julilili42/papers huggingface dataset:
Investigating the Dynamic History of a Promontory Ice Rise using Radar Data
2024-06-07

Ice rises hold valuable records revealing the ice dynamics and climatic history of Antarctic coastal areas from the Last Glacial Maximum to today. This history is often reconstructed from isochrone radar stratigraphy and simulations focusing on Raymond arch evolution beneath the divides. However, this relies on complex ice-flow models where many parameters are unconstrained by observations. Our study explores quad-polarimetric, phase-coherent radar data to enhance understanding near ice divides and domes, using Hammarryggen Ice Rise (HIR) as a case study. Analysing a 5 km profile intersecting the dome, we derive vertical strain rates and ice-fabric properties. These align with ice core data near the summit, increasing confidence in tracing signatures from the dome to the flanks. The Raymond effect is evident, correlating with surface strain rates and radar stratigraphy. Stability is inferred over millennia for the saddle connecting HIR to the mainland, but dome ice-fabric appears relatively young compared to 2D model predictions. In a broader context, quad-polarimetric measurements provide valuable insights into ice-flow models, particularly for anisotropic rheology. Including quad-polarimetric data advances our ability to reconstruct past ice flow dynamics and climatic history in ice rises.
Glyphosate contamination in European rivers not from herbicide application?
2024-06-07

<title>Abstract</title> The most widely used herbicide glyphosate contaminates surface waters around the globe. Both agriculture and urban applications are discussed as sources for glyphosate. To better delineate these sources, we investigated long-term time series of concentrations of glyphosate and its main transformation product aminomethylphosphonic acid (AMPA) in a large meta-analysis of about 100 sites in the USA and Europe. The U.S. data reveal pulses of glyphosate and AMPA when the discharge of the river is high, likely indicating mobilization by rain after herbicide application. In contrast, European concentration patterns of glyphosate and AMPA show a typical cyclic-seasonal component in their concentration patterns, correlating with patterns of wastewater markers such as pharmaceuticals, which is consistent with the frequent detection of these compounds in wastewater treatment plants. Our large meta-analysis clearly shows that for decades, municipal wastewater was a very important source of glyphosate has been municipal wastewater. In addition, European river water data show rather high and constant basic mass fluxes of glyphosate all over the year, not expected from herbicide application. From our meta-analysis, we define criteria for a source of glyphosate, which was hidden so far. Details from the meta-analysis and the knowledge that AMPA is a known transformation product of aminopolyphohsphonates let us hypothesize that also these antiscalants are an important source for glyphosate in Europe, where these compounds are used in detergents.
Symptomatic Clusters Related to Amyloid Positivity in Cognitively Unimpaired Individuals
2024-06-07

Background: The NIA-AA Research Framework on Alzheimer’s disease (AD) proposes a transitional stage (stage 2) characterized by subtle cognitive decline, subjective cognitive decline (SCD) and mild neurobehavioral symptoms (NPS). Objective: To identify participant clusters based on stage 2 features and assess their association with amyloid positivity in cognitively unimpaired individuals. Methods: We included baseline data of N = 338 cognitively unimpaired participants from the DELCODE cohort with data on cerebrospinal fluid biomarkers for AD. Classification into the AD continuum (i.e., amyloid positivity, A+) was based on Aβ42/40 status. Neuropsychological test data were used to assess subtle objective cognitive dysfunction (OBJ), the subjective cognitive decline interview (SCD-I) was used to detect SCD, and the Neuropsychiatric Inventory Questionnaire (NPI-Q) was used to assess NPS. A two-step cluster analysis was carried out and differences in AD biomarkers between clusters were analyzed. Results: We identified three distinct participant clusters based on presented symptoms. The highest rate of A+ participants (47.6%) was found in a cluster characterized by both OBJ and SCD. A cluster of participants that presented with SCD and NPS (A+:26.6%) and a cluster of participants with overall few symptoms (A+:19.7%) showed amyloid positivity in a range that was not higher than the expected A+ rate for the age group. Across the full sample, participants with a combination of SCD and OBJ in the memory domain showed a lower Aβ42/ptau181 ratio compared to those with neither SCD nor OBJ. Conclusions: The cluster characterized by participants with OBJ and concomitant SCD was enriched for amyloid pathology.
Causal analysis of absolute and relative risk reductions
2024-06-07

Any new medical innovation must first prove its benefits with reliable evidence from clinical trials. Evidence is frequently expressed using two metrics, summarizing treatment benefits based on either absolute risk reductions (ARRs) or relative risk reductions (RRRs). While both metrics are derived from the same data (e.g., observed frequencies of a disease in a treatment and control group), they implement conceptually and causally distinct ideas. Here, we analyze these risk reductions measures from a causal modeling perspective, revealing an isomorphic relationship to central measure of causal strength and causal Bayesian networks. First, we show that ARR is equivalent to the Delta P metric, while RRR is equivalent to the causal power metric, thus clarifying the implicit causal assumptions. Second, we show how this formal equivalence establishes a relationship with causal Bayesian networks theory, offering a basis for incorporating risk reduction metrics into a computational causal modeling framework. Drawing on these analyses, we demonstrate that under dynamically varying baseline risks, ARRs are inadequate when generalizing treatment effects to novel contexts. Specifically, the inherent assumption of a linear parameterization of the underlying causal graph leads to incorrect conclusions when generalizing to baseline risks differing from those from which the original effect was obtained. For instance, generalizing the effect of a vaccine to new contexts with different baseline risks (e.g., from one population to another). Our analyses highlight the shared principles underlying risk reduction metrics and measures of causal strength, emphasizing the potential for explicating causal structure and inference in epidemiological research.
Historical development of job profiles in adult education in Germany through the interaction of intermediaries from a conventional-theoretical perspective
2024-06-07

Job profiles in German adult education are manifold, and the requirements for engaging in professional activity are only minimally regulated by the state. It is primarily the providers in adult education who decide on access to the professional field and influence the historical development of job profiles. The article presents findings from a historical longitudinal analysis of job advertisements and other historical documents, focusing on the historical genesis of job profiles in adult education in the Federal Republic of Germany. The analysis draws on theoretical concepts from “Economics of Convention” (EC) research on occupational categories, intermediaries, and labor markets. The results demonstrate that the conception, dissemination, and long-term establishment of a specific job profile for adult education were only made possible through the interaction of different actors, each taking on different intermediary functions.
Impacts of Clinical Decision Support Systems on the Relationship, Communication, and Shared Decision-Making Between Health Care Professionals and Patients: Multistakeholder Interview Study
2024-06-08

Background Clinical decision support systems (CDSSs) are increasingly being introduced into various domains of health care. Little is known so far about the impact of such systems on the health care professional–patient relationship, and there is a lack of agreement about whether and how patients should be informed about the use of CDSSs. Objective This study aims to explore, in an empirically informed manner, the potential implications for the health care professional–patient relationship and to underline the importance of this relationship when using CDSSs for both patients and future professionals. Methods Using a methodological triangulation, 15 medical students and 12 trainee nurses were interviewed in semistructured interviews and 18 patients were involved in focus groups between April 2021 and April 2022. All participants came from Germany. Three examples of CDSSs covering different areas of health care (ie, surgery, nephrology, and intensive home care) were used as stimuli in the study to identify similarities and differences regarding the use of CDSSs in different fields of application. The interview and focus group transcripts were analyzed using a structured qualitative content analysis. Results From the interviews and focus groups analyzed, three topics were identified that interdependently address the interactions between patients and health care professionals: (1) CDSSs and their impact on the roles of and requirements for health care professionals, (2) CDSSs and their impact on the relationship between health care professionals and patients (including communication requirements for shared decision-making), and (3) stakeholders’ expectations for patient education and information about CDSSs and their use. Conclusions The results indicate that using CDSSs could restructure established power and decision-making relationships between (future) health care professionals and patients. In addition, respondents expected that the use of CDSSs would involve more communication, so they anticipated an increased time commitment. The results shed new light on the existing discourse by demonstrating that the anticipated impact of CDSSs on the health care professional–patient relationship appears to stem less from the function of a CDSS and more from its integration in the relationship. Therefore, the anticipated effects on the relationship between health care professionals and patients could be specifically addressed in patient information about the use of CDSSs.
Mother-infant feeding interactions in mothers with and without eating disorder history: Results of a structured observational study
2024-06-08

Whilst examined mothers with ED history are more concerned about feeding their children, ED psychopathology does not affect the quality of mother-infant interaction during feeding at the transition to autonomous eating at ten months of age.
Bursts boost nonlinear encoding in electroreceptor afferents
2024-06-08

Abstract Nonlinear mechanisms are at the heart of neuronal information processing, for example to fire an action potential, the membrane voltage must exceed a threshold nonlinearity. Even though, linear encoding schemes are commonly used and often successfully describe large parts of sensory encoding nonlinear mechanisms such as thresholds and saturations are well known to be crucial to encode behaviorally relevant features in the stimulus space not captured by linear methods. Here we analyze the role of bursts in p-type electroreceptor afferents (P-units) in the weakly electric fish Apteronotus leptorhynchus . It is long known that subpopulations of these cells fire bursts of action potentials while others do not. Previous research suggests, that the non-bursting cells are better at encoding the stimulus time-course while bursting neurons are better suited to encode special features in the stimulus. We here show, based on the analysis of experimental data and modeling, that bursts affect the linear as well as the nonlinear encoding. Theoretical work predicts that in simple leaky-integrate-and-fire model neurons, two periodic stimuli interact nonlinearly when the sum of the two frequencies matches the neuron’s baseline firing rate as quantified by the second-order susceptibility. Indeed, such nonlinear responses have been found in non-bursting P-units when stimulated by two beats simultaneously but only in those cells, that exhibit very low levels of intrinsic noise. In this study, we found that bursts strongly enhance these nonlinear responses which may play a critical role in the detection of weak intruder signals in the presence of a strong female signal, i.e. an electrosensory cocktail party.
Impairment of brain function in a mouse model of Alzheimer’s disease during the pre-depositing phase: the role of α7 nicotinic acetylcholine receptors
2024-06-08

Abstract Alzheimer’s disease (AD) is an age-dependent incurable neurodegenerative disorder accompanied by neuroinflammation, amyloid accumulation and memory impairment. It begins decades before the first clinical symptoms appear, and identifying early biomarkers is key for developing disease-modifying therapies. We show now in a mouse model of AD that before any amyloid deposition the brains of 1.5-month-old mice contain increased levels of pro-inflammatory cytokines IL-1β and IL-6, decreased levels of nicotinic acetylcholine receptors (nAChRs) in the brain and brain mitochondria and increased amounts of α7 nAChR-bound Aβ 1-42 , along with impaired episodic memory and increased risk of apoptosis. Both acute (1-week-long) and chronic (4-month-long) treatments with α7-selective agonist PNU282987, starting at 1.5 months of age, were well tolerated. The acute treatment did not affect the levels of soluble Aβ 1-42 but consistently upregulated the α7 nAChR expression, decreased the level of α7- Aβ 1-42 complexes and improved episodic memory of 1.5-month-old mice. The chronic treatment, covering the disease development phase, strongly upregulated the expression of all abundant brain nAChRs, reduced both free and α7-coupled Aβ 1-42 within the brain, had anti-inflammatory and antiapoptotic effects, and potently upregulated cognition, thus identifying α7 nAChRs as both early biomarker and potent therapeutic target for fighting this devastating disease.
Solving the retention time repeatability problem of hydrophilic interaction liquid chromatography
2024-06-10

Hydrophilic interaction (liquid) chromatography (HILIC) has become the first choice LC mode for the separation of hydrophilic analytes. Numerous studies reported the poor retention time repeatability of HILIC. The problem was often ascribed to slow equilibration and insufficient re-equilibration time to establish the sensitive semi-immobilized water layer at the interface of the polar stationary phase and the bulk mobile phase. In this study, we compare retention time repeatability in HILIC for borosilicate glass and PFA (co-polymer of tetrafluoroethylene and perfluoroalkoxyethylene) solvent bottles. During this study, we observed peak patterns shifting towards higher retention times (for metabolites and peptides) and lower retention times (oligonucleotide sample) with ongoing analysis time when standard borosilicate glass bottles were used as solvent reservoirs. It was hypothesized that release of ions (sodium, potassium, borate, etc.) from the borosilicate glass bottles leads to alterations (thickness and electrostatic screening effects) in the semi-immobilized water layer which is adsorbed to the polar stationary phase surface under acetonitrile-rich eluents in HILIC with concomitant shifts in retention. When PFA solvent bottles were employed instead of borosilicate glass, retention time repeatability was greatly improved and changed from average 8.4 % RSD for the tested metabolites with borosilicate glass bottles to 0.14 % RSD for the PFA solvent bottles (30 injections over 12 h). Similar improvements were observed for peptides and oligonucleotides. This simple solution to the retention time repeatability problem in HILIC might contribute to a better acceptance of HILIC, especially in fields like targeted and untargeted metabolomics, peptide and oligonucleotide analysis.
How can we quantify, explain, and apply the uncertainty of complex soil maps predicted with neural networks?
2024-06-10

Artificial neural networks (ANNs) have proven to be a useful tool for complex questions that involve large amounts of data. Our use case of predicting soil maps with ANNs is in high demand by government agencies, construction companies, or farmers, given cost and time intensive field work. However, there are two main challenges when applying ANNs. In their most common form, deep learning algorithms do not provide interpretable predictive uncertainty. This means that properties of an ANN such as the certainty and plausibility of the predicted variables, rely on the interpretation by experts rather than being quantified by evaluation metrics validating the ANNs. Further, these algorithms have shown a high confidence in their predictions in areas geographically distant from the training area or areas sparsely covered by training data. To tackle these challenges, we use the Bayesian deep learning approach "last-layer Laplace approximation", which is specifically designed to quantify uncertainty into deep networks, in our explorative study on soil classification. It corrects the overconfident areas without reducing the accuracy of the predictions, giving us a more realistic uncertainty expression of the model's prediction. In our study area in southern Germany, we subdivide the soils into soil regions and as a test case we explicitly exclude two soil regions in the training area but include these regions in the prediction. Our results emphasize the need for uncertainty measurement to obtain more reliable and interpretable results of ANNs, especially for regions far away from the training area. Moreover, the knowledge gained from this research addresses the problem of overconfidence of ANNs and provides valuable information on the predictability of soil types and the identification of knowledge gaps. By analyzing regions where the model has limited data support and, consequently, high uncertainty, stakeholders can recognize the areas that require more data collection efforts.
Nitric oxide modulates contrast suppression in a subset of mouse retinal ganglion cells
2024-06-10

Neuromodulators have major influences on the regulation of neural circuit activity across the nervous system. Nitric oxide (NO) has been shown to be a prominent neuromodulator in many circuits and has been extensively studied in the retina. Here, it has been associated with the regulation of light adaptation, gain control, and gap junctional coupling, but its effect on the retinal output, specifically on the different types of retinal ganglion cells (RGCs), is still poorly understood. In this study, we used two-photon Ca 2+ imaging and multi-electrode array (MEA) recordings to measure light-evoked activity of RGCs in the ganglion cell layer in the ex vivo mouse retina. This approach allowed us to investigate the neuromodulatory effects of NO on a cell type-level. Our findings reveal that NO selectively modulates the suppression of temporal responses in a distinct subset of contrast-suppressed RGC types, increasing their activity without altering the spatial properties of their receptive fields. Given that under photopic conditions, NO release is triggered by quick changes in light levels, we propose that these RGC types signal fast contrast changes to higher visual regions. Remarkably, we found that about one-third of the RGC types, recorded using two-photon Ca 2+ imaging, exhibited consistent, cell type-specific adaptational response changes throughout an experiment, independent of NO. By employing a sequential-recording paradigm, we could disentangle those additional adaptational response changes from drug-induced modulations. Taken together, our research highlights the selective neuromodulatory effects of NO on RGCs and emphasizes the need of considering non-pharmacological activity changes, like adaptation, in such study designs.
SGLT2 Inhibitors Correct Fluid Overload in Adult Kidney Transplant Recipients—A Prospective Observational Study
2024-06-10

In this longitudinal observational study, we measured urinary glucose concentration, body composition and volume status (bioimpedance spectroscopy) and plasma renin and aldosterone concentrations in n = 22 kidney transplant recipients (KTRs) initiating on SGLT2I at baseline (BL), and after 1 week and 1, 3, and 6 months. Estimated glomerular filtration rate (eGFR) decreased by −2 mL/min/1.73 m 2 (IQR −10–0) after 1 week and remained stable thereafter. Urinary glucose concentration was 10 (3–24) g/g creatinine after 1 week and correlated with eGFR (r 2 = 0.273; p = 0.057). SGLT2I did not affect HbA1c, fasting blood glucose, body weight, fat or lean mass. SGLT2I decreased fluid overload dependent on baseline overhydration (OH, r 2 = 0.54, p = 0.0003) without occurrence of dehydration. Plasma aldosterone increased at day 7, while plasma renin did not change significantly. In conclusion, SGLT2I corrected fluid overload in patients with elevated overhydration at baseline, while in euvolemic KTRs fluid status remained stable without reduction of body water below the reference range, thus promoting the safety of SGLT2I therapy in patients following kidney transplantation. Glucosuria, together with effects of SGLT2I on blood glucose control and body weight, is attenuated in KTRs dependent on eGFR.
Mobile digital gait analysis captures effects of botulinum toxin in hereditary spastic paraplegia
2024-06-10

Abstract Background and purpose Hereditary spastic paraplegias (HSPs) comprise a group of inherited neurodegenerative disorders characterized by progressive spasticity and weakness. Botulinum toxin has been approved for lower limb spasticity following stroke and cerebral palsy, but its effects in HSPs remain underexplored. We aimed to characterize the effects of botulinum toxin on clinical, gait, and patient‐reported outcomes in HSP patients and explore the potential of mobile digital gait analysis to monitor treatment effects and predict treatment response. Methods We conducted a prospective, observational, multicenter study involving ambulatory HSP patients treated with botulinum toxin tailored to individual goals. Comparing data at baseline, after 1 month, and after 3 months, treatment response was assessed using clinical parameters, goal attainment scaling, and mobile digital gait analysis. Machine learning algorithms were used for predicting individual goal attainment based on baseline parameters. Results A total of 56 patients were enrolled. Despite the heterogeneity of treatment goals and targeted muscles, botulinum toxin led to a significant improvement in specific clinical parameters and an improvement in specific gait characteristics, peaking at the 1‐month and declining by the 3‐month follow‐up. Significant correlations were identified between gait parameters and clinical scores. With a mean balanced accuracy of 66%, machine learning algorithms identified important denominators to predict treatment response. Conclusions Our study provides evidence supporting the beneficial effects of botulinum toxin in HSP when applied according to individual treatment goals. The use of mobile digital gait analysis and machine learning represents a novel approach for monitoring treatment effects and predicting treatment response.