Interpretation Of Data Research Articles

3D magnetotelluric inversion with arbitrary data orientation angles

The survey setup of 3D magnetotelluric inversion is typically chosen to best reflect the underlying geological structure. Typically, the individual observation azimuths are distinct from this preferred survey layout orientation; in fact, measured electric and magnetic fields are aligned with local geomagnetic North. When that happens, either data preprocessing (rotation) is required before the inversion may be performed, or the orientation of the modeling grid is aligned with the data orientations. None of these approaches is optimal, possibly resulting in a reduction of data quality, the loss of statistical accuracy of the error estimates, or in unnecessary grid cells, resulting in increased computation times. In situations when the data quality may be improved by allowing distinct individual site orientations, such as when cultural noise or faulty instrumentation cause a degradation of one of the modes, data loss due to rotation is especially detrimental. In this paper, we present a modified 3D magnetotelluric inversion algorithm, based on the ModEM software, that allows for direct interpretation of observations with arbitrary data orientation angles (ModEM ADORA). In ModEM ADORA, the modeling responses are rotated to the observation orientation angles after each forward calculation and before the data misfit is evaluated. This allows for direct interpretation of observation data with distinct orientations at each site and (as needed for some historical data) at each frequency. The derivative of the penalty functional is computed by pre-multiplying the derivatives of the data functionals (or the complete Jacobian matrix) by the relevant sparse block-diagonal rotation operators. We describe the principle and implementation of ModEM ADORA, and provide a collection of synthetic and real data tests for validation and an analysis of effectiveness of the ADORA option of ModEM. We show that by decoupling modeling grid and sensor orientations, ModEM ADORA supports both computational efficiency and ease of use, as well as allows for the maximum quality and quantity of inverted data.

The Fragility of Statistical Findings in Cervical Disc Arthroplasty: a Systematic Review of Randomized Controlled Trials.

This study employs both the fragility index (FI) and fragility quotient (FQ) to assess the level of robustness in the cervical disc arthroplasty (CDA) literature. We hypothesize that dichotomous outcomes involving CDA would exhibit statistical vulnerability. A PubMed search was conducted to evaluate dichotomous data for randomized controlled trials (RCTs) in CDA literature from 2000 to 2023. The FI of each outcome was calculated through the reversal of a single outcome event until significance was reversed. The FQ was calculated by dividing each fragility index by the study sample size. The interquartile range (IQR) was also calculated for the FI and FQ. Of the 1561 articles screened, 111 met the search criteria, with 35 RCTs evaluating CDA included for analysis. Six hundred and ninety-three outcome events with 130 significant (P < 0.05) outcomes and 563 nonsignificant (P ≥ 0.05) outcomes were identified. The overall FI and FQ for all 693 outcomes were 5 (IQR 3-7) and 0.019 (IQR 0.011-0.043). Fragility analysis of statistically significant outcomes and nonsignificant outcomes both revealed an FI of 5. All of the studies reported loss to follow-up (LTF) data where 65.7% (23) did not report or reported an LTF greater or equal to 5. The literature regarding CDA RCTs lacks statistical robustness and may misrepresent the conclusions with the sole use of the P value. By implementing the FI and FQ along with the P value, we believe the interpretation and contextualization of the clinical data surrounding CDA will be better understood.

A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data

BackgroundSurvival prediction using high-dimensional molecular data is a hot topic in the field of genomics and precision medicine, especially for cancer studies. Considering that carcinogenesis has a pathway-based pathogenesis, developing models using such group structures is a closer mimic of disease progression and prognosis. Many approaches can be used to integrate group information; however, most of them are single-model methods, which may account for unstable prediction.MethodsWe introduced a novel survival stacking method that modeled using group structure information to improve the robustness of cancer survival prediction in the context of high-dimensional omics data. With a super learner, survival stacking combines the prediction from multiple sub-models that are independently trained using the features in pre-grouped biological pathways. In addition to a non-negative linear combination of sub-models, we extended the super learner to non-negative Bayesian hierarchical generalized linear model and artificial neural network. We compared the proposed modeling strategy with the widely used survival penalized method Lasso Cox and several group penalized methods, e.g., group Lasso Cox, via simulation study and real-world data application.ResultsThe proposed survival stacking method showed superior and robust performance in terms of discrimination compared with single-model methods in case of high-noise simulated data and real-world data. The non-negative Bayesian stacking method can identify important biological signal pathways and genes that are associated with the prognosis of cancer.ConclusionsThis study proposed a novel survival stacking strategy incorporating biological group information into the cancer prognosis models. Additionally, this study extended the super learner to non-negative Bayesian model and ANN, enriching the combination of sub-models. The proposed Bayesian stacking strategy exhibited favorable properties in the prediction and interpretation of complex survival data, which may aid in discovering cancer targets.

The ground penetrating radar response recognition of hidden dam defects using multi-output convolutional neural network

The Ground Penetrating Radar (GPR) data interpretation of dam defects detection by manual process is a heavy task. Although the convolutional neural network (CNN) is applicable to detect road defects with the help of other auxiliary instruments and techniques, it is still a challenge for the dam defect detection by deep learning. To overcome this problem, a multi-output CNN model for GPR response recognition of dam defects is proposed. The training dataset and test dataset are produced from a large number of GPR responses with different types of dam defects forward modeling by GPRMax3.0 software. The results show that the highest recognition accuracy of the training dataset and test dataset is about 98% and 97%, respectively. To verify the effectiveness, the new proposed method is used to recognize the hidden GPR responses of dam defects in the real GPR data. The results show that the types and locations of dam defects in the real GPR data can be recognized and classified automatically, efficiently and accurately.

Development of mobile CAT for patient feedback on pediatric consultations based on Rasch analysis of online techniques.

The Rasch Rating Scale Model (RSM) is widely used in questionnaire analysis, providing insights into how individuals respond to item-level stimuli. Existing software for Rasch RSM parameter estimation, while powerful, often presents a steep learning curve. An accessible online tool can greatly benefit novice users, particularly students and clinicians, by simplifying the analytical process. This study introduces an online tool, an intuitive online RSM analysis tool designed to facilitate questionnaire data analysis for applied researchers, students, and clinicians. The online tool employs the joint maximum likelihood method for estimation, yielding estimates, standard errors (SE), and fit statistics iteratively. A unique feature of the tool is its ability to visualize estimates on Google Maps with an opacity setting of 0, enhancing data interpretation through a user-friendly interface. This study outlines the estimation process and key features, employing data from 200 proxy participants who answered 20 5-point questions regarding doctor-patient and doctor-family interactions in pediatric consultations. Mobile computerized adaptive testing (CAT) was employed. The online tool offers 5 essential visual displays often utilized in Rasch analyses, including the Wright Map, KIDMAP, category probability curve, performance plot, and differential item functioning (DIF) graph. DIF analysis revealed that 2 items, concerning the doctor attentiveness and empathy toward the child illness, exhibited differences in female proxy participants' responses, indicating lower satisfaction with pediatricians. The online tool emerges as a user-friendly and efficient RSM analysis tool with notable advantages for newcomers, improving data visualization and comprehension. Its capacity to pinpoint key areas of concern, such as gender-related satisfaction disparities among proxy participants, enhances its utility in questionnaire analysis. The online tool holds promise as a valuable resource for researchers, students, and clinicians seeking accessible Rasch analysis solutions.

Salt-pillow formation during inversion of evaporite-filled half graben – Insights from seismic data interpretation and integrated analogue-numerical modelling

The Mid-Polish Anticlinorium is a regional structure that formed during Late Cretaceous inversion of the Permian-Mesozoic Polish Basin. Within the anticlinorium, local salt pillows built of the Upper Permian (Zechstein) evaporites are often located above reverse faults that accommodated basement inversion. Seismic data from the Szubin area in central Poland were used to guide a combined analogue and numerical modelling study to test whether locally thicker evaporites deposited within a half-graben, could indeed give rise to a salt pillow formed above the half-graben's hanging wall during its inversion. The results of the two approaches are internally consistent and prove that such a genetic relationship is fully viable, with the most important variables being the size of the half-graben, the viscosity of the salt, and the presence of any erosion of the pillow structure. Thus, the existence of salt pillows along the major inversion zones of the Mid-Polish Anticlinorium could possibly be used as indicators for the location of syn-depositional half-grabens during deposition of the Zechstein evaporites. This in turn might suggest that even in the basin center, shallower areas might have existed above the footwalls of such half-graben during Zechstein deposition, characterized by smaller thicknesses and somewhat different facies arrangement. Similar concepts likely apply to other intracontinental salt basins that experienced rifting and then inversion.

COMPARATIVE ANALYSIS OF GENERATIVE ARTIFICIAL INTELLIGENCE (AI) VERSUS MANUAL DATA ANALYSIS IN ORTHOPAEDIC PATIENT DATA

As patient data continues to grow, the importance of efficient and precise analysis cannot be overstated. The employment of Generative Artificial Intelligence (AI), specifically Chat GPT-4, in the realm of medical data interpretation has been on the rise. However, its effectiveness in comparison to manual data analysis has been insufficiently investigated.This quality improvement project aimed to evaluate the accuracy and time-efficiency of Generative AI (GPT-4) against manual data interpretation within extensive datasets pertaining to patients with orthopaedic injuries.A dataset, containing details of 6,562 orthopaedic trauma patients admitted to a district general hospital over a span of two years, was reviewed. Two researchers operated independently: one utilised GPT-4 for insights via prompts, while the other manually examined the identical dataset employing Microsoft Excel and IBM® SPSS® software. Both were blinded on each other's procedures and outcomes. Each researcher answered 20 questions based on the dataset including injury details, age groups, injury specifics, activity trends and the duration taken to assess the data.Upon comparison, both GPT-4 and the manual researcher achieved consistent results for 19 out of the 20 questions (95% accuracy). After a subsequent review and refined prompts (prompt engineering) to GPT-4, the answer to the final question aligned with the manual researcher's findings. GPT-4 required just 30 minutes, a stark contrast to the manual researcher's 9-hour analytical duration.This quality improvement project emphasises the transformative potential of Generative AI in the domain of medical data analysis. GPT-4 not only paralleled the accuracy of manual analysis but also achieved this in significantly less time. For optimal accurate results, data analysis by AI can be enhanced through human oversight. Adopting AI-driven approaches, particularly in orthopaedic data interpretation, can enhance efficiency and ultimately improve patient care. We recommend future investigations on large and more varied datasets to reaffirm these outcomes.

INVESTIGASI KEMAMPUAN BERNALAR ILMIAH SISWA MELALUI IMPLEMENTASI MODEL PROBLEM BASED LEARNING MATERI FISIKA FLUIDA STATIS

In this 21st century, students are required to have skills in facing the challenges of globalization, one of which is scientific reasoning skills. Scientific reasoning is the ability to think systematically and logically to solve problems. Six indicators of scientific reasoning ability include conversion reasoning, proportional reasoning, variable control, probabilistic reasoning, correlation reasoning, and hypothesis-deductive reasoning. Scientific reasoning ability can be improved by using learning models that encourage students to think at a higher level, one of which is the Problem Based Learning (PBL) model. The purpose of this research is to determine the scientific reasoning ability of students through the application of the PBL model on static fluid physics material. This type of research is descriptive quantitative and qualitative. This research was conducted in class XI MIPA 4 SMAN 3 Jember. Data collection techniques in this study using observation techniques, interviews, tests, and documentation. Data analysis techniques in this study are data collection, data reduction, data presentation, data interpretation, and conclusion drawing. The results obtained are the average score of students' reasoning ability on the six indicators of scientific reasoning in the very good category. So it is concluded that the PBL model can improve students' scientific reasoning ability in static fluid physics material.

In vivo neuropil density from anatomical MRI and machine learning.

Brain energy budgets specify metabolic costs emerging from underlying mechanisms of cellular and synaptic activities. While current bottom-up energy budgets use prototypical values of cellular density and synaptic density, predicting metabolism from a person's individualized neuropil density would be ideal. We hypothesize that in vivo neuropil density can be derived from magnetic resonance imaging (MRI) data, consisting of longitudinal relaxation (T1) MRI for gray/white matter distinction and diffusion MRI for tissue cellularity (apparent diffusion coefficient, ADC) and axon directionality (fractional anisotropy, FA). We present a machine learning algorithm that predicts neuropil density from in vivo MRI scans, where ex vivo Merker staining and in vivo synaptic vesicle glycoprotein 2A Positron Emission Tomography (SV2A-PET) images were reference standards for cellular and synaptic density, respectively. We used Gaussian-smoothed T1/ADC/FA data from 10 healthy subjects to train an artificial neural network, subsequently used to predict cellular and synaptic density for 54 test subjects. While excellent histogram overlaps were observed both for synaptic density (0.93) and cellular density (0.85) maps across all subjects, the lower spatial correlations both for synaptic density (0.89) and cellular density (0.58) maps are suggestive of individualized predictions. This proof-of-concept artificial neural network may pave the way for individualized energy atlas prediction, enabling microscopic interpretations of functional neuroimaging data.

Oral bacteria relative abundance in faeces increases due to gut microbiota depletion and is linked with patient outcomes.

The detection of oral bacteria in faecal samples has been associated with inflammation and intestinal diseases. The increased relative abundance of oral bacteria in faeces has two competing explanations: either oral bacteria invade the gut ecosystem and expand (the 'expansion' hypothesis), or oral bacteria transit through the gut and their relative increase marks the depletion of other gut bacteria (the 'marker' hypothesis). Here we collected oral and faecal samples from mouse models of gut dysbiosis (antibiotic treatment and DSS-induced colitis) and used 16S ribosomal RNA sequencing to determine the abundance dynamics of oral bacteria. We found that the relative, but not absolute, abundance of oral bacteria increases, reflecting the 'marker' hypothesis. Faecal microbiome datasets from diverse patient cohorts, including healthy individuals and patients with allogeneic haematopoietic cell transplantation or inflammatory bowel disease, consistently support the 'marker' hypothesis and explain associations between oral bacterial abundance and patient outcomes consistent with depleted gut microbiota. By distinguishing between the two hypotheses, our study guides the interpretation of microbiome compositional data and could potentially identify cases where therapies are needed to rebuild the resident microbiome rather than protect against invading oral bacteria.

Grammatical Analysis of the Quality of Abdi Dalem's Written Language Variety of Ngayogyakarta Hadiningrat Palace, Indonesia

Abdi Dalem is the King's Assistant at the Ngayogyakarta Hadiningrat Palace, Indonesia, which has a special variety of written language. This research aims to formulate the variety of Abdi Dalem written language in the kedhaton (inner core) environment of Ngayogyakarta Hadiningrat Palace as well as grammatical analysis to improve the quality of the variety of written language. This research is descriptive qualitative research. The form of this research data is a variety of Abdi Dalem written language in the kedhaton (inner core) environment of the palace in the form of regulations, bulletins, official letters and Abdi Dalem training books. The data were collected by identifying the places and samples, reviewing related literature, reviewing various written languages, and doing an interview. Data analysis techniques are describing Abdi Dalem, linguistic analysis of Abdi Dalem's written language, and data interpretation. The results of the research show that the variety of Abdi Dalem's official written language consists of 1) the Bagongan variety of Javanese, 2) Indonesian. The grammatical inaccuracy of Abdi Dalem's variety of written language consists of 1) Bagongan lexicon inaccuracy, 2) New Javanese vocabulary inaccuracy, 3) inaccuracy in the use of the New Javanese Ngoko (N) affix, 4) spelling inaccuracy. With this grammatical analysis, it is hoped that it can improve the quality of Abdi Dalem Ngayogyakarta Hadiningrat Palace's variety of written language.

P-index: A Generalized Statistical Aggregation Metric

Data aggregation serves as a crucial technique in data analysis, particularly in the context of data dimensionality reduction. The most common approach involves employing statistical aggregation metrics such as mean, median, and others to characterize a dataset. However, different statistical aggregation metrics may yield disparate results, making the discrimination and selection of suitable metrics for describing data a research topic. Addressing this, the paper proposes a generalized statistical aggregation metric, categorizing several commonly used metrics under specific scenarios. This model offers a novel perspective for choosing among different statistical aggregation metrics and assists researchers in developing a deeper understanding of common statistical aggregation metrics. The introduction of this generalized metric provides a flexible and comprehensive method for considering multiple statistical measures simultaneously, enabling a more accurate grasp of the dataset’s essence and offering reliable insights for decision-making. The study contributes a new viewpoint to the selection and application of statistical aggregation metrics, advancing discussions in multi-objective optimization and data interpretability.

The First International eDNA Workshop in Hong Kong: A beginner's guide for the next‐generation eDNA researcher

AbstractThe field of environmental DNA (eDNA) has rapidly advanced in recent years, providing a non‐invasive and time‐saving method for assessing biodiversity. The First International Environmental DNA (eDNA) Workshop in Hong Kong was held from 16 to 27 October 2023 and provided early career professionals with hands‐on training and collaboration opportunities in eDNA research. With support from The Croucher Foundation Limited (Hong Kong), the workshop covered all stages of an eDNA‐based research project, including study design, field sampling, eDNA extraction, library preparation, bioinformatics, statistical data analysis, and ethics in scientific research. Participants gained insights into the principles and practical applications of eDNA technology, emphasizing the importance of careful experimental design and data interpretation. The workshop also highlighted the need for standardized protocols, comprehensive and local DNA reference databases, and careful selection of primer sets to overcome current issues and limitations. Workshop participants expressed enthusiasm for the potential of eDNA metabarcoding as a valuable tool for ecological monitoring, biodiversity assessment, and conservation decision‐making. The future of eDNA research looks promising overall, with continued advancements in technology, collaboration among researchers, and the integration of eDNA into large‐scale ecological monitoring. Future iterations of the Hong Kong International eDNA workshop will continue to provide hands‐on training and collaboration opportunities for early career professionals interested in eDNA research, focusing on addressing current limitations and challenges in the field.

Science learning STEM-R approach: A study of students' reflective and critical thinking

This study aimed to i) analyse learning problems in science at boarding schools for junior high school students and ii) determine the effectiveness of science learning in the context of Islamic teachings using the science, technology, engineering, and mathematics, -religion (STEM-R) approach to improve reflective and critical thinking skills. The research method applied in this study was a sequential exploratory mixed method. The research design consisted of five stages: i) qualitative data gathering, ii) qualitative data analysis, iii) quantitative data gathering, iv) quantitative data analysis, and v) data interpretation. The subjects of this study were 192 students from boarding schools and were divided into 6 groups. The research results obtained were the school, parents, science teachers and religion teachers’ vision and mission causing students to like or dislike science. An excellent teacher is viewed by students as someone who can make learning enjoyable, exhibit creativity in presenting educational material, possess the ability to motivate, link science and religion, engage in investigative activities, infuse humour, establish connections between science and everyday life, and communicate concepts effectively. Moreover, distinctions in reflective thinking and critical thinking abilities were observed between the experimental group and the control group. Learning that connects science and Islamic religion and examines STEM-R aspects is able to develop students' thinking skills.

Target and Nontarget Screening to Support Capacity Scaling for Substance Use Assessment through a Statewide Wastewater Surveillance Network in New York.

Wastewater-based epidemiology (WBE) has been widely implemented around the world as a complementary tool to conventional surveillance techniques to inform and improve public health responses. Currently, wastewater surveillance programs in the U.S. are evaluating integrated approaches to address public health challenges across multiple domains, including substance abuse. In this work, we demonstrated the potential of online solid-phase extraction coupled with liquid chromatography-high-resolution mass spectrometry to support targeted quantification and nontargeted analysis of psychoactive and lifestyle substances as a step toward understanding the operational feasibility of a statewide wastewater surveillance program for substance use assessment in New York. Target screening confirmed 39 substances in influent samples collected from 10 wastewater treatment plants with varying sewershed characteristics and is anticipated to meet the throughput demands as the statewide program scales up to full capacity. Nontarget screening prioritized additional compounds for identification at three confidence levels, including psychoactive substances, such as opioid analgesics, phenethylamines, and cathinone derivatives. Consumption rates of 12 target substances detected in over 80% of wastewater samples were similar to those reported by previous U.S.-based WBE studies despite the uncertainty associated with back-calculations. For selected substances, the relative bias in consumption estimates was sensitive to variations in monitoring frequency, and factors beyond human excretion (e.g., as indicated by the parent-to-metabolite ratios) might also contribute to their prevalence at the sewershed scale. Overall, our study marks the initial phase of refining analytical workflows and data interpretation in preparation for the incorporation of substance use assessment into the statewide wastewater surveillance program in New York.

Towards a better understanding of the geochemical proxy record of complex carbonate archives

Carbonate archives record a brief snapshot of the ambient Earth’s surface conditions at their deposition. However, the geologically reasonable extraction and interpretation of geochemical proxy data from ancient, diagenetically altered rock archives is fraught with problems. Three issues stand out: the dichotomy between petrographic and geochemical alteration; the lack of quantitative age constraints for specific diagenetic phases resulting in a poorly constrained admixture of local, basin-wide and over-regional (far-field) features; and an often insufficient understanding of the temperatures and compositions of diagenetic fluids. Here, the archive of Devonian marine limestones exposed to multiple far-field diagenetic events is used as an example to explore the above-listed issues. Methods applied include petrography, micro XRF, fluid inclusion data, clumped isotopes, δ13C and δ18O isotopes, 87Sr/86Sr ratios and quartz trace element data. Devonian limestones studied here were overprinted by two cross-cutting regional fault zones (T ≈ 230 °C) by multiple events between the Variscan Orogeny and the late Paleogene. The following processes are recorded: (i) protolith deposition and partial dolomitisation during rapid burial in the Middle/Late Devonian (T ≈ 180 °C); (ii) deep burial to ca 6.5 km and tectonic/hydrothermal overprint during the Variscan Orogeny in the Carboniferous (T ≈ 90–230 °C); (iii) rapid uplift to 1–2 km burial depth at the end of the Variscan Orogeny and hypogene karstification (T ≈ 50 to 100 °C) initiated by regional geology in the Permian/Triassic; (iv) tectonic/hydrothermal overprint during the opening of the Proto-Atlantic Ocean between the Early Jurassic and the Early Cretaceous (T ≈ 50 to 130 °C); (v) tectonic/hydrothermal overprint including renewed hypogene karstification and hydrothermal calcite cement precipitation (T ≈ 50 to 180 °C) during Alpine Orogeny between the Late Cretaceous and late Paleogene. Despite this complex series of diagenetic events, the protolith limestones largely preserved their respective Middle/Late Devonian dissolved inorganic carbon (DIC) and 87Sr/86Sr signatures. This study documents that geochemical proxy data, placed into their petrographic, paleotemperature, and local to over-regional context, significantly increases the ability to extract quantitative information from ancient carbonate rock archives. Research shown here has wider relevance for carbonate archive research in general.

Predicting Scale Deposition in Oil Reservoirs Using Machine Learning Optimization Algorithms

Scale deposition, a form of formation damage, not only affects the reservoir but also damages the well and equipment. This phenomenon occurs due to changes in temperature, pressure, and the injection of incompatible salt water, leading to ionic reactions. This study investigated permeability reduction due to scale deposition and examined how parameters such as temperature, pressure drop, and ion concentration affect the prediction accuracy. The scale deposits investigated in this study include CaSO4, BaSO4, and SrSO4. This paper uses Python to employ different machine-learning algorithms to predict the results. Each machine learning model has certain hyper-parameters that need adjustment. Failure to do so will result in reduced accuracy and incomplete interpretation of input data. The accuracy of the support vector regression (SVR) algorithm was significantly affected by the variation of the epsilon parameter in the dataset used. Therefore, before hyperparameter optimization, SVR had the lowest accuracy at 0.575. After adjusting the hyper-parameters, our findings show that SVR had the highest increase in R-squared value, which was 0.900, and the most minor growth in KNN, which went from 0.995 to 0.996. Additionally, the highest accuracy value for K-Nearest Neighbor is 0.996. Furthermore, most errors were related to SVR and XGBoost algorithms, while the most negligible errors were for the Decision Tree and KNN algorithms.

How borehole wall elasticity affects wellbore storage capacity in periodic hydraulic tests

Characterizing fluid transport and pore pressure diffusion is key for monitoring and understanding many natural (e.g., seismically active zones and volcanic systems) and engineered environments (e.g., enhanced geothermal reservoirs and CO2 underground storage). Borehole hydraulic testing allows for inferring relevant properties of the probed subsurface volume, such as, for example, its transmissivity and diffusivity, assessing the governing flow regime as well as detecting the presence of hydraulic boundaries. Periodic hydraulic tests (PHT) achieve these objectives using an oscillatory fluid injection procedure while measuring the fluid pressure response in monitoring boreholes. The relevant information on the pressure diffusion process occurring in a probed formation is retrieved from the phase shifts and amplitude ratios between the flow rate and the interval pressure. In general, the interpretation of PHT data relies on the assumption that the pressure diffusion process is largely unaffected by the deformation of the probed rock volume. To assess this assumption, we present a conceptual PHT model based on the theory of poroelasticity to investigate the role played by hydromechanical coupling (HMC) effects during PHT and to assess whether and to what extent additional mechanical information can be extracted from these tests. These effects include the influence of the borehole wall deformation on the wellbore storage coefficient Sw, which quantifies the difference between the injected flow rates and those actually entering the porous formation. We show that, in homogeneous formations, the commonly taken approach based on the uncoupled diffusion solution for radial flow reproduces the results of our HMC model. However, neglecting the deformation effect of the borehole wall on the storage capacity of the system during injection can lead to underestimations of both transmissivity and diffusivity, particularly at shorter oscillation periods. We present analytical expressions for the borehole wall deformation contribution to Sw, which depends on the shear modulus and does not change with the oscillatory period. We show that it is possible to obtain the effective Sw along with the transmissivity and diffusivity using observations at various oscillatory periods. In the presence of hydraulic boundaries, such as no-flow or constant-pressure boundaries, at a given distance from the injection borehole, Sw becomes period-dependent, introducing apparent variations in the inferred shear modulus of the formation if the associated HMC effects are not accounted for. Our results thus highlight the importance of accounting for HMC effects in PHT data interpretation, not only to reliably and accurately characterize and monitor hydraulic properties but also to fully exploit the potential of PHT to provide additional information on the mechanical behaviour of the probed rock volume.

Efficiency of machine learning to predict gas accumulations

A growing number of research geophysicists are drawn to machine learning in geophysical applications. At the moment, direct applications of machine learning cover an expansive range of tasks related to the interpretation of seismic data. Beginning with computer-assisted seismic well tying, faults and horizons are automatically interpreted, velocity models are optimized, geobodies are extracted, and seismic facies are classified. Our goal is to demonstrate how artificial neural networks may be used to teach a computer to categorize seismic data and anticipate possible gas accumulations, starting from selecting training data, through filtering the best discriminating attributes, monitoring the learning process, and ending with running the predictions. This workflow was applied on a Dana Gas Messininan gas field in the Nile Delta province of Egypt. The network was trained on a few seismic points in the vicinity of one successful well as positive training examples. The network not only accurately predicted the gas accumulation near the training points but also the other nearby stratigraphically compartmentalized gas accumulations, aligning with production and pressure data of the hidden wells. Similar findings were obtained when the results were compared to classical amplitude variation with offset elastic inversion; however, the machine learning methodology is far faster and easier than the inversion in terms of procedural steps and required work.

Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine

BackgroundMetabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed. ResultsSystematic optimization was performed using fractional factorial experiments for both serum (n=57) and urine (n=24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n=76) and untargeted (n=1,949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2≥0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n=69) and untargeted (n=2,348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n=41 significant metabolites for serum, and n=55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y)>0.95, Q2(Y)>0.65, p-value<0.001 for both matrices). SignificanceThis study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.