Miracle-dz/UniFAR-FTUs huggingface dataset:
Modifying Surface Charges of a Thermophilic Laccase Toward Improving Activity and Stability in Ionic Liquid

The multicopper oxidase enzyme laccase holds great potential to be used for biological lignin valorization alongside a biocompatible ionic liquid (IL). However, the IL concentrations required for biomass pretreatment severely inhibit laccase activity. Due to their ability to function in extreme conditions, many thermophilic enzymes have found use in industrial applications. The thermophilic fungal laccase from Myceliophthora thermophila was found to retain high levels of activity in the IL [C2C1Im][EtSO4], making it a desirable biocatalyst to be used for lignin valorization. In contrast to [C2C1Im][EtSO4], the biocompatibility of [C2C1Im][OAC] with the laccase was markedly lower. Severe inhibition of laccase activity was observed in 15% [C2C1Im][OAc]. In this study, the enzyme surface charges were modified via acetylation, succinylation, cationization, or neutralization. However, these modifications did not show significant improvement in laccase activity or stability in [C2C1Im][OAc]. Docking simulations show that the IL docks close to the T1 catalytic copper, likely interfering with substrate binding. Although additional docking locations for [OAc]- are observed after making enzyme modifications, it does not appear that these locations play a role in the inhibition of enzyme activity. The results of this study could guide future enzyme engineering efforts by showing that the inhibition mechanism of [C2C1Im][OAc] toward M. thermophila laccase is likely not dependent upon the IL interacting with the enzyme surface.
Extracellular Volume Fraction Derived From Dual-Layer Spectral Detector Computed Tomography for Diagnosing Cervical Lymph Nodes Metastasis in Patients With Papillary Thyroid Cancer: A Preliminary Study

Objectives The current study evaluates the performance of dual-energy computed tomography (DECT) derived extracellular volume (ECV) fraction based on dual-layer spectral detector CT for diagnosing cervical lymph nodes (LNs) metastasis from papillary thyroid cancer (PTC) and compares it with the value of ECV derived from conventional single-energy CT (SECT). Methods One hundred and fifty-seven cervical LNs (81 non-metastatic and 76 metastatic) were recruited. Among them, 59 cervical LNs (27 non-metastatic and 32 metastatic) were affected by cervical root artifact on the contrast-enhanced CT images in the arterial phase. Both the SECT-derived ECV fraction (ECVS) and the DECT-derived ECV fraction (ECVD) were calculated. A Pearson correlation coefficient and a Bland–Altman analysis were performed to evaluate the correlations between ECVD and ECVS. Receiver operator characteristic curves analysis and the Delong method were performed to assess and compare the diagnostic performance. Results ECVD correlated significantly with ECVS (r = 0.925; p <0.001) with a small bias (−0.6). Metastatic LNs showed significantly higher ECVD (42.41% vs 22.53%, p <0.001) and ECVS (39.18% vs 25.45%, p <0.001) than non-metastatic LNs. By setting an ECVD of 36.45% as the cut-off value, optimal diagnostic performance could be achieved (AUC = 0.813), which was comparable with that of ECVS (cut-off value = 34.99%; AUC = 0.793) (p = 0.265). For LNs affected by cervical root artifact, ECVD also showed favorable efficiency (AUC = 0.756), which was also comparable with that of ECVS (AUC = 0.716) (p = 0.244). Conclusions ECVD showed a significant correlation with ECVS. Compared with ECVS, ECVD showed comparable performance in diagnosing metastatic cervical LNs in PTC patients, even though the LNs were affected by cervical root artifacts on arterial phase CT.
Characterization of Necroptosis-Related Molecular Subtypes and Therapeutic Response in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high morbidity and mortality and is usually associated with therapeutic resistance and poor prognosis because of individual biological heterogeneity. There is an unmet need to screen for reliable parameters, especially immunotherapy-related biomarkers to predict the patient’s outcomes. Necroptosis is a special caspase-independent form of necrotic cell death associated with the pathogenesis, progression, and prognosis of multiple tumors but the potential connection between necroptosis-related genes (NRGs) and LUAD still remains unclear. In this study, we expounded mutational and transcriptional alterations of 67 NRGs in 522 LUAD samples and proposed a consensus-clustering subtype of these patients into two cohorts with distinct immunological and clinical prognosis characteristics. Cluster B patients were associated with a better prognosis and characterized by relatively lower expression of NRGs, higher immune scores in the tumor microenvironment (TME), more mild clinical stages, and downregulated expression of immunotherapy checkpoints. Subsequently, the NRG score was further established to predict the overall survival (OS) of LUAD patients using univariate Cox, LASSO, and multivariate Cox regression analyses. The immunological characteristics and potential predictive capability of NRG scores were further validated by 583 LUAD patients in external datasets. In addition to better survival and immune-activated conditions, low-NRG-score cohorts exhibited a significant positive correlation with the mRNA stem index (mRNAsi) and tumor mutation burden (TMB) levels. Combined with classical clinical characteristics and NRG scores, we successfully defined a novel necroptosis-related nomogram to accurately predict the 1/3/5-year survival rate of individual LUAD patients, and the potential predictive capability was further estimated and validated in multiple test datasets with high AUC values. Integrated transcriptomic analysis helps us seek vital NRGs and supplements a novel clinical application of NRG scores in predicting the overall survival and therapeutic benefits for LUAD patients.
Editorial: Novel Insights Into the Genetics of Growth Disorders

Although in the popular culture the definition of “genetic disease” is mainly associated to the presence of clinical signs such as intellectual disability, heart malformations or facial dysmorphisms, the history of Medical Genetics demonstrates that actually one of the first conditions associated to a genetic disease has been represented by short stature. This disorder, representing a common medical concern that paediatricians have to face in their daily practice, can be associated to different genetic conditions, with the first evidence coming from the identification of the 45, X karyotype as the cause of the Turner Syndrome (TS), characterized by short stature as one of the main clinical signs (Zinn et al., 1993). The comparison between the phenotype of TS and Klinefelter syndrome, due to a 47, XXY karyotype and characterized by tall stature, suggested the presence of one or more genes affecting human stature mapping on both X and Y sex chromosomes. Since these two chromosomes share genes only on their pseudoautosomal regions (PAR1 and PAR2), it was quite obvious to conclude that the gene(s) involved in short stature was located within these regions. Finally, in 1997, the SHOX gene, mapping within the PAR1 region, was cloned and identified as responsible both for a number of cases of idiopathic short stature and for the majority of cases of Leri-Weill dyschondrosteosis (Rao et al., 1997). In a short time, search for alterations in SHOX has become crucial in the molecular diagnosis of short stature (Stuppia et al., 2003; Stuppia et al., 2010; Genoni et al., 2018), in particular due to the evidence of a good response to GH treatment showed by patients with SHOX deficit (Benabbad et al., 2017). Moreover, the presence of variants affecting height also outside the SHOX coding region extended the spectrum of genotype–phenotype correlation in short stature (BenitoSanz et al., 2005; Babu et al., 2021; Fanelli et al., 2022). The identification of the SHOX gene and its analysis in the diagnostic workflow of children with short stature prompted other researchers to investigate the role played by other genes in the pathogenesis of different forms of short stature. Currently there are more than 200 mendelian syndromes or skeletal dysplasia associated with short stature for which the genetic cause have been identified. In some patients with short stature, variants of moderate effect explain less extreme phenotypes than those seen in disorders with classical syndromic phenotype. For example, biallelic variants in the natriuretic peptide receptor 2 (NPR2) (Wu et al., 2022) lead to acromesomelic displasia weather monoallelic mutations have been detected in short stature patients without any other distinctive feature (Hisado-Oliva et al., 2018) Pathogenic variants in FGFR3, have been found in a wide range of phenotypes, namely achondroplasia, hypochondroplasia and more recently in patients with milder forms short stature (Kant et al., 2015). Edited and reviewed by: Jordi Pérez-Tur, Institute of Biomedicine of Valencia (CSIC), Spain
The Pre-main Sequence: Challenges and Prospects for Asteroseismology

Stars do not simply pop up on the main sequence. Before the stars arrive on the zero-age main sequence, they form in the collapses of molecular clouds, gain matter through accretion processes, and compress their cores until hydrogen can burn in full equilibrium. Although this evolutionary phase lasts a relatively short time, it is the imprint of these important physical processes that is often ignored by simplified assumptions. While asteroseismology offers a great tool to investigate these physical processes, studying pre-MS oscillations in turn has the potential to further advance the field. Asteroseismology of pre-main sequence stars faces observational and theoretical challenges. The remnants of their birth environment which is often still surrounding the young stars causes variability that can interfere with the signal of pulsations. The lack of long time-base satellite observations in addition limits the applications of the method. Theoretical models of pre-main sequence stars include several assumptions and simplifications that influence the calculation of pulsation frequencies and excitation properties of pulsation modes. Keeping all this in mind, the prospects for pre-main sequence asteroseismology are manifold. An improved understanding of the structure of young stellar objects has the potential to answer some of the open questions of stellar evolution, including angular momentum transport and the formation of magnetic fields. While gyrochronology, for example, struggles to determine the ages of the youngest clusters, pulsations in pre-main sequence stars can function as an independent age indicator yielding higher precision for single stars. The increasing interest of stellar astrophysics in general to investigate the formation and early evolution of stars and planets illustrates the growing importance of pre-main sequence asteroseismology. In this work we discuss its potential for an advancement of our understanding of stellar structure and evolution.
A Comparative Study of SOC Estimation Based on Equivalent Circuit Models

This article presents a comparative study of the state of charge (SOC) estimation using Kalman filter (KF)-based estimators and H-infinity filter. The aim of this research is to obtain the optimal estimator by evaluating the SOC accuracy, robustness, and computation time under varying current noise assumptions. In the KF-based estimators, the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF) are mostly used in the SOC estimation area. The mixed driving cycle profiles are used to test the battery to simulate the complex driving conditions in real electric vehicles (EVs). Also, white noise and bias noise are added into the current data to imitate the inaccurate sensors in EVs. The normal equivalent circuit models (ECMs) and augmented ECMs with varying RC branches are thoroughly compared to acquire the best estimator under varying situations.
Multi-Sequence MR-Based Radiomics Signature for Predicting Early Recurrence in Solitary Hepatocellular Carcinoma ≤5 cm

Purpose To investigate the value of radiomics features derived from preoperative multi-sequence MR images for predicting early recurrence (ER) in patients with solitary hepatocellular carcinoma (HCC) ≤5 cm. Methods One hundred and ninety HCC patients were enrolled and allocated to training and validation sets (n = 133:57). The clinical–radiological model was established by significant clinical risk characteristics and qualitative imaging features. The radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm in the training set. The combined model was formed by integrating the clinical–radiological risk factors and selected radiomics features. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC). Results Arterial peritumoral hyperenhancement, non-smooth tumor margin, satellite nodules, cirrhosis, serosal invasion, and albumin showed a significant correlation with ER. The AUC of the clinical–radiological model was 0.77 (95% CI: 0.69–0.85) and 0.76 (95% CI: 0.64–0.88) in the training and validation sets, respectively. The radiomics model constructed using 12 radiomics features selected by LASSO regression had an AUC of 0.85 (95% CI: 0.79–0.91) and 0.84 (95% CI: 0.73–0.95) in the training and validation sets, respectively. The combined model further improved the prediction performance compared with the clinical–radiological model, increasing AUC to 0.90 (95% CI: 0.85–0.95) in the training set and 0.88 (95% CI: 0.80–0.97) in the validation set (p < 0.001 and p = 0.012, respectively). The calibration curve fits well with the standard curve. Conclusions The predictive model incorporated the clinical–radiological risk factors and radiomics features that could adequately predict the individualized ER risk in patients with solitary HCC ≤5 cm.
Investigation of Underlying Association Between Whole Brain Regions and Alzheimer’s Disease: A Research Based on an Artificial Intelligence Model

Alzheimer’s disease (AD) is the most common form of dementia, causing progressive cognitive decline. Radiomic features obtained from structural magnetic resonance imaging (sMRI) have shown a great potential in predicting this disease. However, radiomic features based on the whole brain segmented regions have not been explored yet. In our study, we collected sMRI data that include 80 patients with AD and 80 healthy controls (HCs). For each patient, the T1 weighted image (T1WI) images were segmented into 106 subregions, and radiomic features were extracted from each subregion. Then, we analyzed the radiomic features of specific brain subregions that were most related to AD. Based on the selective radiomic features from specific brain subregions, we built an integrated model using the best machine learning algorithms, and the diagnostic accuracy was evaluated. The subregions most relevant to AD included the hippocampus, the inferior parietal lobe, the precuneus, and the lateral occipital gyrus. These subregions exhibited several important radiomic features that include shape, gray level size zone matrix (GLSZM), and gray level dependence matrix (GLDM), among others. Based on the comparison among different algorithms, we constructed the best model using the Logistic regression (LR) algorithm, which reached an accuracy of 0.962. Conclusively, we constructed an excellent model based on radiomic features from several specific AD-related subregions, which could give a potential biomarker for predicting AD.
Cmri2spec: Cine MRI Sequence to Spectrogram Synthesis via A Pairwise Heterogeneous Translator

Multimodal representation learning using visual movements from cine magnetic resonance imaging (MRI) and their acoustics has shown great potential to learn shared representation and to predict one modality from another. Here, we propose a new synthesis framework to translate from cine MRI sequences to spectrograms with a limited dataset size. Our framework hinges on a novel fully convolutional heterogeneous translator, with a 3D CNN encoder for efficient sequence encoding and a 2D transpose convolution decoder. In addition, a pairwise correlation of the samples with the same speech word is utilized with a latent space representation disentanglement scheme. Furthermore, an adversarial training approach with generative adversarial networks is incorporated to provide enhanced realism on our generated spectrograms. Our experimental results, carried out with a total of 63 cine MRI sequences alongside speech acoustics, show that our framework improves synthesis accuracy, compared with competing methods. Our framework thereby has shown the potential to aid in better understanding the relationship between the two modalities.
Architecture for Variable Bitrate Neural Speech Codec with Configurable Computation Complexity

Low bitrate speech codecs have become an area of intense research. Traditional speech codecs, which use signal processing methods to encode and decode speech, often suffer from quality issues at low bitrates. A neural speech codec, which uses a deep neural network in the compression pipeline, can help alleviate this issue. In this paper we present a new neural speech codec that: 1) supports variable bitrates 2) supports packet losses of up to 120 ms and 3) can operate at low-compute and high-compute modes. Our codec uses a hierarchical VQ-VAE (HVQVAE) for encoding and decoding spectral features at different bitrates. The decoded features are fed to a vocoder for speech synthesis. Depending upon the end user’s computing resources, the decoder either uses a powerful WaveRNN or a parametric vocoder for speech synthesis. Our experiments demonstrate that our HVQVAE + WaveRNN setup achieves high audio quality.
Enrich Features for Few-Shot Point Cloud Classification

Recently, many existing fully supervised methods for point cloud classification have strongly promoted the development of point cloud learning. However, these methods require a lot of labeled data as support, which is challenging to obtain. To alleviate this problem, we propose a novel few-shot point cloud classification method to classify new categories given a few labeled samples. Specifically, we apply the feature supplement module to enrich the geometric information of points and then aggregate multi-scale features through the channel-wise attention module while reducing the computational complexity. Finally, we introduce a classifier to classify the point cloud features under the few-shot learning setup to predict its label. We carry out experimental verification on the benchmark dataset and achieve state-of-the-art performance.
Speaker-Targeted Audio-Visual Speech Recognition Using a Hybrid CTC/Attention Model with Interference Loss

Audio-visual (AV)-automatic speech recognition (ASR) can improve speech recognition accuracy by using lip images, especially in noisy environments. The recently proposed AV Align system integrates speech and image features based on a cross-modal attention mechanism, where attention weights for visual features are estimated by using acoustic features as queries. Although AV Align shows an improvement in recognition accuracy in background noise environments, we have observed that the recognition accuracy degrades significantly in interference speaker environments, where a target speech and an interfering speech overlap each other. In order to improve the speech recognition accuracy of the target speaker in such situations, we propose a method that combines the auxiliary loss function that maximizes the recognition accuracy of the interference speaker and the CTC loss function for training the AV-ASR model. The experimental results using the TCD-TIMIT dataset show that the use of these auxiliary loss functions improves the performance of target-speaker speech recognition in interference speaker environments.
Mining Hard Samples Locally And Globally For Improved Speech Separation

Speech separation dataset typically consists of hard and non-hard samples, and the former is minority and latter majority. The data imbalance problem biases the model towards non-hard samples and weakens the generalization capability. Given that the average separation performance is sufficiently good, improving hard samples may contribute more to back-end tasks. In this paper, we propose two methods to alleviate data imbalance in speech separation task, based on local and global hard sample mining. For the local, we propose weighted loss to compensate for hard samples by increasing their weights in each batch. For the global, we perform global hard sample mining and re-sample to increase the proportion of hard samples in the training set. Because hard sample mining using objective loss in dynamic mixing leads to local results, we propose an indirect method using speaker-specific parameters, based on the fact that pitch median difference and x-vector cosine distance of two speakers in a mixture are closely correlated with separation SI-SNRi. Experimental results show that both methods decrease the percentage of hard samples in the test set than using dynamic mixing only while keeping the average SI-SNRi comparable, and the global method shows more promising results than the local one.
Time-Domain Acoustic Contrast Control with A Spatial Uniformity Constraint for Personal Audio Systems

Personal audio systems with multiple sound zones for listeners to enjoy different music/audio contents privately in a shared physical space have attracted great research interest in the past two decades. Acoustic Contrast Control (ACC) is one of the most popular methods for generating multiple personal sound zones because it produces the minimum inter-zone interference. However, the ACC method has been found to be inferior to the pressure matching method in terms of sound quality due to an uneven frequency response and nonuniform spatial sound field distribution in the bright zone. This paper proposes a spatial uniformity constraint on time-domain broadband ACC in addition to the frequency response trend estimation constraint with the aim of ensuring a uniform sound field distribution in the bright zone. Simulation results with measured room impulse responses demonstrate that the proposed algorithm reduces the magnitude variations in the bright zone to be less than 1 dB higher than the just noticeable level difference at a cost of a perceptually negligible degradation in acoustic contrast.
Deep Adaptation Control for Acoustic Echo Cancellation

We propose a general framework for adaptation control using deep neural networks (NNs) and apply it to acoustic echo cancellation (AEC). First, the optimal step-size that controls the adaptation is derived offline by solving a constrained nonlinear optimization problem that minimizes the adaptive filter misadjustment. Then, a deep NN is trained to learn the relation between the input data and the optimal step-size. In real-time, the NN infers the optimal step-size from streaming data and feeds it to an NLMS filter for AEC. This data-driven method makes no assumptions on the acoustic setup and is entirely non-parametric. Experiments with 100 h of real and synthetic data show that the proposed method outperforms the competition in echo cancellation, speech distortion, and convergence during both single-talk and double-talk.