LaSEEB

NeuroFeedback Training: Intensity analysis

Supervisors:
Fernando Melício
Agostinho Rosa

Uso de Processadores Gráficos para Simulações baseadas em Agentes

Orientadores:
Nuno Fachada
Agostinho Rosa

Abstract
Agent-based modelling is a programming and simulation paradigm. It is a bottom-up approach where
interacting agents are its basic building blocks. However, ABMs must undergo a process of independent implementation and verification to achieve credibility, where different replications of an ABM should provide similar
statistical results. Yet the reality is quite different and too often we observe many replications of a given model
presenting wildly different results. This may happen because ABMs are very sensitive to implementation details,
and the lack of a complete and formal model description often leaves researchers with the burden of creating
implementations based on personal interpretations of the model.
In this work we provide a complete description of the Heatbugs model based on its NetLogo implementation.
To accomplish this, we use the ODD protocol, which allows us to formalize the model so that future independent
replications are able to yield similar results. In addition, we provide results analysis for performance and
statistical alignment for two Heatbugs model replications: 1) a single threaded CPU implementation in C; and,
2) a parallel GPU implementation in OpenCL.

Molecular models of a protein’s structure can give detailed insight into mechanisms underlying its function, especially when viewed combined with sequence features.

In theory, 3D structural models are now available for many proteins, however in practice it is often complex to find all appropriate models and view them with sequence features. Thus it was developed Aquaria, a new web resource that provides 49 million pre-calculated structural models using homology from sequence to structure – 10 times more than currently available from other resources.

Using Aquaria we surveyed not only the visible proteome, but also the ‘unknown’ or ‘dark’ proteome, i.e., regions of proteins that remain stubbornly inaccessible to both experimental structure determination and modeling. Building upon a recent structural modeling study covering 546,000 proteins across many organisms, it was found 44–54% of the proteome in eukaryotes and viruses is dark, compared with only 14% for archaea and bacteria. Surprisingly, most dark proteins cannot be accounted for by (expected) conventional explanations, beside that the dark proteome has unexpected features.

Therefore, this work suggests several new directions for research in structural and computational biology. This work will help focus future research efforts to shed light on the remaining dark proteome.

Neuroengineering

Scientific Area:
Bioengineering – Biosignals and biomedical systems

Weight:
Credits: 6 ECTS
Contact time: 56h
Autonomous time: 112h
Total time: 168h

Rationale

Neuroengineering is a recently developed and rapidly changing domain in Biomedical Engineering that employs engineering methodologies to address the problems of “understanding, repairing, enhancing or otherwise exploring the properties of neural systems”. Its main goal is to develop tools to investigate the human brain and artificial devices to interact with it in order to repair and/or enhance its function, particularly through brain-computer interfaces and neuroprosthetics.

Neuroengineering draws on disciplines ranging from Neuroscience to Biophysics, Electrical Engineering, Computer Science, Materials Science, and Tissue Engineering. Faculty with relevant and complementary expertise in engineering within IST (mostly DBE, but also DEEC and DEI) will join with neuroscience faculty from CNP and FMUL, in order to provide a course on Neuroengineering.

Objectives

The main objective of the course is to provide students with comprehensive background knowledge of the most important areas in the field of Neuroengineering, including the existing challenges and the main concepts and techniques that can be used to address them.

Students successfully completing the course are expected to: 1) have basic knowledge about the organization, structure, function and pathological modifications of neural systems; 2) have general knowledge about the principles, methodologies and applications of the main engineering techniques used to study and interact with neural systems, with the objectives of brain monitoring, diagnosing, modulating, repairing, enhancing or interfacing with machines; and 3) be prepared to critically evaluate different problems and techniques in Neuroengineering.

Synopsis

The course will be organized as a series of teaching modules addressing a number of specific topics in Neuroengineering, primarily aimed at students with a background in engineering (1st cycle / BSc in engineering). Each module will last 1-2 weeks, and will be organized by an expert in the field.

The course will take a multidisciplinary approach, targeting state-of-the-art techniques, and it will include conventional lectures as well as seminars by invited experts and journal club classes for the discussion of relevant scientific literature.

Programme (modules)

Opening (Fernando Lopes da Silva)
Current challenges for neuroengineering
Neuroscience basics I (Zach Mainen, CNP) – 1 week
Neural systems and behavior
Brain cells and circuitry
Neuroscience basics II (Ana Sebastião and Isabel Pavão Martins, FMUL) – 1 week
Neural communication, plasticity and degeneration
Cognitive function and dysfunction
Computational neuroscience (Tiago Maia and Christian Machens, FMUL and CNP) – 1 week
Neural coding and neural networks
Computational cognitive neuroscience
Neuroimaging (Patrícia Figueiredo and Rita Nunes, IST – DBE) – 2 weeks
Electroencephalography (EEG) and magnetoencephalography (MEG): invasive and non-invasive recordings of spontaneous and event-related activity.
Magnetic resonance imaging (MRI): image formation and reconstruction; structural, functional, perfusion and diffusion imaging contrasts.
Diffuse optical imaging (DOI) by near infrared spectroscopy (NIRS)
Positron emission tomography (PET): molecular imaging using radiotracers for brain metabolism and function.
Neural monitoring and diagnosis (Ana Fred, IST - DBE) – 1 week
Statistical inference and model-based classification methods for diagnosis and monitoring of brain disorders
Detection and monitoring of brain activity patterns for emotion assessment and human identification
Unsupervised learning of brain activity patterns and longitudinal studies
Neural interfaces (João Sanches, Fernando Lopes da Silva, IST – DBE) – 1 week
Fundamentals of brain computer interfaces. Neurophysiology, EEG data acquisition and signal processing
Direct EEG Interfaces, VEP, P300 and ERD/ERS
Motor imagery and rehabilitation
Applications
Neural modulation (Agostinho Rosa, IST – DBE) – 1 week
Neurofeedback using EEG and NIRS.
Neural stimulation: Deep Brain Stimulation (DBS), Transcranial Direct Current Stimulation (TDCS), Transcranial Magnetic Stimulation (TMS)
Self-adaptive immersive neural stimulation
Clinical and performance enhancement applications
Neural tissue engineering (Margarida Diogo, IST – DBE) – 1 week
Biomolecular-based strategies (e.g. neurotrophic factors) for neural regeneration
Cellular-based strategies for neural regeneration (stem cell-based and mature neural cell-based strategies) and disease modeling
A-cellular biomaterial-based strategies for neural regeneration
Advanced tissue engineering strategies combining biomaterial scaffolds, biochemical cues and cells
Microsystems and nanotechnology for neuroengineering (João Pedro Code, IST – DBE) – 1 week
Nanoparticle engineering for interaction with neural cells, targeted delivery of drugs, and advanced molecular imaging technologies
Microsystems for neuroscience on a chip and for microengineereing neural development
Cognitive robotics (José Alberto Santos-Víctor, IST – DEEC) – 1 week
Sensorimotor coordination
Non-verbal communication
Tools for rehabilitation
Complex brain networks (Arlindo Oliveira and Alexandre Francisco, IST – DEI) – 1 week
Theory and basic concepts of complex networks
Properties, representation, processing and analysis of large networks
Applications to brain networks

Evaluation method:
Exam (70%): two dates during exam period, covering all the modules’ topics.
Student presentation (30%): two sessions during the last week of the semester, paper or essay regarding one of the course topics

Bibliography:
Neural Engineering, Bin He Ed., 2nd ed. 2013 Edition (ISBN-13: 978-1461452263)
Lectures notes provided by the course faculty.

Schedule (tentative):

Month    Day    Module    Responsible
1    Sep    13    Introduction    Fernando Lopes da Silva (moved to 4 October)
        14        
2        20    Neuroscience basics I    Zach Mainen - Qa2 17:00
        21        
3        27    Neuroscience basics II    Ana Sebastião Qa2 17:00
        28        
4    Oct    4    Computational Neuroscience    Tiago Maia (moved forward)
        5        
5        11    Neuroimaging I    Patricia Figueiredo
        12        
6        18    Neuroimaging II    Rita Nunes
        19        
7        25    Neural monitoring and diagnosis    Ana Fred
        26        
8    Nov    1    Neural interfaces    João Sanches
        2        
9        8    Neural modulation    Agostinho Rosa
        9        
10        15    Neural tissue engineering    Margarida Diogo
        16        
11        22    Microsystems and nanotechnology for neuroengineering     João Pedro Conde 
        23        
12        29    Cognitive robotics    José Santos-Víctor
        30        
13    Dec    6    Complex brain networks    Arlindo Oliveira e Alexandre Francisco
        7        
14        13    Student presentations    all
        14

Nuno Fachada just pos-graduated (Sept 13, 2016) magnum cum laudae at IST-Ulisboa.
Well done !!

SAC 2017 website
Regular papers submission
SRC papers submission

The "dark proteome"—protein regions whose structures are completely unknown—is a key remaining frontier in our understanding of biological systems. A new study now shows that roughly half of the proteome in eukaryotes and viruses is dark, highlighting the need for more sensitive tools to explore the full expanse of the protein universe.

Despite tremendous progress in characterizing the protein universe, many proteins reside in the dark proteome since they have regions of unknown structure. Exploring the dark proteome could clarify future research directions as studies of dark matter have done in physics. After all, this analogy has inspired surveys of other unknown protein properties, such as the “dark matter of the protein universe”—orphan protein sequences that do not match any known sequence profiles.

Turning to the Dark Side of the Protein Universe

The Systems Biology of Cancer group investigates the circadian regulation of tumour-driving mechanisms. The group includes both dry lab facilities at the Institute for Theoretical Biology and wet lab facilities at the Molecular Cancer Research Centre. We work together with bioinformaticians, physicists, molecular biologists and medical doctors and closely collaborate with the European Molecular Biology Laboratory (EMBL) where the group members are encouraged to attend conferences and courses.
Our group uses a systems biology approach involving wet-lab experiments, genome wide screening of gene expression of human and murine cells, bioinformatics and computational models, to understand the dynamic interplay between cancer and the circadian clock. With such a methodology, we aim to investigate the pathways which connect the circadian clock to cancer regulation at the transcriptional (including splicing) and translational levels.

Homepage: https://itb.biologie.hu-berlin.de/wiki/groups/relogio/start

PhD student position in Computational Systems Biology (m/f)

We seek highly motivated students to work on different aspects of our research. Available projects
include mathematical modelling of the circadian clock, development of circadian regulatory networks
in a cancer context and analysis of alternative splicing switches in tumorigenesis.
Profile
- MSc degree (or equivalent) in Bioinformatics, Applied Mathematics, Computer Science or a similar field
‐ Strong programming skills including R and experience in mathematical modelling
- Knowledge of biochemistry and molecular biology would be beneficial
- Ability to work in an interdisciplinary environment and good communication skills including fluent in spoken and written English
The PhD position is available for a period of three years.
How to apply: Please address inquiries and applications electronically (incl. short letter of motivation, full CV, contact of two referees) to Angela Relógio (angela.relogio@charite.de).

PhDposition2016.pdf

Unexpected features of the dark proteome
Nelson Perdigãoa,b, Julian Heinrichc, Christian Stoltec, Kenneth S. Sabird,e, Michael J. Buckleyc, Bruce Taborc, Beth Signald, Brian S. Glossd, Christopher J. Hammangd, Burkhard Rostf, Andrea Schafferhansf, and Seán I. O’Donoghuec,d,g,1. PNAS. doi: 10.1073/pnas.1508380112

Significance

A key remaining frontier in our understanding of biological systems is the “dark proteome”—that is, the regions of proteins where molecular conformation is completely unknown. We systematically surveyed these regions, finding that nearly half of the proteome in eukaryotes is dark and that, surprisingly, most of the darkness cannot be accounted for. We also found that the dark proteome has unexpected features, including an association with secretory tissues, disulfide bonding, low evolutionary conservation, and very few known interactions with other proteins. This work will help future research shed light on the remaining dark proteome, thus revealing molecular processes of life that are currently unknown.

Abstract
We surveyed the “dark” proteome–that is, regions of proteins never observed by experimental structure determination and inaccessible to homology modeling. For 546,000 Swiss-Prot proteins, we found that 44–54% of the proteome in eukaryotes and viruses was dark, compared with only ∼14% in archaea and bacteria. Surprisingly, most of the dark proteome could not be accounted for by conventional explanations, such as intrinsic disorder or transmembrane regions. Nearly half of the dark proteome comprised dark proteins, in which the entire sequence lacked similarity to any known structure. Dark proteins fulfill a wide variety of functions, but a subset showed distinct and largely unexpected features, such as association with secretion, specific tissues, the endoplasmic reticulum, disulfide bonding, and proteolytic cleavage. Dark proteins also had short sequence length, low evolutionary reuse, and few known interactions with other proteins. These results suggest new research directions in structural and computational biology.

structure prediction protein disorder transmembrane proteins secreted proteins unknown unknowns

Footnotes
To whom correspondence should be addressed. Email: sean@odonoghuelab.org.
Author contributions: S.I.O. designed research; N.P., J.H., K.S.S., M.J.B., B.T., B.S., B.S.G., C.J.H., and A.S. performed research; N.P., J.H., C.S., B.R., A.S., and S.I.O. analyzed data; and S.I.O. wrote the paper with contributions from N.P.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Data deposition: This work is accompanied by an online resource (darkproteins.org) that provides periodically updated versions of Datasets S1 and S2, and provides facilities to interactively explore these data.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1508380112/-/DCSupplemental.

Freely available online through the PNAS open access option.

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