Observer Bias Research Articles

Evaluation of the efficacy of NB-UVB phototherapy and topical decapeptide versus NB-UVB phototherapy, topical decapeptide, and carbon dioxide laser in the treatment of stable vitiligo

Background: Vitiligo is an acquired disorder of autoimmune destruction of melanocytes. The management challenges include stabilizing the disease and devising methods for inducing repigmentation. Phototherapy and laser therapy offer promising results, along with topical treatments such as Basic Fibroblast Growth Factor (bFGF)-related topical decapeptide. Aims: This prospective interventional study aimed to assess and compare the efficacy of a triple combination therapy of fractional CO2 laser, Narrow Band-Ultraviolet B (NB-UVB) phototherapy, and topical decapeptide with dual combination of NB-UVB phototherapy and topical decapeptide in the treatment of stable vitiligo. Methods: Patients with stable vitiligo were enrolled for 15 months and divided into two groups: one receiving dual combination therapy and the other the triple combination therapy. Lesions were evaluated using a quartile-grading scale at regular intervals over 16 weeks. Vitiligo noticeability scale (VNS) was used by an independent observer (who was not a part of the study) and by the patient/attendant to eliminate any observation bias. Statistical analysis was done using Pearson chi-square test. Results: Of the 32 enrolled patients, the triple combination therapy demonstrated superior repigmentation outcomes compared to dual therapy, with statistically significant differences observed at all assessment intervals. Side effects were mild and transient. Conclusion: The study highlights the efficacy of triple combination therapy in treating stable vitiligo, particularly for patients with refractory vitiligo. Further research with larger sample sizes and longer follow-up periods is warranted to validate these findings and optimize treatment protocols.

Automated image acquisition and analysis of graphene and hexagonal boron nitride from pristine to highly defective and amorphous structures

Defect-engineered and even amorphous two-dimensional (2D) materials have recently gained interest due to properties that differ from their pristine counterparts. Since these properties are highly sensitive to the exact atomic structure, it is crucial to be able to characterize them at atomic resolution over large areas. This is only possible when the imaging process is automated to reduce the time spent on manual imaging, which at the same time reduces the observer bias in selecting the imaged areas. Since the necessary datasets include at least hundreds if not thousands of images, the analysis process similarly needs to be automated. Here, we introduce disorder into graphene and monolayer hexagonal boron nitride (hBN) using low-energy argon ion irradiation, and characterize the resulting disordered structures using automated scanning transmission electron microscopy annular dark field imaging combined with convolutional neural network-based analysis techniques. We show that disorder manifests in these materials in a markedly different way, where graphene accommodates vacancy-type defects by transforming hexagonal carbon rings into other polygonal shapes, whereas in hBN the disorder is observed simply as vacant lattice sites with very little rearrangement of the remaining atoms. Correspondingly, in the case of graphene, the highest introduced disorder leads to an amorphous membrane, whereas in hBN, the highly defective lattice contains a large number of vacancies and small pores with no indication of amorphisation. Overall, our study demonstrates that combining automated imaging and image analysis is a powerful way to characterize the structure of disordered and amorphous 2D materials, while also illustrating some of the remaining shortcomings with this methodology.

Automating hock wound detection in dairy cattle

Hock scoring in dairy cattle is a crucial welfare assessment tool used to evaluate the condition of a cow's hocks, particularly for signs of injury, swelling, or lesions. These scores provide insight into the overall well-being of the animals and are essential for ensuring proper management and housing conditions. Accurate hock scoring is vital because it can indicate issues such as poor bedding quality or inadequate space, which directly affect the health and productivity of the herd. Traditionally, hock scoring is performed manually by trained observers. However, consistency in scoring can be a challenge. Two studies were conducted to quantify inconsistency in hock scoring. In one study, manual scoring repeatability was measured. In the second study, manual and video scoring repeatability was measured. Repeatability was quantified with a weighted Cowen's kappa metric. Manual scoring was found to be inconsistent but more consistent than video scoring. This variability highlights the need for a more reliable, objective method of scoring. To address this, we explored the automation of hock score detection using artificial intelligence. Specifically, we employed a simple U-net semantic segmentation algorithm to detect wounds on the hocks without classifying them into specific categories. Automating the detection process can reduce observer bias, improve consistency, and allow for continuous monitoring of large herds. This approach holds promise for enhancing animal welfare by providing a more efficient and accurate method of assessing hock health in dairy cattle.

Determination of forest canopy cover of different sized stands with diverse structure using ALS data: case study of the Białowieża Forest (Poland)

ABSTRACT The need for objective methods to determine tree canopy cover (CC) across large numbers of stands has led to the development of techniques that utilise airborne laser scanning (ALS) data, which provides a reproducible and detailed representation of canopy geometry. We developed a method for determining CC area and evaluated the estimation accuracy for stands of different sizes, structure and composition. This method is based on tree crown geometries obtained from ALS data, and verified with field measurements using data for 3245 stands of the Białowieża Forest District in Poland. In relatively large stands (3–5 ha), the theoretical error of prediction decreased from 0.13 to 0.10 with increasing stand area. In stands larger than 10 ha, however, the error in estimating CC was less than ±0.10. Although every estimation method comes with its own assumptions and errors, the presented method eliminates the subjectivity in observer bias prevalent in traditional field-based ocular assessments and provides a more transparent and methodologically uniform approach for estimating CC in forests.

Identification of positions in human aldolase a that are neutral for apparent KM

According to evolutionary theory, many naturally-occurring amino acid substitutions are expected to be neutral or near-neutral, with little effect on protein structure or function. Accordingly, most changes observed in human exomes are also expected to be neutral. As such, accurate algorithms for identifying medically-relevant changes must discriminate rare, non-neutral substitutions against a background of neutral substitutions. However, due to historical biases in biochemical experiments, the data available to train and validate prediction algorithms mostly contains non-neutral substitutions, with few examples of neutral substitutions. Thus, available training sets have the opposite composition of the desired test sets. Towards improving a dataset of these critical negative controls, we have concentrated on identifying neutral positions – those positions for which most of the possible 19 amino acid substitutions have little effect on protein structure or function. Here, we used a strategy based on multiple sequence alignments to identify putative neutral positions in human aldolase A, followed by biochemical assays for 147 aldolase substitutions. Results showed that most variants had little effect on either the apparent Michaelis constant for substrate fructose-1,6-bisphosphate or its apparent cooperativity. Thus, these data are useful for training and validating prediction algorithms. In addition, we created a database of these and other biochemically characterized aldolase variants along with aldolase sequences and characteristics derived from sequence and structure analyses. This database is publicly available at https://github.com/liskinsk/Aldolase-variant-and-sequence-database.

The disc origin of the Milky Way bulg. The high velocity dispersion of metal-rich stars at low latitudes

Previous studies of the chemo-kinematic properties of stars in the Galactic bulge have revealed a puzzling trend. Along the bulge minor axis, and close to the Galactic plane, metal-rich stars display a higher line-of-sight velocity dispersion compared to metal-poor stars, while at higher latitudes metal-rich stars have lower velocity dispersions than metal-poor stars, similar to what is found in the Galactic disc. In this work, we re-examine this issue, by studying the dependence of line-of-sight velocity dispersions on metallicity and latitude in APOGEE Data Release 17, confirming the results of previous works. We then analyse an $N$-body simulation of a Milky Way-like galaxy, also taking into account observational biases introduced by the APOGEE selection function. We show that the inversion in the line-of-sight velocity dispersion-latitude relation observed in the Galactic bulge -- where the velocity dispersion of metal-rich stars becomes greater than that of metal-poor stars as latitude decreases -- can be reproduced by our model. We show that this inversion is a natural consequence of a scenario in which the bulge is a boxy or peanut-shaped structure, whose metal-rich and metal-poor stars mainly originate from the thin and thick disc of the Milky Way, respectively. Due to their cold kinematics, metal-rich, thin disc stars are efficiently trapped in the boxy, peanut-shaped bulge, and at low latitudes show a strong barred morphology, which -- given the bar orientation with respect to the Sun-Galactic centre direction -- results in high velocity dispersions that are larger than those attained by the metal-poor populations. Extremely metal-rich stars in the Galactic bulge, which have received renewed attention in the literature, do follow the same trends as those of the metal-rich populations. The line-of-sight velocity-latitude relation observed in the Galactic bulge for metal-poor and metal-rich stars are thus both an effect of the intrinsic nature of the Galactic bulge (i.e. mostly secular) and of the angle at which we observe it from the Sun.

Optimal 16S rRNA gene amplicon sequencing analysis for oral microbiota to avoid the potential bias introduced by trimming length, primer, and database.

16S rRNA gene amplicon sequencing analysis is used to investigate bacterial communities; however, the estimated bacterial composition can differ from the original due to experimental and analytical biases. Therefore, this study determines the optimal conditions to minimize the potential biases in 16S rRNA gene amplicon sequencing analysis using different DNA samples, trimming lengths, primers, and databases. The results of the mock1 community with 250 and 300 bp paired-end (PE), comprising 15 bacteria from various environments, showed the highest similarity between the theoretical value and the data using the Greengenes2 and the V3 region at the genus level and V5-V6 at the species level. In the 300 bp PE sequencing analysis of the mock2 community, comprising six major oral bacteria, the data using the V3-V4, V4, and V5-V6 regions with the SILVA, Greengenes2, and the Human Oral Microbiome Database (HOMD) showed the highest similarity to the theoretical values at the genus level. At the species level, the data using the V3-V4 and V4 regions with Greengenes2 and the data using the V1-V2 with HOMD exhibited the highest alignment with the theoretical values. In the species analysis of the dental calculus samples with 300 bp PE, the Shannon index value was higher with the V1-V2 region and HOMD than with others. Our results suggest that the optimal conditions for oral microbiome analysis are the combinations of 300 bp PE and the V3-V4 or V4 region with the Greengenes2 and the V1-V2 with HOMD.IMPORTANCEThe 16S rRNA gene amplicon sequencing analysis is frequently used in oral microbiome research. However, this method can have biases that distort the experimental data, which depend on the methodological steps, including sequencing length, trimming length, selected amplification regions, and referenced databases. In this study, a combination of 300 bp PE and the primer targeting the V3-V4 or V4 regions with the Greengenes2 and the V1-V2 region with the HOMD was the most bias-minimizing condition for oral microbiota analysis. In addition, this is the first report of such analyses in modern Japanese dental calculus. The methods used in this study will aid in setting appropriate conditions for sequence analysis of microbiota obtained not only from the oral cavity but also from any environment.

Euclid II. The VIS instrument

This paper presents the specification, design, and development of the Visible Camera (VIS) on the European Space Agency's mission. VIS is a large optical-band imager with a field of view of 0.54 deg$^2$ sampled at with an array of 609 Megapixels and a spatial resolution of . It will be used to survey approximately 14 000 deg$^2$ of extragalactic sky to measure the distortion of galaxies in the redshift range $z=0.1$--1.5 resulting from weak gravitational lensing, one of the two principal cosmology probes leveraged by With photometric redshifts, the distribution of dark matter can be mapped in three dimensions, and the extent to which this has changed with look-back time can be used to constrain the nature of dark energy and theories of gravity. The entire VIS focal plane will be transmitted to provide the largest images of the Universe from space to date, specified to reach AB with a signal-to-noise ratio S/N in a single broad E (r+i+z)$ band over a six-year survey. The particularly challenging aspects of the instrument are the control and calibration of observational biases, which lead to stringent performance requirements and calibration regimes. With its combination of spatial resolution, calibration knowledge, depth, and area covering most of the extra-Galactic sky, VIS will also provide a legacy data set for many other fields. This paper discusses the rationale behind the conception of VIS and describes the instrument design and development, before reporting the prelaunch performance derived from ground calibrations and brief results from the in-orbit commissioning. VIS should reach fainter than AB =25$ with $ S/N 10$ for galaxies with a full width at half maximum of in a diameter aperture over the Wide Survey, and $m_ AB for a Deep Survey that will cover more than 50 deg$^2$. The paper also describes how the instrument works with the telescope and survey, and with the science data processing, to extract the cosmological information.

Diagnostic accuracy of transcranial ultrasonography for detecting stenosis in patients with acute ischaemic stroke: a systematic review and meta-analysis.

Transcranial ultrasonography, including transcranial doppler (TCD) and transcranial colour-coded duplex/sonography (TCCD/TCCS), offers potential benefits in the evaluation of cerebrovascular patency, but its accuracy remains under scrutiny. Hence, this study aims to investigate the diagnostic accuracy of transcranial ultrasonography for detecting stenosis in acute ischaemic stroke patients. We systematically reviewed the databases such as CINAHL, SCOPUS, EMBASE, MEDLINE, Cochrane Library, Google Scholar, and ScienceDirect from inception till June 2023. Studies were eligible if they compared the diagnostic role of transcranial ultrasonography with established techniques, and reported accuracy parameters such as sensitivity, specificity, positive and negative likelihood ratios. Subgroup analysis was performed for TCD and TCCD/TCCS. Seventeen studies involving 1496 participants were included. The pooled sensitivity and specificity of transcranial ultrasonography were 90% and 95% respectively. For TCD, the sensitivity and specificity were 87% and 93% respectively. TCCD/TCCS showed sensitivity 98% and specificity 97%. High clinical value was evident in both TCD and TCCD/TCCS, with heterogeneity across studies but no observable publication bias. Transcranial ultrasonography, including both TCD and TCCD/TCCS, demonstrates high diagnostic accuracy in detecting stenosis in patients with acute ischaemic stroke. These findings support the utility of these methods as reliable, non-invasive tools for cerebrovascular assessment.

Extreme-value modelling of migratory bird arrival dates: Insights from citizen science data

Abstract Citizen science mobilises many observers and gathers huge datasets but often without strict sampling protocols, resulting in observation biases due to heterogeneous sampling effort, which can lead to biased predictions. We develop a spatiotemporal Bayesian hierarchical model for bias-corrected estimation of arrival dates of the first migratory bird individuals at their breeding sites. Higher sampling effort could be correlated with earlier observed dates. We implement data fusion of two citizen-science datasets with fundamentally different protocols (BBS, eBird) and obtain posterior distributions of the latent process, which contains four spatial components endowed with Gaussian process priors: species niche; sampling effort; position and scale parameters of annual first arrival date. The data layer consists of four response variables: counts of observed eBird locations (Poisson); presence-absence at observed eBird locations (Binomial); BBS occurrence counts (Poisson); first arrival dates (Generalised Extreme-Value). We devise a Markov Chain Monte Carlo scheme and check by simulation that the latent process components are identifiable. We apply our model to several migratory bird species in the northeastern United States for 2001−2021 and find that the sampling effort significantly modulates the observed first arrival dates. We exploit this relationship to effectively bias-correct predictions of the true first arrivals.

Prosthetic Joint Infection Research Models in NZW Rabbits: Opportunities for Standardization-A Systematic Review.

Prosthetic joint infection (PJI) is a major complication following total arthroplasty. Rising antimicrobial resistance (AMR) to antibiotics will further increase therapeutic insufficiency. New antibacterial technologies are being developed to prevent PJI. In vivo models are still needed to bridge the translational gap to clinical implementation. Though rabbit models have been used most frequently, there is no consensus about methodology and measured outcomes. The PubMed, Scopus, and EMBASE databases were searched for literature on PJI in rabbit models. Data extraction included bias control, experimental design, and outcome measures of the NZW rabbit models in the articles. A total of 60 articles were included in this systematic literature review. The articles were divided into six groups based on the PJI intervention: no intervention used (21%), revision surgery (14%), prevention with only antibiotics (21%), prevention with surface modifications (7%), prevention with coatings (23%), and others (14%). Despite the current availability of guidelines and recommendations regarding experimental design, bias control, and outcome measures, many articles neglect to report on these matters. Ultimately, this analysis aims to assist researchers in determining suitable clinically relevant methodologies and outcome measures for in vivo PJI models using NZW rabbits to test new antimicrobial technologies.

Extending the Asteroseismic Calibration of the Stellar Rossby Number

The stellar Rossby number (Ro) is a dimensionless quantity that is used in the description of fluid flows. It characterizes the relative importance of Coriolis forces on convective motions, which is central to understanding magnetic stellar evolution. Here we present an expanded sample of Kepler asteroseismic targets to help calibrate the relation between Ro and Gaia color, and we extend the relation to redder colors using observations of the mean activity levels and rotation periods for a sample of brighter stars from the Mount Wilson survey. Our quadratic fit to the combined sample is nearly linear between 0.55 < G BP − G RP < 1.2, and can be used to estimate Ro for stars with spectral types between F5 and K3. The strong deviation from linearity in the original calibration may reflect an observational bias against the detection of solar-like oscillations at higher activity levels for the coolest stars.

Ultraluminous X-ray sources with He star companions

ABSTRACT Ultraluminous X-ray sources (ULXs) are non-nuclear point-like objects observed with extremely high X-ray luminosity that exceeds the Eddington limit of a $\rm 10\, M_\odot$ black hole. A fraction of ULXs has been confirmed to contain neutron star (NS) accretors due to the discovery of their X-ray pulsations. The donors detected in NS ULXs are usually luminous massive stars because of the observational biases. Recently, the He donor star in NGC 247 ULX-1 has been identified, which is the first evidence of a He donor star in ULXs. In this paper, we employed the stellar evolution code mesa (Modules for Experiments in Stellar Astrophysics) to investigate the formation of ULXs through the NS+He star channel, in which a He star transfers its He-rich material onto the surface of an NS via Roche lobe overflow. We evolved a large number of NS+He star systems and provided the parameter space for the production of ULXs. We found that the initial NS+He star systems should have $\sim 0.7\!-\!2.6 \, \mathrm{M}_\odot$ He star and $\sim 0.1\!-\!2500\, \mathrm{d}$ orbital period for producing ULXs, eventually evolving into intermediate-mass binary pulsars. According to binary population synthesis calculations, we estimated that the Galactic rate of NS ULXs with He donor stars is in the range of $\sim 1.6\!-\!4.0\times 10^{-4}\, {\rm yr}^{-1}$, and that there exist $\sim 7-20$ detectable NS ULXs with He donor stars in the Galaxy.

Automated long-term monitoring of stereotypical movement in polar bears under human care using machine learning

The welfare of animals under human care is often assessed by observing behaviours indicative of stress or discomfort, such as stereotypical behaviour (SB), which often shows as repetitive, invariant pacing. Traditional behaviour monitoring methods, however, are labour-intensive and subject to observer bias. Our study presents an innovative automated approach utilising computer vision and machine learning to non-invasively detect and analyse SB in managed populations, exemplified by a longitudinal study of two polar bears. We designed an animal tracking framework to localise and identify individual animals in the enclosure. After determining their position on the enclosure map via homographic transformation, we refined the resulting trajectories using a particle filter. Finally, we classified the trajectory patterns as SB or normal behaviour using a lightweight random forest approach with an accuracy of 94.9 %. The system not only allows for continuous, objective monitoring of animal behaviours but also provides insights into seasonal variations in SB, illustrating its potential for improving animal welfare in zoological settings. Ultimately, we analysed 607 days for the occurrence of SB, allowing us to discuss seasonal patterns of SB in both the male and female polar bear monitored. This work advances the field of animal welfare research by introducing a scalable, efficient method for the long-term, automated detection and monitoring of stereotypical behaviour, paving the way for its application across various settings and species that can be continuously monitored with cameras. We made the code publicly available at https://github.com/team-vera/stereotypy-detector.

Genetic analysis of selection bias in a natural experiment: Investigating in-utero famine effects on elevated body mass index in the Dutch Hunger Winter Families Study.

Natural-experiment designs that compare survivors of in-utero famine exposure to unaffected controls suggest that in-utero undernutrition predisposes to development of obesity. However, birth rates drop dramatically during famines. Selection bias could arise if factors that contribute to obesity also protect fertility and/or fetal survival under famine conditions. We investigated this hypothesis using genetic analysis of a famine-exposed birth cohort. We genotyped participants in the Dutch Hunger Winter Families Study (DHWFS, N=950; 45% male), of whom 51% were exposed to the 1944-1945 Dutch Famine during gestation and 49% were their unexposed same-sex siblings or "time controls" born before or after the famine in the same hospitals. We computed body-mass index (BMI) polygenic indices (PGIs) in DHWFS participants and compared BMI PGIs between famine-exposed and control groups. Participants with higher polygenic risk had higher BMIs (Pearson r=0.42, p<0.001). However, differences between BMI PGIs of famine-exposed participants and controls were small and not statistically different from zero across specifications (Cohen's d=0.10, p>0.092). Our findings did not indicate selection bias, supporting the validity of the natural-experiment design within DHWFS. In summary, our study outlines a novel approach to explore the presence of selection bias in famine and other natural experiment studies.

A novel deep learning model for cabbage leaf disease detection and classification

Manual observation bias in identifying cabbage leaf diseases necessitates efficient automated detection systems in agriculture. In this study, a deep learning-based approach was developed for the automatic recognition and categorization of cabbage diseases, focusing on aphids, worm cuts, and black rot. Using 1400 images, including healthy and diseased samples, our method employs novel deep learning models. EfficientNetB7, MobileNetV2, and DenseNet201 were utilized to successfully identify three types of cabbage leaf diseases with accuracies of 98.56%, 98.26%, and 97.64%, respectively. EfficientNetB7 achieved the highest accuracy of 98.56% for disease classification. The system's ability to automatically extract features from images enhances disease detection, crucial for mitigating yield losses. Through visualization techniques, we analyzed the location of unhealthy regions in the leaves, the filter, and the intermediate layer.

Five New Heartbeat Star Systems with Tidally Excited Oscillations Discovered Based on TESS Data

Heartbeat stars (HBSs) with tidally excited oscillations (TEOs) are ideal astrophysical laboratories for studying the internal properties of the systems. In this paper, five new HBSs exhibiting TEOs are discovered using TESS photometric data. The orbital parameters are derived using a corrected version of Kumar et al.'s model based on the Markov Chain Monte Carlo method. The TEOs in these objects are examined, and their pulsation phases and modes are identified. The pulsation phases of the TEOs in TIC 266809405, TIC 266894805, and TIC 412881444 are consistent with the dominant l = 2, m = 0, or ±2 spherical harmonic. For TIC 11619404, although the TEO phase is close to the m = +2 mode, the m = 0 mode cannot be excluded because of the low inclination in this system. The TEO phase in TIC 447927324 shows a large deviation (>2σ) from the adiabatic expectations, suggesting that it is expected to be a traveling wave or that the pulsations are nonadiabatic. In addition, these TEOs occur at relatively low orbital harmonics, and we cautiously suggest that this may be an observational bias. These objects are valuable sources for studying the structure and evolution of eccentricity orbit binaries and extending the TESS HBS catalog with TEOs.

The Influence of the Magnetic Braking Laws on the Evolution of Persistent and Transient Low-mass X-Ray Binaries

Swift J1858.6−0814 (hereafter J1858) is a transient neutron star (NS) low-mass X-ray binary (LMXB). There is controversy regarding its donor mass derived from observations and theoretical calculations. In this paper, we adopt seven magnetic braking (MB) prescriptions suggested in the literature and different metallicity Z to simulate the evolution of the LMXB. Our results show that, employing the MB model proposed by A. Reiners & S. Mohanty (“rm12”), the convection- and rotation-boosted (“carb”) model, and the intermediate (“inter”) and convection-boosted (“cboost”) models in K. X. Van et al. can match (part of) the observational parameters of J1858 well. We then apply our method to other observed LMXBs and find that the “rm12” and “inter” MB laws are most promising in explaining transient LMXBs. In comparison, the simulations with the “cboost” and “carb” MB laws are more inclined to reproduce persistent LMXBs and ultracompact X-ray binaries, respectively. Our results, though subject to computational and/or observational bias, show that it is challenging to find a unified MB law that applies to the NS LMXB subpopulations simultaneously, indicating our lack of understanding of the true MB law. In addition, we explore the influence of various MB laws on the magnitude of the bifurcation periods in LMXBs.

Physics-Informed Deep-Learning Models Improve Forecast Scalability, Reliability

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 4042557, “Physics-Informed Deep-Learning Models for Improving Shale and Tight Forecast Scalability and Reliability,” by Kainan Wang, SPE, Lichi Deng, and Yuzhe Cai, SPE, Chevron, et al. The paper has not been peer reviewed. _ In the complete paper, the authors present a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight reservoir forecasting. Their approach is applied to synthetic cases and many wells in the Permian Basin. Through hindsight studies, these models have been demonstrated to generate realistic and diverse production curves, capture the physics of unconventional flow, quantify well-production-outlook uncertainty, and help interpretation of subsurface uncertainty. Introduction In the realm of unconventional asset development, scalable forecasting is a key component in forecast reliability. In recent years, data-driven machine-learning models and workflows have emerged as potent tools for predicting well performance, particularly in scenarios where wells share similar reservoir properties, completion designs, and operational conditions. A novel approach known as physics-informed machine learning (PIML) has gained prominence. These models leverage the strengths of machine learning while honoring the constraints imposed by physical laws. By incorporating observational, inductive, or learning bias, they bridge the gap between empirical data and fundamental physics. In this paper, the authors showcase the application of a PIML system tailored specifically for unconventional reservoirs. Their methodology involves the selection of an appropriate physics modeling framework, coupled with the development of multiple machine-learning models that augment the limited field data set. Data Acquisition and Generation Bottomhole pressure (BHP) data are key to reliable forecasting because they reflect constraints to well production. Downhole pressure gauges can provide measurements of BHP accurately, but these measurements are not always available. Numerical models can be used to correlate surface measurements to BHP with some accuracy, taking into consideration the well configuration and fluid properties, yet it is too time-consuming to develop such models for many wells in relation to the speed needed for unconventional reservoir development. An in-house machine-learning model was developed for BHP calculation to further augment the data set of gauge measurements and numerical modeling. Through a workflow that chooses the wells with higher-fidelity data as a training set, and careful calibration and iterations between incrementally adding wells of high-fidelity data to reduce uncertainty, the trained machine- learning model is able to predict BHP accurately for many more wells. Another type of engineering data important for reliable well-performance analysis and forecasting is reservoir-fluid description. In this study, the authors leveraged an internally developed pressure/volume/temperature (PVT) machine-learning model trained on proprietary PVT data, which was also shown to provide reliable predictions to key PVT properties such as saturation pressure, formation volume factor, and viscosity from limited inputs at the reservoir and separator level. These PVT models not only enhance scalability of any downstream forecasting models but also play a crucial role in physics-informed uncertainty analysis for these models.

Outcome measures and their importance for effective equine back pain rehabilitation

Effective rehabilitation for equine back pain is required due to the frequency of dysfunction effecting performance and welfare. Reliable and valid measures should be used establish a baseline in pain and dysfunction levels, and used to monitor change during rehabilitation, as well as support future research assessing the efficacy of treatment interventions. Such measures are collectively known as outcome measures and can include specific objective markers of impairments such as reduced range of motion, or pain on palpation. The aim of this narrative review is to discuss the options and tools available to use as outcome measures in relation to the treatment and rehabilitation of back pain in the horse. In this paper outcome measures critically discussed relate to conformation and standing posture, muscle dimensions, palpation, range of motion, objective gait analysis, functional analysis, owner reported outcome measures and pain scales. Recommendations are made to assist with selection of a suitable outcome measure. In conclusion, complex laboratory-based analysis systems are available to be gold-standard truly objective systems, although realism for clinical practitioners working in the field limits their use. There is a requirement for new outcome measures that are not subject to observer bias nor time or financially consuming, however it is possible to use those available currently with assist in clinical decision making for the benefit of the horse.