Healthy adults favor stable left/right hand choices over performance at an unconstrained reach-to-grasp task.

Reach-to-grasp actions are fundamental to the daily activities of human life, but few methods exist to assess individuals' reaching and grasping actions in unconstrained environments. The Block Building Task (BBT) provides an opportunity to directly observe and quantify these actions, including left/right hand choices. Here we sought to investigate the motor and non-motor causes of left/right hand choices, and optimize the design of the BBT, by manipulating motor and non-motor difficulty in the BBT's unconstrained reach-to-grasp task. We hypothesized that greater motor and non-motor (e.g. cognitive/perceptual) difficulty would drive increased usage of the dominant hand. To test this hypothesis, we modulated block size (large vs. small) to influence motor difficulty, and model complexity (10 vs. 5 blocks per model) to influence non-motor difficulty, in healthy adults (n = 57). Our data revealed that increased motor and non-motor difficulty led to lower task performance (slower task speed), but participants only increased use of their dominant hand only under the most difficult combination of conditions: in other words, participants allowed their performance to degrade before changing hand choices, even though participants were instructed only to optimize performance. These results demonstrate that hand choices during reach-to grasp actions are more stable than motor performance in healthy right-handed adults, but tasks with multifaceted difficulties can drive individuals to rely more on their dominant hand.

Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias.

BACKGROUND: Accurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs. OBJECTIVE: Develop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG. METHODS: We developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model). RESULTS: Of the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording. CONCLUSION: Accurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.

A new approach to estimation of the proportional hazards model based on interval-censored data with missing covariates.

This paper discusses the fitting of the proportional hazards model to interval-censored failure time data with missing covariates. Many authors have discussed the problem when complete covariate information is available or the missing is completely at random. In contrast to this, we will focus on the situation where the missing is at random. For the problem, a sieve maximum likelihood estimation approach is proposed with the use of I-spline functions to approximate the unknown cumulative baseline hazard function in the model. For the implementation of the proposed method, we develop an EM algorithm based on a two-stage data augmentation. Furthermore, we show that the proposed estimators of regression parameters are consistent and asymptotically normal. The proposed approach is then applied to a set of the data concerning Alzheimer Disease that motivated this study.

Transcription factor MoMsn2 targets the putative 3-methylglutaconyl-CoA hydratase-encoding gene MoAUH1 to govern infectious growth via mitochondrial fusion/fission balance in Magnaporthe oryzae.

Mitochondrial quality and quantity are essential for a cell to maintain normal cellular functions. Our previous study revealed that the transcription factor MoMsn2 plays important roles in the development and virulence of Magnaporthe oryzae. However, to date, no study has reported its underlying regulatory mechanism in phytopathogens. Here, we explored the downstream target genes of MoMsn2 using a chromatin immunoprecipitation sequencing (ChIP-Seq) approach. In total, 332 target genes and five putative MoMsn2-binding sites were identified. The 332 genes exhibited a diverse array of functions and the highly represented were genes involved in metabolic and catalytic processes. Based on the ChIP-Seq data, we found that MoMsn2 plays a role in maintaining mitochondrial morphology, likely by targeting a number of mitochondria-related genes. Further investigation revealed that MoMsn2 targets the putative 3-methylglutaconyl-CoA hydratase-encoding gene (MoAUH1) to control mitochondrial morphology and mitophagy, which are critical for the infectious growth of the pathogen. Meanwhile, the deletion of MoAUH1 resulted in phenotypes similar to the ΔMomsn2 mutant in mitochondrial morphology, mitophagy and virulence. Overall, our results provide evidence for the regulatory mechanisms of MoMsn2, which targets MoAUH1 to modulate its transcript levels, thereby disturbing the mitochondrial fusion/fission balance. This ultimately affects the development and virulence of M. oryzae.

Polyethyleneimine surface-modified silver-selenium nanocomposites for anti-infective treatment of wounds by disrupting biofilms.

Bacterial biofilm formation is associated with the pathogenicity of pathogens and poses a serious threat to human health and clinical therapy. Complex biofilm structures provide physical barriers that inhibit antibiotic penetration and inactivate antibiotics via enzymatic breakdown. The development of biofilm-disrupting nanoparticles offers a promising strategy for combating biofilm infections. Hence, polyethyleneimine surface-modified silver-selenium nanocomposites, Ag@Se@PEI (ASP NCs), were designed for synergistic antibacterial effects by destroying bacterial biofilms to promote wound healing. The results ofin vitroantimicrobial experiments showed that, ASP NCs achieved efficient antibacterial effects againstStaphylococcus aureus (S. aureus)andEscherichia coli (E. coli)by disrupting the formation of the bacterial biofilm, stimulating the outbreak of reactive oxygen species and destroying the integrity of bacterial cell membranes. Thein-vivobacterial infection in mice model showed that, ASP NCs further promoted wound healing and new tissue formation by reducing inflammatory factors and promoting collagen fiber formation which efficiently enhanced the antibacterial effect. Overall, ASP NCs possess low toxicity and minimal side effects, coupled with biocompatibility and efficient antibacterial properties. By disrupting biofilms and bacterial cell membranes, ASP NCs reduced inflammatory responses and accelerated the healing of infected wounds. This nanocomposite-based study offers new insights into antibacterial therapeutic strategies as potential alternatives to antibiotics for wound healing.

Deep learning models to predict primary open-angle glaucoma.

Glaucoma is a major cause of blindness and vision impairment worldwide, and visual field (VF) tests are essential for monitoring the conversion of glaucoma. While previous studies have primarily focused on using VF data at a single time point for glaucoma prediction, there has been limited exploration of longitudinal trajectories. Additionally, many deep learning techniques treat the time-to-glaucoma prediction as a binary classification problem (glaucoma Yes/No), resulting in the misclassification of some censored subjects into the nonglaucoma category and decreased power. To tackle these challenges, we propose and implement several deep-learning approaches that naturally incorporate temporal and spatial information from longitudinal VF data to predict time-to-glaucoma. When evaluated on the Ocular Hypertension Treatment Study (OHTS) dataset, our proposed convolutional neural network (CNN)-long short-term memory (LSTM) emerged as the top-performing model among all those examined. The implementation code can be found online (https://github.com/rivenzhou/VF_prediction).

Profiling lncRNA in psoriatic skin using scRNA-seq.

The expressions of long noncoding RNAs (lncRNAs) and their roles in epidermal differentiation have been previously defined using bulk RNA-seq. Despite their tissue-specific expression profiles, most lncRNAs are not well-annotated at the single cell level. Here, we evaluated the use of scRNA-seq to profile and characterize lncRNAs using data from 6 psoriasis patients with paired uninvolved and lesional psoriatic skin. Despite their overall lower expression, we were able to detect >7,000 skin-expressing lncRNAs and their cellular source. Differential gene expression analysis revealed 137 differentially expressed lncRNAs in lesional skin of psoriasis (PP) and identified 169 cell type-specific lncRNAs. Keratinocytes had the highest number of differentially expressed lncRNA in psoriatic skin, which we validated using spatial transcriptomic data. We further showed that expression of keratinocyte-specific lncRNA, AC020916.1, upregulated in lesional skin, is significantly correlated with expressions of genes participating in cell proliferation/epidermal differentiation, including SPRR2E and transcription factor ZFP36, particularly in the psoriatic skin. Our study highlights the potential for using scRNA-seq to profile skin-expressing lncRNA transcripts and to infer their cellular origins, providing a crucial approach that can be applied to the study of other inflammatory skin conditions.

Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling.

Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA significantly outperformed a single-image baseline in 19/20 head-to-head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.

Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling.

Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA significantly outperformed a single-image baseline in 19/20 head-to-head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.

Propensity score weighting methods for causal subgroup analysis with time-to-event outcomes.

Evaluating causal effects of an intervention in pre-specified subgroups is a standard goal in comparative effectiveness research. Despite recent advancements in causal subgroup analysis, research on time-to-event outcomes has been lacking. This article investigates the propensity score weighting method for causal subgroup survival analysis. We introduce two causal estimands, the subgroup marginal hazard ratio and subgroup restricted average causal effect, and provide corresponding propensity score weighting estimators. We analytically established that the bias of subgroup-restricted average causal effect is determined by subgroup covariate balance. Using extensive simulations, we compare the performance of various combinations of propensity score models (logistic regression, random forests, least absolute shrinkage and selection operator, and generalized boosted models) and weighting schemes (inverse probability weighting, and overlap weighting) for estimating the causal estimands. We find that the logistic model with subgroup-covariate interactions selected by least absolute shrinkage and selection operator consistently outperforms other propensity score models. Also, overlap weighting generally outperforms inverse probability weighting in terms of balance, bias and variance, and the advantage is particularly pronounced in small subgroups and/or in the presence of poor overlap. We applied the methods to the observational Comparing Options for Management: PAtient-centered REsults for Uterine Fibroids study to evaluate the causal effects of myomectomy versus hysterectomy on the time to disease recurrence in a number of pre-specified subgroups of patients with uterine fibroids.

Quantifying differences in fMRI preprocessing pipelines via OGRE (One-step General Registration and Extraction).

Volumetric preprocessing methods continue to enjoy great popularity in the analysis of functional MRI (fMRI) data. Among these methods, the software packages FSL (FMRIB, Oxford, UK) and FreeSurfer (LCN, Charlestown, MA) are omnipresent throughout the field. However, it remains unknown what advantages an integrated FSL+FreeSurfer preprocessing approach might provide over FSL alone. Here we developed the One-step General Registration and Extraction (OGRE) pipeline to combine FreeSurfer and FSL tools for brain extraction and registration, for FSL volumetric analysis of fMRI data. We compared preprocessing approaches in a dataset wherein adult human volunteers (N=26) performed a precision drawing task during fMRI scanning. OGRE's preprocessing, compared to traditional FSL preprocessing, led to lower inter-individual variability across the brain, more precise brain extraction, and greater detected activation in sensorimotor areas contralateral to movement. This demonstrates that the introduction of FreeSurfer tools via OGRE preprocessing can improve fMRI data analysis, in the context of FSL's volumetric analysis approach. The OGRE pipeline provides a turnkey method to integrate FreeSurfer-based brain extraction and registration with FSL analysis of task fMRI data.

Healthy adults favor stable left/right hand choices over performance at an unconstrained reach-to-grasp task.

Reach-to-grasp actions are fundamental to the daily activities of human life, but few methods exist to assess individuals' reaching and grasping actions in unconstrained environments. The Block Building Task (BBT) provides an opportunity to directly observe and quantify these actions, including left/right hand choices. Here we sought to investigate the motor and non-motor causes of left/right hand choices, and optimize the design of the BBT, by manipulating motor and non-motor difficulty in the BBT's unconstrained reach-to-grasp task We hypothesized that greater motor and non-motor (e.g. cognitive/perceptual) difficulty would drive increased usage of the dominant hand. To test this hypothesis, we modulated block size (large vs. small) to influence motor difficulty, and model complexity (10 vs. 5 blocks per model) to influence non-motor difficulty, in healthy adults (n=57). We hypothesized that healthy adults with high non-dominant hand performance in a precision drawing task should be more likely to use their non-dominant hand in the BBT. Our data revealed that increased motor and non-motor difficulty led to lower task performance (slower speed), but participants only increased use of their dominant hand only under the most difficult combination of conditions: in other words, participants allowed their performance to degrade before changing hand choices, even though participants were instructed only to optimize performance. These results demonstrate that hand choices during reach-to grasp actions are more stable than motor performance in healthy right-handed adults, but tasks with multifaceted difficulties can drive individuals to rely more on their dominant hand. Statements and Declarations: Dr. Philip and Washington University in St. Louis have a licensing agreement with PlatformSTL to commercialize the iPad app used in this study.

MoLrp1-mediated signaling induces nuclear accumulation of MoMsn2 to facilitate fatty acid oxidation for infectious growth of the rice blast fungus.

Fatty acid ß-oxidation is critical for fatty acid degradation and cellular development. In the rice blast fungus Magnaporthe oryzae, fatty acid ß-oxidation is reported to be important mainly for turgor generation in the appressorium. However, the role of fatty acid ß-oxidation during invasive hyphal growth is rarely documented. We demonstrated that blocking peroxisomal fatty acid ß-oxidation impaired lipid droplet (LD) degradation and infectious growth of M. oryzae. We found that the key regulator of pathogenesis, MoMsn2, which we identified previously, is involved in fatty acid ß-oxidation by targeting MoDCI1 (encoding dienoyl-coenzyme A [CoA] isomerase), which is also important for LD degradation and infectious growth. Cytological observations revealed that MoMsn2 accumulated from the cytosol to the nucleus during early infection or upon treatment with oleate. We determined that the low-density lipoprotein receptor-related protein MoLrp1, which is also involved in fatty acid ß-oxidation and infectious growth, plays a critical role in the accumulation of MoMsn2 from the cytosol to the nucleus by activating the cyclic AMP signaling pathway. Our results provide new insights into the importance of fatty acid oxidation during invasive hyphal growth, which is modulated by MoMsn2 and its related signaling pathways in M. oryzae.

Characteristics and sources of amine-containing particles in the urban atmosphere of Liaocheng, a seriously polluted city in North China during the COVID-19 outbreak.

The Chinese government issued an unprecedentedly strict lockdown policy to control the spread of the novel coronavirus disease 2019 (COVID-19), significantly mitigating air pollution because of the dramatic reduction of industrial and traffic emissions. To explore the impact of COVID-19 lockdown (LCD) on organic aerosols, the mixing states and evolution processes of amine-containing particles were studied using a single particle aerosol mass spectrometer from January to March 2020 in Liaocheng, which is a seriously polluted city in North China. The counts and percentages of amine-containing particles in total obtained particles during the pre-LCD (547832, 29.8 %) were higher than those during the LCD (283983, 20.7 %) and post-LCD (102026, 18.4 %), mainly due to the reduced emission strength of amines and suppressed gas-to-particle partitioning of amines during the LCD and post-LCD. 74(C2H5)2NH2+ was the most abundant amine marker, which accounted for 98.2 %, 98.4 %, and 96.7 % of all amine-containing particles during the pre-LCD, LCD, and post-LCD, respectively. Correlation analysis and temporal variations indicated that the gas-to-particle partitioning of amines was facilitated by the stronger acidic environment and lower temperature, while the effect of RH and aerosol liquid water content was minor. The A-OC particles were the most abundant type (accounting for ~40 %) throughout the observation period. The temporal profiles and correlation analysis suggested that the impact of the increased O3 on the amines and their oxidation products (e.g., trimethylamine oxide) was minor. The identified particle types, correlation analysis, and the potential source contribution function results implied that the amine-containing particles were mainly derived from local and surrounding sources during the LCD, while those were mainly affected by long-range transport during the pre-LCD and post-LCD. Our results could deepen the comprehension of the sources and atmospheric processing of amines in the urban area of North China during the COVID-19 outbreak.

The Peroxisomal-CoA Synthetase MoPcs60 Is Important for Fatty Acid Metabolism and Infectious Growth of the Rice Blast Fungus.

Fatty acid metabolism is important for the maintenance of fatty acid homeostasis. Free fatty acids, which are toxic in excess, are activated by esterification with coenzyme A (CoA) and then subjected to ß-oxidization. Fatty acid ß-oxidation-related genes play critical roles in the development and virulence of several phytopathogens. In this study, we identified and characterized a peroxisomal-CoA synthetase in the rice blast fungus Magnaporthe oryzae, MoPCS60, which is a homolog of PCS60 in budding yeast. MoPCS60 was highly expressed during the conidial and early infectious stages and was induced under oleate treatment. Targeted deletion of MoPCS60 resulted in a significant reduction in growth rate when oleate and olive oil were used as the sole carbon sources. Compared with the wild-type strain Guy11, the ΔMopcs60 mutant exhibited fewer peroxisomes, more lipid droplets, and decreased pathogenicity. The distribution of MoPcs60 varied among developmental stages and was mainly localized to peroxisomes in the hyphae, conidia, and appressoria when treated with oleate. Our results suggest that MoPcs60 is a key peroxisomal-CoA synthetase involved in fatty acid ß-oxidation and pathogenicity in rice blast fungi.

Contrasting compositions and sources of organic aerosol markers in summertime PM2.5 from urban and mountainous regions in the North China Plain.

Although the chemical compositions and sources of organic aerosols (OAs) have been extensively investigated at the summit of Mt. Tai in the North China Plain (NCP), their vertical distributions and characterizations in the Mt. Tai region is not well known. To better understand the vertical variations of OAs in the urban and mountainous atmosphere, PM2.5 samples were collected simultaneously on a daytime/nighttime basis at two sites of different altitudes (Taian urban site: 20 m above ground; the summit of Mt. Tai: 1534 m a.s.l.) during the summer of 2016. The concentrations of all the determined chemical compounds (e.g., OC, EC, inorganic ions, saccharides, n-alkanes, PAHs and hopanes) except for biogenic secondary organic aerosol (BSOA) tracers decreased with the increase in sampling height, indicating the relatively larger contribution of anthropogenic pollutants to OAs at the lower heights. The relatively low concentration levels of biomass burning tracers (e.g., levoglucosan, galactosan and mannosan) and the insignificant correlations of levoglucosan with carbonaceous species demonstrated a negligible effect of biomass burning on the mountaintop atmosphere. The enhanced concentrations of BSOA tracers were observed with the increase of height, largely due to the more intensive secondary oxidation of volatile organic compounds (VOCs) under the stronger radiation conditions at the summit. The daytime concentrations of carbonaceous species, primary sugars, sugar alcohols, PAHs and low molecular weight n-alkanes were significantly higher than those in nighttime at Mt. Tai, suggesting that these chemical compounds at the summit of Mt.Tai aerosols were transported from the ground surface by valley breezes in daytime. There was no correlation between BSOA tracers and relative humidity (RH) or liquid water content (LWC) at both the sites, because both the high RH and LWC can suppress the acid-catalyzed formation of BSOA due to the dilution of the aerosol acidity.

Enhanced photochemical formation of secondary organic aerosols during the COVID-19 lockdown in Northern China.

To eliminate the spread of a novel coronavirus breaking out in the end of 2019 (COVID-19), the Chinese government has implemented a nationwide lockdown policy after the Chinese lunar New Year of 2020, resulting in a sharp reduction in air pollutant emissions. To investigate the impact of the lockdown on aerosol chemistry, the number fraction, size distribution and formation process of oxalic acid (C2) containing particles and its precursors were studied using a single particle aerosol mass spectrometer (SPAMS) at the urban site of Liaocheng in the North China Plain (NCP). Our results showed that five air pollutants (i.e., PM2.5, PM10, SO2, NO2, and CO) decreased by 30.0-59.8% during the lockdown compared to those before the lockdown, while O3 increased by 63.9% during the lockdown mainly due to the inefficient titration effect of O3 via NO reduction. The increased O3 concentration can boost the atmospheric oxidizing capacity and further enhance the formation of secondary organic aerosols, thereby significantly enhancing the C2 particles and its precursors as observed during the lockdown. Before the lockdown, C2 particles were significantly originated from biomass burning emissions and their subsequent aqueous-phase oxidation. The hourly variation patterns and correlation analysis before the lockdown suggested that relative humidity (RH) and aerosol liquid water content (ALWC) played a key role in the formation of C2 particles and the increased aerosol acidity can promote the conversion of precursors such as glyoxal (Gly) and methyglyoxal (mGly) into C2 particles in the aqueous phase. RH and ALWC decreased sharply but O3 concentration and solar radiation increased remarkably during the lockdown, the O3-dominated photochemical pathways played an important role in the formation of C2 particles in which aerosol acidity was ineffective. Our study indicated that air pollution treatment sponges on a joint-control and balanced strategy for controlling numerous pollutants.