A complete low-power, low-cost and wireless solution for bridge structural health monitoring is presented. This work includes monitoring nodes with modular hardware design and low power consumption based on a control and resource management board called CoreBoard, and a specific board for sensorization called SensorBoard is presented. The firmware is presented as a design of FreeRTOS parallelised tasks that carry out the management of the hardware resources and implement the Random Decrement Technique to minimize the amount of data to be transmitted over the NB-IoT network in a secure way. The presented solution is validated through the characterization of its energy consumption, which guarantees an autonomy higher than 10 years with a daily 8 min monitoring periodicity, and two deployments in a pilot laboratory structure and the Eduardo Torroja bridge in Posadas (Córdoba, Spain). The results are compared with two different calibrated commercial systems, obtaining an error lower than 1.72% in modal analysis frequencies. The architecture and the results obtained place the presented design as a new solution in the state of the art and, thanks to its autonomy, low cost and the graphical device management interface presented, allow its deployment and integration in the current IoT paradigm.

The field of microfluidics encompasses the study of fluid behavior within micro-channels and the development of miniature systems featuring internal compartments or passageways tailored for fluid control and manipulation. Microfluidic devices capitalize on the unique chemical and physical properties exhibited by fluids at the microscopic scale. In contrast to their larger counterparts, microfluidic systems offer a multitude of advantages. Their implementation facilitates the investigation and utilization of reduced sample, solvent, and reagent volumes, thus yielding decreased operational expenses. Owing to their compact dimensions, these devices allow for the concurrent execution of multiple procedures, leading to expedited experimental timelines. Over the past two decades, microfluidics has undergone remarkable advancements, evolving into a multifaceted discipline. Subfields such as organ-on-a-chip and paper-based microfluidics have matured into distinct fields of study. Nonetheless, while scientific progress within the microfluidics realm has been notable, its translation into autonomous end-user applications remains a frontier to be fully explored. This paper sets forth the central objective of scrutinizing the present research paradigm, prevailing limitations, and potential prospects of customizable microfluidic devices. Our inquiry revolves around the latest strides achieved, prevailing constraints, and conceivable trajectories for adaptable microfluidic technologies. We meticulously delineate existing iterations of microfluidic systems, elucidate their operational principles, deliberate upon encountered limitations, and provide a visionary outlook toward the future trajectory of microfluidic advancements. In summation, this work endeavors to shed light on the current state of microfluidic systems, underscore their operative intricacies, address incumbent challenges, and unveil promising pathways that chart the course toward the next frontier of microfluidic innovation.

Heterologous prime-boost has broken the protective immune response bottleneck of the COVID-19 vaccines. however, the underlying mechanisms have not been fully elucidated. Here, we investigated antibody responses and explored the response of germinal center (GC) to priming with inactivated vaccines and boosting with heterologous adenoviral-vectored vaccines or homologous inactivated vaccines in mice. Antibody responses were dramatically enhanced by both boosting regimens. Heterologous immunization induced more robust GC activation, characterized by increased Tfh cell populations and enhanced helper function. Additionally, increased B-cell activation and antibody production were observed in a heterologous regimen. Libra-seq was used to compare the differences of S1-, S2- and NTD-specific B cells between homologous and heterologous vaccination, respectively. S2-specific CD19+ B cells presented increased somatic hypermutations (SHMs), which were mainly enriched in plasma cells. Moreover, a heterologous booster dose promoted the clonal expansion of B cells specific to S2 and NTD regions. In conclusion, the functional role of Tfh and B cells following SARS-CoV-2 heterologous vaccination may be important for modulating antibody responses. These findings provide new insights for the development of SARS-CoV-2 vaccines that induce more robust antibody response.

The present retrospective study investigated the clinical features and prognosis of secondary hematological malignancies (SHMs) in patients with sarcoma at Korea Cancer Center Hospital (Seoul, South Korea). Patients who had been diagnosed with SHMs after having received treatment for sarcoma between January 2000 and May 2023 were enrolled. Clinical data were collected from the patients\' medical records. Clinical characteristics were analyzed, including SHM incidence, type and prognosis. Of 2,953 patients with sarcoma, 18 (0.6%) were diagnosed with SHMs. Their median age at the time of sarcoma diagnosis was 39.5 (range, 9-72) years, and 74% (n=14) of these patients were male. The histological features of sarcoma varied, with osteosarcoma diagnosed in nine patients (50%). All patients with sarcoma underwent surgical treatment, and 16 (88.8%) received chemotherapy. The most common type of SHMs was acute myeloid leukemia (n=6; 33.3%), followed by myelodysplastic syndrome (n=5; 27.7%). The median latency period between the sarcoma diagnosis and SHM identification was 30 (range, 11-121) months. A total of 13 (72.2%) patients received treatment for the SHM. The median overall survival after SHM diagnosis was 15.7 (range, 0.4-154.9) months. The incidence of SHMs in sarcoma in the present study was consistent with that reported previously. The presence of SHMs was associated with a poor patient prognosis, especially if treatment for SHMs was not administered.

Somatic hypermutation (SHM) drives affinity maturation and continues over months in SARS-CoV-2-neutralizing antibodies (nAbs). However, several potent SARS-CoV-2 antibodies carry no or only a few mutations, leaving the question of how ongoing SHM affects neutralization unclear. Here, we reverted variable region mutations of 92 antibodies and tested their impact on SARS-CoV-2 binding and neutralization. Reverting higher numbers of mutations correlated with decreasing antibody functionality. However, for some antibodies, including antibodies of the public clonotype VH1-58, neutralization of Wu01 remained unaffected. Although mutations were dispensable for Wu01-induced VH1-58 antibodies to neutralize Alpha, Beta, and Delta variants, they were critical for Omicron BA.1/BA.2 neutralization. We exploited this knowledge to convert the clinical antibody tixagevimab into a BA.1/BA.2 neutralizer. These findings broaden our understanding of SHM as a mechanism that not only improves antibody responses during affinity maturation but also contributes to antibody diversification, thus increasing the chances of neutralizing viral escape variants.

The damage identification of railway bridges poses a formidable challenge given the large variability in the environmental and operational conditions that such structures are subjected to along their lifespan. To address this challenge, this paper proposes a novel damage identification approach exploiting continuously extracted time series of autoregressive (AR) coefficients from strain data with moving train loads as highly sensitive damage features. Through a statistical pattern recognition algorithm involving data clustering and quality control charts, the proposed approach offers a set of sensor-level damage indicators with damage detection, quantification, and localization capabilities. The effectiveness of the developed approach is appraised through two case studies, involving a theoretical simply supported beam and a real-world in-operation railway bridge. The latter corresponds to the Mascarat Viaduct, a 20th century historical steel truss railway bridge that remains active in TRAM line 9 in the province of Alicante, Spain. A detailed 3D finite element model (FEM) of the viaduct was defined and experimentally validated. On this basis, an extensive synthetic dataset was constructed accounting for both environmental and operational conditions, as well as a variety of damage scenarios of increasing severity. Overall, the presented results and discussion evidence the superior performance of strain measurements over acceleration, offering great potential for unsupervised damage detection with full damage identification capabilities (detection, quantification, and localization).

Recent developments in networked and smart sensors have significantly changed the way Structural Health Monitoring (SHM) and asset management are being carried out. Since the sensor networks continuously provide real-time data from the structure being monitored, they constitute a more realistic image of the actual status of the structure where the maintenance or repair work can be scheduled based on real requirements. This review is aimed at providing a wealth of knowledge from the working principles of sensors commonly used in SHM, to artificial-intelligence-based digital twin systems used in SHM and proposes a new asset management framework. The way this paper is structured suits researchers and practicing experts both in the fields of sensors as well as in asset management equally.

Advances in high-throughput sequencing technologies have facilitated the large-scale characterization of B cell receptor (BCR) repertoires. However, the vast amount and high diversity of the BCR sequences pose challenges for efficient and biologically meaningful analysis. Here, we introduce fastBCR, an efficient computational approach for inferring B cell clonal families from massive BCR heavy chain sequences. We demonstrate that fastBCR substantially reduces the running time while ensuring high accuracy on simulated datasets with diverse numbers of B cell lineages and varying mutation rates. We apply fastBCR to real BCR sequencing data from peripheral blood samples of COVID-19 patients, showing that the inferred clonal families display disease-associated features, as well as corresponding antigen-binding specificity and affinity. Overall, our results demonstrate the advantages of fastBCR for analyzing BCR repertoire data, which will facilitate the identification of disease-associated antibodies and improve our understanding of the B cell immune response.

In this paper, a novel railway track monitoring approach is proposed that employs acceleration responses measured on an in-service train to detect the loss of stiffness in the track sub-layers. An Artificial Neural Network (ANN) algorithm is developed that works with the energies of the train acceleration responses. A numerical model of a half-car train coupled with a track profile is employed to simulate the train vertical acceleration. The energy of acceleration signals measured from 100 traversing trains is used to train the ANN for healthy track conditions. The energy is calculated every 15 m along the track, each of which is called a slice. In the monitoring phase, the trained ANN is used to predict the energies of a set of train crossings. The predicted energies are compared with the simulated ones and represented as the prediction error. The damage is modeled by reducing the soil stiffness at the sub-ballast layer that represents hanging sleepers. A damage indicator (DI) based on the prediction error is proposed to visualize the differences in the predicted energies for different damage cases. In addition, a sensitivity analysis is performed where the impact of signal noise, slice sizes, and the presence of multiple damaged locations on the performance of the DI is assessed.

Background: In the Clean Sky 2 project DIMES, the cyclic loading of a section of an A320 wing with pre-existing damage was carried out. Methods: We present a Digital Image Correlation (DIC) prototype system to monitor crack propagation in the aircraft wing. This system includes a mount for easy installation and adjustment in a confined space. Results: Strain localization and evaluation due to crack propagation was successfully observed in the Region-of-Interest (ROI) during cyclic fatigue loading. The results from the DIC prototype system were supported by conventional contact Resistance Strain Gauge (RSG) sensors acting as a far-field monitor. Conclusions: Future improvements, the combination of two DIC modules for a stereo DIC system and the potential of the DIC system for ground-based tests and Structural Health Monitoring (SHM) applications are also discussed.