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Abstract Primary biliary cholangitis (PBC) is a chronic autoimmune liver disease characterized by multilineage immune dysregulation, which subsequently causes inflammation, fibrosis, and even cirrhosis of liver. Due to the limitation of traditional assays, the local hepatic immunopathogenesis of PBC has not been fully characterized. Here, we utilize single-cell RNA sequencing technology to depict the immune cell landscape and decipher the molecular mechanisms of PBC patients. We reveal that cholangiocytes and hepatic stellate cells are involved in liver inflammation and fibrosis. Moreover, Kupffer cells show increased levels of inflammatory factors and decreased scavenger function related genes, while T cells exhibit enhanced levels of inflammatory factors and reduced cytotoxicity related genes. Interestingly, we identify a liver-resident Th1-like population with JAK-STAT activation in the livers of both PBC patients and murine PBC model. Finally, blocking the JAK-STAT pathway alleviates the liver inflammation and eliminates the liver-resident Th1-like cells in the murine PBC model. In conclusion, our comprehensive single-cell transcriptome profiling expands the understanding of pathological mechanisms of PBC and provides potential targets for the treatment of PBC in patients.

AbstractAngelica hirsutiflora Liu et al.1961, is a perennial herb in the Apiaceae family that is endemic to Taiwan. In this study, the complete circular chloroplast genome of A. hirsutiflora was reconstructed and annotated using Illumina sequencing. The size of the chloroplast genome is 154,266 bp, consisting of two inverted repeats (IRs, 25,075 bp) separated by a large single-copy region (LSC, 86,569 bp) and a small single-copy region (SSC, 17,547 bp). The GC content of the chloroplast genome is 37.6%. There are 114 different genes in the chloroplast genome of A. hirsutiflora, including 80 protein-coding genes, 30 tRNA genes and four rRNA genes. A maximum-likelihood phylogenetic analysis showed that A. hirsutiflora forms a distinct clade, and separated from other species within the genus Angelica. This study provided insights into the evolutionary relationships among different species of Angelica.

High-locality and hidden landslides, due to its significant characteristics of being difficult to access, identify, and monitor, have strong suddenness and destructiveness when they occur. Continuous monitoring and risk assessment of these landslides are of great significance. Traditional artificial ground survey methods and ground monitoring equipment have the characteristics of high risk, low efficiency, easy damage to equipment, and frequent offline false alarms. Thus, based on unmanned aerial vehicle (UAV) tilt photogrammetry, this study attempts to provide a digital twin method to characterize high-locality and hidden landslides by monitoring and analyzing the deformation and spatiotemporal evolution of geological disasters. This study uses UAV tilt photogrammetry technology to obtain 10 periods of aerial survey data of the Baige landslide on the Jinsha River in Tibet as the research area from April 2019 to September 2021. A multi-temporal digital twin landslide body is constructed, and high-precision quantitative monitoring of multi-dimensional factors, such as the overall sliding characteristics, local micro deformation, and collapse volume of the Baige landslide, is achieved, which are applied to the monitoring and warning of Baige landslide. The results show that there are signs of continuous deformation in the Baige landslide during the monitoring period from 2019 to 2021, and strong deformation mainly occurs at both sides and rear edges of the landslide, gradually expanding, and posing a risk of collapse and river blockage. The multi-temporal digital twin method and application of landslide deformation monitoring on qualitative and quantitative characteristics description and risk assessment of geological disasters are further analyzed. The method in this study has the advantages of fast and flexible, comprehensive coverage, and not limited by complex and dangerous terrain conditions, which could provide information for the large gradient deformation monitoring and engineering practice of slope disasters, such as high-locality and hidden landslides.

Abstract Electrophysiological working memory (WM) research shows brain areas communicate via macroscopic oscillations across frequency bands, generating nonlinear amplitude modulation (AM) in the signal. Traditionally, AM is expressed as the coupling strength between the signal and a prespecified modulator at a lower frequency. Therefore, the idea of AM and coupling cannot be studied separately. In this study, 33 participants completed a color recall task while their brain activity was recorded through EEG. The AM of the EEG data was extracted using the Holo-Hilbert spectral analysis (HHSA), an adaptive method based on the Hilbert-Huang transforms. The results showed that WM load modulated parieto-occipital alpha/beta power suppression. Furthermore, individuals with higher frontal theta power and lower parieto-occipital alpha/beta power exhibited superior WM precision. In addition, the AM of parieto-occipital alpha/beta power predicted WM precision after presenting a target-defining probe array. The phase-amplitude coupling (PAC) between the frontal theta phase and parieto-occipital alpha/beta AM increased with WM load while processing incoming stimuli, but the PAC itself did not predict the subsequent recall performance. These results suggest frontal and parieto-occipital regions communicate through theta-alpha/beta PAC. However, the overall recall precision depends on the alpha/beta AM following the onset of the retro cue.

In the field of financial risk management, the accuracy of portfolio Value-at-Risk (VaR) forecasts is of critical importance to both practitioners and academics. This study pioneers a comprehensive evaluation of a univariate model that leverages high-frequency intraday data to improve portfolio VaR forecasts, providing a novel contrast to both univariate and multivariate models based on daily data. Existing research has used such high-frequency-based univariate models for index portfolios, it has not adequately studied their robustness for portfolios with diverse risk profiles, particularly under changing market conditions, such as during crises. Our research fills this gap by proposing a refined univariate long-memory realized volatility model that incorporates realized variance and covariance metrics, eliminating the necessity for a parametric covariance matrix. This model captures the long-run dependencies inherent in the volatility process and provides a flexible alternative that can be paired with appropriate return innovation distributions for VaR estimation. Empirical analyses show that our methodology significantly outperforms traditional univariate and multivariate Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models in terms of forecasting accuracy while maintaining computational simplicity and ease of implementation. In particular, the inclusion of high-frequency data in univariate volatility models not only improves forecasting accuracy but also streamlines the complexity of portfolio risk assessment. This research extends the discourse between academic research and financial practice, highlighting the transformative impact of high-frequency data on risk management strategies within the financial sector.

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

Objectives Identification of endometrial cancers (EC) with mismatch repair deficiency (dMMR) or microsatellite instability-high (MSI-H) is essential for Lynch syndrome screening and treatment stratification. We aimed to assess the utility of immunohistochemistry (IHC) staining for MMR protein expression and polymerase chain reaction (PCR)-based MSI assays in EC and the correlation between MMR/MSI status and various clinicopathological parameters. Methods We reviewed the clinical and pathological information of 333 patients with EC. MMR protein expression was assessed as retained or lost to determine MMR status by IHC staining, and MSI status was identified by PCR capillary electrophoresis (PCR-CE) testing with a National Cancer Institute (NCI) panel. The correlation of MMR/MSI status with clinicopathological features was determined by statistical analysis. Discrepant results were further analyzed using an alternative PCR-CE MSI (Promega panel) method, MLH1 promoter methylation assays, and next-generation sequencing (NGS). Results Among the EC patients, the overall percentage of dMMR was 25.2%, and the overall percentage of MSI-H was 24%. Among the dMMR patients, 50 (59.5%) showed loss of MLH1 and PMS2 expression, 19 (22.6%) loss of MSH2 and MSH6 expression, and seven (8.3%) and eight (9.5%) loss of PMS2 and MSH6 expression, respectively. The dMMR subgroup was significantly younger than the pMMR subgroup, especially for

AimsOur aim was to differentiate patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from cognitively normal (CN) individuals and predict the progression from MCI to AD within a 3-year longitudinal follow-up. A newly developed Holo-Hilbert Spectral Analysis (HHSA) was applied to resting state EEG (rsEEG), and features were extracted and subjected to machine learning algorithms.MethodsA total of 205 participants were recruited from three hospitals, with CN (n = 51, MMSE > 26), MCI (n = 42, CDR = 0.5, MMSE ≥ 25), AD1 (n = 61, CDR = 1, MMSE < 25), AD2 (n = 35, CDR = 2, MMSE < 16), and AD3 (n = 16, CDR = 3, MMSE < 16). rsEEG was also acquired from all subjects. Seventy-two MCI patients (CDR = 0.5) were longitudinally followed up with two rsEEG recordings within 3 years and further subdivided into an MCI-stable group (MCI-S, n = 36) and an MCI-converted group (MCI-C, n = 36). The HHSA was then applied to the rsEEG data, and features were extracted and subjected to machine-learning algorithms.Results(a) At the group level analysis, the HHSA contrast of MCI and different stages of AD showed augmented amplitude modulation (AM) power of lower-frequency oscillations (LFO; delta and theta bands) with attenuated AM power of higher-frequency oscillations (HFO; beta and gamma bands) compared with cognitively normal elderly controls. The alpha frequency oscillation showed augmented AM power across MCI to AD1 with a reverse trend at AD2. (b) At the individual level of cross-sectional analysis, implementation of machine learning algorithms discriminated between groups with good sensitivity (Sen) and specificity (Spec) as follows: CN elderly vs. MCI: 0.82 (Sen)/0.80 (Spec), CN vs. AD1: 0.94 (Sen)/0.80 (Spec), CN vs. AD2: 0.93 (Sen)/0.90 (Spec), and CN vs. AD3: 0.75 (Sen)/1.00 (Spec). (c) In the longitudinal MCI follow-up, the initial contrasted HHSA between MCI-S and MCI-C groups showed significantly attenuated AM power of alpha and beta band oscillations. (d) At the individual level analysis of longitudinal MCI groups, deploying machine learning algorithms with the best seven features resulted in a sensitivity of 0.9 by the support vector machine (SVM) classifier, with a specificity of 0.8 yielded by the decision tree classifier.ConclusionIntegrating HHSA into EEG signals and machine learning algorithms can differentiate between CN and MCI as well as also predict AD progression at the MCI stage.

Abstract Purpose In soft tissue reconstructive surgery, perforator localization and flap harvesting have always been critical challenges, but augmented reality (AR) has become a dominant technology to help map perforators. Methods The lateral circumflex femoral artery (LCFA) and its perforators were reconstructed by CTA in consecutive patients (N = 14). Then, the anterolateral thigh perforators and the points from which the perforators emerged from the deep fascia were marked and projected onto the skin surface. As the virtual images were projected onto patients according to bony markers, the courses of the LCFA and its perforators were depicted on the skin surface for intraoperative guidance. Finally, the locations of the emergence points were verified by intraoperative findings and compared to those determined by handheld Doppler ultrasound. Results The sources, locations, and numbers of perforators were determined by CTA. The perforators and their emergence points were accurately mapped on the skin surface by a portable projector to harvest the anterolateral thigh perforator flap. During the operation, the accuracy of the CTA & AR method was 90.2% (37/41), and the sensitivity reached 97.4% (37/38), which were much higher than the corresponding values of Doppler ultrasound. Additionally, the differences between the AR-marked points and the intraoperative findings were much smaller than those seen with Doppler ultrasound (P

This study generated two fused protoplasts of Antrodia cinnamomea and Cordyceps militaris in two ways. The protoplasts of A. cinnamomea were inactivated by heat to inactivate biochemical processes and enzymatic activities in the cytoplasm, and the protoplasts of C. militaris were inactivated by UV radiation to invalidate their genome function, then they were fused under optimal conditions to get a fusion rate as (7.42 ± 0.8) × 10-6 fusants/mL; the new fusants were abbreviated as Ac-Cm. On the other hand, when A. cinnamomea and C. militaris were treated with heat and UV oppositely using similar experiments, the fusion rate was (9.70 ± 0.68) × 10-5 fusants/mL, and the new fusants were abbreviated as Cm-Ac. We selected each of two best-growing fused colonies Ac-Cm-1, Ac-Cm-2, Cm-Ac-1, and Cm-Ac-2, together with parental A. cinnamomea and C. militaris, and studied their morphology, growth antagonism tests, and genetic relationships by 18S rRNA sequencing. In comparison with the initial cultures of 4 fusants, the yields of adenosine, biomass, cordycepic acid, cordycepin, total polysaccharide, and total triterpenoids were increased up 1.305−50.1563 times in the optimal medium conditions. For gene stability tests, those of the four fusants and their outputs were stabilized within 10 generations.

In order to address the issue of nobiletin's limited bioavailability, nobiletin nanoparticles (NNP) were created for the first time in this research employing an anti-solvent under ultrasonication-cis/reverse homogenization. Dimethyl sulfoxide (DMSO) was used as the solvent and deionized water as the anti-solvent to create the nobiletin solution. The optimal surfactant dose of surfactant dose of 0.43%; an ultrasonic period of 8.1 min, ultrasonic at a temperature of 64 °C and a solution concentration of 8.33 mg/mL, the method was optimized to obtain the minimum NNP diameter of 199.89 ± 0.02 nm. A dual optimization process of response surface PBD and BBD was used to minimize the size of HNP particles. Additionally, scanning electron microscopy revealed that the specific surface area of the NNP dramatically increased with the reduction of NNP particle size, and dissolving studies indicated the solubility and dissolution studies showed that NNP had substantially greater solubility and dissolution rates than raw nobiletin per unit time; as a result, the NNP produced by anti-solvent precipitation with a twofold homogenization system supported by ultrasound had a realistic potential for growth.

The 14-3-3 (GRF, general regulatory factor) regulatory proteins are highly conserved and are widely distributed throughout the eukaryotes. They are involved in the growth and development of organisms via target protein interactions. Although many plant 14-3-3 proteins were identified in response to stresses, little is known about their involvement in salt tolerance in apples. In our study, nineteen apple 14-3-3 proteins were cloned and identified. The transcript levels of Md14-3-3 genes were either up or down-regulated in response to salinity treatments. Specifically, the transcript level of MdGRF6 (a member of the Md14-3-3 genes family) decreased due to salt stress treatment. The phenotypes of transgenic tobacco lines and wild-type (WT) did not affect plant growth under normal conditions. However, the germination rate and salt tolerance of transgenic tobacco was lower compared to the WT. Transgenic tobacco demonstrated decreased salt tolerance. The transgenic apple calli overexpressing MdGRF6 exhibited greater sensitivity to salt stress compared to the WT plants, whereas the MdGRF6-RNAi transgenic apple calli improved salt stress tolerance. Moreover, the salt stress-related genes (MdSOS2, MdSOS3, MdNHX1, MdATK2/3, MdCBL-1, MdMYB46, MdWRKY30, and MdHB-7) were more strongly down-regulated in MdGRF6-OE transgenic apple calli lines than in the WT when subjected to salt stress treatment. Taken together, these results provide new insights into the roles of 14-3-3 protein MdGRF6 in modulating salt responses in plants.

Bupleurum kaoi Liu, Chao, and Chuang is an endemic and endangered herb in Taiwan. In this study, the complete circular chloroplast genome of B. kaoi was reconstructed and annotated using Illumina sequencing. The genome size of B. kaoi is 155,938 bp, including a pair of inverted repeat regions (IRs: 26308 bp), separated by a large single-copy (LSC) region of 85,784 bp and a small single-copy (SSC) region of 17,538 bp. The GC content of the chloroplast genome is 37.6%. There are 113 different genes in the chloroplast genome of B. kaoi, including 79 protein-coding genes, 30 tRNA genes, and four rRNA genes. A maximum-likelihood phylogenetic analysis showed that Bupleurum species is the monophyletic group, and B. kaoi belongs to subgenus Bupleurum and is closely related to B. scorzonerifolium.

Electroencephalography (EEG) can reveal the abnormalities of dopaminergic subcortico-cortical circuits in patients with Parkinson’s disease (PD). However, conventional time-frequency analysis of EEG signals cannot fully reveal the non-linear processes of neural activities and interactions. A novel Holo-Hilbert Spectral Analysis (HHSA) was applied to reveal non-linear features of resting state EEG in 99 PD patients and 59 healthy controls (HCs). PD patients demonstrated a reduction of β bands in frontal and central regions, and reduction of γ bands in central, parietal, and temporal regions. Compared with early-stage PD patients, late-stage PD patients demonstrated reduction of β bands in the posterior central region, and increased θ and δ2 bands in the left parietal region. θ and β bands in all brain regions were positively correlated with Hamilton depression rating scale scores. Machine learning algorithms using three prioritized HHSA features demonstrated “Bag” with the best accuracy of 0.90, followed by “LogitBoost” with an accuracy of 0.89. Our findings strengthen the application of HHSA to reveal high-dimensional frequency features in EEG signals of PD patients. The EEG characteristics extracted by HHSA are important markers for the identification of depression severity and diagnosis of PD.

Abstract Microorganisms capable of decomposing cellulose, xylan, starch and protein were individually isolated from swine manure compost and soil in this study. The correlations with pH, carbon source concentration, C/N ratio and enzyme activity among these isolated microorganisms were also investigated. Furthermore, the effect of additional inoculation in the compost was studied by measuring variations in the C/N ratio, enzyme activity and compost maturation rate. The inoculated microorganisms used in this study included four bacterial isolates and one commercial microorganism Phanerochaete chrysosporium. The results indicated that the isolated Kitasatospora phosalacinea strain C1, which is a cellulose-degraded microorganism, presented the highest enzyme activity at 31 ℃ and pH 5.5, while the C/N ratio was 0.8%. The isolated xylan-degraded microorganism Paenibacillus glycanilyticus X1 had the highest enzyme activity at 45 ℃ and pH 7.5, while the C/N ratio was 0.5%. The starch-degraded microorganism was identified as Bacillus licheniformis S3, and its highest enzyme activities were estimated to be 31 ℃ and pH 7.5 while the C/N ratio was 0.8%. The highest enzyme activity of the protein-degraded microorganism Brevinacillus agri E4 was obtained at 45 ℃ and pH 8.5, while the C/N ratio was 1.0%. The rate of temperature increase in the compost inoculated with P. chrysosporium was only higher than that of the compost without inoculation, and its compost maturation level was also lower than that of other composts with additional inoculation. The optimal initial C/N ratio of the compost was 27.5 and the final C/N ratio was 18.9. The composting results also indicated that the secondary inoculation would benefit compost maturation, and the lowest final C/N ratio of 17.0 was obtained.

Abstract The detection of event-related potentials (ERPs) through electroencephalogram (EEG) analysis is a well-established method for understanding brain functions during a cognitive process. To increase the signal-to-noise ratio (SNR) and stationarity of the data, ERPs are often filtered to a wideband frequency range, such as 0.05–30 Hz. Alternatively, a natural-filtering procedure can be performed through empirical mode decomposition (EMD), which yields intrinsic mode functions (IMFs) for each trial of the EEG data, followed by averaging over trials to generate the event-related modes. However, although the EMD-based filtering procedure has advantages such as a high SNR, suitable waveform shape, and high statistical power, one fundamental drawback of the procedure is that it requires the selection of an IMF (or a partial sum of a range of IMFs) to determine an ERP component effectively. Therefore, in this study, we propose an intrinsic ERP (iERP) method to overcome the drawbacks and retain the advantages of event-related mode analysis for investigating ERP components. The iERP method can reveal multiple ERP components at their characteristic time scales and suitably cluster statistical effects among modes by using a tailored definition of each mode’s neighbors. We validated the iERP method by using realistic EEG data sets acquired from a face perception task and visual working memory task. By using these two data sets, we demonstrated how to apply the iERP method to a cognitive task and incorporate existing cluster-based tests into iERP analysis. Moreover, iERP analysis revealed the statistical effects between (or among) experimental conditions more effectively than the conventional ERP method did.

Background Dietary potassium (K+) has emerged as a modifiable factor for cardiovascular and kidney health in the general population, but its role in people with chronic kidney disease (CKD) is unclear. Here, we hypothesize that CKD increases the susceptibility to the negative effects of low and high K+ diets. Methods We compared the effects of low, normal and high KChloride (KCl) diets and a high KCitrate diet for 4 weeks in male rats with normal kidney function and in male rats with CKD using the 5/6th nephrectomy model (5/6Nx). Results Compared with rats with normal kidney function, 5/6Nx rats on the low KCl diet developed more severe extracellular and intracellular K+ depletion and more severe kidney injury, characterized by nephromegaly, infiltration of T cells and macrophages, decreased estimated glomerular filtration rate and increased albuminuria. The high KCl diet caused hyperkalemia, hyperaldosteronism, hyperchloremic metabolic acidosis and severe hypertension in 5/6Nx but not in sham rats. The high KCitrate diet caused hypochloremic metabolic alkalosis but attenuated hypertension despite higher abundance of the phosphorylated sodium chloride cotransporter (pNCC) and similar levels of plasma aldosterone and epithelial sodium channel abundance. All 5/6Nx groups had more collagen deposition than the sham groups and this effect was most pronounced in the high KCitrate group. Plasma aldosterone correlated strongly with kidney collagen deposition. Conclusions CKD increases the susceptibility to negative effects of low and high K+ diets in male rats, although the injury patterns are different. The low K+ diet caused inflammation, nephromegaly and kidney function decline, whereas the high K+ diet caused hypertension, hyperaldosteronism and kidney fibrosis. High KCitrate attenuated the hypertensive but not the pro-fibrotic effect of high KCl, which may be attributable to K+-induced aldosterone secretion. Our data suggest that especially in people with CKD it is important to identify the optimal threshold of dietary K+ intake. [ABSTRACT FROM AUTHOR]