Deep Samples Research Articles

MPN-RRT*: A New Method in 3D Urban Path Planning for UAV Integrating Deep Learning and Sampling Optimization

The increasing deployment of unmanned aerial vehicles (UAVs) in complex urban environments necessitates efficient and reliable path planning algorithms. While traditional sampling-based methods such as Rapidly exploring Random Tree Star (RRT*) are widely adopted, their computational inefficiency and suboptimal path quality in intricate 3D spaces remain significant challenges. This study proposes a novel framework (MPN-RRT*) that integrates Motion Planning Networks (MPNet) with RRT* to enhance UAV navigation in 3D urban maps. A key innovation lies in reducing computational complexity through dimensionality reduction, where 3D urban terrains are sliced into 2D maze representations while preserving critical obstacle information. Transfer learning is applied to adapt a pre-trained MPNet model to the simplified maps, enabling intelligent sampling that guides RRT* toward promising regions and reduces redundant exploration. Extensive MATLAB simulations validate the framework’s efficacy across two distinct 3D environments: a sparse 200 × 200 × 200 map and a dense 800 × 800 × 200 map with no-fly zones. Compared to conventional RRT*, the MPN-RRT* achieves a 47.8% reduction in planning time (from 89.58 s to 46.77 s) and a 19.8% shorter path length (from 476.23 m to 381.76 m) in simpler environments, alongside smoother trajectories quantified by a 91.2% reduction in average acceleration (from 14.67 m/s² to 1.29 m/s²). In complex scenarios, the hybrid method maintains superior performance, reducing flight time by 14.2% and path length by 13.9% compared to RRT*. These results demonstrate that the integration of deep learning with sampling-based planning significantly enhances computational efficiency, path optimality, and smoothness, addressing critical limitations in UAV navigation for urban applications. The study underscores the potential of data-driven approaches to augment classical algorithms, providing a scalable solution for real-time autonomous systems operating in high-dimensional dynamic environments.

Comparison of Heavy Metal Pollution, Health Risk, and Sources Between Surface and Deep Layers for an Agricultural Region Within the Pearl River Delta: Implications for Soil Environmental Research

During the past decades, agricultural soil heavy metal pollution has been becoming increasingly severe due to urbanization and industrialization. However, the impact of externally input heavy metals on deep soils remains unclear because most previous relevant research only focused on surface soils. In the present study, Concentrations of eight heavy metals (Cu, Zn, Ni, Pb, Cr, Cd, As, and Hg) were determined for 72 pairs of surface and deep soil samples collected from an agricultural region close to the Pearl River estuary. Subsequently, heavy metal pollution and potential health risks were assessed using the Geo-accumulation Index and Potential Ecological Risk Index, a dose response model and Monte Carlo simulation, respectively. Principal component analysis (PCA) and the positive matrix factorization (PMF) receptor model were combined to analyze heavy metal sources. The results indicated that average concentrations of all heavy metals exceeded their corresponding background values. Cd was identified as the main pollutant due to its extremely high values of Igeo and Er. Unacceptable potential heavy metal non-carcinogenic and carcinogenic risks indicated by respectively calculated HI and TCR, higher than thresholds 1.0 and 1.0 × 10−4, mainly arose from heavy metals As, Cd, Cr, and Ni through food ingestion and dermal absorption. Anthropogenic sources respectively contributed 19.7% and 38.9% for soil As and accounted for the main contributions to Cd, Cu, and Hg (Surface: 90.2%, 65.4%, 67.3%; Deep: 53.8%, 54.6%, 56.2%) within surface and deep layers. These results indicate that soil heavy metal contents with deep layers were also significantly influenced by anthropogenic input. Therefore, we suggest that both surface and deep soils should be investigated simultaneously to gain relatively accurate results for soil heavy metal pollution and source apportionments.

Hydrogeochemical Characterization and Determination of Arsenic Sources in the Groundwater of the Alluvial Plain of the Lower Sakarya River Basin, Turkey

Arsenic (As) contamination in groundwater represents a major global public health threat, particularly in alluvial aquifer systems where redox-sensitive geochemical processes facilitate the mobilization of naturally occurring trace elements. This study investigates groundwater quality, particularly focusing on the origin of arsenic contamination in shallow and deep alluvial aquifers of the Lower Sakarya River Basin, which are crucial for drinking, domestic, and agricultural uses. Groundwater samples were collected from 34 wells—7 tapping the shallow aquifer (<60 m) and 27 tapping the deep aquifer (>60 m)—during wet and dry seasons for the hydrogeochemical characterization of groundwater. Environmental isotope analysis (δ18O, δ2H, 3H) was conducted to characterize origin and groundwater residence times, and the possible hydraulic connection between shallow and deep alluvial aquifers. Mineralogical and geochemical characterization of the sediment core samples were carried out using X-ray diffraction and acid digestion analyses to identify mineralogical sources of As and other metals. Pearson correlation coefficient analyses were also applied to the results of the chemical analyses to determine the origin of metal enrichments observed in the groundwater, as well as related geochemical processes. The results reveal that 33–41% of deep groundwater samples contain arsenic concentrations exceeding the WHO and Turkish drinking water standard of 10 µg/L, with maximum values reaching 373 µg/L. Manganese concentrations exceeded the 50 µg/L limit in up to 44% of deep aquifer samples, reaching 1230 µg/L. On the other hand, iron concentrations were consistently low, remaining below the detection limit in nearly all samples. The co-occurrence of As and Mn above their maximum contaminant levels was observed in 30–33% of the wells, exhibiting extremely low sulfate concentrations (0.2–2 mg/L), notably low dissolved oxygen concentration (1.45–3.3 mg/L) alongside high bicarbonate concentrations (450–1429 mg/L), indicating localized varying reducing conditions in the deep alluvial aquifer. The correlations between molybdenum and As (rdry = 0.46, rwet = 0.64) also indicate reducing conditions, where Mo typically mobilizes with As. Arsenic concentrations also showed significant correlations with bicarbonate (HCO3−) (rdry = 0.66, rwet = 0.80), indicating that alkaline or reducing conditions are promoting arsenic mobilization from aquifer materials. All these correlations between elements indicate that coexistence of As with Mn above their MCLs in deep alluvial aquifer groundwater result from reductive dissolution of Mn/Fe(?) oxides, which are primary arsenic hosts, thereby releasing arsenic into groundwater under reducing conditions. In contrast, the shallow aquifer system—although affected by elevated nitrate, sulfate, and chloride levels from agricultural and domestic sources—exhibited consistently low arsenic concentrations below the maximum contaminant level. Seasonal redox fluctuations in the shallow zone influence manganese concentrations, but the aquifer’s more dynamic recharge regime and oxic conditions suppress widespread As mobilization. Mineralogical analysis identified that serpentinite, schist, and other ophiolitic/metamorphic detritus transported by river processes into basin sediments were identified as the main natural sources of arsenic and manganese in groundwater of deep alluvium aquifer.

A Fatal Complication in Intensive Care Units: Tracheoesophageal Fistula

Objective: Tracheoesophageal fistula is an abnormal connection between the posterior wall of the trachea and the anterior wall of the adjacent esophagus. Its etiology includes trauma related to endotracheal cuff, damage to the posterior wall during tracheostomy procedure, poor health status, respiratory infections, and steroid treatments. This study aims to investigate the demographic characteristics of patients with tracheoesophageal fistula, the timing of tracheostomy, duration of intubation, and the characteristics of microorganisms isolated from these patients. Material and Method: The study included patients who developed tracheoesophageal fistula while receiving mechanical ventilation through orotracheal intubation or tracheostomy in the intensive care unit. Data collected included age, sex, reason for intensive care unit admission, day of tracheoesophageal fistula occurrence, tracheostomy status, mortality, medication usage, and microorganisms isolated from tracheal aspirate cultures. Results: Thirty patients were included. The mean duration of mechanical ventilation before tracheoesophageal fistula development was 41.3 days, with 27 patients (90%) having tracheostomies. The average day of tracheostomy application was 21.7 days. The mean duration of mechanical ventilation was 68.5 days, and the average intensive care unit stay was 71.2 days. Of 65 deep tracheal aspirate cultures samples, 24 patients (80%) exhibited growth, predominantly Klebsiella pneumoniae (26.1%). Mortality occurred in 86.7% of patients, while 13.3% were discharged in healty. Conclusion: In conclusion, tracheoesophageal fistula, which can be observed in intensive care units, is a highly mortal complication. The majority of these patients exhibited prolonged mechanical ventilation, microorganism growth in tracheal aspirate cultures, steroid use, hypoalbuminemia, use of neuromuscular agents, and high SAPS III and APACHE II scores.

Deep Infections After Open and Closed Fractures.

The purpose of this study was to describe the culture and speciation results of patients with surgical site infection (SSI) from the PREPARE and Aqueous-PREP studies from the PREP-IT Investigators. Patients with suspected SSI underwent collection of deep or organ tissue samples for culture. The culture positivity rate was estimated as a percentage along with the exact binomial 95% confidence interval (CI). Microbial species were reported as percentages. Comparisons between open and closed fractures were conducted with the Z-test for proportions. Significance was set at p < 0.05. Among the 2 primary studies, a total of 484 cases (defined as an anatomic fracture area; some patients had multiple fractures, which were each defined as a case if they developed an infection) had culture samples taken from deep or organ tissue. The culture positivity rate was 96.7% (95% CI, 94.7% to 98.0% [468 of 484 cases]). There were no significant differences (p = 0.507) in culture positivity between open fractures (97.2% [95% CI, 94.5% to 98.6%]; 273 of 281 cases) and closed fractures (96.1% [95% CI, 92.4% to 98.0%]; 195 of 203 cases). There was information on microbial species in 84.4% (395) of 468 cases. For patients with positive cultures, 43.3% (171 of 395 cases) were polymicrobial infections. Open fractures (47.8% [111 of 232 cases]), compared with closed fractures (36.8% [60 of 163 cases]), were more likely to be polymicrobial (p = 0.029). Staphylococcus aureus microbes (methicillin-sensitive S. aureus, methicillin-resistant S. aureus, and coagulase-negative S. aureus) accounted for 43.3% (462 of 1,066) of all positive cultures. The median time to infection was 58.5 days (95% CI, 49.0 to 67.0 days). The median time to infection was not significantly different in cases of open fractures (61.0 days [95% CI, 51.0 to 71.0 days]) compared with closed fractures (54.0 days [95% CI, 43.0 to 67.0 days]) (hazard ratio [HR], 0.92 [95% CI, 0.72 to 1.12]). SSIs associated with gram-negative bacteria had a shorter median time to infection at 46.0 days (95% CI, 36.0 to 58.0 days) compared with SSIs not associated with gram-negative bacteria at 70.0 days (95% CI, 56.0 to 88.0 days) (HR, 1.79 [95% CI, 1.55 to 2.03]). There was also a shorter median time to infection for patients with polymicrobial infections (47.0 days [95% CI, 38.8 to 52.1 days]) compared with patients with monomicrobial infections (78.6 days [95% CI, 57.2 to 86.8 days]) (HR, 1.26 [95% CI, 1.03 to 1.49]). In patients with SSI, tissue samples yielded high rates of microbial culture results. There was a higher proportion of gram-negative organisms in open fractures. Gram-negative infections were also associated with earlier time to infection. Clinicians should not hesitate to take deep-tissue culture samples in patients with suspected SSI and should be prepared to encounter polymicrobial infections. Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

STATE AND STRENGTH-DEFORMABILITY PROPERTIES OF THE MIRNY MINING AND PROCESSING PLANT (MPP) TECHNOGENIC TAILINGS DAM DEPOSITS AS A FACTOR OF DAM STABILITY

The occurrence and development of exogeodynamic processes caused by liquefaction and fluidity of deposits that comprise the mine tailings dam may provoke emergency situations (breakthroughs). To prevent such situations, objects like these are covered by regular geomonitoring.The article presents the results of the study of the current state of the technogenic tailings dam deposits of the enrichment factory No.3 of the MPP (Mirny, Yakutia). Severe climatic, structural-geological and permafrost-hydrogeological conditions of the study area, as well as the thawed state of the technogenic strata (+0.5 to +3.1 °C) require to carry out a thorough control over the state of the dam in order to ensure its effective and safe operation.Studies of the material composition and properties of technogenic deposits were carried out based on the data on five drilled 31–58 m deep boreholes and sampling intervals. The granulometric and mineralogical compositions, as well as physical, physical-chemical and mechanical properties were determined according to standard methods using modern equipment (Analysette 22 NanoTec granulometric analyser, automated test complex ASIS).It has been found that the technogenic strata are layers of loose rock represented mainly by silty-clayey (sandy loam, loam and clay), less often by sandy material. Most of the clay varieties transform from solids to fluid-plastics. The most common lithological variety is silty loam, comprising ~40 % of the total volume sampled. The mineral composition of sediments is characterized by a high content of dolomite, calcite, quartz, serpentine and clay minerals. All clay deposits types have been found to have low strength (C=0.003–0.050 MPa, φ=3.9–15.7°) and high deformability (E=1.6–5.6 MPa). They also exhibit "dangerous" process-forming properties – plasticity, fluidity and flowability, which give rise to the development of exogenous geological processes (erosion, slumps, landslides, etc.), and consequently, influence the stability of the mine tailings dam.

State-of-the-art modeling of cementation exponent in tight and deep carbonate pore systems via rigorous numerical strategies

The primary objective of this study is to develop a robust method for estimating the cementation exponent in carbonate reservoirs, particularly focusing on tight and deep formations. For the first time, hybrid heuristic algorithms, namely Multilayer Perceptron Neural Network (MLPNN) optimized by three Evolutionary Algorithms (EAs), are employed to simulate the cementation exponent concerning diverse pore types and total porosity. Using a unique database of 112 data points from carbonate reservoirs, especially tight and deep samples, this novel approach demonstrates significant precision. Amongst all models, the MLPNN-SCG model shows an Average Absolute Relative Deviation (AARD) of 5.57% and Root Mean Squared Error (RMSE) of 0.17, outperforming commonly used literature correlations. Comprehensive error analysis and data processing validate that MLPNN-SCG provides highly accurate estimates of the cementation exponent. Sensitivity analysis reveals the high relevancy factors of connected vugs (non-fabric-selective dissolution), moldic, and interparticle pore types on the modeling output. Moreover, the trustworthiness of databank and accuracy of the proposed MLPNN-SCG are confirmed by Williams’ plot, showing about 93.75% of the databank within the applicability domain. This study stands out due to the application of hybrid MLPNN optimized by three EAs and the rare focus on cementation exponent modeling via pore descriptions.

Deep Gaussian process priors for Bayesian image reconstruction

Abstract In image reconstruction, an accurate quantification of uncertainty is of great importance for informed decision making. Here, the Bayesian approach to inverse problems can be used: the image is represented through a random function that incorporates prior information which is then updated through Bayes' formula. However, finding a prior is difficult, as images often exhibit non-stationary effects and multi-scale behaviour. Thus, usual Gaussian process priors are not suitable. Deep Gaussian processes, on the other hand, encode non-stationary behaviour in a natural way through their hierarchical structure. To apply Bayes' formula, one commonly employs a Markov chain Monte Carlo (MCMC) method. In the case of deep Gaussian processes, sampling is especially challenging in high dimensions: the associated covariance matrices are large, dense, and changing from sample to sample. A popular strategy towards decreasing computational complexity is to view Gaussian processes as the solutions to a fractional stochastic partial differential equation (SPDE). In this work, we investigate efficient computational strategies to solve the fractional SPDEs occurring in deep Gaussian process sampling, as well as MCMC algorithms to sample from the posterior. Namely, we combine rational approximation and a determinant-free sampling approach to achieve sampling via the fractional SPDE. We test our techniques in standard Bayesian image reconstruction problems: upsampling, edge detection, and computed tomography. In these examples, we show that choosing a non-stationary prior such as the deep GP over a stationary GP can improve the reconstruction. Moreover, our approach enables us to compare results for a range of fractional and non-fractional regularity parameter values.

Effect of coal mining subsidence on soil microbial communities in mining areas with high groundwater levels.

In high groundwater level mining areas, coal mining has led to severe surface subsidence, which not only alters soil physicochemical properties but also destabilizes microbial communities, ultimately impairing ecosystem functions. This study explores the impact of mining-induced subsidence on soil microbial communities in regions with high groundwater and aims to uncover the mechanisms driving these shifts. We collected surface, middle, and deep soil samples from the subsidence areas of both the deep (T1) and shallow (T2) coal seams of the No. 3 Coal Mine in Jining, Shandong province, along with their respective control areas (W1 and W2). The physicochemical properties and microbial community composition of these samples were analyzed. The results indicated that coal mining subsidence significantly alters soil properties and reshaped microbial community structures. Compared to non-subsidence areas, soil nutrient content in subsidence areas decreased by 7.96-43.95%, while soil pH (pH) decreased by 6.33%. In contrast, soil water content (SWC) and bulk density (BD) increased by approximately 12.63 and 6.04%, respectively. Concurrently, microbial community richness and diversity declined by 16.41 and 6.65%, respectively. Despite this decline, the relative abundance of certain microbial taxa, including Actinobacteria, Chloroflexi, and Myxococcota, was higher in subsidence areas. Structural equation modeling further revealed that coal mining subsidence, in conjunction with soil physicochemical properties, accounted for 51-72% of the observed variation in microbial communities. Among the measured factors, soil available potassium (AK) had a significant direct influence on microbial communities, whereas SWC emerged as the most influential indirect factor. These findings provide critical insights into the ecological consequences of coal mining subsidence in high groundwater level areas and offer valuable guidance for land reclamation and soil ecological restoration efforts.

Ventilator-Associated Pneumonia: An Update on the Role of Lung Ultrasound in Adult, Pediatric, and Neonatal ICU Practice.

Ventilator-associated pneumonia (VAP) remains one of the most common and challenging intensive care unit (ICU)-acquired infections, significantly contributing to mortality, morbidity, and healthcare costs. The diagnosis relies on quantitative analysis of a deep microbiological sample; a combination of clinical and radiological signs is commonly used to raise VAP suspicion in clinical practice. Traditional imaging methods such as chest radiography and computed tomography have limitations in critically ill patients under mechanical ventilation. Lung ultrasound (LUS) has emerged in the last years as a valuable tool in the assessment and monitoring of critically ill patients, including for diagnosis and management of VAP, due to its noninvasive bedside applicability and absence of radiation exposure. This last quality is of particular interest in the specific population of children and newborns, where radiation exposure should be further avoided. LUS allows for daily monitoring of lung aeration and provides a quantitative assessment through the LUS aeration score; an unexpected increase of LUS aeration score may raise the suspicion of superinfection. Key ultrasonographic findings, such as subpleural consolidations and consolidations with dynamic linear-arborescent air bronchogram, improve diagnostic specificity for VAP. Similarly to what happens with traditional radiology, the Ventilator-associated Pneumonia Lung Ultrasound Score (VPLUS) combines ultrasound signs with clinical parameters like purulent secretions to enhance diagnostic accuracy. Furthermore, LUS aeration score plays a crucial role in monitoring the response to treatment, enabling assessment of lung reaeration over time. It helps differentiate between treatment responders and nonresponders, guiding therapy adjustments and identifying complications. This review highlights the evolving role of LUS in the early diagnosis, monitoring, and treatment of VAP across various ICU settings, including its application in adult, pediatric, and neonatal care.

Antibiogram use on dairy cattle for bovine respiratory disease: Factors associated with bacterial pathogen identification and prediction of bacterial recovery using machine learning.

Effective isolation of bacterial pathogens for bovine respiratory disease (BRD) is a critical step for accurate diagnosis of the agent associated with this disease on the dairy. Limited information is available on factors associated with herd-level bacterial pathogen recovery for BRD clinical cases, which are important data to help identify strategies to support the successful collection of a minimum number of each organism over time to generate cumulative antibiogram susceptibility testing reports. Our objective was to evaluate factors associated with the recovery of common pathobionts (Pasteurella multocida and Mannheimia haemolytica) in BRD clinical cases from preweaning calves, heifers, and cows at 3 California dairy farms over 2 yr. A second objective was to test the predictability of isolating these organisms in BRD clinical cases using the factors evaluated in the first objective utilizing machine learning (ML). During monthly herd visits, deep nasopharyngeal samples were collected from calves, heifers, and cows diagnosed with BRD over 2 yr. Samples were cultured in aerobic conditions, and pathogens were isolated through colony morphology and validated with MALDI-TOF MS. Evaluation of factors influencing bacterial recovery was initially tested for independence, followed by a logistic regression model and a stepwise logistic feature selection in SAS, and ML classifiers with leave-one-out cross-validation in Python packages. For our study, samples were collected from a total of 301 BRD clinical cases: 146 samples with a culture-positive for P. multocida, 63 samples with a culture-positive for M. haemolytica, and 3 samples with a culture-positive for Histophilus somni. For factors associated with the culture-positive of P. multocida in BRD clinical cases, an interaction between age and season was identified, where cows had overall lower odds of being culture-positive independently of the season when compared with calves in the spring and summer and heifers in the fall and winter. For factors associated with the culture-positive of M. haemolytica in BRD clinical cases, an interaction was also observed between age and season, but the farm further played a role in the odds of being culture-positive, with one farm having considerably greater odds than the remaining ones. Machine learning models with cross-validation showed a weak ability to distinguish positive from negative cases when using age, season, and farm for all scenarios according to F1-scores and receiver operating characteristic analysis. Differences in predictive abilities, factor importance, and the still limited number of predictors in these ML analyses further indicate a potential for building more robust future models once datasets are expanded and more robust algorithms are considered. Overall, correctly identifying factors that may be associated with the prevalence of BRD pathogens and, therefore, recovery of these pathogens is critical for the development of antibiogram programs for evaluation of the antimicrobial susceptibility of BRD pathogens.

Deep Fake Audio Recognition Using Deep Learning

Abstract - Deep fake audio is incredibly lifelike synthetic audio that can be produced because to recent advancements in deep learning algorithms. This poses a major threat to digital communications' legitimacy, security, and privacy. Deep fake audio detection has become a critical challenge since current techniques cannot keep up with the rapid advancements in audio synthesis technology. The objective of this study is to develop a dependable deep fake audio detection system using Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. The proposed method consistently distinguishes between real and false sounds using two advanced audio feature extraction techniques: spectrograms and mel-frequency cepstral coefficients (MFCCs). The RNN and LSTM-based models are trained and evaluated on a range of datasets of deep fake and actual audio samples to guarantee their effectiveness in practical settings. The importance of deep fake audio detection for privacy protection, maintaining the legitimacy of digital communications, and ensuring the veracity of audio evidence in court is highlighted by this study. By demonstrating how deep learning approaches may be used to counter the growing threat of deep fake audio, the findings pave the way for further advancements in this important subject. Key Words: Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Mel-frequency cepstral coefficients (MFCCs), Deep Learning

Deep learning-based compressed sampling reconstruction algorithm for digitizing intensive neutron ToF signals

Deep learning-based compressed sampling reconstruction algorithm for digitizing intensive neutron ToF signals

Spatial Distribution and Source Identification of Trace Metals in Topsoil in Nanyang City, Henan Province, China

ABSTRACTTrace metals (TMs) in soil have garnered widespread attention due to their adverse impacts on crop production and human health. In this study, 385 topsoil samples (0–20 cm) and 118 deep soil samples (150–200 cm) were collected from Nanyang City to investigate the spatial distribution, contamination, and source allocation of TMs. Geographic information system analysis, contamination factor (CF), geo‐accumulation index (Igeo), and positive matrix factorization (PMF) model were utilized. The results showed that the mean contents of Cu, Hg, Pb, and Zn in the study area's topsoil (25.50, 0.035, 28.51, and 79.46 mg kg−1) exceeded background values. CF and Igeo results indicated that over 60% of soil samples were contaminated by Hg, the main contaminant in soil. By combining correlation and PMF analysis, three sources of TMs were identified for (a) Pb (75.4%), Hg (63.7%), and Zn (55.6%), primarily associated with industrial emissions and atmospheric deposition; (b) Co (57.5%), Cr (50.8%), Cu (59.2%), and Ni (46.4%) mainly originated from natural sources; and (c) As (72.8%) primarily from agricultural activities. The proportions of the three sources were 36.27%, 39.76%, and 23.97%, respectively. Anthropogenic sources contributed the most to soil TMs (60.24%), higher than natural sources, indicating substantial accumulation of TMs in topsoil due to significant anthropogenic activities. This study provided useful information for environmental management planning, decision‐making, and contamination assessment.

Recent Advances in the Use of Drill Cuttings for Determining Subduction Zone Structure, Stratigraphy, and Stress State

Abstract Obtaining in situ samples from active subduction systems is critical for assessing the material properties and geological evolution of rocks and sediments that host plate boundary deformation, and advancing our understanding of the processes that lead to fault locking and rupture. However, accessing and coring these materials is challenging, and commonly requires riser drilling. The International Ocean Discovery Program (IODP) Nankai Trough Seismogenic Zone Experiment (NanTroSEIZE) project has successfully used ultradeep riser‐drilling to collect deep crust samples via core or cuttings. This paper reviews analytical methods and challenges associated with interpreting subduction zone processes from cuttings. A key advantage of analyzing cuttings is the ability to collect real time data on the lithological, biostratigraphical, structural and geochemical properties of the drilled materials. Combining these data with logging‐while‐drilling and mud gas logging data permits the generation of depth profiles of lithological variation and deformation structures. Significant limitations of cuttings include small sample size, contamination from cement and drilling fluids, the formation of drilling‐induced cohesive aggregates (DICAs), and vertical mixing during ascent. While it is impossible to overcome all these limitations, this study provides and includes examples illustrating how these issues can impact the assessment of the geological formation. Despite these challenges, cuttings have advanced our knowledge of subduction zone stratigraphy, fault friction, fluid flow, and stress distribution. This has significantly improved our understanding of earthquake mechanics, megathrust fault processes and locking/rupture mechanisms along plate fault boundaries. Future riser‐drilling operations are therefore crucial for understanding megathrust earthquakes and fault behavior.

Study on the Physical Properties of Deep Coalbed Methane Reservoirs

China has abundant deep coalbed methane (CBM) resources. Exploration and development efforts this year have revealed that deep CBM reservoirs are characterized by large resource volumes, intact coal structures, high gas content, high gas saturation, and significant development potential. However, significant differences in reservoir properties between deep and shallow coal seams, particularly in pore structure and fracture characteristics, pose challenges. These differences obscure gas migration patterns and make it difficult to establish effective development techniques. To address these challenges, this study examines deep coal reservoir samples from the Turpan-Hami and Junggar Basins. A comprehensive analysis of reservoir properties was conducted using multiple experimental methods. The findings reveal the multi-scale pore development and fracture characteristics of deep coal reservoirs, providing a theoretical basis for further exploration and development of deep CBM resources.

Mycoplamsa bovis Outbreak and Maintenance of Subclinical Infections in An Exposed Cohort of Juvenile American Bison (Bison bison).

The American bison (Bison bison) is an ecologically, economically, and culturally significant species that is exceptionally vulnerable to disease caused by Mycoplasma bovis. In contrast to livestock in which M. bovis is one of many infectious agents comprising the bovine respiratory disease complex, infection in bison is characterized by severe pneumonia and potential for systemic disease in the absence of coinfecting pathogens. In bison, morbidity and mortality are highest in adult cows, whereas calves and yearlings infrequently present with clinical disease. The infection dynamics of M. bovis in young bison exposed during an outbreak have not been fully characterized. Herein, we describe a severe outbreak of M. bovis in a closed, extensively managed herd from which we established a cohort of young bison for longitudinal observation, sampling, and testing. Our findings indicate that M. bovis can colonize the nasopharynx of calves and yearlings during an outbreak, often without causing apparent clinical signs. Although some animals cleared the infection during a 12-mo follow-up study, others remained PCR and culture positive, highlighting the potential for asymptomatic carriage in bison calves as a source of subsequent outbreaks. Using a paired swabbing approach, we show that sampling the superficial nasal cavity is adequate for detection of M. bovis during an outbreak. Over time, however, deep sampling of the nasopharynx is necessary to maximize detection of subclinical infections. Uncertainty in detection using PCR on nasal swab samples can complicate herd assessments and limit the ability to fully assess risk. This study emphasizes the difficulty of identifying chronic carriers following an outbreak and underscores the need for further research to inform M. bovis management and minimize risk in the sensitive and iconic American bison.

Serial ‘deep-sampling’ PCR of fragmented DNA reveals the wide range of Trypanosoma cruzi burden among chronically infected human, macaque, and canine hosts, and allows accurate monitoring of parasite load following treatment

Infection with the protozoan parasite Trypanosoma cruzi is generally well-controlled by host immune responses, but appears to be rarely eliminated. The resulting persistent, low-level infection results in cumulative tissue damage with the greatest impact generally in the heart in the form of chagasic cardiomyopathy. The relative success in immune control of T. cruzi infection usually averts acute phase death but has the negative consequence that the low-level presence of T. cruzi in hosts is challenging to detect unequivocally. Thus, it is difficult to identify those who are actively infected and, as well, problematic to gauge the impact of treatment, particularly in the evaluation of the relative efficacy of new drugs. In this study, we employ DNA fragmentation and high numbers of replicate PCR reaction (‘deep-sampling’) and to extend the quantitative range of detecting T. cruzi in blood by at least three orders of magnitude relative to current protocols. When combined with sampling blood at multiple time points, deep sampling of fragmented DNA allowed for detection of T. cruzi in all infected hosts in multiple host species, including humans, macaques, and dogs. In addition, we provide evidence for a number of characteristics not previously rigorously quantified in the population of hosts with naturally acquired T. cruzi infection, including, a &gt;6 log variation between chronically infected individuals in the stable parasite levels, a continuing decline in parasite load during the second and third years of infection in some hosts, and the potential for parasite load to change dramatically when health conditions change. Although requiring strict adherence to contamination–prevention protocols and significant resources, deep-sampling PCR provides an important new tool for assessing therapies and for addressing long-standing questions in T. cruzi infection and Chagas disease.

Serial 'deep-sampling' PCR of fragmented DNA reveals the wide range of Trypanosoma cruzi burden among chronically infected human, macaque, and canine hosts, and allows accurate monitoring of parasite load following treatment.

Infection with the protozoan parasite Trypanosoma cruzi is generally well-controlled by host immune responses, but appears to be rarely eliminated. The resulting persistent, low-level infection results in cumulative tissue damage with the greatest impact generally in the heart in the form of chagasic cardiomyopathy. The relative success in immune control of T. cruzi infection usually averts acute phase death but has the negative consequence that the low-level presence of T. cruzi in hosts is challenging to detect unequivocally. Thus, it is difficult to identify those who are actively infected and, as well, problematic to gauge the impact of treatment, particularly in the evaluation of the relative efficacy of new drugs. In this study, we employ DNA fragmentation and high numbers of replicate PCR reaction ('deep-sampling') and to extend the quantitative range of detecting T. cruzi in blood by at least three orders of magnitude relative to current protocols. When combined with sampling blood at multiple time points, deep sampling of fragmented DNA allowed for detection of T. cruzi in all infected hosts in multiple host species, including humans, macaques, and dogs. In addition, we provide evidence for a number of characteristics not previously rigorously quantified in the population of hosts with naturally acquired T. cruzi infection, including, a >6 log variation between chronically infected individuals in the stable parasite levels, a continuing decline in parasite load during the second and third years of infection in some hosts, and the potential for parasite load to change dramatically when health conditions change. Although requiring strict adherence to contamination-prevention protocols and significant resources, deep-sampling PCR provides an important new tool for assessing therapies and for addressing long-standing questions in T. cruzi infection and Chagas disease.

Evaluation of Pore-Fracture Structures and Gas Content in Deep Coal Reservoir of Yan’an Gas Field, Ordos Basin

Research has delved into the main controlling factors for the evolution of the pore-fracture structure in deep coal samples. The gas content is influenced by multiple factors, among which the pore-fracture structure in deep coal samples stands as one of the key determinants. To ascertain the evolution of the pore-fracture structure and the main controlling factors of the gas content in deep coal samples of the Yan’an Gas Field, 16 coal samples were collected from the Yan’an Gas Field in the Ordos Basin in this study. A series of laboratory tests and analyses were then carried out. According to the test results, the major controlling factors for the evolution of the pore-fracture structure of the samples were analyzed in accordance with the proximate analysis components, maceral components, mineral composition of the coal samples, and Ro,max, in conjunction with the pore volume and specific surface area of nanopores. Meanwhile, based on the in situ desorption experiment, the major controlling factors of the gas content in coal were explored. First, based on the SEM and hand specimen identification, the pore-fracture structure of the samples is relatively well developed. Calcite filling the fractures of samples can be seen in the hand specimens of samples. This indicates that the mineral composition has a very important influence on the evolution of the pore-fracture structure of samples. Secondly, this study indicates that pore-fracture structure evolution is influenced by multiple factors, primarily ash content and fixed carbon. As ash content increased, the mesopore surface area and volume rose across all sample types, with Type C showing the highest increase (78.1% in surface area and 12.4% in volume compared to Type A). Conversely, micropore characteristics declined, with Type C exhibiting a 4.8% drop in surface area and a 4.7% reduction in volume. The Ro,max of the samples is generally higher than 2.8%, which has a multifaceted impact on pore-fracture structure evolution. Finally, the gas content is mainly controlled by pore volume and the specific surface area of nanopores, with industrial components and maceral compositions showing minimal direct influence. This suggests that gas content results from the combined effects of material composition and pore-fracture structure evolution. Inorganic minerals like quartz and calcite indirectly affect gas content by influencing pore structure development—occupying spaces while also creating new pores, especially through calcite dissolution. Conversely, clay minerals generally hinder pore development by filling spaces with limited fracture-forming capacity. The main purpose of this study is to evaluate the gas content of coal samples in Yan‘an Gas Field. There are few studies on this area by previous scholars.