Range Of Variation Research Articles

Simulation of nanopore sequencing signal data with tunable parameters.

In silico simulation of high-throughput sequencing data is a technique used widely in the genomics field. However, there is currently a lack of effective tools for creating simulated data from nanopore sequencing devices, which measure DNA or RNA molecules in the form of time-series current signal data. Here, we introduce Squigulator, a fast and simple tool for simulation of realistic nanopore signal data. Squigulator takes a reference genome, a transcriptome, or read sequences, and generates corresponding raw nanopore signal data. This is compatible with basecalling software from Oxford Nanopore Technologies (ONT) and other third-party tools, thereby providing a useful substrate for development, testing, debugging, validation, and optimization at every stage of a nanopore analysis workflow. The user may generate data with preset parameters emulating specific ONT protocols or noise-free "ideal" data, or they may deterministically modify a range of experimental variables and/or noise parameters to shape the data to their needs. We present a brief example of Squigulator's use, creating simulated data to model the degree to which different parameters impact the accuracy of ONT basecalling and downstream variant detection. This analysis reveals new insights into the nature of ONT data and basecalling algorithms. We provide Squigulator as an open-source tool for the nanopore community.

Predicting radiation-induced immune suppression in lung cancer patients treated with stereotactic body radiation therapy.

Stereotactic body radiation therapy (SBRT) is known to modulate the immune system and contribute to the generation of anti-tumor T cells and stimulate T cell infiltration into tumors. Radiation-induced immune suppression (RIIS) is a side effect of radiation therapy that can decrease immunological function by killing naive T cells as well as SBRT-induced newly created effector T cells, suppressing the immune response to tumors and increasing susceptibility to infections. RIIS varies substantially among patients and it is currently unclear what drives this variability. Models that can accurately predict RIIS in near real time based on treatment plan characteristics would allow treatment planners to maintain current protocol specific dosimetric criteria while minimizing immune suppression. In this paper, we present an algorithm to predict RIIS based on a model of circulating blood using early stage lung cancer patients treated with SBRT. This Python-based algorithm uses DICOM data for radiation therapy treatment plans, dose maps, patient CT data sets, and organ delineations to stochastically simulate blood flow and predict the doses absorbed by circulating lymphocytes. These absorbed doses are used to predict the fraction of lymphocytes killed by a given treatment plan. Finally, the time dependence of absolute lymphocyte count (ALC) following SBRT is modeled using longitudinal blood data up to a year after treatment. This model was developed and evaluated on a cohort of 64 patients with 10-fold cross validation. Our algorithm predicted post-treatment ALC with an average error of cells/L with 89% of the patients having a prediction error below 0.5×109 cells/L. The accuracy was consistent across a wide range of clinical and treatment variables. Our model is able to predict post-treatment ALC<0.8 (grade 2 lymphopenia), with a sensitivity of 81% and a specificity of 98%. This model has a ∼38-s end-to-end prediction time of post treatment ALC. Our model performed well in predicting RIIS in patients treated using lung SBRT. With near-real time model prediction time, it has the capability to be interfaced with treatment planning systems to prospectively reduce immune cell toxicity while maintaining national SBRT conformity and plan quality criteria.

Future climate projection across Tanzania under CMIP6 with high-resolution regional climate model

Climate change is one of the most pressing challenges faced by developing countries due to their lower adaptive capacity, with far-reaching impacts on agriculture. The mid-century period is widely regarded as a critical moment, during which adaptation is deemed essential to mitigating the associated impacts. This study presents future climate projections across Tanzania using the latest generation of global climate models (CMIP6) combined with a high-resolution regional climate model. The findings indicate that, the trends in temperature and precipitation in Tanzania from 1991 to 2020, minimum temperatures showed the highest variability with a trend of 0.3 °C, indicating significant fluctuations in minimum temperature over the decades. Maximum temperatures also showed high variability with a trend of 0.4 °C. There is a range of variability in precipitation per decade for different regions in Tanzania, with some regions experiencing significant decreases in precipitation of up to − 90.3 mm and − 127.6 mm. However, there were also regions that experienced increases in precipitation, although these increases were generally less than 4.8 mm over the decades. The projections of minimum and maximum temperatures from 2040 to 2071 under the Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP 5–8.5 are projected to increase by 0.14 °C to 0.21 °C per decade, across different regions. The average projected precipitation changes per decade vary across regions. Some regions are projected to experience increases in precipitation. Other regions are projected to show decreases in precipitation within the range of − 0.6 mm to 15.5 mm and − 1.5 mm to 47.4 mm under SSP2–4.5 and SSP5–8.5 respectively. Overall, both scenarios show an increase in projected temperatures and precipitation for most regions in Tanzania, with some areas experiencing more significant increases compared to others. The changes in temperatures and precipitation are expected to have significant impacts on agriculture and water resources in Tanzania.

Evaluation of the support vector regression (SVR) and the random forest (RF) models accuracy for streamflow prediction under a data-scarce basin in Morocco

Streamflow prediction is a key variable for water resources management. It becomes more important in semi-arid regions such as the Tensift river basin in Morocco, where water resources are facing a severe drought and the demand is continuously increasing. The present analysis focuses on evaluating Machine Learning techniques, namely support vector regression (SVR) and Random Forest (RF) against the multiple linear regression (MLR) for daily streamflow forecasting in the mountainous sub-basin of Rheraya between 2003 and 2016. The results show that SVR performed best, followed by RF and MLR. In measurable terms and regarding mean performance, SVR exhibited the higher Nash–Sutcliffe efficiency score (NSE = 0.59) and a lower root mean squared error (RMSE = 1.18 m3s-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {m}^3\\,\ ext {s}^{-1}$$\\end{document}) compared to RF (NSE = 0.53, RMSE = 1.18 m3s-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {m}^3\\,\ ext {s}^{-1}$$\\end{document}) and MLR (NSE = 0.54, RMSE = 1.01 m3s-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {m}^3\\,\ ext {s}^{-1}$$\\end{document}). Furthermore,the available time series was too short to properly capture the full range of streamflow variability, which reduced the prediction performance outside of the calibration conditions. These findings suggest that ML algorithms, particularly SVR, can provide accurate streamflow estimation useful for water resources management when trained on a representative period. The results highlight the capacity of Machine Learning algorithms, specifically SVR, to augment streamflow prediction for enhanced water resource management in arid regions.

Unraveling the diversity of hyphal explorative traits among Rhizophagus irregularis genotypes.

Differences in functioning among various genotypes of arbuscular mycorrhizal (AM) fungi can determine their fitness under specific environmental conditions, although knowledge of the underlying mechanisms still is very fragmented. Here we compared seven homokaryotic isolates (genotypes) of Rhizophagus irregularis, aiming to characterize the range of intraspecific variability with respect to hyphal exploration of organic nitrogen (N) resources, and N supply to plants. To this end we established two experiments (one in vitro and one in open pots) and used 15N-chitin as the isotopically labeled organic N source. In Experiment 1 (in vitro), mycelium of all AM fungal genotypes transferred a higher amount of 15N to the plants than the passive transfer of 15N measured in the non-mycorrhizal (NM) controls. Noticeably, certain genotypes (e.g., LPA9) showed higher extraradical mycelium biomass production but not necessarily greater 15N acquisition than the others. Experiment 2 (in pots) highlighted that some of the AM fungal genotypes (e.g., MA2, STSI) exhibited higher rates of targeted hyphal exploration of chitin-enriched zones, indicative of distinct N exploration patterns from the other genotypes. Importantly, there was a high congruence of hyphal exploration patterns between the two experiments (isolate STSI always showing highest efficiency of hyphal exploration and isolate L23/1 being consistently the lowest), despite very different (micro) environmental conditions in the two experiments. This study suggests possible strategies that AM fungal genotypes employ for efficient N acquisition, and how to measure them. Implications of such traits for local mycorrhizal community assembly still need to be understood.

Embracing epistemic uncertainty: a risk evaluation method for pollutants in stormwater

ABSTRACT In this study, we show that pollutants of emerging concern are, by nature, prone to the emergence of epistemic uncertainty. We also show that the current uncertainty quantification methods used for pollutant modelling rely almost exclusively on parameter uncertainty, which is not adequate to tackle epistemic uncertainty affecting the model structure. We, therefore, suggest a paradigm shift in the current pollutant modelling approaches by adding a term explicitly accounting for epistemic uncertainties. In a proof-of-concept, we use this approach to investigate the impact of epistemic uncertainty in the fluctuation of pollutants during wet-weather discharge (input information) on the distribution of mass of pollutants (output distributions). We found that the range of variability negatively impacts the tail of output distributions. The fluctuation time, associated with high covariance between discharge and concentration, is a major driver for the output distributions. Adapting to different levels of epistemic uncertainty, our approach helps to identify critical unknown information in the fluctuation of pollutant's concentration. Such information can be used in a risk management context and to design smart monitoring campaigns.

Morphological and yield trait-based evaluation and selection of chili (Capsicum annuum L.) genotypes suitable for both summer and winter seasons

Abstract Chili (Capsicum annuum L.) is one of the most important vegetable cum spice crops grown throughout the world. Evaluation of genotypes based on morphological and yield contributing traits provides the opportunity to assess variability and select superior genotypes. The present investigation was carried out from April 2018 to November 2018 in the summer season and from December 2018 to June 2019 in the winter season in a net house and field, respectively, in the Department of Genetics and Plant Breeding, Patuakhali Science and Technology University, Patuakhali, Bangladesh. A set of 30 qualitative and 13 quantitative characteristics developed by Biodiversity International were recorded to evaluate the genotypes for morphological and yield-contributing traits. The estimated Shannon–Weaver diversity for qualitative traits ranged from 0 to 1.334, where the maximum diversity was recorded for fruit color at an intermediate stage, stem color after transplanting, fruit surface, stigma position, calyx margin shape, and fruit set, while the minimum diversity (H′ = 0) was observed for the traits corolla shape and anthocyanin spots, indicating no diversity of these traits. Thirteen quantitative traits were also analyzed for the selection of the genotypes with the greatest yield. The quantitative traits also exhibited a wide range of variability according to descriptive statistics and analysis of variance. Moreover, the present study revealed a high heritability for almost all of the quantitative traits, which was confirmed by the values of genotypic coefficient of variation, phenotypic coefficient of variation, h 2 b, and GA for both the summer and winter seasons. This suggests that these traits are predominantly governed by additive genes, making them highly amenable to effective selection. The heatmap analysis based on the morphological quantitative traits revealed five clusters for both the summer and winter seasons for the studied chili genotypes, four distinct clusters in the summer season, and three distinct clusters in the winter season for the parameters studied. Using the multi-trait genotype–ideotype distance index based on multiple trait information, the genotypes R-06, R-07, C0525-2, BARI (Bangladesh Agricultural Research Institute) Morich-2, Tengakhali, and Rcy 002 were selected as promising for both the summer and winter seasons. Hence, they are recommended for commercial cultivation or use as parent materials in future breeding for the development of new cultivars adaptable to climate change.

Amorphization transition and electrical properties of La2−xCexCuO4 polycrystalline materials

In contrast to crystals, amorphous materials exhibit distinctive properties and applications due to their unique electronic structure and surface energy. Moreover, the amorphous modification of crystalline materials also results in numerous notable and unique properties. In this study, we propose the amorphous modification of polycrystal La2CuO4 with CeO2, examining the composition of the two phases and changes in the crystal structure. Our findings indicate that the degree of ordering of the La2CuO4 phase significantly decreases as the CeO2 content increases, leading to a resistivity increase in 3–4 orders of magnitude. Furthermore, within the 40–400 K, the transition of the Efros–Shklovskii variable range hopping (E–S VRH) transport mechanism between localized states to the Mott 3D VRH and the crossover around 230 K is observed. The method presented here has great potential in modifying polycrystalline materials by using amorphization to make a sudden increase in resistivity.

Response Surface Optimization for Compliant Joint of Humanoid Robot Using ANSYS - Design of Experiment

Objective: A Compliant Joint of humanoid robot is a spring-loaded assembly, which is used to interact safely with the environment, and it helps to stabilized sudden shock and vibration in the robotic system. At the moment, compliant joints are required to optimize their size and dimensions which result into optimized weight and factor of safety of humanoid robot. Methods: Analysis is carried out using Response Surface Methodology (RSM) and Multi-Objective Genetic Algorithm (MOGA) using ANSYS. The current study employed goal-driven optimisation using ANSYS Workbench to minimise weight and achieve the required factor of safety range for the compliant joint. To find out range of variables such as rim thickness, shaft diameter, base thickness, module thickness and spoke thickness affecting on responses such as factor of safety and geometrical mass of compliant joint single factor single response parametric analysis is carried out. Findings: Based on trend of preliminary analysis variable range and combinations are selected to study interaction effect of parameters to obtain favorable factor of safety and low geometrical mass. The optimized compliant joint is compared with various design and validated through the developed actual module. Novelty: Eventually, the geometry mass of the compliant joint was reduced from 0.8604 kg to 0.6449 kg, resulting in a lighter weight (24.06% reduction) with a 1.7533 factor of safety and more compact in size (outer diameter is shrink from 142 to 126 mm). Keywords: Compliant joint, Goal driven optimization, Response surface optimization, Design of experiment, Humanoid robot

“Let’s talk about sex, fertility and menopause”: The Irish experience for those diagnosed with metastatic breast cancer (MBC)—An all-Ireland patient-led survey (CTRIAL-IE 23-05).

12057 Background: Therapeutic advances have improved MBC survival rates, but patient expressed needs such as sexual wellness have not been evaluated in Ireland to date. Heretofore, the patient’s voice has not been central to such an assessment, compromising MBC care. Methods: An anonymous online survey was designed by a cohort of 30 patients with MBC facilitated by a multidisciplinary breast cancer specialist taskforce. A questionnaire asked individuals to report their experience across three domains: sexual wellness, fertility and menopause. Patients with MBC 18 years or older living on the island of Ireland were eligible to participate. The survey was publicised in the media and in oncology clinics nationally and was available for 10 weeks. The survey consisted of a mixture of open and closed questions and results were summarised by means of counts and percentages for categorical variables and by means of mean, standard deviation, median and range for continuous variables. Results: Between July-October 2023, 246 patients completed the survey. The median age was 52.5 and 98% of responders were female. Over 75% of patients were not offered advice or support on the effects of MBC and the side effects of treatment on sexual wellness. This is despite over 60% of patients experiencing changes in their sex lives, with only 22.8% of those seeking medical advice. Those who did not seek advice stated fear of being dismissed or misunderstood (26.9%) and being uncomfortable discussing the topic (44.4%) as their reasoning. Less than half of patients &lt; 50 years considered that sufficient information was provided on the impact of treatment on fertility. Over 95% of patients were menopausal or experienced permanent changes to their menstrual cycle but 29.3% of patients who sought support reported that they did not receive it. Participants who did not seek support, were asked what supports could have been provided (Table). Conclusions: Patient-led research demonstrates that the patients’ voice is imperative to identify meaningful health interventions. MBC impacts sexual wellness for the majority of patients, a minority of whom seek medical advice, and a majority of whom are uncomfortable discussing the topic. The majority of participants &lt;50 years felt oncofertility advice was insufficient, suggesting that improvements are needed in the information and support given to younger cancer patients regarding their fertility. Support with menopausal symptoms was commonly sought but resources were limited. The majority of patients would welcome availability of a complex menopause clinic and access to improved curated information resources on sexual wellness and fertility. [Table: see text]

Beyond Rubik: The Embodiment–Presence–Interactivity Cube Applied to Exercise

Evidence-based interventions are needed to promote engagement in physical activity. Audio-visual stimuli are frequently employed to enhance the exercise experience. Nonetheless, there is a paucity of research that examines the qualities of technological devices that are employed. Using the Embodiment–Presence–Interactivity Cube (Flavián et al., 2019) as a guiding conceptual framework, the aim of this registered report was to examine how each dimension of the cube (i.e., embodiment, presence and interactivity) influenced a range of exercise-related affective and perceptual variables. A counterbalanced within-subjects design was employed (N = 24). Participants completed 20-min exercise bouts on a cycle ergometer under four conditions: Television, augmented reality, 360° video and virtual reality. A repeated-measures ANOVA indicated a significant Condition × Timepoint interaction for affective valence (p = .046), with greater embodiment offered by technological devices leading to more positive responses. Analyses also indicated main effects of condition for exercise enjoyment, remembered pleasure and forecasted pleasure, with greater presence of technological devices leading to more positive responses. Technologies that combine high levels of embodiment, presence and interactivity (e.g., virtual reality) appear to yield several benefits in terms of in-task (e.g., affective valence) and post-task (e.g., remembered pleasure) responses for exercise conducted at ventilatory threshold.

Validation of oscillometric ratio and maximum gradient methods for non-invasive blood pressure measurement with intra-arterial blood pressure measurements as reference.

Most non-invasive blood pressure (BP) measurements are carried out using instruments which implement either the Ratio or the Maximum Gradient oscillometric method, mostly during cuff deflation, but more rarely during cuff inflation. Yet, there is little published literature on the relative advantages and accuracy of these two methods. In this study of 40 lightly sedated individuals aged 64.1 ± 9.6 years, we evaluate and compare the performance of the oscillometric ratio (K) and gradient (Grad) methods for the non-invasive estimation of mean pressure, SBP and DBP with reference to invasive intra-arterial values. There was no significant difference between intra-arterial estimates of mean pressure made via Korotkoff sounds (MP-OWE) or the gradient method (MP-Grad). However, 17.7% of MP-OWE and 15% of MP-Grad were in error by more than 10 mmHg. SBP-K and SBP-Grad underestimated SBP by 14 and 18 mmHg, whilst accurately estimating DBP with mean errors of 0.4 ± 5.0 and 1.7 ± 6.1 mmHg, respectively. Relative to the reference standard SBP-K, SBP-Grad and DBP-Grad were estimated with a mean error of -4.5 ± 6.6 and 1.4 ± 5.6 mmHg, respectively, noting that using the full range of recommended ratios introduces errors of 12 and 7 mmHg in SBP and DBP, respectively. We also show that it is possible to find ratios which minimize the root mean square error (RMSE) and the mean error for any particular individual cohort. We developed linear models for estimating SBP and SBP-K from a range of demographic and non-invasive OWE variables with resulting mean errors of 0.15 ± 5.6 and 0.3 ± 5.7 mmHg, acceptable according to the Universal standard.

Mexican physicians' knowledge and attitudes towards hereditary colorectal cancer risk management.

e22552 Background: Genetic cancer risk assessment (GCRA) has become an integral component of the management of patients with colorectal cancer (CRC). Understanding clinical criteria and interpreting screening tests to identify patients who should be prioritized for genetic testing is especially critical in resource-constrained settings such as Mexico, where access to genetic testing is limited. Methods: We designed a self-applicable survey consisting of 18 items, divided into three sections: 7 on knowledge, 5 on clinical approaches, and 6 on access to genetic testing. The aim was to assess the practice patterns of Mexican physicians in contact with patients at risk for hereditary CRC. Initially, the survey was piloted among oncology fellows to refine and improve its relevance and applicability. Following adjustments, it was disseminated using a QR code at the annual meeting of the Mexican Society of Oncology (SMeO) in September 2023. Descriptive statistics were performed using frequency and proportions for categorical variables and median and range for quantitative variables. Results: A total of 78 physicians completed the survey, with a median age of 37 years (range, 27-70). Among them, 47.2% were female, 82% were medical oncologists, with a median practice duration of 6 years (range, 1- 40). 52.6% practice in cancer community centers, 83.4% were employed in public primary care settings, and 60% were affiliated to academic centers. Referral criteria for GCRA was identified correctly in 66.7% who recognized age at CRC diagnosis &lt; 50 years, 57.7% synchronous or metachronous CRC, 50% deficient mismatch repair CRC, and 37.2% PREMM5 score of ≥2.5%. Lynch syndrome, Familial Adenomatous Polyposis (FAP), and Cowden syndrome were identified as syndromes with an increased CRC risk by 76.9%, 71.8% and 56.7% of respondents, respectively. Decision-making based on likely pathogenic and pathogenic variants was indicated by 26.9% and 69.2%, while 12.8% reported making decisions based on variants of uncertain significance (VUS). Regarding clinical approach, 74.4% inquired about a family history of cancer at the initial consultation, and 66.7% referred patients for genetic counseling. The primary obstacles to accessing GCRA were the cost of genetic testing (43.6%) and the absence of medical genetics services at their centers (39.7%). Only 23% reported the availability of cascade testing within their practice. Conclusions: The identification of patients at risk of hereditary CRC was suboptimal among Mexican physicians. A high proportion of respondents were not fully qualified to interpret correctly genetic testing results. These indicate a need for awareness and educational programs to improve providers’ knowledge on predisposition cancer syndromes.

Characterization of cancer clinical trials in the community setting.

e13506 Background: Extending clinical trials to community oncology sites is an imperative to meet accrual goals, ensure generalizable results, and facilitate optimal and equitable clinical care. The types of trials attractive to and successful in the community, however, are likely different from those run primarily at academic centers. We analyzed the characteristics of clinical trials open at a network of community oncology practices across the United States in the last 2 years, in order to inform both sponsors and sites about the studies most well-suited for a community setting. Methods: We analyzed characteristics from every clinical trial entered in OncoTrials, a clinical trial screening and tracking tool utilized exclusively by US community oncology practices, between 9/1/2021 through 10/09/2023 via knowledge of the trial NCT number and data available through API from clinicaltrials.gov. Descriptive statistics, including median and range for continuous variables and percentages for categorical variables are reported. Results: 1,310 clinical trials were open across 64 community oncology practices. Sponsors were classified as 80% industry (n=1,054), 15% NIH (n=195), 1% network (n=13) and 5% other (n=48). 93% of studies were interventional, 5% observational, 1% registry and 1% expanded access. Types of interventional studies (n=1,215) and the number of sites that ran them are shown (Table). The median target enrollment across all studies was 235 patients. Individual trials were open at a median of 2 community practices in our cohort (range 1-22 practices); 521 clinical trials (40%) were open at only one practice and 34 trials (2.6%) were open at more than 10 practices. The mean number of unique trials open at a practice during our 2 year observation period was 54 (median 35, range 1-264). Among these trials, the most commonly associated tumor types were lung (15%), pan-solid tumors (14%), breast (11%), lymphoma (6%), colorectal (6%), prostate (5%), gynecologic (4%), bladder (4%), head and neck (4%) and melanoma (4%). Conclusions: Community oncology practices supported a robust portfolio of clinical trials, most commonly focused on treatment of solid tumors. The vast majority were interventional (phase 2-3) and industry-sponsored. Half of sites ran Phase I trials, and Phase IV trials were uncommon but had broad participation across sites. Notably, a large proportion of studies were only open at one single site in a cohort of 64 community oncology practices. This highlights the opportunity for sponsors to leverage networks, consortia, and further research to ensure that appropriate studies are accessible among a broader range of community research sites. [Table: see text]

Self-expressed needs and gaps in our care of metastatic breast cancer (MBC): An all-Ireland patient-led online survey (CTRIAL-IE 23-05).

12058 Background: Therapeutic advances have improved MBC survival rates, but patient self-expressed information needs have not been evaluated in Ireland to date. Heretofore, the patient’s voice has not been central to such an assessment, potentially leading to incorrect assumptions about MBC care. Methods: An anonymous online survey was designed by a cohort of 30 patients with MBC facilitated by a multidisciplinary breast cancer specialist taskforce. A questionnaire asked individuals to report their experience across three domains: credible information sources, palliative care access, and mental health. Patients with MBC 18 years or older living on the Island of Ireland were eligible to participate. The survey was publicised in the media and in oncology clinics nationally and was available for 10 weeks. The survey consisted of a mixture of open and closed questions and results were summarized by means of counts and percentages for categorical variables and by means of mean, standard deviation, median and range for continuous variables. Results: Between July-October 2023, 246 patients completed the survey. The median age of responders was 52.5. 99.4% of patients wished to have access to their personal medical records. Over 95% sought information about metastatic breast cancer outside of the clinic. The go-to information source varied (mostly online). No dedicated comprehensive information source on MBC was identified. Over 83% of patients with MBC were amenable to early palliative care referral but oncology teams did not raise this to the majority. Over 87% of patients reported mental health issues and the majority did seek support (88.4%). Living with uncertainty while awaiting scan results commonly led to feelings of anxiousness (79.5%), fearfulness (54.1%) and depression (15.2%). Conclusions: Patient-led research demonstrates that the patients’ voice is imperative to identify meaningful health interventions. System change including rapid streamlined access to personal medical records and the provision of a credible online information source dedicated to MBC may empower patients to enable them to live well for as long as possible. Routine discussion around early palliative care referral and an opt out approach may be of benefit. Acknowledging the high burden of mental health issues among this patient group and ongoing development of support services is crucial. Minimising the time patients live with uncertainty waiting for scan results should be prioritised.

Mangrove extent reflects estuarine typology and lifecycle events

Ecosystem services provided by mangrove forests are increasingly recognised worldwide. These values have led to numerous ecosystem protection and restoration measures, including recent blue carbon economic incentives to re-establish mangroves at scale. However, our current ability to predict mangrove extent is based mainly on inundation averages (e.g. hydroperiod) or tidal planes of mature mangrove forests. This study investigates the validity of existing methods to accurately predict mangrove extent by comparing the location of existing mangroves at 27 sites (in 23 estuaries in southeast Australia) against commonly used approaches. To this aim, the downslope and upslope surface elevation limits of the mangrove species Avicenna marina were measured on-site using high-resolution RTK-GPS equipment across different estuarine typologies, including drowned river valleys, strong and weakly tidal estuaries, tidal lakes, and intermittently open/closed estuaries. These measurements were analysed against long-term (8–30+ years) tidal records to estimate inundation characteristics within the estuary. Study results indicate that the commonly used tidal plane method for predicting mangrove extent can incorrectly predict vertical extent by over 40 per cent. Likewise, hydroperiod is a poor predictor of extent as similar hydroperiods can be calculated across significantly different estuarine inundation patterns.Interestingly, the results indicate that mangrove elevations and inundation patterns are not uniform within or across estuaries, although there is uniformity across related estuarine typologies. For instance, inundation patterns in drowned river valley estuaries are more consistent (less extreme inundation and exposure events) than in estuaries with a variable tidal range, such as tidal lakes. Ultimately, this study highlights that predictions of mangrove extent should consider estuarine typology, the lifecycle of mangroves, and potential physio-adaptations to changing inundation patterns. Importantly, future tidal conditions may encourage biophysical adaptations in mangroves which may confound the extent of existing predictive techniques.

An Analysis of Postgraduate Studies on Reading Culture

Reading culture is the love of reading, the ability to read continuously, and the ability to read effectively and critically. The reading culture of the individuals who make up a society is a strong foundation for the future. The objective of this study was to examine postgraduate theses on reading culture according to a range of variables. A total of 23 approved postgraduate theses, 10 doctoral theses and 13 master’s theses on reading culture between 2014 and 2024 registered at the YÖK National Thesis Centre were identified. The data analysis of the theses under examination was conducted using the content analysis method, which is a qualitative research method. The research revealed that the inaugural study on reading culture at the graduate level was conducted in 2014. The majority of studies were conducted in 2023, with the highest number of studies conducted at Ankara University. Of the studies, 13 were at the master’s level and 10 were at the doctoral level. The majority of studies were conducted by supervisors holding the title of professor. All studies were conducted in Turkish, and the qualitative research model was employed. The population and sample of the studies were predominantly teachers. Upon examination of the studies, it becomes evident that the significance of the concept of reading culture has been elucidated through the examination of reading culture in Turkey from various vantages. In accordance with the findings of the study, a series of recommendations were put forth.

Study of conduction mechanisms of InSeSb nano-chalcogenide alloys

The electrical conduction mechanisms for bulk samples of In0.1Se0.9−x Sb x (x = 0, 0.04, 0.08 and 0.12) nano-chalcogenide system, synthesized by the melt-quenching technique are investigated through current–voltage (I–V) characteristics. For the detailed study of conduction mechanism pellets of bulk samples are prepared. A thorough examination of electrical conductivity is done in the temperature range of 295–318 K and 0–50 V voltage range. From I–V measurements it is observed that samples are showing ohmic nature at lower field and non-ohmic nature at relatively higher field values. The temperature dependence of DC conductivity is analyzed using the Arrhenius relationship which is found to increase with Sb content. The value of activation energy and pre-exponential factor are calculated, which revealed that the conduction is due to the hopping of charge carriers among the localized states. Different parameters of Mott’s variable range hopping such as degree of disorder T 0, density of localized states N(E F), hopping distance (R hop), and hopping energy (W) are calculated. For the high field conduction process Poole–Frenkel, and Schottky processes are studied.

Stream Algal Biomass Associations with Environmental Variables in a Temperate Rainforest

Benthic algae and autotrophic seston are important bases of stream food webs, and several different environmental factors may influence their biomass. We explored how benthic algae and autotrophic seston biomass (using chlorophyll-a as a proxy for algal biomass) were associated with stream temperature, channel width, canopy cover, stream cardinal orientation, benthic macroinvertebrate functional feeding group abundance, salmonid biomass, and water velocity in 16 small, fish-bearing streams in the temperate rainforest of the Olympic Peninsula in Washington State, USA, in the summer of 2020. We performed a mixed-effects regression analysis of extracted chlorophyll-a (chl-a) and then used model averaging to determine significant (α = 0.05) algal–environmental associations for benthic algae and autotrophic seston separately. We found that benthic algae chl-a concentration increased significantly with stream temperature (p = 0.0085) and decreased significantly with water velocity (p = 0.0053). For autotrophic seston, we found that chl-a concentration increased significantly with benthic macroinvertebrate predator abundance (p = 0.0007) and stream temperature (p = 0.0160). This study underscores the need to consider a broad range of environmental variables when making research and management decisions concerning stream ecology.

Prediction of Positive Patient Health Questionnaire-2 Screening Using Area Deprivation Index in Primary Care.

Depression is recognized as a significant public health issue in the United States. The National Survey on Drug Use and Health reports that 21.0 million adults aged 18 or older had major depressive disorder in 2020, including 14.8 million experiencing a major depressive episode with severe impairment. The aim is to predict the positivity of Patient Health Questionnaire-2 (PHQ-2) outcomes among patients in primary care settings by analyzing a range of variables, including socioeconomic status, demographic characteristics, and health behaviors, thereby identifying those at increased risk for depression. Employing a machine learning approach, the study utilizes retrospective data from electronic health records across 15 primary care clinics in South Florida to explore the relationship between social determinants of health (SDoH), including area of deprivation index (ADI) and PHQ-2 positivity. The study encompasses 15 primary care clinics located in South Florida, where a diverse patient population receives care. Analysis included 94,572 patient visits; 74,636 records were included in the study. If a zip+4 was not available or an ADI score did not exist, the visit was not included in the final analysis. Screening involved the PHQ-2, assessing depressed mood and anhedonia, with a cutoff >2 indicating positive screening. ADI was used to assess SDoH by matching patients' residential postal codes to ADI national percentiles. Demographics, sexual history, tobacco use, caffeine intake, and community involvement were also evaluated in the study. Over 40 machine learning algorithms were explored for their accuracy in predicting PHQ-2 outcomes, using software tools including Scikit-learn and stats models in Python. Variables were normalized, scored, and then subjected to predictive regression models, with Random Forest showing outstanding performance. Feature engineering and correlation analysis identified ADI, age, education, visit type, coffee intake, and marital status as significant predictors of PHQ-2 positivity. The area under the curve and model accuracies varied across clinics, with specific clinics showing higher predictive accuracy and others (p > .05). The study concludes that the ADI, as a proxy for SDoH, alongside other individual factors, can predict PHQ-2 positivity. Health organizations can use this information to anticipate health needs and resource allocation.