Data Estimation Research Articles

Global extinction of Slender‐billed Curlew (Numenius tenuirostris)

In the current biodiversity crisis, conservation efforts are often focused on extinction prevention. However, it can be difficult to determine if a species is extinct, especially if the species has an extensive range, including being a transcontinental migrant, or is poorly known. The breeding range of the migratory Critically Endangered Slender‐billed Curlew Numenius tenuirostris is uncertain, and the species has an extensive non‐breeding range that spans central Asia, eastern Europe, the Middle East, the Mediterranean basin and the northwest African coast. There have been no incontrovertible sightings since 1995. In this time, extensive and intensive searches for the species have continued. Using an IUCN extinction probability framework, we incorporate potential threats to the species, search effort and past records (museum specimens and confirmed and unconfirmed sightings, all of which are primarily from its non‐breeding range) to assess the probability of extinction. The model indicates that there is a 96.0% probability that Slender‐billed Curlew is no longer extant, classing it as Extinct according to IUCN Red List guidelines. Posterior probability Bayesian extinction date estimation modelling suggests an extinction year around the time of the 1995 sighting. Although several threats to the species have been suggested, those that definitively drove the species to extinction will never be known. Other species of Numenius are under a range of pressures, and many are recognized as globally threatened. To ensure the continued survival of all shorebird species, we advocate flyway‐scale concerted, coordinated action, and caution against complacency even for widespread but threatened taxa in Europe.

A simple phylogenetic approach to analyze hypermutated HIV proviruses reveals insights into their dynamics and persistence during antiretroviral therapy

Abstract Hypermutated proviruses, which arise in a single HIV replication cycle when host antiviral APOBEC3 proteins introduce extensive G-to-A mutations throughout the viral genome, persist in all people living with HIV receiving antiretroviral therapy (ART). But, hypermutated sequences are routinely excluded from phylogenetic trees because their extensive mutations complicate phylogenetic inference, and as a result we know relatively little about their within-host evolutionary origins and dynamics. Using >1400 longitudinal single-genome-amplified HIV env-gp120 sequences isolated from six women over a median 18 years of follow-up − including plasma HIV RNA sequences collected over a median 9 years between seroconversion and ART initiation, and >500 proviruses isolated over a median 9 years on ART − we evaluated three approaches for masking hypermutation in nucleotide alignments. Our goals were to 1) reconstruct phylogenies that can be used for molecular dating and 2) phylogenetically infer the integration dates of hypermutated proviruses persisting during ART. Two of the approaches (stripping all positions containing putative APOBEC3 mutations from the alignment, or replacing individual putative APOBEC3 mutations in hypermutated sequences with the ambiguous base R) consistently normalized tree topologies, eliminated erroneous clustering of hypermutated proviruses, and brought env-intact and hypermutated proviruses into comparable ranges with respect to multiple tree-based metrics. Importantly, these corrected trees produced integration date estimates for env-intact proviruses that were highly concordant with those from benchmark trees that excluded hypermutated sequences, supporting the use of these corrected trees for molecular dating. Subsequent molecular dating of hypermutated proviruses revealed that these sequences spanned a wide age range, with the oldest ones dating to shortly after infection. This indicates that hypermutated proviruses, like other provirus types, begin to be seeded into the proviral pool immediately following infection, and can persist for decades. In two of the six participants, hypermutated proviruses differed from env-intact ones in terms of their age distributions, suggesting that different provirus types decay at heterogeneous rates in some hosts. These simple approaches to reconstruct hypermutated provirus’ evolutionary histories reveal insights into their in vivo origins and longevity, towards a more comprehensive understanding of HIV persistence during ART.

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.

A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks

Our understanding of the impact of climate change on water availability and natural hazards in high-mountain regions is limited due to the spatial and temporal scarcity of ground observations of precipitation and snow. Freely available, satellite-based information about the snowpack is currently mainly limited to indirect measurements of snow-covered area or very coarse-scale snow water equivalent (SWE), but only for flat areas in lowlands without vegetation cover. Novel space-based laser altimeters, such as ICESat-2, have the potential to provide high-resolution snow depth data in worldwide mountain regions where no ground observations exist. However, these space-based laser altimeters come with spatial gaps between ground tracks, obtained without repetition at a give location. To overcome these drawbacks, here, we present a combined probabilistic data assimilation and deep learning approach to reconstruct spatio-temporal SWE from observations of snow depth along ground tracks, imitating ICESat-2 tracks in view of a potential future global application.Our approach is based on assimilating SWE and snow cover information in a degree-day model with an iterative ensemble smoother (IES) which allows temporally reconstructing SWE along hypothetical ground tracks separated by 3 km. As input, the degree-day model uses daily precipitation and downscaled air temperature from the ERA5 reanalysis. A feedforward neural network (FNN) is then used for spatial propagation of the daily mean and standard deviation of the updated SWE ensemble members obtained from the IES. The combined IES-FNN approach provides uncertainty-aware spatio-temporally continuous estimates of SWE.We tested our approach in the alpine Dischma valley (Switzerland) using high-resolution snow depth maps obtained from photogrammetric techniques mounted on airplanes and unmanned aerial system observations. Our results show that the IES-FNN model provides reliable estimates at a resolution of approximately 100 m. Even assimilating only one SWE observation during the year (combined with satellite-based melt-out date estimates) produces satisfying results when evaluating the IES-FNN SWE reconstructions on independent dates and smaller (<4 km2) areas: mean absolute error of 86 mm (78 mm) at Schürlialp (Latschüelfurgga) for average SWE of 180 mm (254 mm), and average spatial linear correlation with the reference SWE of 0.51 (0.48). However, the assimilated SWE observation must not be too early in the accumulation season or too late in the melt season when the snowpack is starting or ending to accumulate or melt, respectively. Smaller distances between ground tracks (1500 m and 500 m) show improved performance of the IES-FNN approach in space, with no significant improvement in terms of temporal reconstruction.Applying the IES-FNN approach to e.g., real ICESat-2 data, remains challenging due to the higher uncertainties associated with these data. However, the approach we propose remains potentially very helpful in addressing the problem of scarcity of ground observations of precipitation and snow in high-mountain regions.

Development and validation of a machine learning model integrated with the clinical workflow for inpatient discharge date prediction.

Discharge date prediction plays a crucial role in healthcare management, enabling efficient resource allocation and patient care planning. Accurate estimation of the discharge date can optimize hospital operations and facilitate better patient outcomes. In this study, we employed a systematic approach to develop a discharge date prediction model. We collaborated closely with clinical experts to identify relevant data elements that contribute to the prediction accuracy. Feature engineering was used to extract predictive features from both structured and unstructured data sources. XGBoost, a powerful machine learning algorithm, was employed for the prediction task. Furthermore, the developed model was seamlessly integrated into a widely used Electronic Medical Record (EMR) system, ensuring practical usability. The model achieved a performance surpassing baseline estimates by up to 35.68% in the F1-score. Post-deployment, the model demonstrated operational value by aligning with MS GMLOS and contributing to an 18.96% reduction in excess hospital days. Our findings highlight the effectiveness and potential value of the developed discharge date prediction model in clinical practice. By improving the accuracy of discharge date estimations, the model has the potential to enhance healthcare resource management and patient care planning. Additional research endeavors should prioritize the evaluation of the model's long-term applicability across diverse scenarios and the comprehensive analysis of its influence on patient outcomes.

Species limits and hybridization in Andean leaf-eared mice (Phyllotis).

Leaf-eared mice (genus Phyllotis) are among the most widespread and abundant small mammals in the Andean Altiplano, but species boundaries and distributional limits are often poorly delineated due to sparse survey data from remote mountains and high-elevation deserts. Here we report a combined analysis of mitochondrial DNA variation and whole-genome sequence (WGS) variation in Phyllotis mice to delimit species boundaries, to assess the timescale of diversification of the group, and to examine evidence for interspecific hybridization. Estimates of divergence dates suggest that most diversification of Phyllotis occurred during the past 3 million years. Consistent with the Pleistocene Aridification hypothesis, our results suggest that diversification of Phyllotis largely coincided with climatically induced environmental changes in the mid- to late Pleistocene. Contrary to the Montane Uplift hypothesis, most diversification in the group occurred well after the major phase of uplift of the Central Andean Plateau. Species delimitation analyses revealed surprising patterns of cryptic diversity within several nominal forms, suggesting the presence of much undescribed alpha diversity in the genus. Results of genomic analyses revealed evidence of ongoing hybridization between the sister species Phyllotis limatus and P. vaccarum and suggest that the contemporary zone of range overlap between the two species represents an active hybrid zone.

Node Date Estimation of Diversified Species of Ray-Finned Fish Based on Morphological and Molecular (Vitellogenin 3) Characters Using Bayesian Analysis

Fish and other oviparous and ovoviviparous animals contain vitellogenin (Vtg), an egg yolk precursor phospholipoglycoprotein, which is the prerequisite for oocyte growth during oogenesis. Vtg is also employed as a biomarker to assess the reproductive health of female fish. In this study, we select 34 ray-finned fish under the class Actinopterygii to investigate the evolutionary time scale of their diversification age based on the molecular (vtg3 gene) as well as the combined (morphological and vtg3 gene) characters using different evolutionary models in Bayesian analysis. The analysis indicates that the species of different orders evolved in different epochs. In the case of molecular-based analysis, the orders Cyprinodontiformes and Perciformes are diversified in early Eocene to Oligocene, Anabantiformes in late Eocene, Cypriniformes in Oligocene &amp; Salmoniformes Miocene. In the case of morpho-molecular based analysis the order Cyprinodontiformes is diversified in early Eocene to Oligocene, Anabantiformes in late Eocene, Perciformes in late Eocene to Oligocene, Cypriniformes in Oligocene to Miocene &amp; Salmoniformes Oligocene to Pliocene. However, both analyses suggest that most of the selected species are diversified in Miocene, Oligocene, Eocene, and Paleocene Epoch between 5 – 65 MYA.

Improving the estimation accuracy of wheat maturity date by coupling WheatGrow with satellite images

Accurate estimation of wheat maturity date (MD) is helpful to make reasonable harvest planning and guarantee crop yield and quality. In this study, wheat phenology extracted from satellite images was assimilated into WheatGrow model to develop wheat maturity date estimation model. Theoretical uncertainty was introduced into assimilation system as the error covariance matrix of remote sensing observations, which improved the performance of maturity date estimation model. Compared with the simulated maturity date of crop growth model and assimilation system combined with the constant uncertainty (Assimilation1), the accuracy of assimilation system combined with the theoretical uncertainty (Assimilation2) was higher (r = 0.81, RMSE = 4.5 d). Assimilation2 has better performance and robustness in different years and different subregions. The mean relative errors between the estimated values of Assimilation2 and the observations were generally small and concentrated in the range of −5 % to 5 %. The estimated maturity date showed latitude variation in spatial distribution in the Huang-Huai-Hai Plain (HHHP). In addition, the trend of wheat maturity date from 2001 to 2020 in the central region of HHHP was significant (p < 0.05), and the mean change rate of maturity date reached 3–6 d/10a. However, the overall change trend of maturity date in the HHHP was not significant. Temperature was main driver affecting the spatiotemporal variation of wheat maturity date. The regional wheat maturity date estimation model can provide technical support for wheat maturity date estimation at regional scale.

Identification and Characterization of an Alphacoronavirus in Rhinolophus sinicus and a Betacoronavirus in Apodemus ilex in Yunnan, China.

Coronaviruses (CoVs), the largest positive-sense RNA viruses, have caused infections in both humans and animals. The cross-species transmission of CoVs poses a serious threat to public health. Rodents and bats, the two largest orders of mammals, serve as significant natural reservoirs for CoVs. It is important to monitor the CoVs carried by bats and rodents. In this study, we collected 410 fecal samples from bats and 74 intestinal samples from rats in Yunnan Province, China. Using RT-PCR, we identified one positive sample for alphacoronavirus (TC-14) from Rhinolophus sinicus (Chinese rufous horseshoe bat) and two positive samples for betacoronavirus (GS-53, GS-56) from Apodemus ilex (Rodentia: Muridae). We successfully characterized the complete genomes of TC-14 and GS-56. Phylogenetic analysis revealed that TC-14 clustered with bat CoV HKU2 and SADS-CoV, while GS-56 was closely related to rat CoV HKU24. The identification of positive selection sites and estimation of divergence dates further helped characterize the genetic evolution of TC-14 and GS-56. In summary, this research reveals the genetic evolution characteristics of TC-14 and GS-56, providing valuable references for the study of CoVs carried by bats and rodents in Yunnan Province.

Explainable dating of greek papyri images

Greek literary papyri, which are unique witnesses of antique literature, do not usually bear a date. They are thus currently dated based on palaeographical methods, with broad approximations which often span more than a century. We created a dataset of 242 images of papyri written in “bookhand” scripts whose date can be securely assigned, and we used it to train algorithms for the task of dating, showing its challenging nature. To address data scarcity, we extended our dataset by segmenting each image into its respective text lines. By using the line-based version of our dataset, we trained a Convolutional Neural Network, equipped with a fragmentation-based augmentation strategy, and we achieved a mean absolute error of 54 years. The results improve further when the task is cast as a multi-class classification problem, predicting the century. Using our network, we computed precise date estimations for papyri whose date is disputed or vaguely defined, employing explainability to understand dating-driving features.

Date estimation of fabrication and repair of Color garments encouragement banner

The Color Garments Encouragement Banner was designated a Korean Heritage in 2014 to recognize it as the most significant object of the color garments encouragement campaign. However, despite its significance, nothing is known about its manufacture. Therefore, this study attempted to analyze the materials of the banners to estimate when they were manufactured and repaired. The investigation of materials on the banner involved visual examination, literature review, microscopy, SEM–EDS, FT-IR, Py-GC–MS, ICP-MS, and LC–MS. The fabric, patch, and threads comprising the artifact were identified as cotton. FT-IR and Py-GC–MS confirmed that the repair patch was a woven blend of polyester and cotton yarns. EDS analysis indicated that the polyester was treated with titanium delustering. ICP-MS detected high concentrations of chromium that were not used in traditional dyeing techniques. The azo and sulfur compounds were identified by LC–MS analysis. The material layered on the grommet patch was thought to be a mixture of Pb, Ti with CaCO3 and BaSO4. Based on the overall results, the production date of the banner was narrowed down to the late 1920s, and the repair date to the mid-1950s. Although the materials used could not be identified owing to the limitations of the applicable analysis. Nonetheless, it is hoped that the analyses conducted in this study can serve as a scientific foundation for dating modern cultural heritage objects with limited handed-down record and historical documentation.

Estimation of return dates and return levels of extreme rainfall in the city of Douala, Cameroon

The problem of extreme phenomena with a more precise estimation of their return periods for early warnings, notably to preserve the safety of populations and properties, arises all over the world. This work develops another aspect in the estimation of Return Levels (RLs) and Return Periods (RPs) of extreme precipitation in particular and natural risk in general. In particular, it gives the Return Dates (RDs) with their Confidence Intervals (CIs). The RPs, the RLs and their CIs for extreme rainfall were also investigated. These estimates were made by approaching the peak over a threshold chosen by the Generalized Pareto Distribution (GPD). The CIs of RPs and RLs were determined by the Delta method. The daily rainfall data used were obtained from the data of the synoptic report for the period 2011 to 2021 for the Douala weather station (more details can be found on http://www.ogimet.com/guia.phtml.en). To validate the methods used, real cases of floods occurred in Douala city were considered: for example, a local press compiled flood dates and mentioned that a flood occurred on the April 16, 2013 in this city. Following the data of synoptic report, the corresponding amount of rainfall was around 150 mm. The results obtained have shown a RD on the August 12, 2014. The confidence intervals of return levels and return dates determined by the Delta method were [131.66, 168.456] and [June 23, 2014, January 02, 2015], respectively. These results are in agreement with the data of synoptic report since the rainfall amounts was 132.2 mm (belonging to the confidence interval of return levels), on the August 11, 2014 (belonging to the confidence interval of return dates). These predictions of RDs and RLs with their CIs, at reasonable time scales, can help for efficient management of floods and thus, improve early warnings for safety of populations and goods.

Using Geostationary Satellite Observations to Improve the Monitoring of Vegetation Phenology

Geostationary satellite data enable frequent observations of the Earth’s surface, facilitating the rapid monitoring of land covers and changes. However, optical signals over vegetation, represented by the vegetation index (VI), exhibit an anisotropic effect due to the diurnal variation in the solar angle during data acquisition by geostationary satellites. This effect, typically characterized by the bi-directional reflectance distribution function (BRDF), can introduce uncertainties in vegetation monitoring and the estimation of phenological transition dates (PTDs). To address this, we investigated the diurnal variation in the normalized difference vegetation index (NDVI) with solar angles obtained from geostationary satellites since the image had fixed observation angles. By establishing a temporal conversion relationship between instantaneous NDVI and daily NDVI at the local solar noon (LSNVI), we successfully converted NDVIs obtained at any time during the day to LSNVI, increasing cloud-free observations of NDVI by 34%. Using different statistics of the time series vegetation index, including LSNVI, daily averaged NDVI (DAVI), and angular corrected NDVI (ACVI), we extracted PTD at five typical sites in China. The results showed a difference of up to 41.5 days in PTD estimation, with the highest accuracy achieved using LSNVI. The use of the proposed conversion approach, utilizing time series LSNVI, reduced the root mean square error (RMSE) of PTD estimation by 9 days compared with the use of actual LSNVI. In conclusion, this study highlights the importance of eliminating BRDF effects in geostationary satellite observations and demonstrates that the proposed angular normalization method can enhance the accuracy of time series NDVI in vegetation monitoring.

A simple phylogenetic approach to analyze hypermutated HIV proviruses reveals insights into their dynamics and persistence during antiretroviral therapy.

Hypermutated proviruses, which arise in a single HIV replication cycle when host antiviral APOBEC3 proteins introduce extensive G-to-A mutations throughout the viral genome, persist in all people living with HIV receiving antiretroviral therapy (ART). But, the within-host evolutionary origins of hypermutated sequences are incompletely understood because phylogenetic inference algorithms, which assume that mutations gradually accumulate over generations, incorrectly reconstruct their ancestor-descendant relationships. Using > 1400 longitudinal single-genome-amplified HIV env-gp120 sequences isolated from six women over a median 18 years of follow-up - including plasma HIV RNA sequences collected over a median 9 years between seroconversion and ART initiation, and > 500 proviruses isolated over a median 9 years on ART - we evaluated three approaches for removing hypermutation from nucleotide alignments. Our goals were to 1) reconstruct accurate phylogenies that can be used for molecular dating and 2) phylogenetically infer the integration dates of hypermutated proviruses persisting during ART. Two of the tested approaches (stripping all positions containing putative APOBEC3 mutations from the alignment, or replacing individual putative APOBEC3 mutations in hypermutated sequences with the ambiguous base R) consistently normalized tree topologies, eliminated erroneous clustering of hypermutated proviruses, and brought env-intact and hypermutated proviruses into comparable ranges with respect to multiple tree-based metrics. Importantly, these corrected trees produced integration date estimates for env-intact proviruses that were highly concordant with those from benchmark trees that excluded hypermutated sequences, indicating that the corrected trees can be used for molecular dating. Use of these trees to infer the integration dates of hypermutated proviruses persisting during ART revealed that these spanned a wide age range, with the oldest ones dating to shortly after infection. This indicates that hypermutated proviruses, like other provirus types, begin to be seeded into the proviral pool immediately following infection, and can persist for decades. In two of the six participants, hypermutated proviruses differed from env-intact ones in terms of their age distributions, suggesting that different provirus types decay at heterogeneous rates in some hosts. These simple approaches to reconstruct hypermutated provirus' evolutionary histories, allow insights into their in vivo origins and longevity, towards a more comprehensive understanding of HIV persistence during ART.

Caveat emptor!—wiggle-matching European wood samples (AD 46–AD 286)

AbstractThis study suggests that there may be considerable difficulties in providing accurate calendar age estimates in the Roman period in Europe, between ca. AD 60 and ca. AD 230, using the radiocarbon calibration datasets that are currently available. Incorporating the potential for systematic offsets between the measured data and the calibration curve using the ΔR approach suggested by Hogg et al. (2019), only marginally mitigates the biases in calendar date estimates observed. At present, it clearly behoves researchers in this period to “caveat emptor” and validate the accuracy of their calibrated radiocarbon dates and chronological models against other sources of dating information.

Impact of Variable Sampling on Estimates of HIV-1 Reservoir Formation Dates.

Timing of human immunodeficiency virus-1 (HIV-1) reservoir formation is important for informing HIV cure efforts. It is unclear how much of the variability seen in dating reservoir formation is due to sampling and gene-specific differences. We used a Bayesian extension of root to tip regression (bayroot) to reestimate formation date distributions in participants from Swedish and South African cohorts, and assessed the impact of variable timing, frequency, and depth of sampling on these estimates. Significant shifts in formation date distributions were only observed with use of faster-evolving genes, while timing, frequency, and depth of sampling had minor or no significant effect on estimates.

An improved crop calendar model for winter wheat incorporating vernalization and winter survival to project changes in phenology

Crop phenology and associated crop calendars are necessary for designing breeding goals and for developing effective management practices. Winter wheat is a representative biennial, the cultivation schedule of which is constrained by winter climate conditions, particularly the processes of vernalization and winter survival. Here, we present improvements to a rule-based crop calendar model by incorporating these factors so that it can be used to accurately estimate the phenological events of winter wheat from daily meteorological data. We tested the improved model in Hokkaido, the northernmost Japanese island, which is characterized by seasonal snow cover and a wet summer. The results confirmed that implementing these factors contributed to accurate estimates of peak occurrence dates of winter wheat phenological events. Furthermore, we applied the improved model to simulate wheat phenology under 2 K and 4 K warmer scenarios. The results showed a delayed sowing period up to approximately one month and slight advancements in both flowering and harvesting, leading to a shorter growth period. While this shortened period may be largely compensated by a decrease in the snow-covered period, the shifts in the vegetative and reproductive phases may modify sink-source balance of wheat. We also assessed the risks of pollination failure and preharvest sprouting, both of which are associated with the timing of precipitation, based on the number of rainy days around flowering, and the incidence of precipitation over two consecutive days around the time of harvesting. Our simulations suggested increased risk of pollination failure and reduced risk of preharvest sprouting, leading to an increase in the probability of crop failure. These findings underscore the importance of implementing adaptation measures to mitigate precipitation-related risk under future climates. Further, the findings provide valuable insights for winter wheat breeders and agronomists, thereby facilitating crop production adaptation.

Estimating Key Phenological Dates of Multiple Rice Accessions Using Unmanned Aerial Vehicle-Based Plant Height Dynamics for Breeding

Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height (PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle (UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading (IH) and full heading (FH), and panicle initiation (PI), and growth period after transplanting (GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model (DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest (RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th (R2 = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features (CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI (R2 = 0.834, RMSE = 4.344 d), IH (R2 = 0.877, RMSE = 2.721 d), and FH (R2 = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.

Observations of type Ia supernova SN 2020nlb up to 600 days after explosion, and the distance to M85

The type Ia supernova (SN Ia) SN 2020nlb was discovered in the Virgo Cluster galaxy M85 shortly after explosion. Here we present observations that include one of the earliest high-quality spectra and some of the earliest multi-colour photometry of a SN Ia to date. We calculated that SN 2020nlb faded 1.28 ± 0.02 mag in the B band in the first 15 d after maximum brightness. We independently fitted a power-law rise to the early flux in each filter, and found that the optical filters all give a consistent first light date estimate. In contrast to the earliest spectra of SN 2011fe, those of SN 2020nlb show strong absorption features from singly ionised metals, including Fe II and Ti II, indicating lower-excitation ejecta at the earliest times. These earliest spectra show some similarities to maximum-light spectra of 1991bg-like SNe Ia. The spectra of SN 2020nlb then evolve to become hotter and more similar to SN 2011fe as it brightens towards peak. We also obtained a sequence of nebular spectra that extend up to 594 days after maximum light, a phase out to which SNe Ia are rarely followed. The [Fe III]/[Fe II] flux ratio (as measured from emission lines in the optical spectra) begins to fall around 300 days after peak; by the +594 d spectrum, the ionisation balance of the emitting region of the ejecta has shifted dramatically, with [Fe III] by then being completely absent. The final spectrum is almost identical to SN 2011fe at a similar epoch. Comparing our data to other SN Ia nebular spectra, there is a possible trend where SNe that were more luminous at peak tend to have a higher [Fe III]/[Fe II] flux ratio in the nebular phase, but there is a notable outlier in SN 2003hv. Finally, using light-curve fitting on our data, we estimate the distance modulus for M85 to be μ0 = 30.99 ± 0.19 mag, corresponding to a distance of 15.8+1.4-1.3 Mpc.

Performance Analysis of Handwritten Text Augmentation on Style-Based Dating of Historical Documents

One of the main questions paleographers aim to answer while studying historical manuscripts is when they were produced. Automatized methods provide tools that can aid in a more accurate and objective date estimation. Many of these methods are based on the hypothesis that handwriting styles change over periods. However, the sparse availability of digitized historical manuscripts poses a challenge in obtaining robust systems. The presented research extends previous research that explored the effects of data augmentation by elastic morphing on the dating of historical manuscripts. Linear support vector machines were trained on k-fold cross-validation on textural and grapheme-based features extracted from the Medieval Paleographical Scale, early Aramaic manuscripts, the Dead Sea Scrolls, and volumes of the French Royal Chancery collection. Results indicate training models with augmented data can improve the performance of historical manuscript dating by 1–3% in cumulative scores, but also diminish it. Data augmentation using elastic morphing can both improve and decrease date prediction of historical manuscripts and should be carefully considered. Moreover, further enhancements are possible by considering models tuned to the features and documents’ scripts.