Childhood PM2.5 exposure and upward mobility in the United States.

Although it is well documented that exposure to fine particulate matter (PM2.5) increases the risk of several adverse health outcomes, less is known about its relationship with economic opportunity. Previous studies have relied on regression modeling, which implied strict assumptions regarding confounding adjustments and did not explore geographical heterogeneity. We obtained data for 63,165 US census tracts (86% of all census tracts in the United States) on absolute upward mobility (AUM) defined as the mean income rank in adulthood of children born to families in the 25th percentile of the national income distribution. We applied and compared several state-of-the-art confounding adjustment methods to estimate the overall and county-specific associations of childhood exposure to PM2.5 and AUM controlling for many census tract-level confounders. We estimate that census tracts with a 1 µg/m3 higher PM2.5 concentrations in 1982 are associated with a statistically significant 1.146% (95% CI: 0.834, 1.458) lower AUM in 2015, on average. We also showed evidence that this relationship varies spatially between counties, exhibiting a more pronounced negative relationship in the Midwest and the South.

The increasingly dominant role of climate change on length of day variations.

The melting of ice sheets and global glaciers results in sea-level rise, a pole-to-equator mass transport increasing Earth's oblateness and resulting in an increase in the length of day (LOD). Here, we use observations and reconstructions of mass variations at the Earth's surface since 1900 to show that the climate-induced LOD trend hovered between 0.3 and 1.0 ms/cy in the 20th century, but has accelerated to 1.33 [Formula: see text] 0.03 ms/cy since 2000. We further show that surface mass transport fully explains the accelerating trend in the Earth oblateness observed in the past three decades. We derive an independent measure of the decreasing LOD trend induced by Glacial Isostatic Adjustment (GIA) of [Formula: see text]0.80 [Formula: see text] 0.10 ms/cy, which provides a constraint for the mantle viscosity. The sum of this GIA rate and lunar tidal friction fully explains the secular LOD trend that is inferred from the eclipse record in the past three millennia prior to the onset of contemporary climate change. Projections of future climate warming under high emission scenarios suggest that the climate-induced LOD rate may reach 2.62 [Formula: see text] 0.79 ms/cy by 2100, overtaking lunar tidal friction as the single most important contributor to the long-term LOD variations.

Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics.

Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell resolution where each captured location (spot) may contain a mixture of cells from heterogeneous cell types, and several cell-type decomposition methods have been proposed to estimate cell type proportions of each spot by integrating with single-cell RNA sequencing (scRNA-seq) data. However, these existing methods did not fully consider the effect of distribution difference between scRNA-seq and ST data for decomposition, leading to biased cell-type-specific genes derived from scRNA-seq for ST data. To address this issue, we develop an instance-based transfer learning framework to adjust scRNA-seq data by ST data to correctly match cell-type-specific gene expression. We evaluate the effect of raw and adjusted scRNA-seq data on cell-type decomposition by eight leading decomposition methods using both simulated and real datasets. Experimental results show that data adjustment can effectively reduce distribution difference and improve decomposition, thus enabling for a more precise depiction on spatial organization of cell types. We highlight the importance of data adjustment in integrative analysis of scRNA-seq with ST data and provide guidance for improved cell-type decomposition.

Sea-level rise in Southwest Greenland as a contributor to Viking abandonment.

The first records of Greenland Vikings date to 985 CE. Archaeological evidence yields insight into how Vikings lived, yet drivers of their disappearance in the 15th century remain enigmatic. Research suggests a combination of environmental and socioeconomic factors, and the climatic shift from the Medieval Warm Period (~900 to 1250 CE) to the Little Ice Age (~1250 to 1900 CE) may have forced them to abandon Greenland. Glacial geomorphology and paleoclimate research suggest that the Southern Greenland Ice Sheet readvanced during Viking occupation, peaking in the Little Ice Age. Counterintuitively, the readvance caused sea-level rise near the ice margin due to increased gravitational attraction toward the ice sheet and crustal subsidence. We estimate ice growth in Southwestern Greenland using geomorphological indicators and lake core data from previous literature. We calculate the effect of ice growth on regional sea level by applying our ice history to a geophysical model of sea level with a resolution of ~1 km across Southwestern Greenland and compare the results to archaeological evidence. The results indicate that sea level rose up to ~3.3 m outside the glaciation zone during Viking settlement, producing shoreline retreat of hundreds of meters. Sea-level rise was progressive and encompassed the entire Eastern Settlement. Moreover, pervasive flooding would have forced abandonment of many coastal sites. These processes likely contributed to the suite of vulnerabilities that led to Viking abandonment of Greenland. Sea-level change thus represents an integral, missing element of the Viking story.

The Bering Strait was flooded 10,000 years before the Last Glacial Maximum.

The cyclic growth and decay of continental ice sheets can be reconstructed from the history of global sea level. Sea level is relatively well constrained for the Last Glacial Maximum (LGM, 26,500 to 19,000 y ago, 26.5 to 19 ka) and the ensuing deglaciation. However, sea-level estimates for the period of ice-sheet growth before the LGM vary by > 60 m, an uncertainty comparable to the sea-level equivalent of the contemporary Antarctic Ice Sheet. Here, we constrain sea level prior to the LGM by reconstructing the flooding history of the shallow Bering Strait since 46 ka. Using a geochemical proxy of Pacific nutrient input to the Arctic Ocean, we find that the Bering Strait was flooded from the beginning of our records at 46 ka until [Formula: see text] ka. To match this flooding history, our sea-level model requires an ice history in which over 50% of the LGM's global peak ice volume grew after 46 ka. This finding implies that global ice volume and climate were not linearly coupled during the last ice age, with implications for the controls on each. Moreover, our results shorten the time window between the opening of the Bering Land Bridge and the arrival of humans in the Americas.

Pituitary volumes in patients with adjustment disorder.

The pituitary gland plays an important role in the stress response mechanism. Given the direct link between adjustment disorder and stress, we hypothesized that there might be changes in the pituitary gland in these patients. The study comprised a patient group of 19 individuals with adjustment disorder according to the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition, and 18 healthy controls. The mean pituitary gland volumes of the patient group were not statistically significantly different from those of the healthy control group (80.81 ± 1.82 mm3 in patients with adjustment disorder vs. 81.10 ± 7.04 mm3 in healthy controls, with a statistically nonsignificant difference of P > 0.05). This finding is contrary to our previous findings in anxiety-related disorders. In this regard, adjustment disorder is not similar to anxiety-related disorders in terms of pituitary gland volumes. We should also clearly state that our study is a pioneering study and that studies with large samples are needed to support our findings. The limitations of our study can be attributed to the small sample size, the utilization of a cross-sectional design, and the inclusion of patients using psychotropic drugs.

Glacial isostatic adjustment directed incision of the Channeled Scabland by Ice Age megafloods.

During the last deglaciation, dozens of glacial outburst floods-among the largest known floods on Earth-scoured the Channeled Scabland landscape of eastern Washington. Over this same period, deformation of the Earth's crust in response to the growth and decay of ice sheets changed the topography by hundreds of meters. Here, we investigated whether glacial isostatic adjustment affected routing of the Missoula floods and incision of the Channeled Scabland from an impounded, glacial Lake Columbia. We used modern topography corrected for glacial isostatic adjustment as an input to flood models that solved the depth-averaged, shallow water equations and compared the results to erosion constraints. Results showed that floods could have traversed and eroded parts of two major tracts of the Channeled Scabland-Telford-Crab Creek and Cheney-Palouse-near 18 ka, whereas glacial isostatic adjustment limited flow into the Cheney-Palouse tract at 15.5 ka. Partitioning of flow between tracts was governed by tilting of the landscape, which affected the filling and overspill of glacial Lake Columbia directly upstream of the tracts. These results highlight the impact of glacial isostatic adjustment on megaflood routing and landscape evolution.

RoPE: A robust profile likelihood method for differential gene expression analysis.

Variation in RNA-Seq data creates modeling challenges for differential gene expression (DE) analysis. Statistical approaches address conventional small sample sizes and implement empirical Bayes or non-parametric tests, but frequently produce different conclusions. Increasing sample sizes enable proposal of alternative DE paradigms. Here we develop RoPE, which uses a data-driven adjustment for variation and a robust profile likelihood ratio DE test. Simulation studies show RoPE can have improved performance over existing tools as sample size increases and has the most reliable control of error rates. Application of RoPE demonstrates that an active Pseudomonas aeruginosa infection downregulates the SLC9A3 Cystic Fibrosis modifier gene.

A Novel Risk-Adjusted Metric to Compare Hospitals on Their Antibiotic Prescribing at Hospital Discharge.

BACKGROUND: Antibiotic overuse at hospital discharge is common, but there is no metric to evaluate hospital performance at this transition of care. We built a risk-adjusted metric for comparing hospitals on their overall post-discharge antibiotic use. METHODS: This was a retrospective study across all acute-care admissions within the Veterans Health Administration during 2018-2021. For patients discharged to home, we collected data on antibiotics and relevant covariates. We built a zero-inflated, negative, binomial mixed model with 2 random intercepts for each hospital to predict post-discharge antibiotic exposure and length of therapy (LOT). Data were split into training and testing sets to evaluate model performance using absolute error. Hospital performance was determined by the predicted random intercepts. RESULTS: 1 804 300 patient-admissions across 129 hospitals were included. Antibiotics were prescribed to 41.5% while hospitalized and 19.5% at discharge. Median LOT among those prescribed post-discharge antibiotics was 7 (IQR, 4-10) days. The predictive model detected post-discharge antibiotic use with fidelity, including accurate identification of any exposure (area under the precision-recall curve = 0.97) and reliable prediction of post-discharge LOT (mean absolute error = 1.48). Based on this model, 39 (30.2%) hospitals prescribed antibiotics less often than expected at discharge and used shorter LOT than expected. Twenty-eight (21.7%) hospitals prescribed antibiotics more often at discharge and used longer LOT. CONCLUSIONS: A model using electronically available data was able to predict antibiotic use prescribed at hospital discharge and showed that some hospitals were more successful in reducing antibiotic overuse at this transition of care. This metric may help hospitals identify opportunities for improved antibiotic stewardship at discharge.

CsSHMT3 gene enhances the growth and development in cucumber seedlings under salt stress.

Salt stress is one of the major factors limiting plant growth and productivity. Many studies have shown that serine hydroxymethyltransferase (SHMT) gene play an important role in growth, development and stress response in plants. However, to date, there have been few studies on whether SHMT3 can enhance salt tolerance in plants. Therefore, the effects of overexpression or silencing of CsSHMT3 gene on cucumber seedling growth under salt stress were investigated in this study. The results showed that overexpression of CsSHMT3 gene in cucumber seedlings resulted in a significant increase in chlorophyll content, photosynthetic rate and proline (Pro) content, and antioxidant enzyme activity under salt stress condition; whereas the content of malondialdehyde (MDA), superoxide anion (H2O2), hydrogen peroxide (O2·-) and relative conductivity were significantly decreased when CsSHMT3 gene was overexpressed. However, the content of chlorophyll and Pro, photosynthetic rate, and antioxidant enzyme activity of the silenced CsSHMT3 gene lines under salt stress were significantly reduced, while MDA, H2O2, O2·- content and relative conductivity showed higher level in the silenced CsSHMT3 gene lines. It was further found that the expression of stress-related genes SOD, CAT, SOS1, SOS2, NHX, and HKT was significantly up-regulated by overexpressing CsSHMT3 gene in cucumber seedlings; while stress-related gene expression showed significant decrease in silenced CsSHMT3 gene seedlings under salt stress. This suggests that overexpression of CsSHMT3 gene increased the salt tolerance of cucumber seedlings, while silencing of CsSHMT3 gene decreased the salt tolerance. In conclusion, CsSHMT3 gene might positively regulate salt stress tolerance in cucumber and be involved in regulating antioxidant activity, osmotic adjustment, and photosynthesis under salt stress. KEY MESSAGE: CsSHMT3 gene may positively regulate the expression of osmotic system, photosynthesis, antioxidant system and stress-related genes in cucumber.

Repeated context-specific actions disrupt feedforward adjustments in motor commands in younger and older adults.

The flexibility of the motor system to adjust a planned action before or during the execution of the movement in response to sensory information is critical for preventing errors in motor control. As individuals age, this function declines, leading to an increased incidence of motor errors. Although sensory processing and cognitive decline are known contributors to this impairment, here, we test the hypothesis that repetition of context-specific planned actions interferes with the adjustment of feedforward motor commands. Younger and older participants were instructed to grasp and lift a T-shaped object with a concealed, off-sided center of mass and minimize its roll through anticipatory force control, relying predominantly on predictive model-driven planning (i.e., sensorimotor memories) developed through repeated lifts. We selectively manipulate the number of trial repeats with the center of mass on one side before switching it to the other side of the T-shaped object. The results showed that increasing the number of repetitions improved performance in manipulating an object with a given center of mass but led to increased errors when the object's center of mass was switched. This deleterious effect of repetition on feedforward motor adjustment was observed in younger and older adults. Critically, we show these effects on an internal model-driven motor planning task that relies predominantly on sensorimotor memory, with no differences in sensory inputs from the repetition manipulation. The findings indicate that feedforward motor adjustments are hampered by repetitive stereotyped planning and execution of motor behavior.NEW & NOTEWORTHY Adjusting planned actions in response to sensory stimuli degrades with age contributing to increased incidence of errors ranging from clumsy spills to catastrophic falls. Multiple factors likely contribute to age-related motor inflexibility, including sensory- and cognition-supporting system declines. Here, we present compelling evidence for repetition to disrupt feedforward adjusting of motor commands in younger and older adults, which suggests increases in stereotypy as a deleterious potentiator of motor control errors.

Selection Bias Requires Selection: The Case of Collider Stratification Bias.

In epidemiology, collider stratification bias, the bias resulting from conditioning on a common effect of two causes, is oftentimes considered a type of selection bias, regardless of the conditioning methods employed. In this commentary, we distinguish between two types of collider stratification bias: collider restriction bias due to restricting to one level of a collider (or a descendant of a collider) and collider adjustment bias through inclusion of a collider (or a descendant of a collider) in a regression model. We argue that categorizing collider adjustment bias as a form of selection bias may lead to semantic confusion, as adjustment for a collider in a regression model does not involve selecting a sample for analysis. Instead, we propose that collider adjustment bias can be better viewed as a type of overadjustment bias. We further provide two distinct causal diagram structures to distinguish collider restriction bias and collider adjustment bias. We hope that such a terminological distinction can facilitate easier and clearer communication.

Addressing bias due to measurement error of an outcome with unknown sensitivity in database epidemiological studies.

In epidemiological database studies, the occurrence of an event is measured with error through an indicator whose specificity is often maximised, at the expense of sensitivity. However, if the indicator has low sensitivity, measures of occurrence are underestimated. In association studies, risk difference is biased, and risk ratio may be biased as well, in either direction, if the sensitivity is differential across exposure groups. In this work, we show that if an auxiliary screening indicator can be defined to complement the main indicator, estimates of the positive predictive value of both indicators provide tools to estimate the sensitivity of the primary indicator, or a lower bound thereof. This mitigates bias in estimating the number of cases, prevalence, cumulative incidence, rate (particularly when the event is rare), and in association studies, risk ratio and risk difference. They also allow testing for non-differential sensitivity. While direct estimation of sensitivity is often infeasible, this novel methodology improves evidence based on data obtained from re-use of existing databases, which may prove critical for regulatory and public health decisions.

Effect of disability, homelessness, and neighborhood marginalization on risk-adjustment for hospital performance measurement.

Background It is not known how disability, homelessness, or neighborhood marginalization influence risk-adjusted hospital performance measurement in a universal health care system. Methods We evaluated the effect of including these equity-related factors in risk-adjustment models for in-hospital mortality, and 7- and 30-day readmission in 28 hospitals in Ontario, Canada. We compared risk-adjustment with commonly-used clinical factors to models that also included homelessness, disability, and neighborhood indices of marginalization. We evaluated models in historical data using internal-external cross-validation. We calculated risk-standardized outcome rates for each hospital in a recent reporting period using mixed-effects logistic regression. Results The cohort included 544,805 admissions. Adjustment for disability, homelessness, and neighborhood marginalization had little impact on discrimination or calibration of risk-adjustment models. However, it influenced comparative hospital performance on risk-standardized 30-day readmission rates, resulting in 5 hospitals being reclassified between below-average, average, and above-average groups. No hospitals were reclassified for mortality and 7-day readmission. Conclusion In a system with universally insured hospital services, adjustment for disability, homelessness, and neighborhood marginalization influenced estimates of hospital performance for 30-day readmission but not 7-day readmission or in-hospital mortality. These findings can inform researchers and policymakers as they thoughtfully consider when to adjust for these factors in hospital performance measurement.

Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference.

Nonignorable technical variation is commonly observed across data from multiple experimental runs, platforms, or studies. These so-called batch effects can lead to difficulty in merging data from multiple sources, as they can severely bias the outcome of the analysis. Many groups have developed approaches for removing batch effects from data, usually by accommodating batch variables into the analysis (one-step correction) or by preprocessing the data prior to the formal or final analysis (two-step correction). One-step correction is often desirable due it its simplicity, but its flexibility is limited and it can be difficult to include batch variables uniformly when an analysis has multiple stages. Two-step correction allows for richer models of batch mean and variance. However, prior investigation has indicated that two-step correction can lead to incorrect statistical inference in downstream analysis. Generally speaking, two-step approaches introduce a correlation structure in the corrected data, which, if ignored, may lead to either exaggerated or diminished significance in downstream applications such as differential expression analysis. Here, we provide more intuitive and more formal evaluations of the impacts of two-step batch correction compared to existing literature. We demonstrate that the undesired impacts of two-step correction (exaggerated or diminished significance) depend on both the nature of the study design and the batch effects. We also provide strategies for overcoming these negative impacts in downstream analyses using the estimated correlation matrix of the corrected data. We compare the results of our proposed workflow with the results from other published one-step and two-step methods and show that our methods lead to more consistent false discovery controls and power of detection across a variety of batch effect scenarios. Software for our method is available through GitHub (https://github.com/jtleek/sva-devel) and will be available in future versions of the $\texttt{sva}$ R package in the Bioconductor project (https://bioconductor.org/packages/release/bioc/html/sva.html).

Reformulating provider profiling by grouping providers treating similar patients prior to evaluating performance.

Standard approaches to comparing health providers' performance rely on hierarchical logistic regression models that adjust for patient characteristics at admission. Estimates from these models may be misleading when providers treat different patient populations and the models are misspecified. To address this limitation, we propose a novel profiling approach that identifies groups of providers treating similar populations of patients and then evaluates providers' performance within each group. The groups of providers are identified using a Bayesian multilevel finite mixture of general location models. To compare the performance of our proposed profiling approach to standard methods, we use patient-level data from 119 skilled nursing facilities in Massachusetts. We use simulated and observed outcome data to explore the performance of these profiling methods in different settings. In simulations, our proposed method classifies providers to groups with similar patients' admission characteristics. In addition, in the presence of limited overlap in patient characteristics across providers and misspecifications of the outcome model, the provider-level estimates obtained using our approach identified providers that under- and overperformed compared to the standard regression-based approaches more accurately.

Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials.

Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on individuals in those groups. While offering many advantages, this experimental design introduces challenges that are only partially addressed by existing analytic approaches. First, outcomes are often missing for some individuals within clusters. Failing to appropriately adjust for differential outcome measurement can result in biased estimates and inference. Second, CRTs often randomize limited numbers of clusters, resulting in chance imbalances on baseline outcome predictors between arms. Failing to adaptively adjust for these imbalances and other predictive covariates can result in efficiency losses. To address these methodological gaps, we propose and evaluate a novel two-stage targeted minimum loss-based estimator to adjust for baseline covariates in a manner that optimizes precision, after controlling for baseline and postbaseline causes of missing outcomes. Finite sample simulations illustrate that our approach can nearly eliminate bias due to differential outcome measurement, while existing CRT estimators yield misleading results and inferences. Application to real data from the SEARCH community randomized trial demonstrates the gains in efficiency afforded through adaptive adjustment for baseline covariates, after controlling for missingness on individual-level outcomes.

The experience of albinism in France: a qualitative study on dyads of parents and their adult child with albinism.

BACKGROUND: To date, almost no research on the psychosocial implications of albinism has been conducted in France and an exploration of albinism-related experiences could be beneficial, in order to better understand this condition. The aim of this study was to examine how French people with albinism and their parents live with and adapt to this condition in all the areas of their lives. METHODS: Semi-structured phone interviews were conducted with 9 parent-child dyads, each participating separately. Participants were recruited by convenience sampling, thanks to the combined efforts of a patient association (Genespoir) and professionals from the partner medical referral centers involved in the project. Dyads in which the individual with albinism had any comorbidity were excluded. The interviews were then transcribed and subjected to in-depth thematic analysis. Two codebooks were constructed in a mirrored process: one for people with albinism; the other for their parents. They were finally merged at the end of the coding step. RESULTS: Four main categories were identified: personal perceptions and social representations of albinism, difficulties and obstacles encountered by people with albinism, resources and facilitators, and the importance of parent-child functioning. The results indicated that experiences of stigmatization during childhood and adolescence are common and that people with albinism face challenges in adapting to certain obstacles related to their visual impairments (VI) (e.g., inability to drive a car; eye strain...). Parents emerged as one, if not as the main, source of support for people with albinism throughout their development. Although external support systems exist to assist them in various aspects of their lives, some of them primarily rely on their own personal resources to cope. CONCLUSIONS: This research highlights the importance of a systemic and transdisciplinary approach to make sure families receive the support that best meets their needs.

Enhance the Performance of CZTSSe Solar Cells Through Inhibiting the Cu2+, Tu, and (─COOH) Association Reaction.

The Sol-gel precursor solution reaction mechanism has a significant impact on the Cu2ZnSn(S, Se)4 (CZTSSe) solar cells. It is discovered that in the Cu2ZnSnS4 (CZTS) precursor solution (CZTS-PS) in the preparation, there is an association reaction among Cu2+, thiourea (Tu), and carboxyl (-COOH), which is an important reason for the undesirable CZTSSe solar cells. The strong association reaction generates excessive Cu2+ ions, forming the CuxSe secondary phase on the surface of the CZTSSe absorber. The secondary phase causes a short circuit and deterioration of gadget performance. Following a 6-h aging period for the CZTS-PS, the average photoelectric conversion efficiency (PCE) of the device is enhanced to 8.02%, and there is also an improvement in device uniformity, as evidenced by a decrease in the standard deviation to less than 1. To inhibit the association reaction and eliminate the aging time phenomenon, a strategy is developed using hydrochloric acid to regulate the CZTS-PS environment. This strategy shifts the REDOX reaction in Cu2++Sn2+ toward the formation of Cu1++Sn4+, leading to a decrease in the defect concentrations of VSn(-/0) and CuSn(-/0), which increases the carrier concentration and reduces the impact of band tailing. The average power conversion efficiency (PCE) of the devices improved from 7.45% to 9.26%, the PCE of the best-performing CZTSSe solar cells increased from 9.25% to 9.83%, and the consistency among the devices is further enhanced, as indicated by a reduction in the standard deviation from 0.98 to 0.44. Ultimately, the device performance of the Cu2++Sn2+-DMF system improved by 11.01% (without the MgF2 layer) after optimization. This study serves as a reference for regulating the environment of the CZTS-PS to further enhance the CZTSSe devices' performance, and the photoelectric conversion efficiency is improved by ≈30%.

Comprehensive evaluation of noise reduction methods for single-cell RNA sequencing data.

Normalization and batch correction are critical steps in processing single-cell RNA sequencing (scRNA-seq) data, which remove technical effects and systematic biases to unmask biological signals of interest. Although a number of computational methods have been developed, there is no guidance for choosing appropriate procedures in different scenarios. In this study, we assessed the performance of 28 scRNA-seq noise reduction procedures in 55 scenarios using simulated and real datasets. The scenarios accounted for multiple biological and technical factors that greatly affect the denoising performance, including relative magnitude of batch effects, the extent of cell population imbalance, the complexity of cell group structures, the proportion and the similarity of nonoverlapping cell populations, dropout rates and variable library sizes. We used multiple quantitative metrics and visualization of low-dimensional cell embeddings to evaluate the performance on batch mixing while preserving the original cell group and gene structures. Based on our results, we specified technical or biological factors affecting the performance of each method and recommended proper methods in different scenarios. In addition, we highlighted one challenging scenario where most methods failed and resulted in overcorrection. Our studies not only provided a comprehensive guideline for selecting suitable noise reduction procedures but also pointed out unsolved issues in the field, especially the urgent need of developing metrics for assessing batch correction on imperceptible cell-type mixing.