Subtle Changes Research Articles

Tailored Permeation Through ≈1 nm Thick Carbon Nanomembranes by Subtle Changes in Their Molecular Design.

Due to their nanoscale thickness (≈1 nm) and exceptional selectivity for permeation of gases, nanomembranes made of 2D materials possess high potential for energy-efficient nanofiltration applications. In this respect, organic carbon nanomembranes (CNMs), synthesized via electron irradiation-induced crosslinking of aromatic self-assembled monolayers (SAMs), are particularly attractive, as their structure can be flexibly tuned by choice of molecular precursors. However, tailored permeation of CNMs, defined by their molecular design, has not been yet demonstrated. In this work, it is shown that the permeation of helium (He), deuterium (D2) and heavy water (D2O) for CNMs synthesized from biphenyl-based SAMs on silver (C6H5-C6H4-(CH2)n-COO/Ag, n = 2-6) can be tuned by orders of magnitude by changing the structure of the molecular precursors by just a single methylene unit. The selectivity in permeation of D2O/D2 with an unprecedented value of 200000 can be achieved in this way. The temperature-dependent study reveals a clear correlation between the molecular design and the permeation mechanisms facilitating therewith tailored synthesis of molecular 2D materials for separation technologies.

High-density surface electromyography evaluation of the suprahyoid muscles in patients with Parkinson's disease

Background and purposeHigh-density surface electromyography (HD-sEMG) allows non-invasive analysis of motor unit (MU) activity and recruitment. This study examines MU recruitment features as well as action potential using HD-sEMG in the suprahyoid muscles of patients with Parkinson's disease (PD) and assesses alterations caused by cervical percutaneous interferential current stimulation. MethodsElectrical activity of the suprahyoid muscles during tongue elevation was measured using HD-sEMG. Maximum voluntary contraction (MVC) was measured, then the modified entropy, coefficient of variation, correlation coefficient, and root mean square (RMS) were examined. For comparison, HD-sEMG was also conducted on healthy controls. In addition, this study assessed the impact of cervical percutaneous interferential current stimulation on HD-sEMG in patients with PD (Hoehn–Yahr stages 2–4) over 8 weeks. ResultsTwenty-two patients with PD (mean age 71.7 ± 6.0, 7 females) and 14 healthy controls (mean age 74.9 ± 6.1, 7 females) were included. The RMS at 60 % MVC was significantly lower in patients with PD than in healthy controls (p = 0.007). After cervical percutaneous interferential current stimulation, the correlation coefficients at 40 % and 60 % MVC significantly decreased (p = 0.040 and p = 0.048, respectively), and the RMS at 60 % MVC significantly increased (p = 0.035). ConclusionHD-sEMG of the suprahyoid muscles showed characteristic findings in patients with PD and was able to detect subtle changes due to electrical stimulation intervention. Thus, HD-sEMG could be a promising tool for evaluating swallowing-related muscle conditions and interventions.

Fault diagnosis for multiple redundancy aileron actuator based on parallel-SDP and polar sparse representation

Abstract As a critical component of aircraft flight control systems, the aileron actuator's fault directly affects the performance of flight control performance system and the overall flight safety of aircraft. Therefore, effective fault diagnosis of the aileron actuator becomes particularly important. However, with the development of redundancy design, the structure of aileron actuators more and more complex, and the fault modes are more and more diverse, which increases the difficulty of fault diagnosis significantly. To address this challenge, this study proposes a fault diagnosis method based on Parallel-SDP and Polar Sparse Representation.
First, the current residual signals of force motors are obtained using bi-step observer. Then, the residual signals are transformed into Parallel-SDP images, where each of the four petals of Parallel-SDP image generated from the corresponding channel. The Parallel-SDP image presents global fault information and capture subtle changes of residual signals, which increases the sensitivity of fault diagnosis. Subsequently, rapid single-channel fault localization is achieved by barycenter analysis of Parallel-SDP images. Finally, based on the result of fault localization, the proposed polar sparse representation algorithm is utilized for fault diagnosis of mechanical and control faults separately. Based on the dataset obtained from physical-parameter-simulation, the proposed method was validated, and the accuracy of fault diagnosis of was 99.29%, which is better than conventional fault diagnosis, meanwhile, the computational resources consumption of the proposed method is much less than deeplearning-based method, which is suitable for embedded computing system in aircraft.

Monitoring seismic velocity changes at Campi Flegrei (Italy) using seismic noise interferometry

Campi Flegrei is a volcanic field located west of Naples (Italy) in a densely populated area. Since 2005, its ground has been rising steadily due to the accumulation of fluids at shallow depths. The inflation of volcanic edifices is a possible precursor of an impending eruption. The uplift is accompanied by increasing seismic activity. This raises concerns about the possibility that the volcano may be on the verge of an eruption. To track the fluid movement, it is possible to monitor subtle changes of velocities of seismic waves by exploring ambient seismic noise. By examining different frequency bands, we can observe velocity changes at different depths. We interpret these changes as a monitoring of depth-dependent deformation in addition to the standard monitoring of surface deformation. We observe a velocity decrease in the long-term trend, presumably due to the extension of the hydrothermal system at shallow depths. To explain the long-term changes, we model a spherical pressure source to simulate volumetric strain changes induced by recent fluid activity. The model explains both, surface and subsurface deformation which leads to the opening of microcracks and pores, resulting in the observed velocity decrease. The short-term velocity changes are mainly driven by temperature or groundwater level changes. Once velocity changes are corrected for seasonal effects, remaining short term velocity changes can be associated with volcanic activity and earthquake swarms.

Assessment of biochemical biomarkers and environmental stress indicators in some freshwater fish

The mechanism by which an organism can adapt to subtle environmental changes is predicated on modifications to biochemical processes within the cellular metabolism in response to such changes. Changes in these processes have the potential to induce alterations in cellular structures and tissue organization, as well as establish a causal link between fluctuations in these parameters and stressors exposure. This investigation's main goal and innovation is to evaluate the environmental stress indicators in the aquatic ecosystem of Lake Qarun. Pesticide residues in freshwater fish should be the primary focus of evaluation of environmental stressor concentrations, since they serve as bioindicators at different times and places on a spatiotemporal scale. A thorough analysis of suggestive biochemical biomarker reactions should also be conducted. The effects of environmental stressors, specifically pesticide contamination in Qarun Lake, have been observed and investigated in relation to two fish species: Solea aejabtiaca and Oreochronis niloticus. The results of a hazard assessment conducted at six sampling sites using spatio-temporal data revealed elevated mean values for the pesticides, persistent organic pollutants (POPs), organochlorines, organophosphates, and pyrethroids that were detected. A multi biomarker approach facilitates a more comprehensive understanding of stress responses induced by exposure to pollutants. As a result, the activities of the biochemical biomarkers CYP-450, GST, GSH, and LDH in the blood and liver of fish samples were found to be notably elevated. The suitability of the identified variables for biomonitoring of aquatic pollution was validated, and the data unveiled variations in sensitivity among species, implying that Nile tilapia could potentially function as a bioindicator with high sensitivity. The findings were correlated with the concentrations of detrimental organochlorines, organophosphorus, and pyrethroids in the muscles and gills. The data indicates that pollutants linked to agricultural wastes, runoff, and municipal effluent may be discharged into the lake ecosystem. Consequently, to safeguard the environment, it is essential to enforce and implement policies, acts, and regulations that already exist. Assessing the effects of additional environmental stressors on aquatic ecosystems is another way in which biomarker screening with an integrative approach improves our comprehension of how toxicants impact various levels of biological organization and is particularly useful in realistic environmental exposure scenarios.

Physiological definition for region of interest selection in electrical impedance tomography data: description and validation of a novel method

Objective. Geometrical region of interest (ROI) selection in electrical impedance tomography (EIT) monitoring may lack sensitivity to subtle changes in ventilation distribution. Therefore, we demonstrate a new physiological method for ROI definition. This is relevant when using ROIs to compute subsequent EIT-parameters, such as the ventral-to-dorsal ratio during a positive end-expiratory pressure (PEEP) trial.Approach.Our physiological approach divides an EIT image to ensure exactly 50% tidal impedance variation in the ventral and dorsal region. To demonstrate the effects of our new method, EIT measurements during a decremental PEEP trial in 49 mechanically ventilated ICU-patients were used. We compared the center of ventilation (CoV), a robust parameter for changes in ventro-dorsal ventilation distribution, to our physiological ROI selection method and different commonly used ROI selection methods. Moreover, we determined the impact of different ROI selection methods on the PEEP level corresponding to a ventral-to-dorsal ratio closest to 1.Main results.The division line separating the ventral and dorsal ROI was closer to the CoV for our new physiological method for ROI selection compared to geometrical ROI definition. Moreover, the PEEP level corresponding to a ventral-to-dorsal ratio of 1 is strongly influenced by the chosen ROI selection method, which could have a profound clinical impact; the within-subject range of PEEP level was 6.2 cmH2O depending on the chosen ROI selection method.Significance.Our novel physiological method for ROI definition is sensitive to subtle ventilation-induced changes in regional impedance (i.e. due to (de)recruitment) during mechanical ventilation, similar to the CoV.

Lifelong Changes in the Choroidal Thickness, Refractive Status, and Ocular Dimensions in C57BL/6J Mouse.

To investigate the changes in choroidal thickness (ChT), refractive status, and ocular dimensions in the mouse eye in vivo using updated techniques and instrumentation. High-resolution swept-source optical coherence tomography (SS-OCT), eccentric infrared photoretinoscopy, and custom real-time optical coherence tomography were used to analyze choroidal changes, refractive changes and ocular growth in C57BL/6J mice from postnatal day (P) 21 to month 22. The ChT gradually increased with age, with the thickest region in the para-optic nerve head and thinning outward, and the temporal ChT was globally thicker than the nasal ChT. Retinal thickness remained stable until 4months and subsequently decreased. The average spherical equivalent refraction error was -4.81 ± 2.71 diopters (D) at P21, which developed into emmetropia by P32, reached a hyperopic peak (+5.75±1.38D) at P82 and returned to +0.66 ± 1.86 D at 22months. Central corneal thickness, anterior chamber depth, lens thickness, and axial length (AL) increased continuously before 4months, but subsequently exhibited subtle changes. Vitreous chamber depth decreased with lens growth. ChT was correlated significantly with the ocular parameters (except for retinal thickness) before the age of 4months, but these correlations diminished after 4months. Furthermore, for mice younger than 4months, the difference in the ChT, especially temporal ChT, between the two eyes contributed most to that of axial length and spherical equivalent refraction error. Four months could be a watershed age in the growth of mouse eyes. Large-span temporal recordings of refraction, ocular dimensions, and choroidal changes provided references for the study of the physiological and pathological mechanisms responsible for myopia.

Dynamic 19F-MRI of pulmonary ventilation in lung transplant recipients with and without chronic lung allograft dysfunction

BackgroundBy the time chronic lung allograft dysfunction (CLAD), with its main phenotypes bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), is diagnosed by pulmonary function testing, irreversible damage to the lung allograft may already have occurred. Dynamic 19F-MRI of inhaled perfluoropropane may detect subtle changes in regional lung ventilation and provides a quantitative measure of regional lung function. We assessed feasibility of detecting regional ventilation dysfunction due to CLAD in lung transplant recipients. MethodsDynamic 19F-MRI was performed in ten lung transplant recipients, four without CLAD and six with CLAD (5 BOS, 1 RAS). Gas wash-in and washout dynamics were assessed and regional lung clearance index (RLCI) provided a quantitative metric of regional lung ventilation. ResultsBOS patients had substantially greater variation in regional ventilation compared with stable patients, with more regions of reduced ventilation, especially in the periphery. Tracer washout was homogeneous and rapid in stable patients but highly heterogeneous in CLAD. CLAD patients exhibited significant difference in RLCI between central and peripheral lung regions (p=0.0016) and a wider interquartile range of RLCI for wash-in compared with stable patients (no CLAD 4.1, BOS 10.5, p=0.036). FEV1 (% of baseline) negatively correlated with ventilation during wash-in, most strongly for the periphery (r=-0.844, p=0.0021). ConclusionsDynamic 19F-MRI identified quantifiable differences in regional ventilation in lung transplant recipients with and without CLAD and was well tolerated. Larger longitudinal studies using this approach will determine if early detection of changes in regional ventilation in lung transplant patients allows earlier CLAD detection.

Research on fault diagnosis and feature extraction mechanism visualization of rotating machinery based on improved 1D CNN

With the wide application of intelligent equipment in modern industrial production, it is particularly important to study how to intelligently sense the faults of rotating machinery, improve the diagnosis efficiency and enhance the interpretation ability of the diagnosis process. Although the traditional 1-D CNN performs well in fault diagnosis, it has limitations in capturing subtle changes and complex patterns of fault signals, and its interpretability needs to be improved. Therefore, based on the improved ELCNN model, this paper discusses its diagnosis mechanism in depth, aiming at providing a new idea for intelligent fault diagnosis of rotating machinery. The functions of convolutional layer and S-GAP layer in ELCNN are studied and analyzed. Through single-layer linear convolution, ELCNN can adaptively learn the frequency domain features of the signal and realize the lightweight of the model. At the same time, the S-GAP layer enhances the ability of ELCNN to capture the main peak frequency of fault signals through feature sparseness. The experimental results show that the accuracy of ELCNN in frequency domain feature extraction is more than 80 %. The main peak frequency extracted can effectively help engineers understand the basis of model judgment and improve the reliability of the model.

Gene expression profiles in placenta and their association with anesthesia, delivery mode and maternal diabetes

IntroductionFetal development is dependent on placenta and affected by multiple factors including maternal diabetes. Here we aimed to identify maternal diabetes-associated changes in placentas and analyzed placental gene expression to understand its modulation by maternal diabetes and birth mode. MethodsPlacental RNAseq transcriptome analyses were performed on maternally-derived decidua and fetal-derived villous tissue from pregnancies of mothers with type 1 diabetes (n=14), gestational diabetes (n=6) and without diabetes (n=14). Information on delivery mode and anesthesia were included as covariables. Analyses were performed separately for decidua and fetal tissues and adjusted for sex. ResultsSubstantial placenta gene expression variation was associated with factors other than maternal diabetes, including site, sex, anesthesia type and delivery mode. Two dominant gene expression clusters aligned to anesthesia and delivery mode were observed for decidua and villous tissue. Upregulation of genes within pathways related to organ morphogenesis and downregulation of immune response to steroid- and hypoxia pathway genes was characteristic of placentas from primary cesarean section deliveries with spinal anesthesia. Opposite profiles were observed for placentas from secondary cesarean and epidural anesthesia deliveries. Placentas from vaginal delivery had intermediate gene expression profiles. More subtle changes were associated with maternal diabetes: upregulation of ribosome activity, down-regulation of maternally-derived decidua chemokine signaling pathways and for gestational diabetes, alteration in hypoxia response genes. DiscussionThe findings reveal suppression of immune pathways and upregulation of ribosome activity in the placenta by maternal diabetes highlighting the importance of confounding factors when examining cell and tissue expression profiles. Further studies should determine whether the observed gene expression differences are related to underlying causes for cesarean section deliveries.

Optical Coherence Tomography Split-Spectrum Amplitude-Decorrelation Optoretinography Detects Early Central Cone Photoreceptor Dysfunction in Retinal Dystrophies.

To investigate if split-spectrum amplitude-decorrelation optoretinography (SSADOR) can detect and measure macular cone dysfunction in inherited retinal dystrophies (IRDs). This study was a case series of participants presenting with various IRD pathologies. Participants were recruited from the Ophthalmic Genetics clinic at the Casey Eye Institute from February to August 2023. Multimodal and SSADOR imaging was obtained in all cases. We recruited nine participants, including four with macular dystrophy, one with fundus flavimaculatus, one with cone dystrophy, and three with retinitis pigmentosa. SSADOR decorrelation maps identified areas of cone functional impairment consistent with disease phenotypes. A correlation between the SSADOR signal and retinal sensitivity measured by microperimetry within the central 20° diameter area was observed. Additionally, SSADOR was able to demonstrate a decreased signal in mild cases when microperimetry measurements were still normal but subtle changes were also apparent on structural OCT. SSADOR is sensitive at detecting functional changes in macular cones, even prior to abnormalities in perimetry testing. We highlight the potential benefits of this innovative technology for the early detection of cone dysfunction and their potential contributions to earlier diagnosis and more accurate monitoring of progression. SSADOR is an innovative technology that detects early macular cone function changes, allowing for early diagnosis and precise monitoring of cone dysfunction progression. By serving as a potential clinical trial endpoint, SSADOR facilitates the translation of scientific findings into practical applications, ultimately improving patient care and outcomes.

Acceptability of Nudge in Digital Learning Environment

ABSTRACTDigital nudging is gaining traction in the educational domain to guide students' decision‐making processes and achieve desirable learning outcomes through subtle changes in the digital learning environment. From the information science perspective, these changes are realized through informational cues and human‐computer interface design to affect the influences. Studies have shown nudge effectiveness in influencing students' behaviors, but the extent to which digital nudging affects them and who is susceptible to nudges remains unclear. Although progress has been made in understanding nudge acceptability, research in the context of learning and students' characteristics, such as procrastination behavior, remains limited. To fill this gap, this study surveyed 305 university students to assess their nudge acceptability on two types of nudges: System 1, which involves automatic and intuitive processes, and System 2, which engages deliberate and reflective thinking. The results show that students, regardless of their procrastination tendencies, were receptive to nudges in supporting their learning. System 1 nudge was preferred due to its simplistic and straightforward intervention approach. The insights gained from this study contributed to the advancement of nudge research by demonstrating that students with various procrastination tendencies were receptive to nudging and guiding researchers in designing tailored nudges to maximize effectiveness.

Repeatability and predictability of lying and feeding behaviours in dairy cattle

Subtle changes in behaviour, which may be useful as early indicators of disease or stress in farm animals, can be detected using precision livestock technologies. However, as animals often display differences in their baseline behaviours from one another, individual variation needs to be measured and accounted for if we want to successfully detect abnormalities. In addition to consistent individual differences in their mean behaviour, which are called behavioural types and measured by repeatability, animals may differ in the amount of day-to-day variation around their own mean. This is known as predictability and it is very rarely measured in animals as it requires data with high granularity for extended periods of time, which is hard to obtain without precision technologies. Here we aim to quantify the repeatability and predictability of lying and feeding, two behaviours which have been investigated as potential indicators of disease with inconsistent results, in dairy cows. We used data on daily lying behaviours from leg-mounted sensors for 2183 cows and data on daily feeding behaviours from neck-mounted sensors for 2584 cows belonging to multiple herds to quantify the variation between individuals, the individual differences in predictability and any correlations between individual behaviour type and predictability of the same behaviour. Using multivariate double hierarchical generalised linear models, we were able to quantify individual variation in feeding and lying behaviours of adult cows for the first time. Consistent variation between individuals was present for all measures of lying and feeding with repeatability values of 0.38 for daily feeding time and 0.31 for daily lying time. Individuals also varied in their levels of predictability with coefficients of 0.46 and 0.29 for the daily lying and feeding time respectively. Lastly, there were significant positive correlations between several behaviours and the amount of residual intra-individual variation for the same behaviour (mean lying bout length: 0.93, mean feeding bout length: 0.88), indicating that cows with the longest bouts of the two behaviours were also more variable and unpredictable, perhaps due to an opportunistic strategy. These results highlight the importance of measuring individual variation in behaviour, supporting the evidence of personality traits in cattle, as well as differences in predictability, which must be taken into account in any use of behavioural indicators for early disease diagnosis.

Voice Type Component Profile Model of Glottal Gap Voice in Ex Vivo Canine Larynges

This paper aimed to create a voice type component profile (VTCP) to model a glottal gap during phonation. VTCP was generated from phonation from eight excised canine larynges with induced posterior glottal gaps. Glottal gaps were induced using metal shims with widths ranging from 0.5 to 3.5mm, in 0.5 mm increments. Acoustic data was collected under each gap condition, and a custom MATLAB software was used to analyze the data for VTCP. The trends of each VTC were analyzed with changes in glottal gap size. Increasing glottal gap led to a decrease in VTC1 and an increase in VTC4. Post-Hoc Tukey tests found that for both VTC4 and VTC1, The 1.5mm group differed from the 2, 2.5, and 3.5mm group. Additionally, in both VTC1 and VTC4 there was a significant difference between both the control and 0.5mm shim groups and the 2, 2.5, 3, and 3.5mm shim groups. VTC2 followed a similar trend as VTC1. Average VTC3 was highest at control, and the 1, 2, 2.5, 3, and 3.5mm shim groups significantly differed from the control group. Kendall correlation tests showed that there was a significant weak inverse relationship with glottal gap size and VTC1, VTC2, and VTC3 and a significant weak positive correlational relationship with glottal gap size and VTC4. As glottal gap width increased, phonation exhibited increased turbulence associated with increased breathiness. Periodicity decreased as turbulence increased. Increased turbulence was shown to be derived from the glottal level. The size of the induced glottal gap was the cause of the turbulent signals. The study highlights the potential of VTCP analysis in detecting subtle changes in glottal closure and suggests avenues for further research in diagnosing voice disorders.

How does AI perform in industry chain? A patent claims analysis approach

The development trajectory of AI within the industry chain can offer valuable insights for managers and policymakers. Because the industry chain includes multiple complex nodes, it becomes difficult to showcase the subtle changes in AI at each node. Since patent claims are authoritative legal documents describing technology, we first theoretically demonstrate that integrating them with deep learning can effectively reveal the development of AI within complex nodes. And then, based on claim types and dependencies, we construct a more robust AI Recognition Multiple Attention Mechanism (A&C-Mechanism). Finally, using the battery industry chain (BIC) as a case study, the A&C-Mechanism reveals differences in AI development within the industry chain: (1) The A&C-mechanism can calculate the adjustment weights of patent claims based on variations in claim types and dependencies. Therefore, integrating the A&C-mechanism into NLP models can enhance the models' robustness and sensitivity to the nuanced variations of AI within patent claims; (2) Based on the A&C mechanism, our analysis indicates that AI indeed drives technological upgrades within four BIC nodes of mineral resource extraction (MRE), raw material processing (RMP), finished product manufacturing (FPM), usage, and recycling (UR). However, there is a phenomenon of non-uniform AI development emerging across these nodes; (3) Analyzing the patent application volume and growth rates across the four nodes, we identify that AI development progresses through distinct stages within the industrial chain: early, mid-term, and improvement. With the establishing two coefficients, the AI claim dependency variation coefficient and the AI-NE variation coefficient, we demonstrate that each stage exhibits unique characteristics. AI is used directly in the early stages. As the mid-term stage approaches, AI starts to be optimized and enhanced. During the improvement stage, AI structures, procedures, etc., are adaptively adjusted to better serve each company's goals; (4) Constructing an interaction network of AI with the four nodes based on high-frequency AI named entities within patent claims, we discover that AI development within the industrial chain exhibits iteration and continuity. Moreover, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) remain the cornerstone, serving as the foundation upon which many cutting-edge technologies are built. Digital image processing and machine learning enhance problem-solving across multiple nodes. We discuss our findings and derive implications for research, managers and policymakers.

Temperature-Dependent Rotational Dynamics of a Polar Probe in Non-Polar Aprotic Solvents: Interplay of Solvent Viscosity and Size Effect.

Rotational dynamics of 3-(benzo[d]thiazol-2-yl)-7-(diethylamino)-2H-chromen-2-one (3BT7D2H-one) probe in non-polar aprotic 1,4 dioxane, toluene and cyclohexane solvents of different and comparable viscosity are studied with varying temperature using steady-state and fluorescence depolarization technique. The experimental reorientation time is compared with the hydrodynamic Stoke's-Einstein-Debye(SED). The experimentally measured rotational reorientation times are higher in 1,4 dioxane attributed to its solvent viscosity. In cyclohexane, the solute rotational dynamics behavior shows a transition from sub-slip to SED slip boundary conditions with varying temperatures. Solvent viscosity, size, and subtle changes in the molecular shape of solute play an important role in the rotational dynamics of polar probes in non-polar aprotic solvents.

Noninvasive Left Ventricular Pressure-Strain Loop for Quantitative Assessment of Early Left Ventricular Systolic Dysfunction in Patients With Chronic Kidney Disease.

Patients with chronic kidney disease (CKD) possess a pronounced risk for cardiovascular events. A noninvasive left ventricular pressure-strain loop (LV-PSL) has recently been introduced to detect subtler changes in cardiac function. This study aims to investigate the value of LV-PSL for quantitative assessment of myocardial work (MW) in patients with CKD. Seventy-five patients with CKD were enrolled retrospectively (37 patients with CKD Stages 2-3, and 38 patients with CKD Stages 4-5), and 35 healthy volunteers were included as controls. All subjects underwent transthoracic echocardiography. LV-PSL analysis was performed to estimate LV MW and efficiency. Global work index (GWI), global constructive work (GCW), global wasted work (GWW), and global work efficiency (GWE) were obtained by echocardiography, and the differences among the groups were compared. There was a significant increase in GWW and reduction in GWE in patients with CKD compared to normal controls (p < 0.05). No significant difference in GWI and GCW was observed among the three groups. Multiple linear regression revealed that increased GWW was significantly associated with age, serum creatinine, and systolic pressure, and decreased GWE was associated with age, serum creatinine, and GLS. LV-PSL can be used for noninvasive quantitative assessment of MW in patients with CKD, providing a new sensitive approach for the clinical assessment of myocardial function.

Exploring the Subtle, Novel, and Early Kidney Pathological Changes in Diabetic Nephropathy Using Clustering with Deep Learning

Exploring the Subtle, Novel, and Early Kidney Pathological Changes in Diabetic Nephropathy Using Clustering with Deep Learning

M1 muscarinic receptor activation reverses age-related memory updating impairment in mice

Previously consolidated memories can become temporarily labile upon reactivation.Reactivation-based memory updating is chiefly studied in young subjects, so we aimed to assess this process across the lifespan. To do this, we developed a behavioural paradigm wherein a reactivated object memory is updated with contextual information; 3-month-old and 6-month-old male C57BL/6 mice displayed object memory updating, but 12-month-old mice did not. We found that M1 muscarinic acetylcholine receptor signaling during reactivation was necessary for object memory updating in the young mice. Next, we targeted this mechanism in an attempt to facilitate object memory updating in aging mice. Remarkably, systemic pharmacological M1 receptor activation reversed the age-related deficit. Quantification of cholinergic system markers within perirhinal cortex revealed subtle cellular changes that may contribute to differential performance across age groups. These findings suggest that natural cholinergic change across the lifespan contributes to inflexible memory in the aging brain.

Similar sources but distinct δ13C signatures in adjacent low-temperature travertines from Laguna Amarga (Southern Patagonian Andes)

This study examined the waters and carbonates from two cold spring travertines (ca. 13 °C) located on the sun-exposed (north-facing travertine) and sun-shaded (south-facing travertine) margins of Laguna Amarga, an alkaline lake from the semiarid region of the eastern Patagonian Andes (51°S).The travertines are composed of calcite + low-Mg calcite ± aragonite. Both exhibit similar sedimentological transitions along their longitudinal profiles. In the proximal zones, biologically-influenced carbonates form in wetland-like environments. Spherulitic calcite precipitates in association with extracellular polymeric substances in microbial biofilms containing cyanobacteria-like molds at the vent of the south-facing travertine, while aragonite spherulite formation at the north-facing travertine vent also involves sulfate-reducing bacteria, as indicated by their close association with framboidal pyrite. Downstream, in the intermediate and distal zones, crystalline dendrites predominantly precipitate due to increased turbulence-induced CO2 degassing.Both travertines share a similar range of carbonate 87Sr/86Sr composition (0.70720–0.70740) and isotopic signatures of the spring waters, including δ2H (ca. -110 ‰ VSMOW), δ18Owater (ca. -14 ‰ VSMOW) and δ13C-DIC (ca. -5 ‰ VPDB), suggesting common sources and processes influencing fluid composition. This points to the dissolution of carbonates from mudstone-rich marine units of the Lower Cretaceous (δ13C ca. -1 ‰ VPDB) and Upper Cretaceous (δ13C ca. -10 ‰ VPDB) during shallow subsurface circulation of meteoric waters through the bedrock. The carbon isotopic composition of the deposits resembles those of endogenic travertines (δ13Ctrav − 1.2 to 5.3 ‰ VPDB), with the highest δ13Ctrav values associated with carbonates from the vents. However, the involvement of deep CO2 sources is unclear and epigenic processes capable of producing the observed 13C enrichments are discussed.Despite their common sources, similar sedimentological features and δ18Otrav compositional range (−12.4 to −10.1 ‰ VPDB), the δ13Ctrav values are lower in the south-facing travertine (−1.2 to 1.9 ‰ VPDB) compared to the north-facing travertine (1.8 to 5.3 ‰ VPDB). This disparity is inferred to result from variations in local environmental conditions due to different levels of insolation, which favored the incorporation of soil-derived CO2 in the south-facing travertine and likely increased photosynthetic productivity in the north-facing travertine, thereby shifting their δ13Ctrav signatures to lower and higher values, respectively. These relationships highlight the sensitivity of low-temperature spring carbonates to subtle environmental changes at basin scales.