The notion of information and complexity are important concepts in many scientific fields such as molecular biology, evolutionary theory, and exobiology. Many measures of these quantities, either rely on a statistical notion of information, are difficult to compute, or can only be applied to strings. Based on assembly theory, we propose the notion of a \textit{ladderpath}, which describes how an object can be decomposed into a hierarchical structure using repetitive elements. From the ladderpath two measures naturally emerge: the ladderpath-index and the order-index, which represent two axes of complexity. We show how the ladderpath approach can be applied to both strings and spatial patterns and argue that all systems that undergo evolution can be described as ladderpaths. Further, we discuss possible applications to human language and the origins of life. The ladderpath approach provides a novel characterization of the information that is contained in a single object (or a system) and could aid in our understanding of evolving systems and the origin of life in particular.

Alzheimer’s and Parkinson's diseases (AD and PD) are amongst top of the prevalent neurodegenerative disease. One-third of PD patients are diagnosed with dementia, a pre-symptom of AD, but the underlying mechanism is elusive. Amyloid beta (Aβ) and α-synuclein are two of the most investigated proteins, whose pathological aggregation and spreading are crucial to the pathogenesis of AD and PD, respectively. Transcriptomic studies of the mammalian central nervous system shed light on gene expression profiles at molecular levels, regarding the complexity of neuronal morphologies and electrophysiological inputs/outputs. In the last decade, the booming of the single-cell RNA sequencing technique helped to understand gene expression patterns, alternative splicing, novel transcripts, and signal pathways in the nervous system at single-cell levels, providing insight for molecular taxonomy and mechanistic targets of the degenerative nervous system. Here, we re-visited the cell-cell transmission mechanisms of Aβ and α-synuclein in medi-ating disease propagation, and summarized recent single-cell transcriptome sequencing from different perspectives and discussed its understanding of neurodegenerative diseases.

In order to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional network (TCN) is proposed. Firstly, construct a high-dimensional feature set in the multi-domain of vibration signals, and use comprehensive evaluation indicators to preliminarily screen performance degradation indexes with good sensitivity and strong trend. Secondly, the kernel principal component analysis (KPCA) method is adopted to eliminate redundant information between multi-domain features, and construct a health index (HI) based on convolutional auto-encoder (CAE) network. Thirdly, a TCN-based performance degradation trend prediction model is constructed, and direct multi-step prediction is used to predict the performance degradation trend of the monitored object. On this basis, the validity of the proposed method is verified using the bearing public data, and it is successfully applied to performance degradation trend prediction of rolling contact fatigue specimen. The results show that the feature set can be reduced from 14 dimensions to 4 dimensions by using KPCA, while 98.33% of the information of the original feature set is retained. Furthermore, the method of constructing HI based on CAE network is effective. The change process of the HI constructed truly reflects the performance degradation process of the rolling contact fatigue specimen. Compared with the two commonly used HI construction methods, auto-encoding (AE) network and gaussian mixture model (GMM), this method has obvious advantages. At the same time, the prediction model based on TCN can accurately predict the performance degradation of the rolling contact fatigue specimen with the root mean square error 0.0146 and the mean absolute error 0.0105, which has better performance and higher prediction accuracy than the prediction model based on the long short-term memory (LSTM) network and the gated recurrent unit (GRU). This method has general significance and may be extended to the performance degradation prediction of other mechanical equipment/parts.

Axial compression tests were carried out on 6 square steel tube confined concrete short columns and 6 BFRP square pipe confined concrete axial compression tests. The concrete strength grades were C30, C40, and C50. The test results show that the failure modes of steel pipe and BFRP pipe are obviously different, and the BFRP pipe undergoes brittle failure. Compared with the short columns of concrete confined by BFRP pipes, the ultimate bearing capacity of axial compression is increased by -76.46%, -76.01%, and -73.06%, and the ultimate displacements are -79.20%, -80.78%, -71.71%.

Axial compression tests were carried out on 6 square steel tube confined concrete short columns and 6 BFRP square pipe confined concrete axial compression tests. The concrete strength grades were C30, C40, and C50. The test results show that the failure modes of steel pipe and BFRP pipe are obviously different, and the BFRP pipe undergoes brittle failure. Compared with the short columns of concrete confined by BFRP pipes, the ultimate bearing capacity of axial compression is increased by -76.46%, -76.01%, and -73.06%, and the ultimate displacements are -79.20%, -80.78%, -71.71%.

Antimicrobial and anti-viral materials have attracted significant interest in recent years due to increasing occurrences of nosocomial infections and pathogenic microbial contamination. One method to address this is the combination of photoactive compounds that can produce reactive oxygen species (ROS), such as hydrogen peroxide and hydroxyl radicals to disinfect microbes, with carrier materials that meet the application requirements. Using anthraquinone (AQ) and cellulose nanocrystals (CNCs) as the photoactive and carrier components respectively, this work demonstrates the first covalent incorporation of AQ onto CNCs. The morphology and the photoactive properties were investigated, revealing the structural integrity of the CNCs, and high degree of photoactivity of AQ-CNC materials upon UVA exposure. The AQ-CNCs also exhibited an unexpected persistent generation of ROS under darkness, which adds advantages for antimicrobial applications.

The purpose of this paper is to evaluate the timing of innovative investment in technology product life cycles using a compound binomial option with management flexibility. Considering the business cycles changes in the macroeconomic will affect consumer purchasing power. The focus is how to evaluate the optimal investment strategy and the project value. It was applied to different product stages (three stages including production innovation, manufacture innovation, and operation innovation) and factored to different risks to build a technology innovation strategy model. An aim of this study is the options premium of the best strategy timing for each innovation stage. Its application of the compound binomial options for the manufacture innovation will only be considered after the execution of the production innovation. The same condition is applied to the operation innovation, which will only be considered after the execution of the manufacture innovation. Then, this paper constructs the dynamic investment sequential decision model, assesses the feasibility of an investment strategy, and makes a decision on the appropriate project value and options premium for each stage under the possible change of Gross Domestic Product (GDP). This paper investigates the product life cycle innovation investment topic by using the compound binomial options method and will provide a more flexible strategy decision compared with other trend forecast criteria.

In this paper, the experimental study of heating in 2 h lunch mode of air-conditioned bed system is carried out. The results show that the power consumption of heat conduction heating is only 0.34 kW·h, and the average heat dissipation is 81.3 W, while the power consumption of convection heating is 1.43 kW·h, with an average heat dissipation of 748.7 W. Regardless of power consumption or heat dissipation, convection heating is significantly higher than heat conduction heating, resulting in the room air temperature of the former increased from 12.3 °C to 17.3 °C, while the latter only increased from 14.4 °C to 15.2 °C. The study found that the water temperature of heat conduction heating can meet the thermal comfort needs of the human body in the range of 38~40 °C, while convection heating requires a higher temperature range, In contrast, the grade of hot water required for heat conduction heating is lower. Through the experiment, it is also found that the temperature of convection heating rises fast, but it is easy to appear dry feeling after a long time, while heat conductive heating has a slow temperature rise. there is a cool feeling within 20 min after the heating starts, and then the thermal comfort is better. The air conditioning system in this paper is used for the heating experiment in the winter lunch break mode, compared with convection heating, the overall thermal comfort of heat conduction heating is better and more energy efficient. it is suggested to adopt heat conduction heating mode in the winter heating of this system.

The segmentation of urban scene mobile laser scanning (MLS) data into meaningful street objects is a great challenge due to the scene complexity of street environments, especially in the vicinity of street objects such as poles and trees. This paper proposes a three-stage method for the segmentation of urban MLS data at the object level. The original unorganized point cloud is first voxelized, and all information needed is stored in the voxels. These voxels are then classified as ground and non-ground voxels. In the second stage, the whole scene is segmented into clusters by applying a density-based clustering method based on two key parameters: local density and minimum distance. In the third stage, a merging step and a re-assignment processing step are applied to address the over-segmentation problem and noise points, respectively. We tested the effectiveness of the proposed methods on two urban MLS datasets. The overall accuracies of the segmentation results for the two test sites are 98.3% and 97%, thereby validating the effectiveness of the proposed method.

Arylsulfatase and β-glucuronidase are two important enzymes in human, which play important role on dynamic equilibrium of steroidal estrogens. This work probably for the first time reported that hydrogen peroxide (H2O2), hypochlorite and peracetic acid (PAA) could effectively inhibit the activities of arylsulfatase and/or β-glucuronidase. The 50 percent of inhibitions (IC50) of H2O2, and PAA on arylsulfatase were found to be 142.90±9.00, 91.83±10.01, and 43.46±2.92 μM, respectively. The corresponding respective IC50 of hypochlorite and PAA on β-glucuronidase were 704.90±41.40 and 23.26±0.82 μM, while H2O2 showed no inhibition on β-glucuronidase. It was further revealed that the inhibition of hypochlorite on both arylsulfatase and β-glucuronidase was irreversible. On the contrary, the inhibition by H2O2 and PAA was reversible. Moreover, it was found that the inhibitions of arylsulfatase and/or β-glucuronidase by these three chemicals were pH-dependent, among which the inhibition by H2O2 was competitive and non-competitive for PAA. In general, H2O2 and hypochlorite can be endogenously produced in human, which suggested that the two compounds are potential endocrine disruption compounds (EDCs) as they can cause endocrine disruption via inhibition of arylsulfatase and β-glucuronidase. This work further indicated that any agent that can induce production of H2O2 or hypochlorite in human is potential EDC, which explains why some EDCs with very weak or no estrogenic potency can cause endocrine disruption that confirmed in epidemiological studies.

The development of cost-effective methods for estimating hydraulic conductivity profiles has been an ongoing effort in the field of engineering practice, which can be used to increase availability to clarify the hydrogeological complexity of fractured rock aquifers for the aid of solving groundwater-related problems. A methodology is presented, which combines electrical well logs, fluid conductivity logs, double packer hydraulic tests, Archie’s law, and the Kozeny-Carman-Bear equation to investigate relations between formation factor and hydraulic conductivity. This method was applied to develop hydraulic conductivity profiles based on the data collected from 88 boreholes in Taiwan's mountainous areas. The investigation results include: (1) Well logging signals were suggested to be categorized by rock types to establish effective relationships with hydraulic conductivity. (2) Removing the mud-bearing section data with two proposed data clustering techniques could effectively enhance the correlation between the formation factor and hydraulic conductivity. (3) The predictive models for estimating hydraulic conductivity have been developed for sandstone, schist, and slate. (4) The prevalence of clay content in most of Taiwan's mountainous rock formations has been found, which implies that careful consideration of clay-related issues in complex geologic formations is essential while applying Archie's law theory.

This study aims to develop an elderly care system for improving the interpersonal relationship of the elderly with mild cognitive impairment (MCI) by employing the speaker recognition technique and association functionality of social network platforms. Firstly, the speaker recognition units based on the Gaussian Mixture Model (GMM) and Gaussian Mixture Model-Universal Background Model (GMM-UBM) are implemented to identify the visitor via individual input utterance. After the visitor is identified, the proposed system will be linked to the private database and social network platforms to extract the associated message of two parties. Experimental results indicate that the speaker recognition unit based on GMM-UBM achieves the best performance. Finally, five elderly persons are invited to measure the usability of the proposed system. A questionnaire is used to survey the five elderly persons, and the result indicates that the proposed system is highly potentially applicable in improving the interpersonal relationship of the elderly with MCI.

Air pollution is an important factor affecting human health and daily life. Chinese government is making vigorous efforts to control air pollution. The upgrading of industrial structure is a problem-solving tool in environment and economic growth cases. This paper aims to explore the relationships among environmental regulation, the upgrading of industrial structure and air pollution. The PVAR (Panel Vector Auto Regression) model and moderation effect model has been used to conduct empirical analysis based on panel data of 30 provinces in China from 2004 to 2020. The results of analysis indicate (1) the environmental regulation can significantly reduce the air pollution, but the deterioration of air quality could not effectively influence environmental regulations. (2) The upgrading of industrial structure can weaken the air pollution and air pollution hinders the upgrading of industrial structure. (3) With the improvement of environmental regulation, the industrial structure is constantly upgraded, but the upgrading of the industrial structure has a negative effect on the improvement of environmental regulation. (4) The upgrading of industrial structure can positively moderate the influence of environmental regulation on air pollution.

Fusarium species are one of the filamentous fungal taxa with the most pronounced impact on agri-cultural production and human health. The mycotoxins produced by pathogenic Fusarium not on-ly attack various plants including crops, causing various plant diseases that lead to reduced yields and even death, but also penetrate into the food chain of humans and animals to cause food poi-soning and consequent health hazards. Although sporadic studies have revealed some of the bio-synthetic pathways of Fusarium toxins, they are insufficient to satisfy the need for a comprehen-sive understanding of Fusarium toxin production. In this study, we focused on 35 serious patho-genic Fusarium species with available genomes and systematically analyzed the ubiquity of the dis-tribution of identified Fusarium and non-Fusarium derived fungal toxin biosynthesis gene clusters (BGCs) in these species through the mining of core genes and comparative analysis of correspond-ing BGCs. Additionally, novel sesterterpene synthases and PKS_NRPS clusters were discovered and analysed. This work is the first to systematically analyze the distribution of related mycotoxin biosynthesis in pathogenic Fusarium species. These findings enhance the knowledge of mycotoxin production and provide a theoretical grounding for the prevention of fungal toxin production us-ing biotechnological approaches.

The advancement of mass spectrometry technologies has revolutionised plant metabolomics research by enabling the acquisition of raw metabolomics data. However, the identification, analysis, and visualisation of these data require specialised tools. Existing solutions lack a dedicated plant-specific metabolite database and pose usability challenges. To address these limitations, we developed PlantMetSuite, a web-based tool for comprehensive metabolomics analysis and visualisation. PlantMetSuite encompasses interactive bioinformatics tools and databases specifically tailored for plant metabolomics data, facilitating upstream-to-downstream analysis in metabolomics and supporting integrative multi-omics investigations. PlantMetSuite can be accessed directly through a user's browser without the need for installation or programming skills. The tool is freely available at https://plantmetsuite.verygenome.com/ and will undergo regular updates and expansions to incorporate additional libraries and newly published metabolomics analysis methods. The tool's significance lies in empowering researchers with an accessible and customisable platform for unlocking plant metabolomics insights.

During their growth, bacteria respond to complex environmental changes through equally com-plex and orderly regulatory mechanisms. sRNA has an important role as a post-transcriptional regulator in the environmental adaptation of Bacillus. In this study, we identified an sRNA Bvs091 located in the intergenic region of the Bacillus velezensis, which is widely present in the genus Bacillus and whose expression is upregulated by salt treatment. The deletion of bvs091 in B. ve-lezensis PEBA20 led to a reduced growth rate and a change in the phenotypes, with simple colony structures and low and easily degradable biofilm. Salt tolerance tests indicated that the deletion of bvs091 results in a reduced salt tolerance. galT2 mRNA is supposed to be a direct target of Bvs091 according to results obtained by using the bioinformatic method and performing qRT-PCR analysis. The microscale thermophoresis (MST) analysis showed that Bvs091 could directly bind 28‒51 bases in front of the promoter of galT2. Subsequently, the deletion of galT2 in B. velezensis resulted in increased salt tolerance. Based on the techniques of double plasmids and a half-life period, sRNA Bvs091 is confirmed as a trans-encoded sRNA that negatively regulates the mRNA expression of galT2. In brief, the trans-encoded sRNA Bvs091 acts by pairing the incomplete complementary bases of galT2 mRNA, reducing galT2 mRNA’s stability and affecting the trans-lation efficiency.

For Lentinula edodes, its characteristic flavor is the key determinant for consumer pref-erences. However, the tissue-specific volatile flavor variations of the fruiting body have been overlooked. Here, we comprehensively investigated volatile flavor profiles of different tissues including the pileus skin, context, gill and stipe of the fruiting body of two widely cultivated L. edodes strains (T2 and 0912). We show that the eight-carbon and sulfur com-pounds, which represented 43.2-78.0% and 1.4-42.9% to the total volatile emissions for strain 0912 and T2, dominated their volatiles profiles. The sulfur compounds could rep-resent 32.2% and 42.9% to the total volatile emissions for the context of the strain 0912 and T2, respectively, whereas only represented 1.4% for the stipes of the strain 0912 and 9.0% for the skin of strain T2. Proportions of the predominant C8 compounds (1-Octen-3-one, 1-Octen-3-ol and 3-Octanone) and sulfur compounds (Lenthionine, 1,2,4-Trithiolane, Di-methyl disulfide and Dimethyl trisurfide) changed depending on tissues and strains. We further reveal that the overall volatile flavor profiles varied greatly in different tissues and strains. With machine learning algorithm, we show that volatile profiles could 100% pre-dict strains 0912 and T2. The prediction accuracy for different tissues could also reach 100%. Our results highlight the tissue-specific volatile flavor variations of the L. edodes fruiting body.

Surface-enhanced Raman spectroscopy (SERS) is commonly used for super-selective analysis through nanostructured silver layers in the environment, food quality, biomedicine, and materials science. To fabricate a high-sensitivity but more accessible device of SERS, dc magnetron sputtering technology was used to realize high sensitivity, low cost, stable deposition rate, and rapid mass production. This study investigated various thicknesses of a silver film ranging from 3.0 to 12.1 nm by field-emission-scanning-electron microscope, X-ray diffraction, and X-ray photoelectron spectroscopy. In the rhodamine 6G (R6G) testing irradiated by a He-Ne laser beam, the analytical enhancement factor (AEF) of 9.35x108, the limit of detection (LOD) of 10-8 M, and the relative standard deviation (RSD) of 1.61% were better than other SERS substrates fabricated by the same dc sputtering process because the results show that the 6 nm thickness silver layer has the highest sensitivity, stability, and lifetime. The paraquat and acetylcholine analytes were further investigated and high sensitivity was also achievable. The proposed SERS samples were evaluated and stored in a low humidity environment for up to forty weeks, and no spectrum attenuation could be detected. Soon, the proposed technology to fabricate high sensitivity, repeatability, and robust SERS substrate will be an optimized process technology in multiple applications.

This study aims to identify factors associated with anxiety levels of adults living in Singapore before the pandemic and during the COVID-19 outbreak. Data were collected using a cross-sectional web-based survey conducted from July to November 2020 accruing 264 eligible participants. Ordered logistic regression was used to assess Generalized Anxiety Disorder-7 (GAD-7), ranked as minimal (0-4), mild (5-9), moderate (10-14), and severe (15-21) before the pandemic and during the pandemic. About 74% of participants were female, 50% were aged 25-34, and 50% were married. The GAD-7 level went up from pre-pandemic for both moderate (from 12.5% to 16%) and severe GAD (from 2% to 11%). Alcohol consumption (AOR 1.79, 95% CI 1.04-3.06), loneliness (AOR 1.28, 95% CI 1.05-1.54), and difficulty in switching off social media (AOR 2.21, 95% CI 1.29-3.79) predicted increased GAD-7 levels. The quality of life (AOR 0.84, 95% CI 0.79-0.90) was significantly associated with decreased GAD-7 levels. The results heighten the awareness that early initiation of mental health support is crucial for the population in addition to the various financial support measures provided by the government as they are adapting to live with the COVID-19 pandemic.

Non-melanoma skin cancers (NMSCs) are the leading cause of skin cancer-related morbidity and mortality. Effective strategies are needed to control NMSC occurrence and progression. Non-toxic, plant-derived extracts have been shown to exert multiple anti-cancer effects. Graviola (Annona muricata), a tropical fruit-bearing plant, has been used in traditional medicine against multiple human diseases including cancer. The current study investigated the effects of graviola leaf and stem extract (GLSE) and its solvent-extracted fractions on two human NMSC cell lines, UW-BCC1 and A431. GLSE was found to: i) dose-dependently suppress UW-BCC1 and A431 cell growth, motility, wound closure, and clonogenicity; ii) induce G₀/G₁ cell cycle arrest by downregulating cyclin/cdk factors while upregulating cdk inhibitors, and (iv) induce apoptosis as evidenced by cleavage of caspases-3, -8 and PARP. Further, GLSE suppressed levels of activated hedgehog (Hh) pathway components Smo, Gli 1/2, and Shh while inducing SuFu. GLSE also decreased the expression of pro-apoptotic protein Bax while decreasing the expression of the anti-apoptotic protein Bcl-2. We determined that these activities were concentrated in an acetogenin/alkaloid-rich dichloromethane subfraction of GLSE. Our data identify graviola extracts and their constituents as promising sources for new chemopreventive and therapeutic agent(s) to be further developed for the control of NMSCs