Transition Dates Research Articles

Predictors of and outcomes in arrhythmic mitral valve prolapse

Abstract Background Recently, the European Heart Rhythm Association (EHRA) defined the phenotype of the arrhythmic mitral valve prolapse (AMVP) complex and proposed management approaches. Assessing the incidence of SCD in this small subset of patients is challenging due to the low event-rate and longitudinal real-world data remain scarce. Purpose To determine the predictors of AMVP and study the clinical outcome of patients stratified by ICD indication as proposed by the EHRA consensus statement. Methods All consecutive patients with MVP who underwent cardiac magnetic resonance imaging (CMR) were enrolled in a single-center retrospective registry. AMVP was defined as the presence of frequent or complex ventricular arrhythmias (>5% total PVC burden, non-sustained ventricular tachycardia (VT), VT or ventricular fibrillation) in a patient with MVP without alternative arrhythmic substrates. The medical records of these patients included a comprehensive clinical, rhythmic and echocardiographic characterization allowing to collect the key date of transition to AMVP. A forward conditional multivariable logistic regression was performed to identify clinical risk factors for AMVP reporting the odds ratio (OR) and corresponding 95% confidence interval (95% CI). Incidence rates were calculated for the composite endpoint of SCD, sustained VT, ICD shocks, and all-cause mortality. Results A total of 169 patients (52.1% male, median age 51.4 years) were included, of which 99 (58.6%) were classified as AMVP. Clinical characteristics are presented in Image 1. Patients with non-arrhythmic MVP and AMVP had comparable left ventricular function, mitral valve regurgitation grade and extent of MVP. Multivariable logistic regression identified the presence of late gadolinium enhancement (LGE, OR 2.82, 95%CI 1.45–5.50, p=0.002) and mitral annular disjunction (MAD, OR 1.98, 95%CI 1.02–3.86, p=0.045) on CMR as only predictors of AMVP. Cardiac CMR results are presented in Image 2. The median overall follow-up since first cardiac contact was 8.0 years (IQR 5.0–15.6), while this was 5.0 years (IQR 2.1–7.4 years) after transition to AMVP. The incidence for the composite endpoint in the overall population was 0.4%/year (95%CI 0.2 – 0.9). The 5-year cumulative event rate was 1.8%. According to the EHRA risk stratification the implantation of an ICD would have been reasonable in 69 patients (69.7%). In total, 5 patients were implanted with an ICD for secondary prevention, but all those patients did not classify as AMVP prior to the life threathening arrhythmic event. Conclusion CMR is an important tool in the challenging risk stratification of AMVP given that the presence of MAD and LGE enhancement are predictors of AMVP. There is a lack of longitudinal data to determine when patients transition from MVP to AMVP and how this modifies their outcome.Clinical characteristics and ECG resultsOverview of CMR measurements

Improving the matching degree between remotely sensed phenological dates and physiological growing stages of soybean by a dynamic offset-adjustment strategy

Crop phenology provides crucial information for determining the appropriate timing of farm management practices and predicting crop yields. Satellite remote sensing has become a burgeoning tool for rapid phenological monitoring over wide spatial regions. However, there are significant timing gaps between the satellite-based phenological feature points and ground-observed physiological growing stages of the target. In this study, a dynamic offset-adjustment strategy that aims to improve the matching degree of the above two is proposed and tested with soybean across 16 states in the United States. A series of remotely sensed phenological transition dates that are characteristics of key growing stages of soybean were retrieved using MODIS time series data over the period 2000–2020 and the offset adjustments to the dates were identified by dynamically adjusting offset values till the minimum RMSE between the remote sensing-based and the ground-observed dates of physiological growing stages were obtained. The results indicated that the offset-adjustment strategy can significantly improve the alignments between remotely sensed phenological dates and field-based physiological growing stages of soybean in contrast to these without taking adjustment, with the average RMSE dropping by 58.58 %, 51.59 %, 31.15 %, 25.33 %, 24.67 % in the downturn, peak of season (POS), upturn, stabilization and recession dates, respectively. Among tested remotely sensed characteristics, the end of season (EOS) dates show the greatest alignment with its corresponding physiological growing stage, i.e., the dropping leaves stage. Comparison of the performance of the upturn date and start of season (SOS) in monitoring the date of the emerged stage indicates that the later one exhibits a better consistency with the ground-observed emerged stage after taking the adjustment, with the average RMSE dropping by 56.52 %. The proposed offset-adjustment strategy offers an approach for adjusting remotely sensed characteristics so to make them more consistent with the ground-observed crop physiological growing stages.

The early impacts of primary HPV cervical screening implementation in Australia on the pathology sector: a qualitative study

BackgroundThe transition of Australia’s National Cervical Screening Program from cytology to a molecular test for human papillomavirus (HPV) (locally referred to as the ‘Renewal’), including a longer five-year interval and older age at commencement, significantly impacted all sectors of program delivery. The Renewal had major implications for the roles and requirements of pathology laboratories providing services for the Program. This study aimed to understand the early impacts of the Renewal and its implementation on the pathology sector.MethodsSemi-structured qualitative interviews were conducted with key stakeholders (N = 49) involved in the STakeholder Opinions of Renewal Implementation and Experiences Study (STORIES), 11–20 months after the program transition. A subset of interviews (N = 24) that discussed the pathology sector were analysed using inductive thematic analysis.ResultsFour overarching themes were identified: implementation enablers, challenges, missed opportunities, and possible improvements. Participants believed that the decision to transition to primary HPV screening was highly acceptable and evidence-based, but faced challenges due to impacts on laboratory infrastructure, resources, staffing, and finances. These challenges were compounded by unfamiliarity with new information technology (IT) systems and the new National Cancer Screening Register (‘Register’) not being fully functional by the date of the program transition. The limited availability of self-collection and lack of standardised fields in pathology forms were identified as missed opportunities to improve equity in the Program. To improve implementation processes, participants suggested increased pathology sector involvement in planning was needed, along with more timely and transparent communication from the Government, and clearer clinical management guidelines.ConclusionThe transition to primary HPV screening had a significant and multifaceted impact on the Australian pathology sector reflecting the magnitude and complexity of the Renewal. Strategies to support the pathology sector through effective change management, clear, timely, and transparent communication, as well as adequate funding sources will be critical for other countries planning to transition cervical screening programs.

Assessment of the Quality of Transformation of Financial Reporting of Entities According to IFRS

Background: Mandatory application of IFRS for a specified list of entities brings about additional costs in terms of material, financial and labour resources. Consequently, this encourages the search for ways to minimise costs related to the transition to IFRS adoption, that is, its formalisation. Under such conditions, the need to improve the methodological toolkit for evaluating the process of transformation of financial statements in accordance with the requirements of the IFRS is actualised. This article aims to develop a methodological toolkit for assessing the quality of the financial reporting transformation process per IFRS requirements, using Ukrainian dairy industry enterprises as a case study. Methods: The assessment of the impact of the transition to IFRS reporting on changes in the financial status of entities was carried out using statistical methods of comparison, generalisation, grouping, and coefficients. Conclusions were drawn based on the research findings employing the method of logical generalisation. Results and Conclusions: The method of assessing the impact of the application of IFRS on the financial condition of enterprises in the dairy industry of Ukraine has been improved, particularly in terms of accounting for changes in financial statements as a result of transformational adjustments before and after the date of transition to IFRS. This refinement involves employing financial coefficients as a means of assessment, facilitating the determination of the quality assessment of the process of drawing up the first financial statements of enterprises in accordance with IFRS. New methodological aspects and recommendations for assessing the quality of the transformation process in the context of the application of IFRS will ensure effective management of enterprises in the dairy industry of Ukraine, leading to an increase in the attraction of foreign investments amidst the conditions of European integration processes and the post-war period.

Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data

While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake.

Mismatch between primary and secondary growth and its consequences on wood formation in Qinghai spruce.

The connections between the primary and secondary growth of trees allows better understanding of the dynamics of carbon sequestration in forest ecosystems. The relationship between primary and secondary growth of trees could change due to the diverging responses of meristems to climate warming. In this study, the bud phenology and radial growth dynamics of Qinghai spruce (Picea crassifolia) in arid and semi-arid areas of China in 2019 and 2020 were weekly monitored to analyze their response to different weather conditions and their links with carbon sink. Xylem anatomical traits (i.e. lumen radial diameter and cell wall thickness) were quantified along cell radial files after the end of xylem lignification to calculate the early-to-latewood transition date. Winter and early spring (January-March) were warmer in 2020 with a colder April compared with 2019. Precipitation in April-June was lower in 2020 than in 2019. In 2019, bud phenology occurred earlier, while the onset of xylem formation and the early-to-latewood transition date were delayed. The duration from the beginning of split bud and exposed shoot to the early-to-latewood transition date was positively correlated with the radial width of earlywood (accounting for ~80% of xylem width) and total xylem width. The longer duration of xylem cell division did not increase xylem cell production and radial width. Moreover, the duration from bud burst to the early-to-latewood transition date in 2020 was negatively linked with early phloem cell production as compared with 2019. Our findings suggest that warm conditions in winter and early spring promote the xylogenesis of Qinghai spruce, but might delay bud burst. However, the xylem width increments largely depend on the duration from bud burst to the start of latewood cell division rather than on the earlier xylogenesis and longer duration of xylem cell differentiation induced by warm conditions.

A soil-air temperature model to determine the start of season phenology of deciduous forests

Forest ecosystems play a major role in sequestering atmospheric carbon dioxide, which can help offset the detrimental effects of anthropogenic carbon emissions. However, climate change has and will continue to affect the phenology of forest ecosystems’ carbon uptake, changing both the “carbon uptake transition date” - when forests shift from being a net carbon source to sink - and the “green-up date” reflecting the onset of bud burst. Previous studies have shown that a forest's carbon uptake transition date correlates to the date when soil temperature warms enough to surpass mean annual air temperature (soil-air temperature model). However, we still don't know if this simple relationship holds across different sites or over longer time periods. In this study, we explore the relationship between climate and both types of phenological transition dates using over 200 site years of data between 1997 and 2022. Using flux tower data from 18 sites across North America and Europe, we derive three potential carbon uptake transition dates corresponding to the dates when 10%, 25%, and 50% of seasonal net ecosystem exchange (NEE) amplitude is reached. Using PhenoCam data, we then derive three potential green-up dates corresponding to when 10%, 25%, and 50% of total seasonal green chromatic coordinate (GCC) is reached (the greenness model). We evaluate our model estimates using concordance coefficients, a metric of agreement between two measures, to determine which process, carbon uptake or budburst, is best predicted by the soil-air temperature model and to what extent. We find that variation in phenological relationships can be attributed to different regional and bioclimatic groups, highlighting potential biome-specific strengths and limitations of the soil-air temperature model. This model offers a simple approach to better understand phenological transitions and identify potential and limitations for a simple universal SOS prediction approach in deciduous forests.

Solar-induced chlorophyll fluorescence captures photosynthetic phenology better than traditional vegetation indices

Accurate characterization of plant phenology is of great importance for monitoring global carbon, water, and energy cycling. Remotely sensed satellite observations have been widely used to estimate land surface phenology across multiple spatial scales in the last three decades. Recent development on satellite solar-induced chlorophyll fluorescence (SIF) observations have opened an opportunity to monitor the seasonality of plant growth from the perspective of photosynthesis phenology. The SIF observations from the TROPOspheric Monitoring Instrument (TROPOMI) with high spatial resolution (up to 7 km × 3.5 km pixels) and near-daily global coverage provide unprecedented opportunity to observe photosynthetic and land surface phenology from space. However, the performance of TROPOMI SIF-derived phenology has not been systematically evaluated. In this study, we used flux tower gross primary productivity (GPP) and PhenoCam green chromatic coordinate (Gcc) data as the benchmark to verify phenology metrics derived from satellite observations. The phenology metrics including the start (SOS), end (EOS), length of growing season (LOS), and the peak of growing season (POS) were estimated from TROPOMI SIF, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), near‐infrared reflectance of vegetation (NIRv), and Global Vegetation Phenology product (MCD12Q2), and the latter four were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) across six vegetation types over North America during the period of 2018–2020. We found that the overall agreements of SIF (R2 ranging from 0.30 to 0.63) against GPP-estimated phenology were stronger than NDVI (0.17–0.41), EVI (0.19–0.39), NIRv (0.23–0.62), and MCD12Q2 (0.19–0.48) derived phenological events. In reference to GPP and Gcc-estimated phenology, SIF also generally has the least error and bias compared to other satellite remote sensing-derived phenology metrics. No significant differences were found between SIF and GPP-derived phenology (P>0.05, two-tailed t-test). In addition, we found that the spatial distribution of SIF-derived phenology reflected the expected latitudinal patterns in phenology dates. SOS, EOS, LOS, and POS observed by MCD12Q2 appeared to be earlier, later, longer, and earlier than TROPOMI SIF-derived phenology, respectively. SIF-based phenological transition dates more closely tracked GPP-based phenology dates, indicating TROPOMI SIF could be a great measure to track photosynthesis seasonality and land surface phenology.

Predicting rice phenology and optimal sowing dates in temperate regions using machine learning

AbstractCrop phenology modeling often involves determining variety‐specific growing degree day thresholds, or parameterizing mechanistic crop models. In this work, we used machine learning methods to develop models that provide daily predictions of the probability that rice (Oryza sativa) crops had reached the panicle initiation and flowering growth stages. These per‐date classifications were summarized into per‐paddock growth stage transition dates, which were then compared with field‐sampled reference data, encompassing 15 rice varieties, 10 years, and 380 sites. Leave‐one‐year‐out cross validation was used to provide realistic estimates of model errors. Compared with more complex and computationally intensive algorithms, logistic regression produced competitive results (mean cross‐season validation RMSE 3.9 and 5.2 days for panicle initiation and flowering, respectively). Logistic regression had additional advantages: providing confidence of growth stage predictions at each date (as it is a probabilistic algorithm), and straightforward explainability (as model parameters directly indicated how the various input variables contributed to growth stage predictions). Input variables included accumulated weather, rice variety, and sowing methods. The models were applied to forecasting phenology transition dates of the rice crops planted throughout the Murray and Murrumbidgee valleys. In addition, recommendations for optimal sowing dates were developed, using simulations involving more than 40 years of weather data, with the goal of minimizing the risk of cold‐temperatures during the microspore growth phase, which can severely degrade yield in temperate rice growing regions.

The Early- to Latewood Transition Phenology Is Asynchronous between the Different Parts of Abies forrestii var. smithii in Jiaozi Mountain, Yunnan, China

Phenological synchronicity of different tree parts and habitats can provide valuable insights into the physiological relationships and regulatory factors of different phenologies. The earlywood (EW) to latewood (LW) transition of the xylem is a critical process closely related to the key functions and physiological processes of trees. This study investigated the phenology phase of the EW–LW transition in branches and stems of Smith fir (Abies forrestii var. smithii Viguié & Gaussen) along an altitude gradient (3600 m, 3800 m, and 4000 m) in Jiaozi Mountain, Yunnan, China, in 2021. The study aimed to test the hypothesis that branches transition earlier than stems, but the elevation does not affect the transition time. We monitored the EW–LW transition dates weekly using microcores and then correlated them to the climatic conditions and developmental processes. Our findings revealed that the EW–LW transition timing varied among the different parts of the tree but was synchronized across the different elevations. Furthermore, the transition always occurred earlier in branches than in the stem, but the difference narrowed with increasing altitude. The EW–LW transition was mainly influenced by photoperiod, which plays a crucial role as a signaling factor. Additionally, the tree crown was more sensitive to environmental changes than the stem. The transition time of stems was less sensitive to environmental factors than that of branches. Therefore, our results suggest that the earlywood to latewood transition is regulated by developmental factors, and the photoperiod may indirectly regulate the developmental process of trees as a signaling factor, thus regulating the earlywood to latewood transition time. Our study provides new insights into the developmental regulation and climate sensitivity of tree ring formation.

Corn Phenology Detection Using the Derivative Dynamic Time Warping Method and Sentinel-2 Time Series

Accurate determination of crop phenology information is essential for effective field management and decision-making processes. Remote sensing time series analyses are widely employed to extract the phenological stages. Each crop’s phenological stage has its unique characteristic on the crop plant, while the satellite-derived crop phenology refers to some key transition dates in time series satellite observations. Current techniques primarily estimate specific phenological stages by detecting points with distinctive features on the remote sensing time series curve. But these stages may be different from the Biologische Bundesanstalt, Bundessortenamt and CHemical Industry (BBCH) scale, which is commonly used to identify the phenological development stages of crops. Moreover, when aiming to extract various phenological stages concurrently, it becomes necessary to adjust the extraction strategy for each unique feature. This need for distinct strategies at each stage heightens the complexity of simultaneous extraction. In this study, we utilize the Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series data and propose a phenology extraction framework based on the Derivative Dynamic Time Warping (DDTW) algorithm. This method is capable of simultaneously extracting complete phenological stages, and the results demonstrate that the Root Mean Square Errors (RMSEs, days) of detected phenology on the BBCH scale for corn were less than 6 days overall.

On the variability of ocean surface current in the Bay of Bengal using self-organizing map (SOM)

The self-organizing map (SOM) is implemented to explore various ocean surface current patterns in the Bay of Bengal (BoB) from 1993 – 2015 based on a satellite sensor-derived current product estimated by combining geostrophic, wind-driven, and temperature-driven current components. Four major patterns/groups corresponding to some of the primary current systems in the BoB are obtained (G1, G2, G3, and G4). The start and end of all the patterns drastically varied year to year and are primarily related to positive and negative Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO) years. The correlation of ENSO and the following year's transition date from WBC to SMC is 0.70, while -0.76 for IOD and transition date from EICC to WMC. Through temporal analysis and monthly frequency of occurrence measurements, this study reveals that the first significant dominant pattern is observed during the summer monsoon season when the southwest monsoon current (SMC) is visible near the south of Sri Lanka (pattern G1). The following major pattern is the winter monsoon current (WMC) in the south of Sri Lanka (pattern G4), whose formation is contributed by the northeast monsoon and westward propagating Rossby wave. The other two dominating patterns are East India Coastal Current (EICC, G2) and Western Boundary Current (WBC, G3). SOM analysis is then performed on WBC with 9 nodes (3×3,namedP1toP9), which captures the meandering nature (P3), and meandering in the presence of SMC (P2), the straight nature (P7) and in the presence of SMC (P4). The spatial and temporal analysis of nine SOM patterns (P1 – P9) suggests that the non-meandering nature of WBC (P4) occurs more frequently than meandering WBC (P2).

Using long-term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models.

Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model structures that are best suited to predicting phenological changes under future climate scenarios. Here, we model spring and autumn phenological transition dates obtained from digital repeat photography in a boreal Picea-Sphagnum bog in response to a gradient of whole ecosystem warming manipulations of up to +9°C, using five years of observational data. In spring, seven equally best-performing models for Larix utilized the accumulation of growing degree days as a common driver for temperature forcing. For Picea, the best two models were sequential models requiring winter chilling before spring forcing temperature is accumulated. In shrub, parallel models with chilling and forcing requirements occurring simultaneously were identified as the best models. Autumn models were substantially improved when a CO2 parameter was included. Overall, the combination of experimental manipulations and multiple years of observations combined with variation in weather provided the framework to rule out a large number of candidate models and to identify best spring and autumn models for each plant functional type.

Evaluation of GLDAS soil moisture seasonality in arid climates

ABSTRACT We evaluated the Global Land Data Assimilation System surface soil moisture product (GLDAS v. 2.1) against in situ soil moisture networks in arid climates in Australia and the United States, using common statistical metrics and seasonality metrics. Our results showed that GLDAS performed well (root mean square error (RMSE) = 0.100 m3/m3; unbiased RMSE (ubRMSE) = 0.060 m3/m3; correlation coefficient (R) = 0.555 on average) but systematically overestimated the soil moisture values (Bias = 0.067 m3/m3). The performance was better in Australian Oznet and the U.S. Climate Reference Network (USCRN), compared to the US Soil Climate Analysis Network (SCAN) network. In terms of seasonality, GLDAS soil moisture seasons were biased to start earlier; on average, drying and wetting transitions started 28 and 16 days earlier than in situ data, respectively. The end dates of GLDAS seasonal transitions showed good agreement with in situ data; the errors in transition timings were limited to within a week. This tendency is stronger in the US networks compared to the Australian network.

In‐season crop phenology using remote sensing and model‐guided machine learning

AbstractAccurate in‐season crop phenology estimation (CPE) using remote sensing (RS)‐based machine‐learning methods is challenging because of limited ground‐truth data. In this study, a biophysical crop model was used to guide neural network (NN)‐based, in‐season CPE. Using the Decision Support System for Agrotechnology Transfer (DSSAT), we conducted uncalibrated simulations for corn (Zea mays L.) across Iowa and Illinois in the U.S. Midwest with in‐season weather and historical information for planting and harvest. We investigated guiding the NN CPE method with growth stage (GSTD) and water stress factor (WSF) outputs from these simulations. Results show that guided NNs are able to estimate onset and progression of phenological stages more accurately than an unguided baseline and a crop model‐only method. GSTD guidance improved CPE during seasons when progress deviated from a regional average because of temperature but was detrimental during seasons of delayed planting and harvest. WSF guidance improved CPE during seasons when planting and harvest were delayed by heavy rainfall but performed less well during grainfill and mature stages. Neural network‐based CPE guided by both GSTD and WSF provided the most accurate estimates for pre‐emergence, emerged, silking, and grainfill stages as well as lower RMSE for the median stage transition date than reported in three full‐season CPE studies. An accurate RS method for estimating planting could link DSSAT simulations to the current planting window and improve upon these results. This model‐guided approach can be extended to other crops and regions to unlock in‐season crop risk assessments that are directly linked to crop phenology.

The uncertainty period preceding the clinical defined SPMS diagnosis and the applicability of objective classifiers - A Danish single center study.

The clinical transition from relapsing-remitting multiple sclerosis (RRMS) to secondary progressive MS (SPMS) is often related to a period of diagnostic uncertainty delaying diagnosis. With emerging treatment options for SPMS how to identify RRMS patients at risk of SPMS and when to assign a SPMS diagnosis has become a matter of growing clinical concern. This study aimed to determine the period of diagnostic uncertainty among Danish MS patients. Secondly, this study examined the performance of two objective classifiers in a longitudinal setting regarding their ability to shorten the period of diagnostic uncertainty. By using the Danish Multiple Sclerosis Registry, we identified all patients linked to Rigshospitalet with clinically assigned SPMS from 2010 to 2021. We reviewed all patient records and identified the first mentioned sign of progression (FMP). The time between the dates of FMP and clinically assigned SPMS was defined as the period of diagnostic uncertainty. Secondly, we applied two objective classifiers (the Karolinska Decision tree and the MSBase criteria) to generate suggested transition dates and compared them to the ones obtain from the patient records. Detailed descriptions of the population were made at all mentioned timepoints. In total 138 patients were included. We found a median period of diagnostic uncertainty of 2.12 years. The objective classifiers generated a median suggested transition date 3.44 and 4.48 years earlier than the date of clinically assigned SPMS, but they only provided an earlier SPMS transition date in 50.72% and 55.80% of cases. Our findings emphasize the uncertainty related to the transition from RRMS to SPMS illustrating the need of an improved diagnostic approach. Objective classifiers might have the potential to help reduce the period of diagnostic uncertainty in the future, but in their current form they do not perform satisfactorily enough to solve all difficulties related to detecting SPMS-transition.

Television and the Labor Supply: Evidence from the Digital Television Transition in the UK

<h3>Abstract</h3> I exploit variation in the date of transition from analogue to digital television signal in the UK across more than 40,000 geographical units to investigate the causal impact of television on employment. Using an event study model, I find that the digital transition increases employment probabilities. The impact is driven by mothers, primarily through an increase in part-time employment. I also show that the digital transition keeps children busy by increasing their TV viewing time and reduces the number of hours mothers spend on housework, as well as their reported family burdens.

Phenological assessment of transpiration: The stem-temp approach for determining start and end of season

Field-based assessment of transpiration phenology in boreal tree species is a significant challenge. Here we develop an objective approach that uses stem radius change and its correlation with sapwood temperature to determine the timing of phenological changes in transpiration in mixed evergreen species. We test the stem-temp approach using a five year stem-radius dataset from black spruce (Picea mariana) and jack pine (Pinus banksiana) trees in Saskatchewan (2016–2020). We further compare transpiration phenological transition dates from this approach with tower-based phenological assessment from green chromatic coordinate derived from phenocam images, eddy-covariance-derived evapotranspiration and carbon uptake, tower-based measurements of solar-induced chlorophyll fluorescence and snowmelt timing. The stem-temp approach identified the start and end of four key transpiration phenological phases: (i) the end of temperature-driven cycles indicating the start of biological activity, (ii) the onset of stem rehydration, (iii) the onset of transpiration, and (iv) the end of transpiration-driven cycles. The proposed method is thus useful for characterizing the timing of changes in transpiration phenology and provides information about distinct processes that cannot be assessed with canopy-level phenological measurements alone.

Seasonal Lifting Condensation Level Trends: Implications of Warming and Reforestation in Appalachia’s Deciduous Forest

Lifting condensation level (LCL) has long been used to estimate cloud base heights. However, spatial and temporal patterns of cloud bases embedded within atmospheric currents flowing over mountainous terrain still need to be more adequately described. To advance understanding, hourly observations of barometric pressure and ambient and dew point temperatures from 1948 to 2017 were acquired for seven airports located at 40.21° N (average) and crossing the Allegheny Mountains of the northeastern United States. Daily LCL trends were quantified, and large positive (2.3 m yr−1) and negative (−1.3 m yr−1) LCL trends were found to be greatest near seasonal transition dates (17 April and 9 November 2022). Cool season LCLs (795 m) increased significantly (p &lt; 0.007) at five sites resulting in an average LCL increase of 81 m and implying a deeper and drier sub-cloud layer. Average warm season LCLs (773 m) decreased by 23 m, suggesting a deeper convective cloud layer and less sub-cloud evaporation that may facilitate higher hydrometeor growth and precipitation rates. Collective results indicate divergent seasonally averaged LCLs characterized by more rapid seasonal transitions, warmer and less cloudy cool seasons, and cloudier and more humid warm seasons that may be partly attributable to aggressive reforestation and contribute to more significant rainfall events and higher flood risks.

Assessing Canopy Phenological Variations and Gross Primary Productivity in A Savanna Ecosystem in Yuanjiang, Yunnan Province of Southwest China

Vegetation phenology is an important indicator of environmental change and strongly connected to forest ecosystem productivity change. This study aimed to analyse the pattern of phenological variations derived from digital imagery for the interpretation of ecosystem productivity. For 2014, 2015 and 2016, the seasonal phenological development of savanna was analysed by using towerbased imagery from a digital camera. The green excess index (GEI) was the best at representing the phenological transition dates (PTDs) and useful for investigating the gross primary production (GPP) in the savanna ecosystem. There was a significant correlation between the monthly pattern of the strength of green (Sgreen), green excess index (GEI) and vegetation contrast index (VCI) and GPP throughout the year. Additionally, the annual pattern of colour indices had significant relationship (p &lt; 0.05) with GPP but this was not seasonal. The air temperature (air T) and soil temperature (soil T) were strongly significantly correlated (p &lt; 0.001) with the start of growing season (SGS) and caused the advance in green-up and the timing of the start of the growing season in 2014 and 2016. The short growing season length (GSL) had an impact on the productivity. The colour indices from the digital camera images not only provided the phenological pattern of a forest canopy but also revealed the forest ecosystem productivity by showing the response to environmental factors. Our results indicate that daily continuous digital camera images might be useful for ecologists to use as a tool for future prediction of the long-term phenological modelling.