Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-lasting dust rings in gas-rich disks: sculpting by single and multiple planets

We propose a mechanism by which dust rings in protoplanetary disks can form and be long-lasting compared to gas rings. This involves the existence of a pressure maximum which traps dust either in between two gap-opening planets or at the outermost gap edge of a single or multiple planet system, combined with the decoupling of large dust particles from the gas. We perform 2D gas hydrodynamical simulations of disks with one and two giant planets which may open deep or partial gaps. A gas ring forms in between two planets such that the surface mass density is higher than on either side of it. This ring is a region of pressure maximum where we expect large grains, which are marginally coupled to the gas and would otherwise be subject to radial drift, to collect. Such a pressure maximum also occurs at the outermost gap edge in a disk with one or more planets. We infer the dust evolution in these regions as the gas disk evolves, to understand the longer term behavior of the resulting dust rings. Over time the gas surface density in the ring(s) decreases, which may cause the larger trapped particles to decouple. Consequently, these particles are expected to stay in ring structure(s) longer than the gas. For a Minimum Mass Solar Nebula model, we expect that millimeter and centimeter-sized grains in the outer O(10) au would most likely undergo this trapping and decoupling process.
Mucoadhesive Polymers and Their Applications in Drug Delivery Systems for the Treatment of Bladder Cancer

Bladder cancer (BC) is the tenth most common type of cancer worldwide, affecting up to four times more men than women. Depending on the stage of the tumor, different therapy protocols are applied. Non-muscle-invasive cancer englobes around 70% of the cases and is usually treated using the transurethral resection of bladder tumor (TURBIT) followed by the instillation of chemotherapy or immunotherapy. However, due to bladder anatomy and physiology, current intravesical therapies present limitations concerning permeation and time of residence. Furthermore, they require several frequent catheter insertions with a reduced interval between doses, which is highly demotivating for the patient. This scenario has encouraged several pieces of research focusing on the development of drug delivery systems (DDS) to improve drug time residence, permeation capacity, and target release. In this review, the current situation of BC is described concerning the disease and available treatments, followed by a report on the main DDS developed in the past few years, focusing on those based on mucoadhesive polymers as a strategy. A brief review of methods to evaluate mucoadhesion properties is also presented; lastly, different polymers suitable for this application are discussed.
Molecular Dynamics Simulations on Effect of Surface Roughness of Amorphous Substrate on Nucleation in Liquid Al

In this study, we used molecular dynamics (MD) simulations to investigate the atomic ordering in the liquid aluminum (Al) adjacent to the amorphous substrate with smooth and rough surfaces. This study revealed that the liquid exhibited layering within about 5 atomic layers but no visible in-plane atomic ordering at the interface with the smooth amorphous surface, and neither layering nor in-plane atomic ordering with the rough surface of the amorphous substrate. However, the smooth amorphous surface induced some local ordered structure in the liquid at the interface by a structural templating mechanism, which promoted heterogeneous nucleation by creating a 2-dimensional (2D) nucleus in the third layer. The amorphous substrate with a rough surface had no effect on the nucleation in the liquid, leading to the occurrence of homogeneous nucleation with an undercooling 100 K larger than that of heterogeneous nucleation on the smooth amorphous substrate. This study confirmed that structural templating is a general mechanism for heterogeneous nucleation.
Prediction of Treatment Outcome in Major Depressive Disorder Using Ensemble of Hybrid Transfer Learning and Long Short-Term Memory Based on EEG Signal

Major depressive disorder (MDD) is a widespread global mental disease. The effectiveness of selective serotonin reuptake inhibitors (SSRIs) antidepressants prescribed for MDD patients is limited and pretreatment assessment of treatment outcome is a vital task. In this study, a hybrid model based on transfer learning (TL) of powerful pretrained deep convolutional neural networks (CNNs) empowered with bidirectional long short-term memory (BLSTM) cells and attention mechanism were developed to classify responders (R) and nonresponders (NR) to SSRI antidepressants, using raw data images of pretreatment electro-encephalogram (EEG) signal obtained from 30 MDD patients. TL-LSTM-Attention models based on VGG16, Xception, and Densenet121 models were created for the classification of R/NR. Results show that the highest performance of hybrid TL-LSTM-Attention models was achieved by VGG16-LSTM-Attention with an accuracy of 98.21%, sensitivity of 96.22%, and specificity of 99.67%. An ensemble model based on weighted majority voting among hybrid models is constructed to surpass the performance of each single hybrid model. Our proposed ensemble model gained accuracy, sensitivity, and specificity of 98.84%, 97.80%, and 99.60%, respectively. Hence, the proposed model which is an ensemble of TL-LSTM-Attention models fed by raw data images of EEG signal leads to high confidence in the prediction of antidepressants treatment outcome.
Declaration of Originality

Turbulent mixing of passive scalar field and combustion of gaseous fuel were studied in the context of a non−premixed isothermal and reacting swirling jets discharged from a swirl−stabilised burner, as a function of swirl number. The rate of molecular mixing, which was quantified by the scalar dissipation rate was computed from measured scalar fields that were recorded by using Planar Laser Induced Fluorescence (PLIF) of acetone. The influence of the swirl number on the scalar mixing, unconditional and conditional scalar dissipation rate statistics was investigated. Scalar fields were measured with an average error of ±3%. Scalar dissipation rate was measured with an average error of 12% after de-nosing. The influence of swirl number on combustion characteristics was examined by using Rayleigh scattering with accuracy of 90%. The flow fields in non−reacting and reacting swirling jets were investigated by using Particle Image Velocimetry (PIV). The effect of swirl number on a recirculation zone was shown and discussed. The flow structures were evaluated by using Proper Orthogonal Decomposition. Experimental assessment of presumed filtered density function and subgrid scale (SGS) scalar variance models that are being developed in the context of Large Eddy Simulation (LES) was performed by using the data obtained from measured scalar fields. Measurements were performed in a flow formed by discharging a central jet in the annular stream of swirling air. This is a typical geometry used in swirl−stabilised burners where the central jet is the flow. The measurements were performed at a constant Reynolds number of 28662, based on the area−averaged velocity of 8.46 (m/s) at the exit of the swirl−stabilised burner and the diameter of the annular swirling stream of 50.8(mm). Three swirl numbers S = {0.3, 0.58, 1.07} of the annular swirling stream were considered.
Minimum latency training of sequence transducers for streaming end-to-end speech recognition

Sequence transducers, such as the RNN-T and the Conformer-T, are one of the most promising models of end-to-end speech recognition, especially in streaming scenarios where both latency and accuracy are important. Although various methods, such as alignment-restricted training and FastEmit, have been studied to reduce the latency, latency reduction is often accompanied with a significant degradation in accuracy. We argue that this suboptimal performance might be caused because none of the prior methods explicitly model and reduce the latency. In this paper, we propose a new training method to explicitly model and reduce the latency of sequence transducer models. First, we define the expected latency at each diagonal line on the lattice, and show that its gradient can be computed efficiently within the forward-backward algorithm. Then we augment the transducer loss with this expected latency, so that an optimal trade-off between latency and accuracy is achieved. Experimental results on the WSJ dataset show that the proposed minimum latency training reduces the latency of causal Conformer-T from 220 ms to 27 ms within a WER degradation of 0.7%, and outperforms conventional alignment-restricted training (110 ms) and FastEmit (67 ms) methods.
Multiscale Progressive Segmentation Network for High-Resolution Remote Sensing Imagery

Semantic segmentation of high-resolution remote sensing imageries (HRSIs) is a critical task for a wide range of applications, such as precision agriculture and urban planning. Although convolutional neural networks (CNNs) have made great progress in accomplishing this task recently, there still exist some challenges to address, one of which is simultaneously segmenting objects with large-scale variations in a HRSI. Targeting at this challenge, previous CNNs often adopt multiple convolution kernels in one layer or skip-layer connections between different layers to extract multiscale representations. However, due to the limited learning capacity of each CNN, it tends to make tradeoffs in segmenting different-scale objects. This would lead to unsatisfactory segmentation results for some objects, especially the small or the large ones. In this article, we propose a multiscale progressive segmentation network to address this issue. Instead of forcing one network to deal with all scales of objects, our network attempts to cascade three subnetworks for gradually segmenting objects into small scale, large scale, and other scale. In order to make the subnetwork focus on the specific scale objects, a scale guidance module is designed. It takes advantage of segmentation results from the preceding subnetwork to guide the feature learning of the succeeding one. Additionally, to acquire the final segmentation results, we propose a position-sensitive module for adaptively combining the outputs of the three subnetworks. This module is capable of assigning combination weights of different subnetworks according to their importance. Experiments on two benchmark datasets named Vaihingen and Potsdam indicate that our proposed network can achieve considerable improvements in comparison with several state-of-the-art segmentation models.
A map of neutrophil granules by proteomics

Neutrophils are polymorphonuclear leukocytes that play a key role in the organism defense. These cells enroll in a range of actions to ensure pathogen elimination and orchestrate both innate and adaptative immune responses. The main physiological structures of neutrophils are their storage organelles that are essential since the cell’s activation and participate in all their functions. The storage organelles are divided into 2 types: granules and secretory vesicles. The granules are subdivided into azurophilic, specific and gelatinase. The granules are distinguished by their protein content, and since they play an important role on the neutrophil function, the knowledge of the proteins stored in these organelles can help to better understand these cells. Some proteins are present in high abundance and are used as markers for each storage organelle. These proteins are myeloperoxidase (MPO) for azurophil granules, neutrophil gelatinase associated with lipocalin-2 (NGAL) and lactoferrin (LTF) for specific granules, matrix metalloproteinase-9 (MMP9) for gelatinase granules and alkaline phosphatase (AP) for secretory vesicles. The isolation of neutrophil’s granules, however, is challenging and the existing procedures rely on large sample volumes, about 400 mL of peripheral blood or 3 x 10 neutrophils, not allowing for multiple biological and technical replicates. Therefore, the aim of this study was to develop a miniaturized neutrophil granule’s isolation method and to use biochemical assays, mass spectrometry-based proteomics and a machine learning approach to investigate the protein content of the neutrophil’s storage organelles. With that in mind, 40 mL of the peripheral blood of three apparently healthy volunteers were collected. The neutrophils were isolated, disrupted using nitrogen cavitation and organelles were fractionated with a discontinuous 3-layer Percoll density gradient. The presence of granule’s markers in each fraction was assessed using western blot , gelatin zymography and enzymatic assays. The isolation was proven successful and allowed for a reasonable separation of all neutrophil’s storage organelles in a gradient of less than 1 mL, about 37 times smaller than the methods described in the literature. Moreover, mass spectrometry-based proteomics identified 369 proteins in at least 3 of the 5 samples, and using a machine learning strategy, the localization of 140 proteins was predicted with confidence. Furthermore, this study was the first to investigate the proteome of neutrophil granules using technical and biological replicates, creating a reliable database for further studies. In conclusion, the developed miniaturized method is reproducible, cheaper, and reliable. In addition, it provides a resource for further studies exploring neutrophil granule’s protein content and mobilization during activation with different stimuli.
The Molecular Mechanisms of Panax ginseng in Treating Type 2 Diabetes Mellitus: Network Pharmacology Analysis and Molecular Docking Validation

Background Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder with a high global incidence. Panax ginseng has been used to treat T2DM in traditional medicine, with previous in vitro, in vivo, and clinical trial studies demonstrating its efficacy. This study aimed to determine the mechanism of P. ginseng in treating T2DM by network pharmacology. Methods The bioactive compounds of P. ginseng and corresponding targets of P. ginseng-T2DM were retrieved across multiple databases. The protein-protein interaction network was established using the STRING database and topological analysis helped identify the core target. Using the DAVID tool, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Finally, we checked the binding of core targets and bioactive compounds using molecular docking. Results The P. ginseng-T2DM networks mainly contained 22 bioactive compounds and 314 overlapping targets. The five most significant core targets were SRC, STAT3, MAPK1, AKT1, and PIK3R1. There were 244 GO terms and 95 KEGG pathways (adjusted p < 0.01) that were strongly correlated with diabetes-related signaling pathways, such as insulin resistance, the HIF-1 signaling pathway, the PI3K/Akt signaling pathway, the prolactin signaling pathway, the Rap1 signaling pathway, the Ras signaling pathway, the calcium signaling pathway, and the FoxO signaling pathway. Molecular docking results revealed that the top five core targets had a high binding affinity with the bioactive compounds of P. ginseng. Conclusion The bioactive compounds and targets in P. ginseng ameliorate T2DM by regulating insulin resistance and multiple signaling pathways.
Application of a Hybrid CEEMD-LSTM Model Based on the Standardized Precipitation Index for Drought Forecasting: The Case of the Xinjiang Uygur Autonomous Region, China

Accurate forecasting of droughts can effectively reduce the risk of drought. We propose a hybrid model based on complementary ensemble empirical mode decomposition (CEEMD) and long short-term memory (LSTM) to improve drought prediction accuracy. Taking the Xinjiang Uygur Autonomous Region as an example, the prediction accuracy of the LSTM and CEEMD-LSTM models for the standardized precipitation index (SPI) on multiple timescales was compared and analyzed. Multiple evaluation metrics were used in the comparison of the models, such as the Nash–Sutcliffe efficiency (NSE). The results show that (1) with increasing timescale, the prediction accuracy of the LSTM and CEEMD-LSTM models gradually improves, and both reach their highest accuracy at the 24-month timescale; (2) the CEEMD can effectively stabilize the time-series, and the prediction accuracy of the hybrid model is higher than that of the single model at each timescale; and (3) the NSE values for the hybrid CEEMD-LSTM model at SPI24 were 0.895, 0.930, 0.908, and 0.852 for Fuhai, Kuerle, Yutian, and Hami station, respectively. This indicates the applicability of the hybrid model in the forecasting of drought.
Financial Aid Access During the 2020 Pandemic Period

During the 2020 period of the COVID-19 pandemic, higher education delivery changed drastically. Using document analysis and interviews with staff and students, in this article I address how the practice of financial aid changed during the COVID-19 pandemic. Staff reported that these changes created problems with technology and radically increased workloads. Student stories indicate how changes in financial aid practice could exclude low-income students, and how access to certain technologies delayed students’ receipt of aid.
The multivariate decomposition method for infinite-dimensional integration

We develop the generic "Multivariate Decomposition Method" (MDM) for weighted integration of functions of infinitely many variables $(x_1,x_2,x_3,.)$. The method works for functions that admit a decomposition $f=\sum_{\mathfrak{u}} f_{\mathfrak{u}}$, where $\mathfrak{u}$ runs over all finite subsets of positive integers, and for each $\mathfrak{u}=\{i_1,. ,i_k\}$ the function $f_{\mathfrak{u}}$ depends only on $x_{i_1},. ,x_{i_k}$. Moreover, we assume that $f_{\mathfrak{u}}$ belongs to a normed space $F_{\mathfrak{u}}$, and that a bound on $\|f_{\mathfrak{u}}\|_{F_{\mathfrak{u}}}$ is known. We also specialize MDM to $F_{\mathfrak{u}}$ being the $|\mathfrak{u}|$-tensor product of an anchored reproducing kernel Hilbert space, or a particular non-Hilbert space.
Performance of the Newly Proposed Peguero-Lo Presti Criterion in Adults with Hypertrophic Cardiomyopathy

Background : The classic electrocardiogram (ECG) criteria have been applied to left ventricular hypertrophy (LVH) screening but have low sensitivity. Recently, the newly proposed Peguero-Lo Presti criterion has been proven to be more sensitive in detecting LVH in patients with hypertension than several current ECG criteria. The diagnostic value of the Peguero-Lo Presti criterion in hypertrophic cardiomyopathy (HCM) patients has not been fully evaluated. This study aims to test whether the new Peguero-Lo Presti criterion can improve the diagnostic performance in patients with HCM. Methods : This study included HCM patients and sex-and age-matched healthy control subjects. The diagnostic performance of the Peguero-Lo Presti criterion was evaluated along with the Sokolow-Lyon criterion, Cornell criterion, and total 12-lead voltage criterion. Results : Overall, 63 HCM patients and 63 controls were enrolled. The diagnostic accuracy, sensitivity and specificity of Peguero-Lo Presti criterion were 74.6%, 73.0% and 76.2%, respectively. The Peguero-Lo Presti criterion had the highest sensitivity, while the Cornell criterion and Sokolow-Lyon criterion had the highest specificity (96.8%). The area under the curve (AUC) showed that the Peguero-Lo Presti criterion was 0.809 (95% CI, 0.730–0.874; p < 0.0001), Sokolow-Lyon criterion was 0.841 (95% CI, 0.766–0.900) and total 12-lead voltage criterion was 0.814 (95% CI, 0.735–0.878). There was no significant difference in AUC between Peguero-Lo Presti criterion and Sokolow-Lyon criterion ( p = 0.533), or Peguero-Lo Presti criterion and total 12-lead voltage criterion ( p = 0.908). Receiver operator characteristic curve analysis of the Peguero-Lo Presti criterion showed an optimal cutoff of > 3.15 mV for men (sensitivity: 63.9%; specificity: 80.0%) and > 2.29 mV for women (sensitivity: 78.6%; specificity: 85.7%). Conclusions : The Peguero-Lo Presti criterion provides high sensitivity for ECG diagnosis of HCM patients and can be considered when applicable but this needs to be verified in a larger population.
Resisting Hindutva in the digital Indian diaspora: notes from Australia

ABSTRACT The study of how ethnonationalists mobilize digital media is not matched in size by an investigation into the progressive activists digitally mobilizing against them. This is especially true in diaspora communities, which form the focus of this work. In this paper, we discuss diasporic online progressive activism against one form of ethnonationalism, Hindutva. Situating our argument in the context of the Indian Australian diaspora, we argue that the study of activist media ecologies which seek to resist Hindutva is as critical as the study of Hindutva itself, but that scholarship on this kind resistance in diaspora remains sparse. We suggest that the Australian case – witnessing a critical acceleration in Hindutva mobilization since 2020 – provides a unique opportunity to understand the role of digital media at the nascence of online resistance.
Normalization of code-switched text for speech synthesis

In multilingual communities, code-switching is a common phenomenon. Due to the increase in usage of social media, high level of code-switching is present in social media text as well. These code-switched social media texts are often seen written in monolingual script. Text normalization techniques of the conventional Text-to-Speech (TTS) and machine translation systems may not be able to handle such code-switched texts. Malayalam is a low resource Indic language. Conversational Malayalam contains high level of inter-sentential, intra-sentential as well as intra-word code-switching with English. This paper specifies the techniques for handling Malayalam-English code-switched text data. Evaluation results of experiments conducted on Malayalam-English code-switched data is also presented.
Deep residual spiking neural network for keyword spotting in low-resource settings

We propose a practical solution for the implementation of key-word spotting (KWS) system on portable devices, that features all three properties required for battery-powered portable scenarios: low power usage, small footprint, and high accuracy. In particular, we study an end-to-end KWS system with deep residual Spiking Neural Network (SNN), perform experiments on Google Speech Commands Dataset, and compare with both state-of-the-art ANN and SNN models. First, the proposed solution outperforms its ANN counterpart and other SNN in terms of energy efficiency. Second, it requires a smaller foot-print (86.5K) than other ANN and SNN (210K) models. Third, in terms of classification accuracy, it outperforms the existing power-efficient SNN benchmark by 4% to 17%. The proposed solution is an example of the unparalleled performance of spiking neural network in real-world applications.