Syllabus 2021_22.xlsx

	A	B	C
1	Course code	Course	Teacher(s)
2
3	In-depth disciplinary, thematic or curricular courses
4		Theoretical and Practical Bioinformatics Approaches in Animal and Food Science	P. Stevanato & S. Ravi
5		Prerequisites:	Basic knowledge on molecular biology
6		Target skills and knowledge:	This class is to enable students with an understanding of concepts in the theory and practical of next generation sequencing and analysis of bioinformatics data. At the end of the course, the student is expected to have a good knowledge of various genome sequencing technologies and platforms and related data analysis of the genome, transcriptome, and microbiome.
7		Course contents:	1. Sequence analysis - Understanding a DNA sequence, basic concepts of sequence similarity, identity and homology, database searching: BLAST, FASTA and other sequence analysis tools to assign homology. Primer designing, PCR and Sanger sequence analysis. 2. Transcriptome analysis - Concepts in RNA-seq data analysis: data pre-processing and data-processing steps: mapping algorithms such as BWA and Bowtie2; differential gene expression analysis using RNA-seq data, statistical methods, relative merits of various platforms. Primer design for downstream validation. Measuring gene, lncRNA, siRNA from RNA-seq data. 3. Microbiome Analysis - 16s rRNA data analysis, clustering/phylogenetic tree based of alignment, clustering based on composition. Annotation based on databases, principal component analysis and other clustering tools. 4. SNP analysis - Resequencing of target genes or whole genomes, Gene prediction algorithms, identification of variants – SNPs/SNVs. Concepts behind genome wide association studies. Introduction to various applications.
8		Assessment plan:	Surveys and online written tests
9		Planned learning activities and teaching methods:	Lectures and videos explaining the theoretical concepts and practical online exercises on data analysis
10		Additional notes about suggested reading:	Teaching material in the form of videos will be shared with students
11		Textbooks (and optional supplementary readings):	Referenced scientific papers
12		International short course on Animal and Food Science	?
13		Prerequisites:
14		Target skills and knowledge:
15		Course contents:
16		Assessment plan:
17		Planned learning activities and teaching methods:
18		Additional notes about suggested reading:
19		Textbooks (and optional supplementary readings):
20		Food Microbiota & Human microbiome	A. Giacomini, V. Corich
21		Prerequisites:	Basic knowledge of microbiology
22		Target skills and knowledge:	The course is aimed at familiarizing students with the composition of microbial populations living in foods and their main positive or negative activities. The concept of human microbiome will be also discussed, along with the description of the principal microbial categories and their main effects on human metabolism
23		Course contents:	Composition of microbial populations of main fermented foods Composition of the human microbiome Main functions of the human microbiome.
24		Assessment plan:	The final exam will consist in a written text
25		Planned learning activities and teaching methods:	The course will consist of theoretical classes and a practical session devoted to the knowledge of main bioinformatic tools for the study of microbiomes
26		Additional notes about suggested reading:	All the material of the course will be provided online in the Moodle platform
27		Textbooks (and optional supplementary readings):	Selected scientific papers
28		Food technology	A. Lante
29		Prerequisites:	Basic knowledge of biochemistry
30		Target skills and knowledge:	The course is aimed at familiarizing students with new trends in food science and technology, highlighting how the basic concept of quality has been changed by the strides taken by science and technology
31		Course contents:	Food quality attributes. Emerging technologies in food processing. Focus on food enzymology. Enzyme and cell immobilization. How to determine the environmental impact of food production and dietary choices. The concept of a safe and healthy diet.
32		Assessment plan:	The final exam will consist in a written text
33		Planned learning activities and teaching methods:	The course will consist of theoretical classes and a practical session
34		Additional notes about suggested reading:	All the material of the course will be provided online in the Moodle platform
35		Textbooks (and optional supplementary readings):	Selected scientific papers
36		Investment decisions under uncertainty	C. D'Alpaos & M. Moretto
37		Prerequisites:
38		Target skills and knowledge:	Upon completion of the course students will have acquired a critical understanding of basics principles of capital budgeting and investment decisions under uncertainty and will be able to determine the real-options value for simple capital investment projects.
39		Course contents:	The course reviews current techniques of capital budgeting and details an approach based on the pricing of Real Options which provides a means of quantifying the elements of managerial flexibility in the face of unexpected changes in markets. More specifically: Investment Decisions and Capital Budgeting; Risk, Return and the Opportunity Cost of Capital; Risk and Capital Budgeting: the Capital Asset Pricing Model; Uncertainty and Consumer/Investor Behavior; The Contingent Claim Analysis (CCA); Real Options Theory; Examples (e.g., Managerial Flexibility, Weather Derivatives, Scale Production, Land Use).
40		Assessment plan:	Paper discussion, Final test at the end of the course. Grading is based on class participation and interaction (20%), group work (40%) final test (40%).
41		Planned learning activities and teaching methods:
42		Additional notes about suggested reading:
43		Textbooks (and optional supplementary readings):	a) Brealey, Myers, Allen (2019): Principles of Corporate Finance, McGraw-Hill, International Edition b) Ross, Jordan (2018): Fundamentals of Corporate Finance, McGraw-Hill, International Edition c) Lambrecht (2017): Real Options in Finance, Journal of Banking and Finance, 81, pp. 166-171 d) Schneider, Imai: Valuing Investments in Digital Business Transformation: A Real Options Approach e) Dixit, Pindyck (1994), Investment under Uncertainty, Princeton University Press f) Trigeorgis L. (1996): Real Options: Managerial Flexibility and Strategy in Resource Allocation, MIT Press
44		Econometric models to inform environmental resources management and food policies	M. Thiene, C. Franceschinis
45		Prerequisites:	The course does not require any prerequisite. However, knowledge of descriptive statistics and basic knowledge of the R software (data import and basic syntax) could be helpful. Installation of R is required before the course (detailed installation instructions will be sent).
46		Target skills and knowledge:	The course allows participants to: i) understand how to explore and identify factors that influence environmental resources/services management and demand for food; ii) acquire skills on data collection and data analysis concerning environmental services demand and food choices; iii) acquire knowledge on discrete choice models; iv) learn how to estimate discrete choice models in R; v) apply discrete choice models for the analysis of food and environmental services demand; vi) understand how to use results from econometric analysis to inform environmental resources management and food policies.
47		Course contents:	1. What methods can be used to provide policy makers with relevant information regarding management of natural resources, determinants of environmental services selection and food choice? 2. Basic theoretical background on discrete choice models 3. Discrete choice models estimation in R 4. Tutorial session on valuation and management of environmental resources and drivers of food demand
48		Assessment plan:
49		Planned learning activities and teaching methods:	The course will be held in Zoom. Participants will be explained how to collect and analyze data to investigate the factors that drive demand for food and environmental resources. Then, participants will be introduced to discrete choice models and they will estimate a variety of model specifications in R within a tutorial session focused on valuation and management of environmental resources and drivers of food demand.
50		Additional notes about suggested reading:	The course material will be made available in the Moodle platform.
51		Textbooks (and optional supplementary readings):	None
52		Biostatistics and Clinical epidemiology	L. Desquilbet
53		Prerequisites:
54		Target skills and knowledge:
55		Course contents:	The course is dedicated to basics in biostatistics and study design for causal inference. In detail the course is focused on train student to understand the results obtained from usual statistical tests, to critically read statistics presented in tables, to avoid miss-interpretation of the p-values, to appreciate the a priori statistical power of the study, to interpret the results from univariate survival analyses (Kaplan-Meier curves), to interpret the results from univariate and multivariate regression models and to identify the presence of confounding bias and to discuss the impact of such bias.
56		Assessment plan:
57		Planned learning activities and teaching methods:	This MasterClass is organized in 3 days, dedicated to basics in biostatistics and study design for causal inference. Lectures are during the mornings and practicals during the afternoons. Practicals will be based on results presented in published papers. The examples will be drawn from veterinary medicine, but can easily be extended to clinical research in human medicine.
58		Additional notes about suggested reading:
59		Textbooks (and optional supplementary readings):
60		Experimental design in Crop Science: principles	L. Marini
61		Prerequisites:
62		Target skills and knowledge:
63		Course contents:
64		Assessment plan:
65		Planned learning activities and teaching methods:
66		Additional notes about suggested reading:
67		Textbooks (and optional supplementary readings):
68		Experimental design in Crop Science: from lab to field	L. Marini
69		Prerequisites:
70		Target skills and knowledge:
71		Course contents:
72		Assessment plan:
73		Planned learning activities and teaching methods:
74		Additional notes about suggested reading:
75		Textbooks (and optional supplementary readings):
76		Scientific communication: CRS course	M. F. Mangano
77		Prerequisites:	The course does not require any prerequisite. It addressed to the PhD students at the II year.
78		Target skills and knowledge:	The CSR education provides theoretical and practical instructions on why and for whom we communicate our research within the international academic community, in addition to how. This theoretical and practical approach allows the participants to understand the content while they apply them.
79		Course contents:	The CSR course will consist in 18 hours in 3 days, with lessons of 6 hours each day. Theoretical lectures present basic notions on the key terms of communication of scientific research (first day), necessary to introduce the participants to the construction of written (second day) and oral (third day) text. Theory and practice go together in CSR training. We will apply the theoretical notes in a collective analysis in class of participants’ written and oral texts. We will analyse a few posters on the second day, while we will dedicate a 4 hours-lecture to the analysis of the participants’ oral presentations on June 2022, within their final year reports.
80		Assessment plan:
81		Planned learning activities and teaching methods:	April 5-7, 2022. The lectures will be scheduled from 9.30 to 1 pm and from 2.30 to 5.00 pm.
82		Additional notes about suggested reading:
83		Textbooks (and optional supplementary readings):	none
84		Strategies and tools to reduce environmental impacts: the case of LCA methodology	M. Masiero & M. Zanetti
85		Prerequisites:	none
86		Target skills and knowledge:	At the end of the course, students will have acquired knowledge on the tools used to assess environmental impacts of a product (or service) and the strategies for reducing them.
87		Course contents:	The course consists of a theoretical part in which the LCA (Life Cycle Assessment) methodology will be presented and a practical part which consists of the use of software dedicated to LCA models building. Within the introduction part an overview of recente developments and current trends in tools to incorporate/deal with environmental impacts associated with production will be delivered, with an emphasis on policy and marketing tools and in particular standards and certification ones. Among them, LCA will be addressed in depth. LCA is a transversal technique applicable to all sectors of activity and research. The use of software allows to model the carbon and water footprint, energy, waste production, use of materials and natural resources, as well as costs along the entire supply chain and the entire cycle of life of the studied product or service. The LCA model construction is facilitated by the use of databases, as Ecoinvent, often integrated with the software. In the practical part, doctoral students will be able to use the software to assess the environmental impact of the product (or service) object of their project. The built model allows to identify the most impactful processes and propose reduction strategies.
88		Assessment plan:	Class exercises assigned and supervised by the teachers.
89		Planned learning activities and teaching methods:	After an introduction on climate change and strategies and policies to reduce it, the students will learn how to use a LCA dedicated software. If she/he will, the student could apply LCA to its PhD research subject.
90		Additional notes about suggested reading:	Teaching materials and readings provided during the course
91		Textbooks (and optional supplementary readings):	The teacher will provide the course slides.
92	Linguistics
93		Scientific Writing in English	M.E. Olson
94		Prerequisites:	No prior knowledge is required
95		Target skills and knowledge:
96		Course contents:	-How and why to publish in high impact factor journals -Scientific writing is a group process -Scientific writing follows a simple formula -The "Winning Formula" to structure your manuscript -The importance of the "gap" -Paragraph structure -Connecting paragraphs and sentences -Traps that English sets for non-native speakers -Titles, abstracts, cover letters, and replying to reviewers
97		Assessment plan:
98		Planned learning activities and teaching methods:
99		Additional notes about suggested reading:	All the material of the course will be provided online in the Moodle platform some days before the beginning of the lessons
100		Textbooks (and optional supplementary readings):