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Recent studies have increasingly utilized gradient metrics to investigate the spatial transitions of brain organization, enabling the conversion of macroscale brain features into low-dimensional manifold representations. However, it remains unclear whether alterations exist in the cortical morphometric similarity (MS) network gradient in patients with schizophrenia (SCZ). This study aims to examine potential differences in the principal MS gradient between individuals with SCZ and healthy controls and to explore how these differences relate to transcriptional profiles and clinical phenomenology.
Methods
MS network was constructed in this study, and its gradient of the network was computed in 203 patients with SCZ and 201 healthy controls, who shared the same demographics in terms of age and gender. To examine irregularities in the MS network gradient, between-group comparisons were carried out, and partial least squares regression analysis was used to study the relationships between the MS network gradient-based variations in SCZ, and gene expression patterns and clinical phenotype.
Results
In contrast to healthy controls, the principal MS gradient of patients with SCZ was primarily significantly lower in sensorimotor areas, and higher in more areas. In addition, the aberrant gradient pattern was spatially linked with the genes enriched for neurobiologically significant pathways and preferential expression in various brain regions and cortical layers. Furthermore, there were strong positive connections between the principal MS gradient and the symptomatologic score in SCZ.
Conclusions
These findings showed changes in the principal MS network gradient in SCZ and offered potential molecular explanations for the structural changes underpinning SCZ.
Spin coating is the process of generating a uniform coating film on a substrate by centrifugal forces during rotation. In the framework of lubrication theory, we investigate the axisymmetric film evolution and contact-line dynamics in spin coating on a partially wetting substrate. The contact-line singularity is regularized by imposing a Navier slip model. The interface morphology and the contact-line movement are obtained by numerical solution and asymptotic analysis of the lubrication equation. The results show that the evolution of the liquid film can be classified into two modes, depending on the rotational speed. At lower speeds, the film eventually reaches an equilibrium state, and we provide a theoretical description of how the equilibrium state can be approached through matched asymptotic expansions. At higher speeds, the film exhibits two or three distinct regions: a uniform thinning film in the central region, an annular ridge near the contact line, and a possible Landau–Levich–Derjaguin-type (LLD-type) film in between that has not been reported previously. In particular, the LLD-type film occurs only at speeds slightly higher than the critical value for the existence of the equilibrium state, and leads to the decoupling of the uniform film and the ridge. It is found that the evolution of the ridge can be well described by a two-dimensional quasi-steady analysis. As a result, the ridge volume approaches a constant and cannot be neglected to predict the variation of the contact-line radius. The long-time behaviours of the film thickness and the contact radius agree with derived asymptotic solutions.
Post-traumatic stress disorder (PTSD) is a mental health condition caused by the dysregulation or overgeneralization of memories related to traumatic events. Investigating the interplay between explicit narrative and implicit emotional memory contributes to a better understanding of the mechanisms underlying PTSD.
Methods
This case–control study focused on two groups: unmedicated patients with PTSD and a trauma-exposed control (TEC) group who did not develop PTSD. Experiments included real-time measurements of blood oxygenation changes using functional near-infrared spectroscopy during trauma narration and processing of emotional and linguistic data through natural language processing (NLP).
Results
Real-time fNIRS monitoring showed that PTSD patients (mean [SD] Oxy-Hb activation, 0.153 [0.084], 95% CI 0.124 to 0.182) had significantly higher brain activity in the left anterior medial prefrontal cortex (L-amPFC) within 10 s after expressing negative emotional words compared with the control group (0.047 [0.026], 95% CI 0.038 to 0.056; p < 0.001). In the control group, there was a significant time-series correlation between the use of negative emotional memory words and activation of the L-amPFC (latency 3.82 s, slope = 0.0067, peak value = 0.184, difference = 0.273; Spearman’s r = 0.727, p < 0.001). In contrast, the left anterior cingulate prefrontal cortex of PTSD patients remained in a state of high activation (peak value = 0.153, difference = 0.084) with no apparent latency period.
Conclusions
PTSD patients display overactivity in pathways associated with rapid emotional responses and diminished regulation in cognitive processing areas. Interventions targeting these pathways may alleviate symptoms of PTSD.
Motion primitives play an important role in motion planning for autonomous vehicles, as they effectively address the sampling challenges inherent in nonholonomic motion planning. Employing motion primitives (MPs) is a widely accepted approach in nonholonomic motion planning based on sampling. This study specifically addresses the problem of learning from human-driving data to create human-like trajectories from predefined start-to-end states, which then serve as MP within the sampling-based nonholonomic motion planning framework. In this paper, we propose a deep learning-based method for generating MP that capture human-driving trajectory data features. By processing human-driving trajectory data, we create a Motion Primitive dataset that uniformly covers typical urban driving scenarios. Based on this dataset, a vehicle model long short-term memory neural network model is constructed to learn the features of the human-driving trajectory data. Finally, a framework for the generation of MP for practical applications is given based on this neural network. Our experiments, which focus on the dataset, the MMP generation network, and the generation process, demonstrate that our method significantly improves the training efficacy of the MP generation network. Additionally, the MP generated by our method exhibit higher accuracy compared to traditional methods.
In laser systems requiring a flat-top distribution of beam intensity, beam smoothing is a critical technology for enhancing laser energy deposition onto the focal spot. The continuous phase modulator (CPM) is a key component in beam smoothing, as it introduces high-frequency continuous phase modulation across the laser beam profile. However, the presence of the CPM makes it challenging to measure and correct the wavefront aberration of the input laser beam effectively, leading to unwanted beam intensity distribution and bringing difficulty to the design of the CPM. To address this issue, we propose a deep learning enabled robust wavefront sensing (DLWS) method to achieve effective wavefront measurement and active aberration correction, thereby facilitating active beam smoothing using the CPM. The experimental results show that the average wavefront reconstruction error of the DLWS method is 0.04 μm in the root mean square, while the Shack–Hartmann wavefront sensor reconstruction error is 0.17 μm.
Laser-driven inertial confinement fusion (ICF) diagnostics play a crucial role in understanding the complex physical processes governing ICF and enabling ignition. During the ICF process, the interaction between the high-power laser and ablation material leads to the formation of a plasma critical surface, which reflects a significant portion of the driving laser, reducing the efficiency of laser energy conversion into implosive kinetic energy. Effective diagnostic methods for the critical surface remain elusive. In this work, we propose a novel optical diagnostic approach to investigate the plasma critical surface. This method has been experimentally validated, providing new insights into the critical surface morphology and dynamics. This advancement represents a significant step forward in ICF diagnostic capabilities, with the potential to inform strategies for enhancing the uniformity of the driving laser and target surface, ultimately improving the efficiency of converting laser energy into implosion kinetic energy and enabling ignition.
Significant differences in life-history traits between the southern population (S) and northern (N) population of the cabbage beetle Colaphellus bowringi make it an excellent model for studying inheritance in this insect. In the present study, we observed the life-history traits of pure strains, F1, reciprocal backcross and reciprocal F2 progeny under a photoperiod of L:D 15:9 h at 22 °C. The S population had shorter larval development time, longer pupal time, higher body weight, growth rate and weight loss compared with the N population. In the F1 testing, the larval development time and body weight in hybrid populations were intermediate between the parents, and the paternal parents played a greater role in determining the larval development time, while the maternal parents exhibited a greater role in determining the body weight. The pupal time of hybrid populations was significantly shorter than that of the parents. In the reciprocal backcross testing, both father and grandfather affected the larval development time, while both mother and grandmother affected the body weight. Consistently, in the reciprocal F2 cross testing, the grandfather was more influential in determining the larval development time, and grandmother was more important in determining the body weight. In all tested populations, females had greater body weight, higher growth rate and weight loss than males. Hybridization pattern did not affect sex dimorphism and sex ratio. Overall, these findings suggest that different pathways (maternal or paternal effects) were involved in the inheritance of various life-history traits in C. bowringi.
Most studies on the impact of the COVID-19 pandemic on depression burden focused on the earlier pandemic phase specific to lockdowns, but the longer-term impact of the pandemic is less well studied. In this population-based cohort study with quasi-experimental design, we examined both the short-term and long-term impacts of COVID-19 on depression incidence and healthcare service use among patients with depression.
Methods
Using the territory-wide electronic medical records in Hong Kong, we identified patients with new diagnoses of depression from 2014 to 2022. An interrupted time-series (ITS) analysis examined changes in incidence of depression before and during the pandemic. We then divided patients into nine cohorts based on year of incidence and studied their initial and ongoing service use until December 2022. Generalized linear modeling compared the rates of healthcare service use in the year of diagnosis between patients newly diagnosed before and during the pandemic. A separate ITS analysis explored the pandemic impact on the ongoing service use among preexisting patients.
Results
There was an immediate increase in depression incidence (RR=1.21; 95% CI: 1.10, 1.33; p<0.001) in the population since the pandemic with a nonsignificant slope change, suggesting a sustained effect until the end of 2022. Subgroup analysis showed that increases in incidence were significant among adults and the older population, but not adolescents. Depression patients newly diagnosed during the pandemic used 11 percent fewer resources than the prepandemic patients in the first diagnosis year. Preexisting depression patients also had an immediate decrease of 16 percent in overall all-cause service use since the pandemic, with a positive slope change indicating a gradual rebound.
Conclusions
During the COVID-19 pandemic, service provision for depression was suboptimal in the face of greater demand generated by the increasing depression incidence. Our findings indicate the need to improve mental health resource planning preparedness for future public health crises.
We developed a real-world evidence (RWE) based Markov model to project the 10-year cost of care for patients with depression from the public payer’s perspective to inform early policy and resource planning in Hong Kong.
Methods
The model considered treatment-resistant depression (TRD) and development of comorbidities along the disease course. The outcomes included costs for all-cause and psychiatric care. From our territory-wide electronic medical records, we identified 25,190 patients with newly diagnosed depression during the period from 2014 to 2016, with follow-up until December 2020 for real-world time-to-event patterns. Costs and time varying transition inputs were derived using negative binomial and parametric survival modeling. The model is available as a closed cohort, which studies a fixed cohort of incident patients, or an open cohort that introduces new patients every year. Utilities values and the number of incident cases per year were derived from published sources.
Results
There were 9,217 new patients with depression in 2023. Our closed cohort model projected that the cumulative cost of all-cause and psychiatric care for these patients would reach USD309 million and USD58 million by 2032, respectively. In our open cohort model, 55,849 to 57,896 active prevalent cases would cost more than USD322 million and USD61 million annually in all-cause and psychiatric care, respectively. Although less than 20 percent of patients would develop TRD or its associated comorbidities, they contribute 31 to 54 percent of the costs. The key cost drivers were the number of annual incident cases and the probability of developing TRD and associated comorbidities and of becoming a low-intensity service user. These factors are relevant to early disease stages.
Conclusions
A small proportion of patients with depression develop TRD, but they contribute to a high proportion of the care costs. Our projection also demonstrates the application of RWE to model the long-term costs of care, which can aid policymakers in anticipating foreseeable burden and undertaking budget planning to prepare for future care needs.
A high-energy pulsed vacuum ultraviolet (VUV) solid-state laser at 177 nm with high peak power by the sixth harmonic of a neodymium-doped yttrium aluminum garnet (Nd:YAG) amplifier in a KBe2BO3F2 prism-coupled device was demonstrated. The ultraviolet (UV) pump laser is a 352 ps pulsed, spatial top-hat super-Gaussian beam at 355 nm. A high energy of a 7.12 mJ VUV laser at 177 nm is obtained with a pulse width of 255 ps, indicating a peak power of 28 MW, and the conversion efficiency is 9.42% from 355 to 177 nm. The measured results fitted well with the theoretical prediction. It is the highest pulse energy and highest peak power ever reported in the VUV range for any solid-state lasers. The high-energy, high-peak-power, and high-spatial-uniformity VUV laser is of great interest for ultra-fine machining and particle-size measurements using UV in-line Fraunhofer holography diagnostics.
The objective of this study was to investigate the genetic link between the age at first birth (AFB) and the occurrence of preterm labor and delivery, utilizing Mendelian randomization (MR) data alongside genomewide association analysis (GWAS). We obtained AFB-related GWAS summary data from the European Bioinformatics Institute database and preterm labor and delivery data was sourced from the FinnGen Consortium. The study considered AFB as exposure variables, with the incidence of preterm labor and delivery serving as the outcome variable. Several MR analysis methods, such as inverse-variance weighted (IVW), MR Egger, weighted median, simple, and weighted mode were utilized. Besides MR-Egger intercepts, Cochrane’s Q test evaluated heterogeneity in the MR data, while MR-PRESSO test checked for horizontal pleiotropy. To assess the association’s sensitivity, A leave-one-out approach was utilized to evaluate the sensitivity of the association. The IVW analysis validated that AFB is an independent risk factor for preterm labor and delivery (p < .001). Horizontal pleiotropy was unlikely to bias causality (p > .05). The likelihood of horizontal pleiotropy affecting causality was low (p > .05), and there was no indication of heterogeneity among the genetic variants (p > .05). Ultimately, a leave-one-out analysis confirmed the stability and reliability of this correlation. Our research indicated that AFB is a protective factor for preterm labor and delivery. Further research is required to clarify the possible mechanisms.
The gas dynamics of shock-induced gas filtration through densely packed granular columns with vastly varying shock intensity and the structural parameters are numerically investigated using a coupled Eulerian–Lagrangian approach. The results shed fundamental light on the thermal effects of the shock-induced gas filtration manifested by a distinctive self-heating hot gas layer traversing the medium. The characteristics of the thermal effects in terms of the thermal intensity and uniformity are found to vary with the shock Mach number, Ms, and the filtration coefficient of the granular media, Π. As the incident shock transitions from weak to strong, and (or) the filtration coefficient increases from O(10−5) to O(104), the heating mechanisms transition between three distinct heating modes. A phase diagram of heating modes is established on the parameter space (Ms, Π), which enables us to predict the characteristics of the thermal effect in different shock-induced gas filtrations. The thermal effects markedly accelerate the pressure diffusion due to the additional heat influx when the time scale of the former is smaller than or comparable to the latter. Based on the contour map displaying the coupling degree of the thermal effects and the pressure diffusion, we identify a decoupling criterion whereby the isothermal assumption holds if only the pressure diffusion is concerned. The thermal effects may well bring about considerable thermal shocks which pose a great threat to the integrity of the solid skeleton and further reduce the overall shock resistance performance of the porous media.
A high-energy picosecond 355 nm ultraviolet (UV) laser operating at 100 Hz was demonstrated. A 352 mJ, 69 ps, 1064 nm laser at 100 Hz was realized firstly by cascaded regenerative, laser diode end-pumped single-pass and side-pumped main amplifiers. The stimulated Raman scattering-based beam shaping technique, thermally induced birefringence compensation and 4f spatial filter-image relaying systems were used to maintain a relatively homogeneous beam intensity distribution during the amplification process. By using lithium triborate crystals for second- and third-harmonic generation (THG), a 172 mJ, approximately 56 ps, 355 nm UV laser was achieved with a THG conversion efficiency of 49%. To the best of our knowledge, it is the highest pulse energy of a picosecond 355 nm UV laser so far. The beam quality factor ${M}^2$ and pulse energy stability were ${M}_x^2$=3.92, ${M}_y^2$=3.71 and root mean square of 1.48%@3 hours. This laser system could play significant roles in applications including photoconductive switch excitation, laser drilling and laser micro-fabrication.
Femtosecond oscillators with gigahertz (GHz) repetition rate are appealing sources for spectroscopic applications benefiting from the individually accessible and high-power comb line. The mode mismatch between the potent pump laser diode (LD) and the incredibly small laser cavity, however, limits the average output power of existing GHz Kerr-lens mode-locked (KLM) oscillators to tens of milliwatts. Here, we present a novel method that solves the difficulty and permits high average power LD-pumped KLM oscillators at GHz repetition rate. We propose a numerical simulation method to guide the realization of Kerr-lens mode-locking and comprehend the dynamics of the Kerr-lens mode-locking process. As a proof-of-principle demonstration, an LD-pumped Yb:KGW oscillator with up to 6.17-W average power and 184-fs pulse duration at 1.6-GHz repetition rate is conducted. The simulation had a good agreement with the experimental results. The cost-effective, compact and powerful laser source opens up new possibilities for research and industrial applications.
Explosive dispersal of granular media widely occurs in nature across various length scales, enabling engineering applications ranging from commercial or military explosive systems to the loss prevention industry. However, the correlation between the explosive dispersal behaviour and the structure of dispersal system is far from completely understood, thereby compromising the prediction of the explosive dispersal outcome resulting from a specific dispersal system. Here, we investigate the dispersal behaviours of densely packed particle rings driven by the enclosed pressurized gases using coarse-grained computational fluid dynamics–discrete parcel method. Distinct dispersal modes emerge from the dispersal systems with vastly varying sets of the macro- and micro-scale structural parameters in terms of the dispersal completeness and the spatial uniformity of the dispersed mass. Further investigation reveals the variation in the dispersal modes arises from the collective effects of multiscale gas–particle coupling relationships. Specifically, the macroscale coupling dictates the cyclic momentum/energy transfer between gases and particle ring as an entirety. The mesoscale coupling relates to the inter-pore gas filtration through the thickness of the particle ring, leading to the mass/energy reduction of the explosive source. The microscale coupling involves the individual particle dynamics influenced by the local flow parameters. A persistent macroscale coupling results in an incomplete dispersal which takes the form of an aggregated annular band, whereas the meso- and micro-scale couplings alter the macroscale coupling to a different extent. By incorporating the effects of the variety of structural parameters on the multiscale gas–particle coupling relationships, a non-dimensional parameter referred to as the modified mass ratio is constructed, which shows an explicit correlation with the dispersal mode. We proceed to establish a dispersal ring model in the continuum frame which accounts for the macro and meso-scale coupling effects. This model proves to be capable of successfully predicting the ideal and validated failed dispersal modes.
Accurately converting satellite instantaneous evapotranspiration (λETi) over time to daily evapotranspiration (λETd) is crucial for estimating regional evapotranspiration from remote sensing satellites, which plays an important role in effective water resource management. In this study, four upscaling methods based on the principle of energy balance, including the evaporative fraction method (Eva-f method), revised evaporative fraction method (R-Eva-f method), crop coefficient method (Kc-ET0 method) and direct canopy resistance method (Direct-rc method), were validated based on the measured data of the Bowen ratio energy balance system (BREB) in maize fields in northwestern (NW) and northeastern (NE) China (semi-arid and semi-humid continental climate regions) from 2021 to 2023. Results indicated that Eva-f and R-Eva-f methods were superior to Kc-ET0 and Direct-rc methods in both climatic regions and performed better between 10:00 and 11:00, with mean absolute errors (MAE) and coefficient of efficiency (ɛ) reaching <10 W/m2 and > 0.91, respectively. Comprehensive evaluation of the optimal upscaling time using global performance indicators (GPI) showed that the Eva-f method had the highest GPI of 0.59 at 12:00 for the NW, while the R-Eva-f method had the highest GPI of 1.18 at 11:00 for the NE. As a result, the Eva-f approach is recommended as the best way for upscaling evapotranspiration in NW, with 12:00 being the ideal upscaling time. The R-Eva-f method is the optimum upscaling method for the Northeast area, with an ideal upscaling time of 11:00. The comprehensive results of this study could be useful for converting λETi to λETd.
This study aimed to understand the potassium voltage-gated channel KQT-like subfamily, member 1 gene polymorphism in a rural elderly population in a county in Guangxi and to explore the possible relationship between its gene polymorphism and blood sugar. The 6 SNP loci of blood DNA samples from 4355 individuals were typed using the imLDRTM Multiple SNP Typing Kit from Shanghai Tianhao Biotechnology Co. The data combining epidemiological information (baseline questionnaire and physical examination results) and genotyping results were statistically analyzed using GMDR0.9 software and SPSS22.0 software. A total of 4355 elderly people aged 60 years and above were surveyed in this survey, and the total abnormal rate of glucose metabolism was 16·11 % (699/4355). Among them, male:female ratio was 1:1·48; the age group of 60–69 years old accounted for the highest proportion, with 2337 people, accounting for 53·66 % (2337/4355). The results of multivariate analysis showed that usually not doing farm work (OR 1·26; 95 % CI 1·06, 1·50), TAG ≥ 1·70 mmol/l (OR 1·19; 95 % CI 1·11, 1·27), hyperuricaemia (OR 1·034; 95 % CI 1·01, 1·66) and BMI ≥ 24 kg/m2 (OR 1·06; 95 % CI 1·03, 1·09) may be risk factors for abnormal glucose metabolism. Among all participants, rs151290 locus AA genotype, A allele carriers (AA+AC) were 0.70 times more likely (0.54 to 0.91) and 0.82 times more likely (0.70 to 0.97) to develop abnormal glucose metabolism than CC genotype carriers, respectively. Carriers of the T allele at the rs2237892 locus (CT+TT) were 0.85 times more likely to have abnormal glucose metabolism than carriers of the CC genotype (0.72 to 0.99); rs2237897 locus CT gene. The possibility of abnormal glucose metabolism in the carriers of CC genotype, TT genotype and T allele (CT + TT) is 0·79 times (0·67–0·94), 0·74 times (0·55–0·99) and 0·78 times (0·66, 0·92). The results of multifactor dimensionality reduction showed that the optimal interaction model was a three-factor model consisting of farm work, TAG and rs2237897. The best model dendrogram found that the interaction between TAG and rs2237897 had the strongest effect on fasting blood glucose in the elderly in rural areas, and they were mutually antagonistic. Environment–gene interaction is an important factor affecting abnormal glucose metabolism in the elderly of a county in Hechi City, Guangxi.
In order to improve the global convergence performance of the super-twisting sliding mode control (STSMC) for the uncertain hybrid mechanism, especially in the high-speed scenario, and enhance the robustness of hybrid mechanism system to the uncertainties with a wide range of changes, an intelligent fixed-time super-twisting sliding mode control (IFTSTSMC) is proposed. Firstly, a fixed-time super-twisting sliding mode control (FTSTSMC) algorithm is designed by adding the exponential power terms with the fixed-time performance parameters in sliding variables and the transcendental function of the super-twisting algorithm in order to enhance the global convergence performance of the STSMC. Secondly, the existence condition of FTSTSMC for the uncertain hybrid mechanism is analyzed. The IFTSTSMC is designed by introducing RBF neural network to break through the limited range of uncertainties in FTSTSMC and enhance the robustness of hybrid mechanism system to the uncertainties with a wide range of changes. Then, the Lyapunov stability of the proposed method and the global fixed-time convergence of the system are proved theoretically. Finally, the effectiveness and superiority of the proposed control method are verified by the simulation and the automobile electro-coating conveying prototype experiment comparing with two classical finite-time sliding mode control methods.
This study investigated the impact of diallyl disulfide (DADS) on oxidative stress induced by hydrogen peroxide (H2O2) in ovine rumen epithelial cells (RECs). Initially, the effects of DADS were evaluated on cellular reactive oxygen species (ROS) levels, antioxidant capacity in RECs were estimated. Then, RNA-seq analysis was conducted in DADS-treated and untreated cells to analyze the differential gene expression, as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Finally, the effects of DADS on Kelch-like ECH associated protein 1/the nuclear factor erythroid 2-related factor 2 (Keap1/Nrf2) signaling pathway in RECs were evaluated. Results showed that DADS remarkably enhanced superoxide dismutase (SOD) activity and total antioxidant capacity (T-AOC) (P < 0.05) while reducing ROS and malonaldehyde production (P < 0.05) in H2O2-treated RECs. Transcriptomic analysis revealed that DADS might influence glutathione synthesis through cysteine and methionine metabolism, thereby affecting the transcription of genes involved in immunity and oxidative stress. The DADS treatment resulted in increased nuclear translocation of Nrf2 and upregulation of mRNA and protein levels of quinone oxidoreductase 1, heme oxygenase 1, and Nrf2. The Nrf2-specific inhibitor nullified the protective effects of DADS on malonaldehyde formation induced by H2O2 and decreased T-AOC and SOD activities. In conclusion, DADS demonstrated the ability to alleviate oxidative stress in RECs by promoting antioxidative capacity through the Keap1/Nrf2 signaling pathway.