Browse Articles

Towards a mitogenomic phylogeny of mud dragons (Kinorhyncha): two new mitogenomes from the Pycnophyidae family

Abstract Mud dragons (Kinorhyncha) are an understudied phylum of microscopic marine invertebrates inhabiting a wide range of marine sediments, from shallow coastal habitats to abyssal depths, and occurring from tropical to polar regions. Despite their broad distribution, their minute size often makes them difficult to study. Consequently, molecular resources for the group remain extremely limited and only three complete mitogenomes have been published from this phylum to date. To help fill this substantial gap in genetic data, we sequenced and annotated two new complete mitogenomes from the family Pycnophyidae, Pycnophyes greenlandicus Higgins & Kristensen, 1988 and Cristaphyes cryopygus (Higgins & Kristensen, 1988). Obtained mtDNA sequences were compared with available transcriptomic data from other Kinorhyncha species, providing a more comprehensive basis for the phylogenetic analysis of the phylum. The results showcased unexpected rearrangements in the gene order across all examined taxa, an unusual cox1 –tRNA-Glu genes overlap and an unstable phylogenetic position within the Pycnophyidae family. These new sequences not only significantly expand the mitogenomic data available for Kinorhyncha, but also provide an important step toward a better understanding of phylogenetic relationships within the phylum.

Linking soil physicochemical properties to leaf nutrient composition in olive orchards on semi-arid calcareous soils

Evidence from multifeature whole-report in visual short-term memory suggests that not all misbinding is swapping

Abstract Forgetting is an everyday part of life but its precise mechanisms are incompletely understood. Recall errors are often not random. Rather, people often incorrectly report information about the wrong object in memory. In short term memory, this has been referred to as “misbinding”. Here, it has commonly been assumed that the features of an object get swapped around in mind. However, an alternative mechanism is that information about a feature of one object might be lost, and replaced by another object’s feature. Commonly-used cued recall approaches are blind to this distinction, but testing multiple objects on the same trial has the power to detect this. We asked people to report all features of all objects from an array ( multifeature whole-report ) in any order ( free recall ). This enabled us to directly quantify these subtypes of misbinding in memory. We introduce a probabilistic model that shows that misbinding actually includes a mixture of symmetric swaps and asymmetric misattributions where a forgotten feature gets replaced by a feature of another object in memory without a reciprocal exchange. This distinction is observed even when memory objects are encoded sequentially.

Music and body motion contribute asymmetrically to emotion perception in traditional Chinese plucked-instrument performance

Uncertainty-aware instance-wise feature selection with adaptive graph regularization

Physics informed activation functions and loss functions for signal reconstruction and digital twinning

Numerical simulation of CO generation and migration patterns in goaf based on coupled multi-physics fields

Abstract To explore early prediction methods for goaf spontaneous combustion, a numerical simulation was conducted to investigate the generation and migration laws of indicator gases in the goaf. A multi-physics coupling model integrating flow field, temperature field and concentration field was adopted to systematically analyze the spatio-temporal evolution of the temperature field and indicator gases. Programmed heating experiments revealed that CO exhibits a good correlation with temperature at low stages. Accordingly, CO was determined as the indicator gas in the numerical simulation, and the oxygen consumption rate, CO generation rate and heat release intensity were obtained. The multi-physics coupling results demonstrate that thermal buoyancy is the dominant driving force controlling the vertical migration and spatial distribution of CO in the goaf. Under the combined effect of air leakage and thermal buoyancy, CO accumulates in the upper, deep and return side regions of the goaf, which should be prioritized for monitoring. The findings provide important theoretical and engineering support for the prevention and control of goaf spontaneous combustion.

Moving from table to graph in physics-informed spatio-temporal symbolic regression

Abstract Symbolic Regression (SR) is a powerful technique for discovering analytical mathematical expressions that describe observed numerical data. Traditionally, SR models work on data in tabular form, imposing a purely functional mapping without considering the underlying spatio-temporal dependencies or the governing physical laws. Such approaches are ill-suited for physical problems, where data evolves dynamically across time and space and is governed by ordinary/partial differential equations (ODEs/PDEs). Moreover, as SR are commonly measured by their ability to obtain generalized equations from a relatively small amount of data, representing the data efficiently plays a central role in the performance of such models. In this study, we propose a simple yet powerful solver-agnostic approach for SR fitting by using a dual representation — one that preserves explainability, while the other is physically informed. Our main novelty lies in combining the standard tabular representation with a graph-based spatio-temporal representation in a unified SR fitting framework that can enhance existing SR solvers without modifying their internal search mechanism. Namely, the method uses both tabular and graph-based data representation, where nodes in the graph are associated with spatio-temporal coordinates and dynamic state variables, while edges encode spatial or temporal dependencies. This approach allows for the direct generation of differential equations that describe the underlying physical system by implicitly incorporating spatio-temporal patterns and constraints. Benchmarks across multiple synthetic datasets originated from functional, ordinary/partial different equations (O/PDE), integral, and delayed ODE, demonstrate the ability of the proposed method to recover governing equations with high accuracy, even in noisy settings, improving a wide range of SR out-of-the box. These results indicate that enriching SR with graph-based spatio-temporal structure provides a practical pathway toward more robust and physically consistent equation discovery. At the same time, the current framework assumes that a meaningful spatio-temporal neighborhood structure can be constructed and is validated primarily on controlled synthetic benchmark systems.

Effects of exergames on depression, anxiety, and sleep in adolescents with subthreshold depression: a randomized controlled trial

Longitudinal changes in cognition, sleep, and psychological distress following the MORE program in head and neck cancer patients undergoing chemoradiotherapy

Abstract The purpose of this study was to evaluate changes in subjective cognitive function, sleep quality and psychological distress among head and neck cancer (HNC) patients receiving concurrent chemoradiotherapy (CRT) participating in the Multimodal Oncology Rehabilitation Exercise (MORE) program. A total of 118 HNC patients (median age 47 years, 78.8% males) participated in the supervised program, which included physical exercises, cognitive exercises and psychosocial strategies, delivered three times weekly over eight weeks. Outcomes were assessed at baseline, four, eight (discharge) and twelve weeks (follow-up) using subjective outcome measures. The linear mixed-effects models were employed to evaluate changes in outcomes over time. Results revealed significant improvements in perceived cognitive impairment (β: 3.45, p  < 0.001), sleep (β: 2.8, p  < 0.001), and psychological distress (β: − 2.4, p  < 0.001) by the 12th week (follow-up). These benefits were not influenced by demographic or clinical characteristics. These findings highlight the potential of structured, multimodal exercise programs as adjuncts to supportive care for improving cognition, sleep and psychological distress among HNC patients, particularly in resource constrained settings. To the best of our knowledge, this is the first study to evaluate the longitudinal changes of such an intervention among Indian HNC patients, underscoring the need for future large-scale randomized controlled trials.

Dynamic capability assessment and enhancement in chinese construction enterprises under digital transformation: An integrated cloud model approach

Analyzing climate change trends and projection of their effects on wood equilibrium moisture content using CMIP6 models under SSP scenarios in Iran

Impact of JAK-inhibition on pain and biphasic P2X7R expression on CD4+ T cells in experimental arthritis

Eco-friendly activated carbon derived from pomegranate peel for amoxicillin removal: batch adsorption, kinetic modeling, and thermodynamics

Abstract Activated carbon derived from waste pomegranate peels was investigated for the removal of amoxicillin (AMX) from aqueous solutions. The prepared adsorbent exhibited a high BET surface area (1307 m²/g) and a well-developed micro–mesoporous structure. Under optimal conditions (pH 2, 0.05 g, 25 °C, 50 mg/L), a maximum removal efficiency of 97% was achieved, while 87% removal was maintained at near-neutral pH (pH 6). Increasing the initial concentration reduced removal efficiency due to adsorption site saturation, whereas increasing temperature decreased adsorption, confirming the exothermic nature of the process. Kinetic studies showed that the pseudo-second-order model provided the best fit, indicating that surface-controlled interactions govern the adsorption rate. Equilibrium data were better described by the Freundlich model, suggesting heterogeneous adsorption behavior, although the Langmuir model also indicated a high monolayer adsorption capacity (qₘₐₓ = 100 mg g⁻¹). Thermodynamic parameters (ΔG° = −6.13 to − 5.16 kj/mol, ΔH° = −15.59 kj/mol, ΔS° = −32.29 J/mol K) confirmed that the adsorption process is spontaneous and exothermic. The relatively low ΔH° value indicates that adsorption is predominantly governed by physisorption mechanisms. Overall, the results demonstrate that this low-cost and sustainable adsorbent is a promising alternative for efficient antibiotic removal from water.

Machine learning-based risk prediction of 28-day mortality for sepsis patients with augmented renal clearance

Multi-scale temporal convolution attention network for state-of-charge estimation in Li-ion batteries

Differences in emergency ambulance use between older and younger individuals in Denmark: a nationwide register-based study

Enhanced recovery of high-quality DNA from limited FFPE tissue for advancing cancer genomics

Abstract Archived formalin-fixed paraffin-embedded (FFPE) tissue samples are crucial assets in cancer research. However, despite the availability of commercial DNA purification kits, researchers face persistent challenges. The limited tissue availability, formalin-induced crosslinking, and DNA fragmentation in FFPE blocks often results in low DNA yield and compromised quality, hindering downstream applications. In this study, we optimized and standardized DNA purification methods to achieve high-yield, high-integrity DNA, making it ideal for DNA library preparation and high-throughput sequencing. Therefore, we optimized DNA extraction protocols using two Qiagen kits, the QIAamp DNA FFPE Tissue Kit and the QIAamp DNA FFPE Advanced Kit. The optimized methods significantly improved the yield and integrity of DNA isolated from limited FFPE tissue samples, with the advanced kit demonstrating superior performance and efficiency. This resulted in a two- to threefold increase in DNA concentration, as determined by spectrophotometric (NanoDrop) and fluorometric (Qubit) analyses. Gel electrophoresis and DNA integrity number (DIN) assessments confirm enhanced fragment preservation, suitable for downstream applications. Libraries prepared using DNA from the advanced protocol demonstrated higher sequencing quality and microbial diversity resolution. This study demonstrated that optimizing DNA purification methods for FFPE tissue samples can substantially improve the yield and integrity of DNA, paving the way for more accurate and comprehensive genomic analyses.

Enzymatic indicators reveal drought sensitivity of the deadwood–soil system in temperate forests

Abstract Drought can disrupt biogeochemical functioning in forest ecosystems by limiting microbial activity and extracellular enzyme production. We investigated the effects of simulated moisture deficit on the activity of five enzymes involved in C, N and P acquisition in a deadwood–soil system: β-glucosidase (BG), β-D-cellobiosidase (CB), β-xylosidase (XYL), N-acetyl-β-D-glucosaminidase (NAG) and phosphatase (PH). Deadwood of six temperate tree species (broadleaf and conifer) was exposed to drought and control conditions over two years (2023–2024), and enzyme activities were measured seasonally in both deadwood and the underlying soil. Drought conditions led to a pronounced reduction in the activity of all analyzed enzymes in deadwood, frequently exceeding 50% compared to control treatments, which indicates strong moisture limitation of microbial processes during wood decomposition. Enzymatic activity in soil beneath deadwood also decreased, although the smaller absolute changes observed in soil are likely related to lower initial activity levels rather than increased resistance to drought. The extent of decline varied among enzymes and wood species, with β-glucosidase and β-xylosidase showing differences of approximately 30–40% between treatments in soil, and with some species (e.g. beech and spruce) exhibiting weaker responses to drought. Enzyme activity in both substrates followed a consistent seasonal pattern, with maxima in summer and minima in winter. The increasing divergence between control and drought treatments in the second-year highlights cumulative effects of prolonged water limitation. The results demonstrate that extracellular enzyme activity is highly sensitive to moisture availability and that deadwood and underlying soil respond differently to drought. This provides mechanistic insight into how drought may alter microbial functioning and decomposition dynamics at the deadwood–soil interface in forest ecosystems. General Linear Models, correlation analysis and PCA consistently indicated moisture as a major factor associated with enzymatic variation, whereas temperature showed no significant relationships with enzyme activities. Reduced enzyme activity under drought suggests a limitation of microbial decomposition processes and nutrient acquisition, particularly for carbon-, nitrogen- and phosphorus-related pathways, potentially constraining microbial metabolism and slowing organic matter turnover at the deadwood–soil interface.

Towards precision agriculture for assessing germination rates and density of rice seedling using hierarchical convolutional neural network on drone imagery