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Abstract Background Pathogenic heterozygous mutations in the progranulin gene (GRN) are a key cause of frontotemporal dementia (FTD), leading to significantly reduced biofluid concentrations of the progranulin protein (PGRN). This has led to a number of ongoing therapeutic trials aiming to treat this form of FTD by increasing PGRN levels in mutation carriers. However, we currently lack a complete understanding of factors that affect PGRN levels and potential variation in measurement methods. Here, we aimed to address this gap in knowledge by systematically reviewing published literature on biofluid PGRN concentrations. Methods Published data including biofluid PGRN concentration, age, sex, diagnosis and GRN mutation were collected for 7071 individuals from 75 publications. The majority of analyses (72%) had focused on plasma PGRN concentrations, with many of these (56%) measured with a single assay type (Adipogen) and so the influence of mutation type, age at onset, sex, and diagnosis were investigated in this subset of the data. Results We established a plasma PGRN concentration cut-off between pathogenic mutation carriers and non-carriers of 74.8 ng/mL using the Adipogen assay based on 3301 individuals, with a CSF concentration cut-off of 3.43 ng/mL. Plasma PGRN concentration varied by GRN mutation type as well as by clinical diagnosis in those without a GRN mutation. Plasma PGRN concentration was significantly higher in women than men in GRN mutation carriers (p = 0.007) with a trend in non-carriers (p = 0.062), and there was a significant but weak positive correlation with age in both GRN mutation carriers and non-carriers. No significant association was seen with weight or with TMEM106B rs1990622 genotype. However, higher plasma PGRN levels were seen in those with the GRN rs5848 CC genotype in both GRN mutation carriers and non-carriers. Conclusions These results further support the usefulness of PGRN concentration for the identification of the large majority of pathogenic mutations in the GRN gene. Furthermore, these results highlight the importance of considering additional factors, such as mutation type, sex and age when interpreting PGRN concentrations. This will be particularly important as we enter the era of trials for progranulin-associated FTD.

Autonomous vehicles (AVs) rely heavily on sensors to perceive their surrounding environment and then make decisions and act on them. However, these sensors have weaknesses, and are prone to failure, resulting in decision errors by vehicle controllers that pose significant challenges to their safe operation. To mitigate sensor failures, it is necessary to understand how they occur and how they affect the vehicle’s behavior so that fault-tolerant and fault-masking strategies can be applied. This survey covers 108 publications and presents an overview of the sensors used in AVs today, categorizes the sensor’s failures that can occur, such as radar interferences, ambiguities detection, or camera image failures, and provides an overview of mitigation strategies such as sensor fusion, redundancy, and sensor calibration. It also provides insights into research areas critical to improving safety in the autonomous vehicle industry, so that new or more in-depth research may emerge.

Chagas disease (CD) is a vector-borne Neglected Zoonotic Disease (NZD) caused by a flagellate protozoan, Trypanosoma cruzi, that affects various mammalian species across America, including humans and domestic animals. However, due to an increase in population movements and new routes of transmission, T. cruzi infection is presently considered a worldwide health concern, no longer restricted to endemic countries. Dogs play a major role in the domestic cycle by acting very efficiently as reservoirs and allowing the perpetuation of parasite transmission in endemic areas. Despite the significant progress made in recent years, still there is no vaccine against human and animal disease, there are few drugs available for the treatment of human CD, and there is no standard protocol for the treatment of canine CD. In this review, we highlight human and canine Chagas Disease in its different dimensions and interconnections. Dogs, which are considered to be the most important peridomestic reservoir and sentinel for the transmission of T. cruzi infection in a community, develop CD that is clinically similar to human CD. Therefore, an integrative approach, based on the One Health concept, bringing together the advances in genomics, immunology, and epidemiology can lead to the effective development of vaccines, new treatments, and innovative control strategies to tackle CD.

In Portugal, heterozygous loss-of-function mutations in the progranulin (GRN) gene account for approximately half of the genetic mediated forms of frontotemporal dementia (FTD). GRN mutations reported thus far cause FTD through a haploinsufficiency disease mechanism. Herein, we aim to unveil the GRN mutation spectrum, investigated in 257 FTD patients and 19 family members from the central/north region of Portugal using sequencing methods. Seven different pathogenic variants were identified in 46 subjects including 40 patients (16%) and 6 relatives (32%). bvFTD was the most common clinical presentation among the GRN mutation patients, who showed a global pattern of moderate-to-severe frontotemporoparietal deficits in the neuropsychological evaluation. Interestingly, two mutations were novel (p.Thr238Profs*18, p.Leu354Profs*16), and five were previously described, although three of them only in the Portuguese population, suggesting a population-specific GRN mutational spectrum. The subjects harboring a GRN mutation showed a significant reduction in serum PGRN levels, supporting the pathogenic nature of these variants. This work broadens the mutation spectrum of GRN and the identification of the underlying GRN mutations provided an accurate genetic counselling and allowed the enrolment of subjects with GRN mutations (both asymptomatic and symptomatic) in ongoing clinical trials, which is essential to test new drugs for the disease.

Complexity is the key element of software quality. This article investigates the problem of measuring code complexity and discusses the results of a controlled experiment to compare different views and methods to measure code complexity. Participants (27 programmers) were asked to read and (try to) understand a set of programs, while the complexity of such programs is assessed through different methods and perspectives: (a) classic code complexity metrics such as McCabe and Halstead metrics, (b) cognitive complexity metrics based on scored code constructs, (c) cognitive complexity metrics from state-of-the-art tools such as SonarQube, (d) human-centered metrics relying on the direct assessment of programmers’ behavioral features (e.g., reading time, and revisits) using eye tracking, and (e) cognitive load/mental effort assessed using electroencephalography (EEG). The human-centered perspective was complemented by the subjective evaluation of participants on the mental effort required to understand the programs using the NASA Task Load Index (TLX). Additionally, the evaluation of the code complexity is measured at both the program level and, whenever possible, at the very low level of code constructs/code regions, to identify the actual code elements and the code context that may trigger a complexity surge in the programmers’ perception of code comprehension difficulty. The programmers’ cognitive load measured using EEG was used as a reference to evaluate how the different metrics can express the (human) difficulty in comprehending the code. Extensive experimental results show that popular metrics such as V(g) and the complexity metric from SonarSource tools deviate considerably from the programmers’ perception of code complexity and often do not show the expected monotonic behavior. The article summarizes the findings in a set of guidelines to improve existing code complexity metrics, particularly state-of-the-art metrics such as cognitive complexity from SonarSource tools.

The distinction between sporadic and genetic behavioural-variant frontotemporal dementia (bvFTD) regarding some neuropsychological (NP) features remains challenging. Specifically, progranulin (GRN)-associated bvFTD frequently presents with early episodic memory impairment and some degree of parietal dysfunction which are supporters of Alzheimer’s disease (AD) diagnosis. In this context, we aimed to characterize the NP profile of GRN-bvFTD as compared to sporadic-bvFTD and AD in patients with mild dementia (Mini-Mental State Examination score ≥ 17 and Clinical Dementia Rating Scale score ≤ 1. We identified 21 patients at Centro Hospitalar e Universitário de Coimbra, Portugal with GRN mutations belonging to fifteen different families. As our focus was bvFTD variants, FTD-related aphasic forms (3 patients) were excluded. The remaining 18 GRN-bvFTD were further matched with 18 sporadic-bvFTD and 18 AD patients according to disease staging, age and education. All patients completed the Mini-Mental State Examination, Montreal Cognitive Assessment (MoCA) and a comprehensive NP assessment battery. Results were converted into z-scores. Differences between groups in individual NP measures and NP domains were assessed through non-parametric tests (Kruskal-Wallis test analysis) and eta squared (ŋ2) was calculated as a measure of effect size. Group comparisons show that GRN patients have worse performances on verbal retrieval processes (P = 0.039, ŋ2 = 0.110) and visuoconstructive abilities (P = 0.039, ŋ2 = 0.190) than sporadic bvFTD forms. When compared to AD, GRN patients present a higher impairment in frontal (P = 0.001, ŋ2 = 0.211) and parietal (P = 0.041, ŋ2 = 0.129) measures and a better performance in memory tasks (P = 0.020, ŋ2 = 0.120). Sporadic-bvFTD forms are worse than AD in frontal measures (P = 0.032, ŋ2 = 0.200), being better in both memory (P = 0.010, ŋ2 = 0.131) and visuospatial skills (P = 0.023, ŋ2 = 0.231). Considering these results, we conclude that GRN-bvFTD patients present a NP profile that associates the typical patterns of FTD and AD deficits. This is particularly expressive in visuoconstructive abilities, which was the more discriminative feature between groups, followed by episodic verbal memory. This study was approved by the Institutional Ethics Committee of Centro Hospitalar e Universitário de Coimbra, Portugal (CE-029/2019) on June 24, 2019.

Ultra-short-term HRV features assess minor autonomous nervous system variations such as variations resulting from cognitive stress peaks during demanding tasks. Several studies compare ultra-short-term and short-term HRV measurements to investigate their reliability. However, existing experiments are conducted in low cognitively demanding environments. In this paper, we propose to evaluate these measurements’ reliability under cognitively demanding tasks using a near real-life setting. For this purpose, we selected 31 HRV features, extracted from data collected from 21 programmers performing code comprehension, and compared them across 18 different time frames, ranging from 3 min to 10 s. Statistical significance and correlation tests were performed between the features extracted using the larger window (3 min) and the same features extracted with the other 17 time frames. We paired these analyses with Bland–Altman plots to inspect how the extraction window size affects the HRV features. The main results show 13 features that presented at least 50% correlation when using 60-second windows. The HF and mNN features achieved around 50% correlation using a 30-second window. The 30-second window was the smallest time frame considered to have reliable measurements. Furthermore, the mNN feature proved to be quite robust to the shortening of the time resolution.

Digital transformation has increased the demand for skilled Information Technology (IT) professionals, to an extent that universities cannot satisfy it with newly graduated students. Furthermore, the economical downturn has created difficulties and scarcity of opportunities in other areas of activity. This combination of factors led to the need to consider requalification programmes that enable individuals with diverse specialisations and backgrounds to realign their careers to the IT area. This has led to the creation of many coding bootcamps, providing intensive full-time courses focused on unemployed people or unhappy with their jobs, and individuals seeking a career change. A multidisciplinary group of higher education teachers, in collaboration with several industry stakeholders, have designed and promoted an embedded systems programming course, using an intensive project-based learning approach comprising 6 months of daylong classes and a 9 months internship. Having finished two editions of the programme, a questionnaire was presented to the students that finished successfully, in order to evaluate the long-term benefits to graduates and companies. This paper presents a brief discussion of the programme organisation and pedagogical methodologies, as well as the results of the questionnaire, conducted following a Goal–Question–Metric (GQM) approach. The results demonstrate very positive outcomes, both for graduates and companies.

An emergent research area in software engineering and software reliability is the use of wearable biosensors to monitor the cognitive state of software developers during software development tasks. The goal is to gather physiologic manifestations that can be linked to error-prone scenarios related to programmers’ cognitive states. In this paper we investigate whether electroencephalography (EEG) can be applied to accurately identify programmers’ cognitive load associated with the comprehension of code with different complexity levels. Therefore, a controlled experiment involving 26 programmers was carried. We found that features related to Theta, Alpha, and Beta brain waves have the highest discriminative power, allowing the identification of code lines and demanding higher mental effort. The EEG results reveal evidence of mental effort saturation as code complexity increases. Conversely, the classic software complexity metrics do not accurately represent the mental effort involved in code comprehension. Finally, EEG is proposed as a reference, in particular, the combination of EEG with eye tracking information allows for an accurate identification of code lines that correspond to peaks of cognitive load, providing a reference to help in the future evaluation of the space and time accuracy of programmers’ cognitive state monitored using wearable devices compatible with software development activities.

Frontotemporal lobar degeneration, the neuropathological substrate of frontotemporal dementia (FTD), is characterized by the deposition of protein aggregates, including tau. Evidence has shown concomitant amyloid pathology in some of these patients, which seems to contribute to a more aggressive disease. Our aim was to evaluate cerebrospinal fluid (CSF) amyloid-beta as a predictor of the mortality of FTD patients. We included 99 patients diagnosed with FTD—both behavioral and language variants—with no associated motor neuron disease, from whom a CSF sample was collected. These patients were followed prospectively in our center, and demographic and clinical data were obtained. The survival analysis was carried through a Cox regression model. Patients who died during follow up had a significantly lower CSF amyloid-beta1−42 than those who did not. The survival analysis demonstrated that an increased death rate was associated with a lower CSF amyloid-beta1−42 (HR = 0.999, 95% CI = [0.997, 1.000], p = 0.049). Neither demographic nor clinical variables, nor CSF total tau or p-tau were significantly associated with this endpoint. These results suggest that amyloid deposition in FTD patients may be associated with a higher mortality.