LaSEEB

5ª edição dos Prémios Ciências do Desporto, atribuídos pelo Comité Olímpico de Portugal (COP) e pela Fundação Millennium BCP, com a parceria da revista VISÃO. Este ano as categorias premiadas são “Treino Desportivo”, “Psicologia e Pedagogia do Desporto” e “Medicina do Desporto”.

[[https://visao.sapo.pt/atualidade/desporto/2020-09-21-ja-sao-conhecidos-os-vencedores-dos-premios-ciencias-do-desporto-de-2020/]]

https://sites.google.com/laseeb.org/pdsb-2020

Neurofeedback training of athletes is now the new agenda.

Manually scheduled Exam timetabling will be a task of the past, namely at ISEL-IPL and hopefully for most higher education institutions.

Musician is probably one interesting and close analogy model of scientific researcher a real bridge between art and science.
A musician needs inspiration to compose some new or innovative melody or music that pleases a world community (not only a specific part of it), have to present a finished result/product, likely a plethora of songs to fill a CD-Rom (around minimum 30 minutes of music, sometimes even a complete symphony or opera). All the instruments used chords and rhythm have to be meticulously organized in perfect timing and harmony etc etc
During concerts, one might have to adapt in real-time to the public reaction and probably even some improvisation needed to come out of trouble.
Actually it is harder than scientific research in requirements of personal aptitudes.
Lets welcome musicians to science field, SO ALL THIS IS TO INVITE YOU TO WATCH

https://www.rtp.pt/noticias/pais/outras-historias-frequencia-m-pex_v1134339

A encrypted file written in Portuguese "secret.txt" can be found in www.VitalLibery.pt
Anyone want to try?

PSilva

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