Astrocytic TIMP-1 regulates production of Anastellin, an inhibitor of oligodendrocyte differentiation and FTY720 responses.

Astrocyte activation is associated with neuropathology and the production of tissue inhibitor of metalloproteinase-1 (TIMP1). TIMP1 is a pleiotropic extracellular protein that functions both as a protease inhibitor and as a growth factor. Astrocytes that lack expression of Timp1 do not support rat oligodendrocyte progenitor cell (rOPC) differentiation, and adult global Timp1 knockout (Timp1KO) mice do not efficiently remyelinate following a demyelinating injury. Here, we performed an unbiased proteomic analysis and identified a fibronectin-derived peptide called Anastellin (Ana) that was unique to the Timp1KO astrocyte secretome. Ana was found to block rOPC differentiation in vitro and enhanced the inhibitory influence of fibronectin on rOPC differentiation. Ana is known to act upon the sphingosine-1-phosphate receptor 1, and we determined that Ana also blocked the pro-myelinating effect of FTY720 (or fingolimod) on rOPC differentiation in vitro. Administration of FTY720 to wild-type C57BL/6 mice during MOG35-55-experimental autoimmune encephalomyelitis ameliorated clinical disability while FTY720 administered to mice lacking expression of Timp1 (Timp1KO) had no effect. Analysis of Timp1 and fibronectin (FN1) transcripts from primary human astrocytes from healthy and multiple sclerosis (MS) donors revealed lower TIMP1 expression was coincident with elevated FN1 in MS astrocytes. Last, analyses of proteomic databases of MS samples identified Ana peptides to be more abundant in the cerebrospinal fluid (CSF) of human MS patients with high disease activity. A role for Ana in MS as a consequence of a lack of astrocytic TIMP-1 production could influence both the efficacy of fingolimod responses and innate remyelination potential in the MS brain.

Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures.

Sound event detection is an important facet of audio tagging that aims to identify sounds of interest and define both the sound category and time boundaries for each sound event in a continuous recording. With advances in deep neural networks, there has been tremendous improvement in the performance of sound event detection systems, although at the expense of costly data collection and labeling efforts. In fact, current state-of-the-art methods employ supervised training methods that leverage large amounts of data samples and corresponding labels in order to facilitate identification of sound category and time stamps of events. As an alternative, the current study proposes a semi-supervised method for generating pseudo-labels from unsupervised data using a student-teacher scheme that balances self-training and cross-training. Additionally, this paper explores post-processing which extracts sound intervals from network prediction, for further improvement in sound event detection performance. The proposed approach is evaluated on sound event detection task for the DCASE2020 challenge. The results of these methods on both "validation" and "public evaluation" sets of DESED database show significant improvement compared to the state-of-the art systems in semi-supervised learning.

Astrocytic TIMP-1 regulates production of Anastellin, a novel inhibitor of oligodendrocyte differentiation and FTY720 responses.

Astrocyte activation is associated with neuropathology and the production of tissue inhibitor of metalloproteinase-1 (TIMP1). TIMP1 is a pleiotropic extracellular protein that functions both as a protease inhibitor and as a growth factor. We have previously demonstrated that murine astrocytes that lack expression of Timp1 do not support rat oligodendrocyte progenitor cell (rOPC) differentiation, and adult global Timp1 knockout ( Timp1 KO ) mice do not efficiently remyelinate following a demyelinating injury. To better understand the basis of this, we performed unbiased proteomic analyses and identified a fibronectin-derived peptide called anastellin that is unique to the murine Timp1 KO astrocyte secretome. Anastellin was found to block rOPC differentiation in vitro and enhanced the inhibitory influence of fibronectin on rOPC differentiation. Anastellin is known to act upon the sphingosine-1-phosphate receptor 1 (S1PR1), and we determined that anastellin also blocked the pro-myelinating effect of FTY720 (or fingolimod) on rOPC differentiation in vitro . Further, administration of FTY720 to wild-type C57BL/6 mice during MOG 35-55 -EAE ameliorated clinical disability while FTY720 administered to mice lacking expression of Timp1 in astrocytes ( Timp1 cKO ) had no effect. Analysis of human TIMP1 and fibronectin ( FN1 ) transcripts from healthy and multiple sclerosis (MS) patient brain samples revealed an inverse relationship where lower TIMP1 expression was coincident with elevated FN1 in MS astrocytes. Lastly, we analyzed proteomic databases of MS samples and identified anastellin peptides to be more abundant in the cerebrospinal fluid (CSF) of human MS patients with high versus low disease activity. The prospective role for anastellin generation in association with myelin lesions as a consequence of a lack of astrocytic TIMP-1 production could influence both the efficacy of fingolimod responses and the innate remyelination potential of the the MS brain. Significance Statement: Astrocytic production of TIMP-1 prevents the protein catabolism of fibronectin. In the absence of TIMP-1, fibronectin is further digested leading to a higher abundance of anastellin peptides that can bind to sphingosine-1-phosphate receptor 1. The binding of anastellin with the sphingosine-1-phosphate receptor 1 impairs the differentiation of oligodendrocytes progenitor cells into myelinating oligodendrocytes in vitro , and negates the astrocyte-mediated therapeutic effects of FTY720 in the EAE model of chronic CNS inflammation. These data indicate that TIMP-1 production by astrocytes is important in coordinating astrocytic functions during inflammation. In the absence of astrocyte produced TIMP-1, elevated expression of anastellin may represent a prospective biomarker for FTY720 therapeutic responsiveness.

Fusion-ConvBERT: Parallel Convolution and BERT Fusion for Speech Emotion Recognition.

Speech emotion recognition predicts the emotional state of a speaker based on the person's speech. It brings an additional element for creating more natural human-computer interactions. Earlier studies on emotional recognition have been primarily based on handcrafted features and manual labels. With the advent of deep learning, there have been some efforts in applying the deep-network-based approach to the problem of emotion recognition. As deep learning automatically extracts salient features correlated to speaker emotion, it brings certain advantages over the handcrafted-feature-based methods. There are, however, some challenges in applying them to the emotion recognition problem, because data required for properly training deep networks are often lacking. Therefore, there is a need for a new deep-learning-based approach which can exploit available information from given speech signals to the maximum extent possible. Our proposed method, called "Fusion-ConvBERT", is a parallel fusion model consisting of bidirectional encoder representations from transformers and convolutional neural networks. Extensive experiments were conducted on the proposed model using the EMO-DB and Interactive Emotional Dyadic Motion Capture Database emotion corpus, and it was shown that the proposed method outperformed state-of-the-art techniques in most of the test configurations.

Fusion of Heterogeneous Adversarial Networks for Single Image Dehazing.

In this paper, we propose a novel image dehazing method. Typical deep learning models for dehazing are trained on paired synthetic indoor dataset. Therefore, these models may be effective for indoor image dehazing but less so for outdoor images. We propose a heterogeneous Generative Adversarial Networks (GAN) based method composed of a cycle-consistent Generative Adversarial Networks (CycleGAN) for producing haze-clear images and a conditional Generative Adversarial Networks (cGAN) for preserving textural details. We introduce a novel loss function in the training of the fused network to minimize GAN generated artifacts, to recover fine details, and to preserve color components. These networks are fused via a convolutional neural network (CNN) to generate dehazed image. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods on both synthetic and real-world hazy images.

Genomics-Driven Immunoproteomics: An Integrative Platform to Uncover Important Biomarkers for Human Diseases.

Genomics-driven immunoproteomics (GDI) is a platform that helps identify antigenic protein targets of mutations and other deoxyribonucleic acid (DNA) variations that are commonly associated with pathological states. This platform utilizes data generated from deep sequencing of exomic DNA or ribonucleic acid (RNA) as input to synthesize mutant peptides into microarrays, which then can be used to detect antigenic proteins that invoke immune response in patients. The technology has been used to detect antigenic targets of multiple sclerosis, an autoimmune disease [1], and cancer to identify mutant proteins that invoke immune response in breast cancer patients [2]. This technology has many potential applications to select genomic changes that are specifically recognized by the immune system in a rapid and efficient manner.

Extracellular vesicle fibrinogen induces encephalitogenic CD8+ T cells in a mouse model of multiple sclerosis.

Extracellular vesicles (EVs) are emerging as potent mediators of intercellular communication with roles in inflammation and disease. In this study, we examined the role of EVs from blood plasma (pEVs) in an experimental autoimmune encephalomyelitis mouse model of central nervous system demyelination. We determined that pEVs induced a spontaneous relapsing-remitting disease phenotype in MOG35-55-immunized C57BL/6 mice. This modified disease phenotype was found to be driven by CD8+ T cells and required fibrinogen in pEVs. Analysis of pEVs from relapsing-remitting multiple sclerosis patients also identified fibrinogen as a significant portion of pEV cargo. Together, these data suggest that fibrinogen in pEVs contributes to the perpetuation of neuroinflammation and relapses in disease.

Discovery of putative breast cancer antigens using an integrative platform of genomics-driven immunoproteomics.

Recent advances in cancer immuno-therapeutics such as checkpoint inhibitors, chimeric antigen-receptor T cells, and tumor infiltrating T cells (TIL) are now significantly impacting cancer patients in a positive manner. Although very promising, reports indicate no more than 25% of cases result in complete remission. One of the limitations of these treatments is the identity of putative cancer antigens in each patient, as it is technically challenging to identify cancer antigens in a rapid fashion. Thus, identification of cancer antigens followed by targeted treatment will increase the efficacy of cancer immunotherapies. To achieve this goal, a combined technologies platform of deep genomic sequencing and personalized immune assessment was devised, termed Genomics Driven Immunoproteomics (GDI). Using this technological platform, we report the discovery of 149 tumor antigens from human breast cancer patients. Significant number of these putative cancer antigens arise from single nucleotide variants (SNVs), as well as insertions and deletions that results into frame-shift mutations. We propose a general model of anti-cancer immunity and suggest that the GDI platform may help identify patient-specific tumor antigens in a timely fashion for precision immunotherapies.

Integrative proteomics, genomics, and translational immunology approaches reveal mutated forms of Proteolipid Protein 1 (PLP1) and mutant-specific immune response in multiple sclerosis.

In order to gain mechanistic insights into multiple sclerosis (MS) pathogenesis, we utilized a multi-dimensional approach to test the hypothesis that mutations in myelin proteins lead to immune activation and central nervous system autoimmunity in MS. Mass spectrometry-based proteomic analysis of human MS brain lesions revealed seven unique mutations of PLP1; a key myelin protein that is known to be destroyed in MS. Surprisingly, in-depth genomic analysis of two MS patients at the genomic DNA and mRNA confirmed mutated PLP1 in RNA, but not in the genomic DNA. Quantification of wild type and mutant PLP RNA levels by qPCR further validated the presence of mutant PLP RNA in the MS patients. To seek evidence linking mutations in abundant myelin proteins and immune-mediated destruction of myelin, specific immune response against mutant PLP1 in MS patients was examined. Thus, we have designed paired, wild type and mutant peptide microarrays, and examined antibody response to multiple mutated PLP1 in sera from MS patients. Consistent with the idea of different patients exhibiting unique mutation profiles, we found that 13 out of 20 MS patients showed antibody responses against specific but not against all the mutant-PLP1 peptides. Interestingly, we found mutant PLP-directed antibody response against specific mutant peptides in the sera of pre-MS controls. The results from integrative proteomic, genomic, and immune analyses reveal a possible mechanism of mutation-driven pathogenesis in human MS. The study also highlights the need for integrative genomic and proteomic analyses for uncovering pathogenic mechanisms of human diseases.

Online multi-object tracking with efficient track drift and fragmentation handling.

This paper addresses the problem of multi-object tracking in complex scenes by a single, static, uncalibrated camera. Tracking-by-detection is a widely used approach for multi-object tracking. Challenges still remain in complex scenes, however, when this approach has to deal with occlusions, unreliable detections (e.g., inaccurate position/size, false positives, or false negatives), and sudden object motion/appearance changes, among other issues. To handle these problems, this paper presents a novel online multi-object tracking method, which can be fully applied to real-time applications. First, an object tracking process based on frame-by-frame association with a novel affinity model and an appearance update that does not rely on online learning is proposed to effectively and rapidly assign detections to tracks. Second, a two-stage drift handling method with novel track confidence is proposed to correct drifting tracks caused by the abrupt motion change of objects under occlusion and prolonged inaccurate detections. In addition, a fragmentation handling method based on a track-to-track association is proposed to solve the problem in which an object trajectory is broken into several tracks due to long-term occlusions. Based on experimental results derived from challenging public data sets, the proposed method delivers an impressive performance compared with other state-of-the-art methods. Furthermore, additional performance analysis demonstrates the effect and usefulness of each component of the proposed method.

Dialogue enabling speech-to-text user assistive agent system for hearing-impaired person.

A novel approach for assisting bidirectional communication between people of normal hearing and hearing-impaired is presented. While the existing hearing-impaired assistive devices such as hearing aids and cochlear implants are vulnerable in extreme noise conditions or post-surgery side effects, the proposed concept is an alternative approach wherein spoken dialogue is achieved by means of employing a robust speech recognition technique which takes into consideration of noisy environmental factors without any attachment into human body. The proposed system is a portable device with an acoustic beamformer for directional noise reduction and capable of performing speech-to-text transcription function, which adopts a keyword spotting method. It is also equipped with an optimized user interface for hearing-impaired people, rendering intuitive and natural device usage with diverse domain contexts. The relevant experimental results confirm that the proposed interface design is feasible for realizing an effective and efficient intelligent agent for hearing-impaired.

A draft map of rhesus monkey tissue proteome for biomedical research.

Though the rhesus monkey is one of the most valuable non-human primate animal models for various human diseases because of its manageable size and genetic and proteomic similarities with humans, proteomic research using rhesus monkeys still remains challenging due to the lack of a complete protein sequence database and effective strategy. To investigate the most effective and high-throughput proteomic strategy, comparative data analysis was performed employing various protein databases and search engines. The UniProt databases of monkey, human, bovine, rat and mouse were used for the comparative analysis and also a universal database with all protein sequences from all available species was tested. At the same time, de novo sequencing was compared to the SEQUEST search algorithm to identify an optimal work flow for monkey proteomics. Employing the most effective strategy, proteomic profiling of monkey organs identified 3,481 proteins at 0.5% FDR from 9 male and 10 female tissues in an automated, high-throughput manner. Data are available via ProteomeXchange with identifier PXD001972. Based on the success of this alternative interpretation of MS data, the list of proteins identified from 12 organs of male and female subjects will benefit future rhesus monkey proteome research.

Large-scale proteomic characterization of melanoma expressed proteins reveals nestin and vimentin as biomarkers that can potentially distinguish melanoma subtypes.

Melanoma is an aggressive type of skin cancer, which accounts for only 4% of skin cancer cases but causes around 75% of skin cancer deaths. Currently, there is a limited set of protein biomarkers that can distinguish melanoma subtypes and provide an accurate prognosis of melanoma. Thus, we have selected and profiled the proteomes of five different melanoma cell lines from different stages of progression in comparison with a normal melanocytes using tandem mass spectrometry. We also profiled the proteome of a solid metastatic melanoma tumor. This resulted in the identification of 4758 unique proteins, among which ∼200-300 differentially expressed proteins from each set were found by quantitative proteomics. Correlating protein expression with aggressiveness of each melanoma cell line and literature mining resulted in the final selection of six proteins: vimentin, nestin, fibronectin, annexin A1, dipeptidyl peptidase IV, and histone H2A1B. Validation of nestin and vimentin using 40 melanoma samples revealed pattern of protein expression can help predict melanoma aggressiveness in different subgroups of melanoma. These results, together with the combined list of 4758 expressed proteins, provide a valuable resource for selecting melanoma biomarkers in the future for the clinical and research community.

Voice activity detection in noisy environments based on double-combined fourier transform and line fitting.

A new voice activity detector for noisy environments is proposed. In conventional algorithms, the endpoint of speech is found by applying an edge detection filter that finds the abrupt changing point in a feature domain. However, since the frame energy feature is unstable in noisy environments, it is difficult to accurately find the endpoint of speech. Therefore, a novel feature extraction algorithm based on the double-combined Fourier transform and envelope line fitting is proposed. It is combined with an edge detection filter for effective detection of endpoints. Effectiveness of the proposed algorithm is evaluated and compared to other VAD algorithms using two different databases, which are AURORA 2.0 database and SITEC database. Experimental results show that the proposed algorithm performs well under a variety of noisy conditions.

Defective sphingosine 1-phosphate receptor 1 (S1P1) phosphorylation exacerbates TH17-mediated autoimmune neuroinflammation.

Sphingosine 1-phosphate (S1P) signaling regulates lymphocyte egress from lymphoid organs into systemic circulation. The sphingosine phosphate receptor 1 (S1P1) agonist FTY-720 (Gilenya) arrests immune trafficking and prevents multiple sclerosis (MS) relapses. However, alternative mechanisms of S1P-S1P1 signaling have been reported. Phosphoproteomic analysis of MS brain lesions revealed S1P1 phosphorylation on S351, a residue crucial for receptor internalization. Mutant mice harboring an S1pr1 gene encoding phosphorylation-deficient receptors (S1P1(S5A)) developed severe experimental autoimmune encephalomyelitis (EAE) due to autoimmunity mediated by interleukin 17 (IL-17)-producing helper T cells (TH17 cells) in the peripheral immune and nervous system. S1P1 directly activated the Jak-STAT3 signal-transduction pathway via IL-6. Impaired S1P1 phosphorylation enhances TH17 polarization and exacerbates autoimmune neuroinflammation. These mechanisms may be pathogenic in MS.

Tyrosine phosphorylation of CD13 regulates inflammatory cell-cell adhesion and monocyte trafficking.

CD13 is a large cell surface peptidase expressed on the monocytes and activated endothelial cells that is important for homing to and resolving the damaged tissue at sites of injury. We showed previously that cross-linking of human monocytic CD13 with activating Abs induces strong adhesion to endothelial cells in a tyrosine kinase- and microtubule-dependent manner. In the current study, we examined the molecular mechanisms underlying these observations in vitro and in vivo. We found that cross-linking of CD13 on U937 monocytic cells induced phosphorylation of a number of proteins, including Src, FAK, and ERK, and inhibition of these abrogated CD13-dependent adhesion. We found that CD13 itself was phosphorylated in a Src-dependent manner, which was an unexpected finding because its 7-aa cytoplasmic tail was assumed to be inert. Furthermore, CD13 was constitutively associated with the scaffolding protein IQGAP1, and CD13 cross-linking induced complex formation with the actin-binding protein α-actinin, linking membrane-bound CD13 to the cytoskeleton, further supporting CD13 as an inflammatory adhesion molecule. Mechanistically, mutation of the conserved CD13 cytoplasmic tyrosine to phenylalanine abrogated adhesion; Src, FAK, and ERK phosphorylation; and cytoskeletal alterations upon Ab cross-linking. Finally, CD13 was phosphorylated in isolated murine inflammatory peritoneal exudate cells, and adoptive transfer of monocytic cell lines engineered to express the mutant CD13 were severely impaired in their ability to migrate into the inflamed peritoneum, confirming that CD13 phosphorylation is relevant to inflammatory cell trafficking in vivo. Therefore, this study identifies CD13 as a novel, direct activator of intracellular signaling pathways in pathophysiological conditions.

Subcellular fractionation for identification of biomarkers: serial detergent extraction by subcellular accessibility and solubility.

Cellular localization of proteins is one of the most valuable sources of information regarding spatiotemporal biological events involved in human disease. This information is sometimes enhanced by carrying out protein isolation using a process known as subcellular fractionation. This involves the sequential extraction of proteins from specific compartments and/or organelles within the cell. Additionally, subcellular fractionation for biomarker discovery enables the in-depth analysis of biomolecules by reducing the complexity of the protein mixture. In this chapter, four custom fractionation approaches and one commercial kit are compared for their efficacy and compatibility with subsequent proteomic analysis.

Reinforced AdaBoost learning for object detection with local pattern representations.

A reinforced AdaBoost learning algorithm is proposed for object detection with local pattern representations. In implementing AdaBoost learning, the proposed algorithm employs an exponential criterion as a cost function and Newton's method for its optimization. In particular, we introduce an optimal selection of weak classifiers minimizing the cost function and derive the reinforced predictions based on a judicial confidence estimate to determine the classification results. The weak classifier of the proposed method produces real-valued predictions while that of the conventional AdaBoost method produces integer valued predictions of +1 or -1. Hence, in the conventional learning algorithms, the entire sample weights are updated by the same rate. On the contrary, the proposed learning algorithm allows the sample weights to be updated individually depending on the confidence level of each weak classifier prediction, thereby reducing the number of weak classifier iterations for convergence. Experimental classification performance on human face and license plate images confirm that the proposed method requires smaller number of weak classifiers than the conventional learning algorithm, resulting in higher learning and faster classification rates. An object detector implemented based on the proposed learning algorithm yields better performance in field tests in terms of higher detection rate with lower false positives than that of the conventional learning algorithm.

LC-MS/MS Identification of a Bromelain Peptide Biomarker from Ananas comosus Merr.

Bromelain (Br) is a cysteine peptidase (GenBank AEH26024.1) from pineapple, with over 40 years of clinical use. The constituents mediating its anti-inflammatory activity are not thoroughly characterized and no peptide biomarker exists. Our objective is to characterize Br raw material and identify peptides in the plasma of Br treated mice. After SDS-PAGE in-gel digestion, Br (VN#3507; Middletown, CT, USA) peptides were analyzed via LC/MS/MS using 95% protein probability, 95% peptide probability, and a minimum peptide number = 5. Br spiked mouse plasma (1 ug/ul) and plasma from i.p. treated mice (12 mg/kg) were assessed using SRM. In Br raw material, we identified seven proteins: four proteases, one jacalin-like lectin, and two protease inhibitors. In Br spiked mouse plasma, six proteins (ananain, bromelain inhibitor, cysteine proteinase AN11, FB1035 precursor, FBSB precursor, and jacalin-like lectin) were identified. Using LC/MS/MS, we identified the unique peptide, DYGAVNEVK, derived from FB1035, in the plasma of i.p. Br treated mice. The spectral count of this peptide peaked at 6 hrs and was undetectable by 24 hrs. In this study, a novel Br peptide was identified in the plasma of treated mice for the first time. This Br peptide could serve as a biomarker to standardize the therapeutic dose and maximize clinical utility.

Identification of differentially expressed proteins from primary versus metastatic pancreatic cancer cells using subcellular proteomics.

Pancreatic cancer is an aggressive disease with nearly equal yearly rates of diagnosis and death. Current therapies have failed to improve outcomes due to rapid disease progression and late stage at presentation. Recently, pathways involved in progression and metastasis have been elucidated; however, new knowledge has not generated more effective therapies. We report on the use of subcellular fractionation and liquid chromatography (LC)-mass spectrometry to identify 3,907 proteins in four pancreatic cancer cell lines, 540 of which are unique to primary cancer cells, and 487 unique to cells derived from metastatic sites. Statistical analysis identified 134 proteins significantly differentially expressed between the two populations. The subcellular localization of these proteins was determined and expression levels for four targets were validated using western blot techniques. These identified proteins can be further investigated to determine their roles in progression and metastasis and may serve as therapeutic targets in the development of more effective treatments for pancreatic cancer.