The Combinatorics of Tandem Duplication

Tandem duplication is an evolutionary process whereby a segment of DNA is replicated and proximally inserted. The different configurations that can arise from this process give rise to some interesting combinatorial questions. Firstly, we introduce an algebraic formalism to represent this process as a word producing automaton. The number of words arising from n tandem duplications can then be recursively derived. Secondly, each single word accounts for multiple evolutions. With the aid of a bi-coloured 2d- tree, a Hasse diagram corresponding to a partially ordered set is constructed, from which we can count the number of evolutions corresponding to a given word. Thirdly, we implement some subtree prune and graft operations on this structure to show that the total number of possible evolutions arising from n tandem duplications is $\prod_{k=1}^n(4^k - (2k + 1))$. The space of structures arising from tandem duplication thus grows at a super-exponential rate with leading order term $\mathcal{O}(4^{\frac{1}{2}n^2})$

On the hull and interval numbers of oriented graphs

In this work, for a given oriented graph $D$, we study its interval and hull numbers, denoted by ${in}(D)$ and ${hn}(D)$, respectively, in the geodetic, ${P_3}$ and ${P_3^*}$ convexities. This last one, we believe to be formally defined and first studied in this paper, although its undirected version is well-known in the literature. Concerning bounds, for a strongly oriented graph $D$, we prove that ${hn_g}(D)\leq m(D)-n(D)+2$ and that there is a strongly oriented graph such that ${hn_g}(D) = m(D)-n(D)$. We also determine exact values for the hull numbers in these three convexities for tournaments, which imply polynomial-time algorithms to compute them. These results allows us to deduce polynomial-time algorithms to compute ${hn_{P_3}}(D)$ when the underlying graph of $D$ is split or cobipartite. Moreover, we provide a meta-theorem by proving that if deciding whether ${in_g}(D)\leq k$ or ${hn_g}(D)\leq k$ is NP-hard or W[i]-hard parameterized by $k$, for some $i\in\mathbb{Z_+^*}$, then the same holds even if the underlying graph of $D$ is bipartite. Next, we prove that deciding whether ${hn_{P_3}}(D)\leq k$ or ${hn_{P_3^*}}(D)\leq k$ is W[2]-hard parameterized by $k$, even if the underlying graph of $D$ is bipartite; that deciding whether ${in_{P_3}}(D)\leq k$ or ${in_{P_3^*}}(D)\leq k$ is NP-complete, even if $D$ has no directed cycles and the underlying graph of $D$ is a chordal bipartite graph; and that deciding whether ${in_{P_3}}(D)\leq k$ or ${in_{P_3^*}}(D)\leq k$ is W[2]-hard parameterized by $k$, even if the underlying graph of $D$ is split. We also argue that the interval and hull numbers in the oriented $P_3$ and $P_3^*$ convexities can be computed in polynomial time for graphs of bounded tree-width by using Courcelle's theorem

A new method based on noise counting to monitor the frontend electronics of the LHCb muon detector

A new method has been developed to check the correct behaviour of the frontend electronics of the LHCb muon detector. This method is based on the measurement of the electronic noise rate at different thresholds of the frontend discriminator. The method was used to choose the optimal discriminator thresholds. A procedure based on this method was implemented in the detector control system and allowed the detection of a small percentage of frontend channels which had deteriorated. A Monte Carlo simulation has been performed to check the validity of the method

Heritability of peach tree resistance to bacterial leaf spot.

The objective of this work was to evaluate the broad-sense heritability reaction to bacterial leaf spot (Xanthomonas arboricola pv. pruni), in peach tree populations obtained from directed crosses. Disease severity and defoliation of the genotypes were evaluated in field conditions, with posterior measurement of the healthy leaf area duration (HAD). The observed average heritability (0.51) indicates that the use of the evaluated genitors can be effective for the development of cultivars with higher resistance to the disease.Notas Científicas. Título em português: Herdabilidade de resistência de pessegueiro à bacteriose foliar

Wave profile and tide monitoring system for scalable implementation

A versatile, miniaturized, cost-effective, low-power wave profile and tide monitoring system, capable of long-term and scalable deployment, was developed to integrate pressure and temperature sensors in an RS485 network, for standalone operation with organized memory or real-time shared data monitoring. The pressure and temperature sensors are controlled by low-power microcontrollers, that communicate the data periodically to a datalogger, that depending on the application, store it in a removable SD card or send it to a server via Wi-Fi. The data is then analyzed to compensate for the loss in amplitude sensitivity according to the sensor’s depth. The wave profile can be sampled at a maximum rate of 100 Hz, with a 1 cm resolution. The system was tested successfully in real-life conditions, in rivers Douro and Cávado, and off the coast of Viana do Castelo.João Rocha was supported by the doctoral Grant PRT/BD/154322/2023 financed by the Portuguese Foundation for Science and Technology (FCT), and with funds from Portuguese State Budget, European Social Fund (ESF) and Por_Norte, under MIT Portugal Program. This work is co-funded by the projects K2D: Knowledge and Data from the Deep to Space (POCI-01-0247-FEDER-045941), SONDA (PTDC/EME-SIS/1960/2020), ATLÂNTIDA (NORTE-01-0145-FEDER-000040) and CMEMS - UIDB/04436/2020 and UIDP/04436/2020

Dependence of the energy resolution of a scintillating crystal on the readout integration time

The possibilty of performing high-rate calorimetry with a slow scintillating crystal is studied. In this experimental situation, to avoid pulse pile-up, it can be necessary to base the energy measurement on only a fraction of the emitted light, thus spoiling the energy resolution. This effect was experimentally studied with a BGO crystal and a photomultiplier followed by an integrator, by measuring the maximum amplitude of the signals. The experimental data show that the energy resolution is exclusively due to the statistical fluctuations of the number of photoelectrons contributing to the maximum amplitude. When such number is small its fluctuations are even smaller than those predicted by Poisson statistics. These results were confirmed by a Monte Carlo simulation which allows to estimate, in a general case, the energy resolution, given the total number of photoelectrons, the scintillation time and the integration time

A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images

Background and objective: In patients with suspected Coronary Artery Disease (CAD), the severity of stenosis needs to be assessed for precise clinical management. An automatic deep learning-based algorithm to classify coronary stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) in multiplanar reconstruction images acquired with Coronary Computed Tomography Angiography (CCTA) is proposed. Methods: In this retrospective study, 288 patients with suspected CAD who underwent CCTA scans were included. To model long-range semantic information, which is needed to identify and classify stenosis with challenging appearance, we adopted a token-mixer architecture (ConvMixer), which can learn structural relationship over the whole coronary artery. ConvMixer consists of a patch embedding layer followed by repeated convolutional blocks to enable the algorithm to learn long-range dependences between pixels. To visually assess ConvMixer performance, Gradient-Weighted Class Activation Mapping (Grad-CAM) analysis was used. Results: Experimental results using 5-fold cross-validation showed that our ConvMixer can classify significant coronary artery stenosis (i.e., stenosis with luminal narrowing ≥50%) with accuracy and sensitivity of 87% and 90%, respectively. For CAD-RADS 0 vs. 1–2 vs. 3–4 vs. 5 classification, ConvMixer achieved accuracy and sensitivity of 72% and 75%, respectively. Additional experiments showed that ConvMixer achieved a better trade-off between performance and complexity compared to pyramid-shaped convolutional neural networks. Conclusions: Our algorithm might provide clinicians with decision support, potentially reducing the interobserver variability for coronary artery stenosis evaluation

Wave profile and tide monitoring system for scalable implementation

Apresentação de Poster em conferência Nacional.Presentation of a wave profile and tide monitoring system, with low-cost and low-power pressure sensors connected to a datalogger in a wired or acustic network

Machine learning prediction models for mitral valve repairability and mitral regurgitation recurrence in patients undergoing surgical mitral valve repair

Background: Mitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence. Methods: 1000 patients who underwent MV repair at our institution between 2008 and 2018 were enrolled. Patients were followed longitudinally for up to three years. Clinical and echocardiographic data were included in the analysis. Endpoints were MV repair surgical failure with consequent MV replacement or moderate/severe MR (>2+) recurrence at one-month and mod-erate/severe MR recurrence after three years. Results: 817 patients (DS1) had an echocardiographic examination at one-month while 295 (DS2) also had one at three years. Data were randomly divided into training (DS1: n = 654; DS2: n = 206) and validation (DS1: n = 164; DS2 n = 89) cohorts. For intra-operative or early MV repair failure assessment, the best area under the curve (AUC) was 0.75 and the complexity of mitral valve prolapse was the main predictor. In predicting moderate/severe recurrent MR at three years, the best AUC was 0.92 and residual MR at six months was the most important predictor. Conclusions: Machine learning algorithms may improve prognosis after MV repair procedure, thus improving indications for correct candidate selection for MV surgical repair