Imprecise Estimates Research Articles

Identification and quantitation of monoclonal proteins in polyclonal hypergammaglobulinemia background: a retrospective study

Abstract Background Hypergammaglobulinemia poses a challenge for identifying and quantitating monoclonal proteins (M-protein). The Increased polyclonal immunoglobulin background can lead to imprecise estimation of the M-protein when quantitated by serum protein electrophoresis (SPEP). This is especially true using the perpendicular drop method of peak integration. Hypergammaglobulinemia also makes it difficult to detect small M-proteins on immunofixation electrophoresis. Overestimation of M-protein concentration can lead to unnecessary follow-ups while missing small M-proteins can mask the underlying diagnosis for patients. In this study, we retrospectively evaluated the prevalence and concentration of M-protein within polyclonal hypergamma backgrounds using immunocapture matrix-assisted laser desorption ionization time-of-flight mass-spectrometry (MASS-FIX). Methods We retrospectively analyzed 47061 unique patients tested for SPEP and MASS-FIX in 2022 at Mayo Clinic. If a patient underwent multiple tests during this period, only the first result was included in the analysis. Polyclonal hypergammaglobulinemia was defined as a gamma fraction >1.7g/dL by SPEP. MASS-FIX was used to identify and isotype M-proteins. MASS-FIX was then combined with total immunoglobin quantitation (IgG, IgA or IgM) using nephelometry to quantify concentration of the M- protein using mathematical modeling (method currently under validation). Results Of the 47,061 unique patients, 24,796 were normal by SPEP (53%). M-spikes were present in 5,808 cases (12%). Hypogammaglobulinemia (gamma fraction < 0.5g/dL) in 2897 cases (6%). Small non-quantifiable M-proteins by SPEP were present in 8363 cases (18%). [JC2] There were 4222 hypergamma cases in the cohort (9%), without a quantitative M-spike on SPEP. Of these, 16.1% (680/4222) patients had an M-protein by MASS-FIX. When polyclonal hypergamma was the sole SPEP abnormality, 5.5% were MASS-FIX positive. However, when a small abnormality pattern was observed within the polyclonal hypergamma background on SPEP, 50% of patients were MASS-FIX positive. The most common isotype was IgG (27.2%), followed by IgM (10.2%), IgA (8.1%), and biclonal (3.7%), which is similar to overall isotype distribution in other backgrounds. Using quantitative MASS-FIX, we identified that 95% MASS-FIX positive small monoclonal proteins in polyclonal hypergamma are < 1 g/dL, which is the recommended limited of quantitation for M-protein using SPEP in setting of hypergammaglobulinemia. Conclusion Majority of polyclonal hypergammaglobulinemia cases (83.9%) do not have a monoclonal protein. MASS-FIX allows quantitation of small monoclonal proteins below the SPEP limit of quantitation, which can enhance our understanding of conditions which present with small M-proteins in a polyclonal hypergammaglobulinemia background. Small visual abnormalities within polyclonal hypergamma background on SPEP should be followed-up with immunofixation or MASS-FIX to confirm the presence or absence of M- proteins.

Concentration of kidney markers and detection of exosomes in urine samples collected in cotton wool balls in preterm and term neonates

Collecting urine samples in neonates by catheterisation or suprapubic puncture causes trauma, whereas self-adhesive collection bags can damage fragile skin. An alternative method is the collection of samples from urine-soaked cotton wool balls placed in diapers. The aim of this study was to compare the concentration of albumin, creatinine, neutrophil gelatinase-associated lipocalin (NGAL), and uromodulin between clean-catch urine and samples collected in cotton balls in neonates and assess the efficiency of exosome extraction. Standard clean-catch urine samples (SSs) were assayed for albumin, creatinine, NGAL, and uromodulin using commercial ELISA kits. Concentrations were compared to the same urine samples extracted immediately from soaked cotton wool balls (sample 2, S2) or the urine extracted from cotton wool balls placed in diaper in a warm incubator for 2 h before extraction (sample 3, S3). Exosomes were extracted from all three samples of one patient for visualisation by electron microscopy. Twenty-six infants (17 males) of median gestational age at birth of 32+1 weeks had urine collected at a median age of 29 days at 37+6 weeks corrected age. Concentrations in S2 and S3 were within 10% of the concentration of SS in 46% and 35% of specimens for albumin, 69% and 58% for creatinine, 12% and 12% for NGAL, and 27% and 15% for uromodulin, respectively, without consistent positive or negative bias. Urine albumin/creatinine ratios (UACRs) were 4.3% less in S2 and 4.5% less in S3 than in SS. Exosomes were extracted and visualised from all three sample types. Neonatal urine samples extracted from cotton wool balls can be used to screen for relevant albuminuria ​but provide imprecise estimates of NGAL and uromodulin. The proof of exosome extraction from urine collection in cotton wool balls opens the potential to examine exosomal cargo.

Social determinants of injection drug use-associated bacterial infections and treatment outcomes: systematic review and meta-analysis.

Individual injecting practices (e.g., intramuscular injecting, lack of skin cleaning) are known risk factors for injection drug use-associated bacterial and fungal infections; however, social contexts shape individual behaviours and health outcomes. We sought to synthesize studies assessing potential social determinants of injecting-related infections and treatment outcomes. We searched five databases for studies published between 1 January 2000 and 18 February 18 2021 (PROSPERO CRD42021231411). We included studies of association (aetiology), assessing social determinants, substance use, and health services exposures influencing development of injecting-related infections and treatment outcomes. We pooled effect estimates via random effects meta-analyses. We screened 4,841 abstracts and included 107 studies. Several factors were associated with incident or prevalent injecting-related infections: woman/female gender/sex (adjusted odds ratio [aOR] 1.57, 95% confidence interval [CI] 1.36-1.83; n=20 studies), homelessness (aOR 1.29, 95%CI 1.16-1.45; n=13 studies), cocaine use (aOR 1.31, 95%CI 1.02-1.69; n=10 studies), amphetamine use (aOR 1.74, 95%CI 1.39-2.23; n=2 studies), public injecting (aOR 1.40, 95%CI 1.05-1.88; n=2 studies), requiring injecting assistance (aOR 1.78, 95%CI 1.40-2.27; n=8 studies), and use of opioid agonist treatment (aOR 0.92, 95%CI 0.89-0.95; n=9 studies). Studies assessing outcomes during treatment (e.g., premature hospital discharge) or afterward (e.g., rehospitalization; all-cause mortality) typically had smaller sample sizes and imprecise effect estimates. Injecting-related infections and treatment outcomes may be shaped by multiple social contextual factors. Approaches to prevention and treatment should look beyond individual injecting practices towards addressing the social and material conditions within which people live, acquire and consume drugs, and access health care.

Racism and health and wellbeing among children and youth–An updated systematic review and meta-analysis

BackgroundEvidence of racism's health harms among children and youth is rapidly increasing, though attention to impacts on physical health and biomarker outcomes is more emergent. We performed a systematic review of recent publications to examine the association between racism and health among children and youth, with a meta-analysis of the specific relationships between racism and physical health and biomarkers. MethodsWe conducted a systematic literature search using four databases: Medline, PsycINFO, PubMed, and ERIC. Four inclusion criteria were used to identify eligible studies: (1) exposure was experiences of racism, (2) outcome was health and wellbeing, (3) quantitative methods were used to estimate the association between racism and health outcomes, and (4) the effect size of associations between racism and health and wellbeing was reported for participants aged 0–24 years. Correlation coefficients were used to report the pooled effect size for each outcome indicator. ResultsThere were 463 eligible studies included in the screening process, with 42 studies focusing on physical health or biomarker outcomes. Random-effects meta-analysis found minimal to moderate positive associations between racism and C-reactive protein, Interleukin 6, body mass index (BMI), obesity, systolic blood pressure, salivary cortisol, asthma, and somatic symptoms. There were marginal positive associations between racism and Tumour Necrosis Factor-α, cortisol collected via saliva, urine and hair, BMI-z score, and diastolic blood pressure, with imprecise estimates and wide confidence intervals. ConclusionsRacism is associated with negative physical health and biomarker outcomes that relate to multiple physiological systems and biological processes in childhood and adolescence. This has implications for health and wellbeing during childhood and adolescence and future chronic disease risk. Collective and structural changes to eliminate racism and create a healthy and equitable future for all children and youth are urgently required.

A Closed-Form Analytical Conversion between Zernike and Gatinel–Malet Basis Polynomials to Present Relevant Aberrations in Ophthalmology and Refractive Surgery

The Zernike representation of wavefronts interlinks low- and high-order aberrations, which may result in imprecise clinical estimates. Recently, the Gatinel–Malet wavefront representation has been introduced to resolve this problem by deriving a new, unlinked basis originating from Zernike polynomials. This new basis preserves the classical low and high aberration subgroups’ structure, as well as the orthogonality within each subgroup, but not the orthogonality between low and high aberrations. This feature has led to conversions relying on separate wavefront reconstructions for each subgroup, which may increase the associated numerical errors. This study proposes a robust, minimised-error (lossless) analytical approach for conversion between the Zernike and Gatinel–Malet spaces. This method analytically reformulates the conversion as a nonhomogeneous system of linear equations and computationally solves it using matrix factorisation and decomposition techniques with high-level accuracy. This work fundamentally demonstrates the lossless expression of complex wavefronts in a format that is more clinically interpretable, with potential applications in various areas of ophthalmology, such as refractive surgery.

Deep estimation of the intensity and timing of natural selection from ancient genomes.

Leveraging past allele frequencies has proven to be key for identifying the impact of natural selection across time. However, this approach suffers from imprecise estimations of the intensity (s) and timing (T) of selection, particularly when ancient samples are scarce in specific epochs. Here, we aimed to bypass the computation of allele frequencies across arbitrarily defined past epochs and refine the estimations of selection parameters by implementing convolutional neural networks (CNNs) algorithms that directly use ancient genotypes sampled across time. Using computer simulations, we first show that genotype-based CNNs consistently outperform an approximate Bayesian computation (ABC) approach based on past allele frequency trajectories, regardless of the selection model assumed and the number of available ancient genotypes. When applying this method to empirical data from modern and ancient Europeans, we replicated the reported increased number of selection events in post-Neolithic Europe, independently of the continental subregion studied. Furthermore, we substantially refined the ABC-based estimations of s and T for a set of positively and negatively selected variants, including iconic cases of positive selection and experimentally validated disease-risk variants. Our CNN predictions support a history of recent positive and negative selection targeting variants associated with host defence against pathogens, aligning with previous work that highlights the significant impact of infectious diseases, such as tuberculosis, in Europe. These findings collectively demonstrate that detecting the footprints of natural selection on ancient genomes is crucial for unravelling the history of severe human diseases.

Staging of Cervical Cancer: What has Changed?

In India, cervical cancer is the second most common cause of cancer-related fatalities and the fourth most common malignancy worldwide affecting women. India accounts for 25% of all cervical cancer-related deaths worldwide each year. The main drawbacks of clinical staging were the imprecise estimation of tumor size and the challenge of determining the involvement of pelvic and para-aortic lymph nodes with the few studies that FIGO allowed to be done for staging of cancer cervix. The use of 2009 staging approach showed that when many cases were operated based only on clinical findings, they subsequently required adjuvant therapy; hence, treatment-related morbidity was negatively impacted by these errors. Changes have been made to the staging of cervical cancer according to the 2018 revised International Federation of Gynecology and Obstetrics (FIGO) guidelines. Correction to cancer of the cervix staging was published recently in 2024. The horizontal extent (lateral extent) of the disease is not taken into consideration for staging in cases of microinvasive disease. Three subgroups have been identified based on the stratification of tumor size: IB1 ≤ 2cm, IB2 > 2- ≤ 4cm, and IB3 > 4cm. Pathology and imaging modalities are added to clinical diagnosis for staging of cancer cervix. The involvement of lymph nodes (LNs) is now classified based on pathology (p) or imaging (r) which specifies that lymph node involvement is diagnosed using pathology (p) or imaging (r). Stage IIIC has been added [IIIC1 (involvement of pelvic nodes) and IIIC2 (involvement of para-aortic nodes)] is assigned to the case in the event of lymph node positive status. Pathological assessment takes precedence over radiological and clinical findings. The involvement of vascular/lymphatic spaces should not change the staging. The lower staging should be assigned when there is doubt about stage. Overall, the revised FIGO staging of cancer cervix (2024) has a number of advantages, including the inclusion of imaging and pathology, tumor size and LN-based categorization. More studies on staging of cancer cervix in different populations using revised staging of cancer cervix will help to prognosticate use of this staging.

Community-based complex interventions to sustain independence in older people, stratified by frailty: a systematic review and network meta-analysis.

Sustaining independence is important for older people, but there is insufficient guidance about which community health and care services to implement. To synthesise evidence of the effectiveness of community services to sustain independence for older people grouped according to their intervention components, and to examine if frailty moderates the effect. Systematic review and network meta-analysis. Studies: Randomised controlled trials or cluster-randomised controlled trials. Participants: Older people (mean age 65+) living at home. Interventions: community-based complex interventions for sustaining independence. Comparators: usual care, placebo or another complex intervention. Living at home, instrumental activities of daily living, personal activities of daily living, care-home placement and service/economic outcomes at 1 year. We searched MEDLINE (1946-), Embase (1947-), CINAHL (1972-), PsycINFO (1806-), CENTRAL and trial registries from inception to August 2021, without restrictions, and scanned reference lists. Interventions were coded, summarised and grouped. Study populations were classified by frailty. A random-effects network meta-analysis was used. We assessed trial-result risk of bias (Cochrane RoB 2), network meta-analysis inconsistency and certainty of evidence (Grading of Recommendations Assessment, Development and Evaluation for network meta-analysis). We included 129 studies (74,946 participants). Nineteen intervention components, including 'multifactorial-action' (multidomain assessment and management/individualised care planning), were identified in 63 combinations. The following results were of low certainty unless otherwise stated. For living at home, compared to no intervention/placebo, evidence favoured: multifactorial-action and review with medication-review (odds ratio 1.22, 95% confidence interval 0.93 to 1.59; moderate certainty) multifactorial-action with medication-review (odds ratio 2.55, 95% confidence interval 0.61 to 10.60) cognitive training, medication-review, nutrition and exercise (odds ratio 1.93, 95% confidence interval 0.79 to 4.77) and activities of daily living training, nutrition and exercise (odds ratio 1.79, 95% confidence interval 0.67 to 4.76). Four intervention combinations may reduce living at home. For instrumental activities of daily living, evidence favoured multifactorial-action and review with medication-review (standardised mean difference 0.11, 95% confidence interval 0.00 to 0.21; moderate certainty). Two interventions may reduce instrumental activities of daily living. For personal activities of daily living, evidence favoured exercise, multifactorial-action and review with medication-review and self-management (standardised mean difference 0.16, 95% confidence interval -0.51 to 0.82). For homecare recipients, evidence favoured the addition of multifactorial-action and review with medication-review (standardised mean difference 0.60, 95% confidence interval 0.32 to 0.88). Care-home placement and service/economic findings were inconclusive. High risk of bias in most results and imprecise estimates meant that most evidence was low or very low certainty. Few studies contributed to each comparison, impeding evaluation of inconsistency and frailty. Studies were diverse; findings may not apply to all contexts. Findings for the many intervention combinations evaluated were largely small and uncertain. However, the combinations most likely to sustain independence include multifactorial-action, medication-review and ongoing review of patients. Some combinations may reduce independence. Further research is required to explore mechanisms of action and interaction with context. Different methods for evidence synthesis may illuminate further. This study is registered as PROSPERO CRD42019162195. This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR128862) and is published in full in Health Technology Assessment; Vol. 28, No. 48. See the NIHR Funding and Awards website for further award information.

Effects of Diel Oxygen Cycling and Benthic Macrofauna on Sediment Oxygen Demand

This field study examined how sediment macroinfauna change patterns of sediment oxygen demand (SOD) throughout a diel oxygen cycle. Sediments with a greater faunal presence would be expected to have greater overall SOD, and at night may alter their behavior and influence SOD depending on their response to low-oxygen stress. Dynamic faunal bioturbation or bioirrigation behavior would also result in corresponding variation in SOD values on short time scales. In situ flow-through benthic metabolism chambers were used to measure SOD at a high temporal resolution in discrete sediment patches. Sediments with more macroinfauna had greater average SOD over the diel cycle, consistent with previous studies. Where more macroinfauna were present, they drove greater SOD during nightly low oxygen, presumably by enhancing their burrowing and irrigation activities. SOD was also more variable on a sub-diel timescale in sediments with more macroinfauna. Sediment oxygen demand is dynamic and highly sensitive both temporally, on very short timescales, and spatially, in terms of resident fauna, and their interaction produces heretofore unaccounted complexity in patterns of SOD particularly in shallow coastal systems. Extrapolations of temporally and spatially limited SOD measurements to a system-wide scale that do not account for the short-term and spatially variable effects of fauna may produce imprecise and misleading estimates of this critical ecosystem function.

Quantifying error in effect size estimates in attention, executive function, and implicit learning.

Accurate quantification of effect sizes has the power to motivate theory and reduce misinvestment of scientific resources by informing power calculations during study planning. However, a combination of publication bias and small sample sizes (∼N = 25) hampers certainty in current effect size estimates. We sought to determine the extent to which sample sizes may produce errors in effect size estimates for four commonly used paradigms assessing attention, executive function, and implicit learning (attentional blink, multitasking, contextual cueing, and serial response task). We combined a large data set with a bootstrapping approach to simulate 1,000 experiments across a range of N (13-313). Beyond quantifying the effect size and statistical power that can be anticipated for each study design, we demonstrate that experiments with lower N may double or triple information loss. We also show that basing power calculations on effect sizes from similar studies yields a problematically imprecise estimate between 40% and 67% of the time, given commonly used sample sizes. Last, we show that skewness of intersubject behavioral effects may serve as a predictor of an erroneous estimate. We conclude with practical recommendations for researchers and demonstrate how our simulation approach can yield theoretical insights that are not readily achieved by other methods such as identifying the information gained from rejecting the null hypothesis and quantifying the contribution of individual variation to error in effect size estimates. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Glucagon-Like Peptide-1 Receptor Agonists and Risk for Suicidal Ideation and Behaviors in U.S. Older Adults With Type 2 Diabetes : A Target Trial Emulation Study.

A major concern has recently emerged about a potential link between glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and increased risk for suicidal ideation and behaviors based on International Classification of Diseases codes. To investigate the association between GLP-1 RAs, compared with sodium-glucose cotransporter-2 inhibitors (SGLT2is) or dipeptidyl peptidase-4 inhibitors (DPP4is), and risk for suicidal ideation and behaviors in older adults with type 2 diabetes (T2D). Two target trial emulation studies comparing propensity score (PS)-matched cohorts for GLP-1 RAs versus SGLT2is and GLP-1 RAs versus DPP4is. U.S. national Medicare administrative data from January 2017 to December 2020. Older adults (≥66 years) with T2D; no record of suicidal ideation or behaviors; and a first prescription for a GLP-1 RA, SGLT2i, or DPP4i. The primary end point was a composite of suicidal ideation and behaviors. New GLP-1 RA users were matched 1:1 on PS to new users of an SGLT2i or DPP4i in each pairwise comparison. A Cox proportional hazards regression was used to estimate the hazard ratio (HR) and 95% CIs within matched groups. This study included 21 807 pairs of patients treated with a GLP-1 RA versus an SGLT2i and 21 402 pairs of patients treated with a GLP-1 RA versus a DPP4i. The HR of suicidal ideation and behaviors associated with GLP-1 RAs relative to SGLT2is was 1.07 (95% CI, 0.80 to 1.45; rate difference, 0.16 [CI, -0.53 to 0.86] per 1000 person-years); the HR relative to DPP4is was 0.94 (CI, 0.71 to 1.24; rate difference, -0.18 [CI, -0.92 to 0.57] per 1000 person-years). Low event rate; imprecise estimates; unmeasured confounders, such as body mass index; and potential misclassification of outcomes. Among Medicare beneficiaries with T2D, this study found no clear increased risk for suicidal ideation and behaviors with GLP-1 RAs, although estimates were imprecise and a modest adverse risk could not be ruled out. American Foundation for Pharmaceutical Education, Pharmaceutical Research and Manufacturers of America Foundation, National Institute on Aging, and National Institute of Diabetes and Digestive and Kidney Diseases.

Prenatal antidepressant use and risk of congenital malformations: A population-based cohort study

Previous studies examining antidepressants and congenital-malformations were primarily conducted in western countries, and many were constrained by important methodological limitations. This population-based study identified 465,069 women (including 1,705 redeemed ≥1 prescription of antidepressants during first-trimester) aged 15–50 years who delivered their first and singleton child between 2003 and 2018 in a predominantly-Chinese population in Hong Kong, using territory-wide medical-record database of public-healthcare services, and employed propensity-score fine-stratification-weighted logistic-regression analyses to evaluate risk of any major and organ/system-specific congenital-malformations following first-trimester exposure to antidepressants. Major malformation overall was not associated with any antidepressant (weighted-odds-ratio wOR, 0.88 [95 %CI, 0.44–1.76]), specific drug-class, or individual antidepressants. Exposure to any antidepressant was associated with increased risk of cardiac (wOR, 1.82 [95 %CI, 1.07–3.12]) and respiratory anomalies (wOR,4.11 [95 %CI, 1.61–10.45]). Exposure to selective-serotonin-reuptake-inhibitors (SSRI) and multiple-AD-classes were associated with respiratory and cardiac anomalies, respectively. However, these identified associations were not consistently affirmed across sensitivity analyses, precluding firm conclusion. Observed associations of specific cardiac defects with serotonin-norepinephrine-reuptake-inhibitors (SNRI), tricyclic-antidepressants (TCA) and multiple-AD-classes were noted with wide confidence-intervals, suggesting imprecise estimation. Overall, our findings suggest that first-trimester antidepressant exposure was not robustly associated with increased risk of congenital-malformations. Further research clarifying comparative safety of individual antidepressants on specific malformations is warranted.

DSTAN: A Deformable Spatial-temporal Attention Network with Bidirectional Sequence Feature Refinement for Speckle Noise Removal in Thyroid Ultrasound Video.

Thyroid ultrasound video provides significant value for thyroid diseases diagnosis, but the ultrasound imaging process is often affected by the speckle noise, resulting in poor quality of the ultrasound video. Numerous video denoising methods have been proposed to remove noise while preserving texture details. However, existing methods still suffer from the following problems: (1) relevant temporal features in the low-contrast ultrasound video cannot be accurately aligned and effectively aggregated by simple optical flow or motion estimation, resulting in the artifacts and motion blur in the video; (2) fixed receptive field in spatial features integration lacks the flexibility of aggregating features in the global region of interest and is susceptible to interference from irrelevant noisy regions. In this work, we propose a deformable spatial-temporal attention denoising network to remove speckle noise in thyroid ultrasound video. The entire network follows the bidirectional feature propagation mechanism to efficiently exploit the spatial-temporal information of the whole video sequence. In this process, two modules are proposed to address the above problems: (1) a deformable temporal attention module (DTAM) is designed after optical flow pre-alignment to further capture and aggregate relevant temporal features according to the learned offsets between frames, so that inter-frame information can be better exploited even with the imprecise flow estimation under the low contrast of ultrasound video; (2) a deformable spatial attention module (DSAM) is proposed to flexibly integrate spatial features in the global region of interest through the learned intra-frame offsets, so that irrelevant noisy information can be ignored and essential information can be precisely exploited. Finally, all these refined features are rectified and merged through residual convolution blocks to recover the clean video frames. Experimental results on our thyroid ultrasound video (US-V) dataset and the DDTI dataset demonstrate that our proposed method exceeds 1.2 1.3dB on PSNR and has clearer texture detail compared to other state-of-the-art methods. In the meantime, the proposed model can also assist thyroid nodule segmentation methods to achieve more accurate segmentation effect, which provides an important basis for thyroid diagnosis. In the future, the proposed model can be improved and extended to other medical image sequence datasets, including CT and MRI slice denoising. The code and datasets are provided at https://github.com/Meta-MJ/DSTAN .

Analysis of State-of-Health Estimation Approaches and Constraints for Lithium-Ion Batteries in Electric Vehicles

Energy storage systems (ESS) are seeing rapid market growth due to the changing worldwide landscape of electricity distribution and consumption. An ESS must possess the capability to oversee the functioning of the system’s modules under abnormal circumstances, while also having the ability to supervise, manage, and optimize the performance of one or more battery modules. At present, the condition of batteries is assessed based on two factors: the level of charge (SOC) and the overall condition (SOH). By using these two characteristics, it becomes feasible to compute the anticipated battery lifespan and evaluate a battery’s efficiency. The assessment of SOH is a crucial determinant in guaranteeing the effectiveness, dependability, and security of batteries in electric vehicles (EVs). Nevertheless, the safety issues resulting from the imprecise estimation and forecasting of battery health status have garnered significant attention in academic circles. This study presents a comprehensive evaluation of several SOH monitoring techniques. In order to achieve this objective, various scientific and technical literatures are examined and the corresponding methodologies are categorized into distinct groupings. The groupings are categorized based on the manner in which the procedure is executed: methods and techniques used in experiments and models. This paper provides a comprehensive overview of the benefits and drawbacks of several SOH assessment and prediction techniques, along with the associated obstacles in SOH estimation.

Two-phase visual servoing for capturing tumbling non-cooperative satellites with a space manipulator

In this paper, a visual servoing approach is developed to capture the docking rings of tumbling non-cooperative satellites with a space manipulator. The primary challenge addressed is the potential for the docking ring to leave the monocular camera’s field-of-view as the manipulator approaches the target, due to the ring’s large size. To solve this issue, a two-phase visual servoing scheme combining a monocular camera and a three-line structured light vision system is proposed. In an effort to augment the success rate and safety of capture operations, several constraints are formulated, encompassing manipulator’s kinematics, monocular camera’s field-of-view, obstacle avoidance, structured light’s breakpoints and smooth capture. Subsequently, a nonlinear model predictive controller is proposed to manage these constraints in real-time and regulate the system. System models are established based on image moments and pose for each phase, selecting these features as visual feedback to simplify the formulation of servo constraints and avoid the complex circle-based pose measurement. Furthermore, to ensure unbiased predictions, the model disturbances arising from the imprecise estimation of target motion parameter are observed using an extended Kalman filter, which are then incorporated into the predictive control framework. The simulation results demonstrate the effectiveness of this scheme.

The Influence of Flock Variation, Sample Size, Flock Size and Mean Egg Count on the Accuracy and Precision of the Estimated Mean Egg Count

The control of parasitic nematode infection in sheep and other animals is threatened by the evolution of drug resistance in parasite populations. One recommendation to delay the onset of drug resistance is to estimate the flock mean faecal egg counts by sampling a subpopulation and to treat sheep only when egg counts are high. However, there is little research on the accuracy and precision of estimates of the flock mean obtained from samples. In silico sampling was used to quantify the influence of flock variation, sample size, flock size and mean egg count on the accuracy and precision of the estimated mean egg count. Commonly used and recommended sampling schemes gave alarmingly imprecise estimates of the true flock means. Simply providing a point estimate of the flock egg count can be seriously misleading. Therefore, quantiles were provided for the proportion of estimates in a plausible scenario that is likely to require treatment. It may be more informative to use these quantiles to predict the probability that the true flock mean is sufficiently high to consider treatment.

Methodological issues of retrospective surveys for measuring mortality of highly clustered diseases: case study of the 2014–16 Ebola outbreak in Bo District, Sierra Leone

ABSTRACT Background There is a lack of empirical data on design effects (DEFF) for mortality rate for highly clustered data such as with Ebola virus disease (EVD), along with a lack of documentation of methodological limitations and operational utility of mortality estimated from cluster-sampled studies when the DEFF is high. Objectives The objectives of this paper are to report EVD mortality rate and DEFF estimates, and discuss the methodological limitations of cluster surveys when data are highly clustered such as during an EVD outbreak. Methods We analysed the outputs of two independent population-based surveys conducted at the end of the 2014–2016 EVD outbreak in Bo District, Sierra Leone, in urban and rural areas. In each area, 35 clusters of 14 households were selected with probability proportional to population size. We collected information on morbidity, mortality and changes in household composition during the recall period (May 2014 to April 2015). Rates were calculated for all-cause, all-age, under-5 and EVD-specific mortality, respectively, by areas and overall. Crude and adjusted mortality rates were estimated using Poisson regression, accounting for the surveys sample weights and the clustered design. Results Overall 980 households and 6,522 individuals participated in both surveys. A total of 64 deaths were reported, of which 20 were attributed to EVD. The crude and EVD-specific mortality rates were 0.35/10,000 person-days (95%CI: 0.23–0.52) and 0.12/10,000 person-days (95%CI: 0.05–0.32), respectively. The DEFF for EVD mortality was 5.53, and for non-EVD mortality, it was 1.53. DEFF for EVD-specific mortality was 6.18 in the rural area and 0.58 in the urban area. DEFF for non-EVD-specific mortality was 1.87 in the rural area and 0.44 in the urban area. Conclusion Our findings demonstrate a high degree of clustering; this contributed to imprecise mortality estimates, which have limited utility when assessing the impact of disease. We provide DEFF estimates that can inform future cluster surveys and discuss design improvements to mitigate the limitations of surveys for highly clustered data.

Color and Luminance Separated Enhancement for Low-Light Images with Brightness Guidance.

Existing retinex-based low-light image enhancement strategies focus heavily on crafting complex networks for Retinex decomposition but often result in imprecise estimations. To overcome the limitations of previous methods, we introduce a straightforward yet effective strategy for Retinex decomposition, dividing images into colormaps and graymaps as new estimations for reflectance and illumination maps. The enhancement of these maps is separately conducted using a diffusion model for improved restoration. Furthermore, we address the dual challenge of perturbation removal and brightness adjustment in illumination maps by incorporating brightness guidance. This guidance aids in precisely adjusting the brightness while eliminating disturbances, ensuring a more effective enhancement process. Extensive quantitative and qualitative experimental analyses demonstrate that our proposed method improves the performance by approximately 4.4% on the LOL dataset compared to other state-of-the-art diffusion-based methods, while also validating the model's generalizability across multiple real-world datasets.

Variable selection when estimating effects in external target populations.

External validity is an important part of epidemiologic research. To validly estimate effects in specific external target populations using a chosen effect measure (ie, "transport"), some methods require that one account for all effect measure modifiers (EMMs). However, little is known about how including other variables that are not EMMs (ie, non-EMMs) in adjustment sets affects estimates. Using simulations, we evaluated how inclusion of non-EMMs affected estimation of the transported risk difference (RD) by assessing the impacts of covariates that (1) differ (or not) between the trial and the target, (2) are associated with the outcome (or not), and (3) modify the RD (or not). We assessed variation and bias when covariates with each possible combination of these factors were used to transport RDs using outcome modeling or inverse odds weighting. Inclusion of variables that differed in distribution between the populations but were non-EMMs reduced precision, regardless of whether they were associated with the outcome. However, non-EMMs associated with selection did not amplify bias resulting from omission of necessary EMMs. Including all variables associated with the outcome may result in unnecessarily imprecise estimates when estimating treatment effects in external target populations.

Building Engineering Cost Prediction Based On Deep Learning: Model Construction and Real - Time Optimization

Effective project planning, risk mitigation, and stakeholder satisfaction in the construction business are greatly impacted by accurate cost projection. Overspending, setbacks, and ruined projects are all possible results of imprecise cost estimates. For this reason, it is critical to guarantee the viability and success of a project by increasing the precision of cost predictions. Construction project complexity, a myriad of cost variables, and uncertainty are the obstacles that building engineering cost prediction must overcome. Predictions made using traditional approaches are commonly inaccurate because they fail to fully account for the complex interplay between project factors and expenses. Advanced modelling techniques that can handle complicated data and changeable project contexts are necessary to overcome these obstacles. An approach based on deep learning called Deep CostNet for Building Engineering Technique (DCN-BET) Cost Prediction is presented in this research. Its purpose is to solve the problems associated with building engineering cost prediction. The approach uses deep neural networks to extract intricate patterns from massive amounts of project data collected over time. Improved prediction accuracy and real-time optimisation during project execution are made possible by DCN-BET, which captures the nonlinear correlations between project characteristics and costs. Risk assessment and management, cost forecasting for resource allocation, and project budget estimation and planning are among the few of the many construction industry uses for DCN-BET. The effectiveness of DCN-BET is assessed by conducting thorough simulation analyses in contrast to more conventional cost prediction approaches. Training and testing the model with real-world building engineering datasets allows us to evaluate its accuracy and efficacy in project cost prediction. The results show that DCN-BET has the capacity to support real-time optimisation and significantly improved the accuracy of cost predictions, which improved the overall success and efficiency of the project.