Robust audio indexing for Dutch spoken-word collections

Abstract—Whereas the growth of storage capacity is in accordance with widely acknowledged predictions, the possibilities to index and access the archives created is lagging behind. This is especially the case in the oral history domain and much of the rich content in these collections runs the risk to remain inaccessible for lack of robust search technologies. This paper addresses the history and development of robust audio indexing technology for searching Dutch spoken-word collections and compares Dutch audio indexing in the well-studied broadcast news domain with an oral-history case-study. It is concluded that despite significant advances in Dutch audio indexing technology and demonstrated applicability in several domains, further research is indispensable for successful automatic disclosure of spoken-word collections

Apolipoprotein E: from cardiovascular disease to neurodegenerative disorders.

Apolipoprotein (apo) E was initially described as a lipid transport protein and major ligand for low density lipoprotein (LDL) receptors with a role in cholesterol metabolism and cardiovascular disease. It has since emerged as a major risk factor (causative gene) for Alzheimer's disease and other neurodegenerative disorders. Detailed understanding of the structural features of the three isoforms (apoE2, apoE3, and apoE4), which differ by only a single amino acid interchange, has elucidated their unique functions. ApoE2 and apoE4 increase the risk for heart disease: apoE2 increases atherogenic lipoprotein levels (it binds poorly to LDL receptors), and apoE4 increases LDL levels (it binds preferentially to triglyceride-rich, very low density lipoproteins, leading to downregulation of LDL receptors). ApoE4 also increases the risk for neurodegenerative diseases, decreases their age of onset, or alters their progression. ApoE4 likely causes neurodegeneration secondary to its abnormal structure, caused by an interaction between its carboxyl- and amino-terminal domains, called domain interaction. When neurons are stressed or injured, they synthesize apoE to redistribute cholesterol for neuronal repair or remodeling. However, because of its altered structure, neuronal apoE4 undergoes neuron-specific proteolysis, generating neurotoxic fragments (12-29 kDa) that escape the secretory pathway and cause mitochondrial dysfunction and cytoskeletal alterations, including tau phosphorylation. ApoE4-associated pathology can be prevented by small-molecule structure correctors that block domain interaction by converting apoE4 to a molecule that resembles apoE3 both structurally and functionally. Structure correctors are a potential therapeutic approach to reduce apoE4 pathology in both cardiovascular and neurological disorders

Lack of evidence for decreased protein stability in the 2397 (Met) haplotype of the leucine rich repeat kinase 2 protein implicated in Parkinson’s disease

Missense mutations in the leucine rich repeat kinase 2 (LRRK2) gene are the leading genetic cause of autosomal dominant familial Parkinson’s disease. We previously reported that two mutations within the ROC domain, namely R1441C and A1442P, exhibit increased protein degradation leading to lowered steady state LRRK2 protein levels in HEK293 cells. More recently, the common WD40 domain LRRK2 haplotype, Met2397, which is a risk factor for Crohn’s disease, has been shown to lower steady state protein levels in HEK293 cells. In view of recent evidence implicating LRRK2 and inflame-mation in PD, we investigated the effects of Met2397 on LRRK2 expression, and compared them to the Thr2397 variant and other LRRK2 mutants. In this study, we transfected HEK293 cells with plasmid constructs encoding the different LRRK2 variants, and analyzed the resulting protein levels by Western blot and flow cytometry. Here we found that both the Met2397 and Thr2397 haplotypes yield similar levels of LRRK2 protein expression and do not appear to impact cell viability in HEK293 cells, compared to other LRRK mutants. Thus, we have concluded that the Met2397 haplotype is unlikely to play a role in LRRK2 mediated or idiopathic PD

The dynamic switch mechanism that leads to activation of LRRK2 is embedded in the DFGψ motif in the kinase domain.

Leucine-rich repeat kinase 2 (LRRK2) is a large multidomain protein, and LRRK2 mutants are recognized risk factors for Parkinson's disease (PD). Although the precise mechanisms that control LRRK2 regulation and function are unclear, the importance of the kinase domain is strongly implicated, since 2 of the 5 most common familial LRRK2 mutations (G2019S and I2020T) are localized to the conserved DFGψ motif in the kinase core, and kinase inhibitors are under development. Combining the concept of regulatory (R) and catalytic (C) spines with kinetic and cell-based assays, we discovered a major regulatory mechanism embedded within the kinase domain and show that the DFG motif serves as a conformational switch that drives LRRK2 activation. LRRK2 is quite unusual in that the highly conserved Phe in the DFGψ motif, which is 1 of the 4 R-spine residues, is replaced with tyrosine (DY2018GI). A Y2018F mutation creates a hyperactive phenotype similar to the familial mutation G2019S. The hydroxyl moiety of Y2018 thus serves as a "brake" that stabilizes an inactive conformation; simply removing it destroys a key hydrogen-bonding node. Y2018F, like the pathogenic mutant I2020T, spontaneously forms LRRK2-decorated microtubules in cells, while the wild type and G2019S require kinase inhibitors to form filaments. We also explored 3 different mechanisms that create kinase-dead pseudokinases, including D2017A, which further emphasizes the highly synergistic role of key hydrophobic and hydrophilic/charged residues in the assembly of active LRRK2. We thus hypothesize that LRRK2 harbors a classical protein kinase switch mechanism that drives the dynamic activation of full-length LRRK2

Risk assessment using transfer learning for grassland fires

© 2019 A new direction of risk assessment research in grassland fire management is data-driven prediction, in which data are collected from particular regions. Since some regions have rich datasets that can easily generate knowledge for risk prediction, and some have no data available, this study addresses how we can leverage the knowledge learned from one grassland risk assessment to assist with a current assessment task. In this paper, we first introduce the transfer learning methodology to map and update risk maps in grassland fire management, and we propose a new grassland fire risk analysis method. In this study, two major grassland areas (Xilingol and Hulunbuir) in northern China are selected as the study areas, and five representative indicators (features) are extracted from grassland fuel, fire climate, accessibility, human and social economy. Taking Xilingol as the source domain (where sufficient labelled data are available) and Hulunbuir as the target domain (which contains insufficient data but requires risk assessment/prediction), we then establish the mapping relationship between grassland fire indicators and the degrees of grassland fire risk by using a transfer learning method. Finally, the fire risk in the Hulunbuir grassland is assessed using the transfer learning method. Experiments show that the prediction accuracy reached 87.5% by using the transfer learning method, representing a significant increase over existing methods

Graph Enabled Cross-Domain Knowledge Transfer

To leverage machine learning in any decision-making process, one must convert the given knowledge (for example, natural language, unstructured text) into representation vectors that can be understood and processed by machine learning model in their compatible language and data format. The frequently encountered difficulty is, however, the given knowledge is not rich or reliable enough in the first place. In such cases, one seeks to fuse side information from a separate domain to mitigate the gap between good representation learning and the scarce knowledge in the domain of interest. This approach is named Cross-Domain Knowledge Transfer. It is crucial to study the problem because of the commonality of scarce knowledge in many scenarios, from online healthcare platform analyses to financial market risk quantification, leaving an obstacle in front of us benefiting from automated decision making. From the machine learning perspective, the paradigm of semi-supervised learning takes advantage of large amount of data without ground truth and achieves impressive learning performance improvement. It is adopted in this dissertation for cross-domain knowledge transfer. (to be continued

Expanding the Role of Synthetic Data at the U.S. Census Bureau

National Statistical offices (NSOs) create official statistics from data collected directly from survey respondents, from government administrative records and from other third party sources. The raw source data, regardless of origin, is usually considered to be confidential. In the case of the U.S. Census Bureau, confidentiality of survey and administrative records microdata is mandated by statute, and this mandate to protect confidentiality is often at odds with the needs of data users to extract as much information as possible from rich microdata. Traditional disclosure protection techniques applied to resolve this tension have resulted in official data products that come no where close to fully utilizing the information content of the underlying microdata. Typically, these products take for the form of basic, aggregate tabulations. In a few cases anonymized public-use micro samples are made available, but these are increasingly under risk of re-identification by the ever larger amounts of information about individuals and firms that is available in the public domain. One potential approach for overcoming these risks is to release products based on synthetic or partially synthetic data where values are simulated from statistical models designed to mimic the (joint) distributions of the underlying microdata rather than making the actual underlying microdata available. We discuss recent Census Bureau work to develop and deploy such products. We also discuss the benefits and challenges involved with extending the scope of synthetic data products in official statistics

CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions