Unsupervised underwater imaging based on polarization and binocular depth estimation.

Scattering caused by suspended particles in the water severely reduces the radiance of the scene. This paper proposes an unsupervised underwater restoration method based on binocular estimation and polarization. Based on the correlation between the underwater transmission process and depth, this method combines the depth information and polarization information in the scene, uses the neural network to perform global optimization and the depth information is recalculated and updated in the network during the optimization process, and reduces the error generated by using the polarization image to calculate parameters, so that detailed parts of the image are restored. Furthermore, the method reduces the requirement for rigorous pairing of data compared to previous approaches for underwater imaging using neural networks. Experimental results show that this method can effectively reduce the noise in the original image and effectively preserve the detailed information in the scene.

Gut microbiota composition links to variation in functional domains across psychiatric disorders.

OBJECTIVE: Changes in microbial composition are observed in various psychiatric disorders, but their specificity to certain symptoms or processes remains unclear. This study explores the associations between the gut microbiota composition and the Research Domain Criteria (RDoC) domains of functioning, representing symptom domains, specifically focusing on stress-related and neurodevelopmental disorders in patients with and without psychiatric comorbidity. METHODS: The gut microbiota was analyzed in 369 participants, comprising 272 individuals diagnosed with a mood disorder, anxiety disorder, attention deficit/hyperactivity disorder, autism spectrum disorder, and/or substance use disorder, as well as 97 psychiatrically unaffected individuals. The RDoC domains were estimated using principal component analysis (PCA) with oblique rotation on a range of psychiatric, psychological, and personality measures. Associations between the gut microbiota and the functional domains were assessed using multiple linear regression and permanova, adjusted for age, sex, diet, smoking, medication use and comorbidity status. RESULTS: Four functional domains, aligning with RDoC's negative valence, social processes, cognitive systems, and arousal/regulatory systems domains, were identified. Significant associations were found between these domains and eight microbial genera, including associations of negative valence with the abundance of the genera Sellimonas, CHKCI001, Clostridium sensu stricto 1, Oscillibacter, and Flavonifractor; social processes with Sellimonas; cognitive systems with Sporobacter and Hungatella; and arousal/regulatory systems with Ruminococcus torques (all pFDR < 0.05). CONCLUSION: Our findings demonstrate associations between the gut microbiota and the domains of functioning across patients and unaffected individuals, potentially mediated by immune-related processes. These results open avenues for microbiota-focused personalized interventions, considering psychiatric comorbidity. However, further research is warranted to establish causality and elucidate mechanistic pathways.

Thickness Measurements with EMAT Based on Fuzzy Logic.

Metal thickness measurements are essential in various industrial applications, yet current non-contact ultrasonic methods face limitations in range and accuracy, hindering the widespread adoption of electromagnetic ultrasonics. This study introduces a novel combined thickness measurement method employing fuzzy logic, with the aim of broadening the applicational scope of the EMAT. Leveraging minimal hardware, this method utilizes the short pulse time-of-flight (TOF) technique for initial thickness estimation, followed by secondary measurements guided by fuzzy logic principles. The integration of measurements from the resonance, short pulse echo, and linear frequency modulation echo extends the measurement range while enhancing accuracy. Rigorous experimental validation validates the method's effectiveness, demonstrating a measurement range of 0.3-1000.0 mm with a median error within ±0.5 mm. Outperforming traditional methods like short pulse echoes, this approach holds significant industrial potential.

Multi-polygenic scores in psychiatry: From disorder specific to transdiagnostic perspectives.

The dense co-occurrence of psychiatric disorders questions the categorical classification tradition and motivates efforts to establish dimensional constructs with neurobiological foundations that transcend diagnostic boundaries. In this study, we examined the genetic liability for eight major psychiatric disorder phenotypes under both a disorder-specific and a transdiagnostic framework. The study sample (n = 513) was deeply phenotyped, consisting of 452 patients from tertiary care with mood disorders, anxiety disorders (ANX), attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders, and/or substance use disorders (SUD) and 61 unaffected comparison individuals. We computed subject-specific polygenic risk score (PRS) profiles and assessed their associations with psychiatric diagnoses, comorbidity status, as well as cross-disorder behavioral dimensions derived from a rich battery of psychopathology assessments. High PRSs for depression were unselectively associated with the diagnosis of SUD, ADHD, ANX, and mood disorders (p < 1e-4). In the dimensional approach, four distinct functional domains were uncovered, namely the negative valence, social, cognitive, and regulatory systems, closely matching the major functional domains proposed by the Research Domain Criteria (RDoC) framework. Critically, the genetic predisposition for depression was selectively reflected in the functional aspect of negative valence systems (R2 = 0.041, p = 5e-4) but not others. This study adds evidence to the ongoing discussion about the misalignment between current psychiatric nosology and the underlying psychiatric genetic etiology and underscores the effectiveness of the dimensional approach in both the functional characterization of psychiatric patients and the delineation of the genetic liability for psychiatric disorders.

Super-resolution non-line-of-sight imaging based on temporal encoding.

Non-line-of-sight (NLOS) imaging techniques have the ability to reconstruct objects beyond line-of-sight view, which would be useful in a variety of applications. In transient NLOS techniques, a fundamental problem is that the time resolution of imaging depends on the single-photon timing resolution (SPTR) of a detector. In this paper, a temporal super-resolution method named temporal encoding non-line-of-sight (TE-NLOS) is proposed. Specifically, by exploiting the spatial-temporal correlation among transient images, high-resolution transient images can be reconstructed through modulator encoding. We have demonstrated that the proposed method is capable of reconstructing transient images with a time resolution of 20 picoseconds from a detector with a limited SPTR of approximately nanoseconds. In systems with low time jitter, this method exhibits superior accuracy in reconstructing objects compared to direct detection, and it also demonstrates robustness against miscoding. Utilizing high-frequency modulation, our framework can reconstruct accurate objects with coarse-SPTR detectors, which provides an enlightening reference for solving the problem of hardware defects.

Hippocampal-Medial Prefrontal Event Segmentation and Integration Contribute to Episodic Memory Formation.

How do we encode our continuous life experiences for later retrieval? Theories of event segmentation and integration suggest that the hippocampus binds separately represented events into an ordered narrative. Using a functional Magnetic Resonance Imaging (fMRI) movie watching-recall dataset, we quantified two types of neural similarities (i.e., "activation pattern" similarity and within-region voxel-based "connectivity pattern" similarity) between separate events during movie watching and related them to subsequent retrieval of events as well as retrieval of sequential order. We demonstrated that compared with forgotten events, successfully remembered events were associated with distinct "activation patterns" in the hippocampus and medial prefrontal cortex. In contrast, similar "connectivity pattern" between events were associated with memory formation and were also relevant for retaining events in the correct order. We applied the same approaches to an independent movie watching fMRI dataset as validation and highlighted again the role of hippocampal activation pattern and connectivity pattern in memory formation. We propose that distinct activation patterns represent neural segmentation of events, while similar connectivity patterns encode context information and, therefore, integrate events into a narrative. Our results provide novel evidence for the role of hippocampal-medial prefrontal event segmentation and integration in episodic memory formation of real-life experience.

Prognostic significance of PD-L1 expression in pancreatic cancer: evidence from an updated meta-analysis.

Recent studies revealed that programmed cell death ligand 1 (PD-L1) expression was associated with unfavorable prognosis in various solid tumors, but its clinical relevance for pancreatic cancer has not yet been well established. This meta-analysis summarizes the potential prognostic value of PD-L1 in pancreatic cancer. A quantitative meta-analysis was performed by a systematic search of databases including PubMed, EMBASE, Web of Science, Cochrane library, Scopus and Ovid for eligible studies on the prognostic significance of PD-L1 in pancreatic cancer patients. Pooled hazard ratios (HRs) and their 95% confidence intervals (CIs) were calculated to evaluate the strength of the link between PD-L1 expression and clinical prognosis of patients. Seventeen eligible studies with 2669 patients were included in our study. A significant association was observed between PD-L1 abundance and poor overall survival (OS) of patients with pancreatic cancers, with a pooled hazard ratio (HR) of 1.902, 95% CI: 1.657-2.184. Sensitivity analysis confirmed the reliability of our results. Subgroup analysis shows that differences in regions and detection methods of PD-L1 did not change the overall predictive value of PD-L1 for poor prognosis in pancreatic cancer patients. This meta-analysis indicated that the expression of PD-L1 is associated with a worse OS in pancreatic cancer patients. Additionally, PD-L1 may act as a potential parameter for predicting poor prognosis and thus providing a promising target for anticancer therapy in pancreatic cancer.

Prior-free imaging unknown target through unknown scattering medium.

Imaging through scattering medium based on deep learning has been extensively studied. However, existing methods mainly utilize paired data-prior and lack physical-process fusion, and it is difficult to reconstruct hidden targets without the trained networks. This paper proposes an unsupervised neural network that integrates the universal physical process. The reconstruction process of the network is irrelevant to the system and only requires one frame speckle pattern and unpaired targets. The proposed network enables online optimization by using physical process instead of fitting data. Thus, large-scale paired data no longer need to be obtained to train the network in advance, and the proposed method does not need prior information. The optimization of the network is a physical-based process rather than a data mapping process, and the proposed method also increases the insufficient generalization ability of the learning-based method in scattering medium and targets. The universal applicability of the proposed method to different optical systems increases the likelihood that the method will be used in practice.

Non-invasive imaging through scattering medium and around corners beyond 3D memory effect.

The three-dimensional (3D) memory effect (ME) has been shown to exist in a variety of scattering scenes. Limited by the scope of ME, speckle correlation technology only can be applied in a small imaging field of view (FOV) with a small depth of field (DOF). In this Letter, an untrained neural network is constructed and used as an optimization tool to restore the targets beyond the 3D ME range. The autocorrelation consistency relationship and the generative adversarial strategy are combined. Only single frame speckle and unaligned real targets are needed for online optimization; therefore, the neural network does not need to train in advance. Furthermore, the proposed method does not need to conduct additional modulation for the system. This method can reconstruct not only hidden targets behind the scattering medium, but also targets around corners. The combination strategy of the generative adversarial framework with physical priors used to decouple the aliasing information and reconstruct the target will provide inspiration for the field of computational imaging.

Investigation of optimal gestational weight gain based on the occurrence of adverse pregnancy outcomes for Chinese women: a prospective cohort study.

OBJECTIVE: To investigate recommendations for appropriate gestational weight gain (GWG) of Chinese females. METHODS: In total of 3,172 eligible women in the first trimester were recruited into the Chinese Pregnant Women Cohort Study (CPWCS) project. Pregnancy complications and outcomes were collated using the hospital medical records system. The method of occurrence of participants with adverse pregnancy outcomes (Occurrence Method) was conducted to calculate the recommended total GWG for each participant's pre-pregnancy BMI. Occurrence Method data were judged against the Institute of Medicine (IOM) and Japanese recommended criteria in terms of the total occurrence of adverse pregnancy outcomes of pregnant women with appropriate weight gain. RESULTS: The most frequent GWG was ≥ 14 kg and < 16 kg (19.4%), followed by ≥ 10 kg and < 12 kg (15.5%) and ≥ 12 kg and < 14 kg (15.2%). The most frequently occurring adverse pregnancy outcomes were cesarean sections for underweight (30.0%), normal weight (40.4%), overweight (53.6%) and obese (53.7%) women. A large for gestational age (LGA) accounted for 18.0% of the overweight and 20.9% of the obesity group. Gestational diabetes mellitus (GDM) occurred in 16.9% of overweight and 23.1% of obese women. The recommended total GWG in a Chinese women population is ≥ 8 and < 12 kg if underweight, ≥ 12 and < 14 kg for normal weight, ≥ 8.0 and < 10.0 kg if overweight, and < 8 kg for women with obesity. CONCLUSIONS: Current Chinese recommendations provide the optimal ranges of GWG to minimize the occurrence of undesirable pregnancy outcomes for each group of pre-pregnancy BMIs in a Chinese population. TRIAL REGISTRATION: Registered with ClinicalTrials ( NCT03403543 ).

Traditional Chinese medicine symptom normalization approach leveraging hierarchical semantic information and text matching with attention mechanism.

Traditional Chinese medicine (TCM) symptom normalization is difficult because the challenges of the symptoms having different literal descriptions, one-to-many symptom descriptions and different symptoms sharing a similar literal description. We propose a novel two-step approach utilizing hierarchical semantic information that represents the functional characteristics of symptoms and develop a text matching model that integrates hierarchical semantic information with an attention mechanism to solve these problems. In this study, we constructed a symptom normalization dataset and a TCM normalization symptom dictionary containing normalization symptom words, and assigned symptoms into 24 classes of functional characteristics. First, we built a multi-label text classifier to isolate the hierarchical semantic information from each symptom description and count the corresponding normalization symptoms and filter the candidate set. Then we designed a text matching model of mixed multi-granularity language features with an attention mechanism that utilizes the hierarchical semantic information to calculate the matching score between the symptom description and the normalization symptom words. We compared our approach with other baselines on real-world data. Our approach gives the best performance with a Hit@ 1, 3, and 10 of 0.821, 0.953, and 0.993, respectively, and a MeanRank of 1.596, thus outperforming significantly regarding the symptom normalization task. We developed an approach for the TCM symptom normalization task and demonstrated its superior performance compared with other baselines, indicating the promise of this research direction.

Association among pre-pregnancy body mass index, gestational weight gain and neonatal birth weight: a prospective cohort study in China.

BACKGROUND: This study aims to explore the relationships between pre-pregnancy body mass index (BMI), gestational weight gain (GWG), rate of GWG during the second and third trimesters (GWGrate) and birth weight among Chinese women. METHODS: Women were enrolled by 24 hospitals in 15 different provinces in mainland China from July 25th, 2017 to 26 November 2018. Pre-pregnancy BMI, GWG and GWGrate were calculated and divided in to different groups. The multinomial logistic regression model and restrictive cubic spline model were used to explore the relationships. RESULTS: Of the 3585 participants, women who were underweight, had insufficient GWG or GWGrate had 1.853-, 1850- or 1.524-fold higher risks for delivering small-for-gestational-age (SGA) infant compared with women who had normal BMI, sufficient GWG or GWGrate. Women who were overweight/obese, had excessive GWG or GWGrate had 1.996-, 1676- or 1.673-fold higher risks for delivering large-for-gestational-age (LGA) infant. The effects of GWG and GWGrate on birth weight varied by pre-pregnancy BMI statuses. Dose-response analysis demonstrated L-shaped and S-shaped relationships between pre-pregnancy BMI, GWG, GWGrate and neonatal birth weight. CONCLUSIONS: Pre-pregnancy BMI, GWG or GWGrate were associated with neonatal birth weight among Chinese women. Both body weight before and during pregnancy should be maintained within the recommendations to prevent abnormal birth weight.

Effects of pre-pregnancy body mass index and gestational weight gain on maternal and infant complications.

BACKGROUND: The potential effects of pre-pregnancy body mass (BMI) and gestational weight gain (GWG) on pregnancy outcomes remain unclear. Thus, we investigated socio-demographic characteristics that affect pre-pregnancy BMIs and GWG and the effects of pre-pregnancy BMI and GWG on Chinese maternal and infant complications. METHODS: 3172 women were enrolled in the Chinese Pregnant Women Cohort Study-Peking Union Medical College from July 25, 2017 to July 24, 2018, whose babies were delivered before December 31, 2018. Regression analysis was employed to evaluate the socio-demographic characteristics affecting pre-pregnancy BMI and GWG values and their effects on adverse maternal and infant complications. RESULTS: Multivariate logistic regression analysis revealed that age groups < 20 years (OR: 1.97), 25-30 years (OR: 1.66), 30-35 years (OR: 2.24), 35-40 years (OR: 3.90) and ≥ 40 years (OR: 3.33) as well as elementary school or education below (OR: 3.53), middle school (OR: 1.53), high school (OR: 1.40), and living in the north (OR: 1.37) were risk factors in maintaining a normal pre-pregnancy BMI. An age range of 30-35 years (OR: 0.76), living in the north (OR: 1.32) and race of ethnic minorities (OR: 1.51) were factors affecting GWG. Overweight (OR: 2.01) and inadequate GWG (OR: 1.60) were risk factors for gestational diabetes mellitus (GDM). Overweight (OR: 2.80) and obesity (OR: 5.42) were risk factors for gestational hypertension (GHp). Overweight (OR: 1.92), obesity (OR: 2.48) and excessive GWG (OR: 1.95) were risk factors for macrosomia. Overweight and excessive GWG were risk factors for a large gestational age (LGA) and inadequate GWG was a risk factor for low birth weights. CONCLUSIONS: Overweight and obesity before pregnancy and an excessive GWG are associated with a greater risk of developing GDM, GHp, macrosomia and LGA. The control of body weight before and during the course of pregnancy is recommended to decrease adverse pregnancy outcomes, especially in pregnant women aged < 20 or > 25 years old educated below university and college levels, for ethnic minorities and those women who live in the north of China. TRIAL REGISTRATION: Registered at Clinical Trials ( NCT03403543 ), September 29, 2017.

Dietary patterns in relation to gestational depression and sleep disturbance in Chinese pregnant women.

AIM: To investigate the association of dietary patterns with gestational depression and sleep disturbance. METHODS: Women in early pregnancy were recruited from the Chinese Pregnant Women Cohort Study (CPWCS) through July 25th, 2017 to November 26th, 2018, and eventually 7615 participants were included in this study. The qualitative food frequency questionnaire (Q-FFQ), Edinburgh Postnatal Depression Scale (EPDS), and the Pittsburgh Sleep Quality Index (PSQI) were used to assess dietary, depression and sleep quality during pregnancy, respectively. Dietary patterns were derived by factor analysis. Logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (95% CI) of each outcome according to quartiles of each dietary pattern. RESULTS: Five dietary patterns were identified. Participants with the highest quartile in plant-based pattern had a significantly lower likelihood of mental problems (OR: 95% CI for depression: 0.66, 0.55-0.79; sleep disturbance: 0.80, 0.68-0.93); Similar results were observed in vitamin-rich pattern (OR: 95% CI for depression: 0.46, 0.38-0.55; sleep disturbance: 0.76, 0.65-0.89); However, contrary results were found in high-fat pattern (OR: 95% CI for depression: 2.15, 1.25-1.85; sleep disturbance: 1.43, 1.22-1.67); In animal protein-rich pattern, participants with the highest quartile had a decreased likelihood of depression (OR: 0.80, 95% CI: 0.67-0.96). As for bean products pattern, participants with the highest quartile had an increased risk of depression (OR: 1.28, 95% CI:1.06-1.53). Interactions of dietary patterns and lifestyles on mental disorders were observed. CONCLUSION: Dietary patterns were associated with gestational depression and sleep disturbance. Relevant departments and maternal and child health personnel should conduct health education for pregnant women and guide them to eat properly.

Highly sensitive atrazine fluorescence immunoassay by using magnetic separation and upconversion nanoparticles as labels.

A high-sensitivity fluorescence immunoassay for atrazine was established. It is making use of hydrophilic NaYF4:Yb/Er upconversion nanoparticles (UCNPs) conjugated to the anti-atrazine antibody as signal probe, and of polystyrene magnetic microspheres (PMMs) conjugated to the coating antigen as the capture probe. The coating antigen on the capture probe competes with atrazine for binding to the antibody on the signal probe to form the immuno complex. The complex is separated from the test system by magnetic action, and its green fluorescence is then measured at excitation/emission wavelengths of 980/552 nm using a fluorescence spectrophotometer equipped with an external 980 nm laser. The method was applied to the determination of atrazine in corn, rice, sugar cane juice, and river water. The immunoassay has a linear range that extends from 0.005 to 10 µg·L-1. The assay also recognizes propazine and prometryn, and it therefore can be applied to detect these three herbicides simultaneously. Sugar cane juice and river water samples can be analyzed directly without any pretreatment. The detection limits for atrazine are 2 ng·L-1 in sugar cane juice and river water samples, and 20 ng·kg-1 in corn and rice samples. The recoveries of spiked samples range from 84.7 to 113.6%. Graphical abstract A sensitive fluorescence immunoassay combining magnetic separation for the detection of atrazine in cereal, juice, and water samples using NaYF4:Yb/Er upconversion nanoparticles as labels.

Visual and fluorometric lateral flow immunoassay combined with a dual-functional test mode for rapid determination of tetracycline antibiotics.

A fluorometric immunochromatographic assay (FICA) is described where ZnCdSe/ZnS quantum dots (QDs) act as fluorescent label and gold nanoparticles (AuNPs) act as quencher. The assay works in the "turn-on" mode, i.e. the fluorescent signal (best measured at excitation/emission wavelengths of 302/525 nm) increases with the increase of analyte concentration. This assay can detect tetracycline antibiotics including tetracycline, oxytetracycline, chlortetracycline, and doxycycline. It is not interfered by other veterinary drugs. The visual limits of detection (LODs) for the tetracycline antibiotics are 2 µg·L-1 in buffer, 20 µg·L-1 in milk, and 40 µg·kg-1 in animal muscle tissue. The assay (including sample treatment) can be performed within 30 min. The FICA based on "turn on" mode is more sensitive than the colloidal gold-based immunochromatographic assay (CGICA) and quantum dot-based immunochromatographic assay (QDICA) based on "turn off" mode using either AuNPs or QDs as signal labels. One strip can simultaneously provide the fluorescent test results in the "turn on" mode on the basis of QD luminescence quenching under UV light. The colorimetric test is of the "turn off" mode based on the formation of a red coloration due to the use of AuNPs under natural light. The use of such a dual-functional test mode allows for rapid semi-quantitative determination of tetracycline antibiotics in milk and tissue samples. Graphical abstract Schematoc of a fluorometric immunochromatographic assay (FICA) based on fluorescence quenching of quantum dot (QD) by gold nanoparticle (AuNP) combined with a dual-functional test mode under UV light (turn on mode) and natural light (turn off mode) to visually detect tetracycline antibiotics.

[Genotoxic damage induced by indium chloride on rat].

OBJECTIVE: To investigate the genotoxic effect of indium chloride using the in vivo bone marrow micronucleus test, and to provide scientific basis for the evaluation of the toxicity of indium compounds. METHODS: Thirty two healthy male adult SPF Wistar rats were selected and randomly divided into four groups( control group, 0. 065, 0. 65, 1. 3 mg/kg group). Rats were treated with indium chloride using non exposed endotracheal injection method. Hematoxylin and eosin( H&E) staining was used to detect histological changes. Oxidative stress status relative to lung damage in rats was evaluated by detecting superoxide dismutase( SOD) and maleic dialdehyde( MDA) activities. The frequency of micronuclei was determined at the end of each culture period. Inductively coupled plasma mass spectrometry( ICP-MS) method was used to detect the content of indium in whole blood and lung tissue of rats. RESULTS: The content of indium in whole blood and lung tissue of rats and the micronucleus rate in rats exposed to indium chloridewere significantly higher than those in the control group( P < 0. 01). The MDA content in the lung tissue of rats in middle and high dose group was significantly increased( P <0. 05), while SOD level was significantly lower than that in control group( P < 0. 05);Pathological changes of lung tissue showed that after exposure to indium chloride, inflammatory cell exudation was present in the alveolar cavity and surrounding areas of the trachea were apparent. Alveolar lumen was filled with intraalveolar proteinaceous material. CONCLUSION: Indium chloride exposure will cause the accumulation of indium in blood and lung, bone marrow micronucleus rate increased, occurrence of oxidative damage and pathological changes.

Principal and independent genomic components of brain structure and function.

The highly polygenic and pleiotropic nature of behavioural traits, psychiatric disorders and structural and functional brain phenotypes complicate mechanistic interpretation of related genome-wide association study (GWAS) signals, thereby obscuring underlying causal biological processes. We propose genomic principal and independent component analysis (PCA, ICA) to decompose a large set of univariate GWAS statistics of multimodal brain traits into more interpretable latent genomic components. Here we introduce and evaluate this novel methods various analytic parameters and reproducibility across independent samples. Two UK Biobank GWAS summary statistic releases of 2240 imaging-derived phenotypes (IDPs) were retrieved. Genome-wide beta-values and their corresponding standard-error scaled z-values were decomposed using genomic PCA/ICA. We evaluated variance explained at multiple dimensions up to 200. We tested the inter-sample reproducibility of output of dimensions 5, 10, 25 and 50. Reproducibility statistics of the respective univariate GWAS served as benchmarks. Reproducibility of 10-dimensional PCs and ICs showed the best trade-off between model complexity and robustness and variance explained (PCs: |rz - max| = 0.33, |rraw - max| = 0.30; ICs: |rz - max| = 0.23, |rraw - max| = 0.19). Genomic PC and IC reproducibility improved substantially relative to mean univariate GWAS reproducibility up to dimension 10. Genomic components clustered along neuroimaging modalities. Our results indicate that genomic PCA and ICA decompose genetic effects on IDPs from GWAS statistics with high reproducibility by taking advantage of the inherent pleiotropic patterns. These findings encourage further applications of genomic PCA and ICA as fully data-driven methods to effectively reduce the dimensionality, enhance the signal to noise ratio and improve interpretability of high-dimensional multitrait genome-wide analyses.

Reverse-Mode Polymer-Stabilized Liquid Crystal Films with Enhanced Peel Strength and Electro-Optical Performance Based on Photoreactive Self-Assembly Alignment Layers and Patterned Polymer Walls.

Polymer-stabilized liquid crystal (PSLC) is a promising material toward the practical application of serving as energy-saving reverse-mode smart windows owing to its superior electro-optical (E-O) properties, simple and efficient processability, and compatibility to most practical circumstances. However, its feeble peel strength originated from low polymer content and poor adhesion between polymer networks and substrates inhibited its large-scale flexible film production. It is still a challenging task to derive good mechanical properties and superior E-O performance for PSLCs at the same time. In this study, a highly durable liquid crystal/polymer composite film showing enhanced peel strength and excellent E-O properties was attained by simultaneously building photoreactive self-assemble alignment layers through an efficient one-step method and the sculpture of a patterned polymer wall structure. This film has comprehensive ascendant E-O properties of lower driving voltages, faster response times, and higher contrast ratio, together with an over 30 times lift of the peel strength. The effectuation mechanisms of the alignment, E-O properties, peel-strength, microstructures, and cyclic durability of the films have been systematically studied. This novel liquid crystal/polymer composite film demonstrates advantages in every aspect of performance compared to traditional PSLC devices, which hoards promising applications in smart windows for cars and buildings.

Genetic liability to major psychiatric disorders contributes to multi-faceted quality of life outcomes in children and adults.

Importance: Psychiatric disorders can have an immense impact on socioeconomic, physical, and social-psychological facets of life. Psychiatric disorders are also highly heritable. Under a liability threshold model, an important question arises as to what extent genetic liability for psychiatric disorders relates to, and possibly impacts on, different aspects of quality of life in the general population. Objective: To characterize the link between psychiatric genetic liability and diverse aspects of quality of life in childhood and adulthood. Design setting and participants: We used data from two multi-site, population-based cohorts, i.e. preadolescent children in the USA enrolled at age 9-10 years from the Adolescent Brain Cognitive Development (ABCD) study (N=4,645) and white British adults between age 40-69 years from the UK Biobank (UKB) study (N=377,664). Due to the current limitations of our genetic methods, only data from unrelated individuals of European descent could be included. Main outcomes and measures: To derive robust measures capturing multiple domains of quality of life in each of the cohorts, we integrated an array of measurements of academic, economic, and physical status, as well as social well-being, in a second-level three-factor confirmatory factor analysis. The genetic liabilities to seven major psychiatric disorders were quantified by a set of polygenic scores (PGSs) derived from the largest genome-wide association studies to date, independent of the target cohorts, of major depressive disorder (MDD, N=142k-173k), anxiety disorders (ANX, N=22k-144k), attention-deficit/hyperactivity disorder (ADHD, N=226k), autism spectrum disorder (ASD, N=55k), schizophrenia (SCZ, N=130k), bipolar disorder (BIP, N=353k-414k), and cannabis use disorder (CUD, N=384k). Using general linear models we assessed associations between PGSs and the estimated latent factors, controlling for age, sex, site, genotyping batch, plate, and genetic ancestry. Results: In each cohort, three latent factors indexing distinct but correlated quality of life domains, (1) educational performance and cognition (Edu, in ABCD) / social economic status (SES, in UKB), (2) physical health (Hea), (3) adverse social experience (Adv, in ABCD) / social well-being (Soc, in UKB), were estimated with excellent model fit indices. In addition, a general factor was derived that captured the covariances between the three latent factors (QoL). In the ABCD cohort, ADHD-PGS was significantly associated with Edu (ß = -0.13, t = -8.29, p = 1.53e-16), Adv (ß = -0.09, t = -5.79, p = 7.81e-09), and general QoL (ß = -0.14, t = -8.74, p = 3.37e-18) factors. In the UKB cohort, all examined disorder PGSs were significantly associated with the general QoL latent factor and at least one first-order subdomain, with ADHD-PGS (ß = -0.06 ~ -0.10, t = -29.1 ~ -52.5, p < 5.91e-186) and MDD-PGS (ß = -0.04 ~ -0.07, t = -23.8 ~ -36.3, p < 3.63e-125) showing the largest effects. Conclusions and relevance: The present study reveals an inverse relationship between psychiatric genetic liabilities and multiple quality of life metrics, with ADHD-associated genetic risk being the main contributor in both children and adults, and MDD additionally showing effects in adults. All effect sizes observed were small, as expected. Understanding potential real-world outcomes of quantitative measures of disorder-related genetic risks in the general population can provide a scientific foundation for societal intervention and policy-making processes, with profound implications for promoting a flourishing society.