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Identifying genetic variants responsible for phenotypic variation under selective pressure has the potential to enable productive gains in natural resource conservation and management. Despite this potential, identifying adaptive candidate loci is not trivial, and linking genotype to phenotype is a major challenge in contemporary genetics. Many of the population genetic approaches commonly used to identify adaptive candidates will simultaneously detect false positives, particularly in nonmodel species, where experimental evidence is seldom provided for putative roles of the adaptive candidates identified by outlier approaches. In this study, we use outcomes from population genetics, phenotype association, and gene expression analyses as multiple lines of evidence to validate candidate genes. Using lodgepole and jack pine as our nonmodel study species, we analyzed 17 adaptive candidate loci together with 78 putatively neutral loci at 58 locations across Canada (N > 800) to determine whether relationships could be established between these candidate loci and phenotype related to mountain pine beetle susceptibility. We identified two candidate loci that were significant across all population genetic tests, and demonstrated significant changes in transcript abundance in trees subjected to wounding or inoculation with the mountain pine beetle fungal associate Grosmannia clavigera. Both candidates are involved in central physiological processes that are likely to be invoked in a trees response to stress. One of these two candidate loci showed a significant association with mountain pine beetle attack status in lodgepole pine. The spatial distribution of the attack‐associated allele further coincides with other indicators of susceptibility in lodgepole pine. These analyses, in which population genetics was combined with laboratory and field experimental validation approaches, represent first steps toward linking genetic variation to the phenotype of mountain pine beetle susceptibility in lodgepole and jack pine, and provide a roadmap for more comprehensive analyses.

The porosity (void caused by technological reasons) in engineering materials always decrease their mechanical characteristics and usually affects the deterioration of the functional mechanical characteristics of the finished products. In the castings the porosity resulting from the specific casting processes phenomena occurs inevitably in the matrix structure. The paper shows this problem in relation to the High-Pressure-Die-Casting (HPDC) technology of Al-Si alloy. The analysis of the experimental results and the results from virtualization of HPDC process allowed to assess the effectiveness of this mixed scenario and improve the quality predictions probability for HPDC, with particular consideration of shrinkage and gas porosities. The problem of the tolerance (admissibility) of porosity occurrence in castings and the castings made of liquid Al-Si alloy to which the gas (hydrogen) was introduced intentionally are signalized.

Background Acupuncture is widely used worldwide; however, studies on its effectiveness have been impeded by limitations regarding the design of appropriate control groups. In clinical research, noninvasive sham acupuncture techniques can only be applied through validation studies. Therefore, this systematic review aimed to evaluate the scope of existing literature on this topic to identify trends. Methods We queried Pubmed, EMBASE, and the Cochrane Central Register of Controlled Trials databases from inception to July 2022 for relevant articles. Author names were used to identify additional relevant articles. Two independent reviewers assessed the identified articles based on the inclusion and exclusion criteria. The following data were extracted: study design, information regarding acupuncturists and participants, general and treatment-related characteristics of the intervention and control groups, participants’ experience of acupuncture, and research findings. Results The database query yielded 673 articles, of which 29 articles were included in the final review. Among these, 18 involved the use of one of three devices: Streitberger ( n  = 5), Park ( n  = 7), and Takakura ( n  = 6) devices. The remaining 11 studies used other devices, including self-developed needles. All the included studies were randomized controlled trials. The methodological details of the included studies were heterogeneous with respect to outcomes assessed, blinding, and results. Conclusions Sham acupuncture validation studies have been conducted using healthy volunteers, with a focus on blind review and technological developments in sham acupuncture devices. However, theren may be language bias in our findings since we could not query Chinese and Japanese databases due to language barriers. There is a need for more efforts toward establishing control groups suitable for various acupuncture therapy interventions. Moreover, there is a need for more rigorous sham acupuncture validation studies, which could lead to higher-quality clinical studies.

Liebig’s law of the minimum (LLM) is often used to interpret empirical biological growth data and model multiple substrates co-limited growth. However, its mechanistic foundation is rarely discussed, even though its validity has been questioned since its introduction in the 1820s. Here we first show that LLM is a crude approximation of the law of mass action, the state of art theory of biochemical reactions, and the LLM model is less accurate than two other approximations of the law of mass action: the synthesizing unit model and the additive model. We corroborate this conclusion using empirical data sets of algae and plants grown under two co-limiting substrates. Based on our analysis, we show that when growth is modeled directly as a function of substrate uptake, the LLM model improperly restricts the organism to be of fixed elemental stoichiometry, making it incapable of consistently resolving biological adaptation, ecological evolution, and community assembly. When growth is modeled as a function of the cellular nutrient quota, the LLM model may obtain good results at the risk of incorrect model parameters as compared to those inferred from the more accurate synthesizing unit model. However, biogeochemical models that implement these three formulations are needed to evaluate which formulation is acceptably accurate and their impacts on predicted long-term ecosystem dynamics. In particular, studies are needed that explore the extent to which parameter calibration can rescue model performance when the mechanistic representation of a biogeochemical process is known to be deficient.

Inertial Measurement Units (IMUs) are beneficial for motion tracking as, in contrast to most optical motion capture systems, IMU systems do not require a dedicated lab. However, IMUs are affected by electromagnetic noise and may exhibit drift over time; it is therefore common practice to compare their performance to another system of high accuracy before use. The 3-Space IMUs have only been validated in two previous studies with limited testing protocols. This study utilized an IRB 2600 industrial robot to evaluate the performance of the IMUs for the three sensor fusion methods provided in the 3-Space software. Testing consisted of programmed motion sequences including 360° rotations and linear translations of 800 mm in opposite directions for each axis at three different velocities, as well as static trials. The magnetometer was disabled to assess the accuracy of the IMUs in an environment containing electromagnetic noise. The Root-Mean-Square Error (RMSE) of the sensor orientation ranged between 0.2° and 12.5° across trials; average drift was 0.4°. The performance of the three filters was determined to be comparable. This study demonstrates that the 3-Space sensors may be utilized in an environment containing metal or electromagnetic noise with a RMSE below 10° in most cases.

Background The International Questionnaire to Measure the Use of Complementary and Alternative Medicine (I-CAM-Q) is a standardized tool for assessing CAM use. While it has been adapted into several languages, this study presents the first Hungarian translation and cultural adaptation. Given the growing interest in CAM in Hungary, a validated Hungarian version is essential for accurate data collection and informed healthcare policy. Methods The I-CAM-Q was translated into Hungarian using a rigorous forward–backward translation protocol involving expert translators, proofreaders, and a reconciliation panel. The final version was pilot-tested among healthy volunteer healthcare workers from ophthalmology departments. The questionnaires were used to assess clarity, cultural relevance, and reliability in this cross-sectional study. Individuals with diagnosed eye diseases were excluded. Results Among ophthalmologically healthy volunteers, 77.8% consulted physicians in the past 12 months, while 8.9% visited chiropractors and spiritual healers, and 2.2% consulted acupuncturists. In the past 3 months, physicians remained the most consulted (73.7%), followed by chiropractors (53.3%). CAM therapies were mainly used for general well-being and acute conditions, with high satisfaction 75–100% of users rated chiropractic, acupuncture, and spiritual healing as “very useful.” Phytomedicines were used primarily for acute illness (41.7%) and general well-being (27.1%), with 68.1% of users rating them as very useful. Dietary supplements were widely consumed, especially Vitamin D (62.9%) and Vitamin C (57.1%), mainly for prevention (46.2%) and general health (38.5%). Meditation and relaxation techniques were also commonly practiced, with 92.3% and 69.2% of users, respectively, rating them as very useful. Conclusions The Hungarian version of the I-CAM-Q was successfully translated, culturally adapted, and validated. It provides a reliable tool for assessing CAM use in Hungary and supports further research into CAM practices and their integration into healthcare.

Lumped hydrological models (LHMs) are indispensable for water resource planning and environmental studies due to simple structures and robust performances. LHMs commonly focus on runoff processes with crude representations for other hydrological processes, such as evapotranspiration (E). Therefore, these models may yield unrealistic water balance partitioning. The challenge is to enhance the LHMs performance while retaining simplicity. The generalized complementary relationship (GCR) is a simple and robust method for estimating E. This study attempted to incorporate GCR into four widely used LHMs (Australian water balance model, GR2M, SIMHYD, and TANK) to test whether the integrated models (GCR‐LHMs) can improve runoff simulation at little cost to the model structure and data requirement. Original LHMs and integrated GCR‐LHMs were tested in 2112 catchments over various climatic conditions. Results show that the GCR‐LHMs outperform original LHMs in most catchments (77.7 ± 5.0%). In addition, the number of catchments that GCR‐LHMs have qualified performance (i.e., Kling‐Gupta coefficient [KGE] more than 0.5) increased by 10.7 ± 3.6% compared with LHMs. The performance of original and integrated models is dependent on the aridity index and normalized vegetation index. However, the improvement in model performance is less catchment characteristics dependent. These results indicate that incorporating GCR into the LHMs improves the model performance under different climatic and vegetation conditions and justifies the integration. GCR integration with LHMs can improve runoff estimation ability (with higher KGE and R‐Square) while retaining model simplicity and readily available input. These findings are valuable for improving the applicability and accuracy of LHMs. Key Points Four widely used lumped hydrological models are integrated with generalized complementary relationship Integration improves model applicability while retaining the simplicity of model structure and readily available input Improved model performance is largely independent of catchment characteristics indicating the general validity of the integration

We develop a method to predict and validate gene models using PacBio single-molecule, real-time (SMRT) cDNA reads. Ninety-eight percent of full-insert SMRT reads span complete open reading frames. Gene model validation using SMRT reads is developed as automated process. Optimized training and prediction settings and mRNA-seq noise reduction of assisting Illumina reads results in increased gene prediction sensitivity and precision. Additionally, we present an improved gene set for sugar beet ( Beta vulgaris ) and the first genome-wide gene set for spinach ( Spinacia oleracea ). The workflow and guidelines are a valuable resource to obtain comprehensive gene sets for newly sequenced genomes of non-model eukaryotes.

The Complementary Relationship of evaporation (CR) theory has gained attention in recent years, in part because it relies solely on standard weather data for estimating evaporation, eliminating the need for land surface moisture and resistance information. CR models show varied skill across diverse landscapes and climates. However, inconsistent parameterization, calibration strategies, station data sets, and geographic coverage have hindered consistent and systematic intercomparison of model performance. This study intercompares four widely used CR models, uncalibrated and calibrated, with eddy covariance data worldwide, and assesses how environmental conditions affect model skill and calibration. Eddy covariance data from 227 FLUXNET and AmeriFLUX stations were quality assured and controlled, filtered, and energy balance closure corrected using open‐source software and benchmarking procedures for reproducibility, after which 82 sites remained. Systematic intercomparison showed that, overall, the Rescaled Power function (Szilagyi et al., 2022, https://doi.org/10.1029/2022wr033095) had the highest skill followed by Sigmoid (Han & Tian, 2018, https://doi.org/10.1029/2017wr021755), while Polynomial model (Brutsaert, 2015, https://doi.org/10.1002/2015wr017720) generally biased high, and Advection‐Aridity (Brutsaert & Stricker, 1979, https://doi.org/10.1029/wr015i002p00443) often produced unrealistic negative values when observed evaporation was low. Two‐parameter calibration greatly improved model skill, with single‐parameter PT‐α calibration performing comparably, suggesting potential to reduce model non‐uniqueness. Calibration and model skill rankings were consistent using energy balance closure corrected and uncorrected data sets. Results indicate that environmental conditions such as aridity index, relative humidity, vapor pressure deficit, and the ratio between radiative and apparent potential evaporation can explain spatial patterns of CR model parameters, revealing opportunities for future development of improved, calibration‐free CR evaporation modeling.

Remote-sensing images constitute an important means of obtaining geographic information. Image super-resolution reconstruction techniques are effective methods of improving the spatial resolution of remote-sensing images. Super-resolution reconstruction networks mainly improve the model performance by increasing the network depth. However, blindly increasing the network depth can easily lead to gradient disappearance or gradient explosion, increasing the difficulty of training. This report proposes a new pyramidal multi-scale residual network (PMSRN) that uses hierarchical residual-like connections and dilation convolution to form a multi-scale dilation residual block (MSDRB). The MSDRB enhances the ability to detect context information and fuses hierarchical features through the hierarchical feature fusion structure. Finally, a complementary block of global and local features is added to the reconstruction structure to alleviate the problem that useful original information is ignored. The experimental results showed that, compared with a basic multi-scale residual network, the PMSRN increased the peak signal-to-noise ratio by up to 0.44 dB and the structural similarity to 0.9776.