Electricity consumption is closely linked to economic growth, social development, and carbon emissions. In order to fill the gap of previous studies on the decomposition of electricity consumption ...drivers that have not adequately considered carbon emission constraint, this study constructs the Kaya extended model of electricity consumption and analyzes the effects of drivers in industrial and residential sectors using the Logarithmic Mean Divisia Index (LMDI) method, and empirically explores the temporal and spatial differences in electricity consumption. Results show that: (1) During 2005–2021, the total final electricity consumption growth in Guangdong was much higher than that in Yunnan, but the average annual growth rate in Guangdong was lower, and the largest growth in both provinces was in the industrial sector. (2) The labor productivity level effect is the primary driver that increases total final electricity consumption (Guangdong: 78.5%, Yunnan: 87.1%), and the industrial carbon emission intensity effect is the primary driver that decreases total final electricity consumption (Guangdong: −75.3%, Yunnan: −72.3%). (3) The year-to-year effect of each driver by subsector is overall positively correlated with the year-to-year change in the corresponding driver, and declining carbon emission intensity is a major factor in reducing electricity consumption. (4) The difference in each effect between Guangdong and Yunnan is mainly determined by a change in the corresponding driver and subsectoral electricity consumption. Policy implications are put forward to promote energy conservation and the realization of the carbon neutrality goal.
Insular subdivisions show distinct patterns of resting state functional connectivity with specific brain regions, each with different functional significance in chronic cigarette smokers. This study ...aimed to explore the altered dynamic functional connectivity (dFC) of distinct insular subdivisions in smokers.
Resting-state BOLD data of 31 smokers with nicotine dependence and 27 age-matched non-smokers were collected. Three bilateral insular regions of interest (dorsal, ventral, and posterior) were set as seeds for analyses. Sliding windows method was used to acquire the dFC metrics of different insular seeds. Support vector machine based on abnormal insular dFC was applied to classify smokers from non-smokers.
We found that smokers showed lower dFC variance between the left ventral anterior insula and both the right superior parietal cortex and the left inferior parietal cortex, as well as greater dFC variance the right ventral anterior insula with the right middle cingulum cortex relative to non-smokers. Moreover, compared to non-smokers, it is found that smokers demonstrated altered dFC variance of the right dorsal insula and the right middle temporal gyrus. Correlation analysis showed the higher dFC between the right dorsal insula and the right middle temporal gyrus was associated with longer years of smoking. The altered insular subdivision dFC can classify smokers from non-smokers with an accuracy of 89.66%, a sensitivity of 96.30% and a specify of 83.87%.
Our findings highlighted the abnormal patterns of fluctuating connectivity of insular subdivision circuits in smokers and suggested that these abnormalities may play a significant role in the mechanisms underlying nicotine addiction and could potentially serve as a neural biomarker for addiction treatment.
In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in ...WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison.
1,3,6-Trigalloylglucose is a natural compound that can be extracted from the aqueous extracts of ripe fruit of
Retz, commonly known as "
". The potential anti-
(HP) activity of this compound has not ...been extensively studied or confirmed in scientific research. This compound was isolated using a semi-preparative liquid chromatography (LC) system and identified through Ultra-high-performance liquid chromatography-MS/MS (UPLC-MS/MS) and Nuclear Magnetic Resonance (NMR). Its role was evaluated using Minimum inhibitory concentration (MIC) assay and minimum bactericidal concentration (MBC) assay, scanning electron microscope (SEM), inhibiting kinetics curves, urea fast test, Cell Counting Kit-8 (CCK-8) assay, Western blot, and Griess Reagent System. Results showed that this compound effectively inhibits the growth of HP strain ATCC 700392, damages the HP structure, and suppresses the Cytotoxin-associated gene A (Cag A) protein, a crucial factor in HP infection. Importantly, it exhibits selective antimicrobial activity without impacting normal epithelial cells GES-1. In vitro studies have revealed that 1,3,6-Trigalloylglucose acts as an anti-adhesive agent, disrupting the adhesion of HP to host cells, a critical step in HP infection. These findings underscore the potential of 1,3,6-Trigalloylglucose as a targeted therapeutic agent against HP infections.
We synthesize monodisperse ionic microgel particles which undergo a large change in volume in response to environmental stimuli such as pH and temperature. In addition, the study elucidates the ...effective uptake and release of rheology modifiers from these microgel particles to alter the bulk viscosity of a surrounding fluid. Moreover, we found that the prepared ionic microgel particles can demonstrate abilities to adsorb and repel iron oxide nanoparticles (Fe3O4-NPs) upon pH variation. The extent of the loading of Fe3O4-NPs within the colloidal particles and morphology can be manipulated by tunable interactions between the Fe3O4-NPs and ionic microgel particles.
Objective:
We have already demonstrated that mesenchymal stem cells from patients with ankylosing spondylitis (ASMSCs) exhibited greater adipogenic differentiation potential than those from healthy ...donors (HDMSCs). Here, we further investigated the expression profile of long noncoding RNA (lncRNA) and mRNA, aiming to explore the underlying mechanism of abnormal adipogenic differentiation in ASMSCs.
Methods:
HDMSCs and ASMSCs were separately isolated and induced with adipogenic differentiation medium for 10 days. Thereafter, lncRNAs and mRNAs that were differentially expressed (DE) between HDMSCs and ASMSCs were identified
via
high-throughput sequencing and confirmed by quantitative real-time PCR (qRT–PCR) assays. Then, the DE genes were annotated and enriched by GO analysis. In addition, protein interaction network was constructed to evaluate the interactions between DE mRNAs and to find hub nodes and study cliques. Besides, co-expression network analysis was carried out to assess the co-expressions between DE mRNA and DE lncRNAs, and competing endogenous RNA (ceRNA) network analysis were conducted to predict the relationships among lncRNAs, mRNAs and miRNAs. The signaling pathways based on the DE genes and the predicted DE genes were enriched by KEGG analysis.
Results:
A total of 263 DE lncRNAs and 1376 DE mRNAs were found during adipogenesis in ASMSCs. qRT–PCR indicated that the expression of the top 20 mRNAs and the top 10 lncRNAs was consistent with the high-throughput sequencing data. Several lncRNAs (NR_125386.1, NR_046473.1 and NR_038937.1) and their target genes (SPN and OR1AIP2), together with the significantly co-expressed pairs of DE lncRNAs and DE mRNAs (SLC38A5-ENST00000429588.1, TMEM61-ENST00000400755.3 and C5orf46-ENST00000512300.1), were closely related to the enhanced adipogenesis of ASMSCs by modulating the PPAR signaling pathway.
Conclusion:
Our study analyzed the expression profiles of DE lncRNAs and DE mRNAs during adipogenesis in ASMSCs and HDMSCs. Several DE lncRNAs, DE mRNAs and signaling pathways that probably participate in the aberrant adipogenesis of ASMSCs were selected for future study. These results will likely provide potential targets for our intervention on fat metaplasia and subsequent new bone formation in patients with AS in the future.
To evaluate the efficacy of the endoscopic lumbar interbody fusion technique across different types of lumbar spondylolisthesis, specifically Grade I and Grade II, and suggest technical optimizations ...based on therapeutic outcomes, complications, and patient satisfaction for both grades.
We analyzed data from 57 L4 to 5 spondylolisthesis patients, all categorized as either Grade I or Grade II, comprising 31 males and 26 females. Of these, 36 were diagnosed with Grade I and 21 with Grade II. All subjects underwent the endoscopic lumbar interbody fusion procedure. Primary evaluation metrics included pre and post-operative Vasual Analogue Scale（VAS） pain scores, Osewewtry Disability Index（ODI） functional scores, surgical duration, intraoperative blood loss, degree of spondylolisthesis correction, complications, and patient satisfaction levels.
At a minimum of 6 months post-operation, the VAS score for the Grade I cohort reduced from an initial 7.30 ± 0.69 to 2.97 ± 0.47, while the Grade II cohort saw a decrease from 7.53 ± 0.56 to 3.37 ± 0.62 (P = 0.0194). The ODI score in the Grade I group declined from 66.88 ± 5.15 % pre-operation to 29.88 ± 6.36 % post-operation, and in the Grade II group, it decreased from 69.33 ± 5.27 % to 34.66 ± 6.01 % (P = 0.0092). The average surgical duration for the Grade I group stood at 155.72 ± 17.75 min, compared to 180.38 ± 14.72 min for the Grade II group (P < 0.001). The mean intraoperative blood loss for the Grade I group was 144.58 ± 28.61 ml, whereas the Grade II group registered 188.23 ± 9.41 ml (P < 0.001). Post-surgery, 83 % of the Grade I patients achieved a correction degree exceeding 80 %, and 61 % of the Grade II patients surpassed 50 % (P = 0.0055). Complication rates were recorded at 8 % for Grade I and 16 % for Grade II. Patient satisfaction reached 94 % in the Grade I cohort and 90 % in the Grade II cohort.
Endoscopic lumbar interbody fusion showcases promising therapeutic outcomes for both Grade I and Grade II lumbar spondylolisthesis. However, surgeries for Grade II spondylolisthesis tend to be lengthier, more challenging, involve greater blood loss, and have a heightened complication risk. Tailored technical adjustments and enhancements are essential for addressing the distinct spondylolisthesis types.
: Abnormalities of cognitive and movement functions are widely reported in Parkinson's disease (PD). The mechanisms therein are complicated and assumed to a coordination of various brain regions. ...This study explored the alterations of global synchronizations of brain activities and investigated the neural correlations of cognitive and movement function in PD patients.
: Thirty-five age-matched patients with PD and 35 normal controls (NC) were enrolled in resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Degree centrality (DC) was calculated to measure the global synchronizations of brain activity for two groups. Neural correlations between DC and cognitive function Frontal Assessment Battery (FAB), as well as movement function Unified Parkinson's Disease Rating Scale (UPDRS-III), were examined across the whole brain within Anatomical Automatic Labeling (AAL) templates.
: In the PD group, increased DC was observed in left fusiform gyrus extending to inferior temporal gyrus, left middle temporal gyrus (MTG) and angular gyrus, while it was decreased in right inferior opercular-frontal gyrus extending to superior temporal gyrus (STG). The DC in a significant region of the fusiform gyrus was positively correlated with UPDRS-III scores in PD (
= 0.41,
= 0.0145). Higher FAB scores were shown in NC than PD (
< 0.0001). Correlative analysis of PD between DC and FAB showed negative results (
< 0.05) in frontal cortex, whereas positive in insula and cerebellum. As for the correlations between DC and UPDRS-III, negative correlation (
< 0.05) was observed in bilateral inferior parietal lobule (IPL) and right cerebellum, whereas positive correlation (
< 0.05) in bilateral hippocampus and para-hippocampus gyrus (
< 0.01).
: The altered global synchronizations revealed altered cognitive and movement functions in PD. The findings suggested that the global functional connectivity in fusiform gyrus, cerebellum and hippocampus gyrus are critical regions in the identification of cognitive and movement functions in PD. This study provides new insights on the interactions among global coordination of brain activity, cognitive and movement functions in PD.
Electricity substitution is an effective measure for reducing pollutant emissions and promoting the consumption of renewable energy. The current research lacks a quantitative analysis method for the ...factors affecting regional electricity substitution. This article proposes a decomposition model of the factors affecting electricity substitution based on Logarithmic Mean Divisia Index method to expand the depth and breadth of electricity substitution. Results show that: (1) Macro‐level electricity substitution in Guangdong province develops steadily, with 1694.93 × 108 kWh of total electricity substitution quantity during the period 2006–2020. The electricity substitution quantity in production sector accounts for 83.92% of the total, which is much larger than that in household sector. (2) Sustained improvement of labour productivity gives the largest contribution (3592.32 × 108 kWh) to the increase of electricity substitution in production sector during the period 2006–2020. Living standard effect has the largest contribution (452.21 × 108 kWh) to the increase of electricity substitution in household sector during the period 2006–2020. Electrification level effect and population effect have significant impact on electricity substitution development, while industrial structure has little impact on electricity substitution. (3) Energy intensity effect and energy consumption growth effect negatively influence electricity substitution in Guangdong province. The decline of energy intensity in the production sector has driven the decrease of electricity substitution quantity, contributing −2540.36 × 108 kWh to electricity substitution during the period 2006–2020. Policy implications like continuously promoting breakthroughs in electricity substitution technologies, improvement of electrification rate in the sectors with potential for substitution, and improvement the price mechanism for fossil fuels and electricity can promote the deepening development of electricity substitution and the realisation of the carbon neutrality goal.
This paper proposes a decomposition model of the factors affecting electricity substitution based on LMDI method to expand the depth and breadth of electricity substitution. Taking Guangdong province as an empirical object, we obtain quantitative decomposition results of the impact of each factor on electricity substitution. After analysis and discussion, targeted suggestions are proposed to deepen the development of electricity substitution.
Ultrasound is a commonly used imaging modality for breast cancer diagnosis and prognosis but suffers from false positives, false negatives and interobserver variability. Deep learning (DL), a subset ...of artificial intelligence, has the potential to improve the efficiency and accuracy of breast ultrasound. This article provides a comprehensive overview of DL applications for breast cancer diagnosis and treatment in ultrasound, including methodological descriptions of various DL models, and clinical applications on noise reduction, lesion localization, risk assessment, diagnosis, response evaluation and outcome prediction. Furthermore, the review highlights the importance of interpretability and small sample size learning of DL-based systems in clinical practice; specific recommendations for further expanding the clinical impact of DL-based systems are also provided.
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