Small Bias Research Articles

Electronic Health Interventions and Cervical Cancer Screening: Systematic Review and Meta-Analysis.

Cervical cancer is a significant cause of mortality in women. Although screening has reduced cervical cancer mortality, screening rates remain suboptimal. Electronic health interventions emerge as promising strategies to effectively tackle this issue. This systematic review and meta-analysis aimed to determine the effectiveness of electronic health interventions in cervical cancer screening. On December 29, 2023, we performed an extensive search for randomized controlled trials evaluating electronic health interventions to promote cervical cancer screening in adults. The search covered multiple databases, including MEDLINE, the Cochrane Central Registry of Controlled Trials, Embase, PsycINFO, PubMed, Scopus, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature. These studies examined the effectiveness of electronic health interventions on cervical cancer screening. Studies published between 2013 and 2022 were included. Two independent reviewers evaluated the titles, abstracts, and full-text publications, also assessing the risk of bias using the Cochrane Risk of Bias 2 tool. Subgroup analysis was conducted based on subjects, intervention type, and economic level. The Mantel-Haenszel method was used within a random-effects model to pool the relative risk of participation in cervical cancer screening. A screening of 713 records identified 14 articles (15 studies) with 23,102 participants, which were included in the final analysis. The intervention strategies used in these studies included short messaging services (4/14), multimode interventions (4/14), phone calls (2/14), web videos (3/14), and internet-based booking (1/14). The results indicated that electronic health interventions were more effective than control interventions for improving cervical cancer screening rates (relative risk [RR] 1.464, 95% CI 1.285-1.667; P<.001; I2=84%), cervical cancer screening (intention-to-treat) (RR 1.382, 95% CI 1.214-1.574; P<.001; I2=82%), and cervical cancer screening (per-protocol; RR 1.565, 95% CI 1.381-1.772; P<.001; I2=74%). Subgroup analysis revealed that phone calls (RR 1.82, 95% CI 1.40-2.38), multimode (RR 1.62, 95% CI 1.26-2.08), SMS (RR 1.41, 95% CI 1.14-1.73), and video- and internet-based booking (RR 1.25, 95% CI 1.03-1.51) interventions were superior to usual care. In addition, electronic health interventions did not show a statistically significant improvement in cervical cancer screening rates among women with HPV (RR 1.17, 95% CI 0.95-1.45). Electronic health interventions had a greater impact on improving cervical cancer screening rates among women in low- and middle-income areas (RR 1.51, 95% CI 1.27-1.79). There were no indications of small study effects or publication bias. Electronic health interventions are recommended in cervical cancer screening programs due to their potential to increase participation rates. However, significant heterogeneity remained in this meta-analysis. Researchers should conduct large-scale studies focusing on the cost-effectiveness of these interventions. CRD42024502884; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=502884.

Impact of Adverse Childhood Experiences on Child Development in South America: A Systematic Review

Abstract Background Adverse Childhood Experiences (ACEs) have garnered attention globally for their detrimental effects on children’s development, such as lower communication, gross and fine motor, and personal-social domains. While extensively studied in high-income countries, there remains a gap in understanding their prevalence and impact in low- and middle-income countries (LMICs). This review aimed to identify, evaluate, and summarize the findings of an association between ACEs and children’s developmental and mental health outcomes in South America Methods A systematic review was conducted to investigate the association between ACEs and childhood development in South America. Databases MEDLINE, PsycINFO, Web of Science, Applied Social Sciences Index and Abstracts, Literatura Latino Americana em Ciências da Saúde, IBECS and CINAHL were searched. Selection criteria encompassed studies from Brazil, Colombia, Argentina, Peru, Venezuela, Chile, Ecuador, Bolivia, Paraguay, Uruguay, Guyana and Suriname examining the effects of ACEs on developmental outcomes and mental health disorders in children under 18 years old. Quality assessment aligned with World Health Organization standards was employed. Results Twenty-four studies met the inclusion criteria, with most categorized as low or very low quality. While the majority demonstrated a significant association between ACEs and developmental or mental health outcomes, a few studies reported null findings. Challenges in study quality, such as small sample sizes and selection bias, were identified. Conclusions Despite limitations in study quality, findings underscore the impact of ACEs on child development and mental health in South America. Because of the low quality of the existent studies the next steps should be to grasp a greater understanding of the issue. This review also highlights the urgency for further research and targeted interventions to mitigate the long-term consequences of ACEs in this region. Key messages • ACEs profoundly affect child development and mental health in South America, necessitating urgent attention and intervention. • Addressing the impact of ACEs in South America is critical for fostering healthier developmental trajectories and enhancing public health outcomes.

“Ajudar” – a practice with people with mental disorders treated with long-acting injectables

Abstract Issue Mental Health problems are one of the main causes of disability and morbidity. We think it is pertinent to intervene, providing special care for people with mental disorders and their families. We developed the project “Ajudar” (“To Help”), in a municipality in Northern Portugal. Description of the problem “Ajudar” started in 2013. Its main goal is to contribute to reducing the impact of mental disorders. Its objectives are to identify people with mental disorders treated with long-acting injectables (LAI) and to promote treatment adherence. We identified the Health Units (HU) where these patients take the medication and verified their compliance. An informatic alert was issued when there was non-compliance and we participated in home visits when patients did not show up. We also checked the situation if there was involuntary treatment. We wanted to know if this practice contributed to treatment compliance and to reduce involuntary treatment. Results We gradually identified more people, from 47 in 2013 to 108 in 2024. In 2013, 7 people with mental disorders treated with LAI had an involuntary treatment, whereas in 2019 it was 4. We didn’t analyze data from 2020 to 2022, due to the COVID-19 pandemic. We don’t have the complete data from 2023 yet. Lessons Within the years of “Ajudar”, there was a more than twofold increase in the identified patients and a reduction of almost fourfold of the patients that had an involuntary treatment. Analyzing these data one must be careful due to small numbers bias. We think that a similar project is feasible in other settings. In our response to this Public Mental Health issue, we collaborated with everyone involved with these patients, from health profissionals to resources in the community (social services, community leaders, etc). Key messages • There was a decrease to almost ¾ in the number of patients that had an involuntary treatment. • Involvement with the community is key.

Improved Variance Estimation From Trimmed Samples

ABSTRACTSeveral methods for the robust estimation of the variance of a normal random variable are based on trimming. For example, the popular estimator is based on trimming the pairs of samples with the largest absolute distance. Another example is least trimmed squares regression, where all unusually large residuals are removed before the estimation of the variance. Here, in this work, we propose two new classes of estimators that use an optimal linear combination of the trimmed samples to achieve a lower mean squared error (MSE). The first class guarantees the smallest variance among all unbiased estimators that are based on linear combinations, while the second class of estimators guarantees the smallest MSE at the cost of some small bias. Through simulations, we demonstrate that our proposed estimators can have considerably smaller MSE than other robust estimators both in the presence and absence of outliers.

Single-stage NBI sampler for PM1 mass and five-stage NBI-NMCI sampler for PM1 mass distribution measurements

In recent years, concerns about the health and environmental risks associated with PM1 particles have increased. However, existing PM1 sampling instruments remain to be improved mainly because their PM1 inlets have particle loading and bounce issues. To address these challenges, PM1 inlets based on the Non-Bouncing Impactor (NBI) technique were developed. These inlets employ vacuum oil-wetted glass fiber filter (GFF) substrates to eliminate particle bounce and incorporate a daily vacuum oil injection to prevent particle loading. The 16.7 L/min PM1 NBI, modified from the PM2.5 M-WINS design with a reduced diameter of the single nozzle, was designed to adapt readily with current standard sampling and monitoring systems. The cut-size (Dpa50) of 0.99 ± 0.02 μm was determined by considering the effects of Reynold number and the ratio of jet-to-plate distance and nozzle diameter. Field tests comparing the 16.7 L/min PM1 NBI sampler to the 9-stage NCTU Micro-orifice Cascade Impactor (NMCI9) revealed small sampling biases, with a mean difference of +0.26 ± 2.28 μg/m3 for PM1 measurements when silicone oil-coated aluminum foil (AF) and GFF-AF were used in the NMCI9. The NBI with the oil-wetted GFF substrate effectively removed particles larger than 1.0 μm, resulting in more accurate PM1 mass concentration measurements. Additionally, the development of a 5-stage NMCI with the 30 L/min PM1 NBI as the first stage enabled detailed measurement of PM1 mass distribution in two modes, which was challenging when measuring the entire mass distribution of ambient aerosols. The distribution of size-dependent water-soluble inorganic ions in PM1 showed dominance of SO42− and NH4+ in PM1 compared to PM2.5. In summary, the PM1 NBI enhances accuracy for long-term atmospheric sampling by addressing particle loading and bounce, making it a more reliable standard sampling instrument.

High performance Au/mix-phase MgZnO/Ga-doped ZnO/In heterojunction solar-blind UV detector with small work voltage

High performance UVC (220 nm–280 nm) detectors with low work voltage is key problem in wide application of UVC optoelectronic device. Here, Au/MgZnO/Ga-doped ZnO/In heterojunction detector is made with both high UVC sensitivity mix-phase MgZnO and high carrier density Ga-doped ZnO conductive layers. The heterostructure detector possess relative high response(0.36A/W)at 254 nm deep UV light without bias voltage. The avalanche breakdown phenomenon within the heterojunction detector, introduced high deep UV response (1390.3 A/W at 3.3 μW/cm2 230 nm) of the Au/MgZnO/Ga-doped ZnO/In heterojunction detector under 5 V bias voltage. The high response of Au/MgZnO/Ga-doped ZnO/In heterojunction detector under low work voltage, is not only much higher than MSM structure MgZnO detector under bias voltage over 25 V, but also higher than reported deep UV detector under small bias voltage. In addition, because of different internal breakdown mechanism, the peak response position of the Au/MgZnO/Ga-doped ZnO/In heterojunction detector is located at shorter wavelength position compared with MSM structure MgZnO detector. Therefore, Au/MgZnO/Ga-doped ZnO/In heterojunction detector is an ideal device structure in low voltage driven detector with relative high response at short wavelength UV light.

The effects of special educational needs and socioeconomic status on teachers' and parents' judgements of pupils' cognitive abilities.

Teachers' and parents' judgements of pupils' cognitive abilities influence pupils' daily learning opportunities and experiences, as these judgements affect the difficulty level of materials and instruction that teachers and parents provide. Over time, these judgements thus significantly shape educational success. However, pupils' characteristics, such as special educational needs (SEN), giftedness and socioeconomic status (SES) can influence and bias judgement accuracy. The present study aimed to investigate the relation between pupils' cognitive abilities and their teachers' and parents' judgements of these abilities, and potential bias in these judgements related to SEN, giftedness, and SES. The sample consisted of 1073 primary school pupils from grades 4-6 from 77 classes in 16 schools, and their teachers and parents. Teachers and parents rated their pupils' cognitive abilities. Pupils completed the COVAT-3, a cognitive ability test. Multilevel analyses revealed that parent judgements were significantly higher than teacher judgements, but both informants' judgements were equally strong related to the cognitive ability scores. When controlling for pupils' assessed cognitive abilities, the results revealed small judgement biases: negative for SEN, positive for giftedness, and finally positive for high SES, but only in teachers. Overall, the results indicated that teachers and parents can judge their pupils abilities to a moderate degree, but they also hold judgement biases related to SEN, giftedness and SES. As these biases can affect pupils' opportunities, it is important to increase teachers' and parents' awareness.

Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds

Sea-ice surface temperature from atmospheric reanalysis has been used as an indicator of ice melt and climate change. However, its performance in atmospheric reanalyses is not fully understood in Antarctica. Here, we quantified biases in six widely-used reanalyses using satellite observations, and found strong and persistent warm biases in most reanalyses examined. Further analysis of the biases revealed two main culprits: incorrect cloud properties, and inappropriate sea-ice representation in the reanalysis products. We found that overestimated cloud simulation can contribute more than 4 K warm bias, with ERA5 exhibiting the largest warm bias. Even in reanalysis with smaller biases, this accuracy is achieved through a compensatory relationship between relatively lower cloud fraction bias and overestimated sea ice insulation effect. A dynamic downscaling simulation shows that differences in sea-ice representation can contribute a 2.3 K warm bias. The representation of ice concentration is the primary driver of the spatial distribution of biases by modulating the coupling between sea ice and clouds, as well as surface heat conduction. The lack of a snow layer in all reanalyses examined also has an impact on biases.

A-043 A Fast, Novel and Analytically Sensitive Clinical Research Method for the Analysis of Free Testosterone using UPLC-MS/MS

Abstract Background The aim of this work was to develop an analytically sensitive, rapid clinical research method for the analysis of free testosterone in serum using a small sample volume. Laboratories have found this difficult, partly due to the inherent low concentrations (less than 3% of total testosterone is free), technical challenges and variability in results have been noted with equilibrium dialysis, which is considered the gold standard. Methods Testosterone certified reference material (Merck, UK) was used to create calibrators in 52.75 mM HEPES buffer adjusted to pH 7.4 to mimic the ionic environment of serum. Precision was evaluated using in-house QC materials prepared in serum (Golden West Biologicals, USA and BioIVT, UK). 200 µL serum was incubated at 37°C with 400 µL pH-adjusted 52.75 mM HEPES buffer for 2 hours, mixing at 800 r.p.m. with Rapid Equilibrium Analysis (RED) inserts and plates (Thermo Scientific™, UK). Testosterone-13C3 (Merck, UK) was spiked into resultant diasylate, then liquid-liquid extraction (LLE) was used to concentrate and clean the sample. The ACQUITY™ UPLC™ I-Class System with the Waters™ ACQUITY UPLC BEH C18, 1.7µm, 2.1 x 100 mm Column using a water/methanol/ammonium fluoride gradient and the Waters Xevo™ TQ Absolute Mass Spectrometer were used for quantification. An accelerated protocol for equilibrium dialysis, using a 2-hour incubation and fast mixing step was used (4 hours or longer is more typical historically) though it is essential to control the pH of the dialysis buffer and, particularly, the temperature of the chamber. Results No significant carryover or matrix effects were demonstrated with linearity from 0.5-650 pg/mL. Addition of high concentrations of endogenous substances (albumin, bilirubin, creatinine, cholesterol, triglycerides and uric acid) did not affect quantification. Analytical sensitivity investigations indicated the analytical sensitivity would allow precise quantification (≤20%CV and ≤15% bias) at 0.50 pg/mL. Coefficients of variation (CV) for total precision and repeatability on 5 analytical runs for low, mid and high QCs at approximately 2.79, 8.80 and 134 pg/mL respectively were all ≤ 8.4% (n=25). Accuracy was assessed by analyzing 45 male external quality assurance samples (NEQAS, UK) ranging in concentration from 50.1-268.6 pg/mL and the Bland-Altman agreement showed a small negative bias of -7.6% compared with the all-laboratory trimmed mean (ALTM), though excellent agreement overall. When LC-MS/MS determined concentrations were processed against Vermeulen equation determined concentrations (total testosterone and SHBG concentrations were provided by the scheme, a concentration of 45 g/L albumin was assumed), again good agreement was shown by the Bland-Altman bias of -7.7%. Conclusions A novel clinical research method using a fast equilibrium dialysis procedure with LLE and LC-MS/MS analysis with only 200 µL of serum has been demonstrated for clinical research use. Sub pg/mL analytical sensitivity, excellent linearity and precision, with minimal matrix effects and a strong agreement with an EQA scheme were established.

Circuits for the spectroscopic readout of bits from molecular quantum-dot cellular automata

Molecular quantum-dot cellular automata (QCA) may provide high speed, low-power, classical information processing in the post-CMOS era. The readout of molecular QCA bits is challenging because the molecules may be much smaller than transistors and even single-electron transistors. This paper builds on a recent proposal for the spectroscopic readout of bits from asymmetric QCA molecules. Here, we propose circuits for fanning out a bit onto a large QCA circuit to increase the spectroscopic signal-to-noise ratio. As the number of molecules in a fanout circuit grows, the internal bias of each asymmetric cell accumulates, and the circuit may become stuck, tolerating only a very small internal bias. We also propose the use of an applied electric field to compensate for a candidate molecule’s internal bias, thereby restoring switchability, even when the internal bias is significant.

How well does a single blood sample represent long-term exposure for epidemiological studies of PFOA among men in the general population?

Many epidemiological studies use a single blood sample per participant to assess exposure, but it is unclear how well a single sample represents longer term exposure. We performed a simulation study using summary statistics for repeated serum PFOA measurements from several previous studies in men to generate plausible serum concentrations over time, taking within-subject correlations into account. Simulated serum concentrations for controls were categorized into quintiles at each time point, and used to determine the extent of misclassification at each time point compared to the “true” long-term average exposure. We then generated case counts by quintile needed to produce an odds ratio (OR) of 1.5 for the highest vs. lowest quintile categorized based on long term exposure, and used the same misclassification rates observed in the controls to simulate misclassified exposure quintiles for cases. Comparing long term vs. single baseline exposure measures for repeated serum samples collected within about 5–13 years of each other revealed similar effect estimates, although there was a small bias to the null. Trend tests across quintiles were mostly significant using either baseline or long-term exposure. For the general population sample of men in Norway, with 5 repeated measurements over 28 years, serum PFOA was substantially lower prior to 1987, and using either of the two earliest samples as the exposure metric, compared to the long term average, produced larger bias to the null and non-significant trend tests; however using later time points as the exposure metric resulted in only a small bias. Using data based on studies of men, single baseline serum samples represented rather well the mean of repeated samples collectedup to 13 years apart, but were not always reliable surrogates for average exposure over 3 decades, during which time PFOA exposure levels in the general population have changed substantially.

Effect of an oncology care management program on ED visits in a rural Medicaid population.

175 Background: Optum’s Cancer Support Program (CSP) provides care management to members with cancer diagnoses facing healthcare barriers. CSP nurses educate members on symptom management, palliative care and advance care planning, address psychosocial needs and close gaps in care. Improving health equity and outcomes is a program priority. In 2023, Optum analyzed outpatient claims for 24,221 Medicaid members with cancer and found that rural members visit emergency departments (ED) 31% more than urban members. As part of a CSP quality and outcomes assessment, we aimed to understand CSP’s impact on ED utilization within a high-risk rural Medicaid population. Methods: Beginning 1/1/23, Medicaid members in rural zip codes with an active cancer diagnosis were stratified as high- or low-risk based on diagnosis and clinical factors. High-risk members were invited to participate in CSP and receive individualized telephonic care management. 2023 outpatient medical claims were evaluated, and rural ED visit rates were compared for high-risk CSP participants and non-participants for approximately 4 months (average CSP engagement) after CSP enrollment or identification (non-participants). Population demographics were tested via a Mood's median test; ED visits per thousand members per year (PTMPY)—engaged, non-engaged, and difference in means—was tested via a 10,000-iteration bootstrap. Results: The study population included 421 members stratified as high-risk. Of those, 38% (161) enrolled in CSP. Reasons for non-enrollment included inability to reach the member (61%) and member refused (39%). There were no statistically significant differences between the groups’ demographic composition (all variables had p ≥ 0.05). Under a 10,000-iteration bootstrap, there were 2,696 ED visits PTMPY, 80% CI [2,258, 3,150] in the participant group and 3,427 ED visits PTMPY, 80% CI [2,904, 3,974] in the non-participant group. The difference in means between the groups was 730 ED visits PTMPY, 80% CI [52, 1,421]. Conclusions: Rural Medicaid members enrolled in CSP experienced lower ED visit rates, suggesting that care management may be effective at reducing ED utilization within rural populations that lack access to wraparound oncology services. Limitations of this study include small sample size and potential enrollment bias. Further analysis is underway to increase the study sample size and identify other factors that may be impacted by care management, thereby improving healthcare equity.

Foreground biases in strong gravitational lensing

Strong gravitational lensing is a competitive tool to probe the dark matter and energy content of the Universe. However, significant uncertainties can arise from the choice of lens model, and in particular the parameterisation of the line of sight. In this work, we consider the consequences of ignoring the contribution of foreground perturbers in lens modelling. We derive the explicit form of the degeneracy between the foreground shear and the ellipticity of a power law lens, which renders the former quantity effectively unmeasurable from strong lensing observables, and biases measurements of the latter by a few percent. Nonetheless, we demonstrate that this degeneracy does not affect measurements of the Einstein radius. Foreground tidal effects are also not expected to bias the slope of the potential, and any biases in this slope should not affect the recovery of the Hubble constant. The foreground convergence term adds an additional uncertainty to the measurement of H 0, and we show that this uncertainty will be on the order of 1% for lensing systems located along random lines of sight. There is evidence to indicate that the probability of strong lensing is higher towards overdense lines of sight, and this could result in a small systematic bias towards overestimations of H 0.

Edinburgh postpartum depression scores are associated with vaginal and gut microbiota in pregnancy.

Prenatal and postpartum depression may be influenced by the composition of host associated microbiomes. As such, the objective of this study was to elucidate the relationship between the human gut or vaginal microbiomes in pregnancy with prenatal or postpartum depression. 140 female participants were recruited at their first prenatal visit and completed the Edinburgh Postnatal Depression Scale (EPDS) to screen for depression and anxiety, in addition the EPDS was completed one month postpartum. Vaginal and stool biospecimens were collected in the third trimester, analyzed using 16S rRNA gene sequencing, and assessed for alpha and beta diversity. Individual taxa differences and clustering using the k-medoids algorithm enabled community state type classification. Participants with higher postpartum EPDS scores had higher species richness and lower abundance of L. crispatus in the vaginal microbiota compared to those with lower EPDS scores. Participants with a higher prenatal EPDS score had lower species richness of the gut microbiome. Participants with a vaginal community state type dominated by L. iners had the highest mean prenatal EPDS scores, whereas postpartum EPDS scores were similar regardless of prenatal vaginal state type. Our small sample size and participant's self-report bias limits generalizability of results. Depression in the prenatal and postpartum period is associated with the composition and diversity of the gut and vaginal microbiomes in the third trimester of pregnancy. These results provide a foundational understanding of the microbial relationships between maternal health and depression for identifying potential therapeutic treatments.

"Doctor ChatGPT, Can You Help Me?" The Patient's Perspective: Cross-Sectional Study.

Artificial intelligence and the language models derived from it, such as ChatGPT, offer immense possibilities, particularly in the field of medicine. It is already evident that ChatGPT can provide adequate and, in some cases, expert-level responses to health-related queries and advice for patients. However, it is currently unknown how patients perceive these capabilities, whether they can derive benefit from them, and whether potential risks, such as harmful suggestions, are detected by patients. This study aims to clarify whether patients can get useful and safe health care advice from an artificial intelligence chatbot assistant. This cross-sectional study was conducted using 100 publicly available health-related questions from 5 medical specialties (trauma, general surgery, otolaryngology, pediatrics, and internal medicine) from a web-based platform for patients. Responses generated by ChatGPT-4.0 and by an expert panel (EP) of experienced physicians from the aforementioned web-based platform were packed into 10 sets consisting of 10 questions each. The blinded evaluation was carried out by patients regarding empathy and usefulness (assessed through the question: "Would this answer have helped you?") on a scale from 1 to 5. As a control, evaluation was also performed by 3 physicians in each respective medical specialty, who were additionally asked about the potential harm of the response and its correctness. In total, 200 sets of questions were submitted by 64 patients (mean 45.7, SD 15.9 years; 29/64, 45.3% male), resulting in 2000 evaluated answers of ChatGPT and the EP each. ChatGPT scored higher in terms of empathy (4.18 vs 2.7; P<.001) and usefulness (4.04 vs 2.98; P<.001). Subanalysis revealed a small bias in terms of levels of empathy given by women in comparison with men (4.46 vs 4.14; P=.049). Ratings of ChatGPT were high regardless of the participant's age. The same highly significant results were observed in the evaluation of the respective specialist physicians. ChatGPT outperformed significantly in correctness (4.51 vs 3.55; P<.001). Specialists rated the usefulness (3.93 vs 4.59) and correctness (4.62 vs 3.84) significantly lower in potentially harmful responses from ChatGPT (P<.001). This was not the case among patients. The results indicate that ChatGPT is capable of supporting patients in health-related queries better than physicians, at least in terms of written advice through a web-based platform. In this study, ChatGPT's responses had a lower percentage of potentially harmful advice than the web-based EP. However, it is crucial to note that this finding is based on a specific study design and may not generalize to all health care settings. Alarmingly, patients are not able to independently recognize these potential dangers.

Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model

BackgroundWrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts.MethodsWe developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL.ResultsIn OPTIMISE cohort, STEPHEN’s estimates of the proportion of time spent sedentary had significantly (p < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06–0.25] vs. 0.23 [0.13–0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06–0.26] vs. 0.42[0.32–0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03–0.25] vs. 0.36[0.15–0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes).ConclusionsSTEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/. The package includes trained models, but users have the flexibility to train their own models.

Century‐Long Variations in Surface Incident Solar Radiation Over Japan——Characterized by Observations and Reanalyses

ABSTRACTSurface incident solar radiation (Rs) is of great importance in determining the energy balance in the Earth system. In this study, we use century‐long homogenized observations over Japan to constrain five 20th century reanalyses to explore their performance in reproducing Rs variation on different time scales (high‐frequency components [HFCs], for signals with cycles less than 10 years; low‐frequency components [LFCs], for signals with cycles more than 10 years) by ensemble empirical mode decomposition (EEMD) method and to quantify the impact factors on the Rs estimations by the sum of tree (SOT) model. It is found that ERA20C, ERA20CM and CERA20C overestimated Rs by 0.48–1.49 W/m−2, while 20CRv2c and 20CRv3 underestimated Rs by −0.63 and −1.18 W/m−2, respectively for 1931–2010. Poor correlation coefficient (R) was found to be 0.10 for ERA20CM and 0.22 for 20CRv2c. 20CRv2c failed for 1931–1960 but improved considerably for 1961–2010. 20CRv3 uses an upgraded model and assimilates more observations compared with its predecessor 20CRv2c; however, only the original components and HFCs in Rs were improved, with nearly no improvement in the LFCs. In general, CERA20C Rs, with small biases and higher R of 0.73 for original signals, 0.83 for HFCs and LFCs, is superior to other reanalyses. No obvious trend in clear sky Rs demonstrated that reanalysed Rs are insensitive to the aerosol forcings. Therefore, cloud cover and water vapour maybe the main factors influenced reanalysed Rs. Most of time, Rs is more sensitive to cloud cover than vapour pressure for all reanalyses except original signals (with contribution ratio of 0.29 for cloud cover and 0.71 for vapour pressure) and LFCs (with contribution ratio of 0.41 for cloud cover and 0.59 for vapour pressure) in CERA20C, and original signals (with contribution ratio of 0.37 for cloud cover and 0.63 for vapour pressure) in ERA20C. This work pointed out that aerosol related processes such as aerosol forcings or aerosol radiative effect in reanalyses should be improved in the future, which will ultimately improve the interaction between aerosol and cloud in Rs simulations.

Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection

Climatic, ecological, and socioeconomic factors are facilitating the spread of mosquito-borne diseases, heightening the importance of vector surveillance and control. Citizen science is proving to be an effective tool to track mosquito populations, but methods are needed to detect and account for small scale sampling biases in citizen science surveillance. In this article we combine two types of traditional mosquito surveillance records with data from the Mosquito Alert citizen science system to explore the ways in which the socioeconomic characteristics of urban neighborhoods result in sampling biases in citizen scientists’ mosquito reports, while also shaping the spatial distribution of mosquito populations themselves. We use Barcelona, Spain, as an example, and focus on Aedes albopictus, an invasive vector species of concern worldwide. Our results suggest citizen scientists’ sampling effort is focused more in Barcelona’s lower and middle income census tracts than in its higher income ones, whereas Ae. albopictus populations are concentrated in the city’s upper-middle income tracts. High resolution estimates of the spatial distribution of Ae. albopictus risk can be improved by controlling for citizen scientists’ sampling effort, making it possible to provide better insights for efficiently targeting control efforts. Our methodology can be replicated in other cities faced with vector mosquitoes to improve public health responses to mosquito-borne diseases, which impose massive burdens on communities worldwide.

Achieving a Noise Limit with a Few-layer WSe2 Avalanche Photodetector at Room Temperature.

We engineered a two-dimensional Pt/WSe2/Ni avalanche photodetector (APD) optimized for ultraweak signal detection at room temperature. By fine-tuning the work functions, we achieved an ultralow dark current of 10-14 A under small bias, with a noise equivalent power (NEP) of 8.09 fW/Hz1/2. This performance is driven by effective dark barrier blocking and a record-long electron mean free path (123 nm) in intrinsic WSe2, minimizing dark carrier replenishment and suppressing noise under an ultralow electric field. Our APD exhibits a high gain of 5 × 105 at a modulation frequency of 20 kHz, effectively balancing gain and bandwidth, a common challenge in traditional photovoltaic-based APDs. By addressing the typical challenges of high noise and low gain and minimizing dependence on high electric fields, this work highlights the potential of 2D materials in developing efficient, low-power, and ultrasensitive photodetections.

A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates

Abstract. Lightning is an important atmospheric process for generating reactive nitrogen, resulting in the production of tropospheric ozone, as well as igniting wildland fires, which result in potentially large emissions of many pollutants and short-lived climate forcers. Lightning is also expected to change in frequency and location with the changing climate. As such, lightning is an important component of Earth system models. Until now, the Canadian Earth System Model (CanESM) did not contain an interactive-lightning parameterization. The fire parameterization in CanESM5.1 was designed to use prescribed monthly climatological lightning. In this study, we have added a logistical regression lightning model that predicts lightning occurrence interactively based on three environmental variables and their interactions in CanESM5.1's atmospheric model, CanAM5.1 (Canadian Atmospheric Model), creating the capacity to interactively model lightning, allowing for future projections under different climate scenarios. The modelled lightning and resulting burned area were evaluated against satellite measurements over the historical period, and model biases were found to be acceptable. Modelled lightning had a small negative bias and excellent land–ocean ratio compared to satellite measurements. The modified version of CanESM5.1 was used to simulate two future climate scenarios (SSP2-4.5 and SSP5-8.5; Shared Socioeconomic Pathway) to assess how lightning and burned area change in the future. Under the higher-emissions scenario (SSP5-8.5), CanESM5.1 predicts almost no change to the global mean lightning flash rate by the end of the century (2081–2100 vs. 2015–2035 average). However, there are substantial regional changes to lightning – particularly over land – such as a mean increase of 6 % in the northern mid-latitudes and decrease of −8 % in the tropics. By the century's end, the change in global total burned area with prescribed climatological lightning was about 2 times greater than that with interactive lightning (42 % vs. 26 % increase, respectively). Conversely, in the northern mid-latitudes the use of interactive lightning resulted in 3 times more burned area compared to that with unchanging lightning (48 % vs. 16 % increase, respectively). These results show that the future changes to burned area are greatly dependent on a model's lightning scheme, both spatially and overall.