Normal Conditions Research Articles

Possible detection of atmospheric bioaerosol via LiDAR: a wavelength-based simulation study

This study explores potential of LiDAR technology to rapidly detect aerosolized biological terror agents in the atmosphere. It assesses the application by simulating extinction coefficients and the Ångström exponent at various wavelengths (266, 1064, 1571, and 2000 nm), focusing on differentiating bioaerosols from typical atmospheric particles. The simulation analysis evaluates changes in aerosol distributions and related extinction coefficient and Ångström exponent shifts under clean, normal, and bad atmospheric conditions. The findings indicate that the 1064 nm wavelength effectively detects bioaerosol presence, with a combination of 1064 nm and 1571 nm providing optimal Ångström exponent use for particle size differentiation. This dual-wavelength approach is highlighted as a practical method for bioaerosol detection, showcasing a significant sensitivity to variations in particle quantity and size, which are critical in biological threat scenarios. In conclusion, the study offers guidance for selecting LiDAR wavelengths for biological agent detection systems. While providing a theoretical framework for practical applications, it also underlines the need for further experimental work to confirm findings and fine-tune technology for real-world monitoring and threat management. This research contributes to the development of effective monitoring strategies against the backdrop of biological terror threats.Graphical

Floral Response to Heat: A Study of Color and Biochemical Adaptations in Purple Chrysanthemums.

Chrysanthemums are among the world's most popular cut flowers, with their color being a key ornamental feature. The formation of these colors can be influenced by high temperatures. However, the regulatory mechanisms that control the fading of chrysanthemum flower color under high-temperature stress remain unclear. This study investigates the impact of high temperatures on the color and biochemical responses of purple chrysanthemums. Four purple chrysanthemum varieties were exposed to both normal and elevated temperature conditions. High-temperature stress elicited distinct responses among the purple chrysanthemum varieties. 'Zi Feng Che' and 'Chrystal Regal' maintained color stability, whereas 'Zi Hong Tuo Gui' and 'Zi lian' exhibited significant color fading, particularly during early bloom stages. This fading was associated with decreased enzymatic activities, specifically of chalcone isomerase (CHI), dihydroflavonol 4-reductase (DFR), and anthocyanidin synthase (ANS), indicating a critical period of color development under heat stress. Additionally, the color fading of 'Zi Lian' was closely related to the increased activity of the peroxidase (POD) and polyphenol oxidase (PPO). Conversely, a reduction in β-glucosidase (βG) activity may contribute significantly to the color steadfastness of 'Zi Feng Che'. The genes Cse_sc027584.1_g010.1 (PPO) and Cse_sc031727.1_g010.1 (POD) might contribute to the degradation of anthocyanins in the petals of 'Zi Hong Tuo Gui' and 'Zi Lian' under high-temperature conditions, while simultaneously maintaining the stability of anthocyanins in 'Zi Feng Che' and 'Chrystal Regal' at the early bloom floral stage. The findings of this research provide new insights into the physiological and biochemical mechanisms by which chrysanthemum flower color responds to high-temperature stress.

Comparison of different control methods on the thermally activated building system (TABS) with large energy flexibility

Thermally Activated Building Systems (TABS) has great energy flexibility potential for the effective demand response, but the inherent large thermal inertia creates challenges for the control. Model Predictive Control (MPC) can improve the control efficiency while achieving certain objectives. However, the modeling mechanism of the TABS thermal behavior can lead to the difference in the controller performances. In this study, the white-box model, grey-box model, and black-box model based MPCs (referred to as W-MPC, G-MPC, and B-MPC) are developed and analyzed in depth for a case TABS with high heat flexibility potential. The performances of MPC strategies are investigated together with the baseline Rule Based Control (RBC) strategy under normal conditions as well as a variety of uncertainty events. In general, MPC strategies show better performance compared to the RBC. The prediction-based control can effectively maintain the indoor temperature within the constraints considering the uncertain disturbances and the gradual outdoor climate change. Moreover, MPC strategies help to increase the cost savings due to the effective load shifting by 30%–50%, and enhance the flexible energy utilization by 14%–29%. In terms of the comparison among the different MPC strategies, W-MPC shows better performance in the room temperature control, which reduces the violation of the indoor temperature constraint by 30% and 15% compared to G-MPC and B-MPC, respectively. G-MPC shows slight superiority concerning the economy and flexible energy usage. The normalized cost saving of G-MPC is approximately 3% and 8% lower compared to the W-MPC and B-MPC, respectively, while the utilization efficiencies of the flexible energy are approximately 6% and 12% higher.

Effects of oligosaccharides on performance, intestinal morphology, microbiota and immune reactions in laying hens challenged with dextran sodium sulfate

The aim of this study was to determine the effect of oligosaccharide extract from bamboo shoot (BOS) on the performance, intestinal morphology, microbiota and immune reaction of laying hens challenged with dextran sodium sulfate (DSS). Thirty-two White Leghorn hens (480 days old) were divided into 4 groups (8 hens each) with similar mean body weights: C (basal diet), D (basal diet + DSS), B (5 g/kg BOS diet), and BD (5 g/kg BOS diet + DSS). They were administered a single oral dose of 4 mL of distilled water/kg body weight with or without 0.45 g of DSS for 7 consecutive days from the 14th d to the 21st d of the experiment. The important findings were that (1) The egg yolk ratio was decreased by DSS treatment, but it was improved by BOS treatment, which also increased the egg shell ratio. (2) The diversity of intestinal microbiota and relative abundance of 4 bacteria genera were increased by BOS treatment. (3) Intestinal morphology was not affected by DSS and BOS, but the leukocytes accumulation in the liver was increased by DSS treatment and suppressed by BOS treatment. (4) Dietary BOS treatment influenced the mRNA expression of Th-1 and Treg cytokines in the liver and Th-17 cytokines in both intestine and liver of laying hens. These results suggest that BOS may enhance egg quality, Th-1 and Th-17 immune function without causing tissue damage under normal condition, and may suppress the excessive inflammatory responses during inflammation.

Динамічні якості пасажирського поїзда зчленованої конструкції

The importance of this work stems from the need for renewing the home rolling stock and integrating it into the European market of transport services. The introduction of speedy train operation calls for increased attention to theoretical studies aimed at ride performance improvement. The analysis of passenger traffic safety for home and European tracks of different and geometries calls for assessing the ride performance of passenger trains. In doing so, steady and transient traffic conditions must be considered. This approach to theoretical studies allows one to improve the safety indices of speedy passenger trains. This paper presents the results of theoretical evaluation of the ride performance of an articulated and a standard passenger train. The cars of both trains are equipped with wheels of different profiles used on the Ukrainian and European railways. An assessment of the predicted dynamic performance of the articulated passenger train shows that its cars demonstrate a better ride performance, thus improving passenger comfort and train operating safety throughout the allowable speed range in normal conditions. It is undesirable for such trains to move in curves of small and medium radius. In addition, the articulated train cost is lower than the traditional train one because of a lower number of trucks. The ride performance is also assessed for cars whose wheels are compatible with different rail profiles, which allows them to be used both on the Ukrainian and on the European railways. As shown by calculations, the universal wear-resistant wheel profile does not impair the car ride performance of the trains considered. better ride performance.

Advanced extraction of PV parameters’ models based on electric field impacts on semiconductor conductivity using QIO algorithm

This article presents a novel approach for parameters estimation of photovoltaic cells/modules using a recent optimization algorithm called quadratic interpolation optimization algorithm (QIOA). The proposed formula is dependent on variable voltage resistances (VVR) implementation of the series and shunt resistances. The variable resistances reduced from the effect of the electric field on the semiconductor conductivity should be included to get more accurate representation. Minimizing the mean root square error (MRSE) between the measured (I–V) dataset and the extracted (V–I) curve from the proposed electrical model is the main goal of the current optimization problem. The unknown parameters of the proposed PV models under the considered operating conditions are identified and optimally extracted using the proposed QIOA. Two distinct PV types are employed with normal and low radiation conditions. The VVR TDM is proposed for (R.T.C. France) silicon PV operating at normal radiation, and eleven unknown parameters are optimized. Additionally, twelve unknown parameters are optimized for a Q6-1380 multi-crystalline silicon (MCS) (area 7.7 cm2) operating under low radiation. The efficacy of the QIOA is demonstrated through comparison with four established optimizers: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Sine Cosine Algorithm (SCA). The proposed QIO method achieves the lowest absolute current error values in both cases, highlighting its superiority and efficiency in extracting optimal parameters for both Single-Crystalline Silicon (SCS) and MCS cells under varying irradiance levels. Furthermore, simulation results emphasize the effectiveness of QIO compared to other algorithms in terms of convergence speed and robustness, making it a promising tool for accurate and efficient PV parameter estimation.

Multifaceted roles of Arabidopsis heat shock factor binding protein in plant growth, development, and heat shock response

Heat shock factor-binding protein (HSBP) is a 10 kDa protein in plants and animals and consists exclusively of a coiled-coil domain. During the recovery phase following a heat shock response, HSBP relocates from the cytoplasm to the nucleus in Arabidopsis (Arabidopsis thaliana), where it interacts with heat shock factors (HSFs). Here, we found that 16 out of the 19 functional known HSFs can interact with HSBP. Besides, our results indicate that HSBP negatively regulates HSF gene expression during normal growth conditions and recovery from heat shock. Expanding our understanding of HSBP's physiological functions during regular growth, co-immunoprecipitation and mass spectrometry analysis identified 16 coiled-coil domain-containing proteins co-immunoprecipitated with HSBP. These proteins encompass HSP70s, all components of the MAIGO2 complex, COP1-interactive protein1 (CIP1), CIP1-like protein, kinesin-like protein for actin-based chloroplast movement1 and 2 (KAC1/2), and HSBP itself. By examining mutant plants lacking HSBP and its interacting proteins, we elucidated their functional relationships. Our findings underscore the indispensability of the HSBP coiled-coil heptad repeat for interacting with its partners and its crucial role in growth, development, and the heat shock response. In addition to its involvement in heat shock response, Arabidopsis HSBP, a discreet small regulatory protein, exerts multiple regulatory functions in hypocotyl elongation, flowering time, chloroplast photo-relocation, and seed development.

Quantum maximum power point tracking (QMPPT) for optimal solar energy extraction

Solar energy is key to achieving a more environmentally responsible future. One way to exploit it is to use semiconductor technology through solar panels to generate clean, sustainable, and controllable energy. However, the use of such solutions must be optimised by methods such as maximum power point tracking (MPPT) to extract the maximum available solar energy. Although MPPT algorithms have been widely used and improved, the use of newer approaches, such as quantum computing, appears to hold the promise of achieving new performance levels, particularly for real-time MPPT implementation. The goal of this work is to develop and test a quantum algorithm for the photovoltaic (PV) energy MPPT problem using quantum particle swarm optimisation. The performance of the classic and quantum MPPT algorithms was evaluated under three main operating conditions: normal, high-temperature, and partial shading conditions. This represents a variety of environmental scenarios that can affect the efficiency of solar power generation. According to the study's results, the classical algorithm recorded 0.15% more power than the quantum algorithm in normal operating conditions, and the quantum algorithm generated 3.33% more power in higher temperature tests and 0.89% more power in the partial shading test. Moreover, the quantum algorithm recorded lower duty cycles for the three tests. While the classical algorithm may have a slight edge in power output under normal operation conditions, the quantum algorithm indicates superior performance in challenging conditions and consistently reveals more promising overall efficiency.

Study on the damage mechanism and evolution model of preloaded sandstone subjected to freezing–thawing action based on the NMR technology

Abstract Investigating the damage degradation of rock during the freezing and thawing process is more consistent with the actual engineering environment, considering its internal initial damage. In this study, the effects of initial damage from preloading and subzero-temperature freezing–thawing on microscopic and macroscopic mechanical properties of sandstone were studied based on the nuclear magnetic resonance (NMR) technique. The results show that the P-wave velocity of the sample decreased, while the porosity increased as the initial damage level increased. The distribution of T2 signal intensity exposed to the low-temperature freezing–thawing–saturation treatment was rather larger than that under normal temperature conditions for samples with different levels of initial damage from preloading, indicating that the low-temperature freezing–thawing condition would promote the porosity and have an obvious increase in damage. A continuum damage model considering subzero-temperature freezing–thawing damage from different pore sizes was finally introduced to describe the damage evolution mechanism of sandstone. The research results can be used to quantitatively evaluate the damage evolution mechanism of sandstone treated by subzero-temperature freezing–thawing without mechanical tests. Furthermore, the analysis and research results show that the damage variables of sandstone obtained by the NMR theory were lower than those of classical rock mechanics theory.

Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions

The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory. Statistical approaches were used to analyze the experimental data before applying machine learning. Seven machine learning techniques specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), and artificial neural network (ANN) were applied for the prediction of methane (CH4), nitrogen (N2), and natural gas mixture viscosities. More than 4304 datasets from real experimental data utilizing pressure, temperature, and gas density were employed for developing ML models. Furthermore, three novel correlations have developed for the viscosity of CH4, N2, and composite gas using ANN. Results revealed that models and anticipated correlations predicted methane, nitrogen, and natural gas mixture viscosities with high precision. Results designated that the ANN, RF, and gradient Boosting models have performed better with a coefficient of determination (R2) of 0.99 for testing data sets of methane, nitrogen, and natural gas mixture viscosities. However, linear regression and NuSVR have performed poorly with a coefficient of determination (R2) of 0.07 and − 0.01 respectively for testing data sets of nitrogen viscosity. Such machine learning models offer the industry and research a cost-effective and fast tool for accurately approximating the viscosities of methane, nitrogen, and gas mixture under normal and harsh conditions.

Fluid-Solid-Thermal Coupled Freezing Modeling Test of Soil under the Low-Temperature Condition of LNG Storage Tank

Due to the extensive utilization of liquid nature gas (abbreviated as LNG) resources and a multitude of considerations, LNG storage tanks are gradually transitioning towards smaller footprints and heightened safety standards. Consequently, underground LNG storage tanks are being designed and constructed. However, underground LNG storage tanks release a considerable quantity of cold into the ground under both accidental and normal conditions. The influence of cold results in the ground freezing, which further compromises the safety of the structure. Existing research has neglected to consider the effects of this. This oversight could potentially lead to serious safety accidents. In this work, a complete set of experiments using a novel LNG underground storage tank fluid-solid-thermal coupled cryogenic leakage scale model were conducted for the first time to simulate the effect of the tank on the soil temperature field, stress field, and displacement field and to analyze the development of the three fields and the results of the effect. This research helps the related personnel to better design, construct, and evaluate the LNG underground storage tanks to avoid the catastrophic engineering risks associated with cryogenic leakage and helps to improve the design process of LNG underground storage tanks.

Navigating urban challenges: Quantifying emergency service accessibility and robustness amid meteorological disasters

The frequent occurrence of meteorological disasters presents a significant challenge to the effective functioning of urban public services. This paper seeks to assess the response capacity of urban emergency services during such disasters by integrating perspectives on accessibility and robustness. Through a case study on emergency medical services (EMS), we develop a framework for evaluating the EMS system in downtown Shanghai under the scenario of typhoon In-Fa. This framework encompasses the calculation of both the accessible area of ambulance vehicles and the accessibility to each resident address. Our findings indicate that under normal conditions, the majority of residential areas in downtown Shanghai can access nearby medical services within 10 min. However, accessibility decreases by 28.85 % and 43.83 % under moderate and severe flooding conditions, respectively. Furthermore, by analyzing changes in the robustness of the service supply network, we identify warning and critical points, offering valuable insights for the formulation of disaster warning and response strategies. This study integrates system-level robustness and address-level accessibility to establish a comprehensive framework for evaluating urban emergency service performance under meteorological disaster scenarios. The proposed approach holds promise for evaluating other types of public services by adapting the framework to account for specific supply and demand dynamics.

Identification and characterization of SmPIC30, an important gene that controls resistance to the auxin herbicide picloram in eggplant

Picloram (4-amino-3,5,6-trichloro-2-pyridinecarboxylic acid) is a synthetic auxin herbicide that often disrupts the growth and development of dicotyledonous plants. Arabidopsis PIC30 (AtPIC30) is a specific target that mediates sensitivity to picloram, but a putative orthologue of AtPIC30 in eggplant (Solanum melongena L.) has not been characterized. In this study, we identified PIC30 genes in eggplant. Analyses of the phylogenetic relationships, gene structures, conserved domains and motifs revealed the evolutionary conservation of PIC30 among eggplant, tomato, pepper and Arabidopsis. Subcellular localization analysis indicated that SmPIC30 was a plasma membrane-localized protein. Tissue-specific expression analysis revealed that SmPIC30 was expressed in flowers, leaves and fruits, with the highest expression in flowers. Interestingly, the expression of SmPIC30 was significantly upregulated by picloram treatment. Virus-induced gene silencing (VIGS) assays indicated that knocking down SmPIC30 enhanced resistance to picloram, furthermore, the transcriptions of the auxin coreceptor encoding gene SmAFB5 was increased and the transcription of the auxin response genes SmIAA19 and SmIAA29 decreased in SmPIC30-knockdown plants. The hypocotyl elongation of the 35S::SmPIC30 transgenic lines (OE) in Arabidopsis were significantly longer than those of non-transgenic plants (Col-0) under normal condition, but the hypocotyl elongation in OE was sensitive to picloram treatment compared to those of Col-0. The present study suggests that SmPIC30 plays an important role in the response to picloram.

Extended kalman filter for quantifying hydrogen leaks in PEM fuel cells by estimating oxygen concentration

Hydrogen transfer leaks are one of the most important life-limiting faults in polymer electrolyte membrane fuel cells (PEMFCs). Hydrogen transfer leaks result in a reduction in the amount of oxygen available in the cathode (air) channel, with large leaks resulting in oxygen starvation and hydrogen emission. This paper aims to develop an adaptive extended Kalman filter (EKF) to estimate the unknown oxygen concentration, which is then used to infer hydrogen leaks. To this end, the paper first develops the lumped model of the fuel cell from a pseudo-2D model of a fuel cell. Next, a (non-adaptive) EKF is developed to estimate the fuel cell states under both normal and oxygen-starved conditions. The adaptive EKF is then implemented by adding the unknown hydrogen leak to the list of estimated states. Finally, the paper demonstrates the efficacy of the proposed adaptive EKF by using it to accurately estimate unknown hydrogen leaks in a high-fidelity virtual fuel cell under excessively noisy conditions.

Durability of Evoked Compound Action Potential (ECAP)-Controlled, Closed-Loop Spinal Cord Stimulation (SCS) in a Real-World European Chronic Pain Population.

Closed-loop spinal cord stimulation (CL-SCS) is a recently introduced system that records evoked compound action potentials (ECAPs) from the spinal cord elicited by each stimulation pulse and uses this information to automatically adjust the stimulation strength in real time, known as ECAP-controlled SCS. This innovative system compensates for fluctuations in the distance between the epidural leads and the spinal cord by maintaining the neural response (ECAP) at a predetermined target level. This data collection study was designed to assess the performance of the first CL-SCS system in a real-world setting under normal conditions of use in multiple European centers. The study analyzes and presents clinical outcomes and electrophysiological and device data and compares these findings with those reported in earlier pre-market studies of the same system. This prospective, multicenter, observational study was conducted in 13 European centers and aimed to gather electrophysiological and device data. The study focused on the real-world application of this system in treating chronic pain affecting the trunk and/or limbs, adhering to standard conditions of use. In addition to collecting and analyzing basic demographic information, the study presents data from the inaugural patient cohort permanently implanted at multiple European centers. A significant decrease in pain intensity was observed for overall back or leg pain scores (verbal numerical rating score [VNRS]) between baseline (mean ± standard error of the mean [SEM]; n = 135; 8.2 ± 0.1), 3 months (n = 93; 2.3 ± 0.2), 6 months (n = 82; 2.5 ± 0.3), and 12 months (n = 76; 2.5 ± 0.3). Comparison of overall pain relief (%) to the AVALON and EVOKE studies showed no significant differences at 3 and 12 months between the real-world data release (RWE; 71.3%; 69.6%) and the AVALON (71.2%; 73.6%) and EVOKE (78.1%; 76.7%) studies. Further investigation was undertaken to objectively characterize the physiological parameters of SCS therapy in this cohort using the metrics of percent time above ECAP threshold (%), dose ratio, and dose accuracy (µV), according to previously described methods. Results showed that a median of 90% (40.7-99.2) of stimuli were above the ECAP threshold, with a dose ratio of 1.3 (1.1-1.4) and dose accuracy of 4.4µV (0.0-7.1), based on data from 236, 230, and 254 patients, respectively. Thus, across all three metrics, the majority of patients had objective therapy metrics corresponding to the highest levels of pain relief in previously reported studies (usage over threshold > 80%, dose ratio > 1.2, and error < 10µV). In conclusion, this study provides valuable insights into the real-world application of the ECAP-controlled CL-SCS system, highlighting its potential for maintaining effective pain relief and objective neurophysiological therapy metrics at levels seen in randomized control trials, and potential for quantifying patient burden associated with SCS system use via patient-device interaction metrics. In the Netherlands, the study is duly registered on the International Clinical Trials Registry Platform (Trial NL7889). In Germany, the study is duly registered as NCT05272137 and in the United Kingdom as ISCRTN27710516 and has been reviewed by the ethics committee in both countries.

Single-Nucleus Transcriptomic Atlas of Human Pericoronary Epicardial Adipose Tissue in Normal and Pathological Conditions.

Pericoronary epicardial adipose tissue (EAT) is a unique visceral fat depot that surrounds the adventitia of the coronary arteries without any anatomic barrier. Clinical studies have demonstrated the association between EAT volume and increased risks for coronary artery disease (CAD). However, the cellular and molecular mechanisms underlying the association remain elusive. We performed single-nucleus RNA sequencing on pericoronary EAT samples collected from 3 groups of subjects: patients undergoing coronary bypass surgery for severe CAD (n=8), patients with CAD with concomitant type 2 diabetes (n=8), and patients with valvular diseases but without concomitant CAD and type 2 diabetes as the control group (n=8). Comparative analyses were performed among groups, including cellular compositional analysis, cell type-resolved transcriptomic changes, gene coexpression network analysis, and intercellular communication analysis. Immunofluorescence staining was performed to confirm the presence of CAD-associated subclusters. Unsupervised clustering of 73 386 nuclei identified 15 clusters, encompassing all known cell types in the adipose tissue. Distinct subpopulations were identified within primary cell types, including adipocytes, adipose stem and progenitor cells, and macrophages. CD83high macrophages and FOSBhigh adipocytes were significantly expanded in CAD. In comparison to normal controls, both disease groups exhibited dysregulated pathways and altered secretome in the primary cell types. Nevertheless, minimal differences were noted between the disease groups in terms of cellular composition and transcriptome. In addition, our data highlight a potential interplay between dysregulated circadian clock and altered physiological functions in adipocytes of pericoronary EAT. ANXA1 (annexin A1) and SEMA3B (semaphorin 3B) were identified as important adipokines potentially involved in functional changes of pericoronary EAT and CAD pathogenesis. We built a complete single-nucleus transcriptomic atlas of human pericoronary EAT in normal and diseased conditions of CAD. Our study lays the foundation for developing novel therapeutic strategies for treating CAD by targeting and modifying pericoronary EAT functions.

Transcription factor OsWRKY11 induces rice heading at low concentrations but inhibits rice heading at high concentrations.

The heading date of rice is a crucial agronomic characteristic that influences its adaptability to different regions and its productivity potential. Despite the involvement of WRKY transcription factors in various biological processes related to development, the precise mechanisms through which these transcription factors regulate the heading date in rice have not been well elucidated. The present study identified OsWRKY11 as a WRKY transcription factor which exhibits a pivotal function in the regulation of the heading date in rice through a comprehensive screening of a clustered regularly interspaced palindromic repeats (CRISPR) ‒ CRISPR-associated nuclease 9 mutant library that specifically targets the WRKY genes in rice. The heading date of oswrky11 mutant plants and OsWRKY11-overexpressing plants was delayed compared with that of the wild-type plants under short-day and long-day conditions. Mechanistic investigation revealed that OsWRKY11 exerts dual effects on transcriptional promotion and suppression through direct and indirect DNA binding, respectively. Under normal conditions, OsWRKY11 facilitates flowering by directly inducing the expression of OsMADS14 and OsMADS15. The presence of elevated levels of OsWRKY11 protein promote formation of a ternary protein complex involving OsWRKY11, Heading date 1 (Hd1), and Days to heading date 8 (DTH8), and this complex then suppresses the expression of Ehd1, which leads to a delay in the heading date. Subsequent investigation revealed that a mild drought condition resulted in a modest increase in OsWRKY11 expression, promoting heading. Conversely, under severe drought conditions, a significant upregulation of OsWRKY11 led to the suppression of Ehd1 expression, ultimately causing a delay in heading date. Our findings uncover a previously unacknowledged mechanism through which the transcription factor OsWRKY11 exerts a dual impact on the heading date by directly and indirectly binding to the promoters of target genes.

Autophagy is suppressed by low temperatures and is dispensable for cold acclimation in Arabidopsis.

Plants have evolved various mechanisms to adapt to the ever-changing external environment. Autophagy is one such mechanism and has been suggested to play a key role in responding to and adapting to abiotic stresses in plants. However, the role of autophagy in adaptation to cold and freezing stresses remains to be characterized in detail. Here, we investigated the role of autophagy in the low-temperature response of Arabidopsis using atg mutants. Both the atg5-1 and atg10-1 mutants exhibited normal freezing tolerance, regardless of cold acclimation. A comparison of fresh weights indicated that the difference in growth between the wild-type and atg plants under cold conditions was rather small compared with that under normal conditions. Analysis of COLD-REGULATED gene expression showed no significant differences between the atg mutants and wild type. Treatment with 3-methyladenine, an inhibitor of autophagy, did not impair the induction of COR15Apro::LUC expression upon exposure to low temperature. Evaluation of autophagic activity using transgenic plants expressing RBCS-mRFP demonstrated that autophagy was rarely induced by cold exposure, even in the dark. Taken together, these data suggest that autophagy is suppressed by low temperatures and is dispensable for cold acclimation and freezing tolerance in Arabidopsis.

SMARTSeiz: Deep Learning With Attention Mechanism for Accurate Seizure Recognition in IoT Healthcare Devices.

The Internet of Things (IoT) is capable of controlling the healthcare monitoring system for remote-based patients. Epilepsy, a chronic brain syndrome characterized by recurrent, unpredictable attacks, affects individuals of all ages. IoT-based seizure monitoring can greatly enhance seizure patients' quality of life. IoT device acquires patient data and transmits it to a computer program so that doctors can examine it. Currently, doctors invest significant manual effort in inspecting Electroencephalograph (EEG) signals to identify seizure activity. However, EEG-based seizure detection algorithms face challenges in real-world scenarios due to non-stationary EEG data and variable seizure patterns among patients and recording sessions. Therefore, a sophisticated computer-based approach is necessary to analyze complex EEG records. In this work, the authors proposed a hybrid approach by combining traditional convolution neural (CN) and recurrent neural networks (RNN) along with an attention mechanism for the automatic recognition of epileptic seizures through EEG signal analysis. This attention mechanism focuses on significant subsets of EEG data for class recognition, resulting in improved model performance. The proposed methods are evaluated using a publicly available UCI epileptic seizure recognition dataset, which consists of five classes: four normal conditions and one abnormal seizure condition. Experimental results demonstrate that the suggested approach achieves an overall accuracy of 97.05% for the five-class EEG recognition data, with an accuracy of 99.52% for binary classification distinguishing seizure cases from normal instances. Furthermore, the proposed intelligent seizure recognition model is compatible with an IoMT (Internet of Medical Things) cloud-based smart healthcare framework.

Hainan four-eyed turtles actively select suitable stones to masquerade according to their own morphology.

Masquerade is a form of camouflage in which animals use their body size, shape, and coloration to resemble inanimate objects in their environment to deceive predators. However, there is a lack of experimental evidence to show that animals actively choose objects that match these body parameters. To explore how the Hainan four-eyed turtle, Sacalia insulensis, masquerades using suitable stones, we used indoor video surveillance technology to study the preferences of juvenile S. insulensis for stones of different sizes, shapes, and colors. The results indicated that under normal conditions, during the day, juvenile S. insulensis preferred larger oval or round stones, while at night, they preferred oval stones that were closer to their own size, with no significant preference for stone color during either time. When disturbed (by a researcher swinging their arm back and forth above the experimental setup every hour to mimic a predator), the turtles showed a preference for brown stones that were closer to their size and oval in shape. These findings suggest that juvenile S. insulensis prefer stones that resemble their carapace size and shape to masquerade when undisturbed, and that this preference is reinforced when they masquerade to reduce the risk of predation. The preference for stones that resemble their carapace color is significant only when there is a disturbance. To the best of our knowledge, this is the first study to provide evidence that vertebrates can selectively choose objects that resemble their own morphology for masquerading to reduce predation risk.