Miracle-dz/UniFAR-FTUs huggingface dataset:
Acoustic responses of underwater superhydrophobic surfaces subjected to an intense pulse

Abstract Underwater stability of an air layer trapped in a micro-structure, plastron, is critical in drag reduction applications. Here, we investigate the wetting state and plastron stability of underwater superhydrophobic surfaces (SHS) under an intense acoustic drive. Flat surfaces and SHS are subjected to short acoustic pulses of different intensities. At low amplitude, the comparison between the results of various surfaces shows that plastron behaves like a water–air interface, whose presence can be detected from the phase of the reflected acoustic waves. At moderate intensity, a wetting transition towards a completely wetting state is observed and shown to be triggered by a sufficiently large acoustic radiation pressure. This wetting transition is well captured by a simplified model by balancing radiation pressure with the critical capillary pressure for the interface sliding. Cavitation clouds appear under strong excitation; their sizes and positions greatly depend on the surface acoustic boundary condition. For SHS in a Cassie–Baxter state (with an air layer), cavitation clouds appear at specific locations (from the solid surface) corresponding to the pressure anti-node of the transient standing wave generated by the reflection. This study unprecedentedly demonstrates the capability of acoustic waves to monitor and characterize plastron stability with low and moderate amplitudes, respectively.
Student Perceptions of a Framework for Facilitating Transfer from Lessons to Exams, and the Relevance of This Framework to Published Lessons

A main goal of academic courses is to help students acquire knowledge and skills that they can transfer to multiple contexts. In this article, we (i) examine students’ responses to test question templates (TQTs), a framework intended to facilitate transfer, and (ii) determine whether similar transfer-promoting strategies are commonly embedded in published biology lessons. ABSTRACT A main goal of academic courses is to help students acquire knowledge and skills that they can transfer to multiple contexts. In this article, we (i) examine students’ responses to test question templates (TQTs), a framework intended to facilitate transfer, and (ii) determine whether similar transfer-promoting strategies are commonly embedded in published biology lessons. In study 1, in surveys administered over several academic quarters, students consistently reported that TQTs helped them transfer course content to exams and the real world; that multiple (two to five) examples were generally needed to understand a given TQT, leading >40% students to create their own additional examples; and that TQTs would be helpful in other science courses. In study 2, among 100 peer-reviewed lessons published by CourseSource or the National Center for Case Study Teaching in Science (NCCSTS), less than 5% of lessons gave students advice about exams or helped students create additional practice problems. The latter finding is not meant as criticism of these excellent lessons, which are a boon to the biology education community. However, with TQT-like prescriptions generally absent from peer-reviewed lessons, biology instructors may wish to supplement the lessons with TQT-like strategies to explicitly connect the material to subsequent exams.
Predicting Microbe-Disease Associations via Multiple Layer Graph Convolutional Network and Attention Mechanism

Recently clinical evidences have confirmed that human diseases are affected by the microbes inhabiting human bodies. Identifying latent microbe-disease associations can provide a deep insight into the pathogenesis of diseases. However, traditional biological experiments are inefficient and expensive to achieve pathogenic microbes for diseases, computational approaches become a new alternative choice. In this work, we introduce a graph neural network method (MLAGCNMDA) with multiple layers of graph convolutional network and attention mechanism to predict potential microbe-disease pairs. In MLAGCNMDA, a heterogeneous network is constructed based on the known microbe-disease associations and multiple similarities between microbes and diseases. Moreover, nodes embedding of the heterogeneous network are learned by a multi-layer graph convolutional network model, in which the attention mechanism is introduced in each graph convolutional layer to distinguish the importance of neighbor nodes. Finally, a bilinear decoder is used to decode the node embedding to reconstruct microbe-disease associations. The experiments show that our method outperforms the baseline methods with reliable average AUCs of 0.945 and 0.946 in the Leave-one-out and 5-fold cross validation assessment framework. Case studies on two diseases, i.e., colorectal carcinoma and liver cirrhosis, further confirm the reliability and effectiveness of our method.
Effects of Photobiomodulation therapy on relieving peripheral and central fatigue in the grip exercise

This study aimed to investigate the effect of photobiomodulation therapy (PBMT) on muscle fatigue (i.e., peripheral and central fatigue) after performing the grip exercise. Eleven healthy college students were recruited to participate in a crossover, randomized, double-blind, placebo-controlled experiment. To induce muscle fatigue, all subjects performed the first grip exercise with a load of the 50% of maximum voluntary contraction force until exhaustion, and then they received PBMT or placebo on the hand immediately to perform the same second grip exercise. Surface electromyography (sEMG) signals were recorded from the first dorsal interossei of right hand to evaluate peripheral fatigue using muscle fatigue index (MFI), and functional Near-Infrared Spectroscopy (fNIRS) signals were recorded from cortical regions to evaluate central fatigue using functional connectivity (FC) analysis. Results showed that muscle fatigue index (MFI) was significantly lower in the PBMT group (p < 0.05) in the second grip exercise, when compared to the placebo group, but there was no significant difference in grip strength duration between the two groups. The PBMT group and the placebo group showed different FC patterns in the second grip exercise, which was manifested by the range of FC between the seed point left primary motor cortex (lM1) and other regions of interest (ROIs) in the placebo group was wider than that in the PBMT group. We concluded that using PBMT immediately after performing the grip exercise had a relieving effect on muscle fatigue in the second grip exercise.
Design of planar wideband surface‐wave excited substrate integrated waveguide horn antenna loaded with metamaterials

In this paper, a wideband planar substrate integrated waveguide (SIW) horn antenna based on surface wave excitation through loading metamaterials is designed. Strong coupling effect is realized between the employed bilayer metamaterial unit with the top layer as a π‐shaped‐slotted patch while the bottom layer as the complementary cross‐shaped‐slotted patch. The equivalent permittivity of the extended substrate in front of the planar horn aperture can be effectively reduced to unity and impedance mismatching for conventional planar SIW horn antenna can be reduced in a wide bandwidth. Results indicate that the working frequency of the antenna is 12.3–15.8 GHz with the relative impedance bandwidth of 24.6%.
Foundry Developments Toward Silicon Nitride Photonics From Visible to the Mid-Infrared

Photonic integration technologies have spread in the past decade by means of foundry models that mirror the electronic-integrated circuit industry developments of the past century. Several monolithic technologies exist, based on silicon and III–V semiconductors. In this paper, we discuss the current state and forthcoming developments of open access photonic foundries whose technology platforms are based on silicon nitride material. The paper presents various silicon nitride technologies and foundries, alongside with access models supported by generic integration and process design kits. Technical features, enabled by different micro-fabrication processes and tools are summarized. Application examples and developments of forthcoming incorporation into these platforms are outlined.
Linear Time-Varying Passivity-Based Attitude Control Employing Magnetic and Mechanical Actuation

Spacecraft attitude control using both magnetic and mechanical actuation is considered. A passivity-based control scheme is adopted in order to guarantee robust closed-loop stability. Motivated by the periodic nature of the Earths magnetic field a linear time-varying input strictly passive system is used for control. Conditions which ensure a linear timevarying system is input strictly passive are given, and are used in conjunction with the linear quadratic regulator formulation to design and synthesize the rate control. Simulation results are included, which highlight the robust nature of the closed-loop subject to both orbit, and spacecraft model uncertainty.
A compact tri‐band impedance transformer for frequency‐dependent complex loads

A compact tri‐band impedance transformer that can match the frequency‐dependent complex loads is presented in this letter. The proposed transformer is cascade of a dual‐band conductance transforming network, a dual‐band susceptance stub network and a dual‐to‐tri‐band transformer. There are four topologies of the transformer are available according to the different loads at the first frequency and the realizability of the transformer. All the parameters are acquired by solving the closed form design equations and a detailed design process is provided. The proposed design method is demonstrated through two examples, and a prototype is fabricated. Good agreement between the simulated and measured results is obtained. Compared with the existing works, the transformer not only can match the frequency‐dependent complex loads, but also obtains wide bandwidths and has a very short electrical length. In summary, the proposed impedance transformer is very suitable for the design of tri‐band active circuits and systems.
Strengthened State Transitions for Complete Invariant Verification in Practical Depth-Induction

Bounded Model Checking (BMC) is often able to handle thousands of system variables by encoding the system and its properties via symbolic formulas and using satisfiability (SAT) solvers for verification. To further ease the verification of state invariants, BMC is augmented with a general induction rule called k -induction; however, this sacrifices completeness. Invariant strengthening, a method proposed to overcome this problem, often requires user intervention which limits its general applicability. This paper presents a systematic method which is able to prove every property that is provable with standard k-induction and, in addition, further properties that the standard technique is unable to prove might be provable as well. Our case studies demonstrate the benefit of our approach with respect to plain k-induction. The main idea is to constrain the state transition relation in a way that the space of reachable states remains unchanged and k -induction is more likely to succeed. We show an implementation of our technique where the user needs only to extend the guard conditions with invariants obtained from the system’s specification. This is always possible if the schedule of the executed transitions is (partially) known a-priori.
Contract governance and value co-creation in virtual community: the moderating effect of psychological ownership

ABSTRACT Previous research has identified the factors that affect the value co-creation in virtual community from the perspective of customer demands, however, less has been studied in the literature from the perspective of firms’ contract governance. Drawing on the literature on this topic, this paper focuses on the impact of contract governance on value co-creation in virtual community and introduces the belongingness, self-efficacy, and self-identity contained in psychological ownership of customers as moderators into the model. The findings show that contract governance has a positive effect on value co-creation in virtual community, psychological ownership has a positive moderating effect, which is mainly reflected in belongingness and self-identity of psychological ownership, but this effect is not obvious in self-efficacy of psychological ownership. This study will provide inspiration for brands to better manage virtual brand communities, enhance customers’ willingness to participate in value co-creation, and expand the research on transaction cost theory and value co-creation theory.
IQMMA: an efficient MS1 intensity extraction using multiple feature detection algorithms for DDA proteomics

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called «features», in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years each relying on its own matching algorithm. Here, we provide an integrated solution, Intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of E. Coli digest spiked at different concentrations into the HeLa digest used as a background and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2 and OpenMS FeatureFinder. Neither of them was found optimal when applied individually to all the datasets employed in this work, however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.
Conformational plasticity and allosteric communication networks govern Shelterin protein TPP1 binding to human telomerase

The molecular binding interaction between the Shelterin complex protein TPP1 and human telomerase enzyme (TERT) triggers the telomerase maintenance mechanism that marks cell lifespan. The TPP1’s structural element deputed to bind TERT is the OB-domain, which is able to interact with TERT’s hTEN (TPP1 binding telomerase domain) through the TEL-patch, a group of amino acids whose mutations provoke harsh pathologies. Indeed, aberrations in the formation of TPP1-TERT het-erodimer can lead to severe diseases like Hoyeraal-Hreidarsson syndrome (HHS), whose patients are affected by short telomeres and extremely poor life expectancy. In the present study, we provide a thorough characterization of the structural properties of the TPP1’s OB-domain by combining data coming from microsecond-long molecular dynamics calculations, time-series analyses, and graph-based networks. Our results show that the conformational plasticity of the TPP1’s TEL-patch region is influenced by a network of long-range amino acid communications, needed for the proper TPP1-hTEN binding. Furthermore, we reveal that in the Glu169Δ and Lys170Δ TPP1 variants, responsible for HHS, the plasticity of the TEL-patch region is reduced, affecting the correct binding to hTEN and in turn the telomere processivity, which eventually leads to accelerated ageing of affected cells. Our study provides an unprecedented structural basis for the design of TPP1-targeting ligands with therapeutic potential against cancer and telomerase deficiency diseases.
Auditory memory of complex sounds in sparsely distributed, highly correlated neurons in the auditory cortex

Listening in complex sound environments requires rapid segregation of different sound sources e.g., speakers from each other, speakers from other sounds, or different instruments in an orchestra, and also adjust auditory processing on the prevailing sound conditions. Thus, fast encoding of inputs and identifying and adapting to reoccurring sounds are necessary for efficient and agile sound perception. This adaptation process represents an early phase of developing implicit learning of sound statistics and thus represents a form of auditory memory. The auditory cortex (ACtx) is known to play a key role in this encoding process but the underlying circuits and if hierarchical processing exists are not known. To identify ACtx regions and cells involved in this process, we simultaneously imaged population of neurons in different ACtx subfields using in vivo 2-photon imaging in awake mice. We used an experimental stimulus paradigm adapted from human studies that triggers rapid and robust implicit learning to passively present complex sounds and imaged A1 Layer 4 (L4), A1 L2/3, and A2 L2/3. In this paradigm, a frozen spectro-temporally complex ‘Target’ sound would be randomly re-occurring within a stream of random other complex sounds. We find distinct groups of cells that are specifically responsive to complex acoustic sequences across all subregions indicating that even the initial thalamocortical input layers (A1 L4) respond to complex sounds. Cells in all imaged regions showed decreased response amplitude for reoccurring Target sounds indicating that a memory signature is present even in the thalamocortical input layers. On the population level we find increased synchronized activity across cells to the Target sound and that this synchronized activity was more consistent across cells regardless of the duration of frozen token within Target sounds in A2, compared to A1. These findings suggest that ACtx and its input layers play a role in auditory memory for complex sounds and suggest a hierarchical structure of processes for auditory memory.
Ozanimod Attenuates Human Cerebrovascular Endothelial Derived MMP-9 Activity and Preserves Barrier Properties Following In Vitro Acute Ischemic Injury

Endothelial integrity is critical in mitigating a vicious cascade of secondary injuries following acute ischemic stroke (AIS). Matrix metalloproteinase-9 (MMP-9), a contributor to endothelial integrity loss, is elevated during stroke and is associated with worsened stroke outcome. We investigated the FDA approved selective sphingosine-1-phosphate receptor 1 (S1PR1) ligand, ozanimod, on the regulation/activity of MMP-9 as well as endothelial barrier components (PECAM-1, claudin-5, and ZO-1) in human brain microvascular endothelial cells (HBMECs) following hypoxia plus glucose deprivation (HGD). We previously reported that S1PR1 activation improves HBMEC integrity; however, specific mechanisms underlying S1PR1 involvement in barrier integrity have not been clearly elucidated. We hypothesized that ozanimod would attenuate an HGD-induced increase in MMP-9 activity which would concomitantly attenuate the loss of integral barrier components. Male HBMECs were treated with ozanimod (0.5nM) or vehicle and exposed to 3h normoxia (21% O2) or HGD (1% O2). Immunoblotting, zymography, qRT-PCR, and immunocytochemical labeling techniques assessed processes related to MMP-9 and barrier markers. We observed that HGD acutely increased MMP-9 activity and reduced claudin-5 and PECAM-1 levels, and ozanimod attenuated these responses. In situ analysis via PROSPER, suggested that attenuation of MMP-9 activity may be a primary factor in maintaining these integral barrier proteins. We also observed that HGD increased intracellular mechanisms associated with augmented MMP-9 activation, however ozanimod had no effect on these targeted factors. Thus, we conclude that ozanimod has the potential to attenuate HGD mediated decreases in HBMEC integrity in part by decreasing MMP-9 activity as well as preserving barrier properties. Graphical Abstract
Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

The modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of regulatory features in genome-wide screens. Most GRN inference methods are therefore forced to model relationships between regulatory genes and their targets with expression as a proxy for the upstream independent features, complicating validation and predictions produced by modeling frameworks. Separating covariance and regulatory influence requires aggregation of independent and complementary sets of evidence, such as transcription factor (TF) binding and target gene expression. However, the complete regulatory state of the system, e.g. TF activity (TFA) is unknown due to a lack of experimental feasibility, making regulatory relations difficult to infer. Some methods attempt to account for this by modeling TFA as a latent feature, but these models often use linear frameworks that are unable to account for non-linearities such as saturation, TF-TF interactions, and other higher order features. Deep learning frameworks may offer a solution, as they are capable of modeling complex interactions and capturing higher-order latent features. However, these methods often discard central concepts in biological systems modeling, such as sparsity and latent feature interpretability, in favor of increased model complexity. We propose a novel deep learning autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor), that scales to single cell genomic data and maintains interpretability to perform GRN inference and estimate TFA as a latent feature. We demonstrate that SupirFactor outperforms current leading GRN inference methods, predicts biologically relevant TFA and elucidates functional regulatory pathways through aggregation of TFs.
Effective networks mediate right hemispheric dominance of human 40 Hz auditory steady-state response.

Auditory steady-state responses (ASSR) are induced from the brainstem to the neocortex when humans hear periodic amplitude-modulated tonal signals. ASSRs have been argued to be a key marker of auditory temporal processing and pathological reorganization of ASSR – a biomarker of neurodegenerative disorders. However, most of the earlier studies reporting the neural basis of ASSRs were focused on looking at individual brain regions. Here, we seek to characterize the large-scale directed information flow among cortical sources of ASSR entrained by 40 Hz external signals. Entrained brain rhythms with power peaking at 40 Hz were generated using both monaural and binaural tonal stimulation. First, we confirm the presence of ASSRs and their well-known right hemispheric dominance during binaural and both monaural conditions. Thereafter, reconstruction of source activity employing individual anatomy of the participant and subsequent network analysis revealed that while the sources are common among different stimulation conditions, differential levels of source activation and differential patterns of directed information flow using Granger causality among sources underlie processing of binaurally and monaurally presented tones. Particularly, we show bidirectional interactions involving the right superior temporal gyrus and inferior frontal gyrus underlie right hemispheric dominance of 40 Hz ASSR during both monaural and binaural conditions. On the other hand, for monaural conditions, the strength of inter-hemispheric flow from left primary auditory areas to right superior temporal areas followed a pattern that comply with the generally observed contralateral dominance of sensory signal processing.