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Modular dynamic graph theory metrics effectively capture the patterns of dynamic information interaction during human brain development. While existing research has employed modular algorithms to examine the overall impact of dynamic changes in community structure throughout development, there is a notable gap in understanding the cross-community dynamic changes within different functional networks during early childhood and their potential contributions to the efficiency of brain information transmission. This study seeks to address this gap by tracing the trajectories of cross-community structural changes within early childhood functional networks and modeling their contributions to information transmission efficiency. We analyzed 194 functional imaging scans from 83 children aged 2 to 8 years, who participated in passive viewing functional magnetic resonance imaging sessions. Utilizing sliding windows and modular algorithms, we evaluated three spatiotemporal metrics—temporal flexibility, spatiotemporal diversity, and within-community spatiotemporal diversity—and four centrality metrics: within-community degree centrality, eigenvector centrality, between-community degree centrality, and between-community eigenvector centrality. Mixed-effects linear models revealed significant age-related increases in the temporal flexibility of the default mode network (DMN), executive control network (ECN), and salience network (SN), indicating frequent adjustments in community structure within these networks during early childhood. Additionally, the spatiotemporal diversity of the SN also displayed significant age-related increases, highlighting its broad pattern of cross-community dynamic interactions. Conversely, within-community spatiotemporal diversity in the language network exhibited significant age-related decreases, reflecting the network's gradual functional specialization. Furthermore, our findings indicated significant age-related increases in between-community degree centrality across the DMN, ECN, SN, language network, and dorsal attention network, while between-community eigenvector centrality also increased significantly for the DMN, ECN, and SN. However, within-community eigenvector centrality remained stable across all functional networks during early childhood. These results suggest that while centrality of cross-community interactions in early childhood functional networks increases, centrality within communities remains stable. Finally, mediation analysis was conducted to explore the relationships between age, brain dynamic graph metrics, and both global and local efficiency based on community structure. The results indicated that the dynamic graph metrics of the SN primarily mediated the relationship between age and the decrease in global efficiency, while those of the DMN, language network, ECN, dorsal attention network, and SN primarily mediated the relationship between age and the increase in local efficiency. This pattern suggests a developmental trajectory in early childhood from global information integration to local information segregation, with the SN playing a pivotal role in this transformation. This study provides novel insights into the mechanisms by which early childhood brain functional development impacts information transmission efficiency through cross-community adjustments in functional networks.

Engineers used TCAD tools for semiconductor devices modeling. However, it is computationally expensive and time-consuming for advanced devices with smaller dimensions. Therefore, this work proposes a machine learning-based device modeling algorithm to capture the complex nonlinear relationship between parameters and electrical characteristics of gate-all-around (GAA) nanowire field-effect transistors (NWFETs) from technology computer-aided design (TCAD) simulation results. This method utilizes a densely connected deep neural networks (DenseDNN), which establishes direct connections between layers in the neural networks, provides stronger feature extraction and information transmission capabilities. By incorporating cost-sensitive learning methods, the proposed model focus more on the critical data that determines device characteristics, leading to accurate prediction of key device characteristics under various parameters. Experimental results on a test dataset of 116 NWFETs demonstrate the effectiveness of this method. The DenseDNN model with cost-sensitive learning exhibits better performance than traditional deep neural networks (DNN) with various widths and depths, with a prediction error below 1.62%. Moreover, compared to TCAD simulation results, the model can speedup $10^{6}\times$ .

This comprehensive review critically examines the current state of research on the biological effects of millimeter-wave (MMW) therapy and its potential implications for disease treatment. By investigating both the thermal and non-thermal impacts of MMWs, we elucidate cellular-level alterations, including changes in ion channels and signaling pathways. Our analysis encompasses MMW’s therapeutic prospects in oncology, such as inducing apoptosis, managing pain, and modulating immunity through cytokine regulation and immune cell activation. By employing a rigorous methodology involving an extensive database search and stringent inclusion criteria, we emphasize the need for standardized protocols to enhance the reliability of future research. Although MMWs exhibit promising therapeutic potential, our findings highlight the urgent need for further elucidation of non-thermal mechanisms and rigorous safety assessments, considering the intricate nature of MMW interactions and inconsistent study outcomes. This review underscores the importance of focused research on the biological mechanisms of MMWs and the identification of optimal frequencies to fully harness their therapeutic capabilities. However, we acknowledge the challenges of variable study quality and the necessity for advanced quality control measures to ensure the reproducibility and comparability of future investigations. In conclusion, while MMW therapy holds promise as a novel therapeutic modality, further research is imperative to unravel its complex biological effects, establish safety profiles, and optimize treatment protocols before widespread clinical application.

Prostate cancer is one of the most prevalent malignant tumors in men worldwide, and early diagnosis is essential to improve patient survival. This review provides a comprehensive discussion of recent advances in prostate cancer biomarkers, including molecular, cellular, and exosomal biomarkers. The potential of various biomarkers such as gene fusions (TMPRSS2-ERG), noncoding RNAs (SNHG12), proteins (PSA, PSMA, AR), and circulating tumor cells (CTCs) in the diagnosis, prognosis, and targeted therapies of prostate cancer is emphasized. In addition, this review systematically explores how multi-omics data and artificial intelligence technologies can be used for biomarker discovery and personalized medicine applications. In addition, this review provides insights into the development of specific probes, including fluorescent, electrochemical, and radionuclide probes, for sensitive and accurate detection of prostate cancer biomarkers. In conclusion, this review provides a comprehensive overview of the status and future directions of prostate cancer biomarker research, emphasizing the potential for precision diagnosis and targeted therapy.

Tumors significantly impact individuals’ physical well-being and quality of life. With the ongoing advancements in optical technology, information technology, robotic technology, etc., laser technology is being increasingly utilized in the field of tumor treatment, and laser ablation (LA) of tumors remains a prominent area of research interest. This paper presents an overview of the recent progress in tumor LA therapy, with a focus on the mechanisms and biological effects of LA, commonly used ablation lasers, image-guided LA, and robotic-assisted LA. Further insights and future prospects are discussed in relation to these aspects, and the paper proposed potential future directions for the development of tumor LA techniques.

Abstract Caffeine and its derivatives can effectively bind amyloid beta 16–22 (Aβ16–22) fragment of amyloid beta 1–42 (Aβ1–42), a biomarker for the early diagnosis of Alzheimer's disease (AD), by means of conformation selection, π–π stacking, van der Waals forces, and hydrogen bonding, so as to achieve high specificity and quantitative detection of Aβ1–42. In this study, 3‐mercaptopropionic acid (MPA), conductive polymer poly(thiophene‐3‐acetic acid) (PTAA), and pine‐like/Au pine PTAA (pine PTAA) were applied to modify the electrodes, and the non‐enzymatic caffeine was used as specific biorecognition element to study the analytical performance of the electrochemical sensor platform for Aβ1–42 oligomer (AβO). It was found that caffeine/pine PTAA‐based sensor with large surface area, high active sites, and excellent electrical conductivity demonstrated the widest linear range (10−8 to 100 nM) and highest sensitivity (743.77 Ω/log nM) in comparison. The prepared caffeine‐based sensor was afterward applied to cerebrospinal fluid and blood tests for real sample analysis, demonstrating its potential for practical use in detecting AβO at the attomolar level. Furthermore, the non‐enzymatic caffeine was constructed on the pine‐like PTAA‐modified screen‐printed electrodes for the rapid detection of AβO using portable meter.

The hippocampus is a complex brain structure that plays an important role in various cognitive aspects such as memory, intelligence, executive function, and path integration. The volume of this highly plastic structure is identified as one of the most important biomarkers of specific neuropsychiatric and neurodegenerative diseases. It has also been extensively investigated in numerous aging studies. However, recent studies on aging show that the performance of conventional approaches in measuring the hippocampal volume is still far from satisfactory, especially in terms of delivering longitudinal measures from ultra-high field magnetic resonance images (MRIs), which can visualize more boundary details. The advancement of deep learning provides an alternative solution to measuring the hippocampal volume. In this work, we comprehensively compared a deep learning pipeline based on nnU-Net with several conventional approaches including Freesurfer, FSL and DARTEL, for automatically delivering hippocampal volumes: (1) Firstly, we evaluated the segmentation accuracy and precision on a public dataset through cross-validation. Results showed that the deep learning pipeline had the lowest mean (L = 1.5%, R = 1.7%) and the lowest standard deviation (L = 5.2%, R = 6.2%) in terms of volume percentage error. (2) Secondly, sub-millimeter MRIs of a group of healthy adults with test–retest 3T and 7T sessions were used to extensively assess the test–retest reliability. Results showed that the deep learning pipeline achieved very high intraclass correlation coefficients (L = 0.990, R = 0.986 for 7T; L = 0.985, R = 0.983 for 3T) and very small volume percentage differences (L = 1.2%, R = 0.9% for 7T; L = 1.3%, R = 1.3% for 3T). (3) Thirdly, a Bayesian linear mixed effect model was constructed with respect to the hippocampal volumes of two healthy adult datasets with longitudinal 7T scans and one disease-related longitudinal dataset. It was found that the deep learning pipeline detected both the subtle and disease-related changes over time with high sensitivity as well as the mild differences across subjects. Comparison results from the aforementioned three aspects showed that the deep learning pipeline significantly outperformed the conventional approaches by large margins. Results also showed that the deep learning pipeline can better accommodate longitudinal analysis purposes.

Promoting safe behaviors among construction workers and mitigating unsafe behaviors is an effective approach to enhancing safety performance in the construction industry. Although progress has been made, the research themes concerning construction workers’ safety-related behaviors (CWSRB) and the detailed progress of each theme remain unclear due to differences in review perspectives and conceptual scopes. This study utilized CiteSpace software (V6.2R3 version) to conduct an analysis of co-authorship networks, co-word networks, and co-citations on 563 published articles in this field from 2013 to 2023. This study’s outcomes highlight several key insights: (1) journals such as Safety Science play a pivotal role in the domain; (2) institutions such as the City University of Hong Kong and Hong Kong Polytechnic University, along with prolific authors like Li, are major contributors to the field; (3) the focus of research has evolved from early organizational factors towards a more diverse range of topics, with deep learning emerging as a significant current research hotspot; (4) this study has identified high-cited literature and 11 primary clusters within the field. Current research focuses on five areas: safety-related behavior concepts, influencing factors and consequences, formation mechanisms, interventions, and applications of new technologies. Establishing clear classification criteria for unsafe behaviors, comprehensively understanding the formation mechanisms of safety-related behaviors, evaluating the effectiveness of intervention strategies, and exploring the practical applications of new technologies are future research directions. This study provides researchers with a holistic view of the present state of research and potential avenues for future exploration, thereby deepening the knowledge and comprehension of stakeholders within this domain.

Abstract Introducing oxygen-vacancy into the surface of the non-enzymatic sensor is supposed to be an effective way to improve inherently low catalytic activity and specificity of non-enzymatic sensors. In this work, CuO/C was synthesized at different temperatures using metal-organic frameworks as sacrificial templates to receive additional content of oxygen-vacancy. The product with the highest oxygen vacancy was found at 400 °C (named CuO/C-400 °C), which increased catalytically active sites and enhanced the charge-transfer efficiency. The sensing performance was afterward explored by amperometry under an optimal applied potential at 0.5 V (vs. SCE), presenting a broad detection range from 5.0 µM to 25.325 mM (R2 = 0.9998) with a sensitivity of 244.71 µA mM− 1 cm− 2, and a detection limit of 1 µM. Furthermore, the reliability and selectivity of CuO/C-400 °C sensors were extensively explored in the presence of artificial serum/saliva samples with gradient glucose concentrations. The human blood samples were also detected with high recoveries compared with the clinical Hexokinase method. Hence, the prepared CuO/C-400 °C sensor with a broad detection range and high selectivity can be applied for the diabetes diagnosis ex vivo without further dilution for real-time analysis in practical applications.

Abstract The brain structure and language skills of children are understood to be in a phase of rapid development and are especially represented by key phonological‐semantic expressions that actively develop with age. In the present study, resting‐state functional magnetic resonance imaging data from 85 healthy children were retrospectively analyzed. Correlations of the phonological processing and speeded naming of specific brain regions of interest with age were assessed using the fractional amplitude of low‐frequency fluctuations (fALFF), degree centrality (DC), regional homogeneity (ReHo), and chain mediation effect analysis. Our results suggest that the developmental stages of children's posterior cingulate gyrus (PCC) and right inferior frontal gyrus (IFG) mediate language development in children. Additionally, the functional similarity of the bilateral IFG triangular part was noted during development as was the stronger activation and higher local and whole‐brain connectivity of the left IFG triangular part. Moreover, the PCC displayed stronger activation and higher local connectivity in the same period. Our data suggest that the development of the PCC and right IFG and the similarity of bilateral IFG function are important imaging markers of phonological processing and speeded naming in children and that the PCC and IFG show a more comprehensive development with age.

Abstract Primary liver cancer, with the predominant form as hepatocellular carcinoma (HCC), remains a worldwide health problem due to its aggressive and lethal nature. Transarterial chemoembolization, the first‐line treatment option of unresectable HCC that employs drug‐loaded embolic agents to occlude tumor‐feeding arteries and concomitantly delivers chemotherapeutic drugs into the tumor, is still under fierce debate in terms of the treatment parameters. The models that can produce in‐depth knowledge of the overall intratumoral drug release behavior are lacking. This study engineers a 3D tumor‐mimicking drug release model, which successfully overcomes the substantial limitations of conventional in vitro models through utilizing decellularized liver organ as a drug‐testing platform that uniquely incorporates three key features, i.e., complex vasculature systems, drug‐diffusible electronegative extracellular matrix, and controlled drug depletion. This drug release model combining with deep learning‐based computational analyses for the first time permits quantitative evaluation of all important parameters associated with locoregional drug release, including endovascular embolization distribution, intravascular drug retention, and extravascular drug diffusion, and establishes long‐term in vitro–in vivo correlations with in‐human results up to 80 d. This model offers a versatile platform incorporating both tumor‐specific drug diffusion and elimination settings for quantitative evaluation of spatiotemporal drug release kinetics within solid tumors.

Therapeutic agents can be effectively used for clinically treating cancer rely on critically enhancing their efficacy in the tumor microenvironment under hypoxic conditions. Sonodynamic therapy is a promising strategy for treating cancer that complies with all the requisites of penetrating the deep tissues without inflicting additional trauma. However, the hypoxic tumor microenvironment is not conducive to the sonodynamic therapeutic reagents. This study procured the N-doped TiO2/C nanocomposites by calcining the Ti-containing metal-organic frameworks. Further, the N-doped TiO2/C-PEG was successfully equipped with Tirapazamine (TPZ) with a high drug loading capacity and the sonodynamic effect was found to be better than the TiO2/C due to the smaller bandgap. Further, the N-doped TiO2/C-PEG was successfully equipped with Tirapazamine (TPZ) with a high drug-loading capacity. The resultant N-TiO2/C-PEG@TPZ nanoparticles were found to exhibit high blood circulation stability, responsive drug release, and outstanding efficiency in case of the sonodynamic therapy and chemotherapy. The N-TiO2/C-PEG was also significantly bio-compatible with no obvious toxicity and damage to the blood and major organs of the mice. These preclinical experiments, therefore, substantiated N-TiO2/C-PEG@TPZ to possess an excellent therapeutic effect. Hence, this work can provide a valuable multi-modal treatment method for overcoming the hypoxic microenvironment of the tumors.

Carbon-based nanomaterials, including carbon nanotubes, carbon nanospheres, and carbon nanofibers, are becoming a research hotspot due to their unique structure and good mechanical, thermal, electrical, optical, and chemical properties. With the development of material synthesis technology, they can be functionalized and used in various fields such as energy, environment, and biomedicine. In particular, stimuli-responsive carbon-based nanomaterials have stood out in recent years because of their smart behavior. Researchers have applied carbon-based nanomaterials to different disease treatments based on their stimulus-response properties. In this paper, based on stimuli-responsive carbon-based nanomaterials’ morphology, we categorize them into carbon nanotubes, carbon nanospheres, and carbon nanofibers according to their morphology. Then, their applications in probes, bioimaging, tumor therapy, and other fields are discussed. Finally, we address the advantages and disadvantages of carbon-based stimuli-responsive nanomaterials and discuss their future perspective.

The lighting environment has an important influence on the psychological and physical aspects of a person. On certain occasions, reasonable lighting design can regulate people's emotions and improve their feelings of comfort in a space. Besides, specific lighting can create a specific atmosphere according to space requirements. However, in the study of an individual's affective impressions, there is still some uncertainty about how colored lights affect an individual's moods and impressions toward visual objects. This research improves the understanding of the emotional impact of colored light in space. To better understand the lighting environment in the observation process, the project studied the effects of four groups of lights (green, blue, red, and yellow) on the participants' moods and impressions. Participants watched two sets of visual images under four different lighting conditions and provided feedback on their emotions and evaluations through the Multiple Mood States Scale, Two-Dimensional Mood Scale, and Semantic Differential Scale. The results show that different colors of light have a significant effect on mood, and red light can arouse emotional changes to calm, irritated, relaxed, nervous, stability, and pleasure. At the same time, different colors of light have a certain relevance to participants' impressions and this provides further research value for the design of the colored light environment in an individual's affective impressions. Therefore, this study discusses the feasibility of colored lights as a display method, which has potential application prospects for constructing different space atmospheres.

BackgroundWhite matter hyperintensities (WMHs) are a subtype of cerebral small vessel disease and can be divided into periventricular WMHs (pvWMHs) and deep WMHs (dWMHs). pvWMHs and dWMHs were proved to be determined by different etiologies. This study aimed to develop a 2D Cascade U-net (Cascade U) for the segmentation and differentiation of pvWMHs and dWMHs on 2D T2-FLAIR images.MethodsA total of 253 subjects were recruited in the present study. All subjects underwent 2D T2-FLAIR scan on a 3.0 Tesla MR scanner. Both contours of pvWMHs and dWMHs were manually delineated by the observers and considered as the gold standard. Fazekas scale was used to evaluate the burdens of pvWMHs and dWMHs, respectively. Cascade U consisted of a segmentation U-net and a differentiation U-net and was trained with a combined loss function. The performance of Cascade U was compared with two other U-net models (Pipeline U and Separate U). Dice similarity coefficient (DSC), Matthews correlation coefficient (MCC), precision, and recall were used to evaluate the performances of all models. The linear correlations between WMHs volume (WMHV) measured by all models and the gold standard were also conducted.ResultsCompared with other models, Cascade U exhibited a better performance on WMHs segmentation and pvWMHs identification. Cascade U achieved DSC values of 0.605 ± 0.135, 0.517 ± 0.263, and 0.510 ± 0.241 and MCC values of 0.617 ± 0.122, 0.526 ± 0.263, and 0.522 ± 0.243 on the segmentation of total WMHs, pvWMHs, and dWMHs, respectively. Cascade U exhibited strong correlations with the gold standard on measuring WMHV (R2 = 0.954, p < 0.001), pvWMHV (R2 = 0.933, p < 0.001), and dWMHV (R2 = 0.918, p < 0.001). A significant correlation was found on lesion volume between Cascade U and gold standard (r > 0.510, p < 0.001).ConclusionCascade U showed competitive results in segmentation and differentiation of pvWMHs and dWMHs on 2D T2-FLAIR images, indicating potential feasibility in precisely evaluating the burdens of WMHs.

The protective energy-saving retrofitting (PESR) of heritage buildings (HBs) is one of the essential aspects of building carbon-dioxide abatement. The present work validates and optimizes PESR measures for a typical rammed earth HB (REHB) based on multi-objective optimization and building information modelling (BIM). The addition of internal thermal insulation materials to the exterior wall and roof and the replacement of the exterior windows are investigated. Based on multiple factors (protection methods, U-value limit, and local materials), seven insulation materials and four window types are considered in 28 possible cases. The associated energy consumption, greenhouse gas emissions (GGE) and life cycle cost (LCC) are estimated and compared through comprehensive multi-objective (energy savings, low-carbon and economy) comparison methods. The results indicate that the case involving expanded polystyrene (EPS) internal insulation and 80-series polyurethane windows is optimal, with energy consumption and GGE that are 72% and 50% lower, respectively, than those of the original system and with the lowest cost, wherein 15406 kW h energy, 264 t GGE and 7857 Chinese yuan (CNY) are saved over the entire life. The GGE and LCC at the operation stage account for approximately 90% of the total, of which the associated coal heating accounts for 80%. The optimization method can facilitate the development of innovative energy-saving and low-carbon retrofit methods for HBs.

The relationship between geriatric depression and dementia has been widely debated, and the neurological mechanisms underlying subjective cognitive decline (SCD) associated with social relationships remain elusive. Subclinical geriatric depression (SGD) is common in patients with SCD, and close friends (CFs) have a great influence on a person’s social life. Studies have proven that communication or leisure activities with CFs can improve the cognitive performance of elderly. However, it remains unclear whether the engagement of specific brain regions mediates having CFs, SGD, and SCD. In this study, we aimed to assess the association between social relationships (that is, CFs), SGD, and SCD from the perspective of brain function. We examined the data of 66 patients with SCD and 63 normal controls (NC). Compared with NC, SGD was significantly inversely correlated with the number of CFs in the SCD group. We calculated regional homogeneity (ReHo) of functional magnetic resonance imaging (MRI) data of each subject. At a corrected threshold, the right occipital gyrus (SOG.R) and right fusiform gyrus (FFG.R) exhibited positive correlation with SGD in patients with SCD. Mediation analyses to query the inter-relationships between the neural markers and clinical variables exhibited a best fit of the model with CFs → FFG.R → SGD → SOG.R → SCD. These findings suggested a pathway whereby social relationships alter the function of specific brain regions, and SGD may be an early symptom of SCD. We observed that the FFG.R mediate social relationships and SGD, and the abnormality of the SOG.R may be a key factor in the SCD caused by depression. Moreover, a greater number of CFs may reduce the risk of developing SGD.

Objective: Subjective cognitive decline (SCD) is the first clinical manifestation of the Alzheimer's disease (AD) continuum. Hemodynamic response function (HRF) carries information related to brain pathology and function. The shape of the HRF can be described by three parameters: response height (RH), time-to-peak (TTP), and full-width at half-max (FWHM). We proposed and explored our two hypotheses. Hypothesis 1: HRF was pathologically related to SCD: compared with healthy controls (HC), patients with SCD show HRF aberrations. Hypothesis 2: HRF could be employed as a novel marker of brain imaging for the classification of SCD. Methods: We used resting-state functional magnetic resonance imaging (fMRI) data and performed deconvolution to investigate the HRF parameters in 54 individuals with SCD and 64 HC. Statistical two-sample t tests were performed to investigate between-group differences in HRF parameters. Finally, we used logistic regression to construct a binary classification of SCD and HC. Results: We found altered HRF parameters in the SCD group compared to HC. In the brain regions with altered HRF, we found that RH and FWHM decreased in the SCD group compared to HC, while TTP increased in the SCD group. From the binary logistic regression, we found that the classification accuracy of SCD and HC was 94.07%. Conclusion: The study demonstrated altered HRF parameters in patients with SCD, which could be used as a novel marker of brain function for the classification of SCD.

Although deep learning-based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training setting also has a non-negligible impact on prediction performance. The training setting includes various interdependent components, such as an objective function, a data sampling strategy, and a data augmentation approach. To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components. Extensive experiments are conducted on a publicly available dataset EyePACS. We demonstrate that (1) the DR grading framework is sensitive to input resolution, objective function, and composition of data augmentation; (2) using mean square error as the loss function can effectively improve the performance with respect to a task-specific evaluation metric, namely the quadratically weighted Kappa; (3) utilizing eye pairs boosts the performance of DR grading and; (4) using data resampling to address the problem of imbalanced data distribution in EyePACS hurts the performance. Based on these observations and an optimal combination of the investigated components, our framework, without any specialized network design, achieves a state-of-the-art result (0.8631 for Kappa) on the EyePACS test set (a total of 42,670 fundus images) with only image-level labels. We also examine the proposed training practices on other fundus datasets and other network architectures to evaluate their generalizability. Our codes and pre-trained model are available online.

Abstract Large deformation diffeomorphic metric mapping for curve (LDDMM‐curve) has been widely used in deformation based statistical shape analysis of the mid‐sagittal corpus callosum. A main limitation of LDDMM‐curve is that it is time‐consuming and computationally complex. In this study, down‐sampling strategies for accelerating LDDMM‐curve are investigated and tested on two large datasets, one on Alzheimer's disease (155 Alzheimer's disease, 325 mild cognitive impairment and 185 healthy controls) and the other on first‐episode schizophrenia (92 first‐episode schizophrenia and 106 healthy controls). For both datasets a variety of down‐sampling factors are tested in terms of registration accuracy, registration speed, and most importantly disease‐related patterns. Experimental results revealed that down‐sampling template curve by a factor of 2 can significantly reduce the running time of LDDMM‐curve without sacrificing the registration accuracy. Also, the disease‐induced patterns, or more specifically the group comparison results, were almost identical before and after down‐sampling. It is also shown that there was no need to down‐sample the target population curves but only the single template curve of the study of interest. Comprehensive analyses were conducted.

Abstract Working memory has been examined extensively using the N-back task. However, less is known about the neural bases underlying individual variation in the accuracy rate (AR) and reaction time (RT) as metrics of N-back performance. Whereas AR indexes the overall performance, RT may more specifically reflect the efficiency in updating target identify. Further, studies have associated fluid intelligence (Gf) with working memory, but the cerebral correlates shared between Gf and N-back performance remain unclear. We addressed these issues using the Human Connectome Project dataset. We quantified the differences in AR (critical success index or CSI) and RT between 2- and 0-backs (CSI2–0 and RT2–0) and identified the neural correlates of individual variation in CSI2–0, RT2–0, and Gf, as indexed by the number of correct items scored in the Raven’s Standard Progressive Matrices (RSPM) test. The results showed that CSI2–0 and RT2–0 were negatively correlated, suggesting that a prolonged response time did not facilitate accuracy. At voxel p

Amyloid-β oligomer (AβO) is believed to cause neurotoxicity which is linked to Alzheimer's disease (AD). Therefore, the detection of AβO has been proposed as an effective method for the early diagnosis of AD. In this work, several three-dimensional layers using gold nanoparticles (AuNPs) embedded in different conducting polymer matrix, including poly (thiophene-3-acetic acid), poly (pyrrole-2-carboxylic acid), and poly (pyrrole-3-carboxylic acid), were harnessed as transducers for the large surface area and high electrical conductivity. The cellular prion protein (PrPC) was utilized as the biorecognition element for the specific detection of AβO. The prepared electrochemical sensors were fabricated and compared afterward, in terms of sensitivity and detection range for the highly sensitive detection of AβO. In comparison, the PrPC/AuNPs embedded in Ppy-3-COOH matrix (AuNPs-E-Ppy-3-COOH matrix) was demonstrated to have higher sensitivity and wider detection range (10−9 to 103 nM). Subsequently, through the ex vivo real sample analysis within cerebrospinal fluid and blood test, it is proved that PrPC/AuNPs-E-Ppy-3-COOH matrix can be used for the early diagnosis of AD.

Liver and liver tumor segmentation provides vital biomarkers for surgical planning and hepatic diagnosis. In this paper, we propose and validate a novel level-set method integrating an enhanced edge indicator and an automatically derived initial curve for CT based liver tumor segmentation. At the preprocessing step, the CT image intensity values were truncated to lie in a fixed range to enhance the image contrast surrounding liver and liver tumor. To remove non-liver tissues for subsequent tumor segmentation, liver was firstly segmented using two convolutional neural networks in a coarse-to-fine manner. A 2D slice-based U-net was used to roughly localize the liver and a 3D patch-based fully convolutional network was used to refine the liver segmentation as well as to roughly localize the liver tumor. A novel level-set method was then presented to further refine the tumor segmentation. Specifically, the probabilistic distribution of the liver tumor was estimated using unsupervised fuzzy c-means clustering, which was then utilized to enhance the edge-detector used in level-set. Effectiveness of the proposed pipeline was validated on two publicly-available datasets. Experimental results identified the superior segmentation performance of the proposed pipeline over state-of-the-art methods.

With the continuous increase of carbon emissions and the deterioration of environment, governments have passed various laws and regulations to force enterprises to take carbon emission abatement measures. As one of the regulations, carbon tax plays an important and positive role in environmental protection. For enterprises, the allocation of carbon emission abatement is a common means to promote emission reduction. In this paper, under the uncertain market demand, the option contract is adopted to study the retailer's optimal retail price and order quantity, as well as the manufacturer's optimal ratio of total carbon emission abatement and production quantity under the carbon tax. In addition, We analyze the optimal decisions and expected profits of supply chain members with respect to the carbon tax, total carbon emission abatement and option prices by analytical and numerical study, and find that (1) when the carbon tax increases, the optimal ratio of total carbon emission abatement and the optimal retail price increase, while the optimal order and production quantity decrease, which is contrary to the situation of the total carbon emission abatement; (2) when the option prices increase, the optimal retail price increases while the ratio of total carbon emission abatement and the optimal order and production quantity decrease; (3) the expected profits of the manufacturer and retailer show the same trend with the increase of carbon tax and total carbon emission abatement, while have the opposite trend with the increase of option prices.

An all-inorganic Pb0.99Nb0.02(Zr0.85Sn0.13Ti0.02)0.98O3 (PNZST) antiferroelectric (AFE) thin film was designed to enhance its energy-storage performance by adjusting its orientation. Using a radio frequency (RF) magnetron sputtering technology, 450-nm-PNZST AFE films with (111), (110), and (100) crystal orientations were successfully prepared. All the films showed a dense microstructure and the highly preferred orientations were determined by the orientation of the bottom electrodes. Moreover, the preferred orientation of the AFE thin film had a great influence on the dielectric and energy-storage properties. Meanwhile, the energy storage density of the PNZST AFE thin film with the (100) orientation reached 33.7 J cm-3, which was 43 % higher than that of PNZST AFE thin film with a (111) orientation. All of these results shed light on how the energy-storage performance of PNZST AFE thin films can be enhanced and optimized by adjusting its orientation. This offers a new strategy and innovation, which opens up a route to practical applications in micro-energy-storage systems.

Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination, where infection segmentation is an essential step for assessment and quantification. However, due to the heterogeneity of X-ray imaging and the difficulty of annotating infected regions precisely, learning automated infection segmentation on CXRs remains a challenging task. We propose a novel approach, called DRR4Covid, to learn COVID-19 infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). DRR4Covid consists of an infection-aware DRR generator, a segmentation network, and a domain adaptation module. Given a labeled Computed Tomography scan, the infection-aware DRR generator can produce infection-aware DRRs with pixel-level annotations of infected regions for training the segmentation network. The domain adaptation module is designed to enable the segmentation network trained on DRRs to generalize to CXRs. The statistical analyses made on experiment results have indicated that our infection-aware DRRs are significantly better than standard DRRs in learning COVID-19 infection segmentation (p

The rapid development of medical micromotors draws a beautiful blueprint for the noninvasive or minimally invasive diagnosis and therapy. By combining stimuli-sensitive hydrogel materials, micromotors are bestowed with new characteristics such as stimuli-responsive shape transformation/morphing, excellent biocompatibility and biodegradability, and drug loading ability. Actuated by chemical fuels or external fields (e.g., magnetic field, ultrasound, light, and electric field), hydrogel-based stimuli-responsive (HBSR) micromotors can be utilized to load therapeutic agents into the hydrogel networks or directly grip the target cargos (e.g., drug-loaded particles, cells, and thrombus), transport them to sites of interest (e.g., tumor area and diseased tissues), and unload the cargos or execute a specific task (e.g., cell capture, targeted sampling, and removal of blood clots) in response to a stimulus (e.g., change of temperature, pH, ion strength, and chemicals) in the physiological environment. The high flexibility, adaptive capacity, and shape morphing property enable the HBSR micromotors to complete specific medical tasks in complex physiological scenarios, especially in confined, hard-to-reach tissues, and vessels of the body. Herein, this review summarizes the current progress in hydrogel-based medical micromotors with stimuli responsiveness. The thermo-responsive, photothermal-responsive, magnetocaloric-responsive, pH-responsive, ionic-strength-responsive, and chemoresponsive micromotors are discussed in detail. Finally, current challenges and future perspectives for the development of HBSR micromotors in the biomedical field are discussed.

Nano-sized particles functionalised with short single-stranded (ss)DNAs can act as detectors of complementary DNA strands. Here we consider tri-block-copolymer-based, self-assembling DNA-coated nanoparticles. The copolymers are chemically linked to the DNA strands via azide (N3) groups. The micelles aggregate when they are linked with complementary ssDNA. The advantage of such block-copolymer-based systems is that they are easy to make. Here we show that DNA functionalisation results in inter-micellar attraction, but that N3-groups that have not reacted with the DNA detector strands also change the phase behaviour of the tri-block polymer solution. We studied the triblock copolymer, Pluronic® F108, which forms spherical micelles in aqueous solutions upon heating. We find that the triblock chains ending with either an N3 or N3-DNA complex show a dramatic change in phase behaviour. In particular, the N3-functionalisation causes the chain ends to cluster below the critical micelle temperature (CMT) of pure F108, forming flower-micelles with the N3-groups at the core, while the PPO groups are exposed to the solvent. Above the CMT, we see an inversion with the PPO chains forming the micellar core, while the N3-groups are now aggregating on the periphery, inducing an attraction between the micelles. Our results demonstrate that, due to the two competing self-assembling mechanisms, the system can form transient hydrogels.

Optical coherence tomography (OCT) has considerable application potential in noninvasive diagnosis and disease monitoring. Skin diseases, such as basal cell carcinoma (BCC), are destructive; hence, quantitative segmentation of the skin is very important for early diagnosis and treatment. Deep neural networks have been widely used in the boundary recognition and segmentation of diseased areas in medical images. Research on OCT skin segmentation and laser-induced skin damage segmentation based on deep neural networks is still in its infancy. Here, a segmentation and quantitative analysis pipeline of laser skin injury and skin stratification based on a deep neural network model is proposed. Based on the stratification of mouse skins, a laser injury model of mouse skins induced by lasers was constructed, and the multilayer structure and injury areas were accurately segmented by using a deep neural network method. First, the intact area of mouse skin and the damaged areas of different laser radiation doses are collected by the OCT system, and then the labels are manually labeled by experienced histologists. A variety of deep neural network models are used to realize the segmentation of skin layers and damaged areas on the skin dataset. In particular, the U-Net model based on a dual attention mechanism is used to realize the segmentation of the laser-damage structure, and the results are compared and analyzed. The segmentation results showed that the Dice coefficient of the mouse dermis layer and injury area reached more than 0.90, and the Dice coefficient of the fat layer and muscle layer reached more than 0.80. In the evaluation results, the average surface distance (ASSD) and Hausdorff distance (HD) indicated that the segmentation results are excellent, with a high overlap rate with the manually labeled area and a short edge distance. The results of this study have important application value for the quantitative analysis of laser-induced skin injury and the exploration of laser biological effects and have potential application value for the early noninvasive detection of diseases and the monitoring of postoperative recovery in the future.

Optical coherence tomography (OCT) is widely used in biomedical imaging. However, noise severely affects diagnosing and identifying diseased tissues on OCT images. Here, a noise reduction method based on the external patch prior guided internal clustering and morphological analysis (E2PGICMA) is developed to remove the noise of OCT images. The external patch prior guided internal clustering algorithm is used to reduce speckle noise. The morphological analysis algorithm is employed to the background for contrast enhancement. OCT images of in vivo normal skin tissues were analyzed to remove noise using the proposed method. The estimated standard deviations of the noise were chosen as different values for evaluating the quantitative metrics. The visual quality improvement includes more textures and fine detail preservation. The denoising effects of different methods were compared. Then, quantitative and qualitative evaluations of this proposed method were conducted. The results demonstrated that the SNR, PSNR, and XCOR were higher than those of the other noise-reduction methods, reaching 15.05 dB, 27.48 dB, and 0.9959, respectively. Furthermore, the presented method’s noise reduction ratio (NRR) reached 0.8999. This proposed method can efficiently remove the background and speckle noise. Improving the proposed noise reduction method would outperform existing state-of-the-art OCT despeckling methods.

Carbon ion radiation therapy (CIRT) is the most advanced radiation therapy (RT) available and offers new opportunities to improve cancer treatment and research. CIRT has a unique physical and biological advantage that allow them to kill tumor cells more accurately and intensively. So far, CIRT has been used in almost all types of malignant tumors, and showed good feasibility, safety and acceptable toxicity, indicating that CIRT has a wide range of development and application prospects. In addition, in order to improve the biological effect of CIRT, scientists are also trying to investigate related sensitizing agents to enhance the killing ability of tumor cells, which has attracted extensive attention. In this review, we tried to systematically review the rationale, advantages and problems, the clinical applications and the sensitizing agents of the CIRT. At the same time, the prospects of the CIRT in were prospected. We hope that this review will help researchers interested in CIRT, sensitizing agents, and radiotherapy to understand their magic more systematically and faster, and provide data reference and support for bioanalysis, clinical medicine, radiotherapy, heavy ion therapy, and nanoparticle diagnostics.

Background: Previous first-episode schizophrenia (FES) studies have reported abnormalities in the volume and mid-sagittal size of the corpus callosum (CC), but findings have been inconsistent. Besides, the CC shape has rarely been analyzed in FES. Therefore, in this study, we investigated FES-related CC shape abnormalities using 198 participants [92 FES patients and 106 healthy controls (HCs)].Methods: We conducted statistical shape analysis of the mid-sagittal CC curve in a large deformation diffeomorphic metric mapping framework. The CC was divided into the genu, body, and splenium (gCC, bCC, and sCC) to target the key CC sub-regions affected by the FES pathology. Gender effects have been investigated.Results: There were significant area differences between FES and HC in the entire CC and gCC but not in bCC nor sCC. In terms of the localized shape morphometrics, significant region-specific shape inward-deformations were detected in the superior portion of gCC and the anterosuperior portion of bCC in FES. These global area and local shape morphometric abnormalities were restricted to female FES but not male FES.Conclusions: gCC was significantly affected in the neuropathology of FES and this finding was specific to female FES. This study suggests that gCC may be a key sub-region that is vulnerable to the neuropathology of FES, specifically in female patients. The morphometrics of gCC may serve as novel and efficient biomarkers for screening female FES patients.

Abstract Background Aged people all over the world are prone to fall down accidentally and be injured with fracture, such as the rib fracture. To protect the elderly, the safety vest has been developed to protect them from being injured when falling down. To effectively protect the elderly, more analysis on the protective capacity of a safety vest under different situation are needed. Results Herein, a finite element model based on the computed tomography CT scanning data of a Chinese old female was built, and then used to simulate the process of falling down at different velocities. Analysis and comparison were done on the maximum shear stress, kinetic energy curves and internal energy curves with and without safety vest. The maximum shear stress indicated that the Abbreviated Injury Scale (AIS) 2+ injury risks of rib were 8%, 100% and 100% at the velocities of 1.5 m/s, 2.0 m/s and 2.5 m/s, respectively. The corresponding risks were lowered to 0%, 0% and 60% by the vest, respectively. Furthermore, the vest could absorb the internal energy resulted by the deformation of the thoracic osseous tissue by about 20%, thus decreasing the shear stress and the injury risk. Conclusion It is concluded that the safety vest decreases the injury risk when the elderly fall down, thus protects them from being injured.

White matter hyperintensity (WMH) is associated with various aging and neurodegenerative diseases. In this paper, we proposed and validated a fully automatic system which integrates classical image processing and deep neural network for segmenting WHM from fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) images. In this system, a novel skip connection U-net (SC U-net) was proposed. In addition, an atlas-based method was introduced in the preprocessing stage to remove non-brain tissues (namely skull-stripping) and thus to improve the segmentation accuracy. Effectiveness of the proposed system was validated on a dataset of 60 paired images based on cross-scanner validation. Our experimental results revealed the effectiveness of the skull-stripping strategy. More importantly, compared to two existing state-of-the-art methods for segmenting WHM, including a U-net-like method and another deep learning method, the proposed SC U-net had a faster convergence, a lower loss and a higher segmentation accuracy. Both quantitative and qualitative analyses (via visual examinations) revealed the superior performance of our proposed SC U-net. The mean dice score of the proposed SC U-net was 78.36% which was much higher than those of a U-net-like method (74.99%) and an alternative deep learning method (74.80%). The software environment and model of the proposed system were made publicly accessible at Dockerhub.

The use of molecular imaging technologies for brain imaging can not only play an important supporting role in disease diagnosis and treatment but can also be used to deeply study brain functions. Recently, with the support of reporter gene technology, optical imaging has achieved a breakthrough in brain function studies at the molecular level. Reporter gene technology based on traditional clinical imaging modalities is also expanding. By benefiting from the deeper imaging depths and wider imaging ranges now possible, these methods have led to breakthroughs in preclinical and clinical research. This article focuses on the applications of magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) reporter gene technologies for use in brain imaging. The tracking of cell therapies and gene therapies is the most successful and widely used application of these techniques. Meanwhile, breakthroughs have been achieved in the research and development of reporter genes and their imaging probe pairs with respect to brain function research. This paper introduces the imaging principles and classifications of the reporter gene technologies of these imaging modalities, lists the relevant brain imaging applications, reviews their characteristics, and discusses the opportunities and challenges faced by clinical imaging modalities based on reporter gene technology. The conclusion is provided in the last section.

Neuroimaging studies have associated brain changes in children with future reading and language skills, but few studies have investigated the association between language skills and white matter structure in preschool-aged children. Using 208 data sets acquired in 73 healthy children aged 2–7 years, we investigated the relationship between developmental brain microstructure and phonological processing ability as measured using their phonological processing raw score (PPRS). The correlation analysis showed that across the whole age group, with increasing age, PPRS increased, fractional anisotropy (FA) of the internal capsule and inferior fronto-occipital fasciculus and some other regions increased, and mean diffusivity (MD) of the corpus callosum and internal capsule and some other regions decreased. The results of the mediation analysis suggest that increased FA may be the basis of phonological processing ability development during this period, and the increased number of fiber connections between the right inferior parietal lobule and right supramarginal gyrus may be a key imaging feature of phonological processing ability development. Our study reflects the changes in brain microstructure and contributes to understanding the underlying neural mechanisms of language development in preschool children.

As the major nutrient affecting crop growth, accurate assessing of nitrogen (N) is crucial to precise agricultural management. Although improvements based on ground and satellite data nitrogen in monitoring crops have been made, the application of these technologies is limited by expensive costs, covering small spatial scales and low spatiotemporal resolution. This study strived to explore an effective approach for inversing and mapping the distributions of the canopy nitrogen concentration (CNC) based on Unmanned Aerial Vehicle (UAV) hyperspectral image data in a typical apple orchard area of China. A Cubert UHD185 imaging spectrometer mounted on a UAV was used to obtain the hyperspectral images of the apple canopy. The range of the apple canopy was determined by the threshold method to eliminate the effect of the background spectrum from bare soil and shadow. We analyzed and screened out the spectral parameters sensitive to CNC, including vegetation indices (VIs), random two-band spectral indices, and red-edge parameters. The partial least squares regression (PLSR) and backpropagation neural network (BPNN) were constructed to inverse CNC based on a single spectral parameter or a combination of multiple spectral parameters. The results show that when the thresholds of normalized difference vegetation index (NDVI) and normalized difference canopy shadow index (NDCSI) were set to 0.65 and 0.45, respectively, the canopy’s CNC range could be effectively identified and extracted, which was more refined than random forest classifier (RFC); the correlation between random two-band spectral indices and nitrogen concentration was stronger than that of other spectral parameters; and the BPNN model based on the combination of random two-band spectral indices and red-edge parameters was the optimal model for accurately retrieving CNC. Its modeling determination coefficient (R2) and root mean square error (RMSE) were 0.77 and 0.16, respectively; and the validation R2 and residual predictive deviation (RPD) were 0.75 and 1.92. The findings of this study can provide a theoretical basis and technical support for the large-scale, rapid, and non-destructive monitoring of apple nutritional status.

How to identify the key nodes effectively in urban traffic networks to achieve the equitable resource allocation face to the complex traffic network? This issue needs to be solved in current traffic management. This study considered the urban traffic network topology and network traffic status, put forward an improved model based on the economics of the input-output method by introducing a virtual node to the selected network set up with the flow of urban traffic network, sensor nodes by Leontief inverse matrix calculation coefficient to determine node importance, according to the node importance to deliberate attack traffic network to analyze its robustness, to test the accuracy and practicability of the method. The results show that this improved method adopted to measure the importance of traffic nodes from the global scope has the advantages of fast calculation and simple process and provides a more reliable basis for rational allocation of transport resources.

Due to the continuous improvement of productivity, the transportation demand of freight volume is also increasing. It is difficult to organize freight transportation efficiently when the freight volume is quite large. Therefore, predicting the total amount of goods transported is essential in order to ensure efficient and orderly transportation. Aiming at optimizing the forecast of freight volume, this paper predicts the freight volume in Xi’an based on the Gray GM (1, 1) model and Markov forecasting model. Firstly, the Gray GM (1, 1) model is established based on related freight volume data of Xi’an from 2000 to 2008. Then, the corresponding time sequence and expression of restore value of Xi’an freight volume can be attained by determining parameters, so as to obtain the gray forecast values of Xi’an’s freight volume from 2009 to 2013. In combination with the Markov chain process, the random sequence state is divided into three categories. By determining the state transition probability matrix, the probability value of the sequence in each state and the predicted median value corresponding to each state can be obtained. Finally, the revised predicted values of the freight volume based on the Gray–Markov forecasting model in Xi’an from 2009 to 2013 are calculated. It is proved in theory and practice that the Gray–Markov forecasting model has high accuracy and can provide relevant policy bases for the traffic management department of Xi’an.

Environmental regulation policies are being continuously enriched today. To effectively improve green innovation efficiency through environmental regulations, it is urgent to better understand the impact of different environmental regulations on green innovation efficiency (GIE). However, due to the defects of previous methods for measuring GIE, existing studies may have deviations when analysing the effect of environmental regulations on GIE. To fill this gap, using Shaanxi, China, as a case study, the present study proposes a network data envelopment analysis (DEA) model based on neutral cross-efficiency evaluation to accurately measure the GIE of Shaanxi during the period of 2001–2017. On this basis, this study further analysed the impact of different types of environmental regulations on GIE from three aspects: causality, evolutionary relationships, and effect paths. The results indicate that (1) the GIE of Shaanxi Province showed a “fluctuation-slow growth-steady growth” trend during 2001–2017, and after 2014, the problem of an uncoordinated relationship between technology research and design (R&D) and technology transformation began to appear; (2) there was a linear evolutionary relationship between command-and-control environmental regulation and GIE and a “U”-shaped evolutionary relationship between market-based/voluntary environmental regulation and GIE; and (3) command-and-control environmental regulation and voluntary environmental regulation affected GIE mainly at the technology R&D stage, while market-based environmental regulation ran through the entire process of green innovation activities. This study improves the evaluation methods and theoretical systems of GIE and provides the scientific basis for government decision-makers to formulate environmental regulation policies.

Studies have identified cerebral morphometric markers of binge drinking and implicated cortical regions in support of self-efficacy and stress regulation. However, it remains unclear how cortical structures of self-control play a role in ameliorating stress and alcohol consumption or how chronic alcohol exposure alters self-control and leads to emotional distress. We examined the data of 180 binge (131 men) and 256 non-binge (83 men) drinkers from the Human Connectome Project. We obtained data on regional cortical thickness from the HCP and derived gray matter volumes (GMVs) with voxel-based morphometry. At a corrected threshold, binge relative to non-binge drinking men showed diminished posterior cingulate cortex (PCC) thickness and dorsomedial prefrontal cortex (dmPFC) GMV. PCC thickness and dmPFC GMVs were positively and negatively correlated with self-efficacy and perceived stress, respectively, as assessed with the NIH Emotion Toolbox. Mediation and path analyses to query the inter-relationships between the neural markers and clinical variables showed a best fit of the model with daily drinks → lower PCC thickness and dmPFC GMV → lower self-efficacy → higher perceived stress in men. In contrast, binge and non-binge drinking women did not show significant differences in regional cortical thickness or GMVs. These findings suggest a pathway whereby chronic alcohol consumption alters cortical structures and self-efficacy mediates the effects of cortical structural deficits on perceived stress in men. The findings also suggest the need to investigate multimodal neural markers underlying the interplay between stress, self-control and alcohol use behavior in women.

Studies have examined sex differences in emotion processing in health and illness. However, it remains unclear how these neural processes may relate to individual differences in affective traits. We addressed this issue with a dataset of 970 subjects (508 women) curated from the Human Connectome Project. Participants were assessed with the NIH Toolbox Emotion Measures and fMRI while identifying negative facial emotion and neutral shape targets in alternating blocks. Imaging data were analyzed with published routines and the results were reported at a corrected threshold. Men scored similarly in Anger- but lower in Fear-Affect, as compared to women. Men as compared with women engaged the occipital-temporal visual cortex, retrosplenial cortex (RSC), and both anterior and posterior cingulate cortex to a greater extent during face versus shape identification. Women relative to men engaged higher activation of bilateral middle frontal cortex. In regional brain responses to face versus shape identification, men relative to women showed more significant modulations by both Anger- and Fear- Affect traits. The left RSC and right RSC/precuneus each demonstrated activities during face vs. shape identification in negative correlation with Anger- and Fear- Affect scores in men only. Anger affect was positively correlated with prolonged RT in identifying face vs. shape target in men but not women. In contrast, women relative to men showed higher Fear-Affect score and higher activation in the right middle frontal cortex, which was more strongly correlated with prolonged RT during face vs. shape identification. Together, men and women with higher Fear-Affect demonstrated lower accuracy in identifying negative facial emotion versus neutral shape target, a relationship mediated by activity of the RSC. These findings add to the literature of sex and trait individual differences in emotion processing and may help research of sex-shared and sex-specific behavioral and neural markers of emotional disorders.

The growing need to understand the molecular mechanisms of diseases has prompted the revolution in molecular imaging techniques along with nanomedicine development. Conventional optical coherence tomography (OCT) is a low-cost in vivo imaging modality that provides unique high spatial and temporal resolution anatomic images but little molecular information. However, given the widespread adoption of OCT in research and clinical practice, its robust molecular imaging extensions are strongly desired to combine with anatomical images. A range of relevant approaches has been reported already. In this article, we review the recent advances of molecular contrast OCT imaging techniques, the corresponding contrast agents, especially the nanoparticle-based ones, and their applications. We also summarize the properties, design criteria, merit, and demerit of those contrast agents. In the end, the prospects and challenges for further research and development in this field are outlined.

In recent decades, as a subclass of biomaterials, biologically sensitive nanoparticles have attracted increased scientific interest. Many of the demands for physiologically responsive nanomaterials in applications involving the human body cannot be met by conventional technologies. Due to the field’s importance, considerable effort has been expended, and biologically responsive nanomaterials have achieved remarkable success thus far. This review summarizes the recent advancements in biologically responsive nanomaterials and their applications in biosensing and molecular imaging. The nanomaterials change their structure or increase the chemical reaction ratio in response to specific bio-relevant stimuli (such as pH, redox potentials, enzyme kinds, and concentrations) in order to improve the signal for biologically responsive diagnosis. We use various case studies to illustrate the existing issues and provide a clear sense of direction in this area. Furthermore, the limitations and prospects of these nanomaterials for diagnosis are also discussed.

Social interaction is critical to emotional well-being. Previous studies have suggested sex differences in the perception of social interaction. However, the findings depend on the nature of interactions and whether it involves facial emotions. Here, we explored sex differences in neural responses to the perception of social interaction using the Human Connectome Project data. Participants (n = 969, 505 women) were engaged in a social cognition task with geometric objects moving and colliding to simulate social interaction. Behaviorally, men relative to women demonstrated higher accuracy in perceiving social vs. random interactions. Men vs. women showed higher activation in the right superior temporal gyrus, bilateral occipital and posterior cingulate cortex and precuneus, and women vs. men showed higher activation in the right inferior frontal cortex, during exposure to social vs. random interactions. In whole-brain regressions, the differences in accuracy rate in identifying social vs. random interactions (ARSOC – ARRAN) were associated with higher activation in the paracentral lobule (PCL) and lower activation in bilateral anterior insula (AI), pre-supplementary motor area (preSMA), and left middle frontal gyrus (MFG) in men and women combined, lower activation in bilateral AI, preSMA and left MFG in men alone, and higher activation in the PCL and the medial orbitofrontal cortex in women alone. The latter sex differences were confirmed by slope tests. Further, the PCL activity mediated the correlation between an internalizing syndromal score, as assessed by the Achenbach Self-Report, and (ARSOC – ARRAN) across all subjects. These findings highlighted sex differences in the behavioral and neural processes underlying the perception of social interaction, as well as the influence of internalizing traits on these processes.

Schizophrenia is a type of neurodevelopmental psychiatric disorder. However, to date, scientists have not discovered the etiology and effective treatment of this condition. We injected the early growth response gene (EGR3) into the bilateral hippocampus to build a schizophrenia rat model. Behavioral phenotyping and resting-state functional magnetic resonance imaging (rs-fMRI) were used to analyze the behavioral and cerebral alterations in the schizophrenia rat model. The efficacy of risperidone therapy was also evaluated. We divided 34 rats into four groups: schizophrenia model group (E group), sham-operation group (FE group), healthy control group (H group), and risperidone therapy group (T group). Open field test and Morris water maze were conducted as behavioral experiments. Next, we performed rs-fMRI after four weeks of EGR3 transfection and risperidone treatment and analyzed imaging data using regional homogeneity (ReHo), the amplitude of low-frequency fluctuations (ALFF), and functional connectivity (FC). We examined the difference in behavioral and neural activation among the four groups and considered the correlations between behavior and imaging results. EGR3 gene transfection decreased the total moved distance in the open field test and the duration in the Q5 zone of the Morris water maze. Risperidone treatment reversed the trend and improved the performance of rats in these behavioral tests. Schizophrenia induced several neural alterations in ALFF and ReHo metrics of the rat brain, and risperidone could partly reverse these alterations. The results suggest that similar research is required for schizophrenia and that risperidone may be a novel treatment for dysregulated neural activation in schizophrenia.

Objectives: Vitamin B6 deficiency is associated with a wide spectrum of clinical syndromes. While vitamin B6 status is primarily assessed by measuring the biologically active form of the vitamin, pyridoxal 5-phosphate (PLP), concurrent measurement of the final metabolite 4-pyridoxic acid (PA) can provide additional information regarding supplement intake and hypophosphatasia. The aim of this study is to develop a simple method traceable to the NIST standard reference material 3950 for simultaneous detection of PLP and PA. Design & methods: A one-step reverse phase HPLC method with fluorescence detection was developed by evaluating different derivatization conditions, the use of an internal standard and different calibration strategies. The assay analytical performance was evaluated. Results: Pre-column derivatization with semicarbazide showed the best overall performance in terms of signal to noise ratio, retention time and peak shape when compared to pre- or post-column derivatization with chlorite, pre-column or in-mobile phase derivatization using sodium bisulfite. The final method provided an analytical measurement range from 7.8 to 350 ​nmol/L for PLP and 3.3–339 ​nmol/L for PA, total imprecision