Institute of Science and Environment
Specialisation in Science
DOCSCI-2021-01: BLUE BIOTECH: BIOTECHNOLOGICAL APPLICATIONS OF FISH EXTRACTS
Natural products have always been of unquestionable importance for the identification of molecules useful for humans. As the most diverse vertebrate group, occurring in virtually all aquatic habitats, including in heavily polluted and pathogen-rich ones, fish hold a great potential for the identification of possible molecules of interest for the pharmaceutical, food or aquaculture industries. This project aims to explore the bioactive properties of molecules isolated from extracts (e.g. epidermal mucus) of coastal fish occurring in Macao and in Portugal. The thesis is framed within a broader project (http://ise.usj.edu.mo/research/projects/fishmuc/), developed in cooperation with the Faculty of Biotechnology of Universidade Católica Portuguesa (Portugal). A background in biology, biochemistry or related areas would be valued.
Supervisor: David Gonçalves (david.goncalves@usj.edu.mo)
Keywords: Bioprospection; Mucus; Antibacterial; Marine Biotechnology
DOCSCI-2021-02: IMPACT OF OCEAN ACIDIFICATION IN CHEMICAL COMMUNICATION IN MARINE ANIMALS
Oceans are getting more acidic as a consequence of carbon dioxide accumulation in the atmosphere. The changes in pH are likely to impact different physiological aspects of marine animals, including sensorial systems. Chemical communication, whereby chemical molecules are used to convey information, is the most ancient form of communication and it is likely to be impacted by changes in pH. The project will focus on how chemical communication may be affected by the changing pH conditions of the ocean using marine fish as study models. The thesis is framed within a broader project (http://ise.usj.edu.mo/research/projects/seachem/), developed in cooperation with the Institute of Oceanology, Chinese Academy of Sciences (Qingdao). A background in biology, biochemistry or related areas would be valued.
Supervisor: David Gonçalves (david.goncalves@usj.edu.mo)
Keywords: Ocean Acidification; Chemical Communication; Global Change; Pheromones
DOCSCI-2021-03: ARTIFICIAL INTELIGENCE APPLIED TO BEHAVIOURAL STUDIES
Artificial intelligence (AI) is revolutionizing many fields of knowledge. In particular, deep-learning associated with computer vision is bringing unprecedented advances to the area of object recognition and automatic detection of behavioral patterns. The project aims to apply AI to the recognition of complex animal behavior patterns, using fish as models of study. The project will take advantage of preliminary tools developed in-house. Experience in programming in Python and in electronics would be valued. The thesis is framed within a broader project (http://ise.usj.edu.mo/research/projects/fishstress/), developed in cooperation with Instituto Gulbenkian de Ciência (Portugal).
Supervisor: David Gonçalves (david.goncalves@usj.edu.mo)
Keywords: Artificial Intelligence; Animal Behavior; Machine Learning; Computer Vision
DOCSCI-2021-04: NEUROBIOLOGY OF THE STRESS RESPONSE IN FISH
Stress research in fish is a relatively recent topic but it has been gaining attention, in particular because of the increasing use of fish in animal farming and the need to keep production performance while improving animal welfare. However, stress is difficult to define and to measure objectively because it is intrinsically dependent on the relative appraisal of stimuli. One solution is to try to establish objective end-points of the stress response (hormone levels, activation of brain areas, behavioural assays). The project aims to study the neurobiological mechanisms of the stress response in fish, including the development of stress assays, the impact of early-life stress in adult behaviour and the possible epigenetic transmission of impacts to the next generation, and stress genetic mechanisms in the context of adaptation to captivity. The thesis is framed within a broader project (http://ise.usj.edu.mo/research/projects/fishstress/), developed in cooperation with Instituto Gulbenkian de Ciência (Portugal).
Supervisor: David Gonçalves (david.goncalves@usj.edu.mo)
Keywords: Stress; Animal Welfare; Transgenerational; Neurobiology
DOCSCI-2021-05: LIGHT POLLUTION IN MACAO AND THE IMPACT ON LOCAL ECOSYSTEMS
Light pollution is a form of environmental degradation caused by increased anthropogenic activity and poor energy management. This form of pollution disrupts physiological cycles and behavior of many species, including humans. Previous research has shown that Hong Kong represents the most light-polluted city on Earth. Macao is located near Hong Kong within Southern China, being characterized by higher population density and intense long-lasting outdoor lightening from casinos and hotel industry. This study will represent a pioneer attempt to assess the effects of light pollution on ecosystems of Macao and the potential impact on local biodiversity. The thesis will be integrated as part of an ongoing collaboration between the ISE (USJ), the University of Hong Kong and the Global Night Sky Brightness Monitoring Network.
Supervisor: Raquel Vasconcelos (raquel.vasconcelos@usj.edu.mo)
Keywords: Night Sky Brightness; Physiological Stress; Behavioral Ecology; Environmental Health
DOCSCI-2021-06: NOISE POLLUTION IN MACAO: CHARACTERIZATION AND PUBLIC AWARENESS
The increasing levels of noise pollution are creating a serious hazard to the auditory system and the overall physiology and health condition of animals including humans. Exposure to elevated background noise levels is known to affect hearing function leading to noise-induced hearing loss (NIHL), with a worldwide estimation of more than one billion people at risk of NIHL due to recreational and occupational activities. This proposal aims to evaluate the geographical and temporal variation of the noise levels in Macao territory and identify potential impacts on the local community and biodiversity. The researcher will use data from DSPA to characterize changing patterns in the noise levels over the last decade based on long-term acoustic monitoring stations that exist throughout Macao territory. The work will include mapping of noise levels using GIS tools and appropriate statistical analysis to identify the main problematic areas, evaluate potential environmental impacts, and define possible mitigation strategies. The candidate will also investigate the perception of noise pollution situation by Macao residents through public surveys.
Supervisor: Raquel Vasconcelos (raquel.vasconcelos@usj.edu.mo)
Keywords: Noise; Acoustic Stress; Soundscape; Environmental Health
DOCSCI-2021-07: COMMUNICATION UNDER CHANGING SENSORY ENVIRONMENTS: DANIONELLA TRANSLUCIDA AS A MODEL SYSTEM
Understanding how changes in the environment affect organismal responses and their adaptive potential is paramount in the current scenario of global change. The effects of these changes on the sensory systems and communication, and the ultimate consequences for fitness and evolution of species, have only recently received attention. Environmental disturbance on one communication modality may trigger compensation through other senses, yet surprisingly few studies have examined how environmental pressures impact different sensory systems.
This proposal aims to investigate the development of multimodal communication under different environmental pressures (visual and acoustic) and associated environmental regulation of gene expression. The investigator will use a vocal and genetically tractable fish model, Danionella translucida, which belongs to the same family of zebrafish (Cyprinidae), and it was recently reported as a promising model organism for investigating vocal-auditory neural pathways at the cellular level. This species is highly vocal, remains transparent throughout life, and exhibits the smallest known adult vertebrate brain. The proposal relies on these advantages to establish an integrative research approach combining behavioral, electrophysiology, imaging and genetic tools.
Supervisors: Raquel Vasconcelos (raquel.vasconcelos@usj.edu.mo)
Keywords: Environmental Change; Acoustic Communication; Noise; Transcriptomics
DOCSCI-2021-08: THE EFFECTS OF NOISE POLLUTION ON MARINE VOCAL SPECIES IN THE PEARL RIVER DELTA
The Pearl River Estuary (PRE) and adjacent waters are home to two key species of marine mammals, the Indo-Pacific humpback dolphin or Chinese white dolphin (Sousa chinensis) and the finless porpoise (Neophocaena phocaenoides). The Chinese white dolphin distribution is closely linked to estuarine and tidal influences and it occurs far up the estuary, to the East and West of Macao and across to West Hong Kong. The population is believed to have declined dramatically in the last decade most likely due to anthropogenic activity and associated traffic noise. The finless porpoise is influenced more by oceanic fluctuations and resides in Hong Kong in greater numbers, although increasing mortality rates have been recorded over the last few years. As such, both species are of conservation concern and prey on soniferous fish – sciaenids. Therefore, acoustic studies have great potential to quantify species behavioral patterns and the impacts of prey distribution and anthropogenic factors such as ferryboat noise. This study aims to evaluate changes in occurrence and acoustic behavior of Chinese white dolphin, porpoise and soniferous fish, associated with the presence of anthropogenic noise. The work will include field surveys, passive acoustic monitoring and development of automated detection software tools. The proposal will also define potential noise mitigation strategies.
Supervisors: Raquel Vasconcelos (raquel.vasconcelos@usj.edu.mo)
Keywords: Chinese White Dolphin; Finless Porpoise; Sciaenid; Passive Acoustics
DOCSCI-2021-09: COMPUTATIONAL PEPTIDE DISCOVERY AND OPTIMIZATION FOR BIOPHARMACEUTICAL RESEARCH
Therapeutic peptides are molecules with high specificity that can modulate cell signaling and transport, act as antibiotics, and target protein-protein interactions. However, while computational methods for chemical drug discovery are mature and routinely used, the corresponding methods for peptide research are far from satisfactory. The main challenge here is the larger size of peptide molecules, which have much greater conformational flexibility and computational complexity compared to chemical drugs. The project aims to develop specific computational protocols for peptide discovery and optimization, from activity prediction to sequence design. The candidate will strategically review the state of the art in silico peptide discovery-and-design methods in the research community, including methods developed in-house, and improve them for screening novel peptides, classify functions, predict activities, model protein-peptide complex structures, and rational design peptide for optimal function. Peptides whose functions are of medical importance (e.g. anti-cancer peptides) will be the focus of this study. Candidates are required to have a background in Bioinformatics, Computational Chemistry, Computational Biology or Computer Science.
Supervisor: Shirley Weng In Siu (shirley.siu@usj.edu.mo)
Keywords: Therapeutic Peptide; Biologics; Data Mining and Machine Learning; Molecular Docking
DOCSCI-2021-10: IMPROVE PROTEIN-PEPTIDE DOCKING WITH MACHINE LEARNING AND MOLECULAR DYNAMICS SIMULATIONS
Determining protein-peptide interactions (PPI) is critical for understanding fundamental biological processes and for developing peptides or peptidomimetic medicines. However, the dynamic and volatile nature of PPIs makes experimental characterisation of the structures of protein-peptide complexes difficult. To date, computational prediction of binding modes between proteins and peptides with large conformational changes remains challenging. A recent comparative study of all major docking programs found that the best method could only achieve a success rate of 24% at the top-10 level and only 4% at the top-1 level, indicating that computational predictions are still unreliable. This project aims to investigate ways to improve the accuracy of protein-peptide prediction from at least three perspectives. First, combined global docking and accelerated MD techniques will be explored to identify the binding surface and detect peptide conformations. Second, machine learning and deep learning will be used to improve success rate by classifying native and non-native binding modes. Third, the AlphaFold database, which contains 100,000 high-quality predicted structures, will be explored to support the prediction process or act as a knowledge base for learning atom-atom interaction patterns. Candidates are required to have a background in Bioinformatics, Computational Chemistry, Computational Biology or Computer Science.
Supervisor: Shirley Weng In Siu (shirley.siu@usj.edu.mo)
Keywords: Protein-Peptide Docking; Protein Flexibility; Artificial Intelligence and Deep Learning; Alphafold
DOCSCI-2021-11: AMP IN ACTION: INVESTIGATE THE MODES OF ACTION OF AMPS BY MOLECULAR DYNAMICS SIMULATIONS
Antimicrobial peptides (AMP) are considered promising candidates for the next generation of antibiotics due to their broad spectrum of antimicrobial activity, low toxicity and high potency. The basic mechanism of AMPs is membrane destruction. However, it has been reported that AMPs can interact with and disrupt the functionality of various intracellular targets, nucleic acids and proteins. This complex mechanism involving both extracellular and intracellular targets makes it more difficult to develop resistance to AMPs. This project aims to improve our understanding of how AMP works using computational methods. Molecular dynamics simulation method (MD) is finding increasing application in computational drug discovery and has already been used to gain insight into the mechanisms of action of various antibiotics. In this project, the candidate will study AMPs that have been reported to target membranes and/or proteins, such as the heat shock proteins DnaK and GroEL. The study will involve constructing the complex structures, investigating the energetics and dynamics of the binding interactions, identifying important interactions for binding specificity, and designing peptide variants with improved functionality. Candidates are required to have a background in life sciences, biomedical sciences, Bioinformatics, Computational Chemistry, Computational Biology.
Supervisor: Shirley Weng In Siu (shirley.siu@usj.edu.mo)
Keywords: Antimicrobial Peptides; Antibiotics; Drug Discovery; Intracellular Targets
DOCSCI-2021-12: TARGETING ALZHEIMER’S DISEASE: A NETWORK POLYPHARMCOLOGICAL APPROACH AND COMPUTER-AIDED DRUG DESIGN
Despite two decades of discovering new drugs, there is currently no effective treatment for Alzheimer’s disease (AD). With the number of people suffering from this debilitating disease increasing at an unprecedented rate, there is an urgent need for a cure. The few drugs that are available today for AD cannot cure the disease, but only relieve the symptoms. One problem may be that these drugs are single-targeted and cannot treat the multifactorial factors involved in this complex disease, making current therapy ineffective. In this project, a network-based polypharmacological approach will be used to investigate the complex protein-protein and protein-disease network of AD. Our goal is to identify key targets from the network and then use a computational drug discovery approach to find potential leads for these targets. Existing drugs, food-derived chemicals, collection of natural and chemical compounds will be screened for their potential as AD remedies. This project involves mining of public databases for network construction, developing algorithms to extract useful information from the network, perform virtual screening and molecular study of potential lead molecules, etc. Candidates are required to have a background in Bioinformatics, Computational Chemistry, Computational Biology or Computer Science.
Supervisor: Shirley Weng In Siu (shirley.siu@usj.edu.mo)
Keywords: Alzheimer’s Disease; Neurodegenerative Disease; Drug-Target Network; Computer-Aided Drug Design
DOCSCI-2021-12: DEEP-SEA HYDROTHERMAL PROCESSES
The discovery of hydrothermal fields on the seafloor was one of the greatest scientific discoveries of the last century. However, despite the incredible progress made over the last few decades, still little is known about how mineral deposits associated with hydrothermal systems are formed on the seafloor. This project aims to contribute to the mechanistic and quantitative understanding of processes that set hydrothermal processes, focused on the fluid-rock interactions and consequent mineralization. The study will be based on geological samples (rocks, sediments, and/or deposits) collected at the Mid-Ocean Ridges. The thesis is framed within a broader project (http://ise.usj.edu.mo/research/projects/InSituMin/), developed in cooperation with partners in Australia and Europe. Requirements: Background in Earth Sciences, Geochemistry.
Supervisor: Agata Dias (agata.dias@usj.edu.mo)
Keywords: Deep-Sea Vents; Geochemistry; Metallogenesis
DOCSCI-2021-13: ISOTOPIC AND GEOCHEMICAL INVESTIGATION OF THE MESOZOIC GEOLOGY OF MACAU AND SURROUNDING AREAS OF SOUTHEAST CHINA
The territory of Macau is composed of granitic intrusions belonging to the Southeast China Magmatic Belt (SCMB), located in the southeast area of the Cathaysia block, composed of large volumes of Mesozoic magmatic rocks. Based on previous studies already performed by ISE (http://ise.usj.edu.mo/research/projects/geology-of-macau/; http://ise.usj.edu.mo/research/projects/magic/) and other research teams, Macau is known to be genetically associated with Zhuhai granitic intrusions. No significant and detailed petrological and geochemical studies on these granitic intrusions have been made and moreover there are no comparative studies on the petrogenesis and geodynamics among Macau and the remaining Cathaysian granites. This project aims to integrate the study of the petrology and geochemistry of rocks outcropping in Zhuhai areas with the ones previously characterized in Macau, with the goal of obtaining additional information about the wider geological context to which Macau magmatic intrusions belong. This approach might allow the identification of unknown magmatic episodes in the region and, therefore, add missing links to the proposed model for regional magmatism during the Mesozoic period, contributing to a systematic investigation of the paleo-Pacific subduction.
Requirements: Background in Earth Sciences, Geochemistry.
Supervisor: Agata Dias (agata.dias@usj.edu.mo)
Keywords: Macao Geology; Mesozoic Granites; Petrology; Southeast China Magmatic Belt
