Acellular bacterial ghosts (BGs) are empty non-living bacterial cell envelopes, commonly generated by controlled expression of the cloned lysis gene E of bacteriophage PhiX174. In this study, Vibrio parahaemolyticus ghosts (VPGs) were generated by chemically induced lysis and the method is based on minimum inhibitory concentration (MIC) of sodium hydroxide (NaOH), acetic acid, boric acid, citric acid, maleic acid, hydrochloric acid and sulfuric acid. The MIC values of the respective chemicals were 3.125, 6.25, < 50.0, 25.0, 6.25, 1.56 and 0.781 mg/ml. Except boric acid, the lysis efficiency was reached more than 99.99% at 5 min after treatment of all chemicals. Among those chemicals, NaOH-induced VPGs showed completely DNA-free that was confirmed by quantitative real-time PCR. Besides, lipopolysaccharides (LPS) extracted from the NaOH-induced VPGs showed no distinctive band on SDS-PAGE gel after silver staining. On the other hand, LPS extracted from wild-type bacterial cells as well as the organic acids-induced VPGs showed triple major bands and LPS extracted from the inorganic acids-induced VPGs showed double bands. It suggests that some surface structures in LPS of the NaOH-induced VPGs may be lost, weakened or modified by the MIC of NaOH. Nevertheless, Limulus amoebocyte lysate assay revealed that there is no significant difference in endotoxic activity between the NaOH-induced VPGs and wild-type bacterial cells. Macrophages exposed to the NaOH-induced VPGs at 0.5 × 106 CFU/mL showed cell viability of 97.9%, however the MIC of NaOH did not reduce the cytotoxic effect of wild-type bacterial cells. Like Escherichia coli LPS, the NaOH-induced VPGs are an excellent activator of pro-inflammatory cytokines (IL-1β and iNOS), anti-inflammatory cytokine (IL-10) and dual activities (IL-6) in the stimulated macrophage cells. On the other hand, the induction of TNF-α mRNA was remarkable in the macrophages exposed with wild-type cells. Scanning electron microscopy showed the formation of trans-membrane lysis tunnel structures in the NaOH-induced VPGs. SDS-PAGE and agarose gel electrophoresis also confirmed that cytoplasmic proteins and genomic DNA released from the VPGs to culture medium through the lysis tunnel structures. Taken together, all these results indicated that the NaOH-induced VPGs show the potency of safe, economical and effective inactivated bacterial vaccine candidate.

In this computational study, we investigate the electronic properties of zigzag graphene nano-parallelograms (GNPs), which are parallelogram-shaped graphene nanoribbons of various widths and lengths, using thermally-assisted-occupation density functional theory (TAO-DFT). Our calculations reveal a monotonic decrease in the singlet-triplet energy gap as the GNP length increases. The GNPs possess singlet ground states for all the cases examined. With the increase of GNP length, the vertical ionization potential and fundamental gap decrease monotonically, while the vertical electron affinity increases monotonically. Besides, as the GNP length increases, the symmetrized von Neumann entropy increases monotonically, denoting an increase in the degree of multi-reference character associated with ground-state GNPs. The occupation numbers and real-space representation of active orbitals indicate that there is a transition from the nonradical nature of the shorter GNPs to the increasing polyradical nature of the longer GNPs. In addition, the edge/corner localization of active orbitals is found for the wider and longer GNPs.

In this paper, we introduce ResNetMF, a groundbreaking approach that harnesses the power of residual network matrix factorization to revolutionize recommendation systems. ResNetMF integrates residual networks, renowned for their ability to capture intricate patterns and features, with matrix factorization techniques that excel in modelling user-item interactions. This fusion presents a novel solution that surpasses the limitations of traditional recommendation systems. Through comprehensive experimentation and evaluation of diverse datasets, ResNetMF demonstrates remarkable enhancements in recommendation accuracy and efficiency. By effectively capturing both linear and nonlinear relationships in user-item interactions, ResNetMF provides superior recommendation quality. The outcomes from experiments unequivocally highlight the superiority of ResNetMF over existing state-of-the-art recommendation approaches, thereby validating its innovative nature and underscoring its potential to shape the future of recommendation systems. Through the integration of the deep residual network, ResNetMF approach facilitates the training of neural networks, enabling them to explore the underlying data layers more comprehensively. Extensive experimentation and evaluation across various datasets provide compelling evidence for the superiority of ResNetMF. Moreover, the proposed method utilized natural language processing (NLP) techniques for targeted information dissemination in recommendation systems, emphasizing the importance of personalized and relevant recommendations for user satisfaction and engagement.

For the ground-state properties of gas-phase nanomolecules with multi-reference character, thermally-assisted-occupation density functional theory (TAO-DFT) has been recently found to outperform the widely used Kohn-Sham density functional theory, when the conventional exchange-correlation energy functionals are employed. Aiming to explore solvation effects on the ground-state properties of nanomolecules with multi-reference character at a minimal computational cost, we combine TAO-DFT with the polarizable continuum model (PCM). To show its usefulness, TAO-DFT-based PCM (TAO-PCM) is employed to study the electronic properties of linear acenes in three different solvents (toluene, chlorobenzene, and water). According to TAO-PCM, in the presence of these solvents, the smaller acenes should have nonradical nature, and the larger acenes should have increasing polyradical nature, showing remarkable similarities to the past findings in the gas phase.

Classification of human actions from uni-modal and multi-modal datasets is an ongoing research problem in computer vision. This review is aimed to scope current literature on data-fusion and action-recognition techniques and to identify gaps and future research direction. Success in producing cost-effective and portable vision-based sensors has dramatically increased the number and size of datasets. The rise in number of action recognition datasets intersects with advances in deep-learning architectures and computational support, both of which offer significant research opportunities. Naturally, each action-data modality - such as RGB, depth, skeleton, and infrared - has distinct characteristics; therefore, it is important to exploit the value of each modality for better action recognition. In this article we will focus solely on areas such as data fusion and recognition techniques in the context of vision with a uni-modal and multi-modal perspective. We conclude by discussing research challenges, emerging trends, and possible future research directions.

Thermally-assisted-occupation density functional theory (TAO-DFT) has been an efficient electronic structure method for studying the ground-state properties of large electronic systems with multi-reference character over the past few years. To explore the time-dependent (TD) properties of electronic systems (e.g., subject to an intense laser pulse), in this work, we propose a real-time (RT) extension of TAO-DFT, denoted as RT-TAO-DFT. Besides, we employ RT-TAO-DFT to study the high-order harmonic generation (HHG) spectra and related TD properties of molecular hydrogen H2 at the equilibrium and stretched geometries, aligned along the polarization of an intense linearly polarized laser pulse. The TD properties obtained with RT-TAO-DFT are compared with those obtained with the widely used time-dependent Kohn-Sham (TDKS) method. In addition, issues related to the possible spin-symmetry breaking effects in the TD properties are discussed.

Lipoxygenases are non-heme iron-containing enzymes that catalyze the oxidation of polyunsaturated fatty acids, resulting in the production of lipid hydroperoxides, which are precursors of inflammatory lipid mediators. The enzymes are widely distributed in humans, other eukaryotes, and cyanobacteria. Lipoxygenases hold promise as therapeutic targets for several human diseases, including cancer and inflammation-related disorders. Inhibitors of lipoxygenase have potential applications in pharmaceuticals, cosmetics, and food. Bioactive peptides are short amino acid sequences embedded within parent proteins, which can be released by enzymatic hydrolysis, microbial fermentation, and gastrointestinal digestion. A wide variety of bioactivities have been documented for protein hydrolysates and peptides derived from different biological sources. Recent findings indicate that protein hydrolysates and peptides derived from both edible and non-edible bioresources can act as lipoxygenase inhibitors. In this review, we explore current knowledge on the production of anti-lipoxygenase protein hydrolysates and peptides from millet grains, chia seeds, insects, milk proteins, fish feed, velvet antler blood, fish scales, and feather keratins. We delve into the anti-lipoxygenase activities and modes of action of these protein hydrolysates and peptides, while also highlighting the strengths and shortcomings of previous research. Additionally, we propose potential research directions and areas of improvement that can propel the discovery of anti-lipoxygenase peptides in the near future.

In response to escalating global concerns regarding climate change, nations worldwide have been undertaking structural adjustments in their energy sectors to promote diversification and decarbonization. This study conducts a comparative analysis of the policies and measures adopted by major economies, including the United States, the United Kingdom, Japan, Germany, and France, focusing on climate change, energy structural adjustments, and carbon emission reduction. By analyzing recent carbon emission and highlighting achievements in renewable energy promotion, green industry development, industrial restructuring, and energy production decarbonization, this study aims to provide valuable insights and formulate strategies for constructing an innovative energy system that ensures economic and social development while facilitating rapid growth and wide applications of low-carbon energy sources.

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, autonomous robots have been able to achieve relatively high levels of autonomy. In more unstructured environments, however, the realisation of autonomous mobile robots remains challenging due to limitations in the robots’ external environment understanding. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. The classical navigation approach typically includes robot perception, localisation, environmental mapping, path planning and motion control stages. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots need to be able to understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, and rough or smooth terrain) are required for autonomous robot navigation in unstructured outdoor environments. A wide number of alternative approaches have been proposed in recent years to attempt to address these scene understanding requirements. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments, and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges.

Coronaviruses are positive sense RNA virus belonging to the Coronaviridae family, which are further subdivided into four genera: Alpha, Beta, Gamma, and Delta Coronaviruses. Infectious bronchitis virus and SARS-CoV belong to Beta Coronaviridae family. Infectious bronchitis virus causes respiratory and nephritic signs that includes tracheal rales, urate crystals, lethargy and nasal discharge. In livestock and pets, the Coronavirus infection causes mostly gastrointestinal lesions, which may be prevented through vaccination and biosecurity. Recent infections of SARS-CoV-2 (also known as COVID-19) on farm and pet animals were summarized in this study. Besides, zoo animals were reported with infections in some countries/regions. Although the damage of COVID-19 has not been reported as serious as highly pathogenic avian influenza (HPAI) and African Swine Fever (ASF) on farm animals so far, the transmission mechanism of COVID-19 among group animals/farms and its long-term impacts are still not clear. The impact of Coronavirus on animals and potential prevention strategies, such as vaccine development and farm biosecurity measures, were discussed. Prior to the development of the effective vaccine, the biosecurity measures (e.g., conventional disinfection strategies and innovated technologies) may play roles in preventing potential spread of diseases/viruses.

In order for robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities in order to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged on top of an object to securely grab it between the robotic gripper and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods have driven robotics researchers to explore the application of deep learning methods in task generalised robotic applications. This paper reviews the current state-of-the-art in regards to the application of deep learning methods to generalised robotic grasping and discusses how each element of the deep learning approach has improved the overall performance of robotic grasp detection. A number of the most promising approaches are evaluated and the most successful for grasp detection is identified as the one-shot detection method. The availability of suitable volumes of appropriate training data is identified as a major obstacle for effective utilisation of the deep learning approaches, and the use of transfer learning techniques is identified as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed.

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the identification of characters, compound characters, and word. This study proposes the use of the deep convolutional neural network with dropout to recognize the Brahmi words. This study also proposed a DCNN for Brahmi word recognition and a series of experiments are performed on standard Brahmi dataset. The practical operation of this method was systematically tested on accessible Brahmi image database, achieving 92.47% recognition rate by CNN with dropout respectively which is among the best while comparing with the ones reported in the literature for the same task.

Unpaid family caregivers might suffer losses in income as a result of care provision. Here we used data from the baseline survey of the China Health and Retirement Longitudinal Study to assess the relationship between hours of weekly caregiving provided to grandchildren/parents/parents-in-law and individual’s monthly employment income. Our study sample comprised 3,718 middle-aged Chinese adults who were of working age (45-60 years). For women and men separately, we used a likelihood-based method to determine a caregiving threshold in a two-stage Heckman selection procedure. Instrumental variables were used to rule out the endogeneity of caregiving hours. Our analysis revealed a negative association between caregiving and income for women that depended on a caregiving threshold of 63-hours per week. There was an absence of caregiving-income relationship among men. These results offer new insights into the opportunity costs of unpaid caregiving and support tailored policies to protect the financial well-being of female caregivers.

Unpaid family caregivers must consider the economic trade-off between caregiving and paid employment. Prior literature has suggested labour force participation (LFP) to decline with caregiving intensity, but no study has evaluated this relationship by accounting for the presence of both kinks and discontinuities. Here we used respondents of the China Health and Retirement Longitudinal Study baseline survey who were non-farming, of working-age (aged 45-60) and had a young grandchild and/or a parent/parent-in-law. For women and men separately, a caregiving threshold-adjusted probit model was used to assess the association between LFP and weekly unpaid caregiving hours. Instrumental variables were used to rule out the endogeneity of caregiving hours. Of the 3,718 respondents in the analysis, for men, LFP was significantly and inversely associated with caregiving that involved neither discontinuities nor kinks. For women, a kink was identified at the caregiving threshold of 8 hours per week such that before 8 hours, each caregiving hour was associated with an increase of 0.0257 in the marginal probability of LFP, but each hour thereafter was associated with a reduction of 0.0014 in the marginal probability of LFP. These results have implications for interventions that simultaneously advance policies of health, social care and labour force.

Adsorption is an effective strategy for the removal of pollutants from the wastewater. Herein, a 2-hydroxyterephthalic acid (HTC) modified hypercrosslinked polymer (HTC-HCP) is successfully synthesized via Friedel-Crafts reactions, and used as an adsorbent for the different types of pollutants including organic contaminants and heavy metal ions from wastewater. Excellent adsorption capacities are observed for amines (aniline, p-methylaniline (p-MA), p-chloroaniline (p-CA), and p-aminobenzoic acid (p-ABA)), phenols (phenol, p-chlorophenol (4-CP) Bisphenol A (BPA), 1-Naphthol (1-NP)), dyes (rhodamine B (RhB) and methyl orange (MO)), and metal ions (Pb2+, Hg2+, and Cd2+). The resulting polymers exhibited excellent adsorption performance towards these pollutants. Especially, the removal rate of aniline is above 95% in the concentration of 2.5 mg/L in 40 min at 25 °C. The interaction mechanism has been investigated, and confirmed by FTIR and the theoretical calculation results. It is due to surface complexation and chemisorption between adsorbent and adsorbate. The polymer exhibits good performance such as high adsorption capacity, high separation efficiency, biodegradable properties, and easy regeneration, suggesting that its potential technological applications for the removal of organic compounds and heavy metal ions from actual industrial effluent.

Electrode is a key component in a microbial electrolysis cell (MEC) and it needs significant improvement for practical implementation of MEC. For effective development of electrode technology, accurate and reproducible analytical methods are very important. Linear sweep voltammetry (LSV) is an essential analytical method for evaluating electrode performance; however, it has not been firmly established yet in the MEC field. In this study, biological brush (BB), abiotic brush (AB), Pt wire (PtW), stainless steel wire (SSW) and mesh (SSM)) were tested to explore the most suitable counter electrode in different medium conditions. Coefficient of variation (CV) for Imax of LSV were comparatively analyzed. In BB-anode LSV, SSW (0.48%) and SSM (2.17%) showed higher reproducibility as a counter electrode. In SSM-cathode LSV, BB (1.76%) and PtW (2.01%) produced more reproducible results. In the Ni-AC-SSM-cathode LSV, PtW (3.54%) and BB (8.81%) produced more reproducible result. It shows electrode used in the operation is an appropriate counter electrode in the acetate-added condition. However, in the absence of acetate, PtW (1.24%) and BB (1.71%) produced more reproducible results in SSM cathode and PtW (0.61%) and SSW (1.21%) did in the Ni-AC-SSM-cathode, showing PtW is an appropriate counter electrode. These results also shows that PtW is an appropriate counter electrode in cathode LSV.

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words. Maximum accuracy suggested by system is 95.17% which is better than previously suggested studies.

This review summarizes experimental and theoretical studies of transition metal complexes with two types of novel metal-carbon bonds. One type features complexes with carbones CL2 as ligands, where the carbon(0) atom has two electron lone pairs which engage in double (σ and π) donation to the metal atom [M] CL2. The second part of this review reports complexes which have a neutral carbon atom C as ligand. Carbido complexes with naked carbon atoms may be considered as endpoint of the series [M]-CR3 → [M]-CR2 → [M]-CR → [M]-C.

The aim of the present study was to assess the efficacy of All Cosmos Industries (ACI) bio-organic and bio-chemical fertilizers and ACI N-Fixer (N-Bio Booster) on the paddy yields based on the field trial plots at Langkat, Medan, Indonesia. This application of ACI bio-organic fertilizer (NPK 5/5/5) and ACI bio-chemical (NPK 15/15/15) fertilizer and ACI N-Fixer tests were conducted at the paddy farm at Langkat from May-October 2018. This study employed a factorial randomized complete block design which consisted of two factors, namely: Factor I with four types of fertilizers while Factor II consisted of two paddy varieties (Inpari 30 and Inpari 32). Overall, the filled grains in the ACI treatments are significantly (P< 0.05) higher than those in the control treatments that used Normal Chemical Compound NPK. Overall, total weight per meter² (368-617g) in ACI treatments are also significantly (P< 0.05) higher than those (319-371g) in the control treatments. At harvesting time at 105 days after transplanting, significantly higher (P< 0.05) colony counts (13-15 x 10⁶ CFU/mL) (for ACI treatments), than those (8 x 10⁶ CFU/mL) in the controls positively indicated higher total yields of paddy grains per hectare. It was found that the application of ACI bio-organic and bio-chemical fertilizers and ACI N-Fixer can improve paddy yields of the two rice varieties, between 16.4-38.2% (up to 5.75 MT/ha), in the field trial plots at Langkat. These commercial fertilizers play an imperative role in refining the soil fertility and thereby can increase the yield of rice production. Therefore, it is highly recommended that ACI bio-organic and ACI bio-chemical fertilizers and ACI N-Fixer (N-Bio Booster) can be employed to increase the paddy yield in this region.

The accumulation of waste plastics has a severe impact on the environment, and therefore, the development of efficient chemical recycling methods has become an extremely important task. In this regard, a new strategy of degradation product-promoted depolymerization process was proposed. Using N,N'-dimethyl-ethylenediamine (DMEDA) as depolymerization reagent, an efficient chemical recycling of poly(bisphenol A carbonate) (BPA-PC) material was achieved under mild conditions. The degradation product 1,3-dimethyl-2-imidazolidinone (DMI) was proven to be the critical factor in facilitating the depolymerization process. This strategy does not require catalysts or auxiliary solvents, which is truly green process. This method improves the recycling efficiency of BPA-PC and promotes the development of plastic reutilization.

The fixed stroke fatigue test can not simulate the real load condition of the landing gear, and the variable stroke fatigue test has increasingly become the mainstream. A modular test device for fatigue test for aircraft landing gears was designed, the automatic control of the buffer stroke was realized with ectopic displacement control technology effectively, the horizontal loading cylinder could follow the wheel axis actively with the displacement active control technology, follow-up loading along the vertical direction was realized through constructing a polar coordinate system and making the load line automatic alignment, a split-type dummy wheel was designed to improve the loading accuracy, a booster cylinder was designed to realize the control loading of high pressure combined with the hydraulic control system. The test shows that the technical scheme meets the design requirements, and the fatigue test was completed successfully. The device for landing gear variable stroke fatigue testing can be applied to other landing gear static / fatigue tests.

We mainly describe the influence factors of captured power by huge wave energy harvesters, which the vertical motion of buoy can transform ocean’s potential energy into piezoelectric energy power by waves. By means of semi-analytical solutions and theoretical analysis, related environmental coefficients are analyzed firstly and find the most appropriate wave frequency and geometric structure as reference. Secondly, the numerical results also discuss the impact trend of hydrodynamic parameters and geometric volume on motion, voltage and power with qualitative agreement. The simulation data confirm that structure parameters of the modified model could markedly deliver sufficient power to ocean high electrical equipment with long-time stability.

Background A body composition monitor (BCM) has a role not only in determining over-hydration (OH) but also as an aid to nutritional assessment. For dialysis patient-specific clinical applications of BCM, it is necessary to clarify the relationship between body composition parameters and OH in healthy Chinese individuals. Methods This cross-sectional study involved 314 healthy individuals with a mean age of 45.7±13.1 years. BCM measurements were performed while the subjects were fasting. Results The mean OH level was 0.379±0.81 L. Lean tissue index (LTI) and Lean tissue mass (LTM) were significantly higher in males (p<0.001), while fat tissue index (FTI) was significantly higher in females (p<0.001). In univariate correlation analysis, FTI, Fat, and ATM had a negative correlation with OH in females and all subjects (p<0.05), while LTM and BCM had a positive correlation in all subjects (p<0.05). There was a significant negative correlation between phase angle (PhA) and OH in males, females, and all subjects (r=-0.634, p<0.001; r=-0.666, p<0.001; r=-0.484, p<0.001, respectively). In multivariate linear regression analysis, PhA (b=-1.266, p<0.001), LTM (b=0.987, p<0.001), age (b=-0.307, p<0.001) were independent predictors of OH. Conclusions This study demonstrated that age, LTM and especially PhA, had important roles in predicting OH in healthy Chinese individuals. In the future, PhA may aid in clinical assessment by helping to titrate dry weight among hemodialysis patients with malnutrition.

Acute myocardial infarction (AMI) is a leading cause of morbidity and mortality worldwide. This study aimed to test the efficacy of a newly-developed symptom self-management program on health outcomes among inpatients post-AMI. A pilot randomised, controlled trial was utilised. A convenience sample of 90 was recruited from a tertiary hospital in Singapore. Adult inpatients with post-AMI were randomly assigned to the three groups: IManage-HSet, IManage-FF or control group. Sample size was computed using power analysis. Data were collected through self-reported questionnaires and physiological instruments. Data analyses included univariate statistics, analysis of variance and repeated measure analysis of variance. Ninety participants were recruited with 30 in each group. Participants in the IManage-HSet and IManage-FF group had significantly greater relaxation (ηp2 = 0.11), lower objective stress (ηp2 =0.14), and greater knowledge (ηp2 = 0.62) than those of the control group. The two intervention groups had lower subjective stress (ηp2 = 0.04), depression (ηp2 = 0.01), and anxiety (ηp2 = 0.03) than the control but such differences did not achieve statistical significance. Furthermore, most participants reported that they were satisfied with the programs and they provided positive comments on the contents, educational materials and relaxation practice. This pilot study supports the potential benefits and feasibility of the IManage-VR and IManage-FF interventions on stress, perceived relaxation, knowledge and patient satisfaction. Contents of the symptom management interventions could be added into current cardiac rehabilitation programs. Future RCTs with multi-centered sites and larger sample size are required to further test the intervention. This study was prospectively registered with the International Standard Randomised Controlled Trial on the 5th August 2014 with registration number ISRCTN61027816.

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality.

Plants of the golden Camellia group are not only high-quality camellia germplasm , but also a plant group with rich medicinal and nutritional value, which are used as food, cosmetics and traditional medicine in China. There are approximately 50 species in this group around the world, and more than 30 species of golden camelliaplants have been published in China. The leaves and flowers of these species have similar shapes, so they are often confused as species. Our study used simplified genome sequencing technology to construct a phylogenetic tree of plants in the Chinese golden Camellia group and describe the evolutionary relationships . At the same time, the secondary metabolic indexes of total phenols, total flavonoids, total anthocyanins and ellagic acid in the leaves were determined., and principal component clustering analysis was performed . The results showed that the chemical classification and genetic phylogenetic trees showed some of the same trends, indicating that Camellia chinensis can be used as Biomarkers for chemical type. The research results provide phylogenetic information for the genotype and performance diversity of golden camelliaregionally distributed in China. At the same time, molecular biology phylogenetic methods are used to understand or explain the characteristics of secondary metabolites of golden Camellia , and provide theoretical basis for the search and development of potential bioactive substances.

CO₂ enhanced oil recovery (EOR) has been proven its capability to explore the unconventional tight oil reservoirs and potential for geological carbon storage. Meanwhile, the extremely low permeability pores exaggerate the difficulty CO₂ EOR and geological storage processing in the actual field. This paper initiates the ultrasonic-assisted approach to facilitate the oil-gas miscibility development and finally contribute to unlock more tight oils. First, the physical properties of crude oil with and without ultrasonic treatments were experimentally analysed through gas chromatography (GC), Fourier-transform infrared spectroscopy (FTIR) and viscometer. Second, the oil-gas minimum miscibility pressures (MMPs) were measured from the slim-tube test and the miscibility developments with and without ultrasonic treatments were interpreted from the mixing-cell method. Third, the nuclear-magnetic resonance (NMR) assisted coreflood tests were conducted to physically model the recovery process in porous media and directly obtain the recovery factor. Basically, the ultrasonic treatment (40KHz and 200W for 8 hours) was found to substantially change the oil properties, with viscosity (at 60°C) reduced from 4.1 to 2.8mPa·s, contents of resin and asphaltene decreased from 27.94% and 6.03% to 14.2% and 3.79%, respectively. The FTIR spectrum shows the unsaturated C-H bond, C-O bond and C≡C bond in macromolecules were broken from ultrasonic, which caused the macromolecules (e.g., resin and asphaltenes) to be decomposed into smaller carbon-number molecules. Accordingly, the MMP was determined to be reduced from 15.8 to 14.9MPa from the slim-tube test and the oil recovery factor increased by over 10%. This study reveals the mechanisms of ultrasonic-assisted CO₂ miscible EOR in producing tight oils.

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Un-derstanding vegetation dynamics and its response to climate change and human activities provides essential information for ecological and environmental resource management. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine plat-form and Landsat satellite images, employing trend analysis and other methods to examine spati-otemporal changes in vegetation in the SRB. Additionally, partial correlation and residual analysis were utilized to explore the response of FVC to climate change and human activities. The main results were as follows: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10−3 y–1. The area within 1 km of roads experienced a higher increase of 3 × 10−3 y–1, while the roadless areas experienced a lower increase of 1.1 × 10−3 y–1. Road construction from 2004 to 2006 significantly reduced the FVC within 200 meters of the roads. FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that FVC has a strong positive correlation with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. There is a weak negative correlation between FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) Compared to climate factors, human activities have a greater impact on vegetation dynamics in the SRB. The contributions of climate change to vegetation recovery are increasing. (4) The area changes of dif-ferent land types are closely related to climate factors and human activities, reflecting their impact on vegetation. These results provide valuable information for ecosystem restoration and environ-mental protection in the SRB.

Cycas (family Cycadaceae), spread throughout tropical and subtropical regions, is crucial in con-servation biology. Due to the subtle morphological variations between species, a solid spe-cies-level phylogeny for Cycas is lacking. Because of the rapid progress in high-throughput se-quencing technology, it has become feasible to acquire complete chloroplast (cp) genome se-quences, which provide a molecular foundation for phylogenetic research. In the present study, we employed next-generation sequencing technology to assemble and analyze the chloroplast genomes of six Cycas plants, including their genome structure, GC content, and nucleotide di-versity. The Cycas chloroplast genome spans 162,038 to 162,159 bp and contains 131 genes, in-cluding 86 protein-coding genes, 37 transfer RNA (tRNA) genes, and 8 ribosomal RNA (rRNA) genes. Through a comparative analysis, we found that the chloroplast genome of Cycas was highly conserved, as indicated by the contraction and expansion of the inverted repeat (IR) re-gions and sequence polymorphisms. In addition, several non-coding sites (psbK-psbI, petN-psbM, trnE-UUC-psbD, ndhC-trnM-CAU, and rpl32-trnP-GGG) showed significant varia-tion. The utilization of phylogenetic analysis relying on protein-coding genes has substantiated the division of Cycas primarily into four groups. The application of these findings will prove valuable in evaluating genetic diversity and the phylogenetic connections among closely related species. Moreover, it will provide essential support for the advancement of wild germplasm re-sources.

For decades since the central dogma, cancer biology research has been focusing on the involvement of genes encoding proteins. It has been not until more recent times that a new molecular class has been discovered, named non-coding RNA (ncRNA), which has been shown to play crucial roles in shaping the activity of cells. An extraordinary number of studies into shown that ncRNAs represent an extensive and prevalent group of RNAs, including both oncogenic or tumor suppressive molecules. Henceforth, various clinical trials involving ncRNAs as extra ordinary biomarkers or therapies have started to emerge. In this review, we will focus on the prognostic and diagnostic role of ncRNAs for breast cancer.

Current prenatal genetic evaluation showed a significantly increase in non-invasive screening and the reduction of invasive diagnostic procedures. To evaluate the diagnostic efficacy on detecting common aneuploidies, structural chromosomal rearrangements and pathogenic copy number variants (pCNV), we performed a retrospective analysis on a case series initially analyzed by aneuvysion fluorescence in situ hybridization (FISH) and karyotyping then followed by array comparative genomic hybridization (aCGH). Of the 386 cases retrieved from the past decade, common aneuploidies were detected in 137 cases (35.5%), other chromosomal structural rearrangements were detected in four cases (1%), and pCNV were detected in five cases (1.3%). The relative frequencies for common aneuploidies suggested a under detection of sex chromosome aneuploidies. Approximately 9.5% of cases with common aneuploidies showed a mosaic pattern. Inconsistent results between FISH and karyotyping were noted in cases with pseudo-mosaicism introduced by culture artifact or variable cellular proliferation from cells with mosaic karyotypic complements under in vitro cell culture. Based on findings from this case series, cell-based FISH and karyotyping should be performed to detect common aneuploidies, structural chromosomal abnormalities, and mosaic pattern. DNA-based aCGH and reflex FISH should be performed to detect and confirm genomic imbalances and pCNV. Practice points to ensure the diagnostic accuracy and efficacy were summarized.

The development of the additive manufacturing (AM) technology proffers challenging requirements for forming accuracy and efficiency. In this paper, a hybrid additive manufacturing technology combining fusion-based selective laser melting (SLM) and solid-state cold spraying (CS) was proposed in order to enable the fast production of near-net-shape metal parts. The idea is to fabricate a bulk deposit with a rough contour first via “fast” CS process and then add fine structures and complex features through “slow” SLM. The experimental results show that it is feasible to deposit SLM part onto CS part with good interfacial bonding. However, the CS parts must be subject to heat treatment to improve their cohesion strength before being sending for SLM processing. Otherwise, the high tensile residual stress generated during SLM process will cause fracture and cracks in the CS part. After heat treatment, pure copper deposited by CS undergoes grain growth and recrystallization, resulting in improved cohesive strength and release of the residual stress in the CS parts is released. The tensile test on the SLM/CS interfacial region indicates that the bonding strength increased by 38% from 45±7 MPa to 62±1 MPa after the CS part is subject to heat treatment, and the SLM/CS interfacial bonding strength is higher than the CS parts. This study demonstrates that the proposed hybrid AM process is feasible and promising for manufacturing free-standing SLM-CS components.

In order to explore the genetic diversity of the extremely small population wild plant Habenaria dentata and protect and optimize its wild resources. The genetic diversity of 133 samples from 6 wild populations of H.dentata was analyzed by ISSR molecular marker technique to provide a scientific basis for the screening of improved germplasm resources. The results show that the average number of alleles (Na) was 1.765, the average number of effective alleles (Ne) was 1.424, the average Nei 's gene diversity index (He) was 0.252, the average Shannon information diversity index (I) was 0.381, and the average percentage of polymorphic loci (PPL) was 76.499%. The variation within populations was 77.34%, and the variation between populations was 22.66%. Gene flow (Nm) was 1.705 8, greater than 1. The results of cluster analysis showed that the six populations were mainly divided into four branches and not classified according to geographical location. There was no significant correlation between geographical location and genetic distance between populations (r=0.557, P>0.05). The genetic diversity of H.dentata is high. Among the six wild populations, the genetic diversity of Mulun population is the highest and this population can be used as a key protection unit.

Four previously undescribed highly oxygenated diterpenoids (1–4), zeylleucapenoids A–D, characterized by halimane and labdane skeletons, were isolated from the aerial parts of Leucas zeylanica. Their structures were elucidated primarily by NMR experiments. The absolute configuration of 1 was established by theoretical ECD calculations and X-ray crystallographic analysis, whereas those for 2−4 were assigned by theoretical ORD calculations. Zeylleucapenoid D (4), with an IC₅₀ value of 38.45 μM in RAW264.7 macrophages and nontoxic activity for zebrafish embryo, and obviously inhibited pro-inflammatory cytokines TNF-α and IL-6 in a dose-dependent manner. Compound 4 inhibited of the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). Furthermore, molecular docking analysis indicated the possible mechanism of action for 4 may be bind to targets by hydrogen- and hydrophobic- bond interactions.

Hyperbaric oxygen (HBO) treatment has been proven to attenuate neuroinflammation in rats. This study aimed to determine the potential mechanism underlying the anti-inflammatory effects of HBO treatment on burn-induced neuroinflammation in rats. Thirty-six adult male Sprague–Dawley (SD) rats were randomly assigned to the following six groups (n = 6 per group): (1) sham burn with sham HBO treatment, (2) sham burn with HBO treatment, (3) burn with 1-week sham HBO treatment, (4) burn with 2-week sham HBO treatment, (5) burn with 1-week HBO treatment, and (6) burn with 2-week HBO treatment. SD rats that received third-degree burn injury were used as a full-thickness burn injury model. Subsequently, we analyzed the expression of proteins involved in the galectin-3 (Gal-3)-dependent Toll-like receptor-4 (TLR-4) pathway through enzyme-linked immunosorbent assay (ELISA), immunohistochemistry (IHC) analysis, and Western blotting, and a behavior test was also conducted. The behavior test revealed that HBO treatment significantly suppressed mechanical hypersensitivity in the burn with HBO treatment group compared with the burn with sham HBO treatment group (p < 0.05). ELISA results showed that tumor necrosis factor alpha (TNF-α) and interleukin 1 beta (IL-1β) levels in the dorsal horn of the spinal cord and the skin were significantly decreased in the burn with HBO treatment group compared with the burn with sham HBO treatment group (p < 0.05). Western blotting results demonstrated that HBO treatment significantly reduced the expression of Gal-3 and TLR-4 in the dorsal horn of the spinal cord in the burn with HBO treatment group compared with the burn with sham HBO treatment group (p < 0.05). IHC analysis results showed that the expression of Gal-3, TLR-4, CD68, and CD45 in the dorsal horn of the spinal cord was significantly lower in the burn with HBO treatment group than in the burn with sham HBO treatment group (p < 0.05), and the expression of CD68 and macrophage migration inhibitory factor (MIF) in the right hind paw skin was significantly lower. The expression of vimentin and fibroblast growth factor (FGF) in the right hind paw skin was significantly higher after HBO treatment (p < 0.05). This study proved that early HBO treatment relieves neuropathic pain, inhibits the Gal-3-dependent TLR-4 pathway, and suppresses microglia/macrophage activation in a rat model.

Abstract: The COVID-19 pandemic has disrupted global energy markets and caused significant socio-economic impacts worldwide. The pandemic has also impacted the energy sector, with demand for energy reducing due to lockdowns and reduced economic activity. Hence, this paper aims to provide a comprehensive and analytical review of the impact of COVID-19 on the energy sector and discuss the potential role of artificial intelligence (AI) in mitigating some of these effects. This review examines the changes in energy demand patterns resulting from the pandemic and the implications for the energy industry, including the shifts in policymaking, communication, digital technology, energy conversion, environmental, energy market, and power system operation. The analysis of the energy pattern is discussed according to the timeframes which include pre-, mid-, and post-pandemic periods. Furthermore, we explore how AI can be used to improve energy efficiency, optimize energy use, and reduce energy wastages. The potential for AI to contribute to developing more efficient and sustainable energy systems has also been addressed. Lastly, we highlighted AI’s challenges, which play a significant role in the energy sector's response to the COVID-19 pandemic. The recommendations for AI applications in the energy sector for the transition to a more sustainable energy future are outlined. Information corroborated in this review is expected to provide important guidelines for crafting future research areas and directions in preparing the energy sector for any unforeseen circumstances or pandemic-like situations.

With an increase in energy consumption globally, Fischer-Tropsch (FT) synthesis is a good alternative for producing fuels and chemicals from coal, natural gas or biomass. Among them, coal to liquids has been put into production in countries that have large coal reserves. In this process, Fe-based catalysts are commonly used due to their earth abundance, comparatively wide operation range and ready availability to handle low H2/CO ratio from coal. Despite their extensive applications, the kinetic and mechanistic understandings of Fe carburization and FT reaction on Fe-carbides are relatively limited due to the complexity of phase composition of the applied catalysts. This review summarizes the current state of knowledge of FT synthesis on Fe-carbide with an emphasis on underlying mechanism. Specifically, the employment of model catalyst, such as Raney-Fe, could provide a convenient way to furnish kinetic information regarding Fe carburization and subsequent FT reaction. A major challenge for further understanding catalytic reactions occurring at the Fe-carbide surface is correlating FT activity and selectivity to specific active site. To address this issue, the advancements of both DFT calculations and surface science techniques are highly demanded.

We develop a technique for realizing lithium niobate on insulator (LNOI) waveguides of a multi-centimeter-length with a propagation loss as low as 0.027 dB/cm. Our technique relies on patterning a chromium (Cr) thin film coated on the top surface of LNOI into a hard mask with a femtosecond laser followed by the chemo-mechanical polishing for structuring the LNOI into the waveguides. The surface roughness on the waveguides is determined to be 0.452 nm with an atomic force microscope (AFM). The approach is compatible with other surface patterning technologies such as optical and electron beam lithographies or laser direct writing, enabling high-throughput manufacturing of large-scale LNOI-based photonic integrated circuits.

Gliomas are the most common primary brain tumors in adults. Despite multidisciplinary treatment approaches, the survival rates for patients with malignant glioma have only improved marginally, and few prognostic biomarkers have been identified. Peroxisome proliferator-activated receptor γ (PPARγ) coactivator-1α (PGC-1α) is a crucial regulator of cancer metabolism, playing a vital role in cancer cell adaptation to fluctuating energy demands. In this study, the clinicopathological roles of PGC-1α in gliomas were evaluated. Employing immunohistochemistry, cell culture, siRNA transfection, cell viability assays, western blot analyses, and in vitro and in vivo invasion and migration assays, we explored the functions of PGC-1α in glioma progression. High PGC-1α expression was significantly associated with an advanced pathological stage in patients with glioma and with poorer overall survival. The downregulation of PGC-1α inhibited glioma cell proliferation, invasion, and migration and altered the expression of oncogenic markers. These results conclusively demonstrated that PGC-1α plays a critical role in maintaining the malignant phenotype of glioma cells and indicated that targeting PGC-1α could be an effective strategy to curb glioma progression and improve patient survival outcomes.

(1) Background: Heart failure (HF) is the final stage of multiple cardiac diseases, which has now become a severe public health problem worldwide. β-Adrenergic receptor (β-AR) overactivation is a major pathological factor associated with multiple cardiac diseases and mediates cardiac fibrosis and inflammation. Previous study has demonstrated that Bruton's tyrosine kinase (BTK) mediated cardiac fibrosis by TGF-β related signal pathways, indicating that BTK was a potential drug target for cardiac fibrosis. Zanubrutinib, a second-generation BTK inhibitor, has shown anti-fibrosis effects in previous research. However, it is unclear whether Zanubrutinib can alleviate cardiac fibrosis induced by β-AR overactivation;(2) Methods: In vivo: Male C57BL/6J mice were treated with or without the β-AR agonist isoproterenol (ISO) to establish cardiac fibrosis animal model. Here, results showed that BTK inhibitor Zanubrutinib (ZB) had a great effect on cardiac fibrosis and inflammation induced by β-AR. In vitro: Results showed that ZB alleviated β-AR-induced cardiac fibroblast activation and macrophage proinflammatory cytokine production. Further mechanism studies demonstrated that ZB inhibited β-AR-induced cardiac fibrosis and inflammation by BTK、STAT3、NF-κB and PI3K/Akt signal pathways both in vivo and in vitro; (4) Conclusions: our research provides evidence that ZB ameliorates β-AR-induced cardiac fibrosis and inflammation.

Background: Hurricanes and other wind events are significant disturbances that affect coastal urban forests around the world. Past research has led to the creation of wind resistance ratings for different tree species which can be used in urban forest management efforts to mitigate the effects of these storms. While useful, these ratings have been limited to species common to one global region (Florida, USA).Methods: Drawing on past ratings and data from a global literature review on tropical storm research, we created a machine learning model to broaden both the geographic coverage and the variety of species currently assessed for their resistance to wind.Results: We were able to assign wind resistance ratings to 281 new species based on the available data and our modelling efforts. The model accuracy and agreement with the original ratings when applied to the testing data set was high with 91% accuracy.Conclusions: The resulting list of wind resistance ratings has been adapted into a spreadsheet-based decision aid for managers, allowing them to assess the overall susceptibility of their urban forest to wind events like hurricanes.