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Background: Personality is a unique and relatively stable psychological concept that defines individual human beings. It strongly influences long-term behavioral styles such as emotional expression. This study aims to elucidate the brain functional underpinning behind personality. Methods: A total of 97 young subjects were included. All subjects completed personality, emotion, and cognition scales, and resting-state functional magnetic resonance imaging scan. All subjects were divided into subtypes of harm avoidance (HA) and reward dependence (RD) by clustering analysis. Graph theory analysis and network-based analysis were used to explore the brain functional configurations of personalities. Results: HA subjects showed lower network metrics (P = 0.018) and node metrics (P

To promote the application of steel-reinforced-concrete structures in prefabricated concrete structures, the semi-prefabricated steel-tube double-layer concrete (SPSTDC) column was proposed. The eccentric compression mechanical behavior was studied through theoretical analysis, experiments, and numerical simulations. Three possible failure modes, namely, compression failure, total yield failure, and tension failure, were obtained. Then, corresponding calculation methods of the bearing capacity (Nu) were established and their effectiveness was verified through comparison with test results. The analysis results showed that Nu decreased with the increase in the eccentricity. Nu increased with the precast concrete strength, and its effect on Nu diminished with increasing eccentricity. The post-cast concrete strength had a smaller effect on Nu than the precast concrete strength. Nu increased with the increase in the steel ratio αa, while the steel tube strength had little influence on Nu. The N–M curve of the SPSTDC column could be divided into three segments, corresponding to the three failure modes. For the compression failure mode, the ultimate load Ns decreased with the increase in the ultimate bending moment Ms. For the other two failure modes, the pattern was the opposite. The eccentricity had a significant impact on the horizontal lateral deflections of the SPSTDC columns. With the increase in the eccentricity, the horizontal lateral deflection corresponding to Nu increased. The horizontal lateral displacement of the mid-height section remained the largest compared with those of other sections. The lateral stiffness increased with the increase in the concrete strength, steel strength, and steel ratio. The impact of the precast concrete strength was the most significant while the influence of the steel tube strength was the weakest.

Patients with Parkinson's disease (PD) exhibit heterogenous clinical deficits not only in motor function, other deficits in both sensory and higher-order cognitive processing are also involved. Connectome studies have suggested a primary-to-transmodal gradient and a primary-to-primary gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether these gradients are altered in PD patients and how these alterations associate with neurotransmitter profiles remain unknown. By constructing functional network and calculating its gradient in 134 PD patients and 172 normal controls, we compared functional connectivity gradients between groups and performed spearman correlation to explore the association between neurotransmitter expression and functional network gradient-based alternations in PD. Decreased first gradients were detected mainly in association cortex, including frontal cortex, insula, cingulate, and parietal cortex, corresponding to the decrement of frontoparietal/ventral attention network observed in network-level analyses. Decreased second gradients were observed in primary motor and somatosensory cortex, meeting the decrement of somatomotor network at the network level. Besides, network-level comparisons revealed the increment of visual network in the first gradient and increment of ventral attention network in the second gradient. Transcription-neuroimaging association analyses showed that changes of the first gradient were mainly negatively correlated with nondopaminergic system, while alterations of the second gradient were positively correlated with both dopaminergic and nondopaminergic systems. These results highlight the connectome gradient dysfunction in PD and its linkage with neurotransmitter expression profiles, providing insight into the molecular mechanisms for functional alterations underlying PD.

Digital Twin-Oriented Complex Networked System (DT-CNS) is a modelling paradigm aiming at the representation and analysis of real-world networked systems. DT-CNS models network and the dynamics of and on the networks, including network growth dynamics and the dynamic process on networks. DT-CNS can be built and extended with increasing complexity towards the ultimate goal, a Digital Twin (DT) of reality. DT-CNS, as it approaches a DT, faithfully represents the temporal changes in network and processes, together with their interrelations, based on real-time information acquisition and feedback with reality. To build a DT-CNS with increasing complexity, one of the possible development paths is to increase the heterogeneity of nodes’ features (characteristics of each specific node) and their preferences to create relationships while allowing the networks to evolve with the preference changes under the influence of dynamic process. To achieve that, we created a novel open-source Python tool named DTCNS to implement functionalities including feature representation, network growth based on heterogeneous node features and preferences to create relationships based on features, and the spreading process on networks. The source code, documentation and the full descriptions of the API are available at https://github.com/UTS-CASLab/DT-CNS.

Abstract Objective To observe prosthetic-associated subclinical thrombotic events (PASTE) after transcatheter aortic valve implantation (TAVI) by cardiac CTA, and assess their impact on long-term patient outcomes. Materials We prospectively and consecutively enrolled 188 patients with severe aortic stenosis treated with TAVI from February 2014 to April 2017. At 5 years, 61 of 141 survived patients who had completed annual follow-up CTA (≥ 5 years) were included. We analyzed PASTE by CTA, including hypoattenuated leaflet thickening (HALT), sinus filling defect (SFD), and prosthesis filling defect (PFD). The primary outcome was a major adverse cardiovascular composite outcome (MACCO) of stroke, cardiac re-hospitalization, and bioprosthetic valve dysfunction (BVD); the secondary outcomes were bioprosthetic hemodynamics deterioration (PGmean) and cardiac dysfunction (LVEF). Results During a median follow-up time of 5.25 years, long-term incidence of HALT, SFD, and PFD were 54.1%, 37.7%, and 73.8%, respectively. In the primary outcome, SFD and early SFD were associated with the MACCO (SFD: p = 0.005; early SFD: p = 0.018), and SFD was a predictor of MACCO (HR: 2.870; 95% CI: 1.010 to 8.154, p = 0.048). In the secondary outcomes, HALT was associated with increased PGmean (p = 0.031), while persistent HALT was correlated with ΔPGmean (β = 0.38, p = 0.035). SFD was negatively correlated with ΔLVEF (β = −0.39, p = 0.041), and early SFD was negatively correlated with LVEF and ΔLVEF (LVEF: r = −0.50, p = 0.041; ΔLVEF: r = −0.53, p = 0.030). Conclusions PASTE were associated with adverse long-term outcomes, bioprosthetic hemodynamics deterioration, and cardiac dysfunction. In particular, SFD was a predictor of MACCO and may be a potential target for anticoagulation after TAVI (NCT02803294). Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02803294. Critical relevance statement PASTE, especially SFD, after TAVI based on cardiac CTA findings impacts the long-term outcomes of patients which is a predictor of long-term major adverse outcomes in patients and may be a potential target for anticoagulation after TAVI. Key Points Transcatheter aortic valve implantation is being used more often; associated subclinical thromboses have not been thoroughly evaluated. Prosthetic-associated subclinical thrombotic events were associated with adverse outcomes, bioprosthetic hemodynamics deterioration, and cardiac dysfunction. Studies should be directed at these topics to determine if they should be intervened upon. Graphical Abstract

This study aims to recognize fluorescent excitation images of microcracks in concrete components through the adoption of an Attention Mechanism-based Deep Recurrent Neural Network (RNN) model, thereby enhancing the accuracy and efficiency of crack detection. Considering the significance of concrete crack detection and the limitations in efficiency and accuracy of existing methods, this paper proposes an innovative image processing technique that combines fluorescent excitation methods with deep learning models to achieve earlier and more accurate identification of concrete microcracks. C30 concrete specimens were prepared experimentally and treated with fluorescent solution spray. Fluorescent images of cracks under UV light excitation were collected and processed using a segmented attention mechanism deep RNN model. Various performance evaluation metrics, including mean Intersection over Union (mIoU), mean Image Intersection over Union (miIoU), mean Image Dice Coefficient (miDice), and F1 score, were employed to comprehensively assess the model's performance. The results demonstrate that the proposed model achieved significant effectiveness in concrete crack image recognition, showing higher mIoU, miIoU, miDice, and F1 scores compared to other representative deep learning models, thus proving its advantages in recognition accuracy and efficiency. Particularly, by introducing the segmented attention mechanism, the model could capture microcrack features more effectively, significantly improving the accuracy of crack identification. This method not only provides a new technical approach for the early detection of concrete cracks but also lays the foundation for further development of efficient and accurate crack detection technologies to accommodate more complex engineering application scenarios.

Isolated Bacillus velezensis strain NA16, which produces proteases, amino acids and the transcription levels of different keratinolytic enzymes and disulfide reductase genes in whole gene sequencing, was evaluated during feather degradation. The result shows under optimum fermentation conditions, chicken feather fermentation showed total amino acid concentration of 7599 mg/L, degradation efficiency of 99.3% at 72 h, and protease activity of 1058 U/mL and keratinase activity of 288 U/mL at 48 h. Goose feather fermentation showed total amino acid concentration of 4918 mg/L (96 h), and degradation efficiency was 98.9% at 120 h. Chicken feather fermentation broth at 72 h showed high levels of 17 amino acids, particularly phenylalanine (1050 ± 1.90 mg/L), valine (960 ± 1.04 mg/L), and glutamic (950 ± 3.00 mg/L). Scanning electron microscopy and Fourier transform infrared analysis revealed the essential role of peptide bond cleavage in structural changes and degradation of feathers. Protein purification and zymographic analyses revealed a key role in feather degradation of the 39-kDa protein encoded by gene1031, identified as an S8 family serine peptidase. Whole genome sequencing of NA16 revealed 26 metalloproteinase genes and 22 serine protease genes. Among the proteins, S8 family serine peptidase (gene1031, gene1428) and S9 family peptidase (gene3132) were shown by transcription analysis to play major roles in chicken feather degradation. These findings revealed the transcription levels of different families of keratinolytic enzymes in the degradation of feather keratin by microorganisms, and suggested potential applications of NA16 in feather waste management and amino acid production.

Here, we report on a bifunctional alginate lyase (Vnalg7) expressed in Pichia pastoris, which can degrade natural Undaria pinnatifida into unsaturated guluronic acid di- and trisaccharide without pretreatment. The enzyme activity of Vnalg7 (3620.00 U/mL-culture) was 15.81-fold higher than that of the original alg (228.90 U/mL-culture), following engineering modification. The degradation rate reached 52.75%, and reducing sugar reached 30.30 mg/mL after combining Vnalg7 (200.00 U/mL-culture) and 14% (w/v) U. pinnatifida for 6 h. Analysis of the action mode indicated that Vnalg7 could degrade many substrates to produce a variety of unsaturated alginate oligosaccharides (AOSs), and the minimal substrate was tetrasaccharide. Site-directed mutagenesis showed that Glu238, Glu241, Glu312, Arg236, His307, Lys414, and Tyr418 are essential catalytic sites, while Glu334, Glu344, and Asp311 play auxiliary roles. Mechanism analysis revealed the enzymatic degradation pattern of Vnalg7, which mainly recognizes and attacks the third glycosidic linkage from the reducing end of oligosaccharide substrate. Our findings provide a novel alginate lyase tool and a sustainable and commercial production strategy for value-added biomolecules using seaweeds.

Abstract Objective To determine whether white matter hyperintensity (WMH) volumes in specific regions are associated with Parkinson's disease (PD) compared to non‐PD controls, and to assess their impact on motor signs through cross‐sectional and longitudinal analyses. Methods A total of 50 PD participants and 47 age‐ and gender‐matched controls were enrolled. All PD participants were followed up for at least 2 years. To detect regions of greater WMH in the PD, the WMH volume of each region was compared with the corresponding region in the control group. Linear regression and linear mixed effects models were respectively used for cross‐sectional and longitudinal analyses of the impact of increases in WMH volume on motor signs. Results The PD group had greater WMH volume in the occipital region compared with the control group. Cross‐sectional analyses only detected a significant correlation between occipital WMH volume and motor function in PD. Occipital WMH volume positively correlated with the severity of tremor, and gait and posture impairments, in the PD group. During the follow‐up period, the participants' motor signs progressed and the WMH volumes remained stable, no longitudinal association was detected between them. The baseline occipital WMH volume cannot predict the progression of signs after adjustment for baseline disease duration and the presence of vascular risk factors. Interpretation PD participants in this study were characterized by greater WMH at the occipital region, and greater occipital WMH volume had cross‐sectional associations with worse motor signs, while its longitudinal impact on motor signs progression was limited.

Background: Whether there is hypothalamic degeneration in Parkinson's disease (PD) and its association with clinical symptoms and pathophysiological changes remains controversial. Objectives: We aimed to quantify microstructural changes in hypothalamus using a novel deep learning-based tool in patients with PD and those with probable rapid-eye-movement sleep behavior disorder (pRBD). We further assessed whether these microstructural changes associated with clinical symptoms and free thyroxine (FT4) levels. Methods: This study included 186 PD, 67 pRBD, and 179 healthy controls. Multi-shell diffusion MRI were scanned and mean kurtosis (MK) in hypothalamic subunits were calculated. Participants were assessed using Unified Parkinson's Disease Rating Scale (UPDRS), RBD Questionnaire-Hong Kong (RBDQ-HK), Hamilton Depression Rating Scale (HAMD), and Activity of Daily Living (ADL) Scale. Additionally, a subgroup of PD (n = 31) underwent assessment of FT4. Results: PD showed significant decreases of MK in anterior-superior (a-sHyp), anterior-inferior (a-iHyp), superior tubular (supTub), and inferior tubular hypothalamus when compared with healthy controls. Similarly, pRBD exhibited decreases of MK in a-iHyp and supTub. In PD group, MK in above four subunits were significantly correlated with UPDRS-I, HAMD, and ADL. Moreover, MK in a-iHyp and a-sHyp were significantly correlated with FT4 level. In pRBD group, correlations were observed between MK in a-iHyp and UPDRS-I. Conclusions: Our study reveals that microstructural changes in the hypothalamus are already significant at the early neurodegenerative stage. These changes are associated with emotional alterations, daily activity levels, and thyroid hormone levels.

Abstract Background Immunotoxins are antibody-toxin conjugates that bind to surface antigens and exert effective cytotoxic activity after internalization into tumor cells. Immunotoxins exhibit effective cytotoxicity and have been approved by the FDA to treat multiple hematological malignancies, such as hairy cell leukemia and cutaneous T-cell lymphoma. However, most of the internalized immunotoxin is degraded in lysosomes, and only approximately 5% of free toxin escapes into the cytosol to exert cytotoxicity. Many studies have improved immunotoxins by engineering the toxin fragment to reduce immunogenicity or increase stability, but how the antibody fragment contributes to the activity of immunotoxins has not been well demonstrated. Methods In the current study, we used 32A9 and 42A1, two anti-GPC3 antibodies with similar antigen-binding capabilities and internalization rates, to construct scFv-mPE24 immunotoxins and evaluated their in vitro and in vivo antitumor activities. Next, the antigen-binding capacity, trafficking, intracellular protein stability and release of free toxin of 32A9 scFv-mPE24 and 42A1 scFv-mPE24 were compared to elucidate their different antitumor activities. Furthermore, we used a lysosome inhibitor to evaluate the degradation behavior of 32A9 scFv-mPE24 and 42A1 scFv-mPE24. Finally, the antigen-binding patterns of 32A9 and 42A1 were compared under neutral and acidic pH conditions. Results Although 32A9 and 42A1 had similar antigen binding capacities and internalization rates, 32A9 scFv-mPE24 had superior antitumor activity compared to 42A1 scFv-mPE24. We found that 32A9 scFv-mPE24 exhibited faster degradation and drove efficient free toxin release compared to 42A1 scFv-mPE24. These phenomena were determined by the different degradation behaviors of 32A9 scFv-mPE24 and 42A1 scFv-mPE24 in lysosomes. Moreover, 32A9 was sensitive to the low-pH environment, which made the 32A9 conjugate easily lose antigen binding and undergo degradation in lysosomes, and the free toxin was then efficiently produced to exert cytotoxicity, whereas 42A1 was resistant to the acidic environment, which kept the 42A1 conjugate relatively stable in lysosomes and delayed the release of free toxin. Conclusions These results showed that a low pH-sensitive antibody-based immunotoxin degraded faster in lysosomes, caused effective free toxin release, and led to improved cytotoxicity compared to an immunotoxin based on a normal antibody. Our findings suggested that a low pH-sensitive antibody might have an advantage in the design of immunotoxins and other lysosomal degradation-dependent antibody conjugate drugs.

Abstract Increasing evidence suggests that Parkinson’s disease (PD) exhibits disparate spatial and temporal patterns of progression. Here we used a machine-learning technique—Subtype and Stage Inference (SuStaIn) — to uncover PD subtypes with distinct trajectories of clinical and neurodegeneration events. We enrolled 228 PD patients and 119 healthy controls with comprehensive assessments of olfactory, autonomic, cognitive, sleep, and emotional function. The integrity of substantia nigra (SN), locus coeruleus (LC), amygdala, hippocampus, entorhinal cortex, and basal forebrain were assessed using diffusion and neuromelanin-sensitive MRI. SuStaIn model with above clinical and neuroimaging variables as input was conducted to identify PD subtypes. An independent dataset consisting of 153 PD patients and 67 healthy controls was utilized to validate our findings. We identified two distinct PD subtypes: subtype 1 with rapid eye movement sleep behavior disorder (RBD), autonomic dysfunction, and degeneration of the SN and LC as early manifestations, and cognitive impairment and limbic degeneration as advanced manifestations, while subtype 2 with hyposmia, cognitive impairment, and limbic degeneration as early manifestations, followed later by RBD and degeneration of the LC in advanced disease. Similar subtypes were shown in the validation dataset. Moreover, we found that subtype 1 had weaker levodopa response, more GBA mutations, and poorer prognosis than subtype 2. These findings provide new insights into the underlying disease biology and might be useful for personalized treatment for patients based on their subtype.

The monitoring of pharmaceuticals, personal care products (PCPs), pesticides, and their metabolites through wastewater-based epidemiology (WBE) provides timely information on pharmaceutical consumption patterns, chronic disease treatment rates, antibiotic usage, and exposure to harmful chemicals. However, before applying them for quantitative WBE back-estimation, it is necessary to understand their stability in the sewer system to screen suitable WBE biomarkers thereby reducing research uncertainty. This study investigated the in-sewer stability of 140 typical pharmaceuticals, PCPs, pesticides, and their metabolites across 15 subcategories, using a series of laboratory sewer sediment and biofilm reactors. For the first time, stability results for 89 of these compounds were reported. Among the 140 target compounds, 61 biomarkers demonstrated high stability in all sewer reactors, while 41 biomarkers were significantly removed merely by sediment processes. For biomarkers exhibiting notable attenuation, the influence of sediment processes was generally more pronounced than biofilm, due to its stronger microbial activities and more pronounced diffusion or adsorption processes. Adsorption emerged as the predominant factor causing biomarker removal compared to biodegradation and diffusion. Significantly different organic carbon–water partitioning coefficient (Koc) and distribution coefficient at pH = 7 (logD) values were observed between highly stable and unstable biomarkers, with most hydrophobic substances (Koc > 100 or logD > 2) displaying instability. In light of these findings, we introduced a primary biomarker screening process to efficiently exclude inappropriate candidates, achieving a commendable 77 % accuracy. Overall, this study represents the first comprehensive report on the in-sewer stability of 89 pharmaceuticals, PCPs, pesticides, and their metabolites, and provided crucial reference points for understanding the intricate sewer sediment processes.

Background: Parkinson's disease (PD) patients exhibit an imbalance between neuronal activity and perfusion, referred to as abnormal neurovascular coupling (NVC). Nevertheless, the underlying molecular mechanism and how levodopa, the standard treatment in PD, regulates NVC is largely unknown. Material and methods: A total of 52 drug-naïve PD patients and 49 normal controls (NCs) were enrolled. NVC was characterized in vivo by relating cerebral blood flow (CBF) and amplitude of low-frequency fluctuations (ALFF). Motor assessments and MRI scanning were conducted on drug-naïve patients before and after levodopa therapy (OFF/ON state). Regional NVC differences between patients and NCs were identified, followed by an assessment of the associated receptors/transporters. The influence of levodopa on NVC, CBF, and ALFF within these abnormal regions was analyzed. Results: Compared to NCs, OFF-state patients showed NVC dysfunction in significantly lower NVC in left precentral, postcentral, superior parietal cortex, and precuneus, along with higher NVC in left anterior cingulate cortex, right olfactory cortex, thalamus, caudate, and putamen (P-value

Summary: This study aims to comprehensively review a recently emerging multidisciplinary area related to the application of deep learning methods in cryptocurrency research. We first review popular deep learning models employed in multiple financial application scenarios, including convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning. We also give an overview of cryptocurrencies by outlining the cryptocurrency history and discussing primary representative currencies. Based on the reviewed deep learning methods and cryptocurrencies, we conduct a literature review on deep learning methods in cryptocurrency research across various modeling tasks, including price prediction, portfolio construction, bubble analysis, abnormal trading, trading regulations and initial coin offering in cryptocurrency. Moreover, we discuss and evaluate the reviewed studies from perspectives of modeling approaches, empirical data, experiment results and specific innovations. Finally, we conclude this literature review by informing future research directions and foci for deep learning in cryptocurrency.

Plutella xylostella (Linnaeus) is an important pest of cruciferous plants, which is harmful all over the world, causing serious economic losses, and its drug resistance is increasing rapidly. The sterile insect technique (SIT) is a green control method and does not cause resistance. In this study, transcriptomics and bioinformatics were used to explore the effects of irradiation on the reproductive function of Plutella xylostella, and the response mechanism of sterility under irradiation was initially revealed. We identified 3342 (1682 up-regulated, 1660 down-regulated), 1963 (1042 up-regulated, 921 down-regulated) and 1531 (721 up-regulated, 810 down-regulated) differentially expressed genes (DEGs) in the 200 Gy vs CK (Control Check), 400 Gy vs CK and 400 Gy vs 200 Gy groups, respectively. GO and KEGG analyses were performed for DEGs in each group. The results showed that 200 Gy activated the downstream phosphorylation pathway and inhibited the cytochrome p450 immune response mechanism. 400 Gy promoted protein decomposition and absorption pathways, autophagy pathways, etc. Down-regulated genes were concentrated in the transformation process of energy metabolizing substances such as ATP, phosphorylation signaling pathway, and insulin, while up-regulated genes were concentrated in biological regulation and metabolic processes. Eight genes in the phosphorylation pathway were selected for qRT-PCR verification, and the results showed that the phosphorylation of different dose groups was regulated in different ways. 400 Gy used positive feedback regulation, while the phosphorylation of F1 used negative feedback regulation.

Accurate modelling of complex social systems, where people interact with each other and those interactions change over time, has been a research challenge for many years. This study proposes an evolutionary Digital Twin-Oriented Complex Networked System (DT-CNS) framework that considers heterogeneous node features and changeable connection preferences. We create heterogeneous preference mutation mechanisms to characterise nodes' adaptive decisions on preference mutation in response to interaction patterns and epidemic risks. In this space, we use nodes' interaction utilities to characterise the positive feedback from interactions and negative impact of epidemic risks. We also introduce social capital constraint to harness the density of social connections better. The nodes' heterogeneous preference mutation styles include the (i)inactive style that keeps initial social preferences, (ii) ignorant style that randomly mutates preferences, (iii) egocentric style that optimises individual interaction utility, (iv) cooperative style that optimises the total interaction utilities by group decisions and (v) collaborative style that further allows the cooperative nodes to transfer social capital. Our simulation experiments on evolutionary DT-CNSs reveal that heterogeneous preference mutation styles lead to various interaction and infection patterns. The results also show that (i) increasing social capital enables higher interactions but higher infection risks and uncertainty in decision-making; (ii) group decisions outperform individual decisions by eliminating the unawareness of the decisions of other nodes; (iii) the collaborative nodes under a strict social capital limit can promote interactions, reduce infection risks and achieve higher overall interaction utilities.

The ability to faithfully represent real social networks is critical from the perspective of testing various what-if scenarios which are not feasible to be implemented in a real system as the system’s state would be irreversibly changed. High fidelity simulators allow one to investigate the consequences of different actions before introducing them to the real system. For example, in the context of social systems, an accurate social network simulator can be a powerful tool used to guide policy makers, help companies plan their advertising campaigns or authorities to analyse fake news spread. In this study we explore different Social Network Simulators (SNSs) and assess to what extent they are able to mimic the real social networks. We conduct a critical review and assessment of existing Social Network Simulators under the Digital Twin-Oriented Modelling framework proposed in our previous study. We subsequently extend one of the most promising simulators from the evaluated ones, to facilitate generation of social networks of varied structural complexity levels. This extension brings us one step closer to a Digital Twin Oriented SNS (DT Oriented SNS). We also propose an approach to assess the similarity between real and simulated networks with the composite performance indexes based on both global and local structural measures, while taking runtime of the simulator as an indicator of its efficiency. We illustrate various characteristics of the proposed DT Oriented SNS using a well known Karate Club network as an example. While not considered to be of sufficient complexity, the simulator is intended as one of the first steps on a journey towards building a Digital Twin of a social network that perfectly mimics the reality.

Here, an α-L-arabinofuranosidase (termed TtAbf62) from Thermothelomyces thermophilus is described, which efficiently removes arabinofuranosyl side chains and facilitates arabinoxylan digestion. The specific activity of TtAbf62 (179.07 U/mg) toward wheat arabinoxylan was the highest among all characterized glycoside hydrolase family 62 enzymes. TtAbf62 in combination with endoxylanase and β-xylosidase strongly promoted hydrolysis of barley and wheat. The release of reducing sugars was significantly higher for the three-enzyme combination relative to the sum of single-enzyme treatments: 85.71% for barley hydrolysis and 33.33% for wheat hydrolysis. HPLC analysis showed that TtAbf62 acted selectively on monosubstituted (C-2 or C-3) xylopyranosyl residues rather than double-substituted residues. Site-directed mutagenesis and interactional analyses of enzyme–substrate binding structures revealed the catalytic sites of TtAbf62 formed different polysaccharide-catalytic binding modes with arabinoxylo-oligosaccharides. Our findings demonstrate a “multienzyme cocktail” formed by TtAbf62 with other hydrolases strongly improves the efficiency of hemicellulose conversion and increases biomass hydrolysis through synergistic interaction.

Mêdog is located at the entrance of the water vapor channel of the Yarlung Tsangpo Great Canyon on the southeastern Tibetan Plateau (TP). In this study, the seasonal variation in the microphysical vertical structure of stratiform precipitation at the Mêdog site in 2022 was investigated using micro rain radar (MRR) observations, as there is a lack of similar studies in this region. The average melting layer height is the lowest in February, after which it gradually increases, reaches its peak in August, and then gradually decreases. For lower rain categories, the vertical distribution of small drops remains uniform in winter below the melting layer. The medium-sized drops show slight increases, leading to negative gradients in the microphysical profiles. Slight or evident decreases in concentrations of small drops are observed with decreasing height in the premonsoon, monsoon, and postmonsoon seasons, likely due to significant evaporation. The radar reflectivity, rain rate, and liquid water content profiles decrease with decreasing height according to the decrease in concentrations of small drops. With increasing rain rate, the drop size distribution (DSD) displays significant variations in winter, and the fall velocity decreases rapidly with decreasing height. In the premonsoon, monsoon, and postmonsoon seasons, the concentrations of large drops significantly decrease below the melting layer because of the breakup mechanism, leading to the decreases in the fall velocity profiles with decreasing height during these seasons. Raindrops with sizes ranging from 0.3–0.5 mm are predominant in terms of the total drop number concentration in all seasons. Precipitation in winter and postmonsoon seasons is mainly characterized by small raindrops, while that in premonsoon and monsoon seasons mainly comprises medium-sized raindrops. Understanding the seasonal variation in the vertical structure of precipitation in Mêdog will improve the radar quantitative estimation and the use of microphysical parameterization schemes in numerical weather forecast models over the TP.

Abstract This study aimed to investigate the cortical microstructural/macrostructural degenerative patterns in Parkinson’s disease (PD) patients with mild cognitive impairment (MCI). Overall, 38 PD patients with normal cognition (PD-NC), 38 PD-MCI, and 32 healthy controls (HC) were included. PD-MCI was diagnosed according to the MDS Task Force level II criteria. Cortical microstructural alterations were evaluated with Neurite Orientation Dispersion and Density Imaging. Cortical thickness analyses were derived from T1-weighted imaging using the FreeSurfer software. For cortical microstructural analyses, compared with HC, PD-NC showed lower orientation dispersion index (ODI) in bilateral cingulate and paracingulate gyri, supplementary motor area, right paracentral lobule, and precuneus (P FWE 100 voxels) and the ODI values were associated with the Montreal Cognitive Assessment scores (r = 0.440, P

Gait impairment is a common symptom of Parkinson's disease (PD), but its neural signature remains unclear due to the interindividual variability of gait performance. Identifying a robust gait-brain correlation at the individual level would provide insight into a generalizable neural basis of gait impairment. In this context, this study aimed to detect connectome that can predict individual gait function of PD, and follow-up analyses assess the molecular architecture underlying the connectome by relating it to the neurotransmitter-receptor/transporter density maps. Resting-state functional magnetic resonance imaging was used to detect the functional connectome, and gait function was assessed via a 10 m-walking test. The functional connectome was first detected within drug-naive patients (N = 48) by using connectome-based predictive modeling following cross-validation and then successfully validated within drug-managed patients (N = 30). The results showed that the motor, subcortical, and visual networks played an important role in predicting gait function. The connectome generated from patients failed to predict the gait function of 33 normal controls (NCs) and had distinct connection patterns compared to NCs. The negative connections (connection negatively correlated with 10 m-walking-time) pattern of the PD connectome was associated with the density of the D2 receptor and VAChT transporter. These findings suggested that gait-associated functional alteration induced by PD pathology differed from that induced by aging degeneration. The brain dysfunction related to gait impairment was more commonly found in regions expressing more dopaminergic and cholinergic neurotransmitters, which may aid in developing targeted treatments.

Accurate identification of building structure frequencies forms the basis for damage detection. The structural dynamic response signal, under ambient excitation, can be transformed into a superposition of multiple single-frequency exponentially damped sinusoids combined with random white noise. However, the peak power spectrum of the response signal tends to exhibit line splitting, compromising the precision of frequency identification. This study examines the accuracy characteristics of the single-frequency free damping vibration signal (SFFDVS) and derives the Cramer–Rao lower bound for the frequency estimator. It thoroughly analyzes the factors influencing the accuracy of SFFDVS frequency identification. The study reveals that the primary cause of spectral line splitting is the random delay inherent in SFFDVS. Based on the maximum likelihood method (MLM), this research introduces the MLM algorithm for SFFDVS and provides a simulation analysis. The findings indicate that the MLM estimation algorithm for frequency parameters effectively addresses spectral line splitting and offers robust noise resistance and recognition accuracy.

Background: Rapid eye movement (REM) sleep behavior disorder (RBD) could develop preceding or come after motor symptoms during Parkinson's disease (PD). It remains unknown that whether PD with different timing of RBD onset relative to motor symptoms suggests different spatiotemporal sequence of neurodegeneration. This study aimed to explore the sequence of disease progression in crucially involved brain regions in PD with different timing of RBD onset. Method: We recruited 157 PD, 16 isolated RBD (iRBD), and 78 healthy controls. PD patients were identified as (1) PD with RBD preceding motor symptoms (PD-preRBD, n = 50), (2) PD with RBD posterior to motor symptoms (PD-postRBD, n = 31), (3) PD without RBD (PD-nonRBD, n = 75). The volumes of crucial brain regions, including the basal ganglia and limbic structures in T1-weighted imaging, and the contrast-noise-ratios of locus coeruleus (LC) and substantia nigra (SN) in neuromelanin-sensitive magnetic resonance imaging, were extracted. To simulate the sequence of disease progression for cross-sectional data, an event-based model was introduced to estimate the maximum likelihood sequence of regions' involvement for each group. Then, a statistical parameter, the Bhattacharya coefficient (BC), was used to evaluate the similarity of the sequence. Results: The model predicted that SN occupied the highest likelihood in the maximum likelihood sequence of disease progression in the all PD subgroups, while LC was specifically positioned earlier to SN in iRBD, a prodromal phase of PD. Subsequent early involvement of LC was observed in the both PD-preRBD and PD-postRBD. In contrast, atrophy in the para-hippocampal gyrus but relatively intact LC in the early stage was demonstrated in PD-nonRBD. Then, the similarity comparisons indicated higher BC between PD-postRBD and PD-preRBD (BC = 0.76) but lower BC between PD-postRBD and PD-nonRBD group (BC = 0.41). iRBD had higher BC against PD-preRBD (BC = 0.66) and PD-postRBD (BC = 0.63) but lower BC against PD- nonRBD (BC = 0.48). Conclusion: The spatiotemporal sequence of neurodegeneration between PD-pre and PD-post were similar but distinct from PD-nonRBD. The presence of RBD may be the essential factor for differentiating the degeneration patterns of PD, but the timing of RBD onset has currently proved to be not.

This paper aims to provide a comprehensive critical overview on how entities and their interactions in Complex Networked Systems (CNS) are modelled across disciplines as they approach their ultimate goal of creating a Digital Twin (DT) that perfectly matches the reality. We propose four complexity dimensions for the network representation and five generations of models for the dynamics modelling to describe the increasing complexity level of the CNS that will be developed towards achieving DT (e.g. CNS dynamics modelled offline in the 1st generation v.s. CNS dynamics modelled simultaneously with a two-way real time feedback between reality and the CNS in the 5th generation). Based on that, we propose a new framework to conceptually compare diverse existing modelling paradigms from different perspectives and create unified assessment criteria to evaluate their respective capabilities of reaching such an ultimate goal. Using the proposed criteria, we also appraise how far the reviewed current state-of-the-art approaches are from the idealised DTs. Finally, we identify and propose potential directions and ways of building a DT-orientated CNS based on the convergence and integration of CNS and DT utilising a variety of cross-disciplinary techniques.

Brain iron deposition is a promising marker for human brain health, providing insightful information for understanding aging as well as neurodegenerations, e.g., Parkinson's disease (PD) and Alzheimer's disease (AD). To comprehensively evaluate brain iron deposition along with aging, PD-related neurodegeneration, from prodromal PD (pPD) to clinical PD (cPD), and AD-related neurodegeneration, from mild cognitive impairment (MCI) to AD, a total of 726 participants from July 2013 to December 2020, including 100 young adults, 189 old adults, 184 pPD, 171 cPD, 31 MCI and 51 AD patients, were included. Quantitative susceptibility mapping data were acquired and used to quantify regional magnetic susceptibility, and the resulting spatial standard deviations were recorded. A general linear model was applied to perform the inter-group comparison. As a result, relative to young adults, old adults showed significantly higher iron deposition with higher spatial variation in all of the subcortical nuclei (p

BackgroundUnderstanding genetic influences on Alzheimer’s disease (AD) may improve early identification. Polygenic hazard score (PHS) is associated with the age of AD onset and cognitive decline. It interacts with other risk factors, but the nature of such combined effects remains poorly understood.Materials and methodsWe examined the effect of genetic risk and hippocampal atrophy pattern on episodic memory in a sample of older adults ranging from cognitively normal to those diagnosed with AD using structural MRI. Participants included 51 memory unimpaired normal control (NC), 69 mild cognitive impairment (MCI), and 43 AD adults enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Hierarchical linear regression analyses examined the main and interaction effects of hippocampal subfield volumes and PHS, indicating genetic risk for AD, on a validated episodic memory composite score. Diagnosis-stratified models further assessed the role of PHS.ResultsPolygenic hazard score moderated the relationship between right fimbria/hippocampus volume ratio and episodic memory, such that patients with high PHS and lower volume ratio had lower episodic memory composite scores [ΔF = 6.730, p = 0.011, ΔR2 = 0.059]. This effect was also found among individuals with MCI [ΔF = 4.519, p = 0.038, ΔR2 = 0.050]. In contrast, no interaction effects were present for those NC or AD individuals. A follow-up mediation analysis also indicated that the right fimbria/hippocampus volume ratio might mediate the link between PHS and episodic memory performance in the MCI group, whereas no mediation effects were present for those NC or AD individuals.ConclusionThese findings suggest that the interaction between AD genetic risk and hippocampal subfield volume ratio increases memory impairment among older adults. Also, the results highlighted a potential pathway in which genetic risk affects memory by degrading hippocampal subfield volume ratio in cognitive decline subjects.

Plutella xylostella (Linnaeus) is one of the notorious pests causing substantial loses to numerous cruciferous vegetables across many nations. The sterile insect technique (SIT) is a safe and effective pest control method, which does not pollute the environment and does not produce drug resistance. We used proteomics technology and bioinformatics analysis to investigate the molecular mechanisms responsible for the effects of different doses of radiation treatment on the reproductive ability of male P. xylostella. A total of 606 differentially expressed proteins (DEPs) were identified in the 200 Gy/CK group, 1843 DEPs were identified in the 400 Gy/CK group, and 2057 DEPs were identified in the 400 Gy/200 Gy group. The results showed that after 200 Gy irradiation, the testes resisted radiation damage by increasing energy supply, amino acid metabolism and transport, and protein synthesis, while transcription-related pathways were inhibited. After 400 Gy irradiation, the mitochondria and DNA in the testis tissue of P. xylostella were damaged, which caused cell autophagy and apoptosis, affected the normal life activities of sperm cells, and greatly weakened sperm motility and insemination ability. Meanwhile, Western blotting showed that irradiation affects tyrosine phosphorylation levels, which gradually decrease with increasing irradiation dose.

We report on a high-power acoustic-optical (AO) Q-switched Tm:YLF laser operating at ~1.83 μm by controlling the transmittance of the output coupler. Under the continuous-wave (CW) operation, the maximum output power of 13 W is achieved, and the slope efficiency is up to 32.7%. With a YAG etalon inserted into the cavity, the linewidth is compressed below 0.5 nm with a maximum output power of 12.2 W. In the Q-switched state, the maximum pulsed output power of 10.32 W is achieved with a pulse duration of 150 ns when the repetition rate is 15 kHz. And the maximum pulsed energy of 1.13 mJ is generated with a duration of 131 ns at 5 kHz. As far as we know, this is the highest output power reported for the CW and pulsed 1.83 μm laser. In addition, the relationship between the output wavelength and crystal length is theoretically analyzed, which shows that increasing the loss of 1880 nm is the key to high-power 1.83 μm laser output.

This paper presents a Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (LSTM-HMM) to predict China’s Gross Domestic Product (GDP) fluctuation state within a rolling time window. We compare the predictive power of LSTM-HMM with other dynamic forecast systems within different time windows, which involves the Hidden Markov Model (HMM), Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and LSTM-HMM with an input of monthly Consumer Price Index (CPI) or quarterly CPI within 4-year, 6-year, 8-year and 10-year time window. These forecasting models employed in our empirical analysis share the basic HMM structure but differ in the generation of observable CPI fluctuation states. Our forecasting results suggest that (1) among all the models, LSTM-HMM generally performs better than the other models; (2) the model performance can be improved when model input transforms from quarterly to monthly; (3) among all the time windows, models within 10-year time window have better overall performance; (4) within 10-year time window, the LSTM-HMM, with either quarterly or monthly input, has the best accuracy and consistency.

A high-power narrow-linewidth 1.55 μm pulsed laser, based on MgO:PPLN OPO, has been achieved using a F–P etalon. The pump source is a 1064 nm acousto-optical (AO) Q-switched Nd:YAG laser with a repetition rate of 10 kHz. Under the maximum pump power of 18 W, the signal output power of 2.57 W is demonstrated at 1551.1 nm with a linewidth of 0.07 nm, corresponding to a slope efficiency of 16.1%. Different from traditional inversion lasers, the narrow-linewidth wavelength tunability of approximately 1.55 μm can be realized by changing the temperature.

BackgroundBrain invasion in meningioma has independent associations with increased risks of tumor progression, lesion recurrence, and poor prognosis. Therefore, this study aimed to construct a model for predicting brain invasion in WHO grade II meningioma by using preoperative MRI.MethodsOne hundred seventy-three patients with brain invasion and 111 patients without brain invasion were included. Three mainstream features, namely, traditional semantic features and radiomics features from tumor and tumor-to-brain interface regions, were acquired. Predictive models correspondingly constructed on each feature set or joint feature set were constructed.ResultsTraditional semantic findings, e.g., peritumoral edema and other four features, had comparable performance in predicting brain invasion with each radiomics feature set. By taking advantage of semantic features and radiomics features from tumoral and tumor-to-brain interface regions, an integrated nomogram that quantifies the risk factor of each selected feature was constructed and had the best performance in predicting brain invasion (area under the curve values were 0.905 in the training set and 0.895 in the test set).ConclusionsThis study provided a clinically available and promising approach to predict brain invasion in WHO grade II meningiomas by using preoperative MRI.

Many animal experiments and clinical trials showed that probiotics are effective for the treatment of alcoholic liver disease. Alcohol disrupts the composition of intestinal flora; probiotics modulate the gut microbiota and reverse alcohol-associated intestinal barrier dysfunction by decreasing intestinal mucosal permeability and preventing intestinal bacteria from translocating. Probiotics enhance immune responses and reduce the levels of alcohol-induced inflammatory cytokines and reactive oxygen species (ROS) production in the liver and intestine. Probiotics also increase fatty acid β-oxidation and reduce lipogenesis, combating alcohol-induced hepatic steatosis. In this review, we summarize the current knowledge regarding the mechanism of action of probiotics for reducing the effects of alcoholic liver disease.

The force situation of electrical pole is affected by the actural working condition of distribution network lines including height difference and line angle. By analyzing the influences and introducting a correction coefficient, the selection process of pole would be improved. It could provide enough safety margin to poles and avoid the damage in the case of extreme climate disasters.

At first, it introduced the device used for abandoning lines to protect electric poles from overload in distribution lines. The device set the weak parts in the annular ring, after the deformation of the rings caused by the load, the weak parts moved first, so that the device separated into two parts, finally the electric lines would fall down from the poles. It analyzed the action theory and set up the equivalent experiment to simulate the action process. By the experiment, it shown the reliability of the device in working, and by changing sizes of the annular ring, different action force of the device would be get, so that it could use to protect various levels electric poles.