behaviors such as searching for food sources, feeding, courtship, and oviposition. However, effective management strategies for controlling the insect pest populations under threshold levels are lacking. Therefore, there is an urgent need to formulate eco-friendly pest control strategies based on the disruption of the insect chemosensory system. In this study, we identified 158 putative chemosensory genes based on transcriptomic and genomic data for S. litura, including 45 odorant-binding proteins (OBPs, nine were new), 23 chemosensory proteins (CSPs), 60 odorant receptors (ORs, three were new), and 30 gustatory receptors (GRs, three were new), a number higher than those reported by previous transcriptome studies. Subsequently, we constructed phylogenetic trees based on these genes in moths and analyzed the dynamic expression of various genes in head capsules across larval instars using quantitative real-time polymerase chain reaction. Nine genes–SlitOBP8, SlitOBP9, SlitOBP25, SlitCSP1, SlitCSP7, SlitCSP18, SlitOR34, SlitGR240, and SlitGR242–were highly expressed in the heads of 3- to 5-day-old S. litura larvae. The genes differentially expressed in olfactory organs during larval development might play crucial roles in the chemosensory system of S. litura larvae. Our findings substantially expand the gene inventory for S. litura and present potential target genes for further studies on larval feeding in S. litura.
The aim of this study was to assess the current status of disease-related knowledge and to analyze the relationship among the general condition, illness perception, and psychological status of patients with coronavirus disease 2019 (COVID-19).
A hospital-based cross-sectional study was conducted on 118 patients using convenience sampling. The general questionnaire, disease-related knowledge questionnaire of COVID-19, Illness Perception Questionnaire (IPQ), and Profile of Mood States (POMS) were used to measure the current status of participants.
The overall average score of the disease-related knowledge of patients with COVID-19 was (79.19 ± 14.25), the self-care situation was positively correlated with knowledge of prevention and control (r = 0.265; P = 0.004) and total score of disease-related knowledge (r = 0.206; P = 0.025); the degree of anxiety was negatively correlated with the knowledge of diagnosis and treatment (r = −0.182; P = 0.049). The score of disease-related knowledge was negatively correlated with negative cognition (volatility, consequences, emotional statements) and negative emotions (tension, fatigue, depression) (P < 0.05); positively correlated with positive cognition (disease coherence) and positive emotion (self-esteem) (P < 0.05).
It was recommended that we should pay more attention to the elderly and low-income groups, and increase the knowledge about diagnosis and treatment of COVID-19 and self-care in the future health education for patients.
Why you care: The choice of randomization unit is critical in experiment design, as it affects both the user experience as well as what metrics can be used in measuring the impact of an experiment. When building an experimentation system, you need to think through what options you want to make available. Understanding the options and the considerations to use when choosing amongst them will lead to improved experiment design and analysis.
Why you care: Triggering provides experimenters with a way to improve sensitivity (statistical power) by filtering out noise created by users who could not have been impacted by the experiment. As organizational experimentation maturity improves, we see more triggered experiments being run.
Why you care: Running A/A tests is a critical part of establishing trust in an experimentation platform. The idea is so useful because the tests fail many times in practice, which leads to re-evaluating assumptions and identifying bugs.
As discussed in Chapter 1, running trustworthy controlled experiments is the scientific gold standard in evaluating many (but not all) ideas and making data-informed decisions. What may be less clear is that making controlled experiments easy to run also accelerates innovation by decreasing the cost of trying new ideas, as the quotation from Moran shows above, and learning from them in a virtuous feedback loop. In this chapter, we focus on what it takes to build a robust and trustworthy experiment platform. We start by introducing experimentation maturity models that show the various phases an organization generally goes through when starting to do experiments, and then we dive into the technical details of building an experimentation platform.
Why you care: Understanding the ethics of experiments is critical for everyone, from leadership to engineers to product managers to data scientists; all should be informed and mindful of the ethical considerations. Controlled experiments, whether in technology, anthropology, psychology, sociology, or medicine, are conducted on actual people. Here are questions and concerns to consider when determining when to seek expert counsel regarding the ethics of your experiments.
Why you care: Guardrail metrics are critical metrics designed to alert experimenters about violated assumptions. There are two types of guardrail metrics: organizational and trust-related. Chapter 7 discusses organizational guardrails that are used to protect the business, and this chapter describes the Sample Ratio Mismatch (SRM) in detail, which is a trust-related guardrail. The SRM guardrail should be included for every experiment, as it is used to ensure the internal validity and trustworthiness of the experiment results. A few other trust-related guardrail metrics are also described here.
William Anthony Twyman was a UK radio and television audience measurement veteran (MR Web 2014) credited with formulating Twyman’s law, although he apparently never explicitly put it in writing, and multiple variants of it exist, as shown in the above quotations.
In Chapter 1, we reviewed what controlled experiments are and the importance of getting real data for decision making rather than relying on intuition. The example in this chapter explores the basic principles of designing, running, and analyzing an experiment. These principles apply to wherever software is deployed, including web servers and browsers, desktop applications, mobile applications, game consoles, assistants, and more. To keep it simple and concrete, we focus on a website optimization example. In Chapter 12, we highlight the differences when running experiments for thick clients, such as native desktop and mobile apps.
Why you care: Before you can run any experiments, you must have instrumentation in place to log what is happening to the users and the system (e.g., website, application). Moreover, every business should have a baseline understanding of how the system is performing and how users interact with it, which requires instrumentation. When running experiments, having rich data about what users saw, their interactions (e.g., clicks, hovers, and time-to-click), and system performance (e.g., latencies) is critical.