Artificial Community Research Articles

Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome.

Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being Data-Dependent Acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-Independent Acquisition Mass Spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.

Investigating drug-gut microbiota interactions: reductive and hydrolytic metabolism of oral glucocorticoids by in vitro artificial gut microbiota

Elucidation of the role of gut microbiota in the metabolism of orally administered drugs may improve therapeutic effectiveness and contribute to the development of personalized medicine. In this study, ten different artificial gut microbiota (AGM), obtained by culturing fecal samples in a continuous fermentation system, were challenged for their metabolizing capacity on a panel of six glucocorticoids selected from either prodrugs or drugs. Data from metabolic stability assays highlighted that, while the hydrolysis-mediated conversion of prodrugs to drugs represented only a minor metabolic pathway, significant differences in the stability of parent compounds and in their conversion rates to multiple reductive metabolites were obtained for the selected drugs. In the latter case, a taxonomic composition-dependent ability to convert parent drugs to metabolites was observed. Indeed, the artificial microbial communities dominated by the genus Bacteroides showed the maximal conversion of parent glucocorticoids to several metabolites. Furthermore, the effect of drugs on AGM was also evaluated through shallow shotgun sequencing and flow cytometry-based total bacterial cell count highlighting that these drugs can affect both the taxonomic composition and growth performances of the human gut microbiota.

A new era of synthetic biology-microbial community design.

Synthetic biology conceptualizes biological complexity as a network of biological parts, devices, and systems with predetermined functionalities and has had a revolutionary impact on fundamental and applied research. With the unprecedented ability to synthesize and transfer any DNA and RNA across organisms, the scope of synthetic biology is expanding and being recreated in previously unimaginable ways. The field has matured to a level where highly complex networks, such as artificial communities of synthetic organisms, can be constructed. In parallel, computational biology became an integral part of biological studies, with computational models aiding the unravelling of the escalating complexity and emerging properties of biological phenomena. However, there is still a vast untapped potential for the complete integration of modelling into the synthetic design process, presenting exciting opportunities for scientific advancements. Here, we first highlight the most recent advances in computer-aided design of microbial communities. Next, we propose that such a design can benefit from an organism-free modular modelling approach that places its emphasis on modules of organismal function towards the design of multispecies communities. We argue for a shift in perspective from single organism-centred approaches to emphasizing the functional contributions of organisms within the community. By assembling synthetic biological systems using modular computational models with mathematical descriptions of parts and circuits, we can tailor organisms to fulfil specific functional roles within the community. This approach aligns with synthetic biology strategies and presents exciting possibilities for the design of artificial communities. Graphical Abstract.

Impact of cryoprotective agents on human gut microbes and invitro stabilized artificial gut microbiota communities.

The availability of microbial biobanks for the storage of individual gut microbiota members or their derived and artificially assembled consortia has become fundamental for invitro investigation of the molecular mechanisms behind microbe-microbe and/or microbe-host interactions. However, to preserve bacterial viability, adequate storage and processing technologies are required. In this study, the effects on cell viability of seven different combinations of cryoprotective agents were evaluated by flow cytometry for 53 bacterial species representing key members of the human gut microbiota after one and 3 months of cryopreservation at -80°C. The obtained results highlighted that no universal cryoprotectant was identified capable of guaranteeing effective recovery of intact cells after cryopreservation for all tested bacteria. However, the presence of inulin or skimmed milk provided high levels of viability protection during cryoexposure. These results were further corroborated by cryopreserving 10 artificial gut microbiota produced through invitro continuous fermentation system technology. Indeed, in this case, the inclusion of inulin or skimmed milk resulted in a high recovery of viable cells, while also allowing consistent and reliable preservation of the artificial gut microbiota biodiversity. Overall, these results suggest that, although the efficacy of various cryoprotective agents is species-specific, some cryoprotectants based on glycerol and the addition of inulin or skimmed milk are preferable to retain viability and biodiversity for both single bacterial species and artificial gut microbiota.

The impact of phage and phage resistance on microbial community dynamics.

Where there are bacteria, there will be bacteriophages. These viruses are known to be important players in shaping the wider microbial community in which they are embedded, with potential implications for human health. On the other hand, bacteria possess a range of distinct immune mechanisms that provide protection against bacteriophages, including the mutation or complete loss of the phage receptor, and CRISPR-Cas adaptive immunity. While our previous work showed how a microbial community may impact phage resistance evolution, little is known about the inverse, namely how interactions between phages and these different phage resistance mechanisms affect the wider microbial community in which they are embedded. Here, we conducted a 10-day, fully factorial evolution experiment to examine how phage impact the structure and dynamics of an artificial four-species bacterial community that includes either Pseudomonas aeruginosa wild-type or an isogenic mutant unable to evolve phage resistance through CRISPR-Cas. Additionally, we used mathematical modelling to explore the ecological interactions underlying full community behaviour, as well as to identify general principles governing the impacts of phage on community dynamics. Our results show that the microbial community structure is drastically altered by the addition of phage, with Acinetobacter baumannii becoming the dominant species and P. aeruginosa being driven nearly extinct, whereas P. aeruginosa outcompetes the other species in the absence of phage. Moreover, we find that a P. aeruginosa strain with the ability to evolve CRISPR-based resistance generally does better when in the presence of A. baumannii, but that this benefit is largely lost over time as phage is driven extinct. Finally, we show that pairwise data alone is insufficient when modelling our microbial community, both with and without phage, highlighting the importance of higher order interactions in governing multispecies dynamics in complex communities. Combined, our data clearly illustrate how phage targeting a dominant species allows for the competitive release of the strongest competitor while also contributing to community diversity maintenance and potentially preventing the reinvasion of the target species, and underline the importance of mapping community composition before therapeutically applying phage.

Programmable Enzymatic Reaction Network in Artificial Cell-Like Polymersomes.

The ability to precisely control in vitro enzymatic reactions in synthetic cells plays a crucial role in the bottom-up design of artificial cell models that can recapitulate the key cellular features and functions such as metabolism. However, integration of enzymatic reactions has been limited to bulk or microfluidic emulsions without a membrane, lacking the ability to design more sophisticated higher-order artificial cell communities for reconstituting spatiotemporal biological information at multiple length scales. Herein, droplet microfluidics is utilized to synthesize artificial cell-like polymersomes with distinct molecular permeability for spatiotemporal control of enzymatic reactions driven by external signals and fuels. The presence of a competing reverse enzymatic reaction that depletes the active substrates is shown to enable demonstration of fuel-driven formation of sub-microcompartments within polymersomes as well as realization of out-of-equilibrium systems. In addition, the different permeability characteristics of polymersome membranes are exploited to successfully construct a programmable enzymatic reaction network that mimics cellular communication within a heterogeneous cell community through selective molecular transport.

Entropic Artificial Intelligence and Knowledge Transfer

An overview of entropy applications to illustrate a few possible uses of entropy in knowledge transmission and artificial intelligence. Artificial intelligence uses entropy as a fundamental concept in many diverse applications, including reinforcement learning, data compression, and decision-making. It assists artificial intelligence models in producing wellinformed forecasts and judgments by assessing uncertainty and information content. As such, the purpose of this work is to highlight the importance of entropy and draw the attention of the artificial intelligence research community to it as a potent tool for advancing artificial intelligence. This work also addresses the importance of knowledge transfer (KT), especially intergenerational KT (IGT), in knowledge management. Knowledge entropy (KE) is a concept that is used to measure the complexity of knowledge distribution within an organisation and evaluate the effectiveness of KT activities. Furthermore, the KT model—which is predicated on the ideas of information content and tacitness—is presented. It blends techniques for customisation and codification. A few challenging open problems are presented along with future study options

Effect of target gene sequence evenness and dominance on real-time PCR quantification of artificial sulfate-reducing microbial communities.

Quantitative real-time PCR of phylogenetic and functional marker genes is among the most commonly used techniques to quantify the abundance of microbial taxa in environmental samples. However, in most environmental applications, the approach is a rough assessment of population abundance rather than an exact absolute quantification method because of PCR-based estimation biases caused by multiple factors. Previous studies on these technical issues have focused on primer or template sequence features or PCR reaction conditions. However, how target gene sequence characteristics (e.g., evenness and dominance) in environmental samples affect qPCR quantifications has not been well studied. Here, we compared three primer sets targeting the beta subunit of the dissimilatory sulfite reductase (dsrB) to investigate qPCR quantification performance under different target gene sequence evenness and dominance conditions using artificial gBlock template mixtures designed accordingly. Our results suggested that the qPCR quantification performance of all tested primer sets was determined by the comprehensive effect of the target gene sequence evenness and dominance in environmental samples. Generally, highly degenerate primer sets have equivalent or better qPCR quantification results than a more target-specific primer set. Low template concentration in this study (~105 copies/L) will exaggerate the qPCR quantification results difference among tested primer sets. Improvements to the accuracy and reproducibility of qPCR assays for gene copy number quantification in environmental microbiology and microbial ecology studies should be based on prior knowledge of target gene sequence information acquired by metagenomic analysis or other approaches, careful selection of primer sets, and proper reaction conditions optimization.

Overview of Predation by Birds, Cephalopods, Fish and Marine Mammals on Marine Benthic Amphipods

With about 8000 marine benthic species, the amphipod crustaceans form one of the richest animal groups of the worldwide Ocean. They have colonized a wide range of soft- and hard-bottom natural and artificial habitats extending from the intertidal to hadal zones. Moreover, they show a broad size spectrum, with numerous giant species exceeding 20 cm in length and some species smaller than 2 mm. When biofouling artificial hard surfaces, some tube-building species can form very dense populations comprising up to 100,000 individuals per square meter. Amphipods are important prey for fish and mammals. Along with cephalopod juveniles, they are also included in the trophic diet of shorebirds that consume amphipods mostly during the low tide on tidal flats. They display diel migration, which reinforces the predation by demersal fish in the suprabenthic zone just above the sea bed, as well as by pelagic fish in the water column. Despite their importance in terms of biodiversity and trophic transfer, no general overview is available on the role of benthic amphipods in marine ecosystem food webs. Various methods, including laboratory and field experiments, as well as the analysis of stomach contents and DNA extraction, have been used to identify the prey/predator trophic links. Based on an extensive literature review, this study discusses the role of marine benthic amphipods as potential food for higher trophic levels in natural and artificial hard-bottom communities created via the construction of offshore wind farms.

Metabolomics-Guided Discovery of New Dimeric Xanthones from Co-Cultures of Mangrove Endophytic Fungi Phomopsis asparagi DHS-48 and Phomopsis sp. DHS-11.

The co-culture strategy, which mimics natural ecology by constructing an artificial microbial community, is a useful tool for the activation of biosynthetic gene clusters (BGCs) to generate new metabolites, as well as to increase the yield of respective target metabolites. As part of our project aiming at the discovery of structurally novel and biologically active natural products from mangrove endophytic fungi, we selected the co-culture of a strain of Phomopsis asparagi DHS-48 with another Phomopsis genus fungus DHS-11, both endophyted in mangrove Rhizophora mangle considering the impart of the taxonomic criteria and ecological data. The competition interaction of the two strains was investigated through morphology observation and scanning electron microscopy (SEM), and it was found that the mycelia of the DHS-48 and DHS-11 compacted and tangled with each other with an interwoven pattern in the co-culture system. A new approach that integrates HPLC chromatogram, 1HNMR spectroscopy, UPLC-MS-PCA, and molecular networking enabled the targeted isolation of the induced metabolites, including three new dimeric xanthones phomoxanthones L-N (1-3), along with six known analogs (4-9). Their planar structures were elucidated by an analysis of their HRMS, MS/MS, and NMR spectroscopic data and the absolute configurations based on ECD calculations. These metabolites showed broad cytotoxic activity against the cancer cells assessed, of which compounds 7-9 displayed significant cytotoxicity towards human liver cells HepG-2 with IC50 values ranging from 4.83 μM to 12.06 μM. Compounds 1-6 exhibited weak immunosuppressive activity against the proliferation of ConA-induced (T-cell) and LPS-induced (B-cell) murine splenic lymphocytes. Therefore, combining co-cultivation with a metabolomics-guided strategy as a discovery tool will be implemented as a systematic strategy for the quick discovery of target bioactive compounds.

Open-endedness in synthetic biology: A route to continual innovation for biological design.

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

Many societal entities now have more excellent standards for the efficacy and dependability of identification systems due to the ongoing advancement of computer technology. Traditional identification methods, such as keys and smart cards, have been supplanted by biometric systems in highly secure environments. This research presents a smart computational method for automatically authenticating fingerprints for identity (ID) verification and personal identification. Compared to more traditional machine learning algorithms, the results from applying Deep learning (DL) in areas like computer vision, image identification, robotics, and voice processing have generally been positive. Due to their capacity to analyse big data size and deal with fluctuations in biometric data (such as ageing or expression problems), DL has been heavily used by the artificial intelligence research community. Several biometric systems have succeeded with automatic feature extraction employing deep learning approaches like Convolutional Neural Networks (CNNs). In this research, we provide a biometric process that uses convolutional neural networks. This work introduces a deep learning-based biometric identification system that uses Monte Carlo Dropout (MC Dropout). Combining these two systems makes the authentication process more secure and dependable. Fingerprint image enhancement techniques involve the application of Gabor filters and structure-adaptive anisotropic filters, which have proven to be effective in enhancing the clarity and distinctiveness of fingerprint patterns. To improve the efficiency of deep learning models, this work proposes the Inception-Augmentation GAN (IAGAN) model for data augmentation. The study adds to security development by integrating novel biometric identification and authentication approaches with cutting-edge neural network technology. In this research, we provide a new activation function to speed up the convergence of deep neural networks. The results of 99.6% on Gabor filters and 99.8% on the structure-adaptive anisotropic filter with GACNN with MCD show that deep neural networks can excel over competing approaches with enough training data.

Does weed diversity mitigate yield losses?

While intensive control of weed populations plays a central role in current agriculture, numerous studies highlight the multifaceted contribution of weeds to the functionality and resilience of agroecosystems. Recent research indicates that increased evenness within weed communities may mitigate yield losses in contrast to communities characterized by lower diversity, since weed species that strongly affect crop yields, also dominate weed communities, with a concurrent reduction of evenness. If confirmed, this observation would suggest a paradigm shift in weed management towards promoting higher community diversity. To validate whether the evenness of weed communities is indeed linked to higher crop productivity, we conducted two field experiments: one analyzing the effects of a natural weed community in an intercrop of faba bean and oat, and the other analyzing the effects of artificially created weed communities, together with the individual sown weed species, in faba bean, oats and an intercrop of both crops. The evenness of the weed communities ranged from 0.2 to 0.9 in the natural weed community, from 0.2 to 0.7 in faba bean, from 0 to 0.8 in the intercrop and from 0.3 to 0.9 in oats. Neither the natural nor the artificial weed community showed significant effects of evenness on crop grain yield or crop biomass. The results of this study do not validate a positive relationship of crop productivity and weed evenness, possibly due to low weed pressure and the absence of competitive effects but suggest that also less diverse weed communities may be maintained without suffering yield losses. This is expected to have far reaching implications, since not only diverse weed communities, but also higher abundances of few weed species may contribute to ecosystem functions and may support faunal diversity associated with weeds.

Artificial intelligence-powered energy community management for developing renewable energy systems in smart homes

The increase in demand due to scientific innovation and economic expansion raises challenges linked to environmental impact and sustainable market development. This study analyzes the role played by Peer-to-Peer (P2P) energy trading in system operation. Several case studies with four distinct prosumer models, each fitted with diverse Energy Storage Systems (ESS), photovoltaic (PV) systems, and responsive demand technologies such as Electric Vehicles (EV), are examined. An economic analysis is conducted using the developed interface. An energy management system establishes a dynamic market framework, enabling energy trading via P2P market operators and distribution system operators. The implemented trading platform is chosen depending on pricing variations across different time intervals for home models. The P2P energy trading system employs mathematical modeling via deep learning to achieve an optimal solution that minimizes costs. The study uses other domestic models and optimization techniques to explore the amount of energy traded across households and timeframes. Additionally, it investigates the accomplishment of two-way energy transfer in electric cars, the importance of energy storage and photovoltaic systems, and various housing scenarios concerning peer-to-peer energy trading. Additionally, the system's advantages are demonstrated to participants beforehand by validating and presenting data through an input and output system that accepts these instances.

Development of Science in Conjunction with Economic Growth: Global Trends and Patterns

The formation of artificial communities within the framework of various types of mathematical and computer modeling, primarily agent-based, requires confident knowledge about the degree and nature of the interaction of the simulated agents, components and elements of the system being studied. The work reveals patterns of influence of scientific and innovation indicators on the level of the economy in countries around the world. Based on measuring the level of correlations between the publication activity of the countries of the world, the per capita level of R&D expenditures, patent activity and the number of researchers with the level of their economy relative to the population, it is shown that the indicators of the conjugation of the scientific and innovation sphere of countries and their level are quite high, in general intensified from 2010 to 2022. Since 2010, the ratio of the levels of mutual dependence of indicators of scientific and inventive activity and the relative values of national economies has changed; among other indicators, the highest level of correlation in 2022 is characterized by publication activity. The level of interconnection has largely increased due to the sharp intensification of the level of scientific development of rich oil and gas producing countries. Considering Russia’s place in global trends in scientific development, the author concludes that it is advisable to increase funding for scientific research in Russia while maintaining the course of supporting the productivity of fundamental science.

A Synthetic Time-Series Generation Using a Variational Recurrent Autoencoder with an Attention Mechanism in an Industrial Control System.

Data scarcity is a significant obstacle for modern data science and artificial intelligence research communities. The fact that abundant data are a key element of a powerful prediction model is well known through various past studies. However, industrial control systems (ICS) are operated in a closed environment due to security and privacy issues, so collected data are generally not disclosed. In this environment, synthetic data generation can be a good alternative. However, ICS datasets have time-series characteristics and include features with short- and long-term temporal dependencies. In this paper, we propose the attention-based variational recurrent autoencoder (AVRAE) for generating time-series ICS data. We first extend the evidence lower bound of the variational inference to time-series data. Then, a recurrent neural-network-based autoencoder is designed to take this as the objective. AVRAE employs the attention mechanism to effectively learn the long-term and short-term temporal dependencies ICS data implies. Finally, we present an algorithm for generating synthetic ICS time-series data using learned AVRAE. In a comprehensive evaluation using the ICS dataset HAI and various performance indicators, AVRAE successfully generated visually and statistically plausible synthetic ICS data.

Nitrogen addition strengthens the stabilizing effect of biodiversity on productivity by increasing plant trait diversity and species asynchrony in the artificial grassland communities.

Nitrogen (N) enrichment usually weakens the stabilizing effect of biodiversity on productivity. However, previous studies focused on plant species richness and thus largely ignored the potential contributions of plant functional traits to stability, even though evidence is increasing that functional traits are stronger predictors than species richness of ecosystem functions. We conducted a common garden experiment manipulating plant species richness and N addition levels to quantify effects of N addition on relations between species richness and functional trait identity and diversity underpinning the 'fast-slow' economics spectrum and community stability. Nitrogen addition had a minor effect on community stability but increased the positive effects of species richness on community stability. Increasing community stability was found in the species-rich communities dominated by fast species due to substantially increasing temporal mean productivity relative to its standard deviation. Furthermore, enhancement in 'fast-slow' functional diversity in species-rich communities dominated by fast species under N addition increased species asynchrony, resulting in a robust biodiversity-stability relationship under N addition the artificial grassland communities. The findings demonstrate mechanistic links between plant species richness, 'fast-slow' functional traits, and community stability under N addition, suggesting that dynamics of biodiversity-stability relations under global changes are the results of species-specific responses of 'fast-slow' traits on the plant economics spectrum.

Artificial reef biofouling community impacts on ecosystem metabolism and biogeochemical cycling in estuarine waters of the northern Gulf of Mexico

Artificial reefs increase hard bottom habitat and bolster shoreline protection in coastal waters. Reefs are rapidly colonized by biofouling communities which can potentially alter ecosystem metabolism. Despite the high biomass and ubiquitous nature of biofouling communities, little is known about their ecosystem metabolism or role in biogeochemical nutrient cycling. Using a combination of laboratory and field-based methods, this study focused on the metabolic activity of artificial reef biofouling communities and their potential to contribute to water column hypoxia in a shallow subtropical estuary. Study objectives aimed to (1) measure in situ dissolved oxygen (DO) concentrations in surface and bottom waters along reef transects during summer months when episodic hypoxic events typically occur, (2) estimate respiration rates in relation to nutrient regeneration in the absence versus presence of biofouling communities, and (3) examine seasonal shifts in trophic structure by relating elemental C:N ratios and stable nitrogen isotope (δ15N) values of the biofouling community across eight sample periods. In situ DO in bottom waters was low near high profile reefs. Water column respiration and nutrient regeneration rates were comparable to other estuaries. Respiration and regeneration rates of ammonium and phosphate were all manifestly higher in the presence of a biofouling community. Biofouling metabolic-driven processes were higher at low profile reefs than at high profile reefs. Seasonal shifts in biofouling elemental composition indicated by higher C:N and lower δ15N during cooler months and lower C:N and higher δ15N during warmer months reflected a transition from a more autotrophic community in winter to a more heterotrophic community during the rest of the year. Our results demonstrate that artificial reef biofouling communities can potentially contribute to hypoxic conditions in coastal waters of the northern Gulf of Mexico.

Current Status and Potential Invasiveness Evaluation of an Exotic Mangrove Species, Laguncularia racemosa (L.) C.F. Gaertn, on Hainan Island, China

Laguncularia racemosa is widely planted in coastal mudflats as a pioneer species of mangrove afforestation in China; however, whether it exhibits invasiveness as an exotic species has been a topic of debate. In this study, we investigated the distribution, community structure, and population characteristics of L. racemosa on Hainan Island, China, to discuss its potential invasiveness. The results showed that Hainan Island has become an important distribution area for L. racemosa in China, covering a total area of 64.90 ha. In this investigation, a total of 11 true mangrove species, belonging to six families and 11 genera, were discovered in the L. racemosa community throughout Hainan Island. Furthermore, L. racemosa exhibited a strong adaptive range to seawater salinity and demonstrated a high capacity for natural dispersion. L. racemosa exhibited high importance values in the tree, shrub, and seedling layers of this artificial community, indicating its dominance and potential exclusion of other mangrove species. Over time, the importance values of L. racemosa in the tree and shrub layers initially decreased and then increased, while the opposite trend was observed in the seedling layer. Overall, the diversity indexes, including the Simpson index, Shannon–Wiener index, and Pielou community evenness index, were low in the planted L. racemosa forest on Hainan Island, suggesting a relatively simple species composition within the L. racemosa community. Additionally, based on the height levels of the L. racemosa population, we found that it mainly belonged to the first level, characterized by a large number of seedlings on Hainan Island. The number of L. racemosa seedlings gradually decreased in subsequent levels, and higher height levels of L. racemosa appeared with longer planting times, indicating a growing trend of potential invasiveness of L. racemosa on Hainan Island over an extended period. We suggest that the government should be able to list L. racemosa as an invasive species that can be harvested in the future. Based on this, this study provides fundamental data and theoretical references for the sustainable prevention, control, and management of the exotic species L. racemosa on Hainan Island, and also in other mangrove wetlands in China.

Application of IoT Framework for Prediction of Heart Disease using Machine Learning

Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft computing models can be helpful for remedy of various heart diseases. The Food and Drug Administration (FDA) employs several particularly creative thoughts to get their capsules to the client. Artificial Neural Community offers a first-rate chance to deal with heart diseases with advance IoT and cloud applications.