@article{nokey,

title = {Current Trends in Software Engineering Bachelor Theses},

author = {Jacek Dajda and Micha\l Idzik and Jakub Sroka and Miko\laj Sikora Wiktor Paw\lowski and Maciej Smo lka and Przemys\law Jab\lecki and Filip \'{S}lazyk and Maciej Malawski and Emilia Majerz and Aleksandra Pasternak and Witold Dzwinel

},

url = {https://doi.org/10.31577/cai_2021_4_930

},

doi = {https://doi.org/10.31577/cai_2021_4_930},

year  = {2021},

date = {2021-12-14},

journal = {Computing and Informatics},

abstract = {This article presents short analysis and observations on current trends and directions in conducting engineering theses in the field of computer science. This report is based on collected bachelor theses in AGH Computer Science Department for academic year 2020/2021 as well as the conducted competition for the best engineering theses held during XXII KKIO 2021 Software Engineering Conference. The awarded works are briefly presented as an illustration to the drawn conclusions.



},

keywords = {Extreme-scale Data and Computing},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Preface to the Special Issue on Software Engineering},

author = {Bubak, M. and Malawski, M. and S\lota, R. G. },

url = {https://www.cai.sk/ojs/index.php/cai/article/view/2021_4_729/1101},

doi = {https://doi.org/10.31577/cai_2021_4_729},

year  = {2021},

date = {2021-12-14},

journal = {Vol. 40 No. 4 (2021): Computing and Informatics},

keywords = {Extreme-scale Data and Computing},

pubstate = {published},

tppubtype = {article}

}

@article{Sousa2021,

title = {COVID-19 Symptoms App Analysis to Foresee Healthcare Impacts: Evidence from Northern Ireland},

author = {Jos\'{e} Sousa and Jo\~{a}oBarata and Hugo C van Woerdend and Frank Kee},

url = {https://doi.org/10.1016/j.asoc.2021.108324

},

doi = {https://doi.org/10.1016/j.asoc.2021.108324},

year  = {2021},

date = {2021-12-20},

journal = {Applied Soft Computing},

abstract = {Mobile health (mHealth) technologies, such as symptom tracking apps, are crucial for coping with the global pandemic crisis by providing near real-time, in situ information for the medical and governmental response. However, in such a dynamic and diverse environment, methods are still needed to support public health decision-making. This paper uses the lens of strong structuration theory to investigate networks of COVID-19 symptoms in the Belfast metropolitan area. A self-supervised machine learning method measuring information entropy was applied to the Northern Ireland COVIDCare app. The findings reveal: (1) relevant stratifications of disease symptoms, (2) particularities in health-wealth networks, and (3) the predictive potential of artificial intelligence to extract entangled knowledge from data in COVID-related apps. The proposed method proved to be effective for near real-time in-situ analysis of COVID-19 progression and to focus and complement public health decisions. Our contribution is relevant to an understanding of SARS-COV-2 symptom entanglements in localised environments. It can assist decision-makers in designing both reactive and proactive health measures that should be personalised to the heterogeneous needs of different populations. Moreover, near real-time assessment of pandemic symptoms using digital technologies will be critical to create early warning systems of emerging SARS-CoV-2 strains and predict the need for healthcare resources.},

keywords = {Personal Health Data Science},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {AI \& COVID-19},

author = {Davide Bacciu and Emanuela Girardi and Marco Maratea and Jose Sousa

},

url = {https://content.iospress.com/download/intelligenza-artificiale/ia210121?id=intelligenza-artificiale%2Fia210121},

doi = {DOI 10.3233/IA-210121},

year  = {2021},

date = {2021-01-21},

journal = {Intelligenza Artificiale},

abstract = {The COVID-19 pandemic has influenced our lives significantly since March 2020, and a number of initiatives have

been put forward in order to tackle its effects, including those focused on technological solutions. In this paper, we present

one of such initiatives, i.e. the CLAIRE’s taskforce on AI and COVID-19, in which Artificial Intelligence methodologies and

tools are being developed to help the society contrasting the pandemic. We present the different lines of development within

the taskforce, some fields in which they are used, and draw few recommendations},

keywords = {Personal Health Data Science},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer's Disease},

author = {Samar S. M. Elsheikh and Emile R. Chimusa and Alzheimer's Disease Neuroimaging Initiative and Nicola J. Mulder and Alessandro Crimi

},

url = {https://www.frontiersin.org/articles/10.3389/fnhum.2021.761424/pdf},

doi = {https://doi.org/10.3389/fnhum.2021.761424},

year  = {2021},

date = {2021-12-17},

journal = {Front Hum Neurosci},

abstract = {Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging characteristics and genetics data allows a better understanding of the effects of the gene expression on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimer's disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. For instance, here we show that the expression of PLAU gene increases the change over time in betweenness centrality related to the fusiform gyrus. We also show that the betweenness centrality metric impact dementia-related changes in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimer's genetic risk factors in the estimation of regional brain connectivity alterations.



},

keywords = {Computer Vision Data Science},

pubstate = {published},

tppubtype = {article}

}

@article{Kara2021,

title = {An integrated genomic approach to dissect the genetic landscape regulating the cell-to-cell transfer of α-synuclein},

author = {Eleanna Kara and Alessandro Crimi and Anne Wiedmer and John Hardy and Bradley T. Hyman and Adriano Aguzzi},

doi = {https://doi.org/10.1016/j.celrep.2021.109189},

year  = {2021},

date = {2021-06-08},

urldate = {2021-06-08},

abstract = {Neuropathological and experimental evidence suggests that the cell-to-cell transfer of α-synuclein has an important role in the pathogenesis of Parkinson’s disease (PD). However, the mechanism underlying this phenomenon is not fully understood. We undertook a small interfering RNA (siRNA), genome-wide screen to identify genes regulating the cell-to-cell transfer of α-synuclein. A genetically encoded reporter, GFP-2A-αSynuclein-RFP, suitable for separating donor and recipient cells, was transiently transfected into HEK cells stably overexpressing α-synuclein. We find that 38 genes regulate the transfer of α-synuclein-RFP, one of which is ITGA8, a candidate gene identified through a recent PD genome-wide association study (GWAS). Weighted gene co-expression network analysis (WGCNA) and weighted protein-protein network interaction analysis (WPPNIA) show that those hits cluster in networks that include known PD genes more frequently than expected by random chance. The findings expand our understanding of the mechanism of α-synuclein spread.},

keywords = {Computer Vision Data Science},

pubstate = {published},

tppubtype = {article}

}

@article{Sitek2021,

title = {Genome-Wide Association Study of Brain Connectivity Changes for Alzheimer’s Disease},

author = {Alessandro Crimi and Nicola J. Mulder and Emile R. Chimusa and Samar S. M. Elsheikh









},

doi = {https://doi.org/10.1038/s41598-020-58291-1},

year  = {2020},

date = {2020-01-29},

urldate = {2020-01-29},

abstract = {Variations in the human genome have been found to be an essential factor that affects susceptibility to Alzheimer’s disease. Genome-wide association studies (GWAS) have identified genetic loci that significantly contribute to the risk of Alzheimers. The availability of genetic data, coupled with brain imaging technologies have opened the door for further discoveries, by using data integration methodologies and new study designs. Although methods have been proposed for integrating image characteristics and genetic information for studying Alzheimers, the measurement of disease is often taken at a single time point, therefore, not allowing the disease progression to be taken into consideration. In longitudinal settings, we analyzed neuroimaging and single nucleotide polymorphism datasets obtained from the Alzheimer’s Disease Neuroimaging Initiative for three clinical stages of the disease, including healthy control, early mild cognitive impairment and Alzheimer’s disease subjects. We conducted a GWAS regressing the absolute change of global connectivity metrics on the genetic variants, and used the GWAS summary statistics to compute the gene and pathway scores. We observed significant associations between the change in structural brain connectivity defined by tractography and genes, which have previously been reported to biologically manipulate the risk and progression of certain neurodegenerative disorders, including Alzheimer’s disease.},

keywords = {Computer Vision Data Science},

pubstate = {published},

tppubtype = {article}

}

@article{Amoah2016,

title = {Boosting antenatal care attendance and number of hospital deliveries among pregnant women in rural communities: a community initiative in Ghana based on mobile phones applications and portable ultrasound scans},

author = {Benjamin Amoah and Evelyn A. Anto and Prince K. Osei and Kojo Pieterson and Alessandro Crimi},

url = {https://rdcu.be/cxfpM},

doi = {https://doi.org/10.1186/s12884-016-0888-x},

year  = {2016},

date = {2016-06-14},

urldate = {2016-06-14},

abstract = {The World Health Organization has recommended at least four antenatal care (ANC) visits and skilled attendants at birth. Most pregnant women in rural communities in low-income countries do not achieve the minimum recommended visits and deliver without skilled attendants. With the aim of increasing number of ANC visits, reducing home deliveries, and supplementing care given by ANC clinics, a proposed system based on low-cost mobile phones and portable ultrasound scan machines was piloted.},

keywords = {Computer Vision Data Science},

pubstate = {published},

tppubtype = {article}

}

@article{Crimi2014,

title = {Predictive Value of Imaging Markers at Multiple Sclerosis Disease Onset Based on Gadolinium- and USPIO-Enhanced MRI and Machine Learning},

author = {Alessandro Crimi and Olivier Commowick and Adil Maarouf and Jean-Christophe Ferr\'{e} and Elise Bannier and Ayman Tourbah and Isabelle Berry and Jean-Philippe Ranjeva and Gilles Edan and Christian Barillot},

url = {https://doi.org/10.1371/journal.pone.0093024},

doi = {https://doi.org/10.1371/journal.pone.0093024},

year  = {2014},

date = {2014-04-01},

urldate = {2014-04-01},

abstract = {A novel characterization of Clinically Isolated Syndrome (CIS) patients according to lesion patterns is proposed. More specifically, patients are classified according to the nature of inflammatory lesions patterns. It is expected that this characterization can infer new prospective figures from the earliest imaging signs of Multiple Sclerosis (MS), since it can provide a classification of different types of lesions across patients.},

keywords = {Computer Vision Data Science},

pubstate = {published},

tppubtype = {article}

}