Browse Articles

Individual differences elucidate the perceptual benefits associated with robust temporal fine-structure processing

The auditory system is unique among sensory systems in its ability to phase lock to and precisely follow very fast cycle-by-cycle fluctuations in the phase of sound-driven cochlear vibrations. Yet, the perceptual role of this temporal fine structure (TFS) code is debated. This fundamental gap is attributable to our inability to experimentally manipulate TFS cues without altering other perceptually relevant cues. Here, we circumnavigated this limitation by leveraging individual differences across 200 participants to systematically compare variations in TFS sensitivity to performance in a range of speech perception tasks. TFS sensitivity was assessed through detection of interaural time/phase differences, while speech perception was evaluated by word identification under noise interference. Results suggest that greater TFS sensitivity is not associated with greater masking release from fundamental-frequency or spatial cues but appears to contribute to resilience against the effects of reverberation. We also found that greater TFS sensitivity is associated with faster response times, indicating reduced listening effort. These findings highlight the perceptual significance of TFS coding for everyday hearing.

The continuing search for a better mouse trap: Two tests of a practical, low-cost camera trap for detecting and observing small mammals

The advent of digital wildlife cameras has led to a dramatic increase in the use of camera traps for mammalian biodiversity surveys, ecological studies and occupancy analyses. For cryptic mammals such as mice and shrews, whose small sizes pose many challenges for unconstrained digital photography, use of camera traps remains relatively infrequent. Here we use a practical, low-cost small mammal camera platform (the “MouseCam”) that is easy and inexpensive to fabricate and deploy and requires little maintenance beyond camera service. We tested the MouseCam in two applications: a study of small mammal species composition on two transects across a barrier island and a study of small mammal occupancy along a subtle elevation gradient in a mainland forest. The MouseCam was reasonably efficient, with over 78% of all images containing a recognizable small mammal (mouse, vole, rat or shrew). We obtained an accurate estimate of species composition on the island transects, as indicated by comparison with both concurrent and long-term trapping records for the same transects. MouseCams required a smaller expenditure of personnel and transportation resources than would be required for live trapping. They also detected subtle elevation-related differences in species occupancy in the mainland forest for the marsh rice rat, with the species occurring at lower elevations in the forest. This is consistent with the typical occurrence of the marsh rice rat in marshes and wetlands. We also tested devices (barriers, runways) designed to reduce disturbance by mesopredators (e.g., raccoons). Adding an internal barrier to the MouseCam did not reduce use by white-footed mice, whereas adding an external runway did. We believe specialized small mammal camera-based sensors may have wide applicability in field studies of small mammal distribution, abundance and biology.

Identifying representative sequences of protein families using submodular optimization

Image segmentation with traveling waves in an exactly solvable recurrent neural network

We study image segmentation using spatiotemporal dynamics in a recurrent neural network where the state of each unit is given by a complex number. We show that this network generates sophisticated spatiotemporal dynamics that can effectively divide an image into groups according to a scene’s structural characteristics. We then demonstrate a simple algorithm for object segmentation that generalizes across inputs ranging from simple geometric objects in grayscale images to natural images. Using an exact solution of the recurrent network’s dynamics, we present a precise description of the mechanism underlying object segmentation in the network dynamics, providing a clear mathematical interpretation of how the algorithm performs this task. Object segmentation across all images is accomplished with one recurrent neural network that has a single, fixed set of weights. This demonstrates the expressive potential of recurrent neural networks when constructed using a mathematical approach that brings together their structure, dynamics, and computation.

Device-measured physical activity in adults born preterm with very low birth weight and mediation by motor abilities

Physical activity (PA) is beneficial for several health outcomes. Adults born with very low birth weight (VLBW<1500g) undertake less PA than those born at term, have poorer motor abilities and may serve as a model on early life origins of PA. We therefore examined whether motor abilities mediate the association between being born with VLBW and device-measured PA. In a joint assessment of two longitudinal birth cohorts from Finland and Norway, PA was measured by two tri-axial accelerometers in 87 adults born preterm with VLBW and 109 controls born at term. We explored the mediating role of motor abilities assessed by standardized tests on the association between VLBW and device-measured PA. To do this, we examined group differences in metabolic equivalent of task (MET) min/day of moderate to vigorous PA (MVPA), light PA and sedentary. Analyses were adjusted for cohort, age and sex. MVPA was 40.4 (95% confidence interval [CI]: 13.3 to 69.4) MET min/day lower in the VLBW group than the control group. This was in part mediated through gross motor abilities, indicated by the indirect effect on the association between VLBW and MVPA being -15.6 (95% CI: -28.5 to -5.4) MET min/day. In conclusion, adults born preterm with VLBW undertake less MVPA than controls born at term, and gross motor abilities mediate this association. Interventions targeting motor abilities should be examined as potential ways to increase PA.

Study on load reduction and vibration control strategies for semi-submersible offshore wind turbines

Deriving a genetic regulatory network from an optimization principle

Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed mechanistic model of the gap gene network in the Drosophila embryo, optimizing its 50+ parameters to maximize the information that gene expression levels provide about nuclear positions. This optimization is conducted under realistic constraints, such as limits on the number of available molecules. Remarkably, the optimal networks we derive closely match the architecture and spatial gene expression profiles observed in the real organism. Our framework quantifies the tradeoffs involved in maximizing functional performance and allows for the exploration of alternative network configurations, addressing the question of which features are necessary and which are contingent. Our results suggest that multiple solutions to the optimization problem might exist across closely related organisms, offering insights into the evolution of gene regulatory networks.

Seismic anisotropy prediction using ML methods: A case study on an offshore carbonate oilfield

Estimating seismic anisotropy parameters, such as Thomson’s parameters, is crucial for investigating fractured and finely layered geological media. However, many inversion methods rely on complex physical models with initial assumptions, leading to non-reproducible estimates and subjective fracture interpretation. To address these limitations, this study utilizes machine learning methods: support vector regression, extreme gradient boost, multi-layer perceptron, and a convolutional neural network. The abundance of seismic features leads to many feature combinations, making the training and testing of machine learning models challenging. Therefore, a workflow has been developed to systematically inspect seismic features and select the most appropriate one for anisotropy estimation with reasonable accuracy. Synthetic data were generated using an earth model and well data within a finite difference numerical program. After thoroughly investigating synthetic data, the amplitudes of direct and reflected waves in the time and frequency domains were selected as input features to train machine learning methods. Optimizing the machine learning hyperparameters allowed the training and testing procedures to be completed with high accuracy. Subsequently, the optimized machine learning methods were used to predict Thomsen’s parameters, ε and δ, of a shaley formation in the zone area. To validate the predictions, the ε and δ estimated at a well location were compared with those obtained using a physics-based model, resulting in the least relative errors ranging from 2.92% to 7.14%.

Simultaneous measurement of liquid level and R.I. sensor using POF based on twisted structure

Text mining of practical disaster reports: Case study on Cascadia earthquake preparedness

Many practical disaster reports are published daily worldwide in various forms, including after-action reports, response plans, impact assessments, and resiliency plans. These reports serve as vital resources, allowing future generations to learn from past events and better mitigate and prepare for future disasters. However, this extensive practical literature often has limited impact on research and practice due to challenges in synthesizing and analyzing the reports. In this study, we 1) present a corpus of practical reports for text mining and 2) introduce an approach to extract insights from the corpus using select text mining tools. We validate the approach through a case study examining practical reports on the preparedness of the U.S. Pacific Northwest for a magnitude 9 Cascadia Subduction Zone earthquake, which has the potential to disrupt lifeline infrastructures for months. To explore opportunities and challenges associated with text mining of practical disaster reports, we conducted a brief survey of potential user groups. The case study illustrates the types of insights that our approach can extract from a corpus. Notably, it reveals potential differences in priorities between Washington and Oregon state-level emergency management, uncovers latent sentiments expressed within the reports, and identifies inconsistent vocabulary across the field. Survey results highlight that while simple tools may yield insights that are primarily interpretable by experienced professionals, more advanced tools utilizing large language models, such as Generative Pre-trained Transformer (GPT), offer more accessible insights, albeit with known risk associated with current artificial intelligence technologies. To ensure reproducibility, all supporting data and code are made publicly available (DOI: 10.17603/ds2-9s7w-9694).

The influence of training surface on the effectiveness of plyometric training on physical fitness attributes of volleyball players

On escape criterion of an orbit with s−convexity and illustrations of the behavior shifts in Mandelbrot and Julia set fractals

Our study presents a novel orbit with s−convexity, for illustration of the behavior shift in the fractals. We provide a theorem to demonstrate the escape criterion for transcendental cosine functions of the type Tα,β(u) = cos(um)+αu + β, for u , α , β ∈ C and m ≥ 2. We also demonstrate the impact of the parameters on the formatted fractals with numerical examples and graphical illustrations using the MATHEMATICA software, algorithm, and colormap. Moreover, we observe that the Julia set appears when we widen the Mandelbrot set at its petal edges, suggesting that each Mandelbrot set point contains a sizable quantity of Julia set picture data. It is commonly known that fractal geometry may capture the complexity of many intricate structures that exist in our surroundings.

The optimal grouting area and cable distribution design for guaranteeing roadway stability while considering fluid mechanical effects via the CMOEAD algorithm

AbstractWater inrush in roadways frequently occurs in coal mines when the rock mass is enriched with underground water. To avoid underground water flow into the roadway and guarantee the stability of the roadway, grouting and cables are commonly used to prevent water inrush and guarantee the stability of the roadway. In this work, FLAC3D (fast lagrangian analysis of continua 3 dimension) numerical simulation software was used, and the fluid‒mechanical coupling effects were considered. In combination with the CMOEAD (constrained multi-objective evolution algorithm based on decomposition) optimization method, the optimal grouting area and cable distribution were determined: the center point of the ellipse (grouting area) is (0.01, 1.59), the long axis length is 4.73 m, the short axis length is 4.60 m, and the inclination angle of the ellipse is 53.15°. The cable length is 6.51 m, the total number of cables is 11. The grouting area and cable distribution design from numerical simulation results were applied to engineering practice, the degree of water inrush was markedly reduced, and the displacement of the roadway was within control, indicating that the proposed method is workable and reliable.

The proteome is a terminal electron acceptor

Microbial metabolism is impressively flexible, enabling growth even when available nutrients differ greatly from biomass in redox state. Escherichia coli , for example, rearranges its physiology to grow on reduced and oxidized carbon sources through several forms of fermentation and respiration. To understand the limits on and evolutionary consequences of this metabolic flexibility, we developed a coarse-grained mathematical framework coupling redox chemistry with principles of cellular resource allocation. Our models inherit key qualities from both of their antecedents: i) describing diverse metabolic chemistries and ii) enforcing the simultaneous balancing of atom (e.g., carbon), electron, and energy (adenosine triphosphate) flows, as in redox models, while iii) treating biomass as both the product and catalyst of the growth process, as in resource allocation models. Assembling integrated models of respiration, fermentation, and photosynthesis clarified key microbiological phenomena, including demonstrating that autotrophs grow more slowly than heterotrophs because of constraints imposed by the intracellular production of reduced carbon. Our model further predicted that heterotrophic growth is improved by matching the redox state of biomass to the nutrient environment. Through analysis of ≈ 60,000 genomes and diverse proteomic datasets, we found evidence that proteins indeed accumulate amino acid substitutions promoting redox matching. We therefore propose an unexpected mode of genome evolution where substitutions neutral or even deleterious to the individual biochemical or structural functions of proteins can nonetheless be selected due to a redox-chemical benefit to the population.

Tracing fineware production in the Neo-Assyrian empire: Neutron activation analysis of common and Palace Ware in the upper Tigris River Valley, Turkey

In the Iron Age, the Neo-Assyrian empire (c. 900–600 BC) conquered territory across southwest Asia and established regional capitals along its borders to secure its gains. Governors at these centers oversaw resource extraction and craft production for shipment to the imperial heartland in modern-day northern Iraq. Metals and textiles were the crafts most carefully managed by the administration. We know less about centralized control over ceramic production but hypothesize that fineware production and distribution would have been of interest to imperial administrators. A fineware type known as Palace Ware has been found throughout the empire and is considered an indicator of elite Assyrian dining traditions. Excavations at one regional capital, Ziyaret Tepe (ancient Tušhan) produced pottery of various skill levels used by residents. In this study neutron activation analysis (NAA) was used to characterize and compare the fabrics used to make Palace Ware vessels with more common wares to see if the former vessels were imported from the imperial heartland. Palace Ware is macroscopically distinct, but this does not always indicate an import. Chemical composition of the samples fell into four main groups, and both Palace and common ware were found to have similar compositions. Comparison of these data with those from contemporary sites showed that the two main Ziyaret groups matched the chemical composition of pottery from the Assyrian capitals of Nimrud and Nineveh. Our conclusions show that there is considerable homogeneity in the clays of the upper Tigris river valley in Turkey and the lower Tigris in northern Iraq. Given this similarity, it is possible that Palace Ware at Tušhan was produced locally, imported, or both. If it was manufactured locally, as has been shown at the urban center of Tell Sheikh Hamad, potters in the imperial peripheries may have produced fineware pottery independent of direct imperial control.

METTL10 attenuates adriamycin-induced podocyte injury by targeting cell dedifferentiation

Targeting necroptosis in MCF-7 breast cancer cells: In Silico insights into 8,12-dimethoxysanguinarine from Eomecon Chionantha through molecular docking, dynamics, DFT, and MEP studies

Breast cancer remains a significant challenge in oncology, highlighting the need for alternative therapeutic strategies that target necroptosis to overcome resistance to conventional therapies. Recent investigations into natural compounds have identified 8,12-dimethoxysanguinarine (SG-A) from Eomecon chionantha as a potential necroptosis inducer. This study presents the first computational exploration of SG-A interactions with key necroptotic proteins—RIPK1, RIPK3, and MLKL—through molecular docking, molecular dynamics (MD), density functional theory (DFT), and molecular electrostatic potential (MEP) analyses. Molecular docking revealed that SG-A exhibited a stronger affinity for MLKL (-9.40 kcal/mol) compared to the co-crystallized ligand (-6.29 kcal/mol), while its affinity for RIPK1 (-6.37 kcal/mol) and RIPK3 (-7.01 kcal/mol) was lower. MD simulations further demonstrated the stability of SG-A within the MLKL site, with RMSD values stabilizing between 1.4 and 3.3 Å over 300 ns, indicating a consistent interaction pattern. RMSF analysis indicated the preservation of protein backbone flexibility, with average fluctuations under 1.7 Å. The radius of gyration (Rg) results indicated a consistent value of ~15.3 Å across systems, confirming the role of SG-A in maintaining protein integrity. Notably, SG-A maintains two critical H-bonds within the active site of MLKL, reinforcing the stability of the interaction. Principal component analysis (PCA) indicated a significant reduction in MLKL’s conformational space upon SG-A binding, implying enhanced stabilization. Dynamic cross-correlation map (DCCM) analysis further revealed that SG-A induced highly correlated motions, reducing internal fluctuations within MLKL compared to the co-crystallized ligand. MM-PBSA revealed the enhanced binding efficacy of SG-A, with a significant binding free energy of -31.03 ± 0.16 kcal/mol against MLKL, surpassing that of the control (23.96 ± 0.11 kcal/mol). In addition, the individual residue contribution analysis highlighted key interactions, with ARG149 showing a significant contribution (-176.24 kcal/mol) in the MLKL-SG-A complex. DFT and MEP studies corroborated these findings, revealing that the electronic structure of SG-A is conducive to stable binding interactions, characterized by a narrow band gap (~0.16 units) and distinct electrostatic potential favourable for necroptosis induction. In conclusion, SG-A has emerged as a compelling inducer of necroptosis for breast cancer therapy, warranting further experimental validation to fully realize its therapeutic potential.

Study on the microstructural evolution mechanism of the angular contact ball bearing rings during the quenching and tempering process

A mutant ASXL1-BAP1-EHMT complex contributes to heterochromatin dysfunction in clonal hematopoiesis and chronic monomyelocytic leukemia

ASXL transcriptional regulator 1 (ASXL1) is one of the three most frequently mutated genes in age-related clonal hematopoiesis (CH), alongside DNA methyltransferase 3 alpha (DNMT3A) and Tet methylcytosine dioxygenase 2 ( TET2) . CH can progress to myeloid malignancies including chronic monomyelocytic leukemia (CMML) and is also strongly associated with inflammatory cardiovascular disease and all-cause mortality in humans. DNMT3A and TET2 regulate DNA methylation and demethylation pathways, respectively, and loss-of-function mutations in these genes reduce DNA methylation in heterochromatin, allowing derepression of silenced elements in heterochromatin. In contrast, the mechanisms that connect mutant ASXL1 and CH are not yet fully understood. CH/CMML-associated ASXL1 mutations encode C-terminally truncated proteins that enhance the deubiquitinase activity of the ASXL-BAP1 “PR-DUB” deubiquitinase complex, which removes monoubiquitin from H2AK119Ub. Here, we show that ASXL1 mutant proteins interact with the euchromatic histone lysine methyltransferases 1 and 2 (EHMT1–EHMT2) complex, which generates H3K9me1 and me2, the latter a repressive modification in constitutive heterochromatin. Compared to cells from age-matched wild-type mice, we found that expanded myeloid cells from old (≥18-mo-old) Asxl1tm/+ mice, a heterozygous knock-in mouse model of CH, display genome-wide decreases of H3K9me2, H3K9me3, and H2AK119Ub as well as an associated increase in expression of transposable elements (TEs) and satellite repeats. Increased TE expression was also observed in monocytes from ASXL1 -mutant CMML patients compared to monocytes from healthy controls. Our data suggest that mutant ASXL1 proteins compromise the integrity of both constitutive and facultative heterochromatin in an age-dependent manner by reducing the levels of H3K9me2/3 and H2AK119Ub. This increase in TE expression correlated with increased expression of nearby genes, including many interferon-inducible (inflammation-associated) genes (ISGs).

Effects of different educational interventions on cervical cancer knowledge and human papillomavirus vaccination uptake among young women in Japan: Preliminary results of a cluster randomized controlled trial

The incidence and mortality rates of cervical cancer are increasing among young Japanese women. In November 2021, the Japanese Ministry of Health, Labour, and Welfare reinstated the active recommendation of the human papillomavirus (HPV) vaccine, after it had been suspended in June 2013 due to reports of adverse reactions. However, vaccine hesitancy is prevalent in the younger generation in Japan. To identify obstacles to vaccine uptake, we conducted a randomized study using different methods to provide educational content to improve health literacy regarding cervical cancer and HPV vaccination among Japanese female students. We surveyed 188 Japanese female students, divided into three groups according to the intervention: no intervention, print-based intervention, and social networking service-based intervention. Twenty questionnaires and the Communicative and Critical Health Literacy scales were used as health literacy scales. Participants’ knowledge and health literacy improved regardless of the method of education. In fact, participants acquired proper knowledge when given the opportunity to learn about the importance of the disease and its prevention. Therefore, medical professionals in Japan must provide accurate scientific knowledge regarding routine HPV vaccination and the risk of cervical cancer in young women to improve their health literacy and subsequently increase HPV vaccination rates in Japan, which may lead to cervical cancer elimination. Trial registration number: UMIN000036636.