Search

Abstract A search for magnetised quark nuggets (MQN) is reported using acoustic signals from hydrophones placed in the Great Salt Lake (GSL) in the USA. No events satisfying the expected signature were seen. This observation allows limits to be set on the flux of MQNs penetrating the Earth’s atmosphere and depositing energy in the GSL. The expected signature of the events was ​derived from pressure pulses caused by high-explosive cords between the lake surface and bottom at various locations in the GSL. The limits obtained from this search are compared with those obtained from previous searches and are compared to models for the formation of MQNs.

Abstract Quark nuggets are theoretical objects composed of approximately equal numbers of up, down, and strange quarks and are also called strangelets and nuclearites. They have been proposed as a candidate for dark matter, which constitutes ~85% of the universe’s mass and which has been a mystery for decades. Previous efforts to detect quark nuggets assumed that the nuclear-density core interacts directly with the surrounding matter so the stopping power is minimal. Tatsumi found that quark nuggets could well exist as a ferromagnetic liquid with a ~1012-T magnetic field. We find that the magnetic field produces a magnetopause with surrounding plasma, as the earth’s magnetic field produces a magnetopause with the solar wind, and substantially increases their energy deposition rate in matter. We use the magnetopause model to compute the energy deposition as a function of quark-nugget mass and to analyze testing the quark-nugget hypothesis for dark matter by observations in air, water, and land. We conclude the water option is most promising.

Submarine garbage is constantly destroying the marine ecological environment and polluting the ocean. It is critical to use detection methods to quickly locate and identify submarine garbage. The background of submarine garbage images is much more complex than that of natural scene images, with object deformation and missing contours putting higher demands on the detection network. To solve the problem of low accuracy under complex backgrounds, full stage networks with auxiliary focal loss and multi-attention module are proposed for submarine garbage object detection based on YOLO. To maximize the gradient combination, a hierarchical fusion feature mechanism and a segmentation and merging strategy are used in this paper to optimize the difference in gradient combination to obtain full-stage features. Then the criss-cross attention module is used to precisely extract multi-scale features of small object dense regions while removing noise information from complex backgrounds. Finally, the auxiliary focal loss function addresses the issue of unbalanced positive and negative samples, focusing on the learning of difficult samples while improving overall detection precision. Based on comparative experiments and ablation experiments, the FSA networks achieved state-of-the-art performance, and is applicable to the real-time object detection of submarine garbage in complex backgrounds. [ABSTRACT FROM AUTHOR]

We propose a new metric called s-LID based on the concept of Local Intrinsic Dimensionality to identify and quantify hierarchies of kinematic patterns in heterogeneous media. s-LID measures how outlying a grain's motion is relative to its s nearest neighbors in displacement state space. To demonstrate the merits of s-LID over the conventional measure of strain, we apply it to data on individual grain motions in a set of deforming granular materials. Several new insights into the evolution of failure are uncovered. First,s-LID reveals a hierarchy of concurrent deformation bands that prevails throughout loading history. These structures vary not only in relative dominance but also spatial and kinematic scales. Second, in the nascent stages of the pre-failure regime, s-LID uncovers a set of system-spanning, criss-crossing bands: microbands for small s and embryonic-shearbands at large s, with the former being dominant. At the opposite extreme, in the failure regime, fully formed shearbands at large s dominate over the microbands. The novel patterns uncovered from s-LID contradict the common belief of a causal sequence where a subset of microbands coalesce and/or grow to form shearbands. Instead, s-LID suggests that the deformation of the sample in the lead-up to failure is governed by a complex symbiosis among these different coexisting structures, which amplifies and promotes the progressive dominance of the embryonic-shearbands over microbands. Third, we probed this transition from the microband-dominated regime to the shearband-dominated regime by systematically suppressing grain rotations. We found particle rotation to be an essential enabler of the transition to the shearband-dominated regime. When grain rotations are completely suppressed, this transition is prevented: microbands and shearbands coexist in relative parity. [ABSTRACT FROM AUTHOR]

The cystic fibrosis (CF) community seeks to explain heterogeneous outcomes of pulmonary exacerbation (PEX) treatment. Serum and sputum inflammatory mediators may identify people with CF (PwCF) at risk for suboptimal responses. However, lack of an established association between response phenotypes and these mediators limits clinical application. In this pilot study, we prospectively characterized treatment response phenotypes by assessing health-related quality-of-life (HRQoL) during PEX. We also measured lung function and iron-related biochemical parameters in serum and sputum. We classified subjects as sustained symptom-responders (SRs) or non-sustained symptom-responders (NSRs) based on the absence or presence, respectively, of worsened symptom scores after initial improvement. We used linear mixed models (LMMs) to determine whether trends in lung function, hematologic, serum, and sputum indices of inflammation differed between response cohorts. In 20 PwCF, we identified 10 SRs and 10 NSRs with no significant differences in lung function at PEX onset and treatment durations. SRs had better model-predicted trends in lung function than NSRs during PEX. Non-linear trends in serum and sputum iron levels significantly differed between SRs and NSRs. In adults with cystic fibrosis, PEX treatment response phenotypes may be correlated with distinctive trends in serum and sputum iron concentrations. [ABSTRACT FROM AUTHOR]

Accurately segmenting remote sensing images remains challenging due to the diverse target scales and ambiguous structural boundaries. In this work, we propose a semi-supervised boundary segmentation network (BS-GAN) to address these challenges. BS-GAN employs a semi-supervised learning approach to reduce dependency on labeled data while introducing a novel mixed attention (MA) mechanism to enhance segmentation accuracy by aggregating long-range contextual information. Additionally, we develop a Boundary Gating Module (BGM) to refine boundary segmentation through a multi-task learning strategy focused on boundary feature enhancement. Experimental results on three benchmark datasets demonstrate that BS-GAN achieves superior accuracy and generalization capabilities compared to existing segmentation networks. [ABSTRACT FROM AUTHOR]

Accurate segmentation of retinal blood vessels from retinal images is crucial for detecting and diagnosing a wide range of ophthalmic diseases. Our retinal blood vessel segmentation algorithm enhances microfine vessel extraction, improves edge texture clarity, and normalizes vessel distribution. It stabilizes neural network training for complex retinal vascular features. Channel-aware self-attention (CAS) improves microfine vessel segmentation sensitivity. Heterogeneous adaptive pooling (HAP) facilitates accurate vessel edge segmentation through multi-scale feature extraction. The ghost fully convolutional Rectified Linear Unit (GFCReLU) module in the output convolutional layer captures deep semantic information for better vessel localization. Optimization training with Sparrow-Integrated Lion Optimization Algorithm (SLOA) employs sparrow stochastic updating and annealing to fine-tune parameters. The results of the experiments on our homemade dataset and three public datasets are as follows: Mean Intersection over Union (MIoU) of 80.61%, 76.14%, 76.90%, 74.11%; Dice coefficients of 78.97%, 72.51%, 72.84%, 68.93%; and accuracies of 94.83%, 95.74%, 96.67%, 95.81% respectively. The model effectively segments retinal blood vessels, offering potential for diagnosing ophthalmic diseases. Our dataset is available at https://github.com/ZhouGuoXiong/Retinal-blood-vessels-for-segmentation. [ABSTRACT FROM AUTHOR]

Bullying among South African adolescents is a critical public health issue. This study explores the relationship between childhood adversity, peer influence, and personality traits in predicting bullying perpetration. Data from 769 high school learners were analysed using Structural Equation Modelling. Findings indicate that childhood adversity predicts bullying perpetration, mediated by peer influence and moderated by personality traits like conscientiousness, extraversion, and emotional stability. These results support Social Learning Theory, emphasising observed behaviours and peer dynamics in bullying. Practical implications include targeted interventions addressing childhood adversity and fostering positive peer interactions and personality development. [ABSTRACT FROM AUTHOR]

Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation in lung appearance caused by disease progression and differing X-ray settings. While deep learning models have shown remarkable success in segmenting lungs from CXR images with normal or mildly abnormal findings, their performance declines when faced with complex structures, such as pulmonary opacifications. In this study, we propose AMRU++, an attention-based multi-residual UNet++ network designed for robust and accurate lung segmentation in CXR images with both normal and severe abnormalities. The model incorporates attention modules to capture relevant spatial information and multi-residual blocks to extract rich contextual and discriminative features of lung regions. To further enhance segmentation performance, we introduce a data augmentation technique that simulates the features and characteristics of CXR pathologies, addressing the issue of limited annotated data. Extensive experiments on public and private datasets comprising 350 cases of pneumoconiosis, COVID-19, and tuberculosis validate the effectiveness of our proposed framework and data augmentation technique. [ABSTRACT FROM AUTHOR]

The main advantage of DDIM is that it guarantees the quality of the generated images while increasing the efficiency of the generation by modifying the sampling strategy in the diffusion process. DiffusionRig, which addresses the problem of maintaining identity consistency by learning the person-specific facial prior in a tiny personalized dataset, is a successful representation of the DDIM strategy. Based on DiffusionRig, in this article, we propose an improved face attributes editing method based on DDIM to improve naturalness and accuracy of editing results in complex face attribute editing tasks and the generalization ability. Our method combines DDIM and DECA together and use two-stage training strategy. To reduce DiffusionRig's limitations in handling face attribute editing tasks that require nonlinear understanding and fine-tuning, our method also introduces a channel attention mechanism and a depth-separable convolution technique in the training model. In the first stage we trained our model on the open FFHQ dataset, which consists of 30,000 high-resolution face image. In the second stage, the model was refined by using a tiny personalized dataset. Face attribute editing experiments,comparative experiment with DiffusionRig, and a series of ablation experiments have been performed. Then, the validity of our improved method is verified by qualitative and quantitative analysis. [ABSTRACT FROM AUTHOR]

Campylobacter jejuni is a major cause of food- and water-borne bacterial infections in humans. A key factor helping bacteria to survive adverse environmental conditions is biofilm formation ability. Nonetheless, the molecular basis underlying biofilm formation by C. jejuni remains poorly understood. Around thirty genes involved in the regulation and dynamics of C. jejuni biofilm formation have been described so far. We applied random transposon mutagenesis to identify new biofilm-associated genes in C. jejuni strain 81–176. Of 1350 mutants, twenty-four had a decreased ability to produce biofilm compared to the wild-type strain. Some mutants contained insertions in genes previously reported to affect the biofilm formation process. The majority of identified genes encoded hypothetical proteins. In the library of EZ-Tn5 insertion mutants, we found the cydB gene associated with respiration that was not previously linked with biofilm formation in Campylobacter. To study the involvement of the cydB gene in biofilm formation, we constructed a non-marked deletion cydB mutant together with a complemented mutant. We found that the cydB deletion-mutant formed a weaker biofilm of loosely organized structure and lower volume than the parent strain. In the present study, we demonstrated the role of the cydB gene in biofilm formation by C. jejuni. [ABSTRACT FROM AUTHOR]

Perianal fistulizing Crohn's disease (CD) is a severe gastrointestinal disorder causing extensive mucosal damage with limited treatment options. Severe manifestations of the disease appear at higher rates in non-Europeans but the genetic and cellular mechanisms driving the disease phenotypes remain poorly understood. Herein, we tested whether pathologic determinants in the epithelial stem cell compartment could be detected at the transcript level in rectal organoids derived from a diverse patient population. Rectal organoid and mucosal cells from endoscopic biopsies of each patient having perianal fistulizing CD or no disease controls were prepared for and sequenced at the single cell level. After cell type annotations based on expressed marker genes, samples were analyzed by principal components, for differential transcript expression, cell type proportions, and pathway enrichment. After QC, we produced 77,044 rectal organoid cells (n = 13 patients; 8 CD, 5 controls) with high quality sequences that identified 10 distinct epithelial subtypes, that we compared to 141,367 mucosal epithelial cells (n = 29 patients; 18 CD, 11 controls). Consistent with mucosal epithelial cells, rectal organoids prominently displayed disease signatures represented by the stem and transit amplifying regions of the rectal crypt, including alterations in transcriptional signatures of metabolic, epigenetic, and proliferating pathways. Organoids also retained their gender- and ancestral-specific gene expression signatures. However, they lacked many of the inflammatory signatures observed in epithelial cells from diseased mucosa. Perianal CD patient derived rectal organoids reflect gene expression signatures related to disease, gender, and ancestry, suggesting they harbor inherent properties amenable to further patient-specific, disease-related experimentation. [ABSTRACT FROM AUTHOR]

Pulmonary exacerbations (PEx) are clinically impactful events for individuals with CF. Unfortunately, many CF individuals with PEx fail to regain their baseline lung function despite treatment. The objective of this study was to use unbiased proteomic technology to identify novel blood protein biomarkers that change following intravenous (IV) antibiotic treatment and to explore if changes correlate with clinical response by the end of treatment. Blood samples from 25 PEx events derived from 22 unique CF adults were collected within 24 hours of hospital admission, day 5, day 10, and IV antibiotic completion. Three-hundred and forty-six blood proteins were evaluated with label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) quantitative proteomics and immunoassays. Forty-seven plasma proteins changed significantly following 5 days of IV antibiotic treatment (q-value ≤ 0.10). Early change in IGF2R from hospital admission to day 5 correlated with overall change in symptom score (CFRSD-CRISS) by the end of treatment (r = −0.48, p-value = 0.04). Several plasma proteins identified and quantified by label-free LC-MS/MS changed early following treatment with IV antibiotics and many of these proteins are involved in complement activation and inflammatory/immune-related pathways. Early change in IGF2R correlated with symptom response following IV antibiotic treatment and requires further validation as a predictive biomarker of symptom response. [ABSTRACT FROM AUTHOR]

Racism is a pervasive threat to health with differential impact based on race and ethnicity. Considering the continued perpetration and visibility of racism online and in the news, vicarious racism, or "secondhand" racism when hearing about or witnessing racism being committed against members of one's ethnic or racial group, is a particularly urgent threat in the context of such disparities and their subsequent health consequences. The current study examines if frequency of exposure to vicarious racism and the emotional impact of those experiences are linked to psychoactive substance use, and explores the role of ethnic identity in moderating these relationships. In a cross-sectional survey, 504 adult participants aged 18–78 (M age = 30.15, SD = 11.52, 52.6% female) identifying as Black/African American or Latine reported on their experiences with vicarious racism and alcohol, marijuana, and tobacco use over the past 30 days. Logistic regression was utilized to test hypotheses. Primary findings indicate that greater emotional impact of vicarious racism was associated with a 50% increase in odds of alcohol consumption and that ethnic identity moderated the association between vicarious racism and marijuana use. Greater emotional impact of vicarious racism was related to more marijuana use for those lower on ethnic identity, whereas there was no association for those higher on ethnic identity. Vicarious racism was not related to tobacco use. Results suggest that ethnic identity might be protective in the association of vicarious racism on substance use. Further research on this topic is needed as vicarious racism becomes an increasingly common experience among marginalized populations. [ABSTRACT FROM AUTHOR]

Malaria transmission and endemicity in Africa remains hugely disproportionate compared to the rest of the world. The complex life cycle of P. falciparum (Pf) between the vertebrate human host and the anopheline vector results in differential expression of genes within and between hosts. An in-depth understanding of Pf interaction with various human genes through regulatory elements will pave way for identification of newer tools in the arsenal for malaria control. Therefore, the regulatory elements (REs) involved in the over- or under-expression of various host immune genes hold the key to elucidating alternative control measures that can be applied for disease surveillance, prompt diagnosis and treatment. We carried out an RNAseq analysis to identify differentially expressed genes and network elucidation of non-coding RNAs and target genes associated with immune response in individuals with different clinical outcomes. Raw RNAseq datasets, retrieved for analyses include individuals with severe (Gambia—20), symptomatic (Burkina Faso—15), asymptomatic (Mali—16) malaria as well as uninfected controls (Tanzania—20; Mali—36). Of the total 107 datasets retrieved, we identified 5534 differentially expressed genes (DEGs) among disease and control groups. A peculiar pattern of DEGs was observed, with individuals presenting with severe/symptomatic malaria having the highest and most diverse upregulated genes, while a reverse phenomenon was recorded among asymptomatic and uninfected individuals. In addition, we identified 141 differentially expressed micro RNA (miRNA), of which 78 and 63 were upregulated and downregulated respectively. Interactome analysis revealed a moderate interaction between DEGs and miRNAs. Of all identified miRNA, five were unique (hsa-mir-32, hsa-mir-25, hsa-mir-221, hsa-mir-29 and hsa-mir-148) because of their connectivity to several genes, including hsa-mir-221 connected to 16 genes. Six-hundred and eight differentially expressed long non coding RNA (lncRNA) were also identified, including SLC7A11, LINC01524 among the upregulated ones. Our study provides important insight into host immune genes undergoing differential expression under different malaria conditions. It also identified unique miRNAs and lncRNAs that modify and/or regulate the expression of various immune genes. These regulatory elements we surmise, have the potential to serve a diagnostic purpose in discriminating between individuals with severe/symptomatic malaria and those with asymptomatic infection or uninfected, following further clinical validation from field isolates. [ABSTRACT FROM AUTHOR]

This paper presents new relativistic composite polytropic models for compact stars by simultaneously solving Einstein field equations with the polytropic state equation to simulate the spherically symmetric, static matter distribution. Using a non-uniform polytropic index, we get the Tolman–Oppenheimer–Volkoff equation for the relativistic composite polytrope (CTOV). To analyze the star's structure, we numerically solve the CTOV equation and compute the Emden and mass functions for various relativistic parameters and polytropic indices appropriate for neutron stars. The calculation results show that, as the relativistic parameter approaches zero, we recover the well-known Lane-Emden equation from the Newtonian theory of polytropic stars; thus, testing the computational code by comparing composite Newtonian models to those in the literature yields good agreement. We compute composite relativistic models for the neutron star candidates Cen X-3, SAXJ1808.4-3658, and PSR J1614-22304. We compare the findings with various existing models in the literature. Based on the accepted models for PSR J1614-22304 and Cen X-3, the star's core radius is predicted to be between 50 and 60% percent of its total radius, while we found that the radius of the core of star SAXJ1808.4-3658 is around 30% of the total radius. Our findings show that the neutron star structure may be approximated by a composite relativistic polytrope, resulting in masses and radii that are quite consistent with observation. [ABSTRACT FROM AUTHOR]

Aplacophoran molluscs are shell-less and have a worm-like body which is covered by biomineralized sclerites. We investigated sclerite crystallography and the sclerite mosaic of the Solenogastres species Dorymenia sarsii, Anamenia gorgonophila, and Simrothiella margaritacea with electron-backscattered-diffraction (EBSD), laser-confocal-microscopy and FE-SEM imaging. The soft tissue of the molluscs is covered by spicule-shaped, aragonitic sclerites. These are sub-parallel to the soft body of the organism. We find, for all three species, that individual sclerites are untwinned aragonite single crystals. For individual sclerites, aragonite c-axis is parallel to the morphological, long axis of the sclerite. Aragonite a- and b-axes are perpendicular to sclerite aragonite c-axis. For the scleritomes of the investigated species we find different sclerite and aragonite crystal arrangement patterns. For the A. gorgonophila scleritome, sclerite assembly is disordered such that sclerites with their morphological, long axis (always the aragonite c-axis) are pointing in many different directions, being, more or less, tangential to cuticle surface. For D. sarsii, the sclerite axes (equal to aragonite c-axes) show a stronger tendency to parallel arrangement, while for S. margaritacea, sclerite and aragonite organization is strongly structured into sequential rows of orthogonally alternating sclerite directions. The different arrangements are well reflected in the structured orientational distributions of aragonite a-, b-, c-axes across the EBSD-mapped parts of the scleritomes. We discuss that morphological and crystallographic preferred orientation (texture) is not generated by competitive growth selection (the crystals are not in contact), but is determined by templating on organic matter of the sclerite-secreting epithelial cells and associated papillae. [ABSTRACT FROM AUTHOR]

This study presents a thorough numerical evaluation of the crashworthiness properties of a new bio-inspired DNA tubes (BIDNATs) with circular, elliptical, and rectangular cross-sections. Deformation and crashworthiness behaviors are evaluated using axial quasi-static crushing simulations by ABAQUS/Explicit (Abaqus 6.14, https://www.3ds.com/products-services/simulia/products/abaqus/). The study compares the performance of conventional tubes with rectangular and elliptical cross-sections to DNA-inspired tubes. Increasing the rotation angle leads to more helices and a pronounced helix angle, resulting in lower initial peak force (IPF). However, lower cross-section aspect ratios generally have higher IPF and specific energy absorption (SEA) values. BIDNATs with rectangular cross-sections and a 540° rotation angle have the lowest SEA and IPF values across all aspect ratios. Notably, for the 110/100 aspect ratio, the SEA of E110/100 is 71% higher than the conventional tube. Overall, BIDNATs with elliptical cross-sections and a 360° rotation angle exhibit higher SEA values and lower IPF values, particularly for a width (W) of 100 mm. Conventional circular and elliptical tubes generally have SEA values exceeding 6 J/g, with only E110/100 surpassing this among DNA-inspired tubes. The NE110/100 tube has the highest SEA, surpassing E110/100 by 54%, while its IPF is 10% greater than DNA-inspired E110/100. It's worth noting that conventional circular and elliptical tubes have higher IPF values compared to their DNA-inspired counterparts. These findings offer valuable insights for engineers and researchers in the design of crash tubes to improve overall vehicle safety for both occupants and pedestrians. [ABSTRACT FROM AUTHOR]

Efficient and rapid auxiliary diagnosis of different grades of lung adenocarcinoma is conducive to helping doctors accelerate individualized diagnosis and treatment processes, thus improving patient prognosis. Currently, there is often a problem of large intra-class differences and small inter-class differences between pathological images of lung adenocarcinoma tissues under different grades. If attention mechanisms such as Coordinate Attention (CA) are directly used for lung adenocarcinoma grading tasks, it is prone to excessive compression of feature information and overlooking the issue of information dependency within the same dimension. Therefore, we propose a Dimension Information Embedding Attention Network (DIEANet) for the task of lung adenocarcinoma grading. Specifically, we combine different pooling methods to automatically select local regions of key growth patterns such as lung adenocarcinoma cells, enhancing the model's focus on local information. Additionally, we employ an interactive fusion approach to concentrate feature information within the same dimension and across dimensions, thereby improving model performance. Extensive experiments have shown that under the condition of maintaining equal computational expenses, the accuracy of DIEANet with ResNet34 as the backbone reaches 88.19%, with an AUC of 96.61%, MCC of 81.71%, and Kappa of 81.16%. Compared to seven other attention mechanisms, it achieves state-of-the-art objective metrics. Additionally, it aligns more closely with the visual attention of pathology experts under subjective visual assessment. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance (AMR) is nowadays a global health concern as bacterial pathogens are increasingly developing resistance to antibiotics. Monoclonal antibodies (mAbs) represent a powerful tool for addressing AMR thanks to their high specificity for pathogenic bacteria which allows sparing the microbiota, kill bacteria through complement deposition, enhance phagocytosis or inhibit bacterial adhesion to epithelial cells. Here we describe a visual opsono-phagocytosis assay which relies on confocal microscopy to measure the impact of mAbs on phagocytosis of the bacterium Neisseria gonorrhoeae by macrophages. With respect to traditional CFU-based assays, generated images can be automatically analysed by convolutional neural networks. Our results demonstrate that confocal microscopy and deep learning-based analysis allow screening for phagocytosis-promoting mAbs against N. gonorrhoeae, even when mAbs are not purified and are expressed at low concentration. Ultimately, the flexibility of the staining protocol and of the deep-learning approach make the assay suitable for other bacterial species and cell lines where mAb activity needs to be investigated. [ABSTRACT FROM AUTHOR]

Individuals with autism spectrum disorder (ASD) often exhibit atypical hippocampal anatomy and connectivity throughout their lifespan, potentially linked to alterations in the neurogenic process within the hippocampus. In this study, we performed an in-silico analysis to identify single-nucleotide polymorphisms (SNPs) in genes relevant to adult neurogenesis in the C58/J model of idiopathic autism. We found coding non-synonymous (Cn) SNPs in 33 genes involved in the adult neurogenic process, as well as in 142 genes associated with the signature genetic profile of neural stem cells (NSC) and neural progenitors. Based on the potential alterations in adult neurogenesis predicted by the in-silico analysis, we evaluated the number and distribution of newborn neurons in the dentate gyrus (DG) of young adult C58/J mice. We found a reduced number of newborn cells in the whole DG, a higher proportion of early neuroblasts in the subgranular layer (SGZ), and a lower proportion of neuroblasts with morphological maturation signs in the granule cell layer (GCL) of the DG compared to C57BL/6J mice. The observed changes may be associated with a delay in the maturation trajectory of newborn neurons in the C58/J strain, linked to the Cn SNPs in genes involved in adult hippocampal neurogenesis. [ABSTRACT FROM AUTHOR]

This study focuses on enhancing the mechanical properties of thin, soft, free-standing films via a layer-by-layer (LBL) fabrication process called LBL-FP. Soft polymer nanocomposite (PNC) thin films, combining polydimethylsiloxane (PDMS) and single-walled carbon nanotubes (SWCNT) at ultra-low loadings using a unique bottom-up LBL-FP, are examined. Two different structures of layered composites, (i) LBL PNCs- Layered composites with alternating layers of PDMS and SWCNT, (ii) Bulk PNCs- Layered composites with SWCNT dispersed in the bulk of PDMS, are comparatively investigated for their structural and mechanical properties. Silane-functionalized SWCNT strengthens the chemical bonding with PDMS, improving adhesion and dispersion. Mechanical analysis using nanoindentation, delamination, and dynamic analysis highlights the advantages of LBL PNCs with alternating layers of PDMS and SWCNT. Notably, LBL PNC (0.5 wt%) exhibits significant improvements, such as 2.6X increased nanoindentation resistance, 3X improved viscoelasticity, and (2–5)X enhanced tensile properties in comparison with neat PDMS. Due to this, LBL PNCs offer potential for soft, lightweight applications like wearables, electromagnetic interference shielding materials, and strain sensors while advancing composite thin film mechanics. The study emphasizes using a stacked architecture to produce PDMS-SWCNT multilayered PNCs with improved mechanics utilizing ultra-low concentrations of SWCNT. This first-of-its-kind stack design facilitates possibilities for lightweight composites utilizing less fillers. The LBL assembly involves the stacking of alternating layers of different materials, each contributing specific properties to enhance the overall strength and toughness of the structure. [ABSTRACT FROM AUTHOR]

Present studies show that steel fibres can improve the bond of steel bar in steel fibre reinforced concrete (SFRC) with a correlation to the fibre factor and the fibre distribution uniformity. As a foundation of high-flowability SFRC working together with 400 MPa grade hot-rolled ribbed (HRB400) steel bar in reinforced structures, the bond between them was evaluated through a series of pull-out testing on 48 specimens with a central arranged steel bar. The bond behaviours of steel bar were estimated with a constant bond length of 5d (d is the diameter of steel bar) embedded in high-flowability SFRC, the main research parameters included the ingot mill steel fibres with a fibre volume fraction varied from 0.8 to 2.0%, the strength grade C40 and C50 of SFRC or referenced conventional concrete, and the diameter of steel bars varied from 14 to 20 mm. Results showed that the high-flowability SFRC compacted with a slight vibration is beneficial to improve the bond failure pattern since steel fibres effectively eliminate the crack appeared on the SFRC blocks during the pulling out of steel bar, leading to all specimens failed with the steel bar pull out of SFRC blocks. The bond strength was dominant by the SFRC strength, and obviously strengthened with the increase of fibre volume fraction, while the peak-slip was slightly influenced by the diameter of steel bar. By conducting analyses of test data, equations for calculating the bond strength and the peak-slip are proposed accounting for the effect of steel fibres. Then the predicting method for the anchorage length is suggested linking with different design codes for concrete structures. Compared with test results of this study, a little shorter anchorage length of steel bar in SFRC is obtained from the specification of Chinese code JGJ/T46, which should be noticed to ensure a rational anchorage of ribbed steel bar in SFRC with ingot mill steel fibres. [ABSTRACT FROM AUTHOR]

A technique for efficiently multiplying two signed numbers using limited area and high speed is presented in this paper. This work uses both the Booth and Vedic multiplication sutra methodologies to enhance the speed and reduction in the area by using two VLSI architectures of radix encoding techniques—Radix-4 and Radix-8—with the Vedic multiplier. The functionality of the proposed methods is tested using an Artix-7 Field Programmable Gate Array (FPGA-XC7A100T-CSG324) in Xilinx Vivado 2019.1 and ASIC 45 nm technology. Two methods of Booth encoding using Vedic multiplier (Urdhva-Tiryakbhyam sutra) were used to develop, and examine the benefits of rapid computational multiplier. The results of the proposed multiplier for Booth-Vedic-Radix-4 encoding (BVR-4) decrease area by 89% and improve Area-Delay Product (ADP) by 72% for a 16-bit multiplier when subjected to other existing multipliers. The Booth-Vedic-Radix-8 (BVR-8) method shows that there will be an 89% reduction in area and an improvement in ADP by 72% for the 16-bit multiplier. The performance is evaluated regarding area occupancy (i.e., LUTs number) and propagation delay (output time). In terms of resource utilization, the proposed BVR-4 and BVR-8 multipliers outperform all the current designs with a marginal effect on speed and area for narrower bit-width ranges. [ABSTRACT FROM AUTHOR]

Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, and various artifacts in the chest radiographic images. In this paper, we propose a robust deep learning segmentation framework for the anatomical structure in chest radiographs that utilizes a dual encoder–decoder convolutional neural network (CNN). The first network in the dual encoder–decoder structure effectively utilizes a pre-trained VGG19 as an encoder for the segmentation task. The pre-trained encoder output is fed into the squeeze-and-excitation (SE) to boost the network's representation power, which enables it to perform dynamic channel-wise feature calibrations. The calibrated features are efficiently passed into the first decoder to generate the mask. We integrated the generated mask with the input image and passed it through a second encoder–decoder network with the recurrent residual blocks and an attention the gate module to capture the additional contextual features and improve the segmentation of the smaller regions. Three public chest X-ray datasets are used to evaluate the proposed method for multi-organs segmentation, such as the heart, lungs, and clavicles, and single-organ segmentation, which include only lungs. The results from the experiment show that our proposed technique outperformed the existing multi-class and single-class segmentation methods. [ABSTRACT FROM AUTHOR]

Landslides are the most widely distributed geological hazards in the Three Gorges Reservoir Area (TGRA). Understanding the deformation mechanism and evolution of landslides is of great significance for their prevention and control. In this study, we focused on the Zhangjiacitang landslide, a typical bank landslide in the TGRA. We analyzed the relationship between landslide deformation and water level fluctuations and rainfall, based on accumulated displacement monitoring data, to clarify their triggering factors and deformation mechanism. The results show that the Zhangjiacitang landslide is a large-scale accumulation landslide. Under the influence of cyclic water level fluctuations and periodic rainfall, the accumulated displacement–time curve shows a "stepped" characteristic. Heavy rainfall emerged as the primary factor influencing the deformation of the Zhangjiacitang landslide, leading to substantial deformation throughout different periods. The deformation of the landslide exhibited a positive correlation with the intensity of rainfall. In contrast, the impact of water level changes on the landslide deformation was more intricate. A rapid water level drop (> 0.3 m/d) tended to intensify the landslide deformation, while the slow water level drop period (< 0.3 m/d) did not exhibit such an effect. This study emphasizes the need for closely monitoring the landslide status during heavy rainfall periods and rapid water level decline periods. The findings of this study provide a certain reference for landslide monitoring, early warning, prevention and control in the TGRA. [ABSTRACT FROM AUTHOR]

Weakly supervised object localization tasks remain challenging to identify and segment an entire object rather than only discriminative parts of the object. To tackle this problem, corruption-based approaches have been devised, which involve the training of non-discriminative regions by corrupting (e.g., erasing) the input images or intermediate feature maps. However, this approach requires an additional hyperparameter, the corrupting threshold, to determine the degree of corruption and can unfavorably disrupt training. It also tends to localize object regions coarsely. In this paper, we propose a novel approach, Module of Axis-based Nexus Attention (MoANA), which helps to adaptively activate less discriminative regions along with the class-discriminative regions without an additional hyperparameter, and elaborately localizes an entire object. Specifically, MoANA consists of three mechanisms (1) triple-view attentions representation, (2) attentions expansion, and (3) features calibration mechanism. Unlike other attention-based methods that train a coarse attention map with the same values across elements in feature maps, MoANA trains fine-grained values in an attention map by assigning different attention values to each element. We validated MoANA by comparing it with various methods. We also analyzed the effect of each component in MoANA and visualized attention maps to provide insights into the calibration. [ABSTRACT FROM AUTHOR]

Influences of high in-situ stress generally need to be considered when excavating deep underground caverns. The dynamic fracture behaviors of rocks under blast loads were investigated by using the rock-anchored beam excavation in underground powerhouses of Shuangjiangkou Hydropower Station in Sichuan Province, China as the engineering background. To solve the problems of the poor blasting breakage effect of rocks and the difficulty in protecting surrounding rocks during excavation, mechanical properties of granite under static and dynamic loads were investigated and the sequential controlled fracture blasting (SCFB) method was adopted during in-situ tests. Based on the Riedel-Hiermaier-Thoma constitutive model and the strength criterion, software LS-DYNA was employed to simulate the dynamic propagation of blasting-induced cracks. The contour shaping effect obtained via numerical simulation is generally consistent with the test results. The results show that SCFB can to some extent control the direction of crack initiation and rock fracture behavior of the blasthole wall cracks and the spacing of successive bursting holes is about 10 times the diameter of the blastholes when the cracks between the blastholes are shaped the best effect. Moreover, the magnitude and direction of principal in-situ stress can both affect the propagation path and length of blasting-induced cracks. The results of the research on the excavation and construction of deeply buried underground caverns have a certain reference value. [ABSTRACT FROM AUTHOR]

Pulse-coupled neural networks perform well in many fields such as information retrieval, depth estimation and object detection. Based on pulse coupled neural network (PCNN) theory, this paper constructs a visual perception model framework and builds a real image reproduction platform. The model firstly analyzes the structure and generalization ability of neural network multi-class classifier, uses the minimax criterion of feature space as the splitting criterion of visual perception decision node, which solves the generalization problem of neural network learning algorithm. In the simulation process, the initial threshold is optimized by the two-dimensional maximum inter-class variance method, and in order to improve the real-time performance of the algorithm, the fast recurrence formula of neural network is derived and given. The PCNN image segmentation method based on genetic algorithm is analyzed. The genetic algorithm improves the loop termination condition and the adaptive setting of model parameters of PCNN image segmentation algorithm, but the PCNN image segmentation algorithm still has the problem of complexity. In order to solve this problem, this paper proposed an IGA-PCNN image segmentation method combining the improved algorithm and PCNN model. Firstly, it used the improved immune genetic algorithm to adaptively obtain the optimal threshold, and then replaced the dynamic threshold in PCNN model with the optimal threshold, and finally used the pulse coupling characteristics of PCNN model to complete the image segmentation. From the coupling characteristics of PCNN, junction close space of image and gray level characteristics, it determined the local gray mean square error of image connection strength coefficient. The feature extraction and object segmentation properties of PCNN come from the spike frequency of neurons, and the number of neurons in PCNN is equal to the number of pixels in the input image. In addition, the spatial and gray value differences of pixels should be considered comprehensively to determine their connection matrix. Digital experiments show that the multi-scale multi-task pulse coupled neural network model can shorten the total training time by 17 h, improve the comprehensive accuracy of the task test data set by 1.04%, and shorten the detection time of each image by 4.8 s compared with the series network model of multiple single tasks. Compared with the traditional PCNN algorithm, it has the advantages of fast visual perception and clear target contour segmentation, and effectively improves the anti-interference performance of the model. [ABSTRACT FROM AUTHOR]

The estimation of spacecraft pose is crucial in numerous space missions, including rendezvous and docking, debris removal, and on-orbit maintenance. Estimating the pose of space objects is significantly more challenging than that of objects on Earth, primarily due to the widely varying lighting conditions, low resolution, and limited amount of data available in space images. Our main proposal is a new deep learning neural network architecture, which can effectively extract orbiting spacecraft features from images captured by inverse synthetic aperture radar (ISAR) for pose estimation of non-cooperative on orbit spacecraft. Specifically, our model enhances spacecraft imaging by improving image contrast, reducing noise, and using transfer learning to mitigate data sparsity issues via a pre-trained model. To address sparse features in spacecraft imaging, we propose a dense residual U-Net network that employs dense residual block to reduce feature loss during downsampling. Additionally, we introduce a multi-head self-attention block to capture more global information and improve the model's accuracy. The resulting tightly interlinked architecture, named as SU-Net, delivers strong performance gains on pose estimation by spacecraft ISAR imaging. Experimental results show that we achieve the state of the art results, and the absolute error of our model is 0.128 ∘ to 0.4491 ∘ , the mean error is about 0.282 ∘ , and the standard deviation is about 0.065 ∘ . The code are released at https://github.com/Tombs98/SU-Net. [ABSTRACT FROM AUTHOR]

According to data from the U.S. Center for Disease Control and Prevention, as of June 2020, a significant number of African Americans had been infected with the coronavirus disease, experiencing disproportionately higher death rates compared to other demographic groups. These disparities highlight the urgent need to examine the experiences, behaviors, and opinions of the African American population in relation to the COVID-19 pandemic. By understanding their unique challenges in navigating matters of health and well-being, we can work towards promoting health equity, eliminating disparities, and addressing persistent barriers to care. Since Twitter data has shown significant promise as a representation of human behavior and for opinion mining, this study leverages Twitter data published in 2020 to characterize the pandemic-related experiences of the United States' African American population using aspect-based sentiment analysis. Sentiment analysis is a common task in natural language processing that identifies the emotional tone (i.e., positive, negative, or neutral) of a text sample. Aspect-based sentiment analysis increases the granularity of sentiment analysis by also extracting the aspect for which sentiment is expressed. We developed a machine learning pipeline consisting of image and language-based classification models to filter out tweets not related to COVID-19 and those unlikely published by African American Twitter subscribers, leading to an analysis of nearly 4 million tweets. Overall, our results show that the majority of tweets had a negative tone, and that the days with larger numbers of published tweets often coincided with major U.S. events related to the pandemic as suggested by major news headlines (e.g., vaccine rollout). We also show how word usage evolved throughout the year (e.g., outbreak to pandemic and coronavirus to covid). This work also points to important issues like food insecurity and vaccine hesitation, along with exposing semantic relationships between words, such as covid and exhausted. As such, this work furthers understanding of how the nationwide progression of the pandemic may have impacted the narratives of African American Twitter users. [ABSTRACT FROM AUTHOR]

Object detection has been widely applied in various fields with the rapid development of deep learning in recent years. However, detecting small objects is still a challenging task because of the limited information in features and the complex background. To further enhance the detection accuracy of small objects, this paper proposes an efficient single-shot detector with weight-based feature fusion (WFFA-SSD). First, a weight-based feature fusion block is designed to adaptively fuse information from several multi-scale feature maps. The feature fusion block can exploit contextual information for feature maps with large resolutions. Then, a context attention block is applied to reinforce the local region in the feature maps. Moreover, a pyramids aggregation block is applied to combine the two feature pyramids to classify and locate target objects. The experimental results demonstrate that the proposed WFFA-SSD achieves higher mean Average Precision (mAP) under the premise of ensuring real-time performance. WFFA-SSD increases the mAP of the car by 4.12% on the test set of the CARPK. [ABSTRACT FROM AUTHOR]

Numerous studies in animals demonstrate that embryonic exposure to ethanol (EtOH) at low-moderate doses stimulates neurogenesis and increases the number of hypothalamic neurons expressing the peptide, hypocretin/orexin (Hcrt). A recent study in zebrafish showed that this effect on the Hcrt neurons in the anterior hypothalamus (AH) is area specific, evident in the anterior (aAH) but not posterior (pAH) part of this region. To understand specific factors that may determine the differential sensitivity to EtOH of these Hcrt subpopulations, we performed additional measures in zebrafish of their cell proliferation, co-expression of the opioid dynorphin (Dyn), and neuronal projections. In association with the increase in Hcrt neurons in the aAH but not pAH, EtOH significantly increased only in the aAH the proliferation of Hcrt neurons and their number lacking Dyn co-expression. The projections of these subpopulations differed markedly in their directionality, with those from the pAH primarily descending to the locus coeruleus and those from the aAH ascending to the subpallium, and they were both stimulated by EtOH, which induced specifically the most anterior subpallium-projecting Hcrt neurons to become ectopically expressed beyond the aAH. These differences between the Hcrt subpopulations suggest they are functionally distinct in their regulation of behavior. [ABSTRACT FROM AUTHOR]

Tree species recognition accuracy greatly affects forest remote sensing mapping and forestry resource monitoring. The multispectral and texture features of the remote sensing images from the ZiYuan-3 (ZY-3) satellite at two phenological phases of autumn and winter (September 29th and December 7th) were selected for constructing and optimizing sensitive spectral indices and texture indices. Multidimensional cloud model and support vector machine (SVM) model were constructed by the screened spectral and texture indices for remote sensing recognition of Quercus acutissima (Q. acutissima) and Robinia pseudoacacia (R. pseudoacacia) on Mount Tai. The results showed that, the correlation intensities of the constructed spectral indices with tree species were preferable in winter than in autumn. The spectral indices constructed by band 4 showed the superior correlation compared with other bands, both in the autumn and winter time phases. The optimal sensitive texture indices for both phases were mean, homogeneity and contrast for Q. acutissima, and contrast, dissimilarity and second moment for R. pseudoacacia. Spectral features were found to have a higher recognition accuracy than textural features for recognizing on both Q. acutissima and R. pseudoacacia, and winter showing superior recognition accuracy than autumn, especially for Q. acutissima. The recognition accuracy of the multidimensional cloud model (89.98%) does not show a superior advantage over the one-dimensional cloud model (90.57%). The highest recognition accuracy derived from a three-dimensional SVM was 84.86%, which was lower than the cloud model (89.98%) in the same dimension. This study is expected to provide technical support for the precise recognition and forestry management on Mount Tai. [ABSTRACT FROM AUTHOR]

The effective segmentation of the lesion region in gastric cancer images can assist physicians in diagnosing and reducing the probability of misdiagnosis. The U-Net has been proven to provide segmentation results comparable to specialists in medical image segmentation because of its ability to extract high-level semantic information. However, it has limitations in obtaining global contextual information. On the other hand, the Transformer excels at modeling explicit long-range relations but cannot capture low-level detail information. Hence, this paper proposes a Dual-Branch Hybrid Network based on the fusion Transformer and U-Net to overcome both limitations. We propose the Deep Feature Aggregation Decoder (DFA) by aggregating only the in-depth features to obtain salient lesion features for both branches and reduce the complexity of the model. Besides, we design a Feature Fusion (FF) module utilizing the multi-modal fusion mechanisms to interact with independent features of various modalities and the linear Hadamard product to fuse the feature information extracted from both branches. Finally, the Transformer loss, the U-Net loss, and the fused loss are compared to the ground truth label for joint training. Experimental results show that our proposed method has an IOU of 81.3%, a Dice coefficient of 89.5%, and an Accuracy of 94.0%. These metrics demonstrate that our model outperforms the existing models in obtaining high-quality segmentation results, which has excellent potential for clinical analysis and diagnosis. The code and implementation details are available at Github, https://github.com/ZYY01/DBH-Net/. [ABSTRACT FROM AUTHOR]

Lysozyme can kill bacteria by its enzymatic activity or through a mechanism involving its cationic nature, which can facilitate electrostatic interactions with the viral capsid, the negatively charged parts of nucleic acids, and polymerase, so binding to nucleic acids may be another biological function of lysozyme. Here, PCR was used as a research tool to detect the effects of lysozyme on the replication and transcription of nucleic acids after treatment in different ways. We found that lysozyme and its hydrolysate can enter cells and inhibit PCR to varying degrees in vitro, and degraded lysozyme inhibited nucleic acid replication more effectively than intact lysozyme. The inhibition of lysozyme may be related to polymerase binding, and the sensitivity of different polymerases to lysozyme is inconsistent. Our findings provide a theoretical basis for further explaining the pharmacological effects of lysozyme, such as antibacterial, antiviral, anticancer, and immune regulatory activities, and directions for the development of new pharmacological effects of lysozyme and its metabolites. [ABSTRACT FROM AUTHOR]

Retrieval practice performed shortly upon the encoding of information benefits recall of the retrieved information but causes forgetting of nonretrieved information. Here, we show that the forgetting effect on the nonretrieved information can quickly evolve into recall enhancement when retrieval practice is delayed. During a time window of twenty minutes upon the encoding of information, the forgetting effect observed shortly after encoding first disappeared and then turned into recall enhancement when the temporal lag between encoding and retrieval practice was prolonged. Strikingly, recall enhancement continued to emerge when retrieval practice was postponed up to one week. The results illustrate a fast transition from the forgetting of nonretrieved information to recall enhancement. This fast transition is of relevance for daily life, in which retrieval is often selective and delayed. [ABSTRACT FROM AUTHOR]

Aiming at the problem of spontaneous combustion of coal relics caused by the overburden fracture network penetrating the upper and lower coal seams in the process of shallow-buried and short-distance coal seam mining, the 31114 working face of Lijiahao coal mine was used as the research background to study the characteristics of overburden transport and fracture development in shallow-buried and short-distance coal seam mining by using physical similar simulation test; the fractal dimension and image processing techniques were used to quantify the overburden fractures; the classical mechanical models of "solid support beam" and "masonry beam" were combined to analyze the causes of fracture dynamic evolution. The results show that: (1) Before the key seam fracture, the stress in the upper rock seam only changes in a small amount, and the stress in the lower rock seam evolves similarly to the single coal seam mining; when the key seam fracture is broken, the stress in the upper and lower rock seams will change by jumps. (2) The fractal dimension of the fissures rised from 1.4 to 1.5, the total area of fissures is increased from 16,638 pixels to 17,707 pixels, and the total length is increased from 2217 to 3071 pixels; after the main key layer of the overlying rock is broken, the fractal dimension of the fissures is reduced from 1.56 to 1.5, and the total area of fissures is reduced from 31,451 pixels to 29,089 pixels, the total length has increased from 5657 to 6619 pixels. (3) Before the key layer between the coal seams is broken, it will be suspended to form a "fixed beam". After the first break, the broken rock above it will settle synchronously until the rock blocks form a hinged structure and then collapse. After the fall stops, the key layer periodically breaks to form a "masonry beam" structure, and the overlying stratum settles synchronously. [ABSTRACT FROM AUTHOR]

Acrylamide is a toxic reaction product occurring in dry-heated food such as bakery products. To meet the requirements laid down in recent international legal norms calling for reduction strategies in food prone to acrylamide formation, efficient chromatography-based quantification methods are available. However, for an efficient mitigation of acrylamide levels, not only the quantity, but also the contaminant's distributions are of interest especially in inhomogeneous food consisting of multiple ingredients. A promising tool to investigate the spatial distribution of analytes in food matrices is mass spectrometry imaging (MS imaging). In this study, an autofocusing MALDI MS imaging method was developed for German gingerbread as an example for highly processed and instable food with uneven surfaces. Next to endogenous food constituents, the process contaminant acrylamide was identified and visualized keeping a constant laser focus throughout the measurement. Statistical analyses based on relative acrylamide intensities suggest a higher contamination of nut fragments compared to the dough. In a proof-of-concept experiment, a newly developed in-situ chemical derivatization protocol is described using thiosalicylic acid for highly selective detection of acrylamide. This study presents autofocusing MS imaging as a suitable complementary method for the investigation of analytes' distributions in complex and highly processed food. [ABSTRACT FROM AUTHOR]

The Li-enriched oilfield brine is a very important lithium resource. It has gained much attention and become the target of active Li surveys with the growing global demand for Li. However, only little is known about their feature and nature. In the study, hydrochemical data from 155 oil wells tapping the Eocene to Lower Oligocene Qianjiang Formation of the Jianghan Basin, central China indicate that the brines are of the Na–Cl or Na–Ca–Cl type and are characterized by highly variable Li contents of 7.56 to 150 mg/L, with Mg/Li ratios less than 11.65. High Na/Cl and Cl/Br molar ratios indicate distinct contributions from halite dissolution. The Ca excess, Na deficit and Ca/Mg and Ca/Sr molar ratios in the brines imply multiple diagenetic processes, including halite dissolution, dolomitization, albitization and calcite or anhydrite cementation. The lithium contents of these brines have a weak relationship with the salinity and a negative correlation with Cl/Br ratios, possibly indicating that these Qianjiang oilfield brines have been diluted by secondary brines derived from halite dissolution. The spatial distribution patterns for Li and B concentrations of the brines are different from those for salinity and Br contents and show a geographic pattern, indicating that Li enrichment in the Qianjiang brines is likely connected with geothermal sources associated with volcanic activity. [ABSTRACT FROM AUTHOR]

The potential deformation and failure of a slope with typical 3D shapes involve 3D characteristics, such that these factors cannot be simulated using 2D methods. If 3D characteristics are not considered in expressway slope monitoring, an excessive number of monitoring points may be arranged in the stable/safe part, whereas insufficient monitoring points may be arranged in the unstable/dangerous part. In this study, the 3D deformation and failure characteristics of the Lijiazhai slope of the Shicheng–Ji'an Expressway in Jiangxi Province, China were analyzed by 3D numerical simulations using the strength reduction method. The potential 3D slope surface displacement trends, initial position of failure, and maximum depth of potential slip surface were simulated and discussed. The deformation of Slope A was generally small. The slope ranging from the third platform to the slope top was located in Region I, where the deformation was approximately equal to zero. The deformation of Slope B was located in Region V, where the displacement generally was larger than 2 cm in the range from the first–third platforms to the slope top, and the deformation of the trailing edge exceeded 5 cm. The surface displacement monitoring points should be arranged in Region V. Monitoring was then optimized considering the 3D characteristics of the deformation and failure of a slope. Accordingly, surface and deep displacement monitoring networks were effectively arranged in the unstable/dangerous part of the slope. Results may be used as references for similar projects. [ABSTRACT FROM AUTHOR]

This study reports light energy harvesting characteristics of bismuth ferrite (BiFeO3) and BiFO3 doped with rare-earth metals such as neodymium (Nd), praseodymium (Pr), and gadolinium (Gd) dye solutions that were prepared by using the co-precipitation method. The structural, morphological, and optical properties of synthesized materials were studied, confirming that 5–50 nm sized synthesized particles have a well-developed and non-uniform grain size due to their amorphous nature. Moreover, the peaks of photoelectron emission for bare and doped BiFeO3 were observed in the visible region at around 490 nm, while the emission intensity of bare BiFeO3 was noticed to be lower than that of doped materials. Photoanodes were prepared with the paste of the synthesized sample and then assembled to make a solar cell. The natural and synthetic dye solutions of Mentha, Actinidia deliciosa, and green malachite, respectively, were prepared in which the photoanodes were immersed to analyze the photoconversion efficiency of the assembled dye-synthesized solar cells. The power conversion efficiency of fabricated DSSCs, which was confirmed from the I–V curve, is in the range from 0.84 to 2.15%. This study confirms that mint (Mentha) dye and Nd-doped BiFeO3 materials were found to be the most efficient sensitizer and photoanode materials among all the sensitizers and photoanodes tested. [ABSTRACT FROM AUTHOR]

A long standing challenge in biological and artificial intelligence is to understand how new knowledge can be constructed from known building blocks in a way that is amenable for computation by neuronal circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory. Specifically, we ask how recurrent neuronal networks can store and retrieve multiple knowledge structures. We model each structure as a set of binary relations between events and attributes (attributes may represent e.g., temporal order, spatial location, role in semantic structure), and map each structure to a distributed neuronal activity pattern using a vector symbolic architecture scheme.We then use associative memory plasticity rules to store the binarized patterns as fixed points in a recurrent network. By a combination of signal-to-noise analysis and numerical simulations, we demonstrate that our model allows for efficient storage of these knowledge structures, such that the memorized structures as well as their individual building blocks (e.g., events and attributes) can be subsequently retrieved from partial retrieving cues. We show that long-term memory of structured knowledge relies on a new principle of computation beyond the memory basins. Finally, we show that our model can be extended to store sequences of memories as single attractors. [ABSTRACT FROM AUTHOR]

The advent of energy-specific collimation in pencil beam scanning (PBS) proton therapy has led to an improved lateral dose conformity for a variety of treatment sites, resulting in better healthy tissue sparing. Arc PBS delivery has also been proposed to enhance high-dose conformity about the intended target, reduce skin toxicity, and improve plan robustness. The goal of this work was to determine if the combination of proton arc and energy-specific collimation can generate better dose distributions as a logical next step to maximize the dosimetric advantages of proton therapy. Plans were optimized using a novel DyNamically collimated proton Arc (DNA) genetic optimization algorithm that was designed specifically for the application of proton arc therapy. A treatment planning comparison study was performed by generating an uncollimated two-field intensity modulated proton therapy and partial arc treatments and then replanning these treatments using energy-specific collimation as delivered by a dynamic collimation system, which is a novel collimation technology for PBS. As such, we refer to this novel treatment paradigm as Dynamically Collimated Proton Arc Therapy (DC-PAT). Arc deliveries achieved a superior target conformity and improved organ at risk (OAR) sparing relative to their two-field counterparts at the cost of an increase to the low-dose, high-volume region of the healthy brain. The incorporation of DC-PAT using the DNA optimizer was shown to further improve the tumor dose conformity. When compared to the uncollimated proton arc treatments, the mean dose to the 10mm of surrounding healthy tissue was reduced by 11.4% with the addition of collimation without meaningfully affecting the maximum skin dose (less than 1% change) relative to a multi-field treatment. In this case study, DC-PAT could better spare specific OARs while maintaining better target coverage compared to uncollimated proton arc treatments. While this work presents a proof-of-concept integration of two emerging technologies, the results are promising and suggest that the addition of these two techniques can lead to superior treatment plans warranting further development. [ABSTRACT FROM AUTHOR]

Actinic lentigines (AL) or age spots, are skin hyperpigmented lesions associated with age and chronic sun exposure. To better understand the physiopathology of AL, we have characterized the inflammation response in AL of European and Japanese volunteers. Gene expression profile showed that in both populations, 10% of the modulated genes in AL versus adjacent non lesional skin (NL), i.e. 31 genes, are associated with inflammation/immune process. A pro-inflammatory environment in AL is strongly suggested by the activation of the arachidonic acid cascade and the plasmin pathway leading to prostaglandin production, along with the decrease of anti-inflammatory cytokines and the identification of inflammatory upstream regulators. Furthermore, in line with the over-expression of genes associated with the recruitment and activation of immune cells, immunostaining on skin sections revealed a significant infiltration of CD68+ macrophages and CD4 + T-cells in the dermis of AL. Strikingly, investigation of infiltrated macrophage subsets evidenced a significant increase of pro-inflammatory CD80+/CD68+ M1 macrophages in AL compared to NL. In conclusion, a chronic inflammation, sustained by pro-inflammatory mediators and infiltration of immune cells, particularly pro-inflammatory M1 macrophages, takes place in AL. This pro-inflammatory loop should be thus broken to normalize skin and improve the efficacy of age spot treatment., (© 2024. The Author(s).)

The paper reviews the properties of cement composites reinforced with short fibres. The effect of natural fibres was investigated: cotton, sisal, jute, ramie, bamboo, and synthetic fibres: polymer and polypropylene. It was noticed that the fibres change the consistency of the mixture up to 15%. In the composite flexural strength tests, a change in strength by +/− 8% was observed, depending on the type of fibres used. The research shows that the use of natural fibres had a positive effect on the compressive strength by 27%, while the use of synthetic fibres caused its decrease by 4%. Additionally, it was noticed that the chemical composition, the diameter and the total length of the fibres in the element have an impact on the composite shrinkage. The fibre-containing composites showed an 8% higher water absorption compared to the non-fibre samples. The exception is the ramie fibres, which reduce water absorption. In general, a positive effect of natural fibers on the properties of cement composites has been noticed, however, in case of natural fibres application, a thorough further properties investigation is recommended. [ABSTRACT FROM AUTHOR]