Light diffraction by sarcomeres produces iridescence in transmission in the transparent ghost catfish.

Despite the elaborate varieties of iridescent colors in biological species, most of them are reflective. Here we show the rainbow-like structural colors found in the ghost catfish (Kryptopterus vitreolus), which exist only in transmission. The fish shows flickering iridescence throughout the transparent body. The iridescence originates from the collective diffraction of light after passing through the periodic band structures of the sarcomeres inside the tightly stacked myofibril sheets, and the muscle fibers thus work as transmission gratings. The length of the sarcomeres varies from ~1 µm from the body neutral plane near the skeleton to ~2 µm next to the skin, and the iridescence of a live fish mainly results from the longer sarcomeres. The length of the sarcomere changes by ~80 nm as it relaxes and contracts, and the fish shows a quickly blinking dynamic diffraction pattern as it swims. While similar diffraction colors are also observed in thin slices of muscles from non-transparent species such as the white crucian carps, a transparent skin is required indeed to have such iridescence in live species. The ghost catfish skin is of a plywood structure of collagen fibrils, which allows more than 90% of the incident light to pass directly into the muscles and the diffracted light to exit the body. Our findings could also potentially explain the iridescence in other transparent aquatic species, including the eel larvae (Leptocephalus) and the icefishes (Salangidae).

Combining developmental and sleep health measures for autism spectrum disorder screening: an ECHO study.

BACKGROUND: Sleep problems are reported for up to 80% of autistic individuals. We examined whether parsimonious sets of items derived from the Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) and the Brief Infant Sleep Questionnaire (BISQ) are superior to the standard M-CHAT-R in predicting subsequent autism spectrum disorder (ASD) diagnoses. METHODS: Participants from 11 Environmental influences on Child Health Outcomes (ECHO) cohorts were included. We performed logistic LASSO regression models with 10-fold cross-validation to identify whether a combination of items derived from the M-CHAT-R and BISQ are superior to the standard M-CHAT-R in predicting ASD diagnoses. RESULTS: The final sample comprised 1552 children. The standard M-CHAT-R had a sensitivity of 44% (95% CI: 34, 55), specificity of 92% (95% CI: 91, 94), and AUROC of 0.726 (95% CI: 0.663, 0.790). A higher proportion of children with ASD had difficulty falling asleep or resisted bedtime during infancy/toddlerhood. However, LASSO models revealed parental reports of sleep problems did not improve the accuracy of the M-CHAT-R in predicting ASD diagnosis. CONCLUSION: While children with ASD had higher rates of sleep problems during infancy/toddlerhood, there was no improvement in ASD developmental screening through the incorporation of parent-report sleep metrics. IMPACT: Parental-reported sleep problems are common in autism spectrum disorder (ASD). We investigated whether the inclusion of parental-reports of infant/toddler sleep patterns enhanced the effectiveness of developmental screening for autism. We reported higher rates of difficulty falling asleep and resisting bedtime during infancy and toddlerhood among children later diagnosed with ASD; however, we did not find an improvement in ASD developmental screening through the incorporation of parent-report sleep metrics. In our sample, the standard M-CHAT-R had a sensitivity of 39% among children of mothers with government insurance compared with a sensitivity of 53% among children of mothers with employer-based insurance.

Structure-Activity Relationship of pH-Sensitive Doxorubicin-Fatty Acid Prodrug Albumin Nanoparticles.

Albumin has emerged as a versatile drug carrier. To harness albumin as a carrier for doxorubicin (DOX), we synthesized three acid-labile DOX prodrugs using stearic acid (SA), oleic acid (OA), and linoleic acid (LA) as the albumin-binding motif, respectively. Different from conventional albumin nanodrugs (such as Abraxane, with a drug loading of 10%), the DOX prodrugs assembled albumin nanoparticles (NPs) have an ultrahigh drug loading (>35%). Noteworthy, we demonstrated that the saturation of fatty acids exerted great influence on colloidal stability of prodrug NPs, thus affecting their in vivo pharmacokinetics, tumor accumulation and antitumor efficacy. Furthermore, the hydrazone bond-bridged DOX prodrugs could remain intact in the bloodstream but allow DOX to be released in the acidic tumor environment, resulting in improved antitumor efficacy and safety. Our work gives novel insights into the structure-to-efficacy relationship of albumin-bound fatty acid prodrugs and provides a simple strategy for advanced albumin-bound nanomedicines.

Water resource dynamics and protection strategies for inland lakes: A case study of Hongjiannao Lake.

Globally, lakes are drying up and shrinking and inland lakes, in particular, face severe water shortage problems. Thus, the degradation mechanisms and protection measures for inland lakes urgently need to be explored. Hongjiannao Lake (HL), an inland lake on the border of Shaanxi Province and Inner Mongolia Autonomous Region of China, was selected for the present case study. The evolution of HL was analyzed and the current lake water storage was measured on site. The driving factors of water resource changes in HL were discussed based on meteorological and landcover data. The results showed that (1) from 1929 to 2021, the lake area of HL experienced four stages: formation, stability, shrinkage and recovery. The smallest water area was 31.08 km2 in 2015, half the size of lake in the 1960s. (2) Spatially, the morphological changes of HL mainly occurred where the rivers entered the lake. (3) In 2021, the average depth of HL was 3.77 m, and the water storage capacity was 140.56 million m3. (4) The annual average evaporation was 3.36 times the amount of the annual average precipitation in Hongjiannao Basin (HB), but climate change was not the main driver of changes in the HL area. (5) In the past 20 years, cultivated land and artificial surface increased by 3.11% and 1.04%, respectively, whereas grassland and water body decreased by 3.51% and 0.45%, respectively. The expansion of cultivated land and artificial surface, as well as the construction of reservoirs upstream of the lake, hindered the replenishment of water resources to HL. This study recommends a range of strategies for water resource protection in inland lakes, including implementing ecological restoration projects, carrying out inter-basin water transfer measures, improving the efficiency of regional water resource use, and improving industrial structure and distribution.

Research on Image Stitching Algorithm Based on Point-Line Consistency and Local Edge Feature Constraints.

Image stitching aims to synthesize a wider and more informative whole image, which has been widely used in various fields. This study focuses on improving the accuracy of image mosaic and proposes an image mosaic method based on local edge contour matching constraints. Because the accuracy and quantity of feature matching have a direct influence on the stitching result, it often leads to wrong image warpage model estimation when feature points are difficult to detect and match errors are easy to occur. To address this issue, the geometric invariance is used to expand the number of feature matching points, thus enriching the matching information. Based on Canny edge detection, significant local edge contour features are constructed through operations such as structure separation and edge contour merging to improve the image registration effect. The method also introduces the spatial variation warping method to ensure the local alignment of the overlapping area, maintains the line structure in the image without bending by the constraints of short and long lines, and eliminates the distortion of the non-overlapping area by the global line-guided warping method. The method proposed in this paper is compared with other research through experimental comparisons on multiple datasets, and excellent stitching results are obtained.

Latent Class Analysis of Prenatal Substance Exposure and Child Behavioral Outcomes.

OBJECTIVES: To predict behavioral disruptions in middle childhood, we identified latent classes of prenatal substance use. STUDY DESIGN: As part of the Environmental influences on Child Health Outcomes Program, we harmonized prenatal substance use data and child behavior outcomes from 2195 women and their 6- to 11-year-old children across 10 cohorts in the US and used latent class-adjusted regression models to predict parent-rated child behavior. RESULTS: Three latent classes fit the data: low use (90.5%; n = 1986), primarily using no substances; licit use (6.6%; n = 145), mainly using nicotine with a moderate likelihood of using alcohol and marijuana; and illicit use (2.9%; n = 64), predominantly using illicit substances along with a moderate likelihood of using licit substances. Children exposed to primarily licit substances in utero had greater levels of externalizing behavior than children exposed to low or no substances (P = .001, d = .64). Children exposed to illicit substances in utero showed small but significant elevations in internalizing behavior than children exposed to low or no substances (P < .001, d = .16). CONCLUSIONS: The differences in prenatal polysubstance use may increase risk for specific childhood problem behaviors; however, child outcomes appeared comparably adverse for both licit and illicit polysubstance exposure. We highlight the need for similar multicohort, large-scale studies to examine childhood outcomes based on prenatal substance use profiles.

The Differences in Structure and Function of the Cerebellum Between Cantonese-Mandarin Bilinguals and Mandarin Monolinguals: a Multi-model MRI Study.

The effects of the long-term bilingual experience on structure and function of the cerebellum remain unclear. To explore whether there are differences in cerebellar gray matter structure between Cantonese-Mandarin bilinguals and Mandarin monolinguals and whether these different cerebellar structures have different resting-state functional connectivity (RSFC) with the cerebrum between the two groups, 30 Cantonese-Mandarin bilingual and 30 Mandarin monolingual college students were scanned by the T1-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. Voxel-based morphology (VBM) analysis and RSFC analysis were used to analyze the cerebellar gray matter volume (GMV) and cerebellar-cerebro functional connectivity, respectively. Correlation analysis was performed between GMV/RSFC and the rapid automatized naming (RAN) and cognitive control. Compared to Mandarin monolinguals, Cantonese-Mandarin bilinguals showed larger GMV in bilateral cerebellar inferior posterior lobe (including bilateral VIIIa, VIIIb,IX, and right X, Vermis VIIIb, and Vermis IX) and a significant increase in RSFC coupling of the right inferior cerebellar posterior lobe with orbital part of left inferior frontal gyrus (IFG). In addition, there was a positive correlation between average response time (RT) of Mandarin alphanumeric RAN and RSFC between the right inferior posterior lobe of cerebellum and left IFG of all participants. The long-term Cantonese-Mandarin bilingual experience can increase the GMV of the bilateral cerebellar inferior posterior lobe and the RSFC between the right inferior cerebellar posterior lobe with orbital part of left inferior frontal gyrus (IFG).

Cobalt-Nanoparticles-Decorated 3D Porous Nitrogen-Doped Carbon Network for Electrocatalytic Nitrite Reduction to Ammonia.

Electrocatalytic nitrite (NO2-) reduction offers the potential to synthesize high-value ammonia (NH3) while simultaneously removing NO2- pollution from aqueous solutions, but it requires high-efficiency catalysts to drive the complex six-electron reaction. Herein, cobalt-nanoparticle-decorated 3D porous nitrogen-doped carbon network (Co@NC) is proven as a high-efficiency catalyst for the selective electroreduction of NO2- to NH3. Such Co@NC attains a large NH3 yield of 922.7 µmol h-1 cm-2 and a high Faradaic efficiency of 95.4% under alkaline conditions. Furthermore, it shows remarkable electrochemical stability during cyclic electrolysis.

Prenatal Substance Exposure: Associations with Neurodevelopment in Middle Childhood.

OBJECTIVE: Single-substance exposure effects on neurodevelopmental outcomes, such as problem behavior and intelligence quotient (IQ), have been studied in children for decades. However, the long-term consequences of polysubstance exposure are poorly understood. STUDY DESIGN: Longitudinal neurodevelopmental data were gathered from cohorts across the United States through the Environmental Influences on Child Health Outcomes Program. Data on prenatal exposure to opioids, nicotine, marijuana, and alcohol were collected from children ages 6 to 11 years (N = 256). Problem behavior was assessed using the Child Behavior Checklist (school-age version), and verbal IQ (VIQ) and performance IQ (PIQ) were assessed using the Weschler Intelligence Scale for Children, Fifth Edition. We first identified latent profiles in the overall sample, then evaluated differences in profile membership for children with and without prenatal substance exposure. RESULTS: Latent profile analysis identified two mutually exclusive categories: average VIQ and PIQ, with typical problem behavior, and below-average VIQ with average PIQ and clinically significant problem behavior. Children with prenatal nicotine and polysubstance exposures were more likely to be classified in the below-average VIQ, elevated problem behavior profile compared with children without prenatal nicotine exposure. CONCLUSION: The presence of clinically significant behavior problems in children with average PIQ, but below-average VIQ, could represent a unique endophenotype related to prenatal nicotine exposure in the context of other prenatal substance exposures. KEY POINTS: · The neurodevelopmental consequences of prenatal polysubstance exposure are poorly understood.. · Children with prenatal polysubstance exposure exhibited reduced IQ and elevated problem behavior.. · We found significant behavior problems in children with average PIQ and below-average VIQ.. · This may represent a unique endophenotype related to prenatal nicotine exposure..

Physiological Variables Influenced by 'Candidatus Liberibacter asiaticus' Infection in Two Citrus Species.

'Candidatus Liberibacter asiaticus' is the bacterium associated with the citrus disease known as huanglongbing (HLB). This study evaluated the influence of 'Ca. L. asiaticus' infection on a number of key plant physiological variables concerning photosynthesis, cell integrity, reactive oxygen species scavengers' activity, and osmoregulation of two different species of citrus-the pomelo Citrus maxima and the mandarin C. reticulata 'Tankan'-relative to their measured 'Ca. L. asiaticus' infection load. Results indicated that all measured physiological variables except soluble sugar were affected by increased 'Ca. L. asiaticus' infection titers, wherein the variety C. maxima proved overall more resistant than C. reticulata. 'Ca. L. asiaticus' infection was linked in both plants to decrease in chlorophyll concentration, cell membrane permeability, and malondialdehyde, as well as increased free proline and starch contents. Chlorophyll fluorescence measurements taken 9 months after grafting the mandarin C. reticulata with 'Ca. L. asiaticus' scions revealed a significant decrease in the photosynthesis variables maximum photochemical quantum yield of photosystem II (PSII), effective photochemical quantum yield of PSII, and coefficient of photochemical fluorescence quenching assuming interconnected PSII antennae, whereas nonphotochemical fluorescence quenching increased significantly; C. maxima plants, on the other hand, did not show significant differences until the 12th month from infection exposure. The variables superoxide dismutase, catalase, peroxidase, and soluble protein initially increased and later decreased. In addition, progression of 'Ca. L. asiaticus' replication in both citrus species was accompanied by rapid changes in three reactive oxygen species scavenging enzymes in C. maxima, while the pattern was different in C. reticulata. We hypothesize that the observed interspecific differences in physiological change are related to their relative resistance against 'Ca. L. asiaticus' infection. These results provide a scaffold for better describing the pathogenesis, selecting the most resistant breeds, or even validating pertaining omics research; ultimately, these detailed observations can facilitate the diagnosis of 'Ca. L. asiaticus' infection.

Edge-Guided Camouflaged Object Detection via Multi-Level Feature Integration.

Camouflaged object detection (COD) aims to segment those camouflaged objects that blend perfectly into their surroundings. Due to the low boundary contrast between camouflaged objects and their surroundings, their detection poses a significant challenge. Despite the numerous excellent camouflaged object detection methods developed in recent years, issues such as boundary refinement and multi-level feature extraction and fusion still need further exploration. In this paper, we propose a novel multi-level feature integration network (MFNet) for camouflaged object detection. Firstly, we design an edge guidance module (EGM) to improve the COD performance by providing additional boundary semantic information by combining high-level semantic information and low-level spatial details to model the edges of camouflaged objects. Additionally, we propose a multi-level feature integration module (MFIM), which leverages the fine local information of low-level features and the rich global information of high-level features in adjacent three-level features to provide a supplementary feature representation for the current-level features, effectively integrating the full context semantic information. Finally, we propose a context aggregation refinement module (CARM) to efficiently aggregate and refine the cross-level features to obtain clear prediction maps. Our extensive experiments on three benchmark datasets show that the MFNet model is an effective COD model and outperforms other state-of-the-art models in all four evaluation metrics (Sα, Eϕ, Fßw, and MAE).

Fast and Robust Infrared Small Target Detection Using Weighted Local Difference Variance Measure.

Infrared (IR) small-target-detection performance restricts the development of infrared search and track (IRST) systems. Existing detection methods easily lead to missed detection and false alarms under complex backgrounds and interference, and only focus on the target position while ignoring the target shape features, which cannot further identify the category of IR targets. To address these issues and guarantee a certain runtime, a weighted local difference variance measure (WLDVM) algorithm is proposed. First, Gaussian filtering is used to preprocess the image by using the idea of a matched filter to purposefully enhance the target and suppress noise. Then, the target area is divided into a new tri-layer filtering window according to the distribution characteristics of the target area, and a window intensity level (WIL) is proposed to represent the complexity level of each layer of windows. Secondly, a local difference variance measure (LDVM) is proposed, which can eliminate the high-brightness background through the difference-form, and further use the local variance to make the target area appear brighter. The background estimation is then adopted to calculate the weighting function to determine the shape of the real small target. Finally, a simple adaptive threshold is used after obtaining the WLDVM saliency map (SM) to capture the true target. Experiments on nine groups of IR small-target datasets with complex backgrounds illustrate that the proposed method can effectively solve the above problems, and its detection performance is better than seven classic and widely used methods.

Multimodal Sentiment Analysis Representations Learning via Contrastive Learning with Condense Attention Fusion.

Multimodal sentiment analysis has gained popularity as a research field for its ability to predict users' emotional tendencies more comprehensively. The data fusion module is a critical component of multimodal sentiment analysis, as it allows for integrating information from multiple modalities. However, it is challenging to combine modalities and remove redundant information effectively. In our research, we address these challenges by proposing a multimodal sentiment analysis model based on supervised contrastive learning, which leads to more effective data representation and richer multimodal features. Specifically, we introduce the MLFC module, which utilizes a convolutional neural network (CNN) and Transformer to solve the redundancy problem of each modal feature and reduce irrelevant information. Moreover, our model employs supervised contrastive learning to enhance its ability to learn standard sentiment features from data. We evaluate our model on three widely-used datasets, namely MVSA-single, MVSA-multiple, and HFM, demonstrating that our model outperforms the state-of-the-art model. Finally, we conduct ablation experiments to validate the efficacy of our proposed method.

Porous Polymer Cubosomes with Ordered Single Primitive Bicontinuous Architecture and Their Sodium-Iodine Batteries.

Bicontinuous porous materials, which possess 3D interconnected pore channels facilitating a smooth mass transport, have attracted much interest in the fields of energy and catalysis. However, their synthesis remains very challenging. We report a general approach, using polymer cubosomes as the template, for the controllable synthesis of bicontinuous porous polymers with an ordered single primitive (SP) cubic structure, including polypyrrole (SP-PPy), poly-m-phenylenediamine (SP-PmPD), and polydopamine (SP-PDA). Specifically, the resultant SP-PPy had a unit cell parameter of 99 nm, pore diameter of 45 nm, and specific surface area of approximately 60 m2·g-1. As a proof of concept, the I2-adsorbed SP-PPy was employed as the cathode materials of newly emerged Na-I2 batteries, which delivered a record-high specific capacity (235 mA·h·g-1 at 0.5 C), excellent rate capability, and cycling stability (with a low capacity decay of 0.12% per cycle within 400 cycles at 1 C). The advantageous contributions of the bicontinuous structure and I3- adsorption mechanism of SP-PPy were revealed by a combination of ion diffusion experiments and theoretical calculations. This study opens a new avenue for the synthesis of porous polymers with new topologies, broadens the spectrum of bicontinuous-structured materials, and also develops a novel potential application for porous polymers.

Meta-analysis under imbalance in measurement of confounders in cohort studies using only summary-level data.

BACKGROUND: Cohort collaborations often require meta-analysis of exposure-outcome association estimates across cohorts as an alternative to pooling individual-level data that requires a laborious process of data harmonization on individual-level data. However, it is likely that important confounders are not all measured uniformly across the cohorts due to differences in study protocols. This imbalance in measurement of confounders leads to association estimates that are not comparable across cohorts and impedes the meta-analysis of results. METHODS: In this article, we empirically show some asymptotic relations between fully adjusted and unadjusted exposure-outcome effect estimates, and provide theoretical justification for the same. We leverage these results to obtain fully adjusted estimates for the cohorts with no information on confounders by borrowing information from cohorts with complete measurement on confounders. We implement this novel method in CIMBAL (confounder imbalance), which additionally provides a meta-analyzed estimate that appropriately accounts for the dependence between estimates arising due to borrowing of information across cohorts. We perform extensive simulation experiments to study CIMBAL's statistical properties. We illustrate CIMBAL using National Children's Study (NCS) data to estimate association of maternal education and low birth weight in infants, adjusting for maternal age at delivery, race/ethnicity, marital status, and income. RESULTS: Our simulation studies indicate that estimates of exposure-outcome association from CIMBAL are closer to the truth than those from commonly-used approaches for meta-analyzing cohorts with disparate confounder measurements. CIMBAL is not too sensitive to heterogeneity in underlying joint distributions of exposure, outcome and confounders but is very sensitive to heterogeneity of confounding bias across cohorts. Application of CIMBAL to NCS data for a proof-of-concept analysis further illustrates the utility and advantages of CIMBAL. CONCLUSIONS: CIMBAL provides a practical approach for meta-analyzing cohorts with imbalance in measurement of confounders under a weak assumption that the cohorts are independently sampled from populations with the same confounding bias.

Co Nanoparticles Decorated Corncob-Derived Biomass Carbon as an Efficient Electrocatalyst for Nitrate Reduction to Ammonia.

Nitrate (NO3-) is a type of common pollutant in aqueous systems. Electrochemical NO3- reduction is an ecofriendly and sustainable strategy, which can selectively reduce NO3- to highly value-added NH3 and remove NO3- pollutants at the same time. In this work, Co nanoparticles decorated corncob-derived biomass carbon as a highly active electrocatalyst for NO3- to NH3 conversion. Such a catalyst can achieve an amazing Faradaic efficiency of 93.4% and a large NH3 yield of 0.60 mmol h-1 cm-2 in alkaline media. 15N-Labeling experiment proves that the detected NH3 is derived from NO3- electroreduction. In addition, it also displays excellent durability in long-term and cycle-electrolysis tests.

Combination of orthokeratology lens with 0.01% atropine in slowing axial elongation in children with myopia: a randomized double-blinded clinical trial.

BACKGROUND: To evaluate the additive effects of orthokeratology (OK) lenses and 0.01% atropine on slowing axial elongation in myopic children. METHODS: A prospective, randomized, double-blinded, placebo-controlled trial was conducted over a 12-month period. Sixty children aged 8 to 12 years with spherical equivalent refraction from - 1.00 to -4.00 D who had been wearing OK lenses successfully for 2 months (as baseline) were randomly assigned in a 1:1 ratio to combination group (combination of OK lens and 0.01% atropine eye drops) and control group (combination of OK lens and placebo). The primary outcome was change in axial length, along with secondary outcomes including change in pupil diameter (PD) and accommodative amplitude (AMP) at 12 months (measured at 4-month intervals). RESULTS: After 12 months, the overall axial elongation was 0.10 ± 0.14 mm and 0.20 ± 0.15 mm (p = 0.01) in the combination and control groups, respectively. The change in axial length in the two groups showed significant differences only in the first four months (median [Q1, Q3] (95% CI), -0.01 mm [-0.07, 0.05] (-0.06, 0.04) vs. 0.04 mm [0.00, 0.10] (0.02, 0.09); p = 0.04), but no difference thereafter. Multivariate linear regression analysis showed that the axial elongation was significantly slower in the combination group than in the control group (standard ß = -0.10, p = 0.02). PD significantly increased by 0.45 mm [0.20, 0.68] at the 4th month visit (p < 0.001) and then remained stable in the combination group. The PD in the control group and AMP in the two groups remained stable from baseline to 12 months (all p > 0.05). CONCLUSION: The combination therapy was more effective than the OK lens alone in slowing axial elongation after 12 months of treatment, and mainly in the first 4 months. TRIAL REGISTRATION: The First Affiliated Hospital of Zhengzhou University, ChiCTR2000033904. Registered 16/06/2020, http://www.chictr.org.cn/login.aspx?referurl=%2flistbycreater.aspx.

Multi-Scale Strengthened Directional Difference Algorithm Based on the Human Vision System.

The human visual system (HVS) mechanism has been successfully introduced into the field of infrared small target detection. However, most of the current detection algorithms based on the mechanism of the human visual system ignore the continuous direction information and are easily disturbed by highlight noise and object edges. In this paper, a multi-scale strengthened directional difference (MSDD) algorithm is proposed. It is mainly divided into two parts: local directional intensity measure (LDIM) and local directional fluctuation measure (LDFM). In LDIM, an improved window is used to suppress most edge clutter, highlights, and holes and enhance true targets. In LDFM, the characteristics of the target area, the background area, and the connection between the target and the background are considered, which further highlights the true target signal and suppresses the corner clutter. Then, the MSDD saliency map is obtained by fusing the LDIM map and the LDFM map. Finally, an adaptive threshold segmentation method is employed to capture true targets. The experiments show that the proposed method achieves better detection performance in complex backgrounds than several classical and widely used methods.

Materials, Preparation Strategies, and Wearable Sensor Applications of Conductive Fibers: A Review.

The recent advances in wearable sensors and intelligent human-machine interfaces have sparked a great many interests in conductive fibers owing to their high conductivity, light weight, good flexibility, and durability. As one of the most impressive materials for wearable sensors, conductive fibers can be made from a variety of raw sources via diverse preparation strategies. Herein, to offer a comprehensive understanding of conductive fibers, we present an overview of the recent progress in the materials, the preparation strategies, and the wearable sensor applications related. Firstly, the three types of conductive fibers, including metal-based, carbon-based, and polymer-based, are summarized in terms of their principal material composition. Then, various preparation strategies of conductive fibers are established. Next, the primary wearable sensors made of conductive fibers are illustrated in detail. Finally, a robust outlook on conductive fibers and their wearable sensor applications are addressed.

Association between paternal age at childbirth and autism spectrum disorder in offspring. / çˆ¶äº²ç”Ÿè‚²å¹´é¾„ä¸Žå„¿ç«¥å­¤ç‹¬ç—‡è°±ç³»éšœç¢çš„ç›¸å ³æ€§.

OBJECTIVES: To study the association between paternal age at childbirth and the risk of autism spectrum disorder (ASD) in offspring. METHODS: In this cross-sectional study, 71 children with ASD who were diagnosed in the Department of Child Healthcare in six hospitals in Guangzhou, Foshan, Beijing, Wuhan, Hangzhou, and Chongqing of China from August 2016 to March 2017 were enrolled as subjects, and 284 typically developing children matched for age, sex, and maternal age at childbirth with the ASD children served as controls. A self-design questionnaire was used to collect the data on social demography, maternal pregnancy, and delivery. The association between paternal age at childbirth and the development of ASD in offspring was evaluated by the logistic regression analysis. RESULTS: After control for demographic factors and pregnancy- and delivery-related factors, the logistic regression analysis showed that a relatively high paternal age at childbirth was significantly associated with the increased risk of ASD in offspring (OR=1.12, 95%CI: 1.02-1.23, P<0.05). After grouping based on the paternal age, the logistic regression analysis showed that paternal age at childbirth of ≥40 years was significantly associated with the risk of ASD in offspring (before adjustment: OR=7.08, 95%CI: 1.77-28.32, P<0.05; after adjustment: OR=8.50, 95%CI: 1.71-42.25, P<0.05). CONCLUSIONS: High paternal age at childbirth is significantly associated with the increased risk of ASD in offspring, and paternal age at childbirth ≥40 years may be the high-risk age group for ASD in offspring.