Discrete Infomax Codes for Supervised Representation Learning.

For high-dimensional data such as images, learning an encoder that can output a compact yet informative representation is a key task on its own, in addition to facilitating subsequent processing of data. We present a model that produces discrete infomax codes (DIMCO); we train a probabilistic encoder that yields k-way d-dimensional codes associated with input data. Our model maximizes the mutual information between codes and ground-truth class labels, with a regularization which encourages entries of a codeword to be statistically independent. In this context, we show that the infomax principle also justifies existing loss functions, such as cross-entropy as its special cases. Our analysis also shows that using shorter codes reduces overfitting in the context of few-shot classification, and our various experiments show this implicit task-level regularization effect of DIMCO. Furthermore, we show that the codes learned by DIMCO are efficient in terms of both memory and retrieval time compared to prior methods.

The SWI/SNF chromatin remodeling complex regulates germinal center formation by repressing Blimp-1 expression.

Germinal center (GC) reaction is crucial in adaptive immune responses. The formation of GC is coordinated by the expression of specific genes including Blimp-1 and Bcl-6. Although gene expression is critically influenced by the status of chromatin structure, little is known about the role of chromatin remodeling factors for regulation of GC formation. Here, we show that the SWI/SNF chromatin remodeling complex is required for GC reactions. Mice lacking Srg3/mBaf155, a core component of the SWI/SNF complex, showed impaired differentiation of GC B and follicular helper T cells in response to T cell-dependent antigen challenge. The SWI/SNF complex regulates chromatin structure at the Blimp-1 locus and represses its expression by interacting cooperatively with Bcl-6 and corepressors. The defect in GC reactions in mice lacking Srg3 was due to the derepression of Blimp-1 as supported by genetic studies with Blimp-1-ablated mice. Hence, our study identifies the SWI/SNF complex as a key mediator in GC reactions by modulating Bcl-6-dependent Blimp-1 repression.

Inference of dynamic networks using time-course data.

Cells execute their functions through dynamic operations of biological networks. Dynamic networks delineate the operation of biological networks in terms of temporal changes of abundances or activities of nodes (proteins and RNAs), as well as formation of new edges and disappearance of existing edges over time. Global genomic and proteomic technologies can be used to decode dynamic networks. However, using these experimental methods, it is still challenging to identify temporal transition of nodes and edges. Thus, several computational methods for estimating dynamic topological and functional characteristics of networks have been introduced. In this review, we summarize concepts and applications of these computational methods for inferring dynamic networks and further summarize methods for estimating spatial transition of biological networks.

TEMPI: probabilistic modeling time-evolving differential PPI networks with multiPle information.

MOTIVATION: Time-evolving differential protein-protein interaction (PPI) networks are essential to understand serial activation of differentially regulated (up- or downregulated) cellular processes (DRPs) and their interplays over time. Despite developments in the network inference, current methods are still limited in identifying temporal transition of structures of PPI networks, DRPs associated with the structural transition and the interplays among the DRPs over time. RESULTS: Here, we present a probabilistic model for estimating Time-Evolving differential PPI networks with MultiPle Information (TEMPI). This model describes probabilistic relationships among network structures, time-course gene expression data and Gene Ontology biological processes (GOBPs). By maximizing the likelihood of the probabilistic model, TEMPI estimates jointly the time-evolving differential PPI networks (TDNs) describing temporal transition of PPI network structures together with serial activation of DRPs associated with transiting networks. This joint estimation enables us to interpret the TDNs in terms of temporal transition of the DRPs. To demonstrate the utility of TEMPI, we applied it to two time-course datasets. TEMPI identified the TDNs that correctly delineated temporal transition of DRPs and time-dependent associations between the DRPs. These TDNs provide hypotheses for mechanisms underlying serial activation of key DRPs and their temporal associations. AVAILABILITY AND IMPLEMENTATION: Source code and sample data files are available at http://sbm.postech.ac.kr/tempi/sources.zip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of co-occurring transcription factor binding sites from DNA sequence using clustered position weight matrices.

Accurate prediction of transcription factor binding sites (TFBSs) is a prerequisite for identifying cis-regulatory modules that underlie transcriptional regulatory circuits encoded in the genome. Here, we present a computational framework for detecting TFBSs, when multiple position weight matrices (PWMs) for a transcription factor are available. Grouping multiple PWMs of a transcription factor (TF) based on their sequence similarity improves the specificity of TFBS prediction, which was evaluated using multiple genome-wide ChIP-Seq data sets from 26 TFs. The Z-scores of the area under a receiver operating characteristic curve (AUC) values of 368 TFs were calculated and used to statistically identify co-occurring regulatory motifs in the TF bound ChIP loci. Motifs that are co-occurring along with the empirical bindings of E2F, JUN or MYC have been evaluated, in the basal or stimulated condition. Results prove our method can be useful to systematically identify the co-occurring motifs of the TF for the given conditions.

Parameter learning for alpha integration.

In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed α-integration as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), enabling an optimal integration in the sense of minimizing the α-divergence. It also encompasses existing integration methods as its special case, for example, a weighted average and an exponential mixture. The parameter α determines integration characteristics, and the weight vector w assigns the degree of importance to each measure. In most work, however, α and w are given in advance rather than learned. In this letter, we present a parameter learning algorithm for learning α and ω from data when multiple integrated target values are available. Numerical experiments on synthetic as well as real-world data demonstrate the effectiveness of the proposed method.

Principal network analysis: identification of subnetworks representing major dynamics using gene expression data.

MOTIVATION: Systems biology attempts to describe complex systems behaviors in terms of dynamic operations of biological networks. However, there is lack of tools that can effectively decode complex network dynamics over multiple conditions. RESULTS: We present principal network analysis (PNA) that can automatically capture major dynamic activation patterns over multiple conditions and then generate protein and metabolic subnetworks for the captured patterns. We first demonstrated the utility of this method by applying it to a synthetic dataset. The results showed that PNA correctly captured the subnetworks representing dynamics in the data. We further applied PNA to two time-course gene expression profiles collected from (i) MCF7 cells after treatments of HRG at multiple doses and (ii) brain samples of four strains of mice infected with two prion strains. The resulting subnetworks and their interactions revealed network dynamics associated with HRG dose-dependent regulation of cell proliferation and differentiation and early PrPSc accumulation during prion infection. AVAILABILITY: The web-based software is available at: http://sbm.postech.ac.kr/pna.

Flexible Ternary Combination of Gellan Gum, Sodium Carboxymethyl Cellulose, and Silicon Dioxide Nanocomposites Fabricated by Quaternary Ammonium Silane: Rheological, Thermal, and Antimicrobial Properties.

Gellan gum-sodium carboxymethyl cellulose (GC)-based composite films with various concentrations of silicon dioxide (SiO2) nanoparticles and octadecyldimethyl-(3-triethoxy silylpropyl)ammonium chloride (ODDMAC) were successfully prepared by the traditional solution casting method to improve the antimicrobial and water repellent properties. Fourier transform infrared (FT-IR) spectra confirm the formation of hydrogen bonds between the GC and nano-SiO2. The microstructure and physicochemical properties were investigated by FT-IR, wide-angle X-ray diffraction, and scanning electron microscopy (SEM) analyses. The rheological properties of the GC-SiO2 hydrogel were also characterized. The results show that the inclusion of SiO2 nanoparticles significantly improved the viscosity and viscoelastic properties of the GC hydrogel. The GC-SiO2 hydrogel exhibited shear-thinning behavior and its viscosity decreased at high shear rates. The storage and loss moduli of the GC composites increased as the frequency and SiO2 concentration increased. The tensile strength and elongation at break of the GC composites increased by 75.9 and 62%, respectively, with the addition of SiO2 and ODDMAC. In addition, nano-SiO2 decreased the water vapor permeability and increased the hydrophobic properties of the GC-SiO2 composites. Thermogravimetric analysis showed that the T 5% loss was in the range of 99.4-128.6 °C and the char yield was in the range of 20.1-29.9%, which was significantly enhanced by the incorporation of SiO2 nanoparticles. The GC-SiO2 (ODDMAC) nanocomposites effectively shielded the UV light and exhibited high antimicrobial activity against six different pathogens. The simple and cost-effective GC-SiO2 (ODDMAC) nanocomposites gained importance in food packaging and biomedical applications.

Population-Based Epidemiology of Pediatric Patients with Treated Tic Disorders from Real-World Evidence in Korea.

Objectives: Although tic disorder (TD) is a common mental disorder in children and adolescents, epidemiological data based on real-world evidence (RWE) are insufficient. Using RWE, this study sought to examine the prevalence of treated TD, use of medical utilization, and use of prescription drugs among patients with TD with respect to TD type and comorbid psychiatric illness. Methods: We performed a retrospective cross-sectional study. Using the Korean Health Insurance Review and Assessment Service Pediatric Patient Sample data from 2009 to 2016, we analyzed 20,599 patients with TD (Korean Standard Classification of Diseases-6/7 code: F95.x) aged 2-19 years. Results: The annual average TD prevalence was 2.6/1000 population (95% confidence interval, 2.3-2.8/1000). Between 2009 and 2016, a slight increase in TD prevalence was observed from 1.9 to 2.9/1000 population. The TD prevalence rate in male patients was four times higher than that in female patients. Differences were observed in health care utilization and drug prescription types between patients with Tourette syndrome and chronic or transient TD. In addition, more than half of patients with TD had comorbid psychiatric disorders, and one-third of patients with TD had attention-deficit/hyperactivity disorder (ADHD). Patients with TD without comorbidities were frequently prescribed aripiprazole, while patients with TD and comorbid ADHD were frequently prescribed atomoxetine, methylphenidate, risperidone, and aripiprazole. Conclusion: This study described the epidemiological characteristics of TD based on recent RWE from Korea, and its findings can help establish future TD evidence-based clinical guidelines and related policies.

A constrained sequential EM algorithm for speech enhancement.

Speech enhancement is a fundamental problem, the goal of which is to estimate clean speech s(t), given a noise-contaminated signal s(t)+n(t), where n(t) is white or colored noise. This task can be viewed as a probabilistic inference problem which involves estimating the posterior distribution of hidden clean speech, given a noisy observation. Kalman filter is a representative method but is restricted to Gaussian distributions only. We consider the generalized auto-regressive (GAR) model in order to capture the non-Gaussian characteristics of speech. Then we present a constrained sequential EM algorithm where Rao-Blackwellized particle filters (RBPFs) are used in the E-step and model parameters are updated in a sequential manner in the M-step under positivity constraints for noise variance parameters. Numerical experiments confirm the high performance of our proposed method, compared to Kalman filter-based methods, in the task of sequential speech enhancement.

Kruppel-Like Factor 4 Positively Regulates Autoimmune Arthritis in Mouse Models and Rheumatoid Arthritis in Patients via Modulating Cell Survival and Inflammation Factors of Fibroblast-Like Synoviocyte.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes mild to severe joint inflammation. During RA pathogenesis, fibroblast-like synoviocytes (FLS) acquire a tumor-like phenotype and mediate cartilage destruction both directly and indirectly by producing proinflammatory cytokines and matrix metalloproteinases (MMPs). Kruppel-like factor (KLF) 4, a member of the KLF family, plays significant roles in cell survival, proliferation, and differentiation. A recent study reported increased expression of KLF4 in synovial tissue from RA patients. However, its precise role in RA in different models, including mouse autoimmune disease models, remains unclear. In this study, we examined the role of KLF4 during development of autoimmune arthritis in mouse models. To do this, we used KLF4 knockout mice rendered by ribonucleic acid (RNA)-guided endonuclease (RGEN) and performed collagen antibody-induced arthritis (CAIA). We found that deletion of KLF4 reduces inflammation induced by CAIA. In addition, we assessed collagen-induced arthritis (CIA) in control mice and KLF4-overexpressing mice generated by a minicircle vector treatment. Severity of CIA in mice overexpressing KLF4 was greater than that in mice injected with control vector. Finally, we verified the inflammatory roles of KLF4 in CIA by treating Kenpaullone which is used as KLF4 inhibitor. Next, we focused on human/mouse FLS to discover the cellular process involved in RA pathogenesis including proliferation, apoptosis, and inflammation including MMPs. In FLS, KLF4 upregulated expression of mRNA encoding proinflammatory cytokines interleukin (IL)-1ß and IL-6. KLF4 also regulated expression of matrix metallopeptidase 13 in the synovium. We found that blockade of KLF4 in FLS increased apoptosis and suppressed proliferation followed by downregulation of antiapoptotic factor BCL2. Our results indicate that KLF4 plays a crucial role in pathogenesis of inflammatory arthritis in vivo, by regulating apoptosis, MMP expression, and cytokine expression by FLS. Thus, KLF4 might be a novel transcription factor for generating RA by modulating cellular process of FLS.

What is a Right Distal Fusion Level for Prevention of Sagittal Imbalance in Multilevel Posterior Cervical Spine Surgery: C7 or T1?

STUDY DESIGN: This was a retrospective cohort study. OBJECTIVE: To evaluate the sagittal alignment and T1 slope after multilevel posterior cervical fusion surgery depending on the distal fusion level; C7 or T1, and find out the appropriate distal fusion level. SUMMARY OF BACKGROUND DATA: The sagittal balance of the cervical spine is known to be affected by cervical lordosis and T1 slope. However, T1 slope is not a constant parameter that can be frequently changed after the surgery. Furthermore, useful studies to help guide surgeons in decision-making as to the most appropriate distal level of fusion for cervical sagittal balance are very limited. MATERIALS AND METHODS: From 2014 to 2015, 50 patients who underwent multilevel posterior cervical fusion surgery were evaluated and followed up for >2 years. Group 1 was composed of 29 patients whose distal fusion level was C7. Group 2 was composed of 21 patients whose distal fusion level was T1. C1-C2 lordosis, C2-C7 lordosis, C2-C7 sagittal vertical axis (SVA), and T1 slope were measured on preoperative and the last follow-up. RESULTS: In group 1, C2-C7 SVA (23.1→30.4 mm, P=0.043) was worsened, and T1 slope (22.3→32.9 degrees, P=0.001) was increased after the surgery. In group 2, no significant change occurred in C2-C7 SVA after the surgery (25.3 →23.6 mm, P=0.648). The last follow-up T1 slope was similar with preoperative T1 slope (22.7→21.8 degrees, P=0.04) in group 2. CONCLUSIONS: This study showed that sagittal alignment became worse after the multilevel posterior cervical surgery when distal fusion level was stopped at C7, which was associated with increase of T1 slope. However, when we extended the distal fusion level to T1, T1 slope was not changed after the surgery. Therefore, sagittal alignment was maintained after the surgery. On the basis of the results of this study, we recommend distal fusion extends to T1. LEVEL OF EVIDENCE: Level III.

Interrupting oral infection of Porphyromonas gingivalis with anti-FimA antibody attenuates bacterial dissemination to the arthritic joint and improves experimental arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that typically results in strong inflammation and bone destruction in the joints. It is generally known that the pathogenesis of RA is linked to cardiovascular and periodontal diseases. Though rheumatoid arthritis and periodontitis share many pathologic features such as a perpetual inflammation and bone destruction, the precise mechanism underlying a link between these two diseases has not been fully elucidated. Collagen-induced arthritis (CIA) mice were orally infected with Porphyromonas gingivalis (Pg) or Pg preincubated with an anti-FimA antibody (FimA Ab) specific for fimbriae that are flexible appendages on the cell surface. Pg-infected CIA mice showed oral microbiota disruption and increased alveolar bone loss and had synovitis and joint bone destruction. However, preincubation with FimA Ab led to a significant reduction in the severity of both oral disease and arthritis. Moreover, FimA Ab attenuated bacterial attachment and aggregation on human gingival and rheumatoid arthritis synovial fibroblasts. In addition, we discovered bacteria may utilize dendritic cells, macrophages and neutrophils to migrate into the joints of CIA mice. These results suggest that disrupting Pg fimbriae function by FimA Ab ameliorates RA.

Author Correction: Interrupting oral infection of Porphyromonas gingivalis with anti-FimA antibody attenuates bacterial dissemination to the arthritic joint and improves experimental arthritis.

After online publication of this article, the authors noticed an error in the Figure section. The correct statement of this article should have read as below.

Nonnegative tensor factorization for continuous EEG classification.

In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classify multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.

Manipulation of Rat Movement via Nigrostriatal Stimulation Controlled by Human Visually Evoked Potentials.

Here, we report that the development of a brain-to-brain interface (BBI) system that enables a human user to manipulate rat movement without any previous training. In our model, the remotely-guided rats (known as ratbots) successfully navigated a T-maze via contralateral turning behaviour induced by electrical stimulation of the nigrostriatal (NS) pathway by a brain- computer interface (BCI) based on the human controller's steady-state visually evoked potentials (SSVEPs). The system allowed human participants to manipulate rat movement with an average success rate of 82.2% and at an average rat speed of approximately 1.9 m/min. The ratbots had no directional preference, showing average success rates of 81.1% and 83.3% for the left- and right-turning task, respectively. This is the first study to demonstrate the use of NS stimulation for developing a highly stable ratbot that does not require previous training, and is the first instance of a training-free BBI for rat navigation. The results of this study will facilitate the development of borderless communication between human and untrained animals, which could not only improve the understanding of animals in humans, but also allow untrained animals to more effectively provide humans with information obtained with their superior perception.

Publisher Correction: Manipulation of Rat Movement via Nigrostriatal Stimulation Controlled by Human Visually Evoked Potentials.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

Differential learning algorithms for decorrelation and independent component analysis.

Decorrelation and its higher-order generalization, independent component analysis (ICA), are fundamental and important tasks in unsupervised learning, that were studied mainly in the domain of Hebbian learning. In this paper we present a variation of the natural gradient ICA, differential ICA, where the learning relies on the concurrent change of output variables. We interpret the differential learning as the maximum likelihood estimation of parameters with latent variables represented by the random walk model. In such a framework, we derive the differential ICA algorithm and, in addition, we also present the differential decorrelation algorithm that is treated as a special instance of the differential ICA. Algorithm derivation and local stability analysis are given with some numerical experimental results.

A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery.

BACKGROUND: For a self-paced motor imagery based brain-computer interface (BCI), the system should be able to recognize the occurrence of a motor imagery, as well as the type of the motor imagery. However, because of the difficulty of detecting the occurrence of a motor imagery, general motor imagery based BCI studies have been focusing on the cued motor imagery paradigm. NEW METHOD: In this paper, we present a novel hybrid BCI system that uses near infrared spectroscopy (NIRS) and electroencephalography (EEG) systems together to achieve online self-paced motor imagery based BCI. We designed a unique sensor frame that records NIRS and EEG simultaneously for the realization of our system. Based on this hybrid system, we proposed a novel analysis method that detects the occurrence of a motor imagery with the NIRS system, and classifies its type with the EEG system. RESULTS: An online experiment demonstrated that our hybrid system had a true positive rate of about 88%, a false positive rate of 7% with an average response time of 10.36 s. COMPARISON WITH EXISTING METHOD(S): As far as we know, there is no report that explored hemodynamic brain switch for self-paced motor imagery based BCI with hybrid EEG and NIRS system. CONCLUSIONS: From our experimental results, our hybrid system showed enough reliability for using in a practical self-paced motor imagery based BCI.

Long-term cultivation of multipotential neural stem cells from adult rat subependyma.

Cultivation of adult rat neural stem cells (RNSCs) from the ventricular subependyma has been reported to be more difficult than growth of mouse neural stem cells. This is unfortunate, because rats provide useful models of brain function and disease, and implantation of RNSCs in these models could provide critical information on allograft behavior. Growing the cells in an appropriate medium (NS-A+B27 supplement), plating at sufficient densities (>5 cells per mm(2)), and minimizing opportunities for detachment from the substratum made it possible to isolate and cultivate these cells for over 6 months for >50 passages with no apparent change in phenotype. Single clones could be expanded indefinitely and differentiated to form astrocytes, oligodendrocytes, and neurons, demonstrating that the cultures did indeed contain neural stem cells. The cells had a much shorter cell cycle time ( approximately 13 h) than doubling time ( approximately 35 h), suggesting that these cells produce post-mitotic cells in approximately two of three divisions, thus making expansion difficult. The optimization of methods to grow adult RNSCs and identification of characteristics that limit their growth should prove useful in increasing the use of RNSCs for studies of their potential role in brain health and disease.