Current multi-scale biomaterials for tissue regeneration following spinal cord injury.

Spinal cord injury (SCI) may cause loss of motor and sensory function, autonomic dysfunction, and thus disrupt the quality of life of patients, leading to severe disability and significant psychological, social, and economic burden. At present, existing therapy for SCI have limited ability to promote neural function recovery, and there is an urgent need to develop innovative regenerative approaches to repair SCI. Biomaterials have become a promising strategy to promote the regeneration and repair of damaged nerve tissue after SCI. Biomaterials can provide support for nerve tissue by filling cavities, and improve local inflammatory responses and reshape extracellular matrix structures through unique biochemical properties to create the optimal microenvironment at the SCI site, thereby promoting neurogenesis and reconnecting damaged spinal cord tissue. Considering the importance of biomaterials in repairing SCI, this article reviews the latest progress of multi-scale biomaterials in SCI treatment and tissue regeneration, and evaluates the relevant technologies for manufacturing biomaterials.

Atorvastatin exerts a preventive effect against steroid-induced necrosis of the femoral head by modulating Wnt5a release.

Steroid-induced osteonecrosis of the femoral head (SONFH) is a prevalent form of osteonecrosis in young individuals. More efficacious clinical strategies must be used to prevent and treat this condition. One of the mechanisms through which SONFH operates is the disruption of normal differentiation in bone marrow adipocytes and osteoblasts due to prolonged and extensive use of glucocorticoids (GCs). In vitro, it was observed that atorvastatin (ATO) effectively suppressed the impact of dexamethasone (DEX) on bone marrow mesenchymal stem cells (BMSCs), specifically by augmenting their lipogenic differentiation while impeding their osteogenic differentiation. To investigate the underlying mechanisms further, we conducted transcriptome sequencing of BMSCs subjected to different treatments, leading to the identification of Wnt5a as a crucial gene regulated by ATO. The analyses showed that ATO exhibited the ability to enhance the expression of Wnt5a and modulate the MAPK pathway while regulating the Wnt canonical signaling pathway via the WNT5A/LRP5 pathway. Our experimental findings provide further evidence that the combined treatment of ATO and DEX effectively mitigates the effects of DEX, resulting in the upregulation of osteogenic genes (Runx2, Alpl, Tnfrsf11b, Ctnnb1, Col1a) and the downregulation of adipogenic genes (Pparg, Cebpb, Lpl), meanwhile leading to the upregulation of Wnt5a expression. So, this study offers valuable insights into the potential mechanism by which ATO can be utilized in the prevention of SONFH, thereby holding significant implications for the prevention and treatment of SONFH in clinical settings.

Sepsis caused by Phytobacter diazotrophicus complicated with galactosemia type 1 in China: a case report.

BACKGROUND: Phytobacter diazotrophicus (P. diazotrophicus) is an opportunistic pathogen that causes nosocomial outbreaks and sepsis. However, there are no reports of P. diazotrophicus isolated from human blood in China. CASE PRESENTATION: A 27-day-old female infant was admitted to our hospital with fever and high bilirubin levels. The clinical features included jaundice, abnormal coagulation, cholestasis, fever, convulsions, weak muscle tension, sucking weakness, ascites, abnormal tyrosine metabolism, cerebral oedema, abnormal liver function, clavicle fracture, and haemolytic anaemia. The strain isolated from the patient's blood was identified as P. diazotrophicus by whole-genome sequencing (WGS). Galactosemia type 1 (GALAC1) was diagnosed using whole-exome sequencing (WES). Based on drug sensitivity results, 10 days of anti-infective treatment with meropenem combined with lactose-free milk powder improved symptoms. CONCLUSION: P. diazotrophicus was successfully identified in a patient with neonatal sepsis combined with galactosemia. Galactosemia may be an important factor in neonatal sepsis. This case further expands our understanding of the clinical characteristics of GALAC1.

Evaluation of agricultural non-point source pollution using an in-situ and automated photochemical flow analysis system.

Off-line leachate collection from agricultural landscapes cannot guarantee precise evaluation of agricultural non-point source (ANPS) due to geospatial variations, time, and transportation from the field to the laboratory. Implementing an in-situ nitrogen and phosphorous monitoring system with a robust photochemical flow analysis is imperative for precision agriculture, enabling real-time intervention to minimize non-point source pollution and overcome the limitations posed by conventional analysis in laboratory. A reliable, robust and in-situ approach was proposed to monitor nitrogen and phosphorous for determining ANPS pollution. In this study, a home-made porous ceramic probe and the frequency domain reflectometer (FDR) based water content sensors were strategically placed at different soil depths to facilitate the collection of leachates. These solutions were subsequently analyzed by in-situ photochemical flow analysis monitoring system built across the field to estimate the concentrations of phosphorus and nitrogen. After applying both natural and artificial irrigation to the agricultural landscape, at least 10 mL of soil leachates was consistently collected using the porous ceramic probe within 20 min, regardless of the depth of the soil layers when the volumetric soil water contents are greater than 19%. The experimental results showed that under different weather conditions and irrigation conditions, the soil water content of 50 cm and 90 cm below the soil surface was 19.58% and 26.08%, respectively. The average concentrations of NH4+-N, NO3--N, PO43- are 0.584 mg/L, 15.7 mg/L, 0.844 mg/L, and 0.562 mg/L, 16.828 mg/L and 0.878 mg/L at depths of 50 cm and 90 cm below the soil surface, respectively. Moreover, the comparison with conventional laboratory spectroscopic analysis confirmed R2 values of 0.9951, 0.9943, 0.9947 average concentration ranges of NH4+-N, NO3--N, and PO43-, showcasing the accuracy and reliability of robust photochemical flow analysis in-situ monitoring system. The suggested monitoring system can be helpful in the assessment of soil nutrition for precision agriculture.

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Nitrate pollution in groundwater has become a global concern. One of the most important issues in controlling the nitrate pollution of groundwater is to identify the pollution source quickly and accurately. In this review, we firstly summarized the isotopic background values of potential sources of nitrate pollution in groundwater in 17 provinces (cities, autonomous regions) and 29 study areas in China, which could provide the fundamental database for subsequent research. Secondly, we reviewed the research progress of nitrate isotopes combined with multiple tracers for tracing nitrate in groundwater, and discussed their applicable conditions, advantages, and disadvantages. We found that halides and microorganisms combined with nitrate isotopes could accurately trace the pollution sources of domestic sewage, excrement and agricultural activities. The combination of Δ17O and nitrate isotopes could effectively distinguish the source of atmospheric deposition of nitrate in groundwater. The combination of groundwater age and nitrate isotopes could further determine the time scale of nitrate pollution. In addition, we summarized the application cases and compared the characteristics of mass balance mixing model, IsoSource model, Bayesian isotope mixing model, and EMMTE model for quantitative identification of nitrate pollution in groundwater. For the complexity and concealment of groundwater pollution sources, the coupling of nitrate isotopes with other chemical and biological tracing methods, as well as the application of nitrate isotope quantitative models, are effective tools for reliably identifying groundwater nitrate sources and transformation processes.

A novel fish individual recognition method for precision farming based on knowledge distillation strategy and the range of the receptive field.

With the continuous development of green and high-quality aquaculture technology, the process of industrialized aquaculture has been promoted. Automation, intelligence, and precision have become the future development trend of the aquaculture industry. Fish individual recognition can further distinguish fish individuals based on the determination of fish categories, providing basic support for fish disease analysis, bait feeding, and precision aquaculture. However, the high similarity of fish individuals and the complexity of the underwater environment presents great challenges to fish individual recognition. To address these problems, we propose a novel fish individual recognition method for precision farming that rethinks the knowledge distillation strategy and the chunking method in the vision transformer. The method uses the traditional convolutional neural network model as the teacher model, introducing the teacher token to guide the student model to learn the fish texture features. We propose stride patch embedding to expand the range of the receptive field, thus enhancing the local continuity of the image, and self-attention-pruning to discard unimportant tokens and reduce the model computation. The experimental results on the DlouFish dataset show that the proposed method in this paper improves accuracy by 3.25% compared to ECA Resnet152, with an accuracy of 93.19%, and also outperforms other vision transformer models.

Functionalized Single Crystal Perovskite Materials for SERS and Their Potential Detection Applications.

Recent advances in detection and diagnostic tools have improved understanding and identification of plant physiological and biochemical processes. Effective and safe Surface Enhanced Raman Spectroscopy (SERS) can find objects quickly and accurately. Raman enhancement amplifies the signal by 1014-1015 to accurately quantify plant metabolites at the molecular level. This paper shows how to use functionalized perovskite substrates for SERS. These perovskite substrates have lots of surface area, intense Raman scattering, and high sensitivity and specificity. These properties eliminate sample matrix component interference. This study identified research gaps on perovskite substrates' effectiveness, precision, and efficiency in biological metabolite detection compared to conventional substrates. This article details the synthesis and use of functionalized perovskites for plant metabolites measurement. It analyzes their pros and cons in this context. The manuscript analyzes perovskite-based SERS substrates, including single-crystalline perovskites with enhanced optoelectronic properties. This manuscript aims to identify this study gap by comprehensively reviewing the literature and using it to investigate plant metabolite detection in future studies.

GUGA-based MRCI approach with core-valence separation approximation (CVS) for the calculation of the core-excited states of molecules.

We develop and demonstrate how to use the Graphical Unitary Group Approach (GUGA)-based MRCISD with Core-Valence Separation (CVS) approximation to compute the core-excited states. First, perform a normal Self-Consistent-Field (SCF) or valence MCSCF calculation to optimize the molecular orbitals. Second, rotate the optimized target core orbitals and append to the active space, form an extended CVS active space, and perform a CVS-MCSCF calculation for core-excited states. Finally, construct the CVS-MRCISD expansion space and perform a CVS-MRCISD calculation to optimize the CI coefficients based on the variational method. The CVS approximation with GUGA-based methods can be implemented by flexible truncation of the Distinct Row Table. Eliminating the valence-excited configurations from the CVS-MRCISD expansion space can prevent variational collapse in the Davidson iteration diagonalization. The accuracy of the CVS-MRCISD scheme was investigated for excitation energies and compared with that of the CVS-MCSCF and CVS-CASPT2 methods using the same active space. The results show that CVS-MRCISD is capable of reproducing well-matched vertical core excitation energies that are consistent with experiments by combining large basis sets and a rational reference space. The calculation results also highlight the fact that the dynamic correlation between electrons makes an undeniable contribution in core-excited states.

Correlation between the cardiometabolic index and arteriosclerosis in patients with type 2 diabetes mellitus.

BACKGROUND: The cardiometabolic index (CMI) is a new metric derived from the triglyceride-glucose index and body mass index and is considered a potential marker for cardiovascular risk assessment. This study aimed to examine the correlation between the CMI and the presence and severity of arteriosclerosis in patients with type 2 diabetes mellitus (T2DM). METHODS: This study involved 2243 patients with T2DM. The CMI was derived by dividing the triglyceride level (mmol/L) by the high-density lipoprotein level (mmol/L) and then multiplying the quotient by the waist-to-height ratio. Multivariate logistic regression was used to analyze the correlations between the CMI and BMI blood biomarkers, blood pressure, and brachial-ankle pulse wave velocity (baPWV). RESULTS: Patients were categorized into three groups based on their CMI: Group C1 (CMI < 0.775; n = 750), Group C2 (CMI: 0.775-1.355; n = 743), and Group C3 (CMI > 1.355; n = 750). Increased BMI, fasting glucose, insulin (at 120 min), total cholesterol (TC), and baPWV values were observed in Groups C2 and C3, with statistically significant trends (all trends P < 0.05). The CMI was positively correlated with systolic blood pressure (r = 0.74, P < 0.001). Multivariate analysis revealed that an increased CMI contributed to a greater risk for arteriosclerosis (OR = 1.87, 95%CI: 1.66-2.10, P < 0.001). Compared to the C1 group, the C2 group and C3 group had a greater risk of developing arteriosclerosis, with ORs of 4.55 (95%CI: 3.57-5.81, P<0.001) and 5.56 (95%CI: 4.32-7.17, P<0.001), respectively. The association was notably stronger in patients with a BMI below 21.62 kg/m² than in those with a BMI of 21.62 kg/m² or higher (OR = 4.53 vs. OR = 1.59). CONCLUSIONS: These findings suggest that the CMI is a relevant and independent marker of arteriosclerosis in patients with T2DM and may be useful in the risk stratification and management of these patients.

Novel Operation Mechanism and Multifunctional Applications of Bubble Microrobots.

Microrobots have emerged as powerful tools for manipulating particles, cells, and assembling biological tissue structures at the microscale. However, achieving precise and flexible operation of arbitrary-shaped microstructures in 3D space remains a challenge. In this study, three novel operation methods based on bubble microrobots are proposed to enable delicate and multifunctional manipulation of various microstructures. These methods include 3D turnover, fixed-point rotation, and 3D ejection. By harnessing the combined principles of the effect of the heat flow field and surface tension of an optothermally generated bubble, the bubble microrobot can perform tasks such as flipping an SIA humanoid structure, rotating a bird-like structure, and launching a hollow rocket-like structure. The proposed multi-mode operation of bubble microrobots enables diverse attitude adjustments of microstructures with different sizes and shapes in both 2D and 3D spaces. As a demonstration, a biological microenvironment of brain glioblastoma is constructed by the bubble microrobot. The simplicity, versatility, and flexibility of this proposed method hold great promise for applications in micromanipulation, assembly, and tissue engineering.

Effects of polyethylene, polylactic acid, and tire particles on the sediment microbiome and metabolome at high and low temperatures.

Global warming has led to a high incidence of extreme heat events, and the frequent occurrence of extreme heat events has had extensive and far-reaching impacts on wetland ecosystems. The widespread distribution of plastics in the environment, including polyethylene (PE), polylactic acid (PLA), and tire particles (TPs), has caused various environmental problems. Here, high-throughput sequencing techniques and metabolomics were used for the first time to investigate the effects of three popular microplastic types: PE, PLA, and TP, on the sediment microbiome and the metabolome at both temperatures. The microplastics were incorporated into the sediment at a concentration of 3% by weight of the dry sediment (wt/wt), to reflect environmentally relevant conditions. Sediment enzymatic activity and physicochemical properties were co-regulated by both temperatures and microplastics producing significant differences compared to controls. PE and PLA particles inhibited bacterial diversity at low temperatures and promoted bacterial diversity at high temperatures, and TP particles promoted both at both temperatures. For bacterial richness, only PLA showed inhibition at low temperature; all other treatments showed promotion. PE, PLA, and TP microplastics changed the community structure of sediment bacteria, forming two clusters at low and high temperatures. Furthermore, PE, PLA, and TP changed the sediment metabolic profiles, producing differential metabolites such as lipids and molecules, organic heterocyclic compounds, and organic acids and their derivatives, especially TP had the most significant effect. These findings contribute to a more comprehensive understanding of the potential impact of microplastic contamination.IMPORTANCEIn this study, we added 3% (wt/wt) microplastic particles, including polyethylene, polylactic acid, and tire particles, to natural sediments under simulated laboratory conditions. Subsequently, we simulated the sediment microbial and ecosystem responses under different temperature conditions by incubating them for 60 days at 15°C and 35°C, respectively. After synthesizing these results, our study strongly suggests that the presence of microplastics in sediment ecosystems and exposure under different temperature conditions may have profound effects on soil microbial communities, enzyme activities, and metabolite profiles. This is important for understanding the potential hazards of microplastic contamination on terrestrial ecosystems and for developing relevant environmental management strategies.

Salinization of groundwater in shale gas extraction area in the Sichuan Basin, China: Implications for water protection in shale regions with well-developed faults.

The expanding growth of shale gas development has sparked global concern over water-related environmental issues. However, research on groundwater contamination in shale gas areas in China remains limited, impeding environmentally friendly industry practices. To address this gap, we investigated the Wufeng-Longmaxi shale region in the Sichuan Basin, encompassing both operational and prospective shale gas extraction sites, to assess the effects of shale gas operations on shallow groundwater quality. We found there was no significant correlation between groundwater quality and the minimum distance from the shale gas well pads, and some groundwater samples located far from shale gas well pads, rather than those close to pads, were salinized. These findings suggest minimal impacts from shale gas drilling and hydraulic fracturing. The salinized groundwater samples are characterized by high salinity levels and ion concentrations, and are located near fault zones. The primary source of shallow groundwater salinization was derived from the Triassic formation brines confirmed through the assessment of the sensitivity and conservative mixing models. Faults in the study area were identified as pathways for the upward migration of Triassic brines, evidenced by the proximity of salinized samples to fault zones. However, further investigation is required to ascertain whether shale gas extraction activities have induced the migration of formation brines. The occurrence and reactivation of faults, induced by microseismic activities, may pose an increased risk of groundwater contamination in tectonically complex fault zones during shale gas extraction. Therefore, it is imperative to enhance extraction strategies and technologies, particularly in shale regions with well-developed faults, such as optimizing well placement regulation, controlling hydraulic fracturing scale, and strengthening environmental monitoring. By shedding light on potential environmental ramifications of shale gas extraction, especially in fault-rich regions, our study informs water protection strategies and the sustainable advancement of the shale gas industry.

Temperature-dependent effects of microplastics on sediment bacteriome and metabolome.

The increasing prevalence of microplastics in the environment has become a concern for various ecosystems, including wetland ecosystems. Here, we investigated the effects of three popular microplastic types: polyethylene, polylactic acid, and tire particles at 5 °C and 25 °C on the sediment microbiome and metabolome at the 3% (w/w) level. Results indicated that temperature greatly influenced catalase and neutral phosphatase activities, whereas the type of microplastic had a more significant impact on urease and dehydrogenase activities. The addition of microplastic, especially tire particles, increased microbial diversity and significantly altered the microbial community structure and metabolic profile, leading to the formation of different clusters of microbial communities depending on the temperature. Nonetheless, the effect of temperature on the metabolite composition was less significant. Functional prediction showed that the abundance of functional genes related to metabolism and biogeochemical cycling increased with increasing temperature, especially the tire particles treatment group affected the nitrogen cycling by inhibiting ureolysis and nitrogen fixation. These observations emphasize the need to consider microplastic type and ambient temperature to fully understand the ecological impact of microplastics on microbial ecosystems.

Catheter-Assisted Bioinspired Adhesive Magnetic Soft Millirobot for Drug Delivery.

Soft millirobots have evolved into various therapeutic applications in the medical field, including for vascular dredging, cell transportation, and drug delivery, owing to adaptability to their surroundings. However, most soft millirobots cannot quickly enter, retrieve, and maintain operations in their original locations after removing the external actuation field. This study introduces a soft magnetic millirobot for targeted medicine delivery that can be transported into the body through a catheter and anchored to the tissues. The millirobot has a bilayer adhesive body with a mussel-inspired hydrogel layer and an octopus-inspired magnetic structural layer. It completes entry and retrieval with the assistance of a medical catheter based on the difference between the adhesion of the hydrogel layer in air and water. The millirobot can operate in multiple modes of motion under external magnetic fields and underwater tissue adhesion after self-unfolding with the structural layer. The adaptability and recyclability of the millirobots are demonstrated using a stomach model. Combined with ultrasound (US) imaging, operational feasibility within organisms is shown in isolated small intestines. In addition, a highly efficient targeted drug delivery is confirmed using a fluorescence imaging system. Therefore, the proposed soft magnetic millirobots have significant potential for medical applications.

Dual-Responsive Nanorobot-Based Marsupial Robotic System for Intracranial Cross-Scale Targeting Drug Delivery.

Nanorobots capable of active movement are an exciting technology for targeted therapeutic intervention. However, the extensive motion range and hindrance of the blood-brain barrier impeded their clinical translation in glioblastoma therapy. Here, a marsupial robotic system constructed by integrating chemical/magnetic hybrid nanorobots (child robots) with a miniature magnetic continuum robot (mother robot) for intracranial cross-scale targeting drug delivery is reported. For primary targeting on macroscale, the continuum robot enters the cranial cavity through a minimally invasive channel (e.g., Ommaya device) in the skull and transports the nanorobots to pathogenic regions. Upon circumventing the blood-brain barrier, the released nanorobots perform secondary targeting on microscale to further enhance the spatial resolution of drug delivery. In vitro experiments against primary glioblastoma cells derived from different patients are conducted for personalized treatment guidance. The operation feasibility within organisms is shown in ex vivo swine brain experiments. The biosafety of the treatment system is suggested in in vivo experiments. Owing to the hierarchical targeting method, the targeting rate, targeting accuracy, and treatment efficacy have improved greatly. The marsupial robotic system offers a novel intracranial local therapeutic strategy and constitutes a key milestone in the development of glioblastoma treatment platforms.

Effect of in situ ultrasonic wave and influent ammonia nitrogen fluctuation on stability of aerobic granular sludge.

This study elucidates the impact of fluctuating influent conditions and in situ ultrasonic wave exposure on the stability of aerobic granular sludge (AGS) in the treatment of simulated wastewater emanating from rare earth mining operations. During a stable influent period spanning from Day 1 to Day 95, the seed granules underwent an initial disintegration followed by a re-granulation phase. The secondary granulation was achieved on Day 80 and Day 40 for the ultrasonic reactor (R1) and the control reactor (R2), respectively. Notably, granules formed in R1 exhibited a more porous structure compared to those generated in R2. Subsequently, when the ammonia nitrogen in the influent oscillated between 100 and 500 mg/L during Days 96-140, both reactors yielded compact and densely structured granules. Nitrogen removal profiles were comparable between the two reactors: the removal efficiencies for ammonia nitrogen and total inorganic nitrogen escalated from 95% and 80%, respectively, during Days 1-95, to 95% and 90%, respectively, post-Day 140. A suite of performance metrics indicated that steady-state granules from R1 outperformed those from R2 across several parameters. Specifically, the nitrification/denitrification rates, and relative abundance of denitrifying bacteria were all higher in granules from R1. Conversely, the relative abundance of nitrifying bacteria was comparable between granules from both reactors. However, R1 granules demonstrated lower sludge concentration and smaller average particle size than their R2 counterparts. In conclusion, the AGS system demonstrated robust resilience to fluctuating ammonia nitrogen, and the application of ultrasonic waves significantly enhanced granular activity while achieving in situ sludge reduction.

Temporal and spatial dynamic changes of planktonic bacteria community structure in Li River, China: a seasonal survey.

A combined temporal and spatial research approach helps us to evaluate the ecological status of a river scientifically and comprehensively. To understand the response mechanisms of bacteria in the Li River to different environments, we conducted a 1-year study (2020-2021) and collected water samples from 18 sections of the river in October, January, April, and August. 16S sequencing was used to study the composition and structure of bacterial communities in Li River at different temporal and spatial scales. The results showed that NO3--N, TP, T, pH, and DO were significantly different on spatial and temporal scales. Alpha diversity of planktonic bacteria in Li River fluctuated significantly with the season, reaching its highest in summer. Proteobacteria remained the most dominant phylum in all seasons, but the differential microorganisms varied between seasons. Although the abundance of metabolic functions of planktonic bacteria did not show significant differences between seasons, we found that DO, TP, T, and COD were the key environmental factors affecting bacterial metabolism. In addition, the co-occurrence network analysis showed that the autumn network had a higher number of nodes and edges and exhibited a high degree of complexity, while the summer network had the highest degree of modularity and exhibited greater stability. These results deepen our knowledge of the response mechanisms of river microorganisms to temporal and spatial changes and provide a scientific reference for the study of river ecosystems.

Surfactant and antibiotic co-occurrence reshaped the acidogenic process for volatile fatty acids production during sludge anaerobic fermentation.

The overuse of surfactants and antibiotics has led to their high concentration in waste activated sludge (WAS), and these exogenous pollutants have been shown to pose various influences on the subsequent anaerobic treatment process. Previous works have primarily concerned the impacts of individual pollutants on WAS anaerobic fermentation process. This work revealed the synergetic effects of sodium dodecyl benzene sulfonate (SDBS) and sulfadiazine (SDZ) co-occurrence in WAS on the biosynthesis of volatile fatty acids (VFAs). The addition of SDBS in the SDZ reactor significantly increased VFAs generation, and this increase was correlated with the concentration of SDZ. The VFAs production exhibited a 200.0-211.9 % and 5.9-20.4 % increase in comparison with the sole SDZ and SDBS reactor, respectively. The SDBS and SDZ co-occurrence facilitated the solubilization, hydrolysis, and acidification stages of WAS fermentation synchronously. SDBS was effectively to disintegrate the cemented structure of extracellular polymeric substances and meanwhile improve the SDZ solubilization, which increase the SDZ bioavailability as well as biotoxicity to the anaerobic species. Herein, the anaerobic consortia structure was evidently reshaped, and the keystone microbes Acetoanaerobium and Fususibacter, as well-tolerated hydrolytic-acidogenic bacteria, were greatly enriched. Furthermore, the functional microbial metabolic traits responsible for the substrate extracellular hydrolysis (e.g., glsA and MAN2C1), intracellular metabolism (e.g., ALDO and asdA), and fatty acid generation (e.g., aarC) were all upregulated in the SDBS/SDZ co-occurrence reactor.

A deep learning-enabled smartphone platform for rapid and sensitive colorimetric detection of dimethoate pesticide.

A novel deep learning-enabled smartphone platform is developed to assist a colorimetric aptamer biosensor for fast and highly sensitive detection of dimethoate. The colorimetric determination of dimethoate is based on the specific binding of dimethoate and aptamer, which leads to the aggregation of AuNPs in high-concentration NaCl solution, resulting in an obvious color change from red to blue. This color change provides sufficient data for self-learning enabled by a convolutional neural network (CNN) model, which is established to predict dimethoate concentration based on images acquired from a smartphone. To enhance user-friendliness for non-experts, the CNN model is then embedded into a smartphone app, enabling offline detection of dimethoate pesticide in real environments within just 15 min using a pre-configured colorimetric probe. The developed platform exhibits superior performance, achieving a regression coefficient of 0.9992 in the concentration range of 0-10 µM. Moreover, the app's performance is found to be consistent with the ELISA kit. These remarkable findings demonstrate the potential of combining colorimetric biosensors with smartphone-based deep learning methods for the development of portable and affordable tools for pesticide detection.

GraphTS: Graph-represented time series for subsequence anomaly detection.

Automatic detection of subsequence anomalies (i.e., an abnormal waveform denoted by a sequence of data points) in time series is critical in a wide variety of domains. However, most existing methods for subsequence anomaly detection often require knowing the length and the total number of anomalies in time series. Some methods fail to capture recurrent subsequence anomalies due to using only local or neighborhood information for anomaly detection. To address these limitations, in this paper, we propose a novel graph-represented time series (GraphTS) method for discovering subsequence anomalies. In GraphTS, we provide a new concept of time series graph representation model, which represents both recurrent and rare patterns in a time series. Particularly, in GraphTS, we develop a new 2D time series visualization (2Dviz) method, which compacts all 1D time series patterns into a 2D spatial temporal space. The 2Dviz method transfers time series patterns into a higher-resolution plot for easier sequence anomaly recognition (or detecting subsequence anomalies). Then, a Graph is constructed based on the 2D spatial temporal space of time series to capture recurrent and rare subsequence patterns effectively. The represented Graph also can be used to discover single and recurrent subsequence anomalies with arbitrary lengths. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and efficiency.