Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributive Features of Dissolved Organic Carbon in Aquatic Systems in the Source Area of the Yellow River on the Northeastern Qinghai–Tibet Plateau, China

Dissolved organic carbon (DOC) is the main participant in carbon cycles through water pathways. Recent studies have highlighted the roles of aquatic systems in landscape and watershed carbon budgets. This study is based on 261 samples collected between 2016 and 2017, from individual water types (e.g., river/stream, lake/pond, icing/spring, snow/rain, groundwater/ice, and others) in the source area of the Yellow River (SAYR). These samples were analyzed for examining the distributive features of DOC in aquatic systems, especially in relation to environmental factors. It shows that: 1) DOC concentrations in permafrost-related waters (7.2–234.4 mg C·L−1) were often the highest among all aquatic DOC sources (lakes/ponds: 21.3 ± 34.1 mg C·L−1, rivers/streams: 4.3 ± 3.7 mg C·L−1, and groundwater: 1.8 ± 1.4 mg C·L−1); 2) the seasonality of riverine DOC showed declining features in 2016 and high in summer/autumn, followed by a spring freshet in 2017, and a close association with intra-annual precipitation modes; 3) the main controls of aquatic DOC are permafrost presence, precipitation, and NDVI, and they contribute to 38% of variances of environmental variables in affecting variations in aquatic DOC in the SAYR; and 4) a literature review on biodegradable DOC (BDOC) of varied aquatic DOC pools indicates the highest DOC concentrations (48–1,548 mg C·L−1) and BDOC (23–71%) of ground-ice meltwater. Thus, we suggest that in the SAYR, permafrost dynamics dominate aquatic DOC distribution, and permafrost thaw may alter aquatic DOC budgets, eventually becoming an additional source for atmospheric carbon emissions.
Editorial: Combinatory Approaches of Epigenetic Regulators in T Cell-Based Immunotherapy

Combinatory Approaches of Epigenetic Regulators in T cell-Based Immunotherapy T cell-based immunotherapy, which developed based on the understanding of T-cell capacity for antigen-directed cytotoxicity, has emerged as a powerful tool in the armamentarium against cancer (Ribas and Wolchok 2018). Nevertheless, recent challenges include discovering why immunotherapy works so dramatically in some patients but not in others, and how to design a multipronged approach for a complete curative regimen in a patient-speci ﬁ c manner. Accumulating evidence has highlighted the importance of epigenetic mechanisms in determining T-cell fates and the corresponding features of immunotherapy-responsive T cells, while epigenetic drugs have been demonstrated to improve cancer immunotherapy ef ﬁ cacy in many aspects (Liu et al., 2022). In this Research Topic “ Combinatory approaches of epigenetic regulators in T cell-based immunotherapy ” , a diverse range of examples are included that cover methodological approaches of multi-omics analysis and biomarker exploration, together with clinical studies and literature reviews of research progress made in current epigenetic and immunotherapy combinatory approaches. Chimeric antigen receptor T (CAR-T) immunotherapy, which utilized genetically modi ﬁ ed autologous T cells, has shown promising results in treating patients with blood cancers. The paper by Zhou et al. describes a clinical trial study of anti-CD19 CAR-T cell therapy in patients with relapsed/ refractory B-cell lymphoma. Besides therapeutic bene ﬁ t, they demonstrated that the side effects caused by this CAR-T cell therapy can be successfully managed by administering interleukin 6 antagonist, glucocorticoids, and/or plasmapheresis, which provides an effective and safe treatment
Agroclimatic Metrics for the Main Stone Fruit Producing Areas in Spain in Current and Future Climate Change Scenarios: Implications From an Adaptive Point of View

Stone fruit production has enormous economic importance in Spain. Cultivation locations for these fruit species (i.e., peach, apricot, plum, and sweet cherry) cover wide and climatically diverse geographical areas within the country. Climate change is already producing an increase in average temperatures with special intensity in certain areas like the Mediterranean ones. These changes lead to a decrease in the accumulated chill, which can have a profound impact on the phenology of Prunus species like stone fruits due to, e.g., difficulties to cover the chilling requirements to break endodormancy, the occurrence of late frost events, or abnormal early high temperatures. All these factors can severely affect fruit production and quality and therefore provoke very negative consequences from the socio-economic point of view in the incumbent regions. Thus, characterization of current cultivation areas in terms of agroclimatic variables (e.g., chill and heat accumulation and probabilities of frost and early abnormal heat events), based on data from 270 weather stations for the past 20 years, is carried out in this work to produce an informative picture of the current situation. Besides, future climatic projections from different global climate models (data retrieved from the Meteorological State Agency of Spain—AEMET) up to 2065 for two Representative Concentration Pathway scenarios (i.e., RCP4.5 and RCP8.5) are also analyzed. Using the current situation as a baseline and considering the future scenarios, information on the current and future adaptive suitability of the different species/cultivars to the different growing areas can be inferred. This information could be the basis of a decision support tool to help the different stakeholders to take optimal decisions regarding current and future stone fruit or other temperate species cultivation in Spain.
Authentication of Marantodes pumilum (Blume) Kuntze: A Systematic Review

Botanical drug products consist of complex phytochemical constituents that vary based on various factors that substantially produce different pharmacological activities and possible side effects. Marantodes pumilum (Blume) Kuntze (Primulaceae) is one of the most popular Malay traditional botanical drugs and widely recognized for its medicinal use. Many studies have been conducted focusing on the identification of bioactive substances, pharmacological and toxicological activities in its specific varieties but less comprehensive study on M. pumilum authentication. Lack of quality control (QC) measurement assessment may cause different quality issues on M. pumilum containing products like adulteration by pharmaceutical substances, substitution, contamination, misidentification with toxic plant species, which may be detrimental to consumers’ health and safety. This systematic literature review aims to provide an overview of the current scenario on the quality control of botanical drug products as determined by pharmacopoeia requirements specifically for M. pumilum authentication or identification. A systematic search for peer-reviewed publications to document literature search for M. pumilum authentication was performed using four electronic databases: Web of Science, PubMed, Scopus and ScienceDirect for related studies from January 2010 to December 2021. The research studies published in English and related articles for identification or authentication of M. pumilum were the main inclusion criteria in this review. A total 122 articles were identified, whereby 33 articles met the inclusion criteria. Macroscopy, microscopy, chemical fingerprinting techniques using chromatography, spectroscopy and hyphenated techniques, and genetic-based fingerprinting using DNA barcoding method have been used to identify M. pumilum and to distinguish between different varieties and plant parts. The study concluded that a combination of approaches is necessary for authenticating botanical drug substances and products containing M. pumilum to assure the quality, safety, and efficacy of marketed botanical drug products, particularly those with therapeutic claims.
Braiding Dynamics in Active Nematics

In active matter systems, energy consumed at the small scale by individual agents gives rise to emergent flows at large scales. For 2D active nematic microtubule (ANMT) systems, these flows are largely characterized by the dynamics of mobile defects in the nematic director field. As these defects wind about each other, their trajectories trace out braids. We introduce a minimal model of ANMT systems based on the topological properties of these braids. In particular, we consider the topological entropy of braids, which quantifies how chaotic the associated flow must be. Since microtubule bundles, an extensile system, stretch out exponentially in time, the resultant defect movement must correspond to braids with positive topological entropy. Indeed, we conjecture that the emergent defect dynamics are often optimal in that they give braids which maximize the, suitably normalized, topological entropy. We will look at the dynamics of four +1/2 defects on a sphere as a case study, using both simulations and a reinterpretation of experimental data from the literature.
Behavioral Phenotypes of Foxg1 Heterozygous Mice

FOXG1 syndrome (FS, aka a congenital variant of Rett syndrome) is a recently defined rare and devastating neurodevelopmental disorder characterized by various symptoms, including severe intellectual disability, autistic features, involuntary, and continuous jerky movements, feeding problems, sleep disturbances, seizures, irritability, and excessive crying. FS results from mutations in a single allele of the FOXG1 gene, leading to impaired FOXG1 function. Therefore, in establishing mouse models for FS, it is important to test if heterozygous (HET) mutation in the Foxg1 gene, mimicking genotypes of the human FS individuals, also manifests phenotypes similar to their symptoms. We analyzed HET mice with a null mutation allele in a single copy of Foxg1, and found that they show various phenotypes resembling the symptoms of the human FS individuals. These include increased anxiety in the open field as well as impairment in object recognition, motor coordination, and fear learning and contextual and cued fear memory. Our results suggest that Foxg1 HET mice recapitulate at least some symptoms of the human FS individuals.
Regional Downregulation of Dopamine Receptor D1 in Bilateral Dorsal Lateral Geniculate Nucleus of Monocular Form-Deprived Amblyopia Models

Amblyopia is a common eye disease characterized by impaired best-corrected visual acuity. It starts in early childhood and leads to permanent vision reduction if left untreated. Even though many young patients with amblyopia are well treated in clinical practice, the underlying mechanism remains to be elucidated, which limits not only our understanding of this disease but also the therapeutic approach. To investigate the molecular mechanism of amblyopia, primate and rodent models of monocular-deprived amblyopia were created for mRNA screening and confirmation. We obtained 818 differentially expressed genes from the dorsal lateral geniculate nucleus (dLGN) of a primate model of amblyopia. After Gene Ontology and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses, the main enriched pathways were related to neural development. Interestingly, a particular neurotransmitter pathway, the dopaminergic pathway, was identified. The downregulation of dopamine receptor D1 (DRD1) was confirmed in both monkey and mouse samples. Furthermore, the immunofluorescence staining indicated that DRD1 expression was downregulated in both ventrolateral region of the contralateral dLGN and the dorsomedial region of the ipsilateral dLGN in the mouse model. The regions with downregulated expression of DRD1 were the downstream targets of the visual projection from the amblyopic eye. This study suggested that the downregulation of DRD1 in the LGN may be a cause for amblyopia. This may also be a reason for the failure of some clinical cases of levodopa combined with carbidopa applied to amblyopes.
Small Molecule RPI-194 Stabilizes Activated Troponin to Increase the Calcium Sensitivity of Striated Muscle Contraction

Small molecule cardiac troponin activators could potentially enhance cardiac muscle contraction in the treatment of systolic heart failure. We designed a small molecule, RPI-194, to bind cardiac/slow skeletal muscle troponin (Cardiac muscle and slow skeletal muscle share a common isoform of the troponin C subunit.) Using solution NMR and stopped flow fluorescence spectroscopy, we determined that RPI-194 binds to cardiac troponin with a dissociation constant KD of 6–24 μM, stabilizing the activated complex between troponin C and the switch region of troponin I. The interaction between RPI-194 and troponin C is weak (KD 311 μM) in the absence of the switch region. RPI-194 acts as a calcium sensitizer, shifting the pCa50 of isometric contraction from 6.28 to 6.99 in mouse slow skeletal muscle fibers and from 5.68 to 5.96 in skinned cardiac trabeculae at 100 μM concentration. There is also some cross-reactivity with fast skeletal muscle fibers (pCa50 increases from 6.27 to 6.52). In the slack test performed on the same skinned skeletal muscle fibers, RPI-194 slowed the velocity of unloaded shortening at saturating calcium concentrations, suggesting that it slows the rate of actin-myosin cross-bridge cycling under these conditions. However, RPI-194 had no effect on the ATPase activity of purified actin-myosin. In isolated unloaded mouse cardiomyocytes, RPI-194 markedly decreased the velocity and amplitude of contractions. In contrast, cardiac function was preserved in mouse isolated perfused working hearts. In summary, the novel troponin activator RPI-194 acts as a calcium sensitizer in all striated muscle types. Surprisingly, it also slows the velocity of unloaded contraction, but the cause and significance of this is uncertain at this time. RPI-194 represents a new class of non-specific troponin activator that could potentially be used either to enhance cardiac muscle contractility in the setting of systolic heart failure or to enhance skeletal muscle contraction in neuromuscular disorders.
Robust Unstructured Knowledge Access in Conversational Dialogue with ASR Errors

Performance of spoken language understanding (SLU) can be degraded with automatic speech recognition (ASR) errors. We propose a novel approach to improve SLU robustness by randomly corrupting clean training text with an ASR error simulator, followed by self-correcting the errors and minimizing the target classification loss in a joint manner. In the proposed error simulator, we leverage confusion networks generated from an ASR decoder without human transcriptions to generate variety of error patterns for model training. We evaluate our approach on DSTC10 challenge targeted for knowledge-grounded task-oriented conversational dialogues with ASR errors. Experimental results show effectiveness of our proposed approach, boosting the knowledge-seeking turn detection (KTD) F1 significantly from 0.9433 to 0.9904. Knowledge cluster classification is boosted from 0.7924 to 0.9333 in Recall@1. After knowledge document re-ranking, our approach shows significant improvement in all knowledge selection metrics, from 0.7358 to 0.7806 in Recall@1, from 0.8301 to 0.9333 in Recall@5, and from 0.7798 to 0.8460 in MRR@5 (Mean Reciprocal Rank) on the test set. On the recent DSTC10 evaluation, our approach demonstrates significant improvement in knowledge selection, boosting Recall@1 from 0.495 to 0.7105 compared to the official baseline. Our source code is released in GitHub1.
Being Greedy Does Not Hurt: Sampling Strategies for End-To-End Speech Recognition

Maximum Likelihood Estimation (MLE) is currently the most common approach to train large scale speech recognition systems. While it has significant practical advantages, MLE exhibits several drawbacks known in literature: training and inference conditions are mismatched and a proxy objective is optimized instead of word error rate. Recently, the Optimal Completion Distillation (OCD) training method was proposed which attempts to address some of those issues. In this paper, we analyze if the method is competitive over a strong MLE baseline and investigate its scalability towards large speech data beyond read speech, which to our knowledge is the first attempt known in literature. In addition, we propose and analyze several sampling strategies trading off exploration and exploitation of unseen prefixes and their effect on ASR accuracy. We conduct several experiments on both public LibriSpeech data and in-house large scale far-field data and compare models trained with MLE and OCD. Our proposed greedy sampling with soft targets approach proves most effective and yields a 9% rel. word error rate improvement over the i.i. d sampling. Finally, we note that OCD method improves over the MLE without label smoothing by 12%, and underperform by 6% once label smoothing is introduced to MLE.
Improved Simulation of Realistically-Spatialised Simultaneous Speech Using Multi-Camera Analysis in The Chime-5 Dataset

Room simulation is an essential tool in the development of distant microphone ASR and source separation. However, most commonly used simulated datasets adopt uninformed and potentially unrealistic speaker location distributions. In earlier work, we analysed a 50-hour audio-visual dataset of multiparty recordings made in real homes to estimate typical angular separations between speakers. We now refine and extend this work using a multi-camera analysis to estimate full 2-D speaker location distributions. Results show that commonly used simulated datasets use unrealistically large angular separations, but unrealistically small ranges for target to interferer distance ratios. We generate more realistically distributed datasets and use them to re-evaluate state-of-the-art source separation and ASR approaches. Results suggest that imposing realistic angular separation distributions makes datasets more challenging, however, the pattern when using realistic distance ratios is more complicated and can depend on room size.
Towards Controllable and Physical Interpretable Underwater Scene Simulation

The realistic simulation of underwater scenes has important significance for many researches related to underwater vision, such as underwater image restoration, underwater moving object monitoring, etc. To date, however, the existing underwater scene simulation pipelines are either too complicated due to the continuous spectra and camera parameters involved, or difficult to control since the empirically controlled distance based fog effect is usually used by them. In this paper, we try to fill in this research gap by proposing an Underwater Scene Simulation approach, namely USSim, which especially focuses on the influence of ocean water. In USSim, Jerlov water type and depth are regarded as main variables to control the simulation effects. In addition, the spectra of the incident light is decomposed into three primary components and their attenuations are modeled separately, and finally the simulated scene is generated via the hybrid underwater imaging model proposed by us. USSim greatly reduces the computational complexity and enables the fog effect to be controlled by variables with explicit physical meanings. The controllability, physical interpretability and simulation effects of our USSim under different conditions have been verified by extensive experiments. To make our results reproducible, the source code is made online available at https://cslinzhang.github. io/USSim/.
Cascade Multi-Channel Noise Reduction and Acoustic Feedback Cancellation

Acoustic feedback and noise are common problems that corrupt microphone signals and affect the performance of speech and audio signal processing applications and devices. In this paper, a cascade noise reduction (NR) and acoustic feedback cancellation (AFC) algorithm is presented for speech applications where a multi-channel Wiener filter (MWF) based NR is applied first followed by a single-channel prediction-error method (PEM) based adaptive feedback cancellation stage. It is shown that by using a rank-2 estimate of the speech correlation matrix in the NR stage it is possible to obtain a good feedback path estimate for the reference microphone in the AFC stage. Closed-loop simulations with M microphones and 1 loudspeaker are presented using both an M-channel rank-1 and an (M + 1)-channel rank-2 MWF and it is shown that for the considered input signal-to-noise ratios the proposed algorithm increases the added stable gain (ASG) of the system.
A Complex Spectral Mapping with Inplace Convolution Recurrent Neural Networks For Acoustic Echo Cancellation

Recently, deep learning is introduced in acoustic echo cancellation (AEC) and achieves remarkable performance. For deep learning-based AEC, the most important problem is generalization ability in diversity scenarios. Different from most methods which process the entire frequency band, we propose inplace convolution recurrent neural networks (ICRN) for end-to-end AEC, which utilizes inplace convolution and channel-wise temporal modeling to ensure the near-end signal information being preserved. In addition, we employ complex spectral mapping with a multi-task learning strategy for better generalization capability. Experiments conducted on various unmatched scenarios show that the proposed method outperforms previous methods. Moreover, the system has 210K parameters and 1.76G MACs, which is suitable for real-time applications.
AdaPID: An Adaptive PID Optimizer for Training Deep Neural Networks

Deep neural networks (DNNs) have well-documented merits in learning nonlinear functions in high-dimensional spaces. Stochastic gradient descent (SGD)-type optimization algorithms are the ‘workhorse’ for training DNNs. Nonetheless, such algorithms often suffer from slow convergence, sizable fluctuations, and abundant local solutions, to name a few. In this context, the present paper draws ideas from adaptive control of dynamical systems, and develops an adaptive proportional-integral-derivative (AdaPID) solver for fast, stable, and effective training of DNNs. AdaPID relies on second-order moment estimates of gradients to adaptively adjust the PID coefficients. Numerical tests corroborate the merits of AdaPID on several tasks such as image generation using generative adversarial networks (GANs) and image classification using convolutional neural networks (CNNs) as well as long-short term memories (LSTMs).
Chunkfusion: A Learning-Based RGB-D 3D Reconstruction Framework Via Chunk-Wise Integration

Recent years have witnessed a growing interest in online RGB-D 3D reconstruction. On the premise of ensuring the reconstruction accuracy with noisy depth scans, making the system scalable to various environments is still challenging. In this paper, we devote our efforts to try to fill in this research gap by proposing a scalable and robust RGB-D 3D reconstruction framework, namely Chunk-Fusion. In ChunkFusion, sparse voxel management is exploited to improve the scalability of online reconstruction. Besides, a chunk-wise TSDF (truncated signed distance function) fusion network is designed to perform a robust integration of the noisy depth measurements on the sparsely allocated voxel chunks. The proposed chunk-wise TSDF integration scheme can accurately restore surfaces with superior visual consistency from noisy depth maps and can guarantee the scalability of online reconstruction simultaneously, making our reconstruction framework widely applicable to scenes with various scales and depth scans with strong noises and outliers. The outstanding scalability and efficacy of our ChunkFusion have been corroborated by extensive experiments. To make our results reproducible, the source code is made online available at https://cslinzhang.github. io/ChunkFusion/.