Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal Variation in Transcriptomic Profiling of Tetrastigma hemsleyanum Fully Developed Tuberous Roots Enriches Candidate Genes in Essential Metabolic Pathways and Phytohormone Signaling

Tetrastigma hemsleyanum Diels et Gilg (Sanyeqing, SYQ) is a perennial climbing liana and an endemic plant to southern China. Its tuberous roots (TRs) are used in traditional Chinese medicine for treating some diseases such as high fever, pneumonia, asthma, hepatitis, and cancers. However, the mechanisms underlying the development of TR and the content of flavonoids and phenylpropanoids (FPs) are not well-understood. In this study, we performed a transcriptomic analysis of 12 fully developed TR (FD-TR) samples harvested in four seasons [spring (Sp), summer (Su), autumn (Au), and winter (Wi)] using the RNA-Sequencing (RNA-Seq). We obtained a total of 78.54 Gb raw data and 65,578 unigenes. Then, the unigenes were annotated by using six databases such as non-redundant protein database (NR), Pfam, eggNOG, SWISSProt, Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene ontology (GO). The transcriptomic profiling showed closer relationships between the samples obtained in Su and Au than those obtained in Sp and Wi based on the results of both total unigenes and differentially expressed genes (DEGs). Three pathways, including the biosynthesis of FPs, metabolism of starch and sucrose, and signaling of phytohormones, were highly enriched, suggesting a gene-level seasonal variation. Based on the numbers of DEGs, brassinosteroid (BR) signal transduction factors appeared to play a key role in modulating the development of TRs while most of the auxin signaling genes were mainly activated in Wi and Sp FD-TRs. Most genes in the biosynthesis and biodegradation of starch and biodegradation of cellulose were activated in Wi FD-TRs. As determined by the high performance liquid chromatography (HPLC) and aluminum nitrate colorimetric method, the contents of total flavonoids and most detected FP components increased from Sp to Au but decreased in Wi. Enhanced expression levels of some genes in the biosynthetic pathways of FPs were detected in Su and Au samples, which corroborated well with metabolite content. Our findings provide the first transcriptomic and biochemical data on a seasonal variation in the composition of medically important metabolites in SYQ FD-TRs.
Influence of Physical Activity Levels and Functional Capacity on Brain β-Amyloid Deposition in Older Women

Physical activity (PA) and Alzheimer's disease are associated. However, how PA influences the cerebral β-amyloid (Aβ) burden remains unclear. The aim of this study was to determine if PA levels and/or functional capacity (FC) are associated with Aβ plaque deposition, and whether these associations differed according to APOE-ε4 genotype. A total of 117 women (69.7 ± 2.6 years; 33.3% APOE-ε4-carriers) from the Women's Healthy Ageing Project cohort (WHAP) were analyzed. PA was measured using the International Physical Activity Questionnaire and, FC was evaluated using the Timed Up and Go test (TUGt). Positron emission tomography with F-18 Florbetaben was carried out to assess cerebral Aβ burden, and quantified using standardized uptake value rations. The sample was split into PA and TUGt tertiles (T1, T2 and T3), and compared according to APOE-ε4 genotype (positive/negative). There were no significant differences in Aβ accumulation according to PA tertiles and APOE-ε4 genotype. Regarding FC, APOE-ε4+ participants in the first TUGt tertile (high performance) obtained significant lower Aβ accumulations compared with the other two tertiles (p < 0.05). Comparing between genotypes, greater Aβ depositions were found between T2 and T3 in APOE-ε4+ compared with those who were APOE-ε4– (p < 0.05). Values of TUGt ≥ 6.5 s (APOE-ε4+) and 8.5 s (APOE-ε4–) were associated with an increased risk of having higher Aβ retention. In conclusion, low performance in TUGt is associated with a negative effect on brain pathology with increasing cerebral Aβ depositions in older women who are APOE-ε4+. In physically active older women (> 600 METs·min/week), higher PA levels are not associated with reduction in Aβ depositions.
Multi-modal locomotor costs explain sexual size but not shape dimorphism in a leaf-mimicking insect

In most arthropods, adult females are larger than males, and male competition is a race to quickly locate and mate with scattered females (scramble competition polygyny). In this context, smaller males may be favored due to more efficient locomotion leading to higher mobility during mate searching while larger males may benefit from increased speed and higher survivorship. Understanding how body size affects different aspects of the locomotor performance of males is therefore essential to shed light on the evolution of this widespread mating system. Using a combination of empirical measures of flight performance and substrate adhesion, and modelling of body aerodynamics, we show that large body size impairs both flight and landing (attachment) performance in male leaf insects (Phyllium philippinicum), a species where relatively small and skinny males fly through the canopy in search of large sedentary females. Smaller males were more agile in the air, ascended more rapidly during flight, and had a lower risk of detaching from the substrates on which they walk and land. Our models revealed variation in body shape affected body lift and drag, but tradeoffs with weight meant that effects were negligible, suggesting that flight costs do not explain the evolution of strong sexual dimorphism in body shape in this species. Graphical abstract
Traip Mitotic Function Controls Brain Size

Microcephaly is a developmental failure to achieve proper brain size and neuron number. Mutations in diverse genes are linked to microcephaly, including several with DNA damage repair (DDR) functions; however, it is not well understood how these DDR gene mutations limit brain size. One such gene is TRAIP, which has multiple known functions in DDR. We characterized the Drosophila ortholog Traip, finding that loss of Traip causes a brain-specific defect in the Mushroom Body (MB). Traip mutant (traip-) MBs had reduced size and fewer neurons, but no neurodegeneration, consistent with human primary microcephaly disorders. Reduced neuron numbers in traip- were explained by premature caspase-dependent cell death of MB neuroblasts (MB-NBs). Many traip- MB-NBs had prominent chromosome bridges in anaphase, along with polyploidy, aneuploidy, or micronuclei. We found no evidence for an interphase DNA repair role for Traip in MB-NBs; instead, proper MB development requires Traip function during mitosis, where Traip localizes to centrosomes and mitotic spindles. Our results suggest that proper brain size is ensured by the recently described role for TRAIP in unloading stalled replication forks in mitosis, which suppresses DNA bridges and neural stem cell death to promote proper neuron number. Further, the mitotic nature of traip- MB-NB defects and Traip localization suggest a closer etiological link between DDR microcephaly genes like Traip and the centrosome/spindle-related genes more commonly associated with microcephaly.
Predictability and parallelism in the contemporary evolution of hybrid genomes

Hybridization between species is widespread across the tree of life. As a result, many species, including our own, harbor regions of their genome derived from hybridization. Despite the recognition that this process is widespread, we understand little about how the genome stabilizes following hybridization, and whether the mechanisms driving this stabilization tend to be shared across species. Here, we dissect the drivers of variation in local ancestry across the genome in replicated hybridization events between two species pairs of swordtail fish: Xiphophorus birchmanni × X. cortezi and X. birchmanni × X. malinche. We find unexpectedly high levels of repeatability in local ancestry across the two types of hybrid populations. This repeatability is attributable in part to the fact that the recombination landscape and locations of functionally important elements play a major role in driving variation in local ancestry in both types of hybrid populations. Beyond these broad scale patterns, we identify dozens of regions of the genome where minor parent ancestry is unusually low or high across species pairs. Analysis of these regions points to shared sites under selection across species pairs, and in some cases, shared mechanisms of selection. We show that one such region is a previously unknown hybrid incompatibility that is shared across X. birchmanni × X. cortezi and X. birchmanni × X. malinche hybrid populations.
Bcl-3 suppresses Th9 differentiation by regulating glutamine utilization

Bcl-3 is an atypical member of the IκB protein family that plays important and diverse roles in both innate and adaptive immunity, including Th17-dependent autoimmunity models in mice. When naïve mouse splenic CD4+ T cells were cultured under Th17 conditions in vitro, we unexpectedly found that the most highly differentially expressed gene between wild type and Bcl-3-deficient (KO) Th17 cells encoded the cytokine IL-9. We therefore investigated the role of Bcl-3 in Th9 cell differentiation. When naïve CD4+ T cells were cultured under Th9-polarizing conditions in vitro, the extent of Th9 differentiation observed in wild type cells was increased in Bcl-3 KO cells and conversely was decreased in cells overexpressing Bcl-3. The suppressive effect of Bcl-3 on Th9 differentiation was cell-autonomous, and NF-κB inhibitors abolished increased Th9 differentiation in Bcl-3 KO cells. Consistent with this, in the Th9 transfer model of OVA-induced allergic airway inflammation, mice receiving Bcl-3 KO cells had greater immune cell infiltration in the lung than mice receiving wild type cells. Mechanistically, unsupervised transcriptomic analysis revealed differentially expressed genes in KO cells, including the glutamine transporter Slc1a5, which was downregulated. The functional significance of this was suggested by the ability of increasing concentrations of glutamine in the media to reduce the difference in Th9 differentiation between WT and KO cells. Our results suggest a novel role for Bcl-3 as a negative regulator of Th9 differentiation, in part by limiting glutamine accessibility through downregulation of Slc1a5.
DANet: Dynamic Salient Object Detection Networks Leveraging Auxiliary Information

Based on Fully Convolutional Networks, recent salient object detection (SOD) methods achieve impressive results. Some studies improve the classical SOD frameworks by utilizing auxiliary information like fixation points, salient numbers and salient edges. These works adopt auxiliary information by embedding sub-networks into the main network. However, how to incorporate auxiliary information regardless of the specific structure with less coupling is unexplored in SOD. In this paper, we present DANet, a new Dynamic network to leverage arbitrary Auxiliary information for SOD. The proposed framework consists of 1) a Dynamic Weight Generator (DWG) which converts arbitrary auxiliary features into dynamic weights, 2) a Dynamic Bridge Block (DBB) which uses dynamic weight convolution to incorporate auxiliary information from DWG and then refines the fused features, and 3) a two-step training strategy to alleviate the side effect caused by drastic changes among different input images. Extensive experiments demonstrate the effect of different auxiliary information and the proposed framework is a universal method to improve SOD with auxiliary information. Comparison experiments show that DANet achieves state-of-the-art performance without any pre-processing and post-processing.
Utilization of Semantic Planes: Improved Localization and Dense Semantic Map for Monocular SLAM in Urban Environment

In this letter, we propose a novel semantic direct monocular simultaneous localization and mapping (SLAM) system that fuses the semantic information obtained by an advanced deep neural network (DNN) into direct sparse odometry with loop closure(LDSO), with the purpose of improving the localization accuracy and building a dense semantic map of the urban environment. For localization, we apply a point reselection strategy based on coarse semantic plane (CSP) constraints to discard static points inconsistent with the nearby co-plane points of the same semantic class and dynamic points beyond the visible range. Moreover, a point group movement consistency (PGMC) check is utilized to decrease the impact of moving dynamic objects. For the dense semantic map, we model numerous small semantic planes from well-estimated points to measure the depth of each static pixel, rather than conduct stereo matching. Experimental results show that our method is more accurate than LDSO and comparable with ORB-SLAM in terms of localization. Moreover, it is capable of building a dense semantic map of the urban environment for better scene understanding.
Aligning Sets of Temporal Signals with Riemannian Geometry and Koopman Operator

In this paper, we consider the problem of aligning data sets of short temporal signals without any a-priori known correspondence. We present a method combining Koopman operator theory and the Riemannian geometry of symmetric positive-definite (SPD) matrices. First, by taking a Koopman operator theory standpoint, we build feature matrices of the signals using dynamic mode decomposition (DMD). Second, we align these features using parallel transport of SPD matrices, built from the DMD feature matrices. We showcase the performance of the proposed method on simulated observations of a mechanical system and on two real-world applications: sleep stage identification and pre-epileptic seizure prediction.
Improving Multimodal Speech Enhancement by Incorporating Self-Supervised and Curriculum Learning

Speech enhancement in realistic scenarios still remains many challenges, such as complex background signals and data limitations. In this paper, we present a co-attention based framework that incorporates self-supervised and curriculum learning to derive the target speech in noisy environments. Specifically, we first leverage self-supervision to pre-train the co-attention model on the task of audio-visual synchronization. The pre-trained model can focus on the lip of speakers automatically, and then the self-supervised features from the model are combined with a u-net regression network to separate the spectrograms of sound mixtures. To make the training process easier and further improve the performance, we introduce the curriculum learning scheme for the training stage of speech enhancement. Extensive experiments show that our model achieves superior performance over previous self-supervised method for speech enhancement, and demonstrate the generalizability of our approach to the transferred dataset.
Deep Learning Based Hybrid Precoding in Dual-Band Communication Systems

We propose a deep learning-based method that uses spatial and temporal information extracted from the sub-6GHz band to predict/track beams in the millimeter-wave (mmWave) band. In more detail, we consider a dual-band communication system operating in both the sub-6GHz and mmWave bands. The objective is to maximize the achievable mutual information in the mmWave band with a hybrid analog/digital architecture where analog precoders (RF precoders) are taken from a finite codebook. Finding a RF precoder using conventional search methods incurs large signalling overhead, and the signalling scales with the number of RF chains and the resolution of the phase shifters. To overcome the issue of large signalling overhead in the mmWave band, the proposed method exploits the spatiotemporal correlation between sub-6GHz and mmWave bands, and it predicts/tracks the RF precoders in the mmWave band from sub-6GHz channel measurements. The proposed method provides a smaller candidate set so that performing a search over that set significantly reduces the signalling overhead compared with conventional search heuristics. Simulations show that the proposed method can provide reasonable achievable rates while significantly reducing the signalling overhead.
Differential Convolution Feature Guided Deep Multi-Scale Multiple Instance Learning for Aerial Scene Classification

Aerial image classification is challenging for current deep learning models due to the varied geo-spatial object scales and the complicated scene spatial arrangement. Thus, it is necessary to stress the key local feature response from a variety of scales so as to represent discriminative convolutional features. In this paper, we propose a deep multi-scale multiple instance learning (DMSMIL) framework to tackle the above challenges. Firstly, we develop a differential multi-scale dilated convolution feature extractor to exploit the different patterns from different scales. Then, the deep features of each scale are fed into a multiple instance learning module to generate a bag-level probability prediction. Lastly, probability predictions from all the MIL branches are fused to generate the final semantic prediction. Extensive experiments on three widely-utilized aerial scene classification benchmarks demonstrate that our proposed DMSMIL outperforms the state-of-the-art approaches by a large margin.
Non-Parallel Many-To-Many Voice Conversion Using Local Linguistic Tokens

The VQ-VAE based voice conversion models have lately received increasing attention in non-parallel many to many voice conversion, where the encoder extracts the speaker- invariant linguistic content from the input speech using vector quantization and the decoder produces the target speech from the encoder output, conditioned on the target speaker representation. However, it is challenging for the encoder to find a proper balance between removing the speaker information and preserving the linguistic content, which degrades the converted speech quality. To address this issue, we propose the Local Linguistic Tokens (LLTs) model to learn high-quality speaker-invariant linguistic embeddings using the multi-head attention module, which has shown great success in extracting speaking style embeddings in Global Style Tokens (GSTs). Instead of vector quantization, the multihead attention module makes the encoder preserve more linguistic content to enhance the converted speech quality. Both objective and subjective experimental results revealed that, compared with the state-of-the-art VQ-VAE model, the proposed LLTs model achieved significantly better speech quality and comparable speaker similarity. The converted samples are available online for listening. 1
Estimating Severity of Depression From Acoustic Features and Embeddings of Natural Speech

Major depressive disorder, referred to as depression, is a leading cause of disability, absence from work, and premature death. Automatic assessment of depression from speech is a critical step towards improving diagnosis and treatment of depression. Previous works on depression assessment from speech considered various acoustic features extracted from speech to estimate depression severity. But performance of these approaches is not at clinical standards, and thus requires further improvement. In this work, we examine two novel approaches for improving depression severity estimation from short audio recordings of speech. Specifically, in audio recordings of a narrative by individuals diagnosed with major depressive disorder, we analyze spectral-based and excitation source-based features extracted from speech, and significance of sentiment and emotion classification in estimation of depression severity. Initial results indicate synchrony between depression scores and the sentiment and emotion labels. We propose the use of sentiment and emotion based embeddings obtained using machine learning techniques in estimation of depression severity. We also propose use of multi-task training to better estimate depression severity. We show that the proposed approaches provide additive improvements in the estimation of depression severity.
Full-Duplex Multifunction Transceiver with Joint Constant Envelope Transmission and Wideband Reception

This paper introduces and justifies a novel system concept that consists of full-duplex transceivers and uses a multifunction signal for simultaneous two-way communication, jamming and sensing tasks. The proposed device structure and wave-form enable simple-yet-effective interference suppression at the cost of being limited to constant-envelope transmission— this is a weakness only for the communication functionality that becomes limited to frequency-shift keying (FSK) while frequency-modulated continuous wave (FMCW) waveforms are effective for jamming and sensing purposes. We show how the transmission and reception as well as different interference and distortion compensation procedures are implemented in such multifunction transceivers. The system could be also applied for simultaneous spectrum monitoring with the above functions. Finally, we showcase the expected performance of such a system through numerical results.
A Joint Training Framework of Multi-Look Separator and Speaker Embedding Extractor for Overlapped Speech

In multi-talker cases, overlapped speech degrades the speaker verification (SV) performance dramatically. To tackle this challenging problem, speech separation with multi-channel techniques can be adopted to extract each speaker’s signals to improve the SV performance. In this paper, a joint training framework of the front-end multi-look speech separator and the back-end speaker embedding extractor is proposed for multi-channel overlapped speech. To better leverage the complementarity between the speech separator and the speaker embedding extractor, several training strategies are proposed to jointly optimize the two modules. Experimental results show that the proposed joint training framework significantly outperforms the individual SV system by around 52% relative EER reduction. Additionally, the robustness of the proposed framework is further evaluated under different conditions.
Diagnosis of Inter-Turn Faults Based on Fault Harmonic Component Tracking in LSPMSMs Working Under Nonstationary Conditions

Inter-turn faults are one of the most important issues in line-start permanent magnet synchronous motors (LSPMSMs). To date, steady-state operating conditions of LSPMSMs have been analyzed in studies focusing on diagnosing this fault. However, no studies have been conducted for nonstationary operating conditions, such as variable speed or variable load conditions, which are common in the industry. This paper presents a novel approach for fault harmonic component (FHC) tracking based on transient-MCSA to diagnose inter-turn faults in an LSPMSM operating under nonstationary conditions. First, the inter-turn failure model of LSPMS was analyzed to show the fault effect on motor current, and the most dominant inter-turn FHC in the order and frequency domains were determined. Then, using Gabor-OT, inter-turn FHC signals were extracted from motor current signals in the frequency domain and reconstructed in the time domain. Intrinsic mode functions (IMFs) were calculated by decomposing the reconstructed FHC signals (RFHCSs) via ensemble empirical mode decomposition. Using the energies of RFHCSs, calculated based on Gabor coefficients, and the Kullback–Leibler divergences of the selected IMFs, diagnosis of the inter-turn faults and detection of the severity of the faults was performed. The results are in agreement with the results in the literature, thus showing that the proposed method is a successful and useful means for detecting inter-turn faults in LSPMSMs.