Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameter Estimation in Sparse Inverse Problems Using Bernoulli-Gaussian Prior

Sparse coding is now one of the state-of-art approaches for solving inverse problems. In combination with (Fast) Iterative Shrinkage Thresholding Algorithm (ISTA), among other algorithms, one can efficiently get a nice estimator of the sought sparse signal. However, the major drawback of these methods is the tuning of the so-called hyperparameter. In this paper, we first provide an Expectation-Maximization (EM) algorithm to estimate the parameters of a Bernoulli-Gaussian model for denoising a sparse signal corrupted by a white Gaussian noise. Then, building on the Expectation-Maximization interpretation of ISTA, we provide a simple iterative algorithm to blindly estimate all the model parameters in the linear inverse problem context, including the hyperparameter involved in the popular ℓ0 regularized minimization. Moreover, the algorithm directly yields an estimator of the sparse signal.
Compressive Phase Retrieval Based On Sparse Latent Generative Priors

We address the problem of compressive phase retrieval (CPR) based on generative prior. The problem is ill-posed and requires structural assumptions. CPR techniques impose sparsity prior on the signal to perform reconstruction from compressive phaseless measurements. Recent developments in data-driven signal models in the form of generative priors have been shown to outperform sparsity priors with significantly fewer measurements. However, it is possible to improve upon the performance of generative prior based methods by introducing structure in the latent-space. We propose to introduce structure on the signal by enforcing sparsity in the latent-space via proximal method while training the generator. The optimization is called as proximal meta-learning (PML). Enforcing sparsity in the latent space naturally leads to a union-of-submanifolds model in the solution space. The overall framework of imposing sparsity along with PML is called as sparsity-driven latent space sampling (SDLSS). We demonstrate the efficacy of the proposed framework over the state-of-the-art deep phase retrieval (DPR) technique on MNIST and CelebA datasets. We evaluate the performance as a function of the number of measurements and sparsity factor using standard objective measures. The results show that SDLSS performs better at higher compression ratio and has faster recovery compared with DPR.
Csenet: Complex Squeeze-and-Excitation Network for Speech Depression Level Prediction

Automatic speech depression level prediction (SDLP) is a very challenging problem in affective computing. There are many studies that have acquired quite good performances for SDLP. However, most of the input speech features of these studies are based on the amplitude spectrogram, which loses the phase spectrogram information. Therefore, these speech features may lose some important information related to depression. In order to make full use of speech information, this paper proposes a complex squeeze-and-excitation network (CSENet) for SDLP. The complex spectrogram is used as the input speech feature, which contains both amplitude and phase spectrogram. In addition, to acquire a discriminative feature, the squeeze-and-excitation residual network is employed to extract deep speech feature. Finally, the attentive temporal pooling is utilized to dynamically select more important information according to the attention mechanisms. Experimental results on the AVEC 2013 and AVEC 2014 datasets prove the effectiveness of our proposed method. As for the mean absolute error (MAE) evaluation metric on AVEC 2013, our proposed method acquires state-of-the-art performance.
Model-Based Online Learning for Resource Sharing in Joint Radar-Communication Systems

The ever-increasing congestion in the radio spectrum has made coexistence and co-design for radar and communication systems an important problem to address. The radio spectrum is a rapidly time-frequency-space varying resource, and learning is required to use the spectrum and mitigate the interference. This paper proposes a model-based online learning (MBOL) framework to enable a structured way to formulate efficient online learning algorithms for resource sharing in joint radar-communication (JRC) systems. As an example, we apply the MBOL framework for allocating frequency resources in non-cooperative shared spectrum scenarios. The proposed MBOL algorithm learns a predictive model using online convex optimization (OCO) and chooses the best frequency channels in uncertain interference environments. The algorithm outperforms the considered baseline algorithms in terms of regret that quantifies the cost of learning.
Holitouch: Conveying Holistic Touch Illusions by Combining Pseudo-Haptics With Tactile and Proprioceptive Feedback During Virtual Interaction With 3DUIs

Virtual reality technology and immersive virtual environments often support realistic hand representations via hand-posture-sensing controllers or hand tracking for natural hand-based interaction. However, one limiting factor remains as the lack of realistic haptic feedback including tactile and proprioceptive cues; even for simple haptic interactions like touching a virtual object. This paper introduces the Holitouch technique to improve the haptic realism of essential 3D user interface elements such as buttons. Holitouch is a feedback technique based on a wearable device that combines different types of haptic feedback (i.e., 1) pseudo-haptic, 2) tactile, and 3) proprioceptive) to convey the holistic sensation of stiffness, contact, and activation while interacting with 3D buttons. Our approach provides these sensations by utilizing redundant multisensory cues, i.e., congruent feedback, to create plausible illusions of touch. The results of two experiments show that the proposed feedback combination contributes to delivering a holistic sensation when interacting with buttons in VR while having high user acceptance.
Two-Year Event-Free Survival Prediction in DLBCL Patients Based on In Vivo Radiomics and Clinical Parameters

Purpose For the identification of high-risk patients in diffuse large B-cell lymphoma (DLBCL), we investigated the prognostic significance of in vivo radiomics derived from baseline [18F]FDG PET/CT and clinical parameters. Methods Pre-treatment [18F]FDG PET/CT scans of 85 patients diagnosed with DLBCL were assessed. The scans were carried out in two clinical centers. Two-year event-free survival (EFS) was defined. After delineation of lymphoma lesions, conventional PET parameters and in vivo radiomics were extracted. For 2-year EFS prognosis assessment, the Center 1 dataset was utilized as the training set and underwent automated machine learning analysis. The dataset of Center 2 was utilized as an independent test set to validate the established predictive model built by the dataset of Center 1. Results The automated machine learning analysis of the Center 1 dataset revealed that the most important features for building 2-year EFS are as follows: max diameter, neighbor gray tone difference matrix (NGTDM) busyness, total lesion glycolysis, total metabolic tumor volume, and NGTDM coarseness. The predictive model built on the Center 1 dataset yielded 79% sensitivity, 83% specificity, 69% positive predictive value, 89% negative predictive value, and 0.85 AUC by evaluating the Center 2 dataset. Conclusion Based on our dual-center retrospective analysis, predicting 2-year EFS built on imaging features is feasible by utilizing high-performance automated machine learning.
H3K27M Mutation Doesn’t Mean Worse Prognosis in Old Patients

Objective Diffuse midline glioma (DMG), H3K27 altered is a new entity that has become widely recognized. However, studies concerning DMG in adult patients remain rare. We did a retrospective study covering the largest amount of patients to date to analyze the clinicopathological characteristics of diffuse glioma in midline structures of the adult. Methods We reviewed 108 cases of adult DMG, collected their clinical data, and pathological results including H3K27 mutation. Summarized their features and the connection with overall survival in different age groups. Results Among 108 cases, 79 tumors were located at the thalamus. 38 patients had H3K27M mutation, whose average age was 35.7 years. The median overall survival of H3K27M-mutant gliomas and the 80 H3K27M wild-type gliomas were both 12 months. For young patients (age ≤ 35), The median survival time of the H3K27M-mutant was 18 months, while that of the H3K27M wild-type was 37 months. For older patients (age>35), the median survival time of the H3K27M-mutant was 16 months, while that of the H3K27M wild-type was 13 months. Other clinicopathological factors including sex, tumor location, the approach of surgery, histological grade, ATRX, and P53 were statistically irrelevant to prognosis. Conclusion The DMG in adults mainly occurred in the thalamus. H3K27M mutations tend to happen more frequently in young adults, and this genetic alteration results in a worse outcome only in young patients (≤35). For old patients, age is the only independent prognostic factor. Patients who underwent different surgical operations including biopsy, subtotal resection, and total resection had similar prognoses.
Deciphering Metabolic Adaptability of Leukemic Stem Cells

Therapeutic targeting of leukemic stem cells is widely studied to control leukemia. An emerging approach gaining popularity is altering metabolism as a potential therapeutic opportunity. Studies have been carried out on hematopoietic and leukemic stem cells to identify vulnerable pathways without impacting the non-transformed, healthy counterparts. While many metabolic studies have been conducted using stem cells, most have been carried out in vitro or on a larger population of progenitor cells due to challenges imposed by the low frequency of stem cells found in vivo. This creates artifacts in the studies carried out, making it difficult to interpret and correlate the findings to stem cells directly. This review discusses the metabolic difference seen between hematopoietic stem cells and leukemic stem cells across different leukemic models. Moreover, we also shed light on the advancements of metabolic techniques and current limitations and areas for additional research of the field to study stem cell metabolism.
Substrates and Loaded Iron Ions Relative Position Influence the Catalytic Characteristics of the Metalloenzymes Angelica archangelica Flavone Synthase I and Camellia sinensis Flavonol Synthase

Metalloenzymes are a class of enzymes that catalyze through the metal ions they load. Angelica archangelica flavone synthase I (AnFNS I) and Camellia sinensis flavonol synthase (CaFLS), both of which belong to metalloenzymes, have highly similar structures and metal catalytic cores. However, these two enzymes catalyze the same substrate to produce significantly different products. To identify the cause for the differences in the catalytic characteristics of AnFNS I and CaFLS, their protein models were constructed using homology modeling. Structural alignment and molecular docking was also used to elucidate the molecular basis of the differences observed. To analyze and verify the cause for the differences in the catalytic characteristics of AnFNS I and CaFLS, partial fragments of AnFNS I were used to replace the corresponding fragments on CaFLS, and the catalytic characteristics of the mutants were determined by bioconversion assay in E. coli and in vitro catalytic test. The results suggest that the difference in catalytic characteristics between AnFNS I and CaFLS is caused by the depth of the active pockets and the relative position of the substrate. Mutant 10 which present similar dock result with AnFNS I increased the proportion of diosmetin (a flavone) from 2.54 to 16.68% and decreased the proportion of 4′-O-methyl taxifolin (a flavanol) from 47.28 to 2.88%. It was also indicated that the atoms in the substrate molecule that determine the catalytic outcome may be H-2 and H-3, rather than C-2 and C-3. Moreover, it is speculated that the change in the catalytic characteristics at the changes relative spatial position of H-2/H-3 of hesperetin and the loaded carbonyl iron, caused by charged residues at the entrance of the active pocket, is the key factor for the biosynthesis of flavone from flavanone.
Multi-Target Antibacterial Mechanism of Moringin From Moringa oleifera Seeds Against Listeria monocytogenes

Moringin [4-(α-L-rhamnosyloxy) benzyl isothiocyanate] is an isothiocyanate from Moringa oleifera seeds. It is the bioactivated form of the glucosinolate precursor glucomoringin with various health benefits. However, few studies have examined the antibacterial activity of moringin. This study aimed to investigate the antimicrobial activity and mechanism of moringin against Listeria monocytogenes. The minimum inhibitory concentration (MIC), and growth curves were used to evaluate the bacteriostatic effect of moringin against L. monocytogenes. Transcriptome analysis by RNA sequencing was performed to elucidate the underlying mechanism of moringin against L. monocytogenes. The transcriptome results were validated. The results showed that moringin inhibited the growth of L. monocytogenes with a MIC of 400 μM. RNA sequencing results showed that the differences in the expression of genes related to the cell wall and membrane biosynthesis, phosphotransferase system (PTS), oxidative stress, energy metabolism, and DNA binding were significantly affected. As with the transcriptome results, the results of the mechanism verification found that moringin damaged the integrity of the cell wall and cell membrane, stimulated oxidative stress, interfered with energy metabolism and DNA replication, and finally led to the death of L. monocytogenes. The present study provides evidence that moringin exhibits strong antimicrobial activity against L. monocytogenes and insight into its potential mechanism.
An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery

Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional features is an important step towards morphogenetic brain structure characterization of murine models in neurobiological studies. State-of-the-art image segmentation methods register image volumes to standard presegmented templates or well-characterized highly detailed image atlases. Performance of these methods depends critically on the quality of skull-stripping, which is the digital removal of tissue signal exterior to the brain. This is, however, tedious to do manually and challenging to automate. Registration-based segmentation, in addition, performs poorly on small structures, low resolution images, weak signals, or faint boundaries, intrinsic to in vivo MRI scans. To address these issues, we developed an automated end-to-end pipeline called DeepBrainIPP (deep learning-based brain image processing pipeline) for 1) isolating brain volumes by stripping skull and tissue from T2w MRI images using an improved deep learning-based skull-stripping and data augmentation strategy, which enables segmentation of large brain regions by atlas or template registration, and 2) address segmentation of small brain structures, such as the paraflocculus, a small lobule of the cerebellum, for which DeepBrainIPP performs direct segmentation with a dedicated model, producing results superior to the skull-stripping/atlas-registration paradigm. We demonstrate our approach on data from both in vivo and ex vivo samples, using an in-house dataset of 172 images, expanded to 4,040 samples through data augmentation. Our skull stripping model produced an average Dice score of 0.96 and residual volume of 2.18%. This facilitated automatic registration of the skull-stripped brain to an atlas yielding an average cross-correlation of 0.98. For small brain structures, direct segmentation yielded an average Dice score of 0.89 and 5.32% residual volume error, well below the tolerance threshold for phenotype detection. Full pipeline execution is provided to non-expert users via a Web-based interface, which exposes analysis parameters, and is powered by a service that manages job submission, monitors job status and provides job history. Usability, reliability, and user experience of DeepBrainIPP was measured using the Customer Satisfaction Score (CSAT) and a modified PYTHEIA Scale, with a rating of excellent. DeepBrainIPP code, documentation and network weights are freely available to the research community.
Greenland Ice Sheet Surfaces Colonized by Microbial Communities Emit Volatile Organic Compounds

Volatile organic compounds (VOCs) are emitted by organisms for a range of physiological and ecological reasons. They play an important role in biosphere–atmosphere interactions and contribute to the formation of atmospheric secondary aerosols. The Greenland ice sheet is home to a variety of microbial communities, including highly abundant glacier ice algae, yet nothing is known about the VOCs emitted by glacial communities. For the first time, we present VOC emissions from supraglacial habitats colonized by active microbial communities on the southern Greenland ice sheet during July 2020. Emissions of C5–C30 compounds from bare ice, cryoconite holes, and red snow were collected using a push–pull chamber active sampling system. A total of 92 compounds were detected, yielding mean total VOC emission rates of 3.97 ± 0.70 μg m–2 h–1 from bare ice surfaces (n = 31), 1.63 ± 0.13 μg m–2 h–1 from cryoconite holes (n = 4), and 0.92 ± 0.08 μg m–2 h–1 from red snow (n = 2). No correlations were found between VOC emissions and ice surface algal counts, but a weak positive correlation (r = 0.43, p = 0.015, n = 31) between VOC emission rates from bare ice surfaces and incoming shortwave radiation was found. We propose that this may be due to the stress that high solar irradiance causes in bare ice microbial communities. Acetophenone, benzaldehyde, and phenylmaleic anhydride, all of which have reported antifungal activity, accounted for 51.1 ± 11.7% of emissions from bare ice surfaces, indicating a potential defense strategy against fungal infections. Greenland ice sheet microbial habitats are, hence, potential sources of VOCs that may play a role in supraglacial microbial interactions, as well as local atmospheric chemistry, and merit future research efforts.
Soil Depth Significantly Shifted Microbial Community Structures and Functions in a Semiarid Prairie Agroecosystem

The North American Great Plains cover a large area of the Nearctic ecozone, and an important part of this biome is semiarid. The sustainable intensification of agriculture that is necessary to produce food for an ever-increasing world population requires knowledge of the taxonomic and functional structure of the soil microbial community. In this study, we investigated the influence of soil depth on the composition and functions of the microbial communities hosted in agricultural soils of a semiarid agroecosystem, using metagenomic profiling, and compared them to changes in soil chemical and physical properties. Shotgun sequencing was used to determine the composition and functions of the soil microbial community of 45 soil samples from three soil depths (0–15 cm, 15–30 cm, and 30–60 cm) under different agricultural land use types (native prairie, seeded prairie, and cropland) in southwest Saskatchewan. Analysis of community composition revealed the declining abundance of phyla Verrucomicrobia, Bacteroidetes, Chlorophyta, Bacillariophyta, and Acidobacteria with soil depth, whereas the abundance of phyla Ascomycota, Nitrospirae, Planctomycetes, and Cyanobacteria increased with soil depth. Soil functional genes related to nucleosides and nucleotides, phosphorus (P) metabolism, cell division and cell cycle, amino acids and derivatives, membrane transport, and fatty acids were particularly abundant at 30–60 cm. In contrast, functional genes related to DNA and RNA metabolism, metabolism of nitrogen, sulfur and carbohydrates, and stress response were more abundant in the top soil depth. The RDA analysis of functional genes and soil physico-chemical properties revealed a positive correlation between phages and soil organic P concentrations. In the rooting zone of this semiarid agroecosystem, soil microbes express variable structural patterns of taxonomic and functional diversity at different soil depths. This study shows that the soil microbial community is structured by soil depth and physicochemical properties, with the middle soil depth being an intermediate transition zone with a higher taxonomic diversity. Our results suggest the co-existence of various microbial phyla adapted to upper and lower soil depths in an intermediate-depth transition zone.
Vimentin Tail Segments Are Differentially Exposed at Distinct Cellular Locations and in Response to Stress

The intermediate filament protein vimentin plays a key role in cell signaling and stress sensing, as well as an integrator of cytoskeletal dynamics. The vimentin monomer consists of a central rod-like domain and intrinsically disordered head and tail domains. Although the organization of vimentin oligomers in filaments is beginning to be understood, the precise disposition of the tail region remains to be elucidated. Here we observed that electrophilic stress-induced condensation shielded vimentin from recognition by antibodies against specific segments of the tail domain. A detailed characterization revealed that vimentin tail segments are differentially exposed at distinct subcellular locations, both in basal and stress conditions. The 411–423 segment appeared accessible in all cell areas, correlating with vimentin abundance. In contrast, the 419–438 segment was more scantily recognized in perinuclear vimentin and lipoxidative stress-induced bundles, and better detected in peripheral filaments, where it appeared to protrude further from the filament core. These differences persisted in mitotic cells. Interestingly, both tail segments showed closer accessibility in calyculin A-treated cells and phosphomimetic mutants of the C-terminal region. Our results lead us to hypothesize the presence of at least two distinct arrangements of vimentin tail in cells: an “extended” conformation (accessible 419–438 segment), preferentially detected in peripheral areas with looser filaments, and a “packed” conformation (shielded 419–438 segment), preferentially detected at the cell center in robust filaments and lipoxidative stress-induced bundles. These different arrangements could be putatively interconverted by posttranslational modifications, contributing to the versatility of vimentin functions and/or interactions.