Diogo Bastos

Erdafitinib is an oral pan-fibroblast growth factor receptor (FGFR) tyrosine kinase inhibitor approved to treat locally advanced/metastatic urothelial carcinoma (mUC) in patients with susceptible FGFR3/2 alterations (FGFRalt) who progressed after platinum-containing chemotherapy. FGFR-altered tumours are enriched in luminal 1 subtype and may have limited clinical benefit from anti-programmed death-(ligand) 1 [PD-(L)1] treatment. This cohort in the randomized, open-label phase III THOR study assessed erdafitinib versus pembrolizumab in anti-PD-(L)1-naive patients with mUC.Patients ≥18 years with unresectable advanced/mUC, with select FGFRalt, disease progression on one prior treatment, and who were anti-PD-(L)1-naive were randomized 1 : 1 to receive erdafitinib 8 mg once daily with pharmacodynamically guided uptitration to 9 mg or pembrolizumab 200 mg every 3 weeks. The primary endpoint was overall survival (OS). Secondary endpoints included progression-free survival (PFS), objective response rate (ORR), and safety.The intent-to-treat population (median follow-up 33 months) comprised 175 and 176 patients in the erdafitinib and pembrolizumab arms, respectively. There was no statistically significant difference in OS between erdafitinib and pembrolizumab [median 10.9 versus 11.1 months, respectively; hazard ratio (HR) 1.18; 95% confidence interval (CI) 0.92-1.51; P = 0.18]. Median PFS for erdafitinib and pembrolizumab was 4.4 and 2.7 months, respectively (HR 0.88; 95% CI 0.70-1.10). ORR was 40.0% and 21.6% (relative risk 1.85; 95% CI 1.32-2.59) and median duration of response was 4.3 and 14.4 months for erdafitinib and pembrolizumab, respectively. 64.7% and 50.9% of patients in the erdafitinib and pembrolizumab arms had ≥1 grade 3-4 adverse events (AEs); 5 (2.9%) and 12 (6.9%) patients, respectively, had AEs that led to death.Erdafitinib and pembrolizumab had similar median OS in this anti-PD-(L)1-naive, FGFR-altered mUC population. Outcomes with pembrolizumab were better than assumed and aligned with previous reports in non- FGFR-altered populations. Safety results were consistent with the known profiles for erdafitinib and pembrolizumab in this patient population.

Accumulating evidence shows that microbes with their theater of activity residing within the human intestinal tract (i.e., the gut microbiome) influence host metabolism. Some of the strongest results come from recent fecal microbial transplant (FMT) studies that relate changes in intestinal microbiota to various markers of metabolism as well as the pathophysiology of insulin resistance. Despite these developments, there is still a limited understanding of the multitude of effects associated with FMT on the general physiology of the host, beyond changes in gut microbiome composition. We examined the effect of either allogenic (lean donor) or autologous FMTs on the gut microbiome, plasma metabolome, and epigenomic (DNA methylation) reprogramming in peripheral blood mononuclear cells in individuals with metabolic syndrome measured at baseline (pre-FMT) and after 6 weeks (post-FMT). Insulin sensitivity was determined with a stable isotope-based 2 step hyperinsulinemic clamp and multivariate machine learning methodology was used to uncover discriminative microbes, metabolites, and DNA methylation loci. A larger gut microbiota shift was associated with an allogenic than with autologous FMT. Furthemore, the data results of the the allogenic FMT group data indicates that the introduction of new species can potentially modulate the plasma metabolome and (as a result) the epigenome. Most notably, the introduction of Prevotella ASVs directly correlated with methylation of AFAP1, a gene involved in mitochondrial function, insulin sensitivity, and peripheral insulin resistance (Rd, rate of glucose disappearance). FMT was found to have notable effects on the gut microbiome but also on the host plasma metabolome and the epigenome of immune cells providing new avenues of inquiry in the context of metabolic syndrome treatment for the manipulation of host physiology to achieve improved insulin sensitivity.

The MIP Timing Detector will provide additional timing capabilities for detection of minimum ionizing particles (MIPs) at CMS during the High Luminosity LHC era, improving event reconstruction and pileup rejection. The central portion of the detector, the Barrel Timing Layer (BTL), will be instrumented with LYSO:Ce crystals and Silicon Photomultipliers (SiPMs) providing a time resolution of about 30 ps at the beginning of operation, and degrading to 50-60 ps at the end of the detector lifetime as a result of radiation damage. In this work, we present the results obtained using a 120 GeV proton beam at the Fermilab Test Beam Facility to measure the time resolution of unirradiated sensors. A proof-of-concept of the sensor layout proposed for the barrel region of the MTD, consisting of elongated crystal bars with dimensions of about 3 x 3 x 57 mm$^3$ and with double-ended SiPM readout, is demonstrated. This design provides a robust time measurement independent of the impact point of the MIP along the crystal bar. We tested LYSO:Ce bars of different thickness (2, 3, 4 mm) with a geometry close to the reference design and coupled to SiPMs manufactured by Hamamatsu and Fondazione Bruno Kessler. The various aspects influencing the timing performance such as the crystal thickness, properties of the SiPMs (e.g. photon detection efficiency), and impact angle of the MIP are studied. A time resolution of about 28 ps is measured for MIPs crossing a 3 mm thick crystal bar, corresponding to an MPV energy deposition of 2.6 MeV, and of 22 ps for the 4.2 MeV MPV energy deposition expected in the BTL, matching the detector performance target for unirradiated devices.

The European Committee for Future Accelerators (ECFA) Early Career Researcher's (ECR) panel, which represents the interests of the ECR community to ECFA, officially began its activities in January 2021. In the first two years, the panel has defined its own internal structure, responded to ECFA requests for feedback, and launched its own initiatives to better understand and support the diverse interests of early career researchers. This report summarises the panel composition and structure, as well as the different activities the panel has been involved with during the first two years of its existence.

This study investigates the determining factors influencing the intention to use investment aggregator functionality in the context of Open Banking. A survey involving 167 participants was conducted, employing an in-depth analysis through Artificial Neural Networks (ANN). The findings reveal that the construct of "innovativeness" is the most influential factor for individuals’ intention to utilize investment aggregator functionality. Furthermore, “trust,” “social influence,” “optimism,” and “perceived usefulness” were identified as additional significant factors. Notably, individuals already engaged in investment activities, including variable income or fixed income investments, exhibited a higher inclination to adopt technology. These findings contribute to companies and banks considering the implementation of Investment Aggregator technology by providing valuable insights for effective adoption strategies. Additionally, this study offers technical contributions to Open Banking technology and aggregating tools, expanding knowledge in the field and presenting practical recommendations for companies to successfully apply this technology. Moreover, the study contributes to the deepening of the theme and the advancement of knowledge in non-linear methods, enriching the understanding of this evolving field.

One of the most common pitfalls often found in high dimensional biological data sets are correlations between the features. This may lead to statistical and machine learning methodologies overvaluing or undervaluing these correlated predictors, while the truly relevant ones are ignored. In this paper, we will define a new method called pairwise permutation algorithm (PPA) with the aim of mitigating the correlation bias in feature importance values. Firstly, we provide a theoretical foundation, which builds upon previous work on permutation importance. PPA is then applied to a toy data set, where we demonstrate its ability to correct the correlation effect. We further test PPA on a microbiome shotgun dataset, to show that the PPA is already able to obtain biological relevant biomarkers.

The European Committee for Future Accelerators (ECFA) Early-Career Researchers (ECR) Panel was invited by the ECFA Detector R&D Roadmap conveners to collect feedback from the European ECR community. A working group within the ECFA ECR panel held a Townhall Meeting to get first input, and then designed and broadly circulated a detailed survey to gather feedback from the larger ECR community. A total of 473 responses to this survey were received, providing a useful overview of the experiences of ECRs in instrumentation training and related topics. This report summarises the feedback received, and is intended to serve as an input to the ECFA Detector R&D Roadmap process.

One of the most common pitfalls often found in high dimensional biological data sets are correlations between the features. This may lead to statistical and machine learning methodologies overvaluing or undervaluing these correlated predictors, while the truly relevant ones are ignored. In this paper, we will define a new method called \textit{pairwise permutation algorithm} (PPA) with the aim of mitigating the correlation bias in feature importance values. Firstly, we provide a theoretical foundation, which builds upon previous work on permutation importance. PPA is then applied to a toy data set, where we demonstrate its ability to correct the correlation effect. We further test PPA on a microbiome shotgun dataset, to show that the PPA is already able to obtain biological relevant biomarkers.

The target laboratory in this work is an official government laboratory actingon medicine production to answer public health programs of Health Ministry, search and development and productive development partnership. Due the need of enterprises working with organized logistic procedures, it was essential to performance a change on how the laboratory internal logistic worked to suit it to the modern concepts and practices of management, since the concept used acted on a decentralized way with some key activities of logistic being realized outside of the Logistic Department, hampering its integration with the support activities. Therefore, the work tried to identify, through mapping of these activities and business logistic procedures, the spots that needed to be improved to the necessary fit ment. To propose a differen tmodel of management it was used experimentally, the centralization of management of some activities that were decentralized, such as: material catalog management;ordinary consumption items achievement used on production and search; signed contract material management; logistic cooperation management to medicament dispatch. With thein tervention done and the logistic procedure reorganization, results as the stock level sreduction; amount of materials given by providers with delay reduction; improvement on service levels; delivery time reduction of medicines composing the Health Ministry programs; among others, could be reached.

The European Committee for Future Accelerators (ECFA) Early Career Researcher's (ECR) panel, which represents the interests of the ECR community to ECFA, officially began its activities in January 2021. In the first two years, the panel has defined its own internal structure, responded to ECFA requests for feedback, and launched its own initiatives to better understand and support the diverse interests of early career researchers. This report summarises the panel composition and structure, as well as the different activities the panel has been involved with during the first two years of its existence.

The gut microbiota constitutes an important modulator of metabolic heath and an unbalanced microbiota is increasingly recognized as an important risk factor for metabolic disorders, such as obesity, insulin resistance and type 2 diabetes. Seeking for novel treatments to counteract insulin resistance and metabolic disturbances in obese subjects, we explore whether delivering a protective commensal bacteria (Anaerobutyricum soehngenii L2-7) directly into the small intestine of treatment-naïve subjects with metabolic syndrome would ameliorate glucose metabolism (NTR NL6630). We previously identified this butyrate-producing intestinal commensal to be associated with improved insulin sensitivity in metabolic syndrome subjects. Herein, we found that a single-dose of duodenal infusion containing A. soehngenii improved peripheral glucose control and stimulated the secretion of the hormone GLP-1, which is reported to positively act on both insulin secretion and sensitivity. Moreover, by RNA-seq, we found that A. soehngenii-treatment triggered a prominent alteration of the duodenal transcriptome with 73 genes being differentially expressed. Among these, the expression of regenerating islet-protein 1B-encoding gene (REG1B) was the most significantly unregulated by A. soehngenii administration. Strikingly, duodenal REG1B expression negatively correlated with peripheral glucose variability, which was significantly diminished by A. soehngeniiduodenal infusion. Altogether our findings disclose that a single-dose of A. soehngenii is sufficient to greatly impact the duodenal transcriptional profile and ameliorate glucose metabolism in metabolic syndrome individuals, likely through induction of intestinal GLP-1 production. Nonetheless, further studies are needed to delineate the specific pathways involved in induction of REG1B and GLP-1. Notably, duodenal administration of A. soehngenii was also safe and well-tolerated.

One of the most common pitfalls often found in high dimensional biological data sets are correlations between the features. This may lead to statistical and machine learning methodologies overvaluing or undervaluing these correlated predictors, while the truly relevant ones are ignored. In this paper, we will define a new method called \textit{pairwise permutation algorithm} (PPA) with the aim of mitigating the correlation bias in feature importance values. Firstly, we provide a theoretical foundation, which builds upon previous work on permutation importance. PPA is then applied to a toy data set, where we demonstrate its ability to correct the correlation effect. We further test PPA on a microbiome shotgun dataset, to show that the PPA is already able to obtain biological relevant biomarkers.