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Lithium ion batteries (LIBs) are key components of modern electronics and are regularly utilized as their primary power source. Understanding the electrical and mechanical properties of electrode materials plays a major role in the performance improvement of LIBs. In this article, we provide research using PinPoint™ scanning spreading resistance microscopy (SSRM) to effectively measure both electrical and mechanical properties of LIB electrode surfaces at a much higher quality in a high-vacuum environment than in ambient conditions. The data collected in this experiment demonstrate that this technique is an effective means for measuring the quantitative and qualitative topographical, electrical and mechanical data of advanced materials with improved image quality and data accuracy.
Arbitrary word limits on journal articles limit scholarly research, particularly when opportunities for publishing monographs are decreasing. This chapter argues that these limits should be relaxed or even eliminated. Removing arbitrary length limits will improve efficiency by allowing authors to spend less time worrying about said limits and strategizing ways to evade them, resulting in higher-quality articles. This claim is supported by observational evidence.
To propose a new anthropometric index that can be employed to better predict percent body fat (PBF) among young adults and to compare with current anthropometric indices.
All measurements were taken in a controlled laboratory setting in Seoul (South Korea), between 1 December 2015 and 30 June 2016.
Eighty-seven young adults (18–35 years) who underwent dual-energy x-ray absorptiometry (DXA) were used for analysis. Multiple regression analyses were conducted to develop a body fat index (BFI) using simple demographic and anthropometric information. Correlations of DXA measured PBF (DXA_PBF) with previously developed anthropometric indices and the BFI were analysed. Receiver operating characteristic curve analyses were conducted to compare the ability of anthropometric indices to identify obese individuals.
BFI showed a strong correlation with DXA_PBF (r = 0·84), which was higher than the correlations of DXA_PBF with the traditional (waist circumference, r = 0·49; waist to height ratio, r = 0·68; BMI, r = 0·36) and alternate anthropometric indices (a body shape index, r = 0·47; body roundness index, r = 0·68; body adiposity index, r = 0·70). Moreover, the BFI showed higher accuracy at identifying obese individuals (area under the curve (AUC) = 0·91), compared with the other anthropometric indices (AUC = 0·71–0·86).
The BFI can accurately predict DXA_PBF in young adults, using simple demographic and anthropometric information that are commonly available in research and clinical settings. However, larger representative studies are required to build on our findings.
We investigate the spatial distribution, spectral properties and temporal variability of primary producers (e.g. communities of microbial mats and mosses) throughout the Fryxell basin of Taylor Valley, Antarctica, using high-resolution multispectral remote-sensing data. Our results suggest that photosynthetic communities can be readily detected throughout the Fryxell basin based on their unique near-infrared spectral signatures. Observed intra- and inter-annual variability in spectral signatures are consistent with short-term variations in mat distribution, hydration and photosynthetic activity. Spectral unmixing is also implemented in order to estimate mat abundance, with the most densely vegetated regions observed from orbit correlating spatially with some of the most productive regions of the Fryxell basin. Our work establishes remote sensing as a valuable tool in the study of these ecological communities in the McMurdo Dry Valleys and demonstrates how future scientific investigations and the management of specially protected areas could benefit from these tools and techniques.
Postpartum depression and anxiety are under-addressed public health problems with numerous treatment access barriers, including insufficiently available mental health specialist providers.
To examine the effectiveness of nurse-delivered telephone interpersonal psychotherapy (IPT) for postpartum depression. Trial registration ISRCTN88987377.
Postpartum women (n = 241) with major depression (on the Structured Clinical Interview for DSM-IV (SCID-I)) from 36 Canadian public health regions in rural and urban settings were randomly assigned to 12 weekly 60 min nurse-delivered telephone-IPT sessions or standard locally available care. The primary outcome was the proportion of women clinically depressed at 12 weeks post-randomisation, with masked intention-to-treat analysis. Secondary outcomes examined included comorbid anxiety, self-reported attachment and partner relationship quality.
At 12 weeks, 10.6% of women in the IPT group (11/104) and 35% in the control group (35/100) remained depressed (OR = 0.22, 95% CI 0.10–0.46), with the IPT group 4.5 times less likely to be clinically depressed (SCID); 21.2% in the IPT group and 51% in the control group had an Edinburgh Postnatal Depression Scale (EPDS) score >12 (OR = 0.26, 95% CI 0.14–0.48), and attachment avoidance decreased more in the IPT group than in the control group (P = 0.02). Significant differences favoured the IPT group for comorbid anxiety and partner relationship quality at all time points, with no differences in health service or antidepressant use. None of the IPT responders relapsed by 36 weeks. Between-group SCID differences were sustained at 24 weeks, but not at 36 weeks.
Nurse-delivered telephone IPT is an effective treatment for diverse urban and rural women with postpartum depression and anxiety that can improve treatment access disparities.
UK Biobank is a well-characterised cohort of over 500 000 participants including genetics, environmental data and imaging. An online mental health questionnaire was designed for UK Biobank participants to expand its potential.
Describe the development, implementation and results of this questionnaire.
An expert working group designed the questionnaire, using established measures where possible, and consulting a patient group. Operational criteria were agreed for defining likely disorder and risk states, including lifetime depression, mania/hypomania, generalised anxiety disorder, unusual experiences and self-harm, and current post-traumatic stress and hazardous/harmful alcohol use.
A total of 157 366 completed online questionnaires were available by August 2017. Participants were aged 45–82 (53% were ≥65 years) and 57% women. Comparison of self-reported diagnosed mental disorder with a contemporary study shows a similar prevalence, despite respondents being of higher average socioeconomic status. Lifetime depression was a common finding, with 24% (37 434) of participants meeting criteria and current hazardous/harmful alcohol use criteria were met by 21% (32 602), whereas other criteria were met by less than 8% of the participants. There was extensive comorbidity among the syndromes. Mental disorders were associated with a high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation.
The UK Biobank questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed because of selection bias. Built into UK Biobank, these data intersect with other health data to offer unparalleled potential for crosscutting biomedical research involving mental health.
Much of our attention as electroencephalographers is devoted to the identification and localization of spikes and seizures. Atlases, primers, and texts of electroencephalogram (EEG) interpretation provide a wealth of information to guide seizure identification, but often the diagnosis is based on the same principle as Justice Potter Stewart’s maxim for identifying obscenity in Jacobellis v. Ohio: “I know it when I see it.”1 Virtually all of the mathematical seizure detection algorithms currently in use are based on empiric observations of EEG activity that occurs contemporaneously with behavioral seizures, or resembles the electrical activity we see during such behaviors. Ideally, we should be able to derive the parameters for identifying electrographic seizures from a detailed understanding of the underlying neuronal pathophysiology that generates abnormal rhythmic activity, disrupting normal brain circuit functions and behaviors. Unfortunately, we are not there yet. In many cases, however, we have at least a rudimentary knowledge of the neurons and brain structures involved in seizure generation. This chapter will review what we know about how seizures are generated and how that translates into the patterns we observe in EEG recordings.
Maternal systemic inflammation during pregnancy may restrict embryo−fetal growth, but the extent of this effect remains poorly established in undernourished populations. In a cohort of 653 maternal−newborn dyads participating in a multi-armed, micronutrient supplementation trial in southern Nepal, we investigated associations between maternal inflammation, assessed by serum α1-acid glycoprotein and C-reactive protein, in the first and third trimesters of pregnancy, and newborn weight, length and head and chest circumferences. Median (IQR) maternal concentrations in α1-acid glycoprotein and C-reactive protein in the first and third trimesters were 0.65 (0.53–0.76) and 0.40 (0.33–0.50) g/l, and 0.56 (0.25–1.54) and 1.07 (0.43–2.32) mg/l, respectively. α1-acid glycoprotein was inversely associated with birth size: weight, length, head circumference and chest circumference were lower by 116 g (P = 2.3 × 10−6), and 0.45 (P = 3.1 × 10−5), 0.18 (P = 0.0191) and 0.48 (P = 1.7 × 10−7) cm, respectively, per 50% increase in α1-acid glycoprotein averaged across both trimesters. Adjustment for maternal age, parity, gestational age, nutritional and socio-economic status and daily micronutrient supplementation failed to alter any association. Serum C-reactive protein concentration was largely unassociated with newborn size. In rural Nepal, birth size was inversely associated with low-grade, chronic inflammation during pregnancy as indicated by serum α1-acid glycoprotein.
Focused assessment with sonography for trauma (FAST) has been incorporated into the initial evaluation of trauma for decades. It is an important screening tool in the detection of intra-abdominal fluid. The objective of this study was to perform a systematic review of the use and accuracy of FAST as an imaging tool for blunt abdominal trauma in disaster/mass casualty settings. A systematic review of literature was conducted using key words and search terms. Two independent reviewers screened abstracts to determine inclusion using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS). For studies passing QUADAS, a meta-analysis was performed calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). FAST results were compared with the gold standard, which was a combination of CT scan results, operative findings, and medical records of the clinical course. Initial database screening resulted in 133 articles, of which 21 were selected for QUADAS evaluation. Five studies passed QUADAS and were selected in the final meta-analysis, with a total of 4263 patients. The sensitivity of FAST was 92.1% (87.8–95.6), specificity 98.7% (96.0–99.9), PPV 90.7% (70.0–98.0), and NPV 98.8% (98.1–99.5) for the detection of intra-abdominal injury. In our meta-analysis, FAST was both sensitive and specific in the evaluation of trauma in the disaster setting.
The landscape of antimicrobial resistance (AMR) surveillance is changing rapidly. The primary objective of this study was to assess the benefit of linking population-based infection prevention and control surveillance data on methicillin-resistant Staphylococcus aureus (MRSA) to hospital discharge abstract data (DAD). We assessed the value of this novel data linkage for the characterization of hospital-acquired (HA) and community-acquired MRSA (CA-MRSA) cases.
Incident inpatient MRSA surveillance data for all adults (≥18 years) from 4 acute-care facilities in Calgary, Alberta, between April 1, 2011, and March 31, 2017, were linked to DAD. Personal health number (PHN) and gender were used to identify specific individuals, and specimen collection time-points were used to identify specific hospitalization records. A third common variable on admission date between these databases was used to validate the linkage process. Descriptive statistics were used to characterize HA-MRSA and CA-MRSA cases identified through the linkage process.
A total of 2,430 surveillance records (94.6%) were successfully linked to the correct hospitalization period. By linking surveillance and administrative data, we were able to identify key differences between patients with HA- and CA-MRSA. These differences are consistent with previously reported findings in the literature. Data linkage to DAD may be a novel tool to enhance and augment the details of base surveillance data.
Conclusion and recommendations:
This is the first Canadian study linking a frontline healthcare-associated infection AMR surveillance database to an administrative population database. This work represents an important methodological step toward complementing traditional AMR surveillance data practices. Data linkage to other data types, such as primary care, emergency, social, and biological data, may be the basis of achieving more precise data focused around AMR.
Antipseudomonal carbapenems are an important target for antimicrobial stewardship programs. We evaluated the impact of formulary restriction and preauthorization on relative carbapenem use for medical and surgical intensive care units at a large, urban academic medical center using interrupted time-series analysis.
In 2010, South Africa (SA) hosted the Fédération Internationale de Football Association (FIFA) World Cup (soccer). Emergency Medical Services (EMS) used the SA mass gathering medicine (MGM) resource model to predict resource allocation. This study analyzed data from the World Cup and compared them with the resource allocation predicted by the SA mass gathering model.
Prospectively, data were collected from patient contacts at 9 venues across the Western Cape province of South Africa. Required resources were based on the number of patients seeking basic life support (BLS), intermediate life support (ILS), and advanced life support (ALS). Overall patient presentation rates (PPRs) and transport to hospital rates (TTHRs) were also calculated.
BLS services were required for 78.4% (n = 1279) of patients and were consistently overestimated using the SA mass gathering model. ILS services were required for 14.0% (n = 228), and ALS services were required for 3.1% (n = 51) of patients. Both ILS and ALS services, and TTHR were underestimated at smaller venues.
The MGM predictive model overestimated BLS requirements and inconsistently predicted ILS and ALS requirements. MGM resource models, which are heavily based on predicted attendance levels, have inherent limitations, which may be improved by using research-based outcomes.