Problem-based learning (PBL) is a teaching method that appeared in the early 1960s and is widely applied in distinct areas nowadays. In the presented manuscript, we describe a PBL methodology use restricted to applied mathematics for problem solving among a group of engineering students in a Portuguese university. In the pandemic context, it was a huge challenge both for the students and for the teacher. Supported by the available literature, the experiment was defined. As it is well known, teachers are not only knowledge transmitters but also designers of teaching initiatives. Thus, teachers and students both have a large role in PBL methodologies, where collaboration, reflection and concepts discussion are essential. In the presented pedagogical challenge, students were devoted to integrating the previous knowledge acquired and the one acquired during the project. This process improved their new competences—both personal and team work. Despite being a recent pedagogical method, PBL is revealed to be an important teaching tool.

Drug discovery now faces a new challenge, where the availability of experimental data is no longer the limiting step, and instead, making sense of the data has gained a new level of importance, propelled by the extensive incorporation of cheminformatics and bioinformatics methodologies into the drug discovery and development pipeline. These enable, for example, the inference of structure-activity relationships that can be useful in the discovery of new drug candidates. One of the therapeutic applications that could benefit from this type of data mining is proteasome inhibition, given that multiple compounds have been designed and tested for the last 20 years, and this collection of data is yet to be subjected to such type of assessment. This study presents a retrospective overview of two decades of proteasome inhibitors development (680 compounds), in order to gather what could be learned from them and apply this knowledge to any future drug discovery on this subject. Our analysis focused on how different chemical descriptors coupled with statistical tools can be used to extract interesting patterns of activity. Multiple instances of the structure-activity relationship were observed in this dataset, either for isolated molecular descriptors (e.g., molecular refractivity and topological polar surface area) as well as scaffold similarity or chemical space overlap. Building a decision tree allowed the identification of two meaningful decision rules that describe the chemical parameters associated with high activity. Additionally, a characterization of the prevalence of key functional groups gives insight into global patterns followed in drug discovery projects, and highlights some systematically underexplored parts of the chemical space. The various chemical patterns identified provided useful insight that can be applied in future drug discovery projects, and give an overview of what has been done so far.

The BoneMedLeg research project was developed to address current research concerns related to the use of skeletal reference collections for forensic purposes. These concerns were partly addressed by amassing a new reference collection which incorporates unclaimed human remains sourced from two municipal cemeteries in the city of Porto, Portugal. Amassed between 2012 and 2014 the collection was developed with permission from and in partnership with the Municipality of Porto, in a manner that is similar to that of other skeletal reference collections in Portugal. Traditionally, municipalities have bequeathed human remains that are cleared from temporary primary and secondary burial plots at local cemeteries and deemed unclaimed, to museums and universities for research purposes. The BoneMedLeg collection currently includes a total of 95 individuals, of which only 81 are fully identified (38 males and 43 females), with ages ranging from 21 days to 94 years, and a mean age of about 62 years. Years of death range from 1969 to 2003, and years of birth from 1891 to 1969. Only about half of the individuals are documented as to cause of death, which includes a considerable diversity of etiologies, from oncological to cardiovascular system disorders, and also traumatic injuries. The collection is more representative of an unskilled working class and aged population, due to one of the main sourced cemeteries disproportionately serving more socioeconomic disadvantaged communities and reflecting the demographics of the city over the past 40 years. In addition to describing the history and curatorial process of the collection in detail, this paper also discusses its broad legal framework and potential biases in its profile and composition which can inform and help plan future research projects.

Recently, at the 119th European Study Group with Industry, the Energy Solutions Operator EDP proposed a challenge concerning electricity prices simulation, not only for risk measures purposes but also for scenario analysis in terms of pricing and strategy. The main purpose was short-term Electricity Price Forecasting (EPF). This analysis is contextualized in the study of time series behavior, in particular multivariate time series, which is considered one of the current challenges in data mining. In this work a short-term EPF analysis making use of vector autoregressive models (VAR) with exogenous variables is proposed. The results show that the multivariate approach using VAR, with the season of the year and the type of day as exogenous variables, yield a model that explains the intra-day and intra-hour dynamics of the hourly prices.

The goal of this paper is to explore whether combining dental and skeletal maturation data increases the reliability of determining whether an individual is 16 years old or older. This is tentatively done by building probabilistic models for age estimation based on dental and skeletal maturation using a longitudinal sample of eight males, with annual assessments between the ages of 13 and 19, totalling 56 observations. Skeletal maturity was assessed for the radius and ulna using the TW2 method, and dental maturity was assessed for the second and third molars using Demirjian’s scheme. Logistic regression was selected to determine the probability of an individual being 16 years of age and older, by combining dental and skeletal maturity scores and using them separately. The age estimation models combining dental and skeletal maturity scores seem to perform better than either dental or skeletal maturity in isolation. In addition, when in isolation or combination, models based on skeletal maturity scores seem to outperform models based on dental maturity scores. The findings seem to support the notion that dental development is less reliable than skeletal maturity for age estimation in adolescents, but these results have to be confirmed by further studies.