Application of adaptive chaotic dung beetle optimization algorithm to near-infrared spectral model transfer.

A new transfer approach was proposed to share calibration models of the hexamethylenetetramine-acetic acid solution for studying hexamethylenetetramine concentration values across different near-infrared (NIR) spectrometers. This approach combines Savitzky-Golay first derivative (S_G_1) and orthogonal signal correction (OSC) preprocessing, along with feature variable optimization using an adaptive chaotic dung beetle optimization (ACDBO) algorithm. The ACDBO algorithm employs tent chaotic mapping and a nonlinear decreasing strategy, enhancing the balance between global and local search capabilities and increasing population diversity to address limitations observed in traditional dung beetle optimization (DBO). Validated using the CEC-2017 benchmark functions, the ACDBO algorithm demonstrated superior convergence speed, accuracy, and stability. In the context of a partial least squares (PLS) regression model for transferring hexamethylenetetramine-acetic acid solutions using NIR spectroscopy, the ACDBO algorithm excelled over alternative methods such as uninformative variable elimination, competitive adaptive reweighted sampling, cuckoo search, grey wolf optimizer, differential evolution, and DBO in efficiency, accuracy of feature variable selection, and enhancement of model predictive performance. The algorithm attained outstanding metrics, including a determination coefficient for the calibration set (Rc2) of 0.99999, a root mean square error for the calibration set (RMSEC) of 0.00195%, a determination coefficient for the validation set (Rv2) of 0.99643, a root mean squared error for the validation set (RMSEV) of 0.03818%, residual predictive deviation (RPD) of 16.72574. Compared to existing OSC, slope and bias correction (S/B), direct standardization (DS), and piecewise direct standardization (PDS) model transfer methods, the novel strategy enhances the accuracy and robustness of model predictions. It eliminates irrelevant background information about the hexamethylenetetramine concentration, thereby minimizing the spectral discrepancies across different instruments. As a result, this approach yields a determination coefficient for the prediction set (Rp2) of 0.96228, a root mean squared error for the prediction set (RMSEP) of 0.12462%, and a relative error rate (RER) of 17.62331, respectively. These figures closely follow those obtained using DS and PDS, which recorded Rp2, RMSEP, and RER values of 0.97505, 0.10135%, 21.67030, and 0.98311, 0.08339%, 26.33552, respectively. Unlike conventional methods such as OSC, S/B, DS, and PDS, this novel approach does not require the analysis of identical samples across different instruments. This characteristic significantly broadens its applicability for model transfer, which is particularly beneficial for transferring specific measurement samples.

A Sinh-Cosh-Enhanced DBO Algorithm Applied to Global Optimization Problems.

The Dung beetle optimization (DBO) algorithm, devised by Jiankai Xue in 2022, is known for its strong optimization capabilities and fast convergence. However, it does have certain limitations, including insufficiently random population initialization, slow search speed, and inadequate global search capabilities. Drawing inspiration from the mathematical properties of the Sinh and Cosh functions, we proposed a new metaheuristic algorithm, Sinh-Cosh Dung Beetle Optimization (SCDBO). By leveraging the Sinh and Cosh functions to disrupt the initial distribution of DBO and balance the development of rollerball dung beetles, SCDBO enhances the search efficiency and global exploration capabilities of DBO through nonlinear enhancements. These improvements collectively enhance the performance of the dung beetle optimization algorithm, making it more adept at solving complex real-world problems. To evaluate the performance of the SCDBO algorithm, we compared it with seven typical algorithms using the CEC2017 test functions. Additionally, by successfully applying it to three engineering problems, robot arm design, pressure vessel problem, and unmanned aerial vehicle (UAV) path planning, we further demonstrate the superiority of the SCDBO algorithm.

Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications.

The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed "Mean Differential Variation", to enhance the algorithm's ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems.

Prediction Model of Coal Gas Permeability Based on Improved DBO Optimized BP Neural Network.

Accurate measurement of coal gas permeability helps prevent coal gas safety accidents effectively. To predict permeability more accurately, we propose the IDBO-BPNN coal body gas permeability prediction model. This model combines the Improved Dung Beetle algorithm (IDBO) with the BP neural network (BPNN). First, the Sine chaotic mapping, Osprey optimization algorithm, and adaptive T-distribution dynamic selection strategy are integrated to enhance the DBO algorithm and improve its global search capability. Then, IDBO is utilized to optimize the weights and thresholds in BPNN to enhance its prediction accuracy and mitigate the risk of overfitting to some extent. Secondly, based on the influencing factors of gas permeability, effective stress, gas pressure, temperature, and compressive strength, they are chosen as the coupling indicators. The SPSS 27 software is used to analyze the correlation among the indicators using the Pearson correlation coefficient matrix. Additionally, the Kernel Principal Component Analysis (KPCA) is employed to extract the original data. Then, the original data is divided into principal component data for the model input. The prediction results of the IDBO-BPNN model are compared with those of the PSO-BPNN, PSO-LSSVM, PSO-SVM, MPA-BPNN, WOA-SVM, BES-SVM, and DPO-BPNN models. This comparison assesses the capability of KPCA to enhance the accuracy of model predictions and the performance of the IDBO-BPNN model. Finally, the IDBO-BPNN model is tested using data from a coal mine in Shanxi. The results indicate that the predicted outcome closely aligns with the actual value, confirming the reliability and stability of the model. Therefore, the IDBO-BPNN model is better suited for predicting coal gas permeability in academic research writing.

Deliberately introduced dung beetles in Australia: 12 years of occurrence and abundance records from 2001 to 2022.

Since 1968, the Australian Dung Beetle Project has carried out field releases of 43 deliberately introduced dung beetle species for the biological control of livestock dung and dung-breeding pests. Of these, 23 species are known to have become established. For most of these species, sufficient time has elapsed for population expansion to fill the extent of their potential geographic range through both natural and human-assisted dispersal. Consequently, over the last 20 years, extensive efforts have been made to quantify the current distribution of these introduced dung beetles, as well as the seasonal and spatial variation in their activity levels. Much of these data and their associated metadata have remained unpublished, and they have not previously been synthesized into a cohesive dataset. Here, we collate and report data from the three largest dung beetle monitoring projects from 2001 to 2022. Together, these projects encompass data collected from across Australia, and include records for all 23 species of established dung beetles introduced for biocontrol purposes. In total, these data include 22,718 presence records and 213,538 absence records collected during 10,272 sampling events at 546 locations. Most presence records (97%) include abundance data. In total, 1,752,807 dung beetles were identified as part of these data. The distributional occurrence and abundance data can be used to explore questions such as factors influencing dung beetle species distributions, dung beetle biocontrol, and insect-mediated ecosystem services. These data are provided under a CC-BY-NC 4.0 license and users are encouraged to cite this data paper when using the data.

Geographical distribution and phoretic associations of the viviparous nematode Tokorhabditis atripennis with Onthophagus dung beetles in Japan.

Viviparity is generally considered to be rare in animals. In nematodes, only six species of Rhabditida are viviparous. Five of these species have been identified in association with Onthophagus dung beetles, with Tokorhabditis atripennis being repeatedly isolated from the dung beetle Onthophagus atripennis in Japan. T. atripennis is easy to culture in a laboratory setting, and its host, O. atripennis, is distributed all over Japan. Therefore, T. atripennis is an ideal candidate for ecological and evolutionary studies on viviparity. However, the extent of their distribution and relationship with dung beetles, as well as habitats, remain unclear. In the present study, we conducted field surveys and successfully isolated 27 strains of viviparous nematodes associated with tunneler dung beetles from various regions of Japan, all of which were identified as T. atripennis. T. atripennis exhibited a strong association with Onthophagus dung beetles, especially O. apicetinctus and O. atripennis. And it was predominantly found in specific anatomical locations on the beetle bodies, such as the 'groove between pronotum and elytron' and the 'back of the wings'. Our findings suggest that Onthophagus species are the primary hosts for T. atripennis, and T. atripennis exhibits a close relationship with the living environments of tunneler beetles. This association may play a significant role in the evolution of viviparity in nematodes.

Sexually transmitted mutualist nematodes shape host growth across dung beetle species.

Many symbionts are sexually transmitted and impact their host's development, ecology, and evolution. While the significance of symbionts that cause sexually transmitted diseases (STDs) is relatively well understood, the prevalence and potential significance of the sexual transmission of mutualists remain elusive. Here, we study the effects of sexually transmitted mutualist nematodes on their dung beetle hosts. Symbiotic Diplogastrellus monhysteroides nematodes are present on the genitalia of male and female Onthophagus beetles and are horizontally transmitted during mating and vertically passed on to offspring during oviposition. A previous study indicates that the presence of nematodes benefits larval development and life history in a single host species, Onthophagus taurus. However, Diplogastrellus nematodes can be found in association with a variety of beetle species. Here, we replicate these previous experiments, assess whether the beneficial effects extend to other host species, and test whether nematode-mediated effects differ between male and female host beetles. Rearing three relatively distantly related dung beetle species with and without nematodes, we find that the presence of nematodes benefits body size, but not development time or survival across all three species. Likewise, we found no difference in the benefit of nematodes to male compared to female beetles. These findings highlight the role of sexually transmitted mutualists in the evolution and ecology of dung beetles.

Dynamic constitutive identification of concrete based on improved dung beetle algorithm to optimize long short-term memory model.

In order to improve the accuracy of concrete dynamic principal identification, a concrete dynamic principal identification model based on Improved Dung Beetle Algorithm (IDBO) optimized Long Short-Term Memory (LSTM) network is proposed. Firstly, the apparent stress-strain curves of concrete containing damage evolution were measured by Split Hopkinson Pressure Bar (SHPB) test to decouple and separate the damage and rheology, and this system was modeled by using LSTM network. Secondly, for the problem of low convergence accuracy and easy to fall into local optimum of Dung Beetle Algorithm (DBO), the greedy lens imaging reverse learning initialization population strategy, the embedded curve adaptive weighting factor and the PID control optimal solution perturbation strategy are introduced, and the superiority of IDBO algorithm is proved through the comparison of optimization test with DBO, Harris Hawk Optimization Algorithm, Gray Wolf Algorithm, and Fruit Fly Algorithm and the combination of LSTM is built to construct the IDBO-LSTM dynamic homeostasis identification model. The final results show that the IDBO-LSTM model can recognize the concrete material damage without considering the damage; in the case of considering the damage, the IDBO-LSTM prediction curves basically match the SHPB test curves, which proves the feasibility and excellence of the proposed method.

A novel MPPT technology based on dung beetle optimization algorithm for PV systems under complex partial shade conditions.

Solar power is a renewable energy source, and its efficient development and utilization are important for achieving global carbon neutrality. However, partial shading conditions cause the output of PV systems to exhibit nonlinear and multipeak characteristics, resulting in a loss of output power. In this paper, we propose a novel Maximum Power Point Tracking (MPPT) technique for PV systems based on the Dung Beetle Optimization Algorithm (DBO) to maximize the output power of PV systems under various weather conditions. We performed a performance comparison analysis of the DBO technique with existing renowned MPPT techniques such as Squirrel Search Algorithm, Cuckoo search Optimization, Horse Herd Optimization Algorithm, Particle Swarm Optimization, Adaptive Factorized Particle Swarm Algorithm and Gray Wolf Optimization Hybrid Nelder-mead. The experimental validation is carried out on the HIL + RCP physical platform, which fully demonstrates the advantages of the DBO technique in terms of tracking speed and accuracy. The results show that the proposed DBO achieves 99.99% global maximum power point (GMPP) tracking efficiency, as well as a maximum improvement of 80% in convergence rate stabilization rate, and a maximum improvement of 8% in average power. A faster, more efficient and robust GMPP tracking performance is a significant contribution of the DBO controller.

Infrared thermal images using PCSAN-Net-DBOA: An approach of breast cancer classification.

This manuscript proposes thermal images using PCSAN-Net-DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR-IR) dataset for breast cancer classification. The adaptive distorted Gaussian matched-filter (ADGMF) was used in removing noise and increasing the quality of infrared thermal images. Next, these preprocessed images are given into one-dimensional quantum integer wavelet S-transform (OQIWST) for extracting Grayscale statistic features like standard deviation, mean, variance, entropy, kurtosis, and skewness. The extracted features are given into the pyramidal convolution shuffle attention neural network (PCSANN) for categorization. In general, PCSANN does not show any adaption optimization techniques to determine the optimal parameter to offer precise breast cancer categorization. This research proposes the dung beetle optimization algorithm (DBOA) to optimize the PCSANN classifier that accurately diagnoses breast cancer. The BCD-PCSANN-DBO method is implemented using Python. To classify breast cancer, performance metrics including accuracy, precision, recall, F1 score, error rate, RoC, and computational time are considered. Performance of the BCD-PCSANN-DBO approach attains 29.87%, 28.95%, and 27.92% lower computation time and 13.29%, 14.35%, and 20.54% greater RoC compared with existing methods like breast cancer diagnosis utilizing thermal infrared imaging and machine learning approaches(BCD-CNN), breast cancer classification from thermal images utilizing Grunwald-Letnikov assisted dragonfly algorithm-based deep feature selection (BCD-VGG16) and Breast cancer detection in thermograms using deep selection based on genetic algorithm and Gray Wolf Optimizer (BCD-SqueezeNet), respectively. RESEARCH HIGHLIGHTS: The input images are engaged from the breast cancer dataset for breast cancer classification. The ADQMF was used in removing noise and increasing the quality of infrared thermal images. The extracted features are given into the PCSANN for categorization. DBOA is proposed to optimize PCSANN classifier that classifies breast cancer precisely. The proposed BCD-PCSANN-DBO method is implemented using Python.

Predictions of Aeroengines' Infrared Radiation Characteristics Based on HKELM Optimized by the Improved Dung Beetle Optimizer.

To solve the problems of high computational cost and the long time required by the simulation and calculation of aeroengines' exhaust systems, a method of predicting the characteristics of infrared radiation based on the hybrid kernel extreme learning machine (HKELM) optimized by the improved dung beetle optimizer (IDBO) was proposed. Firstly, the Levy flight strategy and variable spiral strategy were introduced to improve the optimization performance of the dung beetle optimizer (DBO) algorithm. Secondly, the superiority of IDBO algorithm was verified by using 23 benchmark functions. In addition, the Wilcoxon signed-rank test was applied to evaluate the experimental results, which proved the superiority of the IDBO algorithm over other current prominent metaheuristic algorithms. Finally, the hyperparameters of HKELM were optimized by the IDBO algorithm, and the IDBO-HKELM model was applied to the prediction of characteristics of infrared radiation of a typical axisymmetric nozzle. The results showed that the RMSE and MAE of the IDBO-HKELM model were 20.64 and 8.83, respectively, which verified the high accuracy and feasibility of the proposed method for predictions of aeroengines' infrared radiation characteristics.

Research on Indoor Environment Prediction of Pig House Based on OTDBO-TCN-GRU Algorithm.

Temperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it provides crucial decision-making support for the precise and targeted regulation of the internal environmental conditions. This approach ensures an optimal living environment, essential for the well-being and healthy development of the pigs. The existing methodologies for forecasting environmental factors in pig houses are currently hampered by issues of low predictive accuracy and significant fluctuations in environmental conditions. To address these challenges in this study, a hybrid model incorporating the improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), and gated recurrent units (GRUs) is proposed for the prediction and optimization of environmental factors in pig barns. The model enhances the global search capability of DBO by introducing the Osprey Eagle optimization algorithm (OOA). The hybrid model uses the optimization capability of DBO to initially fit the time-series data of environmental factors, and subsequently combines the long-term dependence capture capability of TCNs and the non-linear sequence processing capability of GRUs to accurately predict the residuals of the DBO fit. In the prediction of ammonia concentration, the OTDBO-TCN-GRU model shows excellent performance with mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) of 0.0474, 0.0039, and 0.9871, respectively. Compared with the DBO-TCN-GRU model, OTDBO-TCN-GRU achieves significant reductions of 37.2% and 66.7% in MAE and MSE, respectively, while the R2 value is improved by 2.5%. Compared with the OOA model, the OTDBO-TCN-GRU achieved 48.7% and 74.2% reductions in the MAE and MSE metrics, respectively, while the R2 value improved by 3.6%. In addition, the improved OTDBO-TCN-GRU model has a prediction error of less than 0.3 mg/m3 for environmental gases compared with other algorithms, and has less influence on sudden environmental changes, which shows the robustness and adaptability of the model for environmental prediction. Therefore, the OTDBO-TCN-GRU model, as proposed in this study, optimizes the predictive performance of environmental factor time series and offers substantial decision support for environmental control in pig houses.

Mammal dung-dung beetle trophic networks: an improved method based on gut-content DNA.

Background: Dung beetles provide many important ecosystem services, including dung decomposition, pathogen control, soil aeration, and secondary seed dispersal. Yet, the biology of most dung beetles remains unknown. Natural diets are poorly studied, partly because previous research has focused on choice or attraction experiments using few, easily accessible dung types from zoo animals, farm animals, or humans. This way, many links within natural food webs have certainly been missed. In this work, we aimed to establish a protocol to analyze the natural diets of dung beetles using DNA gut barcoding. Methods: First, the feasibility of gut-content DNA extraction and amplification of 12s rDNA from six different mammal dung types was tested in the laboratory. We then applied the method to beetles caught in pitfall traps in Ecuador and Germany by using 12s rDNA primers. For a subset of the dung beetles caught in the Ecuador sampling, we also used 16s rDNA primers to see if these would improve the number of species we could identify. We predicted the likelihood of amplifying DNA using gut fullness, DNA concentration, PCR primer, collection method, and beetle species as predictor variables in a dominance analysis. Based on the gut barcodes, we generated a dung beetle-mammal network for both field sites (Ecuador and Germany) and analyzed the levels of network specificity. Results: We successfully amplified mammal DNA from dung beetle gut contents for 128 specimens, which included such prominent species as Panthera onca (jaguar) and Puma concolor (puma). The overall success rate of DNA amplification was 53%. The best predictors for amplification success were gut fullness and DNA concentration, suggesting the success rate can be increased by focusing on beetles with a full gut. The mammal dung-dung beetle networks differed from purely random network models and showed a moderate degree of network specialization (H2': Ecuador = 0.49; Germany = 0.41). Conclusion: We here present a reliable method of extracting and amplifying gut-content DNA from dung beetles. Identifying mammal dung via DNA reference libraries, we created mammal dung-dung beetle trophic networks. This has benefits over previous methods because we inventoried the natural mammal dung resources of dung beetles instead of using artificial mammal baits. Our results revealed higher levels of specialization than expected and more rodent DNA than expected in Germany, suggesting that the presented method provides more detailed insights into mammal dung-dung beetle networks. In addition, the method could have applications for mammal monitoring in many ecosystems.

Polarisation vision in the dark: green-sensitive photoreceptors in the nocturnal ball-rolling dung beetle Escarabaeus satyrus.

Many insects utilise the polarisation pattern of the sky to adjust their travelling directions. The extraction of directional information from this sky-wide cue is mediated by specialised photoreceptors located in the dorsal rim area (DRA). While this part of the eye is known to be sensitive to the ultraviolet, blue or green component of skylight, the latter has only been observed in insects active in dim light. To address the functional significance of green polarisation sensitivity, we define the spectral and morphological adaptations of the DRA in a nocturnal ball-rolling dung beetle-the only family of insects demonstrated to orient to the dim polarisation pattern in the night sky. Intracellular recordings revealed polarisation-sensitive green photoreceptors in the DRA of Escarabaeus satyrus. Behavioural experiments verified the navigational relevance of this finding. To quantify the adaptive value of green sensitivity for celestial orientation at night, we also obtained the polarisation properties of the night sky in the natural habitat of the beetle. Calculations of relative photon catch revealed that under a moonlit sky the green-sensitive DRA photoreceptors can be expected to catch an order of magnitude more photons compared with the UV-sensitive photoreceptors in the main retina. The green-sensitive photoreceptors - which also show a range of morphological adaptations for enhanced sensitivity - provide E. satyrus with a highly sensitive system for the extraction of directional information from the night sky.

Functional consequences of animal community changes in managed grasslands: An application of the CAFE approach.

In the midst of an ongoing biodiversity crisis, much research has focused on species losses and their impacts on ecosystem functioning. The functional consequences (ecosystem response) of shifts in communities are shaped not only by changes in species richness, but also by compositional shifts that result from species losses and gains. Species differ in their contribution to ecosystem functioning, so species identity underlies the consequences of species losses and gains on ecosystem functions. Such research is critical to better predict the impact of disturbances on communities and ecosystems. We used the "Community Assembly and the Functioning of Ecosystems" (CAFE) approach, a modification of the Price equation to understand the functional consequences and relative effects of richness and composition changes in small nonvolant mammal and dung beetle communities as a result of two common disturbances in North American prairie restorations, prescribed fire and the reintroduction of large grazing mammals. Previous research in this system has shown dung beetles are critically important decomposers, while small mammals modulate much energy in prairie food webs. We found that dung beetle communities were more responsive to bison reintroduction and prescribed fires than small nonvolant mammals. Dung beetle richness increased after bison reintroduction, with higher dung beetle community biomass resulting from changes in remaining species (context-dependent component) rather than species turnover (richness components); prescribed fire caused a minor increase in dung beetle biomass for the same reason. For small mammals, bison reintroduction reduced energy transfer through the loss of species, while prescribed fire had little impact on either small mammal richness or energy transfer. The CAFE approach demonstrates how bison reintroduction controls small nonvolant mammal communities by increasing prairie food web complexity, and increases dung beetle populations with possible benefits for soil health through dung mineralization and soil bioturbation. Prescribed fires, however, have little effect on small mammals and dung beetles, suggesting a resilience to fire. These findings illustrate the key role of re-establishing historical disturbance regimes when restoring endangered prairie ecosystems and their ecological function.

The effect of surface topography on the ball-rolling ability of Kheper lamarcki (Scarabaeidae).

The most effective way to avoid intense inter- and intra-specific competition at the dung source, and to increase the distance to the other competitors, is to follow a single straight bearing. While ball-rolling dung beetles manage to roll their dung balls along nearly perfect straight paths when traversing flat terrain, the paths that they take when traversing more complex (natural) terrain are not well understood. In this study, we investigate the effect of complex surface topographies on the ball-rolling ability of Kheper lamarcki. Our results reveal that ball-rolling trajectories are strongly influenced by the characteristic scale of the surface structure. Surfaces with an increasing similarity between the average distance of elevations and the ball radius cause progressively more difficulties during ball transportation. The most important factor causing difficulties in ball transportation appears to be the slope of the substrate. Our results show that, on surfaces with a slope of 7.5 deg, more than 60% of the dung beetles lose control of their ball. Although dung beetles still successfully roll their dung ball against the slope on such inclinations, their ability to roll the dung ball sideways diminishes. However, dung beetles do not seem to adapt their path on inclines such that they roll their ball in the direction against the slope. We conclude that dung beetles strive for a straight trajectory away from the dung pile, and that their actual path is the result of adaptations to particular surface topographies.

Berberine hydrochloride-loaded dung beetle chitosan/sodium alginate microspheres ameliorate DSS-induced colitis and regulate gut microorganisms in mice.

Berberine hydrochloride (BH) has long been known for its therapeutic efficacy. In the present study, we aimed to treat mice with colitis using dung beetle chitosan (DCS) -transported BH. To achieve this, BH-loaded DCS/sodium alginate microspheres (SA-DCS-BH) were prepared. The SA-DCS-BH was characterized using SEM, DLS, FT-IR, and XRD, then was used for administration and anti-inflammatory examination in mice. SEM and DLS confirmed the surface morphology of the microspheres, and the particle size was relatively uniform. FT-IR and XRD results confirmed that BH was successfully loaded. In vitro and in vivo studies showed that SA-DCS-BH had slow-release ability. After treatment with SA-DCS-BH, DAI was significantly reduced, colon weight and length increased, spleen length and weight reduced, concentrations of pro-inflammatory cytokines in colonic tissues were reduced, and gut microbiota species abundance was modulated. In addition, this study found a correlation between specific microbes and colitis indicators, Muribaculaceae showed sequential growth after receiving BH, SA-CS-BH, and SA-DCS-BH treatments, respectively. It was concluded that SA-DCS-BH effectively delivered the BH to the intestine with slow-release ability and exhibited anti-inflammatory effects by immune response. Compared to commercial chitosan, DCS has potential for modulating intestinal microorganisms and more suitable carrier for intestinal drug delivery systems.

Hotter-is-not-better: A study on the thermal response of a winter active and nocturnal beetle.

While there are numerous examples of thermogenesis processes in poikilothermic insects that maintain a stable temperature for a certain time and in certain parts of the body, there is a lack of information on ectothermic insect species capable of remaining active under "cold" conditions that would be challenging for other species. Such a thermal strategy would imply the existence of a metabolism that can operate at different temperatures without the need to increase body temperature when experiencing cold environmental conditions. This "hotter-is-not-better" thermal strategy is considered ancestral and conjectured to be linked to the origin and evolution of endothermy. In this study, we examined the thermal performance of a large-bodied dung beetle species (Chelotrupes momus) capable of being active during the winter nights in the Iberian Mediterranean region. Field and laboratory results were obtained using thermocamera records, thermocouples, data loggers and spectrometers that measured ultraviolet, visible and near-infrared wavelengths. The thermal data clearly indicated that this species can remain active at a body temperature of approximately 6 °C without the need to warm its body above ambient temperature. Comparing the spectrophotometric data of the species under study with that from other previously examined dung beetle species indicated that the exoskeleton of this particular species likely enhances the absorption of infrared radiation, thereby implying a dual role of the exoskeleton in both heat acquisition and heat dissipation. Taken together, these results suggest that this species has morphological and metabolic adaptations that enable life processes at temperatures that are typically unsuitable for most insect species in the region.

Monthly runoff prediction based on variational modal decomposition combined with the dung beetle optimization algorithm for gated recurrent unit model.

Highly accurate monthly runoff forecasts play a pivotal role in water resource management and utilization. This article proposes a coupling of variational modal decomposition (VMD) and the dung beetle optimization algorithm (DBO) with the gated recurrent unit (GRU) to establish a new monthly runoff forecasting model: the VMD-DBO-GRU. Initially, historical runoff data are decomposed via VMD. Subsequently, the parameters of the GRU are optimized using the DBO, and the decomposed monthly runoff components are inputted into the GRU neural network. Finally, the predictions for each component are consolidated to provide monthly runoff predictions. The model is then validated using monthly runoff data from the Ansha reservoir in Fujian, collected from 1980 to 2020. The results demonstrate a higher prediction accuracy of the VMD-DBO-GRU model compared to BP, SVM, GRU, VMD-GRU, DBO-GRU, and EMD-GRU models, providing a new alternative for conducting monthly runoff prediction.

Dung beetle-associated yeasts display multiple stress tolerance: a desirable trait of potential industrial strains.

BACKGROUND: Stress-tolerant yeasts are highly desirable for cost-effective bioprocessing. Several strategies have been documented to develop robust yeasts, such as genetic and metabolic engineering, artificial selection, and natural selection strategies, among others. However, the significant drawbacks of such techniques have motivated the exploration of naturally occurring stress-tolerant yeasts. We previously explored the biodiversity of non-conventional dung beetle-associated yeasts from extremophilic and pristine environments in Botswana (Nwaefuna AE et.al., Yeast, 2023). Here, we assessed their tolerance to industrially relevant stressors individually, such as elevated concentrations of osmolytes, organic acids, ethanol, and oxidizing agents, as well as elevated temperatures. RESULTS: Our findings suggest that these dung beetle-associated yeasts tolerate various stresses comparable to those of the robust bioethanol yeast strain, Saccharomyces cerevisiae (Ethanol Red™). Fifty-six percent of the yeast isolates were tolerant of temperatures up to 42 °C, 12.4% of them could tolerate ethanol concentrations up to 9% (v/v), 43.2% of them were tolerant to formic acid concentrations up to 20 mM, 22.7% were tolerant to acetic acid concentrations up to 45 mM, 34.0% of them could tolerate hydrogen peroxide up to 7 mM, and 44.3% of the yeasts could tolerate osmotic stress up to 1.5 M. CONCLUSION: The ability to tolerate multiple stresses is a desirable trait in the selection of novel production strains for diverse biotechnological applications, such as bioethanol production. Our study shows that the exploration of natural diversity in the search for stress-tolerant yeasts is an appealing approach for the development of robust yeasts.