爱沙尼亚生命科学大学

A thorough understanding of the global carbon pools and cycle is essential for understanding and predicting the effects of climate change. Coastal waters play a key role in the global carbon cycle but remain poorly understood due to their optical complexity, high spatial variability, and sensitivity to climate change. Satellite remote sensing data can provide high spatial and temporal resolution for carbon monitoring at local, regional, and global scales. This study estimates the potential of Sentinel-2 MSI (S2) and Sentinel-3 OLCI (S3) for mapping carbon fractions in coastal waters. In situ hyperspectral reflectance measurements and concentrations of different carbon fractions, namely Total Organic Carbon (TOC), Dissolved Organic Carbon (DOC), Particulate Organic Carbon (POC), and Dissolved Inorganic Carbon (DIC), were used. Bio-optical and physical water parameters, as well as carbon fractions, were measured in the coastal areas of the eastern Baltic Sea from 2023 to 2024. In situ reflectance spectra were recalculated to S2 and S3 band reflectance using their respective spectral response functions. Regression models, including linear regression and machine learning methods, were developed and validated against in situ data to estimate carbon fractions. For S2, the combinations of green and red or red-edge bands achieved the best performance in carbon retrieval, with R2 values of 0.67, 0.64, and 0.56 for TOC, DOC, and DIC, respectively. The corresponding MAPE values ranged from 14% to 18%, and biases were close to zero. Slightly higher accuracies were obtained with S3 simulations. The S3 models that used reflectance at 490, 620, and 665 nm produced the most accurate estimates for both TOC (R2 = 0.71) and DOC (R2 = 0.67). For DIC, the best predictive model combined information from 510, 665, and 709 nm bands, achieving R2 of 0.62. Among the tested optical proxies, CDOM absorption at 443 nm showed the most consistent relationship with TOC, DOC, and DIC (R2 = 0.56–0.30). The study represents a significant step forward in the remote sensing of coastal waters, and if adopted, the proposed carbon-fraction products could enable substantial progress across various fields, from carbon-cycle research to monitoring and policymaking.

Continuous biothermal monitoring is valuable for evaluating dairy calf health, but conventional methods are usually labor-intensive or invasive. This study evaluated a minimally invasive subcutaneous implant (SCI) system and assessed a LightGBM regression model to mitigate peripheral environmental thermal coupling. In a two-farm trial (n = 720 calves), SCIs were deployed under three presurgical skin preparation protocols. Telemetry was evaluated across development (n = 327 calves; 164,383 hourly sliding-window prediction epochs) and out-of-time (OOT) evaluation cohorts (n = 123 calves; 82,724 epochs; 100 novel, 23 overlapping), separated by an empirical washout buffer (≥ 532 h) to prevent feature leakage. Early implantation failure was low at 1.39% (10/720), with increasing procedural sequence associated with lower failure odds (p = 0.007), whereas skin preparation protocol revealed no statistically detectable difference in 60-day retention (p = 0.77). System performance was constrained by a 9.3% hardware failure rate (67/720; predominantly pre-insertion initialization failures). Although active devices achieved 90.5% telemetry integrity, intention-to-treat data completeness was 82.7%. Raw subcutaneous temperature showed marked disagreement with the gastrointestinal reference (TC) (RMSE = 1.094°C, CCC = 0.16). The LightGBM model reduced ambient dependence, decreasing error to RMSE = 0.403°C on matched OOT epochs, though concordance remained modest (CCC = 0.35). The model systematically underestimated hyperthermic reference temperatures (TC > 39.5°C), dampening peak febrile excursions. While subcutaneous implants demonstrate procedural feasibility, commercial implementation requires eliminating electronic infant mortality, accommodating telemetry gaps, and resolving hyperthermic attenuation.

Species with a wide geographic distribution constitute an ideal model system for characterizing the adaptation of secondary chemistry to local climate and testing the generality of hypotheses about how investments in secondary chemistry scale with relative carbon and nutrient availability. We studied needle carbon (CM), nitrogen (NM) and terpenoid contents per dry mass in Pinus sylvestris collected across a broad geographical (38° to 59° N, latitudes 18° to 29° E; Estonia, Sweden, Romania, Turkey) and altitudinal (26-1980) range. In the Turkish samples, a low CM value corresponded to the lowest levels of mono- and sesquiterpenoids. In addition, the Turkish pine needles lacked diterpenoids. The monoterpenoid profiles revealed the presence of two distinct pine chemotypes: the Turkish pines were characterized as ‘(−)-α-pinene pines’, while the Estonian, Swedish, and Romanian samples were identified as ‘(+)-α-pinene - (+)-3-carene pines’. Overall, Northern pine populations were characterized by higher foliage C and terpenoid contents and greater terpenoid diversity. In addition, the direct correlations of foliage C and terpenoid contents were observed for ‘(+)-α-pinene - (+)-3-carene pines’. Our study highlights major variations in composition and content of Scots pine needle terpenoids among geographically distinct populations.

This paper introduces the Estonian Musculoskeletal Disorders Questionnaire (EMDQ), a practice-oriented tool for assessing the prevalence of musculoskeletal disorders in the workplace. Work-related musculoskeletal disorders (WMSDs) are an occupational health concern. Prevention largely depends on workstation-level interventions, where monitoring musculoskeletal discomfort helps set priorities. Although several tools like Nordic Musculoskeletal Questionnaire (NMQ) and the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) exist for mapping WMSD prevalence, they often face usability challenges for both workers completing the questionnaires and occupational health and safety (OHS) specialists analyzing the data. The EMDQ is designed to address these issues. Available in both paper and electronic formats, it features a gender-neutral body map that divides the body into distinct regions, supporting inclusive dissemination among workers. The questionnaire is intentionally brief to minimize worker effort. For OHS specialists, the EMDQ enables automated data processing that: (1) visualizes the most affected body regions, (2) estimates prevalence within the sample and extrapolates to the entire workforce, and (3) compares company-level data with sectoral averages. Additionally, the EMDQ includes guidance to ensure compliance with GDPR, supporting ethical data handling practices.

The search for predictors of plant diversity has challenged scientists for decades. Here we identify intense photosynthetically active radiation (PAR) as a major factor constraining plant species richness in global grasslands. We show that the strength of the negative relationship between species richness and PAR increases with increasing elevation and that species richness is more strongly correlated with intense PAR than with UV-B radiation, climate variables, and atmospheric nitrogen deposition. In addition to species richness, plant biomass was also negatively correlated with PAR at higher elevations, indicating that intense PAR also constrains plant biomass in montane grasslands. Furthermore, we show that the decrease in plant species richness with increasing PAR is mainly caused by a decrease in species richness of forbs, sedges, and rushes. In contrast, species richness of grasses was only negatively correlated with PAR at high elevations, and species richness of legumes was not significantly correlated with PAR. Our results suggest that PAR constrains plant species richness in global grasslands and limits the extent to which plant species of specific functional groups can migrate uphill in response to climate warming.