Miracle-dz/UniFAR-FTUs huggingface dataset:
Listening to Rhythmic Music Reduces Connectivity within the Basal Ganglia and the Reward System

Music can trigger emotional responses in a more direct way than any other stimulus. In particular, music-evoked pleasure involves brain networks that are part of the reward system. Furthermore, rhythmic music stimulates the basal ganglia and may trigger involuntary movements to the beat. In the present study, we created a continuously playing rhythmic, dance floor-like composition where the ambient noise from the MR scanner was incorporated as an additional instrument of rhythm. By treating this continuous stimulation paradigm as a variant of resting-state, the data was analyzed with stochastic dynamic causal modeling (sDCM), which was used for exploring functional dependencies and interactions between core areas of auditory perception, rhythm processing, and reward processing. The sDCM model was a fully connected model with the following areas: auditory cortex, putamen/pallidum, and ventral striatum/nucleus accumbens of both hemispheres. The resulting estimated parameters were compared to ordinary resting-state data, without an additional continuous stimulation. Besides reduced connectivity within the basal ganglia, the results indicated a reduced functional connectivity of the reward system, namely the right ventral striatum/nucleus accumbens from and to the basal ganglia and auditory network while listening to rhythmic music. In addition, the right ventral striatum/nucleus accumbens demonstrated also a change in its hemodynamic parameter, reflecting an increased level of activation. These converging results may indicate that the dopaminergic reward system reduces its functional connectivity and relinquishing its constraints on other areas when we listen to rhythmic music.
The Orchive : Data mining a massive bioacoustic archive

The Orchive is a large collection of over 20,000 hours of audio recordings from the OrcaLab research facility located o the northern tip of Vancouver Island. It contains recorded orca vocalizations from the 1980 to the present time and is one of the largest resources of bioacoustic data in the world. We have developed a web-based interface that allows researchers to listen to these recordings, view waveform and spectral representations of the audio, label clips with annotations, and view the results of machine learning classiers based on automatic audio features extraction. In this paper we describe such classiers that discriminate between background noise, orca calls, and the voice notes that are present in most of the tapes. Furthermore we show classication results for individual calls based on a previously existing orca call catalog. We have also experimentally investigated the scalability of classiers over the entire Orchive.
Using Design Axioms and Topology to Model Database Semantics

The freedom to combine information stored in a database using the opera tors provided by its datamodel introduces many caveats, such as with view updates and integrity preservation, for the database designer. To alleviate these problems we define a formal model that explicates the database semantics through entity definitions and limits their use along well-defined paths. Our approach is based on six design axioms and concepts borrowed topology. This way we achieve an unified description of both the database intension and its extension. In particular, we show that generalisation I specialisation hierarchies are naturally cast into proper subset hierarchies in the entity type topology. Moreover, the limitations posed on the construction of entity types preserve the Armstrong axioms for functional dependencies. This way our model captures much of the real-world semantic constraints and remains sound and complete.
A time-varying complex dynamical network model and its controlled synchronization criteria

Today, complex networks have attracted increasing attention from various fields of science and engineering. It has been demonstrated that many complex networks display various synchronization phenomena. In this note, we introduce a time-varying complex dynamical network model. We then further investigate its synchronization phenomenon and prove several network synchronization theorems. Especially, we show that synchronization of such a time-varying dynamical network is completely determined by the inner-coupling matrix, and by the eigenvalues and the corresponding eigenvectors of the coupling configuration matrix of the network.
Quantum Computation as Geometry

Quantum computers hold great promise for solving interesting computational problems, but it remains a challenge to find efficient quantum circuits that can perform these complicated tasks. Here we show that finding optimal quantum circuits is essentially equivalent to finding the shortest path between two points in a certain curved geometry. By recasting the problem of finding quantum circuits as a geometric problem, we open up the possibility of using the mathematical techniques of Riemannian geometry to suggest new quantum algorithms or to prove limitations on the power of quantum computers.
Learning Finite State Machines With Self-Clustering Recurrent Networks

Recent work has shown that recurrent neural networks have the ability to learn finite state automata from examples. In particular, networks using second-order units have been successful at this task. In studying the performance and learning behavior of such networks we have found that the second-order network model attempts to form clusters in activation space as its internal representation of states. However, these learned states become unstable as longer and longer test input strings are presented to the network. In essence, the network forgets where the individual states are in activation space. In this paper we propose a new method to force such a network to learn stable states by introducing discretization into the network and using a pseudo-gradient learning rule to perform training. The essence of the learning rule is that in doing gradient descent, it makes use of the gradient of a sigmoid function as a heuristic hint in place of that of the hard-limiting function, while still using the discretized value in the feedback update path. The new structure uses isolated points in activation space instead of vague clusters as its internal representation of states. It is shown to have similar capabilities in learning finite state automata as the original network, but without the instability problem. The proposed pseudo-gradient learning rule may also be used as a basis for training other types of networks that have hard-limiting threshold activation functions.
A System for Semantic Integration of Geologic Maps via Ontologies ∗

This paper describes a prototype system for registering geologic data sets through ontologies to assist in integrating and querying heterogeneous geologic data sets. The system consists of three components: an o tology repository , thedata set registration, andontology-aware applications . User-defined ontologies in OWL are saved and used by the system. Each data set must be registered before it becomes available, and the registration semi-automatically generates a mapping from data sets to ontologies. The mapping between data sets and ontologies are used by applications to explore and extract information from the data set.
Effective, Robust Design of Community Mitigation for Pandemic Influenza: A Systematic Examination of Proposed US Guidance

Background The US government proposes pandemic influenza mitigation guidance that includes isolation and antiviral treatment of ill persons, voluntary household member quarantine and antiviral prophylaxis, social distancing of individuals, school closure, reduction of contacts at work, and prioritized vaccination. Is this the best strategy combination? Is choice of this strategy robust to pandemic uncertainties? What are critical enablers of community resilience? Methods and Findings We systematically simulate a broad range of pandemic scenarios and mitigation strategies using a networked, agent-based model of a community of explicit, multiply-overlapping social contact networks. We evaluate illness and societal burden for alterations in social networks, illness parameters, or intervention implementation. For a 1918-like pandemic, the best strategy minimizes illness to <1% of the population and combines network-based (e.g. school closure, social distancing of all with adults' contacts at work reduced), and case-based measures (e.g. antiviral treatment of the ill and prophylaxis of household members). We find choice of this best strategy robust to removal of enhanced transmission by the young, additional complexity in contact networks, and altered influenza natural history including extended viral shedding. Administration of age-group or randomly targeted 50% effective pre-pandemic vaccine with 7% population coverage (current US H5N1 vaccine stockpile) had minimal effect on outcomes. In order, mitigation success depends on rapid strategy implementation, high compliance, regional mitigation, and rigorous rescinding criteria; these are the critical enablers for community resilience. Conclusions Systematic evaluation of feasible, recommended pandemic influenza interventions generally confirms the US community mitigation guidance yields best strategy choices for pandemic planning that are robust to a wide range of uncertainty. The best strategy combines network- and case-based interventions; network-based interventions are paramount. Because strategies must be applied rapidly, regionally, and stringently for greatest benefit, preparation and public education is required for long-lasting, high community compliance during a pandemic.
Measuring Text Reuse

In this paper we present results from the METER (MEasuring TExt Reuse) project whose aim is to explore issues pertaining to text reuse and derivation, especially in the context of newspapers using newswire sources. Although the reuse of text by journalists has been studied in linguistics, we are not aware of any investigation using existing computational methods for this particular task. We investigate the classification of newspaper articles according to their degree of dependence upon, or derivation from, a newswire source using a simple 3-level scheme designed by journalists. Three approaches to measuring text similarity are considered: n-gram overlap, Greedy String Tiling, and sentence alignment. Measured against a manually annotated corpus of source and derived news text, we show that a combined classifier with features automatically selected performs best overall for the ternary classification achieving an average F1-measure score of 0.664 across all three categories.
A Quest for Survival: A Review of the Early Biomarkers of Pancreatic Cancer and the Most Effective Approaches at Present

Pancreatic cancer (PC) is the most lethal type of cancer; it has the lowest 5-year survival rate among all other types of cancers. More than half of PC cases are diagnosed at an advanced stage due to PC’s insidious and non-specific symptoms. Surgery remains the most efficacious treatment option currently available, but only 10–20% of PC cases are resectable upon diagnosis. As of now, the sole biomarker approved by the United States Food and Drug Administration (US-FDA) for PC is carbohydrate antigen 19-9 (CA19-9); however, its use is limited for early diagnosis. An increasing number of studies have investigated a combination of biomarkers. Lately, there has been considerable interest in the application of a liquid biopsy, including the utilization of microRNAs (miRNAs), circulating tumor DNA (ctDNA), and circulating tumor cells (CTCs). Screening for PC is indicated for high-risk patients; studies on new diagnostic models combined with biomarkers for early detection have also shown promising results in terms of the ability of these models and biomarkers to aid clinicians in deciding on whether to start screening. This review seeks to provide a concise overview of the advancements in relation to existing biomarkers and explore novel strategies for the early detection of PC.
Deficiency of Niemann-Pick Type C-1 Protein Impairs Release of Human Immunodeficiency Virus Type 1 and Results in Gag Accumulation in Late Endosomal/Lysosomal Compartments

ABSTRACT Human immunodeficiency virus type 1 (HIV-1) relies on cholesterol-laden lipid raft membrane microdomains for entry into and egress out of susceptible cells. In the present study, we examine the need for intracellular cholesterol trafficking pathways with respect to HIV-1 biogenesis using Niemann-Pick type C-1 (NPC1)-deficient (NPCD) cells, wherein these pathways are severely compromised, causing massive accumulation of cholesterol in late endosomal/lysosomal (LE/L) compartments. We have found that induction of an NPC disease-like phenotype through treatment of various cell types with the commonly used hydrophobic amine drug U18666A resulted in profound suppression of HIV-1 release. Further, NPCD Epstein-Barr virus-transformed B lymphocytes and fibroblasts from patients with NPC disease infected with a CD4-independent strain of HIV-1 or transfected with an HIV-1 proviral clone, respectively, replicated HIV-1 poorly compared to normal cells. Infection of the NPCD fibroblasts with a vesicular stomatitis virus G-pseudotyped strain of HIV-1 produced similar results, suggesting a postentry block to HIV-1 replication in these cells. Examination of these cells using confocal microscopy showed an accumulation and stabilization of Gag in LE/L compartments. Additionally, normal HIV-1 production could be restored in NPCD cells upon expression of a functional NPC1 protein, and overexpression of NPC1 increased HIV-1 release. Taken together, our findings demonstrate that intact intracellular cholesterol trafficking pathways mediated by NPC1 are needed for efficient HIV-1 production.
Author ' s personal copy Applying evolutionary genetics to schistosome epidemiology

We review how molecular markers and evolutionary analysis have been applied to the study of schistosome parasites, important pathogens that infect over 200 million people worldwide. Topics reviewed include phylogenetics and biogeography, hybridization, infection within snails, mating systems, and genetic structure. Some interesting generalizations include that schistosome species hybridize frequently and have switched definitive hosts repeatedly in evolutionary time. We show that molecular markers can be used to infer epidemiologically relevant processes such as spatial variation in transmission, or to reveal complex patterns of mate choice. Analysis of genetic structure data shows that transmission foci can be structured by watershed boundaries, habitat types, and host species. We also discuss sampling and analytical problems that arise when using larvae to estimate genetic parameters of adult schistosome populations. Finally, we review pitfalls in methodologies such as genotyping very small individuals, statistical methods for identifying clonemates or for identifying sibling groups, and estimating allele frequencies from pooled egg samples. 2010 Elsevier B.V. All rights reserved. * Corresponding author. E-mail address: Michelle.steinauer@oregonstate.edu (M.L. Steinauer).
Late-replicating CNVs as a source of new genes

Summary Asynchronous replication of the genome has been associated with different rates of point mutation and copy number variation (CNV) in human populations. Here, our aim was to investigate whether the bias in the generation of CNV that is associated with DNA replication timing might have conditioned the birth of new protein-coding genes during evolution. We show that genes that were duplicated during primate evolution are more commonly found among the human genes located in late-replicating CNV regions. We traced the relationship between replication timing and the evolutionary age of duplicated genes. Strikingly, we found that there is a significant enrichment of evolutionary younger duplicates in late-replicating regions of the human and mouse genome. Indeed, the presence of duplicates in late-replicating regions gradually decreases as the evolutionary time since duplication extends. Our results suggest that the accumulation of recent duplications in late-replicating CNV regions is an active process influencing genome evolution.
International Union of Pharmacology. XVII. Classification of muscarinic acetylcholine receptors.

Actions of acetylcholine in the periphery are the result of activation of either the ionotropic nicotinic receptor or the metabotropic muscarinic receptor. In the mammalian central nervous system (CNS)c, both nicotinic and muscarinic receptor subtypes are present on neurons, although there is as yet
Ethnic Differences in Body Composition and Obesity Related Risk Factors: Study in Chinese and White Males Living in China

The purpose of this cross-sectional observational study was to identify ethnic differences in body composition and obesity-related risk factors between Chinese and white males living in China. 115 Chinese and 114 white male pilots aged 28–63 years were recruited. Fasting body weight, height and blood pressure were measured following standard procedures. Whole-body and segmental body composition were measured using an 8-contact electrode bioimpedance analysis (BIA) system. Fasting serum glucose, fasting plasma total cholesterol (TC), high-density lipoprotein (HDL) cholesterol, and triglycerides (TG) were assessed using automatic biochemistry analyzer. After adjusting for age and body mass index (BMI), Chinese males had significantly higher percentage of body fat (PBF) both with respect to whole body (Chinese: 23.7%±0.2% vs. Whites: 22.4%±0.2%) and the trunk area (Chinese: 25.0%±0.3% vs. Whites: 23.2%±0.3%) compared to their white counterparts. At all BMIs, Chinese males had significantly higher fasting glucose levels (Chinese: 5.7±1.0 mmol/L vs. Whites: 5.2±1.0 mmol/L) but lower high-density lipoprotein levels (Chinese: 0.8±1.0 mmol/L vs. Whites: 1.0±1.0 mmol/L) than white males. In addition, a marginally significantly higher diastolic blood pressure was found among Chinese men than that among white men (Chinese: 80±1.0 mmHg vs. Whites: 77±1.0 mmHg). Chinese males had more body fat and a greater degree of central fat deposition pattern than that seen in white males in the present study. Furthermore, data on blood pressure, fasting glucose and blood lipids suggest that Chinese men may be more prone to obesity-related risk factors than white men.
Grid roadmap based real time path planning

The probabilistic roadmap (PRM) is forceful for path planning in static environments. Also PRM based methods may be employed for real time path planning in dynamic environments. These methods for real time path planning desire vast amount of time for preprocessing. To mitigate desired time for initialization, we propose a new method based on Grid Roadmap (GRM) which is an edge less roadmap. By suggested roadmap and utilizing a training method for robot manipulator, we attain a shape deformed model for obstacles that cancels our ambition for configuration space. Accordingly, grid roadmap construction will be on workspace. Finally, the planner searches for a collision free path in workspace with dynamic and shape changing obstacles.
Molecular Pathology of Pancreatic Cystic Lesions with a Focus on Malignant Progression

Simple Summary Pancreatic cysts are being identified with increasing frequency, and the prevalence increases with age to 10–25% by the 7th decade of life. They also have the potential for malignant transformation. The effective monitoring of the progression of pancreatic cysts and the early detection of pancreatic cancer is crucial. Here, we review recent advances in the molecular pathology of pancreatic cystic lesions and discuss how the revised tumorigenesis and progression model in an intraductal papillary mucinous neoplasm (IPMN) could provide insights into refining strategies for the risk stratification of pancreatic cysts and the early detection of cancer. Abstract The malignant progression of pancreatic cystic lesions (PCLs) remains understudied with a knowledge gap, yet its exploration is pivotal for effectively stratifying patient risk and detecting cancer at its earliest stages. Within this review, we delve into the latest discoveries on the molecular level, revealing insights into the IPMN molecular landscape and revised progression model, associated histologic subtypes, and the role of inflammation in the pathogenesis and malignant progression of IPMN. Low-grade PCLs, particularly IPMNs, can develop into high-grade lesions or invasive carcinoma, underscoring the need for long-term surveillance of these lesions if they are not resected. Although KRAS and GNAS remain the primary oncogenic drivers of neoplastic development in IPMNs, additional genes that are important in tumorigenesis have been recently identified by whole exome sequencing. A more complete understanding of the genes involved in the molecular progression of IPMN is critical for effective monitoring to minimize the risk of malignant progression. Complicating these strategies, IPMNs are also frequently multifocal and multiclonal, as demonstrated by comparative molecular analysis. Algorithms for preoperative cyst sampling and improved radiomic techniques are emerging to model this spatial and temporal genetic heterogeneity better. Here, we review the molecular pathology of PCLs, focusing on changes associated with malignant progression. Developing models of molecular risk stratification in PCLs which can complement radiologic and clinical features, facilitate the early detection of pancreatic cancer, and enable the development of more personalized surveillance and management strategies are summarized.
Data Visualization Via Collaborative Filtering

Collaborative Filtering (CF) is the most successful approach to Recommender Systems (RS). In this paper, we suggest methods for global and personalized visualization of CF data. Users and items are first embedded into a high-dimensional latent feature space according to a predictor function particularly designated to conform with visualization requirements. The data is then projected into 2-dimensional space by Principal Component Analysis (PCA) and Curvilinear Component Analysis (CCA). Each projection technique targets a di fferent application, and has its own advantages. PCA places all items on a Global Item Map (GIM) such that the correlation between their latent features is revealed optimally. CCA draws personalized Item Maps (PIMs) representing a small subset of items to a specifi c user. Unlike in GIM, a user is present in PIM and items are placed closer or further to her based on their predicted ratings. The intra-item semantic correlations are inherited from the high-dimensional space as much as possible. The algorithms are tested on three versions of the MovieLens dataset and the Netflix dataset to show they combine good accuracy with satisfactory visual properties. We rely on a few examples to argue our methods can reveal links which are hard to be extracted, even if explicit item features are available.