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Microtubule-associated 1B (MAP1B) proteins are expressed at the nervous system level where they control cytoskeleton activity and regulate neurotransmitter release. Here, we report about the identification of a planarian MAP1B factor ( DjMap1B ) that is enriched in cephalic ganglia and longitudinal nerve cords but not in neoblasts, the plentiful population of adult stem cells present in planarians, thanks to which these animals can continuously cell turnover and regenerate any lost body parts. DjMap1B knockdown induces morphological anomalies in the nervous system and affects neoblast commitment. Our data put forward a correlation between a MAP1B factor and stem cells and suggest a function of the nervous system in non-cell autonomous control of planarian stem cells.

A dynamic filtering system that can choose in real time between two different noise filters depending on the dynamics of the measured environment is presented. Unlike other adaptive filters approaches, this system does not require prior knowledge of the environment beyond noise characteristics. We implemented this system into a Brillouin optical time-domain analysis (BOTDA) sensing scheme using a closed-circuit control system for dynamic tracking of the Brillouin Frequency Shift (BFS) along the sensing fiber using a Proportional-Integral-Derivative (PID) controller. Through experiments and numerical simulations, we compare this method to the filtering capabilities of P and PI controllers chosen as optimal in a previous work for closed-circuit BOTDA (CC-BOTDA). Results show that the adaptive noise filter provides a dynamic response comparable to the other controllers, while increasing noise suppression by a factor between 30% and beyond 100%, showing how an adaptive system can improve suppression with only knowledge of the measurement noise.

Fiber optic sensors represent one of the most promising technologies for the monitoring of various engineering structures. A major challenge in the field is to analyze and predict the strain transfer to the fiber core reliably. Many authors developed analytical models of a coated optical fiber, assuming null strain at the ends of the bonding length. However, this configuration only partially reflects real experimental setups in which the cable structure can be more complex and the strains do not drastically reduce to zero. In this study, a novel strain transfer model for surface-bonded sensing cables with multilayered structure was developed. The analytical model was validated both experimentally and numerically, considering two surface-mounted cable prototypes with three different bonding lengths and five load cases. The results demonstrated the capability of the model to predict the strain profile and, differently from the available strain transfer models, that the strain values at the extremities of the bonded fiber length are not null.

Spontaneous Brillouin scattering is widely used to probe the mechanical and thermal state of matter, yet it has been assumed to be intrinsically stable. Jin and colleagues overturn this view by showing that spontaneous Brillouin light carries its own thermally driven noise floor. Their framework predicts—and experiments confirm—a universal upper bound of SNR = 1 under ideal detection conditions which can become even more restrictive than the conventional shot-noise limit in practical Brillouin systems. This discovery introduces a new fundamental limit to Brillouin-based sensing, microscopy and metrology.

Shape sensing with optical fiber sensors is an emerging technology with broad applications across various fields. This study evaluates the metrological performance of shape sensing cables in the presence of fiber core failures, a critical issue in scenarios where cable replacement is impractical due to technological and economic constraints. The impact of core failure is quantified by comparing the uncertainty in key parameters, such as curvature and bending angle, between pristine and damaged cables through Monte Carlo simulations. Results indicate that while core failure degrades performance, shape reconstruction remains achievable. However, the reconstruction becomes sensitive to bending direction due to the loss of core symmetry. Additionally, simulations of how measurement noise propagates into uncertainty in the 3D shape reconstruction are carried out. Analysis of specific shapes, including a circle and a right-handed helix, shows that increasing the number of sensing cores significantly mitigates the adverse effects of core failure. The most notable improvement occurs when the number of cores is increased from four to five. These findings show how shape reconstruction is still possible even in the presence of core damage, and how this changes the behavior of the sensing process.

Optical fiber sensors (OFSs) represent an efficient sensing solution in various structural health monitoring (SHM) applications. However, a well-defined methodology is still missing to quantify their damage detection performance, preventing their certification and full deployment in SHM. In a recent study, the authors proposed an experimental methodology to qualify distributed OFSs using the concept of probability of detection (POD). Nevertheless, POD curves require considerable testing, which is often not feasible. This study takes a step forward, presenting a model-assisted POD (MAPOD) approach for the first time applied to distributed OFSs (DOFSs). The new MAPOD framework applied to DOFSs is validated through previous experimental results, considering the mode I delamination monitoring of a double-cantilever beam (DCB) specimen under quasi-static loading conditions. The results show how strain transfer, loading conditions, human factors, interrogator resolution, and noise can alter the damage detection capabilities of DOFSs. This MAPOD approach represents a tool to study the effects of varying environmental and operational conditions on SHM systems based on DOFSs and for the design optimization of the monitoring system.

BackgroundRobotic rectal resection with da Vinci Si has some technical limitations, which could be overcome by the new da Vinci Xi. We compare short-term surgical and functional outcomes following robotic rectal resection with total mesorectal excision for cancer, with the da Vinci Xi (Xi-RobTME group) and the da Vinci Si (Si-RobTME group).MethodsThe first consecutive 30 Xi-RobTME were compared with a Si-RobTME control group of 30 patients, selected using a one-to-one case-matched methodology from our prospectively collected Institutional database, comprising all cases performed between April 2010 and September 2016 by a single surgeon. Perioperative outcomes were compared. The impact of minimally invasive TME on autonomic function and quality of life was analyzed with specific questionnaires.ResultsThe docking and overall operative time were shorter in the Xi-RobTME group (p < 0.001 and p < 0.05 respectively). The mean differences of overall operative time and docking time were −33.8 min (95% CI −5.1 to −64.5) and −6 min (95% CI −4.1 to −7.9), respectively. A fully-robotic approach with complete splenic flexure mobilization was used in 30/30 (100%) of the Xi-RobTME cases and in 7/30 (23%) of the Si-RobTME group (p < 0.001). The hybrid approach in males and patients with BMI > 25 kg/m2 was necessary in ten patients (45 vs. 0%, p < 0.001) and in six patients (37 vs. 0%, p < 0.05), in the Si-RobTME and Xi-RobTME groups, respectively. There were no differences in conversion rate, mean hospital stay, pathological data, and in functional outcomes between the two groups before and at 1 year after surgery.ConclusionThe technical advantages offered by the da Vinci Xi seem to be mainly associated with a shorter docking and operative time and with superior ability to perform a fully-robotic approach. Clinical and functional outcomes seem not to be improved, with the introduction of the new Xi platform.

Biological evolution has generated a vast array of natural compounds produced by organisms across all domains. Among these, secondary metabolites, selected to enhance an organism’s competitiveness in its natural environment, make them a reservoir for discovering new compounds with cytotoxic activity, potentially useful as novel anticancer agents. Slime secretions, the first barrier between epithelial surfaces and the surrounding environment, frequently contain cytotoxic molecules to limit the growth of parasitic organisms. Planarians, freshwater Triclads, continuously secrete a viscous mucus with multiple physiological functions. The chemical composition of planarian mucus has been only partially elucidated, and there are no studies reporting its cytotoxic or cytostatic effects. In this study, we developed a protocol for collecting mucus from Dugesia japonica specimens and we demonstrated that it inhibits the growth of cancer cells by activating cytostatic and ROS-dependent cytotoxic mechanisms inducing lipid droplet accumulation and mitochondrial membrane reorganization. Although further research is needed to identify the specific chemicals responsible for the anticancer activity of planarian mucus, this work opens up numerous research avenues aimed at better understanding the mechanisms of action of this product for potential therapeutic applications.

This research is based on the idea that certain cognitive-intensive tasks typically carried out by structural engineers—such as choosing how to effectively arrange a building’s structure—can be successfully automated. In this article we investigate two techniques widely used in the field of Artificial Intelligence, namely Monte Carlo Tree Search (MCTS) and Genetic Algorithm (GA). Following a tabula rasa approach, according to which no hints nor external data are used as a reference for navigating the search space, we demonstrate how structural designs of two-dimensional multi-storey reinforced concrete structures can be generated, with no human intervention, by implementing and combining the two techniques from a reinforcement-learning perspective. The design tasks assigned to the developed software agents concern civil structures under static and seismic loads, and the basis for comparison is represented by a combination of construction cost and greenhouse gas emissions associated with the making of the structures. In the article, based on the main concepts of Computational Mechanics, a theoretical framework is introduced, which allows to represent both structures and design tasks in a simple, analytical way. The process of gamification, to which MCTS is often associated, is then described, so that structural design is reduced to the concepts of state, actions and payoff.. Finally, case studies are presented in which different agents—based respectively on GA, MCTS, and a combination of both—are tested. The results suggest that hybrid approaches, where GA operates first followed by MCTS, are the most effective.

Recent advancements in AI and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly complex environments. To address the challenges associated with continuous learning constraints and knowledge capacity in this context, cognitive architectures inspired by human cognition have gained significance. This study contributes to existing research by introducing a cognitive-attentional system employing a constructive neural network-based learning approach for continuous acquisition of procedural knowledge. We replace an incremental tabular Reinforcement Learning algorithm with a constructive neural network deep reinforcement learning mechanism for continuous sensorimotor knowledge acquisition, thereby enhancing the overall learning capacity. The primary emphasis of this modification centers on optimizing memory utilization and reducing training time. Our study presents a learning strategy that amalgamates deep reinforcement learning with procedural learning, mirroring the incremental learning process observed in human sensorimotor development. This approach is embedded within the CONAIM cognitive-attentional architecture, leveraging the cognitive tools of CST. The proposed learning mechanism allows the model to dynamically create and modify elements in its procedural memory, facilitating the reuse of previously acquired functions and procedures. Additionally, it equips the model with the capability to combine learned elements to effectively adapt to complex scenarios. A constructive neural network was employed, initiating with an initial hidden layer comprising one neuron. However, it possesses the capacity to adapt its internal architecture in response to its performance in procedural and sensorimotor learning tasks, inserting new hidden layers or neurons. Experimentation conducted through simulations involving a humanoid robot demonstrates the successful resolution of tasks that were previously unsolved through incremental knowledge acquisition. Throughout the training phase, the constructive agent achieved a minimum of 40% greater rewards and executed 8% more actions when compared to other agents. In the subsequent testing phase, the constructive agent exhibited a 15% increase in the number of actions performed in contrast to its counterparts.