Variation In Patterns Research Articles

The Interplay Between Muscular Activity and Pattern Recognition of Electro-Stimulated Haptic Cues During Normal Walking: A Pilot Study

This pilot study explored how muscle activation influences the pattern recognition of tactile cues delivered using electrical stimulation (ES) during each 10% window interval of the normal walking gait cycle (GC). Three healthy adults participated in the experiment. After identifying the appropriate threshold, ES as the haptic cue was applied to the gastrocnemius lateralis (GL) and biceps brachii (BB) of participants walking on a treadmill. Findings revealed variable recognition patterns across participants, with the BB showing more variability during walking due to its minimal activity compared to the actively engaged GL. Dynamic time warping (DTW) was used to assess the similarity between muscle activation and electro-stimulated haptic perception. The DTW distance between electromyography (EMG) signals and muscle recognition patterns was significantly smaller for the GL (4.87 ± 0.21, mean ± SD) than the BB (8.65 ± 1.36, mean ± SD), showing a 78.6% relative difference, indicating that higher muscle activation was generally associated with more consistent haptic perception. However, individual differences and variations in recognition patterns were observed, suggesting personal variability influenced the perception outcomes. The study underscores the complexity of human neuromuscular responses to artificial sensory stimuli and suggests a potential link between muscle activity and haptic perception.

State-and-Evolution Detection Model for Characterizing Farmland Spatial Pattern Variation in Hengyang Using Long Time Series Remote Sensing Product

Analyzing farmland landscape pattern variations induced by human activities can support effective decision making by governments to improve land use efficiency. However, research on long-term and continuous spatial process changes in farmland is scarce, and spatial pattern changes in farmlands remain insufficiently understood. Moreover, studies in which researchers have utilized dynamic process analysis to describe farmlands are relatively limited. This study aimed to apply the state-and-evolution detection model (SEDM), generated from long-term remote sensing data, to characterize farmland spatial pattern variations in Hengyang City, Hunan Province. Annual farmland data from 1990 to 2022, change type samples, and auxiliary data were collected, and six types of spatial pattern variations (perforation, dissection, shrinkage, creation, enlargement, and aggregation) were defined for the study area. Subsequently, the SEDM was applied based on four landscape indices. Finally, spatiotemporal evolution features, namely evolution times, evolution duration, and dominant evolution pattern, were quantified. The farmland in the study area exhibited a generally upward trend with fluctuations. The maximum area was followed by shrinkage (S), perforation (P), and enlargement (E). In over 70% of the study area, fewer than three evolution times occurred over three decades. The dominant evolution patterns were P–S, S–P, and E–P for single evolution events, and P–S–P, S–P–S, and P–S–S for double events. The model achieved an overall accuracy of 85%, thus demonstrating its effectiveness in characterizing landscape pattern variations and providing valuable insights for researchers and policy makers to develop strategies for farmland protection.

Study on the Application Method of Aquifer Depth Distribution Patterns as Model Input on the Performance of a Physically Based Distributed Hydrologic Model

Groundwater discharge is critical for maintaining river flow during dry seasons, especially in lowland areas. Despite its significance, groundwater resources have often been overlooked highlighting the need for comprehensive studies amidst growing pressure to develop new water resources. This study focuses on the Soyang River Basin, South Korea, including its ungauged northern regions, the nearby DMZ (Demilitarized Zone), using the physically based Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model. A three-year simulation was conducted to examine variable aquifer depth distribution patterns by assuming an inverse relationship between surface elevation and aquifer bottom depth. Three case studies (i.e., equal distribution, linear regression, and logarithmic regression) were evaluated and compared. The method to identity optimal aquifer depth distributions to enhance groundwater simulation accuracy in regions with significant topographical variation was incorporated. Groundwater levels at six monitoring sites showed that altitude-based variable aquifer depths outperformed the equal distribution case. The results showed strong agreement between simulated and observed values, particularly in the linear regression case with an R-squared statistic of 0.858 and Nash–Sutcliffe Efficiency index of 0.789, indicating that linear regression-based aquifer depth estimation can significantly improves long-term runoff modeling and groundwater simulation accuracy. The logarithmic regression case had the lowest relative peak error in peak flow. These findings highlight the importance of adjusting aquifer depth distributions in physically based hydrologic models to better reflect real-world conditions. Overall, this study contributes to advance groundwater modeling by integrating variable aquifer depth distributions into a physically based hydrologic model for large scale watersheds.

Yield prediction through UAV-based multispectral imaging and deep learning in rice breeding trials

ContextPredicting crop yields with high precision and timeliness is essential for crop breeding, enabling the optimization of planting strategies and efficients resource allocation while ensuring food security. Current research in this field typically does not address the problem of yield prediction in the diverse context of breeding experiments involving numerous varieties. However, evaluating the performance of prediction models across multiple varieties is vital for further model refining and enhancing model robustness and adaptability. ObjectiveThis study aims to evaluate the performance of feature- and image-based yield prediction models for yields with multiple varieties to compare their capabilities and determine an appropriate timing for early yield prediction. MethodsThis study combines unmanned aerial vehicle (UAV)-based multispectral remote sensing imagery with machine learning and deep learning-based algorithms to develop rice yield prediction models across multiple varieties. The performances of both feature- and image-based models are evaluated. The feature-based models considered in this study include random forest (RF), deep neural network (DNN), and long short-term memory (LSTM) algorithms, and the image-based models are convolutional neural network (CNN) architectures, including both two-dimensional (2D) and three-dimensional (3D) CNN models. To assess the performance of the multi-variety crop yield prediction models thoroughly, this study considers two sampling scenarios: stratified sampling and group sampling. Results and conclusionsThe results show that the image-based deep learning models outperform the feature-based machine learning models, which indicates their superior robustness in multi-variety scenarios and highlights their significant potential of directly extracting spatiotemporal features from images for yield prediction. The results indicate that the multi-temporal 2D CNN model (i.e., the CNN-M2D model) can achieve the best yield prediction performance among all models, achieving RRMSE = 8.13 % and R2 = 0.73. The prediction results also demonstrate good consistency with the observed data, indicating an efficient capturing of spatial pattern variations in yield across different varieties. Based on the results, with the crops progressing along the growth stages, the accuracy of the yield prediction models improves gradually, achieving the best prediction performance during the flowering to grain-filling stage. Finally, according to the results, the optimal lead time for predicting rice yield is approximately one month before harvest. SignificanceOur study can provide a reference for the research community in yield prediction and high-yield variety selection in breeding trials.

Influence of season, sex, and interspecific interactions on the diel activity patterns of two sympatric African small carnivores

Animal activity patterns vary seasonally and between species, facilitating species coexistence. In Africa, however, factors affecting the activity of many small carnivores remain poorly understood, especially for congeneric and sympatric species whose similar sizes may lead to interspecific competition. Here, we investigated differences and variations in the activity patterns of two sympatric Viverridae species in a seasonal African landscape. We continuously radio-tracked 15 small-spotted genets (Genetta genetta) and five Cape genets (G. tigrina) over 24-h cycles throughout the year. We analysed the effects of season, sex, and interspecific interactions on circadian rhythms using multi-cosinor regression models. Both species maintained a nocturnal activity pattern year-round, decreasing activity significantly during the cold-dry season. This pattern aligns with the thermoregulatory hypothesis—especially for species with an elongated body like genets—suggesting decreased activity under extreme cold weather conditions to conserve energy. Females in both species were less active than males, possibly due to their smaller home ranges, especially during the cold-dry season. These effects were particularly pronounced in Cape genets, which primarily inhabit riverine forests. Female Cape genets adjusted their activity onset, possibly to minimize encounters with males, mostly during the hot-wet season when caring for their offspring. Small-spotted genets shifted their activity onset and peak in riverine forests—areas of potential contact with Cape genets—compared to areas without Cape genets. Overall, our study underscores the critical role of seasonal environmental changes and interspecific interactions in shaping the activity patterns of two carnivore species within a semi-arid Albany Thicket landscape.

Impact of aging on the fracture strength of 3D-printed fixed implant prostheses: A comparative analysis of monolithic and bi-layer with or without fiber-reinforced composite frameworks.

This study aims to evaluate the fracture strength of three-unit, implant-supported fixed dental prostheses (FDPs) constructed from 3D-printed resin. It compares structures with and without fiber-reinforced composite (FRC) frameworks under conditions that mimic artificial aging. Forty FDPs were fabricated and divided into two groups: monolithic (n=20) and bi-layer (n=20). The monolithic group (MG) consisted entirely of FDPs made from 3D-printed resin for permanent restorations, while the bi-layer group (BG) featured FDPs with an FRC framework veneered with the same 3D-printed resin. Each group was subdivided into two subgroups: one subjected to artificial aging (MTG, n=10; BTG, n=10), and the other served as the baseline control (n=10). All subgroups underwent a mechanical bending test, applying a single load to failure using a universal testing machine. There was a statistically significant difference in fracture strength due to the presence of the FRC framework (p<0.01). The framework became the FDP more resistant to load. The mean load to failure and standard deviations were as follows: MG 406.59N (±33.84), MTG 286.20N (±152.15), BG 1142N (±162.88), and BTG 945N (±211.52). Although no statistical differences in strength were observed between aged and nonaged subgroups, variations in failure patterns emerged, with the BG and BTG groups showing a predominance of failures compared to the MG. The inclusion of an FRC framework significantly enhanced the fracture strength of the FDPs. While the aging protocol did not alter thestrength, failures predominantly occurred in the 3D-printed resin superstructure at the connectors. The highest rate of fractures occurred in the premolar connector.

Conservation agriculture enhances maize yields and profitability in Mexico’s semi-arid highlands

Rainfed agriculture in semi-arid regions is affected by variable rainfall patterns, resulting in low yields under conventional farming systems. To address this issue, cropping systems based on conservation agriculture were evaluated in 2 field experiments on 17 farmers’ fields in the semi-arid highlands of Queretaro, Mexico, from 2013 to 2020, to assess yields and profitability. Monocropped maize (Zea mays L.) under conventional tillage was compared to growing maize on permanent beds with soil mulch, either monocropped or in rotation with triticale (X Triticum Secale Wittmack), oats (Avena sativa L.), or common beans (Phaseolus vulgaris L.). In the San Juan del Rio field experiment, maize yields on permanent beds averaged from 2,475 to 3,517 kg ha− 1 over five years, exceeding 70% the yields under conventional tillage. In the Cadereyta field experiment, 4-year average maize yields on permanent beds ranged from 979 to 1,382 kg ha− 1 with no significant difference to those under conventional tillage. In farmers’ fields, maize on permanent beds yielded an average of 3,717 kg ha− 1, 70% higher than with conventional tillage. The most profitable system was permanent beds with a maize-bean rotation in field experiments and with maize monocropping in farmers’ fields. Overall, conservation agriculture can improve maize yields and profitability in Central Mexico’s semi-arid highlands.

Dental Practitioners’ Knowledge and Attitudes Toward the Etiology, Diagnosis, and Treatment of Peri-Implantitis

The objective of this study was to assess the level of knowledge and attitude about the etiology, diagnosis, and management of peri-implantitis among dental practitioners. Methods: An online cross-sectional study on 303 dentists in Saudi Arabia was conducted. A closed-ended survey consisting of 28 questions was designed. Three sections were created, i.e., (1) participants’ characteristics; (2) knowledge of peri-implantitis etiology, risk factors, diagnosis, and complications; and (3) the use of antibiotics to manage peri-implantitis and methods used for diagnosis and treatment. The data were analyzed using mean and percentages. Results: A total of 303 dentists participated in this study with a 78.8% response rate, comprising 80% general dentists and 20% specialists. The majority of dentists demonstrated knowledge about peri-implantitis, with 85.5% identifying bacterial plaque as an etiologic factor and 83.8% recognizing it as an inflammatory reaction. Regarding risk factors, 88.1% identified smoking and 86.1% recognized periodontitis as contributors to peri-implant disease development. In terms of clinical decision making, 63% of participants considered implant mobility as a definitive indication for implant removal, and more than half reported using crater-like bone defects surrounding implants as a diagnostic criterion for peri-implantitis. For antimicrobial therapy, amoxicillin alone (30%) was the most commonly prescribed antibiotic, followed by the combination of amoxicillin with metronidazole (29%). Despite these treatment approaches, it is important to note that currently there are no established, predictable protocols for treating any phase of peri-implant disease. Conclusion: While participating dentists demonstrated a strong understanding of peri-implantitis etiology and risk factors, particularly regarding bacterial plaque and smoking as major contributors, there was considerable variation in antibiotic selection and usage patterns. These findings suggest the need for standardized guidelines and further research to establish evidence-based protocols for managing peri-implant diseases.

Dissecting neurofilament tail sequence-phosphorylation-structure relationships with multicomponent reconstituted protein brushes

Neurofilaments (NFs) are multisubunit, bottlebrush-shaped intermediate filaments abundant in the axonal cytoskeleton. Each NF subunit contains a long intrinsically disordered tail domain, which protrudes from the NF core to form a "brush" surrounding each NF. Precisely how the tails' variable charge patterns and repetitive phosphorylation sites mediate their conformation within the brush remains an open question in axonal biology. We address this problem by grafting recombinant NF tail protein constructs NF-Light, -Medium, and -Heavy (NFL, NFM, and NFH) to surfaces, yielding protein brushes of defined stoichiometry that can be phosphorylated in vitro. Atomic force microscopy measurements reveal that brush height depends on composition monotonically but not always linearly for binary NFL:NFM or NFL:NFH systems, and that NFM-based brushes are highly extended, while brushes incorporating the much larger NFH are surprisingly compact even after multisite phosphorylation. Complementary self-consistent field theory (SCFT) predicts multilayer brush morphologies for NFM and phosphorylated NFH brushes. Further experiments and SCFT analysis with designed mutants reveal that N-terminal negative charges in the NFH tail repel phosphorylated residues to generate the multilayer morphology, while the C-terminal charge-neutral region contributes to multilayer brush morphology but not total brush height. Charge-shuffled NFM variants show that charge segregation promotes brush collapse near physiological ionic strengths. Collectively, this study supports a role for NFM in establishing a dynamic range for NF brush conformation, lending insight into previous in vitro and in vivo findings. More broadly, this work establishes a platform for dissecting contributions of disordered protein sequence to conformation at interfaces.

A Study of the Role of Income, Consumption Patterns and Time in Shaping Food Expenditure in India

Aims: we sought to determine the key factors affecting per capita monthly expenditure on food across Indian states over time. Study Design: This is a quantitative, cross-sectional study that uses existing data from National Sample Survey (NSS), 61st, 66th and 68th rounds. Methodology: We compiled datasets on 10 independent variables believed to influence monthly per capita expenditure (MPCE) across 17 Indian states over three distinct time periods (2004-05, 2009-10, 2011-12). These variables include household size, dependency ratio, milk availability, Gross State Domestic Product (GSDP), literacy rate, a time dummy variable, poverty rate, and consumption patterns of cereals, pulses, and non-vegetarian food. Results: The analysis of 68 observations identified key factors influencing household food consumption expenditure. milk consumption showed a positive correlation with expenditure, yielding a coefficient of 1.106 (p =0.001) as did literacy rate (coefficient = 10.018, p = 0.045), expenditure on non-food items (coefficient = 0.086, p = 0.001), and expenditure share on eggs and meat (coefficient = 29.011, p = 0.001). Time dummy variables indicated rising expenditures over time, with coefficients of 517.76 (p = 0.001) for 2009-10, 703.93 (p = 0.001) for 2011-12, and 2324.29 (p = 0.001) for 2022-23. Overall, the model accounted for 90.9% of the variance in food consumption expenditure. Conclusion: Higher per capita milk availability leads to increased overall food spending, while the time factor indicates a trend of rising expenditures, likely linked to changing consumption patterns and incomes. literacy rate also positively affect food expenditure choices The analysis also shows that spending on protein-rich foods, such as eggs and meat, positively affects overall food expenditure.

Estimation of Future Climate Change in Low Folded Zone, Iraq with the LARS-WG and Five GMS Models under CMIP5 Scenarios

This study investigates the trends of climate change in the Low Folded Zone region of Iraq and provides future projections for daily maximum and minimum temperatures, as well as precipitation. Using the LARS-WG model and five General Circulation Models (GCMs) under three Representative Concentration Pathways (RCP) scenarios, the research aims to provide valuable insights into the anticipated changes in climate variables. The calibration and validation of the model demonstrate its capability to simulate future climatic data, with statistical indices indicating a strong correlation between observed and generated data. The projected future temperatures showed a consistent increase across all selected stations, with average annual maximum temperatures expected to rise by 1.06 to 5.48°C by the end of the twenty-first century. The highest increase in temperatures was predicted under the high-emission scenario RCP8.5. The results also indicated spatial and temporal variations in precipitation patterns, with the annual percentage increase in precipitation ranging from 7.07% to 10.75% for RCP 2.6, 0% to 2.2% for RCP4.5, and 3.7% to 6.8% for RCP8.5. The findings reveal a projected increase in annual temperatures and variable precipitation patterns, highlighting the urgency of proactive measures to address the challenges posed by climate change in the region. The results of this study are crucial for informing decision-makers and planners in developing strategies for climate change adaptation and resource management, particularly in water resource management and agricultural planning.

Assessment Of Spatiotemporal Variation In Inter-State Passenger Flow From Katsina, Katsina State, Nigeria

This paper examines the nature, pattern, and spatiotemporal variation of inter-state passenger movement from Katsina to other states across the country. Two major terminals were purposefully selected for the study, which are the Nigerian Union of Road Transport Workers (NURTW) and Katsina State Transport Authority (KTSTA). Questionnaires were systemically administered to departing passengers. Information on passenger flow was collected from the passenger register at the motor parks. A flow line map was used to show the direction of the flow, while a bar graph was used to show the volume or magnitude and temporal variation of passenger movement from Katsina to other states across the country. The findings reveal that the majority of the passengers were males who were married, and most of them were traders and civil servants; thus, work- or business-related trips account for most of the reasons for travelling. The findings reveal that the NURTW terminal has a wider spatial coverage of 11 states, while the KTSTA terminal has a spatial coverage of 9 states. Kano State is the most popular destination, followed by Kaduna and Abuja. Generally, the findings show a seasonal pattern of passenger flow with higher numbers in the later months of the year (October to December) and lower numbers in the middle of the year (June and July). Recommendations include the enhancement of transport infrastructures to cater to work- or business-related trips, seasonal adjustment, passenger demographic consideration, and promotion of off-peak travel. This is to ensure a more efficient and responsive transport system that meets the needs of travellers and maximises customer satisfaction.

Seasonal Variations in Birth Patterns in Greece: A Comprehensive Analysis of National Data From 1956 to 2022

Seasonal Variations in Birth Patterns in Greece: A Comprehensive Analysis of National Data From 1956 to 2022

Improved understanding of sequence polymorphisms at 42 Y chromosome short tandem repeats for the Chinese Han population

Y-chromosome short tandem repeat (Y-STR) is an important type of genetic markers in the human genome, widely used in molecular anthropology and forensic genetics. However, most Y-STR studies has been focused on the length-based variations resulting from differences in the number of repeat units. Less attention was paid to sequence-based Y-STR variations. Consequently, sequence-based variation characteristics of Y-STRs in Chinese populations remain insufficiently studied. In this study, targeted sequencing of 42 Y-STR loci was performed for 331 Chinese Han males (with an average sequencing depth of 612 ×), unveiling a total of 387 sequence allele types and their frequencies in the population. Repeat pattern variations were observed in seven loci containing multiple repeat units. Across all sequenced repeat and flanking regions, 46 single-nucleotide substitutions and insertion/deletion variations were identified, including 13 mutations not recorded in the dbSNP database. Twenty-seven previously unreported sequence-based alleles were identified. Additionally, differences in Y-STRs between the Chinese Han population and three American populations (African Americans, Caucasians, and Hispanics) were revealed from sequence-based data analysis. In summary, this study provides a detailed summary of the sequence features of 42 Y-STRs in the Chinese Han population, improving our understanding of Y-STRs and providing basic data of sequence variations for the application of Y-STRs.

Heteroblasty in Conchocarpus heterophyllus (A.St.-Hil.) Kallunki & Pirani (Rutaceae): An approach of leaf development from the unifoliolate leaves

Heteroblasty is a phenomenon marked by abrupt changes in the development of organs of certain species. In our research on Conchocarpus heterophyllus, a shrub with palmately arranged leaflets ranging from 3(4) to 5(–7), we observed significant variations in leaf patterns between seedlings, young plants, and plants in the reproductive phase, indicating transition events during plant development. The focus of this study is to thoroughly investigate these events of heteroblasty and heterophylly in C. heterophyllus, with new evidence of the unifoliolate condition in Rutaceae. Morphological and anatomical observations were conducted from mature leaves of individuals at different stages of development. The results show that the heteroblasty in C. heterophyllus is marked by changes in leaf patterns across three stages. In stage 1, seedlings exclusively produce unifoliolate leaves from the first foliage leaf. After the eighth to tenth node from the cotyledons, heterophylly becomes evident with the emergence of leaves containing 2–3 leaflets, in addition to unifoliolate leaves, characterizing the second stage. The third stage is defined by the onset of the reproductive phase, marked by the presence of inflorescences. In this stage, the leaf complexity varies, with the number of leaflets ranging from 2 to 5, and unifoliolate leaves are lacking. Furthermore, our study identified the occurrence of vascular traces of leaflets in the swollen region located at the apex of the petiole of all unifoliolate leaves, occurring in stages 1 and 2. These vascular traces are interpreted as remnants of the lost lateral leaflets in unifoliolate leaves and represent novel evidence of this special condition in compound leaves. Heteroblasty in C. heterophyllus is a genetically controlled phenomenon, and consequently, prospective investigations involving microRNAs, particularly microRNAs 156 and 172, have the potential to provide valuable insights into the genetic mechanisms underlying the transition between different phases.

A conceptual model explaining spatial variation in soil nitrous oxide emissions in agricultural fields

Soil emissions of nitrous oxide contribute substantially to global warming from agriculture. Spatiotemporal variation in nitrous oxide emissions within agricultural fields leads to uncertainty in the benefits of climate-smart agricultural practices. Here, we present a conceptual model explaining spatial variation in temporal patterns of soil nitrous oxide emissions developed from high spatial resolution measurements of soil nitrous oxide emissions, gross nitrous oxide fluxes, and soil physicochemical properties in two maize fields in Illinois, USA. In sub-field locations with consistently low nitrous oxide emissions, soil nitrate and dissolved organic carbon constrained nitrous oxide production irrespective of changes in soil moisture. In sub-field locations where high emissions occurred episodically, soil nitrate and dissolved organic carbon availability were higher, and increases in soil moisture stimulated nitrous oxide production. These findings form the ‘cannon model’ which conceptualizes how sub-field scale variation in soil nitrate and dissolved organic carbon determines where increases in soil moisture can trigger high soil nitrous oxide emissions within agricultural fields.

International practice patterns and perspectives on endovascular therapy for the treatment of cerebral venous thrombosis.

Cerebral venous thrombosis (CVT) accounts for 0.5-1% of all strokes. The role of endovascular therapy (EVT) in the management of CVT remains controversial and variations in practice patterns are not well known. Here, we present a comprehensive, international characterization of practice patterns and perspectives on the use of EVT for CVT.Methods A comprehensive 42-question survey was distributed to stroke clinicians globally from May-October 2023, asking about practice patterns and perspectives on the use of EVT for CVT. The overall response rate was 31% (863 respondents of 2744 invited) across 61 countries. The majority of respondents (74%) supported the use of EVT for CVT in certain clinical situations. Key considerations for decision-making in using EVT favored clinical over radiographic/procedural factors and included worsening level of consciousness (86%) and worsening neurological deficits (76%). In the past three years, 56% of respondents used EVT for the treatment of CVT, with most (49.5%) involved in 2-5 cases. Among interventionalists, significant variability existed in the techniques used for EVT (p<0.001), with aspiration thrombectomy (56%) and stent retriever (51%) being the most used overall. Regionally, interventionalists from China predominately used intra-sinus heparin (56%), while this technique was most commonly ranked as "never indicated" throughout the rest of the world (23%). Post-procedure, low molecular weight heparin was the most used anticoagulant (83%), although North American respondents favored unfractionated heparin (37%), while imaging was primarily split between magnetic resonance (71.8%) and computed tomography (65.9%) arteriography or venography. Our survey reveals significant heterogeneity in approaches to EVT for CVT, and provides a comprehensive characterization of indications, techniques and long-term management used by clinicians internationally. This resource will aid in optimizing patient selection and endovascular treatments for future trials.

Stereotactic ablative radiotherapy for primary kidney cancer – An international patterns of practice survey

PurposeTo conduct an international survey of radiation oncologists treating primary renal cell carcinoma (RCC) with SABR to ascertain the general patterns of SABR use, common dose/treatment/follow-up details, and expected outcomes. Materials and methodsA 51-question survey was created containing the following themes: prevalence and clinical scenarios in which RCC SABR is used, dose-fractionation schedules, treatment delivery details, follow-up/outcome assessments, and implementation barriers. The survey was distributed widely across multiple influential radiation oncology societies and social media, and ran from January to April 2023. ResultsA total of 255 respondents participated, mostly from academic centers within Europe/North America. Of these, 40 % (n = 102) currently offer SABR (50 % having begun within the last 3 years). Common barriers in non-users included lack of referrals by urologists and lack of supportive practice guidelines. Of respondents who do offer SABR, 77 % treat both small (4 cm or less) and large (>4 cm) renal masses. Dose-fractionation strategies varied from 27-52 Gy (3–5 fractions) for multifraction regimens, and 15–34 Gy for single fractions. Apart from treatment for medically inoperable disease, scenarios in which SABR was likely to be offered were for recurrence post surgery/thermal ablation and for oligometastatic kidney lesions. Uncommon scenarios included RCC with renal vein/inferior vena cava thrombosis, and as cytoreductive therapy in metastatic RCC. Expected local control outcomes were generally above 70 %, higher for small versus large renal masses. ConclusionsSABR is a relatively newer indication for primary RCC, offered by less than 50% of respondents, with both consistent and variable practice patterns observed.

Hierarchically Structured and Tunable Hydrogel Patches: Design, Characterization, and Application.

Recent studies show the importance of hydrogel geometry for various applications, such as encoding, micromachines, or tissue engineering. However, fabricating hydrogel structures with micrometer-sized features, advanced geometry, and precise control of porosity remains challenging. This work presents hierarchically structured hydrogels, so-called hydrogel patches, with internally deviating regions on a micron-scale. These regions are defined in a one-step, high-throughput fabrication process via stop-flow lithography. Between the specified projection pattern during fabrication, an interconnecting lower crosslinked and more porous hydrogel network forms, resulting in at least two degrees of crosslinking within the patches. A detailed investigation of patch formation is performed for two material systems and pattern variations, revealing basic principles for reliable patch formation. In addition to the two defined crosslinked regions, further regions are implemented in the patches by adapting the pattern accordingly. The variations in pattern geometry impact the mechanical characteristics of the hydrogel patches, which display pattern-dependent compression behavior due to predefined compression points. Cell culture on patches, as one possible application, reveals that the patch pattern determines the cell area of L929 mouse fibroblasts. These results introduce hierarchically structured hydrogel patches as a promising and versatile platform system with highcustomizability.

Shadows in the forest: Uncovering unusual colouration records in mammals from the Ecuadorian Tropical Andes.

Variations in colouration patterns have been reported in numerous wildlife species, particularly birds. However, the increased use of camera traps for wildlife monitoring has enabled the detection of elusive species and phenotypic variations that might otherwise go undetected. Here, we compiled records of unusual colouration patterns in terrestrial mammals, documented through camera-trap studies over a 12-year period in the Llanganates-Sangay Connectivity Corridor, in the Tropical Andes of Ecuador. We identified colour variations in seven species of terrestrial mammals, including disorders, such as melanism, white spotting/ piebaldism, xanthocromism and progressive greying. Notably, we reported a high prevalence of melanism in wild populations of the clouded oncilla, along with observations on the species' activity patterns. Approximately half of the recorded clouded oncillas were melanistic. We detected significant differences in activty patterns between melanistic and non-melanistc clouded oncilla, with melanistic morphs showing a peak of activity between 3 a.m. and before dawn. The proportion of melanistic individuals suggests that melanism is widespread throughout the corridor. However, its impact on the species' fitness remains unclear.