Deep Samples Research Articles

Numerical Simulation of Mechanical Properties of Deep Gray Sandstone with Holes and Radial Cracks

In order to study the effects of prefabricated pore-fracture on the mechanical properties, crack extension and energy evolution of rocks under uniaxial compression, the effects of rock samples containing pore-fracture deep grey sandstone on the mechanical properties, crack extension and energy evolution of rocks were analysed in detail through the indoor experimental inverse performance of the fine-scale mechanical parameters of the PFC3D model. The results show that the mechanical parameters of deep grey sandstone samples with pore fissures are significantly lower than those of intact samples, and the decrease is closely related to the diameter of the pore as well as the length and width of the fissure, with the increase of the diameter of the pore, the peak strength and the peak strain of deep grey sandstone with single pore are in the trend of attenuation, and the mechanical parameters of deep grey sandstone samples with pore fissures of different lengths and widths are in the trend of attenuation with the increase of fissure widths and lengths of different layers. The mechanical parameters of deep grey sandstone samples with different lengths and widths of fissures show a trend of attenuation with the increase of fissure width and length; based on the analysis of microscopic crack characteristics during loading of deep grey sandstone with pore fissures, it is concluded that the cracks produced by deep grey sandstone samples with pore fissures are mainly distributed at the two ends of the samples, and the middle area has fewer cracks and the number of the cracks produced shows a trend of attenuation with the increase in the diameter of the pore as well as the length and width of the fissure; Based on the energy evolution mechanism, it is concluded that as the pore size of the rock sample increases, the strain node at which the dissipated energy begins to appear becomes progressively earlier, the energy storage capacity of the rock appears to be drastically reduced, and the internal damage becomes more and more severe.

Deterministic equivalent and error universality of deep random features learning*

Abstract This manuscript considers the problem of learning a random Gaussian network function using a fully connected network with frozen intermediate layers and trainable readout layer. This problem can be seen as a natural generalization of the widely studied random features model to deeper architectures. First, we prove Gaussian universality of the test error in a ridge regression setting where the learner and target networks share the same intermediate layers, and provide a sharp asymptotic formula for it. Establishing this result requires proving a deterministic equivalent for traces of the deep random features sample covariance matrices which can be of independent interest. Second, we conjecture the asymptotic Gaussian universality of the test error in the more general setting of arbitrary convex losses and generic learner/target architectures. We provide extensive numerical evidence for this conjecture. In light of our results, we investigate the interplay between architecture design and implicit regularization.

Surface soil sampling underestimates soil carbon and nitrogen storage of long-term cover cropping

Cover cropping is a promising management practice for soil health and climate change mitigation by improving soil organic carbon (SOC) and total nitrogen (TN) stocks. However, limited studies focused on deeper soil layers (>30 cm depth) where soil C is more stable than that in surface soil (≤30 cm depth). Here, deep soil sampling was conducted in a 15-year cover cropping experiment, in a horticulture-grain system on sandy loam soil. The SOC and TN stocks were expressed on an equivalent soil mass basis using a cubic spline model. Overall, long-term cover cropping had significantly greater SOC and TN stocks by 22 % (95 %CI: 5–43 %) and 26 % (95 %CI: 6–49 %), respectively in the 0–120 cm depth, compared to no cover cropping. Additionally, the mean SOC and TN sequestration rate (0–30 cm depth) was 0.53 Mg C ha−1 yr−1 and 0.06 Mg N ha−1 yr−1, respectively. However, if only 0–15 cm depth was evaluated, long-term cover cropping did not significantly affect SOC and TN stocks. These results indicated that shallow sampling (<15 cm depth) may not provide comprehensive information on the effect of long-term cover cropping on soil C and N storage. To better understand the mechanism of bulk soil C and N storage, we investigated their distribution between particulate and mineral-associated organic matter pools (POM and MAOM). We found POM pool was the main store of bulk SOC and TN stocks in surface soils while it was the MAOM pool in deeper soil layers, without soil texture change with soil depth. These findings indicated that soil C and N sources for bulk SOC and TN accrual differed in surface and deeper soils. Our study demonstrated that long-term cover cropping can facilitate SOC accumulation in the soil below 15 cm deep, which calls into question carbon capture protocols that focus on shallow soil depths.

An Improved Physics-Based Dual-Band Model for Satellite-Derived Bathymetry Using SuperDove Imagery

Shallow water bathymetry is critical for environmental monitoring and maritime security. Current widely used statistical models based on passive optical satellite remote sensing often rely on prior bathymetric data, limiting their application to regions lacking such information. In contrast, the physics-based dual-band log-linear analytical model (P-DLA) can estimate shallow water bathymetry without in situ measurements, offering significant potential. However, the quasi-analytical algorithm (QAA) used in the P-DLA is sensitive to non-ideal pixels, resulting in unstable bathymetry estimation. To address this issue and evaluate the potential of SuperDove imagery for bathymetry estimation in regions without prior bathymetric data, this study proposes an improved physics-based dual-band model (IPDB). The IPDB replaces the QAA with a spectral optimization algorithm that integrates deep and shallow water sample pixels to estimate diffuse attenuation coefficients for the blue and green bands. This allows for more accurate estimation of shallow water bathymetry. The IPDB was tested on SuperDove images of Dongdao Island, Yongxing Island, and Yongle Atoll. The results showed that SuperDove images are capable of estimating shallow water bathymetry in regions without prior bathymetric data. The IPDB achieved Root Mean Square Error (RMSE) values below 1.7 m and R2 values above 0.89 in all three study areas, indicating strong performance in bathymetric estimation. Notably, the IPDB outperformed the standard P-DLA model in accuracy. Furthermore, this study outlines four sampling principles that, when followed, ensure that variations in the spatial distribution of sampling pixels do not significantly impact model performance. This study also showed that the blue–green band combination is optimal for the analytical expression of the physics-based dual-band model.

Fluvial-lacustrine influences on linear dune erosion at Lake Caroline in the Simpson Desert of Australia

Linear dunes are frequently examined with regard to their formation and maintenance; however, erosion or degradation of these landforms is less commonly addressed, due in part to the lack of observable changes in the geologic record. This study investigates the significant erosion of linear dunes along Lake Caroline, a playa in the northern Simpson Desert of central Australia. Field observations, laboratory analyses, and remote sensing imagery document recent fluvial and lacustrine interactions at Lake Caroline. Compositional and geochemical sediment analyses from three ∼2 m deep hand-augured sample sites indicate that the playa is primarily filled with sand from linear dunes, but also contains evaporite minerals and weathered local bedrock. Satellite images show that the playa has flooded at least 58 times since 2001. Our results indicate that present-day aeolian processes are sufficient to mobilize and transport sediment but are dominated by fluvial-lacustrine processes, resulting in the playa-adjacent dunes becoming isolated and starved of local sediment. As a result, the dunes are currently eroding into the playa which supports the conclusion that long-distance sediment transport is not responsible for the formation or maintenance of linear dunes as suggested by the linear due extension hypothesis. Burial dates from optically stimulated luminescence (OSL) indicate the linear dunes have been eroding for as much as 24 ka, and sediment samples from the playa near one linear dune suggest an onset of dune formation in the area before 58 ka. This work documents an instance in geologic time during the late Quaternary when aeolian, fluvial, and lacustrine processes are competing with one another, offering new insights into the dynamics of linear dunes in the arid Simpson Desert.

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop.

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. We also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

8159 Single Nuclei RNA-Sequencing Reveals Novel Cellular Diversity in Human Neck Adipose Tissue

Abstract Disclosure: H. Camara: None. S.D. Kodani: Employee; Self; Hoxton Farm. S. Yang: None. V. Efthymiou: None. K. An: None. A. Gupta: None. F. Shamsi: None. A. Streets: None. M.E. Patti: Advisory Board Member; Self; Fractyl Laboratories. Consulting Fee; Self; AstraZeneca, Hamni, MBX Biosciences. Grant Recipient; Self; Dexcom. Y. Tseng: Consulting Fee; Self; LyGenesis. Other; Self; BioHaven. Adipose tissue plays a crucial role in maintaining metabolic health. It fulfills distinct functions through two types of fat: white adipose tissue (WAT), the primary site of triglyceride storage, and brown adipose tissue (BAT) along with related beige fat, specializing in thermogenic energy expenditure. Despite the scarcity of BAT in humans, these thermogenic adipocytes efficiently utilize glucose and triglycerides and act as an endocrine organ by secreting batokines. Hence, increased BAT activity is associated with improved metabolic health in humans. In adults, thermogenic adipose tissue is localized in deep neck planes, exhibiting a gradient from white to beige/brown from superficial to deep layers. Mounting evidence suggests the diverse cell types within the adipose tissue and their dynamic interactions play an important role in its versatile functions. To understand the cellular compositions of human adipose tissue and directly compare the differences between human BAT vs. WAT, we collected 30 paired samples of superficial, intermediate and deep human neck adipose tissue from 15 participants who underwent anterior cervical discectomy fusion or thyroid surgery and subjected the frozen tissues to single-nucleus RNA sequencing (snRNA-seq). The integration of the samples yielded 41,337 good-quality nuclei. As expected, white adipocytes (WAd) were the dominant cell type, comprising 38% of the total cells captured. Further analysis of the WAd cluster revealed increased expression of brown adipocyte-associated genes, such as EBF2 and COBL, and enrichment of oxidative phosphorylation pathway score in cells from deep neck samples, indicating increased thermogenic potential. Bona fide brown adipocytes (BAds), marked by the expression of PPARGC1A, were predominantly located in the deep neck samples (&amp;gt;80%) but comprised less than 2% of the dataset. Despite their low numbers, cell-cell communication analysis by CellChat predicted that BAds account for most of the communication events within this niche, highlighting their endocrine importance. snRNA-seq also captured non-adipocyte cells, including adipocyte progenitors, smooth muscle, immune, Schwann and neuron-associated cells. Among these, a novel Neuron-Associated Cell-1, marked by the expression of CNTNAP2, emerged as the second most abundant in the dataset (∼10% of total). Another neuron-associated cell population marked by expression of DNAH11, though less abundant (∼1%), was enriched in deep neck samples, potentially regulating the high levels of thermogenic potential in this region. Taken together, the identified subpopulations and their gene expression profiles offer new insights into the tissue's role in metabolic regulation. This knowledge could be pivotal for developing targeted therapies for obesity and related disorders. Presentation: 6/1/2024

Accelerated 2D radial Look-Locker T1 mapping using a deep learning-based rapid inversion recovery sampling technique.

Efficient abdominal coverage with T1-mapping methods currently available in the clinic is limited by the breath hold period (BHP) and the time needed for T1 recovery. This work develops a T1-mapping framework for efficient abdominal coverage based on rapid T1 recovery curve (T1RC) sampling, slice-selective inversion, optimized slice interleaving, and a convolutional neural network (CNN)-based T1 estimation. The effect of reducing the T1RC sampling was evaluated by comparing T1 estimates from T1RC ranging from 0.63 to 2.0s with reference T1 values obtained from T1RC =2.5-5s. Slice interleaving methodologies were evaluated by comparing the T1 variation in abdominal organs across slices. The repeatability of the proposed framework was demonstrated by performing acquisition on test subjects across imaging sessions. Analysis of in vivo data based on retrospectively shortening the T1RC showed that with the CNN framework, a T1RC =0.84 s yielded T1 estimates without significant changes in mean T1 (p> 0.05) or significant increase in T1 variability (p> 0.48) compared to the reference. Prospectively acquired data using T1RC =0.84 s, an optimized slice interleaving scheme, and the CNN framework enabled 21 slices in a 20 s BHP. Analyses across abdominal organs produced T1 values within 2% of the reference. Repeatability experiments yielded Pearson's correlation, repeatability coefficient, and coefficient of variation of 0.99, 2.5%, and 0.12%, respectively. The proposed T1 mapping framework provides full abdominal coverage within a single BHP.

Microbial communities and metagenomes in methane-rich deep coastal sediments

Coastal sediments are rich in embedded recalcitrant organic carbons that are biotransformed into methane. In this study, gas composition (carbon dioxide, methane and nitrogen) and chemical indicators (total nitrogen, total carbon, and total sulfate) were examined in five deep sediment cores (up to 130 m in length) obtained from the Hangzhou Bay. The V3-V4 region of the 16S rRNA gene amplicons was amplified and sequenced for the prokaryotic community analysis. The species composition, along with the physicochemical factors of the sediments, revealed a strong correlation with methane content in one of the sediment cores. We then obtained metagenomes of the two sediment samples selected for their high methane content and enrichment of methanogenic Bathyarchaeota with phylogenetic evidence. A total of 27 draft genomes were retrieved through metagenomic binning methodologies and were classified into Bathyarchaeota, Asgard archaea, Planctomycetes, and other microbial groups. The data provided are valuable for understanding the relationship between methane generation and microbial community composition in deep sediment core samples from coastal to marine environments.

Isotope characterisation of groundwater resources in uranium impacted Fazilka, Punjab

AbstractGroundwater is the most reliable source for freshwater supplies in India and abroad. Population rise has affected the groundwater resources both in terms of quality and quantity. Punjab, being an agrarian state, is highly dependent on groundwater resources for both irrigation and drinking purposes. Recent studies have indicated presence of uranium in groundwater especially in southwestern parts of Punjab. In this research, water samples were collected from Fazilka district of Punjab for evaluating the water quality focussing uranium and identifying source and recharge processes of groundwater. Uranium concentration was found in both shallow and deep groundwater samples. Inter-ionic correlations signify that alkalinity and electrical conductivity of the water samples control the uranium mobilization from the aquifer matrix. Isotopic systematics demonstrate that groundwater recharge is mainly through rainwater infiltration.

Case Report: Soft tissue infection with Burkholderia thailandensis capsular variant: case report from the Lao PDR

Background Burkholderia thailandensis is an environmental bacteria closely related to Burkholderia pseudomallei that rarely causes infection in humans. Some environmental isolates have shown to express a capsular polysaccharide known as B. thailandensis capsular variant (BTCV), but human infection has not previously been reported. Although B. thailandednisis has been identified in environmental samples in Laos before, there have not been any human cases reported. Case A 44-year-old man presented to a district hospital in Laos with a short history of fever and pain in his left foot. Physical examination identified a deep soft-tissue abscess in his left foot and an elevated white blood count. A deep pus sample was taken and melioidosis was suspected from preliminary laboratory tests. The patient was initially started on cloxacillin, ceftriaxone and metronidazole, and was then changed to ceftazidime treatment following local melioidosis treatment guidelines. Laboratory methods A deep pus sample was sent to Mahosot Hospital microbiology laboratory where a mixed infection was identified including Burkholderia sp. Conventional identification tests and API 20NE were inconclusive, and the B. pseudomallei-specific latex agglutination was positive. The isolate then underwent a Burkholderia species specific PCR which identified the isolate as B. thailandensis. The isolate was sent for sequencing on the Illumina NovaSeq 6000 system and multi-locus sequence typing analysis identified the isolate had the same sequence type (ST696) as B. thailandensis E555, a strain which expresses a B. pseudomallei-like capsular polysaccharide. Conclusion This is the first report of human infection with B. thailandensis in Laos, and the first report of any human infection with the B. thailandensis capsular variant. Due to the potential for laboratory tests to incorrectly identify this bacteria, staff in endemic areas for B. thailandensis and B. pseudomallei should be aware and ensure that appropriate confirmatory methods are used to differentiate between the species.

Quantifying recharge mechanisms in low-hilly areas of a loess region: Implications for the quantity and quality of groundwater

Groundwater plays a crucial role in sustaining water supply and irrigation in arid regions with thick vadose zones; however, the recharge mechanisms and their impact on water quality remain contentious. For flood-irrigated areas, irrigation water amount is not well known to evaluate the effect of irrigation on the recharge. In this study, we employed multiple tracers to investigate recharge mechanisms in a low-hilly region with some flood-irrigated areas, where irrigation water recharge was estimated by the chloride mass balance method combined with the tritium peak method. Deep soil samples from irrigated lands in vadose zone and groundwater from unconfined aquifers were collected to determine the contents of the water isotopes (2H, 18O, and 3H), chlorides (Cl-), and nitrates (NO3−). The findings revealed unique tritium peaks preserved in the loess vadose zone, where the recharge rate under the non-irrigated site was estimated to be only 8.6 mm yr−1, 2.4 % of the average annual precipitation. According to the estimated irrigated recharge rates, irrigation water dominates the potential diffuse recharge in vadose zone. For the aquifer recharge, we then quantified diffuse recharge (62.2 ± 4.7 %, piston flow) and focused recharge (37.8 ± 4.7 %, preferential flow) via the line-conditioned excess (lc-excess) balance method. Focused recharge occurred in low depressions, where surface runoff carrying sediments with high solutes converged in large rainfall events during wet season. Subsequently, focused mode rapidly recharges groundwater with NO3− and Cl- elevated, deteriorating water quality of phreatic aquifer. Finally, we concluded focused recharge does not dominate aquifer recharge, but significantly affects groundwater quality. These findings underscore the implications for the sustainability of agricultural water and soil resources management, emphasizing the potential impacts of soil conservation on groundwater quality in low-hilly regions.

The Relationship of Materialism and Financial Literacy on Students' Financial Management

Study This aim For know connection materialism and literacy finance to management finance student. Types of research This use approach study quantitative through analysis regression multiple use SPSS application. Object in study This is students across the city palopo . Deep Sampling​ study This use accidental sampling method with total of 120 respondents . Research result show that that materialism No influential or not significant to management finances for students in Palopo City, this give results that level caution students in Palopo City be careful in use the money so that make student can manage it his finances with OK, more rational or not will behave consumer. For literacy finance influential positive and significant to management finance, which means the more tall literacy finance so will make management generated finances​ become the more Good. Keyword: Materialism , Literacy Finance , Management Finance

Content, Sources, and Ecological Risk Assessment of Heavy Metals in Soil of Typical Karst County

Soil heavy metals in karst areas have obvious high background value characteristics. Conducting county-level soil heavy metal ecological risk assessment and identifying heavy metal sources in karst areas are of great significance for soil pollution control and land resource management. Taking Pingguo City, a typical karst county in Guangxi Province, as the study object, 3 151 surface and deep soil samples were collected using the grid method and combined to form 785 analytical samples. The contents of eight heavy metal elements, including As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, were determined. The content characteristics and sources of heavy metals were analyzed using statistical analysis, interpolation analysis, factor analysis, and the absolute principal component-multiple linear regression model （APCS-MLR）. Using the content of heavy metal elements in deep soil （150-200 cm） as background values, the ecological risk assessment of heavy metals in surface soil （0-20 cm） in the study area was conducted using the geo-accumulation index （Igeo） and potential ecological risk index （RI） methods. The results showed that the average content of heavy metal elements in the deep soil of the study area was significantly higher than the background value of the C layer soil in Guangxi Province, and the average content of heavy metal elements in the surface soil was significantly higher than the background value of the A layer soil in Guangxi Province. The spatial distribution of soil heavy metal element content generally showed the characteristics of high in karst areas and low in non-karst areas. The main sources of As, Cr, Ni, Pb, and Zn were soil parent materials, with contribution rates of 74.36%, 84.59%, 93.69%, 79.67%, and 78.17%, respectively. The main sources of Cd were soil parent material sources and unknown sources, with contribution rates of 37.33% and 31.05%, respectively. The main sources of Cu were soil parent materials and unknown sources, with contribution rates of 59.07% and 40.23%, respectively. The main sources of Hg were tectonic activity and mineralization, as well as unknown sources, with contribution rates of 52.49% and 30.65%, respectively. The geo-accumulation index （Igeo） showed that the surface soil was mainly polluted by Cd, with mild or above pollution accounting for 47.78%. The potential ecological risk index （RI） showed that the proportion of surface soil heavy metal comprehensive potential ecological hazards with mild, moderate, strong, and very strong levels was 80.78%, 14.97%, 2.51%, and 1.64%, respectively.

Pollution risk assessment and spatial distribution of potentially hazardous elements in selenium-rich soils in the MenYuan basin of the northeastern Tibetan Plateau

The scientific utilization of selenium(Se)-rich soil necessitates the evaluation of potentially hazardous elements (PHEs) content. Menyuan basin, a representative Se-rich region located on the northern periphery of the Qinghai-Tibet Plateau, was chosen as the research subject. Pollution risk and spatial source analysis was conducted for eight PHEs (As, Cr, Cu, Pb, Zn, Cd, Ni and Hg) using 5297 topsoil samples and 480 deep soil samples. The results showed that more than 50% of the total area has a Se content above 0.24 mg/kg. There are extremely few sampling sites with potentially hazardous elements at risk of contamination, according to single and comprehensive pollution index. Low ecological risk was seen in 93.34% of the topsoil samples. The results show that although the overall level of surface soil pollution in the study area may be deemed negligible. On the surface or in deeper layers, the concentration centers of As, Cr, Ni, Zn, Pb, and other elements show consistency, indicating that underlying parent rocks are the primary source of these elements. The findings presented herein establish a scientific foundation for the secure development and utilization of Se-rich soil.

Optimizing Energy Harvesting in Wireless Body Area Networks: A Deep Reinforcement Learning Approach to Dynamic Sampling

In energy-harvesting wireless body area networks (EH-WBAN), the self-sustainability of body sensor nodes without compromising the service quality criteria is very important. In remote vital signs monitoring applications, the sampling rate of body nodes in each period should be determined to achieve this goal. There are two fundamental challenges in determining the sampling rate: 1) In EH-WBAN, the rate of the harvestable energy is time-dependent and unpredictable. 2) The vital signs of the patient have different change rates relevant to the time. Therefore, a technique is required that can learn the changes of the harvestable energy and the change rates of vital signs simultaneously and specify the proper sampling rate for BNs. The reinforcement learning algorithms are among the efficient solutions for this problem because they are capable of learning environmental uncertainties. Previous RL-based methods for determining the BN's sampling rate have three fundamental problems: 1) focus on the optimization of the transmission energy and ignore the sensing energy, 2) only affect one of the two aspects of energy or sensed data in determining the sampling rate and 3) discretization of the problem space does not ensure the determination of the optimal sampling rate. Therefore, this paper proposes a method named “deep reinforcement learning-based dynamic sampling” (DRDS) which first formulates the sampling rate determination problem as a Markov decision process (MDP) and proposes a deep deterministic policy gradient algorithm (DDPG) to solve it. The proposed method considers both energy and data variability aspects in determining the sampling rate. Two parameters, the super-capacitor’s voltage level, and ambient light intensity, are considered for the energy aspect; for the data variability aspect, the long short-term memory (LSTM) algorithm is developed to predict the change rate of data in the next round. Simulations indicate that by preserving the data integrity, the proposed method can decrease the sampling rate and unnecessary data transmission by about 49.95% and 89.7%, respectively, compared with state-of-the-art methods, and allow the sensor to achieve self-sustainability.

Soil Carbon and Nitrogen Stocks Across Hillslopes Underlain by Continuous Permafrost in the Northern Arctic Foothills, Alaska, United States

ABSTRACTConstraining the variability of soil organic carbon (SOC) and total nitrogen (TN) stocks across hillslopes in Low Arctic permafrost‐affected landscapes remains a significant challenge for improving global estimates of permafrost SOC stocks. We investigated SOC and TN stocks across hillslopes at two sites in the Arctic Foothills of Alaska, United States (Happy Valley and Sagwon Hills). Average SOC and TN stocks for the 0–1‐m depth interval were high (52.0 ± 15.1 kg C m−2 and 2.74 ± 0.82 kg N m−2) and linearly related (R2 = 0.74, p &lt; 0.0001). Unlike soils of other permafrost and nonpermafrost landscapes, variability was greatest within rather than between hillslope positions. Furthermore, SOC and TN stocks in the surface 1 m did not exhibit strong patterns by hillslope position and were only weakly associated with major geomorphic parameters that typically predict SOC and TN stocks well in other landscapes. Although sampling at upper hillslope positions was largely limited to depths of less than 1.5 m due to the presence of coarse fragments in reworked glacial till, deeper observations at lower hillslope positions (footslopes, toeslopes, and basins) revealed significantly larger SOC stocks (92.0 ± 18.0 kg C m−2 at 2 m; 117.1 ± 10.4 kg C m−2 at 3 m). The unique small‐scale variability in ice content, cryoturbation, patterned ground, and organic layer thickness on these broad, Low Arctic sites contributes to the relatively homogeneous distribution of SOC and TN stocks across hillslope positions in the top 1 m, but a future focus on deeper sampling may reveal greater differences in SOC and TN stocks.

Experimental study on soil deformation caused by overexploitation of groundwater.

Due to the overexploitation of deep groundwater, the largest cone of depression in the world has formed in the North China Plain. This led to severe geological hazards, including land subsidence and ground fissures, and also caused economic losses. The prevention and treatment of subsidence needs to rely on the accurate prediction of subsidence amount. According to the one-dimensional consolidation theory and effective stress principle, combined with stratum structure, groundwater flow, stress distribution, and so forth, the high-pressure consolidation test results of 569.6m deep borehole soil samples are adopted; with a specific focus on stress and deformation parameters under exploitation of groundwater condition, the soil-water coupling prediction model of groundwater level lowering depth and land subsidence has been established. Verification with measured subsidence data near the study sites demonstrated that the predicted curve is consistent with the measured one and the differences between them are acceptable. The model can be applied in different areas after making adjustment based on different regional stratigraphic structures. Its key advantage lies in the ability to provide land subsidence prediction for areas lacking monitoring data, making it highly valuable for widespread application. PRACTITIONER POINTS: There is a compressible stratum structure; it is the internal factors of land subsidence. The groundwater level decline causes the soil body stress to change. It is land subsidence of the external factors. Based on the one-dimensional consolidation theory and by combining stratigraphic structures, groundwater flow, and stress distribution, a ground settlement prediction model was established.

Antimicrobial in vitro effects of eight essential oils on Pasteurella multocida and Mannheimia haemolytica from nasopharyngeal swab samples of fattening calves

Essential oils are secondary metabolites of aromatic plants and are used in phytotherapy to treat various diseases. In the present study, eight selected essential oils - ajwain oil (Trachyspermum ammi L.), fennel oil (Foeniculum vulgare Mill. subsp. vulgare var. vulgare), thyme oil chemotype (ct.) thymol (Thymus vulgaris L.), tea tree oil (Melaleuca alternifolia Cheel.), oregano oil (Origanum vulgare L.), mountain savory oil (Satureja montana L.), lemongrass oil (Cymbopogon citratus (DC.) Stapf.) and eucalyptus oil (Eucalyptus globulus Labill.) -were examined for their antibacterial effect against Pasteurella (P.) multocida and Mannheimia (M.) haemolytica isolates from deep nasopharyngeal swab samples of fattening calves using agar diffusion and microdilution. All eight essential oils were effective against the tested isolates. Lemongrass oil proved to be the most potent of all eight essential oils, while fennel oil was only weakly effective. Different antimicrobial effects were observed between the two research methods. The effectiveness of ajwain, thyme, oregano and mountain savory oils was comparable in agar diffusion. However, this could not be reproduced using the microdilution method. P. multocida was found to be more sensitive to all essential oils tested than M. haemolytica. This study shows that the tested essential oils have antimicrobial in-vitro effects on P. multocida and M. haemolytica isolates and that the examination method is associated with the test result.

Hyper-diverse antigenic variation and resilience to transmission-reducing intervention in falciparum malaria

Intervention efforts against falciparum malaria in high-transmission regions remain challenging, with rapid resurgence typically following their relaxation. Such resilience co-occurs with incomplete immunity and a large transmission reservoir from high asymptomatic prevalence. Incomplete immunity relates to the large antigenic variation of the parasite, with the major surface antigen of the blood stage of infection encoded by the multigene and recombinant family known as var. With a stochastic agent-based model, we investigate the existence of a sharp transition in resurgence ability with intervention intensity and identify molecular indicators informative of its proximity. Their application to survey data with deep sampling of var sequences from individual isolates in northern Ghana suggests that the transmission system was brought close to transition by intervention with indoor residual spraying. These results indicate that sustaining and intensifying intervention would have pushed malaria dynamics to a slow-rebound regime with an increased probability of local parasite extinction.