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Semi-supervised learning has gained significant attention in the field of remote sensing due to its ability to effectively leverage both a limited number of labeled samples and a large quantity of unlabeled data. An effective semi-supervised learning approach utilizes unlabeled samples to enforce prediction consistency under minor perturbations, thus reducing the model's sensitivity to noise and suppressing false positives in change-detection tasks. This principle underlies consistency regularization-based methods. However, while these methods enhance noise robustness, they also risk overlooking subtle but meaningful changes, leading to information loss and missed detections. To address this issue, we introduce a simple yet efficient method called Sample Inflation Interpolation (SII). This method leverages labeled sample pairs to mitigate the information loss caused by consistency regularization. Specifically, we propose a novel data augmentation strategy that generates additional change samples by combining existing supervised change samples with calculated proportions of change areas. This approach increases both the quantity and diversity of change samples in the training set, effectively compensating for potential information loss and reducing missed detections. Furthermore, to prevent overfitting, small perturbations are applied to the generated sample pairs and their labels. Experiments conducted on two public change detection (CD) datasets validate the effectiveness of our proposed method. Remarkably, even with only 5% of labeled training data, our method achieves performance levels that closely approach those of fully supervised learning models. [ABSTRACT FROM AUTHOR]

Domain generalization person re-identification (DG-ReID) has gained much attention recently due to the poor performance of supervised re-identification on unseen domains. The goal of domain generalization is to develop a model that is insensitive to domain bias and can perform well across different domains. In this article, We conduct experiments to verify the importance of style factors in domain bias. Specifically, the experiments are to affirm that style bias across different domains significantly contributes to domain bias. Based on this observation, we propose style variable and irrelevant learning (SVIL) to eliminate the influence of style factors on the model. Specifically, we employ a style jitter module (SJM) that enhances the style diversity of a specific source domain and reduces the style differences among various source domains. This allows the model to focus on identity-relevant information and be robust to style changes. We also integrate the SJM module with a meta-learning algorithm to further enhance the model's generalization ability. Notably, our SJM module is easy to implement and does not add any inference cost. Our extensive experiments demonstrate the effectiveness of our approach, which outperforms existing methods on DG-ReID benchmarks. [ABSTRACT FROM AUTHOR]

1,3,6-Trigalloylglucose is a natural compound that can be extracted from the aqueous extracts of ripe fruit of Terminalia chebula Retz, commonly known as "Haritaki". The potential anti-Helicobacter pylori (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. [ABSTRACT FROM AUTHOR]

The permeability of low-permeability cores is generally measured using a pulse decay method. The core of low-permeability rocks, such as shale, often has a layered structure. The applicability of pulse decay testing for layered cores is not clear. In this study, the performance of the pulse decay method on layered cores was comprehensively investigated. Numerical simulations were conducted to investigate the influence of the interlayer permeability ratio, storativity ratio, layer thickness, interlayer location, and number of layers on the pulse decay pressure and pressure derivative curves, as well as the permeability obtained from pulse decay testing. The results revealed that the pressure curves of layered cores exhibit distinct differences from those of homogeneous cores if the upstream permeability is larger than the downstream one. The pressure derivative curve shows more inclined or horizontal straight-line segments than in the homogeneous case. The shapes of the pressure and pressure derivative curves are affected by the upstream and downstream positions of the core, but the tested permeability is not affected. The tested permeability differs from the equivalent model permeability, with an error of up to 22%. If the number of layers is not less than 10, the permeability obtained from the pulse decay test is consistent with that of the equivalent model. These differences are influenced by the interlayer permeability ratio, storativity ratio, layer thickness, interlayer location, and number of layers. To improve the accuracy of permeability analysis in pulse decay testing for layered cores, curve fitting using the characteristics of the pressure derivative curve can be employed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Our meta‐analysis aims to evaluate the association of Asp299Gly and Thr399Ile with rheumatoid arthritis (RA) susceptibility and severity. By manually searching 3 electronic databases (PubMed, Embase and Web of Science), relevant articles were collected. After checking eligibility for every study, this meta‐analysis on eligible studies was performed under 5 genetic models: (1) allelic contrast; (2) heterozygous model; (3) homozygous model; (4) dominant model; (5) recessive model. In Spanish populations, a significantly decreased RA risk was identified in allelic comparison (odds ratio [OR] = 0.73, 95% CI 0.55 ~ 0.96) and dominant model (OR = 0.74, 95% CI 0.56 ~ 0.99) of Asp299Gly polymorphism. A trend of reduced risk was also observed under the heterozygous model (OR = 0.77, 95% CI 0.58 ~ 1.03). As for Thr399Ile, it might also have a protective effect on Spanish populations in allelic comparison (OR = 0.71, 95% CI 0.44 ~ 1.15). In contrast, for both Asp299Gly and Thr399Ile, a higher risk of RA was detected in Chinese Han populations. The frequency of both Asp299Gly and Thr399Ile increased in rheumatoid factor (RF)‐positive subjects in Chinese patients (Asp299Gly, RF+:RF− = 0.165:0.145; Thr399Ile, RF+:RF− = 0.170:0.161) and decreased in Spanish patients (Asp299Gly, RF+:RF− = 0.060:0.073; Thr399Ile, RF+:RF− = 0.046:0.056), but not to a statistically significant extent. Our meta‐analysis suggested that both Asp299Gly and Thr399Ile might have a protective effect on Spanish populations, but the 2 polymorphisms could act as a susceptible factor in Chinese Han populations. To confirm our results, further investigation concerning the functional impacts of Asp299Gly and Thr399Ile are still needed. [ABSTRACT FROM AUTHOR]

The current study investigated the cognitive and neural substrates that underpin writing ability. We explored similarities and differences in writing numbers and words and compared these to language and manual actions in a large group of sub-acute, stroke patients (n = 740). The behavioral data showed association and dissociation in the ability to write words and numbers. Comorbidities of writing deficits with both language and motor impairments were prevalent, with less than a handful showing deficits restricted to the writing tasks. A second analysis with a subset of patients (n = 267) explored the neural networks that mediate writing abilities. Lesion to right temporal contributed to writing words, while lesions to left postcentral contributed to writing numbers. Overlapping neural mechanisms included the bilateral prefrontal cortex, right inferior parietal, left middle occipital and the right cerebellum. With the former regions associated with error pattern typical to writing based on prior knowledge (the lexical route), while lesion to left MOG was associated with errors to the phonological (non-lexical) route. Using principle components extracted from the behavioral data, we showed that right prefrontal and right parietal contributed to the ability to use pen, while lesion to bilateral prefrontal, inferior temporal and cerebellum supported unique use of pen for writing. The behavioral and imaging data suggested that writing numbers and words primarily relied on overlapping cognitive and neural functions. Incidents of pure writing deficits, in the absence of motor or language deficits were rare. Nevertheless, the PCA and neural data suggested that writing abilities were associated with some unique neuro-cognitive functions, specifically dedicated to the use of pen and the ability to transform meaning to motor command. [ABSTRACT FROM AUTHOR]

Objective: The purposes of this study are to investigate the regional homogeneity (ReHo) of spontaneous brain activities in Parkinson's disease (PD) patients with freeze of gait (FOG) and to investigate the neural correlation of movement function through resting-state functional magnetic resonance imaging (RS-fMRI). Methods: A total of 35 normal controls (NC), 33 PD patients with FOG (FOG+), and 35 PD patients without FOG (FOG−) were enrolled. ReHo was applied to evaluate the regional synchronization of spontaneous brain activities. Analysis of covariance (ANCOVA) was performed on ReHo maps of the three groups, followed by post hoc two-sample t -tests between every two groups. Moreover, the ReHo signals of FOG+ and FOG− were extracted across the whole brain and correlated with movement scores (FOGQ, FOG questionnaire; GFQ, gait and falls questionnaire). Results: Significant ReHo differences were observed in the left cerebrum. Compared to NC subjects, the ReHo of PD subjects was increased in the left angular gyrus (AG) and decreased in the left rolandic operculum/postcentral gyrus (Rol/PostC), left inferior opercular-frontal cortex, left middle occipital gyrus, and supramarginal gyrus (SMG). Compared to that of FOG−, the ReHo of FOG+ was increased in the left caudate and decreased in the left Rol/PostC. Within the significant regions, the ReHo of FOG+ was negatively correlated with FOGQ in the left SMG/PostC (r = −0.39, p < 0.05). Negative correlations were also observed between ReHo and GFQ/FOGQ (r = −0.36/−0.38, p < 0.05) in the left superior temporal gyrus (STG) of the whole brain analysis based on AAL templates. Conclusion: The ReHo analysis suggested that the regional signal synchronization of brain activities in FOG+ subjects was most active in the left caudate and most hypoactive in the left Rol/PostC. It also indicated that ReHo in the left caudate and left Rol/PostC was critical for discriminating the three groups. The correlation between ReHo and movement scores (GFQ/FOGQ) in the STG has the potential to differentiate FOG+ from FOG−. This study provided new insight into the understanding of PD with and without FOG. [ABSTRACT FROM AUTHOR]

Background : 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. Methods : 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. Results : 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 (r = 0.41, p = 0.0145). Higher FAB scores were shown in NC than PD (p < 0.0001). Correlative analysis of PD between DC and FAB showed negative results (p < 0.05) in frontal cortex, whereas positive in insula and cerebellum. As for the correlations between DC and UPDRS-III, negative correlation (p < 0.05) was observed in bilateral inferior parietal lobule (IPL) and right cerebellum, whereas positive correlation (p < 0.05) in bilateral hippocampus and para-hippocampus gyrus (p < 0.01). Conclusion : 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. [ABSTRACT FROM AUTHOR]

This work reports new evidence of the versatility of soft and environmentally responsive micron-sized colloidal gel particles as stabilizers at ionic liquid-water droplet interfaces. These particles display a duality with properties ascribed typically to both polymeric and colloidal systems. The utilization of fluorescently labeled composite microgel particles allows in-situ and facile visualization without the necessity of invasive sample preparation. When the prepared particles form monolayers equilibrated at the ionic liquid-water interface on fully covered droplets, the colloidal lattice re-orders itself depending on the surface charge of these particles. Finally, we demonstrate that the spontaneously formed and densely packed layer of microgel particles can be employed for extraction applications, as the interface remains permeable to small active species. [ABSTRACT FROM AUTHOR]

We synthesize monodisperse ionicmicrogel particles which undergoa large change in volume in response to environmental stimuli suchas pH and temperature. In addition, the study elucidates the effectiveuptake and release of rheology modifiers from these microgel particlesto alter the bulk viscosity of a surrounding fluid. Moreover, we foundthat the prepared ionic microgel particles can demonstrate abilitiesto adsorb and repel iron oxide nanoparticles (Fe3O4-NPs) upon pH variation. The extent of the loading of Fe3O4-NPs within the colloidal particles and morphologycan be manipulated by tunable interactions between the Fe3O4-NPs and ionic microgel particles. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]