Search Results (359)

CubeSats are increasingly adopted for space missions due to their low cost and short development cycles. However, their attitude determination and control systems (ADCS) often suffer from limited verification environments and constrained hardware configurations. This study addresses the development and verification of a flight-ready ADCS for the INHA RoSAT 3U CubeSat under realistic constraints in hardware, software, and test infrastructure. A model-based design (MBD) approach is adopted to construct an integrated development pipeline covering algorithm design, simulation, automatic C code generation, and integration with flight software (FSW). The generated code is embedded into a closed commercial onboard computer framework while preserving consistency across model-in-the-loop (MIL) and processor-in-the-loop (PIL) verification stages. To compensate for the lack of full hardware-in-the-loop (HIL) facilities, a FlatSat-based Sensor-to-Actuator test strategy is introduced to validate critical hardware–software interfaces including signal polarity, unit consistency, mounting orientation, and data flow using actual flight hardware. Furthermore, a fault-aware hierarchical attitude control scheme is defined in which the controller transitions to an alternative controller upon actuator fault indications. The presented approach demonstrates a practical ADCS development and verification strategy suitable for resource-constrained CubeSat missions, providing guidance for teams facing similar limitations in cost, resources, and test infrastructure. Full article

This paper presents Nexus, a proof-of-concept low-cost, modular, and reprogrammable multichannel data logger aimed at validating the architectural feasibility of an open and scalable acquisition platform for scientific instrumentation. The system was conceived to address common limitations of commercial data loggers, such as high cost, restricted configurability, and limited autonomy, by relying exclusively on widely available components and open hardware/software resources, thereby facilitating reproducibility and adoption in resource-constrained academic and industrial environments. The proposed architecture supports up to six interchangeable acquisition modules, enabling the integration of up to 20 analog channels with heterogeneous resolutions (24-bit, 12-bit, and 10-bit ADCs), as well as digital acquisition through multiple communication interfaces, including I²C (two independent buses), SPI (two buses), and UART (three interfaces). Quantitative validation was performed using representative acquisition configurations, including a 24-bit ADS1256 stage operating at sampling rates of up to 30 kSPS, 12-bit microcontroller-based stages operating at approximately 1 kSPS, and 10-bit operating at 100 SPS, consistent with stable real-time acquisition and visualization under proof-of-concept constraints. SPI communication was configured with an effective clock frequency of 2 MHz, ensuring deterministic data transfer across the tested acquisition modules. A hybrid data management strategy is implemented, combining high-capacity local storage via USB 3.0 solid-state drives, optional cloud synchronization, and a 7-inch touchscreen human–machine interface based on Raspberry Pi OS for system control and visualization. Power continuity is addressed through an integrated smart uninterruptible power supply, which provides telemetry, automatic source switching, and limited backup operation during power interruptions. As a proof of concept, the system was functionally validated through architectural and interface-level tests, demonstrating stable communication across all supported protocols and reliable acquisition of synthetic and biosignal-like waveforms. The results confirm the feasibility of the proposed modular architecture and its ability to integrate heterogeneous acquisition, storage, and interface subsystems within a unified open-source platform. While not intended as a finalized commercial product, Nexus establishes a validated foundation for future developments in modular data logging, embedded intelligence, and application-specific instrumentation. Full article

Human epidermal growth factor receptor 2 (HER2) is a molecular target of interest in prostate cancer due to its association with poor prognosis and its potential role in androgen receptor signaling. However, earlier clinical trials investigating HER2-targeted therapies, including antibodies and small molecules, have shown limited efficacy. More recent studies using the HER2 antibody-drug conjugate (ADC) trastuzumab deruxtecan (T-DXd) suggest potential therapeutic benefit in prostate cancer. However, its effective utilization requires a HER2 IHC scoring system that accurately represents HER2 expression patterns unique to prostate cancer, which is currently not established. We have developed a modified HER2 IHC scoring system that, unlike the breast and gastrointestinal tumor HER2 IHC grading scales, considers the distinct spatiotemporal expression of HER2 in prostate tumors. In this commentary, we discussed two patients with metastatic prostate cancer who were classified as HER2 IHC 3+ using our prostate cancer-specific scoring system and who demonstrated meaningful clinical responses and responded to treatment with T-DXd. We further review the historical evolution of HER2 testing in prostate cancer, as well as factors that may have contributed to the failure of previous clinical trials targeting HER2 in prostate tumors. Our aim is to highlight the need for developing a standardized HER2 IHC grading model in prostate cancer, which could improve the predictive value of HER2 IHC expression, enabling a more accurate identification of patients likely to benefit from HER2-targeted ADCs. Full article

Human epidermal growth factor receptor 2 (HER2)-low breast cancer is a clinically relevant subgroup defined by low but detectable HER2 protein expression, immunohistochemistry (IHC) score of 1+ or 2+ with negative in situ hybridization findings, positioned at the interface between traditional HER2-positive and HER2-negative disease. The recent introduction of antibody–drug conjugates (ADCs) has increased the clinical significance of borderline HER2 expression and exposed important diagnostic challenges, particularly in cases with very low levels of membrane staining, including the emerging HER2-ultralow category. Background and Objectives: This review summarizes the pathological and biological framework of HER2-low and HER2-ultralow breast cancer and critically appraises the magnitude, direction, and determinants of HER2 variability under systemic therapy. Particular focus is placed on treatment-associated shifts after chemotherapy, intratumoral heterogeneity, and pre-analytical and analytical factors that can influence HER2 assessment, with direct implications for therapeutic stratification and biomarker reassessment. Materials and Methods: A narrative literature review was conducted using PubMed, Scopus, and Web of Science, focusing on studies published within the last five years. Eligible publications included clinical trials, retrospective cohorts, and translational or molecular studies that reported paired HER2 assessment in breast cancer and were interpreted according to American Society of Clinical Oncology/College of American Pathologists-aligned criteria. Results: Across major cohorts, HER2-low appeared to be the most dynamic category, with variability frequently observed following systemic therapy. Beyond treatment-related effects, shifts in HER2 status may be attributable to intratumoral heterogeneity and technical variability, with the greatest impact observed at the IHC 0–1+ interface. Conclusions: Given the clinical relevance of low-level HER2 expression, standardized testing and transparent reporting are essential, and HER2 reassessment may be justified in selected clinical scenarios to optimize access to HER2-directed therapies. Full article

Reliable state estimation is critical for the safe operation of second-life battery systems but is severely hindered by significant parameter heterogeneity arising from diverse historical aging conditions. Traditional static models struggle to adapt to such variability, while online identification methods are prone to divergence under dynamic loads. To overcome these challenges, this paper proposes a Dual-Anchor Adaptive Fusion Framework for robust State of Charge (SOC) estimation and State of Health (SOH) soft-sensing. Specifically, to establish a reliable physical baseline, an automated Dynamic Relaxation Interval Selection (DRIS) strategy is introduced. By minimizing the fitting Root Mean Square Error (RMSE), DRIS systematically extracts high-fidelity parameters to construct two “anchor models” that rigorously define the boundaries of the aging space. Subsequently, a residual-driven Bayesian fusion mechanism is developed to seamlessly interpolate between these anchors based on real-time voltage feedback, enabling the model to adapt to uncalibrated target batteries. Concurrently, a novel “SOH Soft-Sensing” capability is unlocked by interpreting the adaptive fusion weights as real-time health indicators. Experimental results demonstrate that the proposed framework achieves robust SOC estimation with an RMSE of 0.42%, significantly outperforming the standard Adaptive Extended Kalman Filter (A-EKF, RMSE 1.53%), which exhibits parameter drift under dynamic loading. Moreover, the a posteriori voltage tracking residual is compressed to ~0.085 mV, effectively approaching the hardware’s ADC quantization limit. Furthermore, SOH is inferred with a relative error of 0.84% without additional capacity tests. This work establishes a robust methodological foundation for calibration-free state estimation in heterogeneous retired battery packs. Full article

The development of highly dynamic bipedal robots demands sensing capable of capturing key contact-related signals in real time, particularly the Center of Pressure (CoP). CoP is fundamental for locomotion control and state estimation and is also of interest in biomedical applications such as gait analysis and lower-limb assistive devices. To enable reliable CoP acquisition under dynamic walking, this paper presents a foot-mounted measurement system and an online self-calibration method that adapts sensor scale and bias parameters during locomotion using both external foot sensors and the robot’s proprioceptive measurements. We demonstrate an online self-calibration pipeline that updates foot-sensor scale and bias parameters during a walking experiment on a NAO-V5 platform using a sliding window optimization. The reported results indicate improved within-trial consistency relative to an offline-calibrated reference baseline under the tested walking conditions. In addition, the framework reconstructs a digitized estimate of the vertical ground reaction force (vGRF) from load-cell readings; due to ADC quantization and the discrete offline calibration dataset, the vGRF signal may exhibit stepwise behavior and should be interpreted as a reconstructed (digitized) quantity rather than laboratory-grade continuous force metrology. Overall, the proposed sensing-and-calibration pipeline offers a practical solution for dynamic CoP acquisition with low-cost hardware. Full article

Background/Objectives: Preoperative differentiation between oral squamous cell carcinoma (SCC) and minor salivary gland carcinoma (SGC) remains clinically challenging due to overlapping imaging characteristics. This study aimed to develop a diagnostic model based on quantitative dynamic contrast-enhanced MRI (qDCE-MRI) parameters to distinguish SCC from SGC prior to surgery. Methods: Patients with histopathologic confirmed SCC or minor SGC who underwent preoperative 3.0T qDCE-MRI were recruited. Clinical characteristics and pharmacokinetic parameters, including volume transfer constant (K^trans), reverse reflux rate constant (K_ep), volume fraction of extravascular extracellular space (V_e), plasma volume fraction (V_p), time to peak (TTP), maximum concentration (MAXConc), maximal slope (MAXSlope), and area under the concentration-time curve (AUCt), along with the apparent diffusion coefficient (ADC), were extracted. Univariate and multivariable logistic regression analyses were performed to identify independent discriminators. Diagnostic performance was assessed using receiver operating characteristic analysis, and model comparisons were conducted with the DeLong test. Interobserver agreement was evaluated using intraclass correlation coefficients (ICC). Results: All qDCE-MRI parameters demonstrated excellent interobserver agreement (ICC range, 0.82–0.94). Multivariable analysis identified K_ep (OR = 2620.172, p = 0.001), maximal slope (OR = 1.715, p = 0.024), and tumor location (OR = 5.561, p = 0.027) as independent predictors. The qDCE-MRI model achieved superior diagnostic performance compared with the clinical model (AUC: 0.945 vs. 0.747; p = 0.012). Conclusions: A qDCE-MRI–based model incorporating K_ep and MAXSlope was shown to provide excellent accuracy for preoperative differentiation between oral SCC and minor SGC. Full article

Background/Objectives: The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by an enriched stroma, hampering the effectiveness of therapy. This co-clinical study aimed to (1) provide insight into early post-treatment changes in the TME using multiparametric magnetic resonance imaging (mpMRI)-derived quantitative imaging biomarkers (QIBs) in a preclinical PDAC model treated with radiotherapy and correlate these QIBs with histology; (2) evaluate the feasibility of obtaining these QIBs in patients with PDAC using clinically approved mpMRI data acquisitions. Methods: Athymic mice (n = 12) at pre- and post-treatment as well as patients with PDAC (n = 11) at pre-treatment underwent mpMRI including diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) data acquisition sequences. DW and DCE data were analyzed using monoexponential and extended Tofts models, respectively. DeepLIIF quantified the total percentage (%) of tumor cells in hematoxylin and eosin (H&E)-stained tissues from athymic mice. Spearman correlation and Wilcoxon signed rank tests were performed for statistical analysis. Results: In the preclinical PDAC model, mean pre- and post-treatment ADC and K^trans values differed significantly (p < 0.01), changing by 20.50% and 20.41%, respectively, and the median total tumor cells quantified by DeepLIIF was 24% (range: 15–53%). Post-treatment ADC values and relative change in v_e (rΔv_e) showed a significant negative correlation with total tumor cells (ρ = −0.77, p < 0.014 for ADC and ρ = −0.77, p = 0.009 for rΔv_e). In patients with PDAC, pre-treatment mean ADC and K^trans values were 1.76 × 10⁻³ (mm²/s) and 0.24 (min⁻¹), respectively. Conclusions: QIBs in both preclinical and clinical settings underscore their potential for future co-clinical research to evaluate emerging drug combinations targeting both tumor and stroma. Full article

Background and Objectives: Soft tissue sarcomas account for approximately 7% of all malignant tumors in the pediatric population. Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) measurements may provide early functional biomarkers of treatment response by reflecting changes in tumor cellularity. This study evaluated whether ADC-derived parameters can serve as quantitative biomarkers of neoadjuvant chemotherapy response in pediatric rhabdomyosarcoma. Materials and Methods: This retrospective single-center study included 14 patients aged ≤18 years with histopathologically confirmed rhabdomyosarcoma who underwent MRI before treatment and after three cycles of chemotherapy. Twenty-five patients were initially identified; eleven were excluded due to imaging artifacts or absence of baseline examination. ADC maps were generated on 1.5T and 3T scanners. Regions of interest were placed over the entire lesion and areas with the lowest ADC signal. Relative ADC (rADC) was calculated by normalizing tumor ADC to adjacent healthy muscle. Paired t-tests were used to compare pre- and post-treatment values. Results: At baseline, 13/14 patients (93%) demonstrated diffusion restriction. Mean ADC increased from 1.11 × 10⁻³ mm²/s (SD ± 0.48) at baseline to 1.63 × 10⁻³ mm²/s (SD ± 0.67) after treatment. The paired t-test for rADC yielded t = −3.089 (p = 0.0086, 95% CI: −0.79 to −0.14), indicating a statistically significant change. There was a significant difference between the ADC values of the entire lesion and the areas with the lowest signal in tumors with a heterogenic structure, t = 2.862, p = 0.013. Conclusions: ADC and rADC increased significantly after neoadjuvant chemotherapy in pediatric rhabdomyosarcoma, suggesting potential utility as early functional biomarkers of treatment response. These preliminary findings require validation in larger multicenter prospective studies with correlation to histopathological response and clinical outcomes before clinical implementation. Full article

The use of deep learning radiomics to predict whether advanced prostate cancer (PCa) will progress within two years after treatment has been validated, yet there remains a lack of research on estimating time to progression. Patients were enrolled from October 2017 to March 2024. One hundred and eighty-two patients with advanced PCa diagnosed through ultrasound-guided systematic prostate biopsy were enrolled. A deep learning-based radiomics model for predicting progression was firstly developed using pretreatment MR apparent diffusion coefficient (ADC) maps, and the performance of manual (ROIref) versus AI-derived (ROIai) tumor segmentations was compared. Then, survival analysis was performed to compare ROIref-based and ROIai-based radiomics-predicted probabilities in the risk stratification. The area under the receiver operating characteristics curve (AUC) was used to estimate the model efficacy. The model achieved high AUC values for progression prediction in test sets (ROIref: 0.840, ROIai: 0.852). No significant difference was observed between ROIai-based and ROIref-based approaches (ΔAUC = 0.012, p = 0.870) in the test set. Both ROIref-predicted and ROIai-predicted probabilities independently predicted progression in multivariate Cox proportional hazard regression models (p < 0.001) and stratified patients into distinct survival groups (log-rank p < 0.001). Decision curve analysis confirmed equivalent clinical utility across thresholds (0.1–0.6), with net benefit exceeding the “treat all” and “treat none” strategies. In conclusion, deep learning-based radiomics models could effectively predict advanced PCa progression, with AI-derived tumor annotations performing equally to manual expert ones. Full article

This work presents an experimental evaluation of a high-speed analog-to-digital conversion method based on passive reference waveform crossings combined with time-to-digital converter (TDC) time-tagging. Unlike conventional level-crossing event-driven analog-to-digital converters (ADCs) that require dynamically updated digital-to-analog converters (DACs), the proposed architecture compares the input waveform against a broadband periodic sampling function without active threshold control. Crossing instants are detected by a high-speed comparator and converted into rising and falling edge timestamps using a multi-channel TDC. A commercial ScioSense GPX2-based time-tagger with 30 ps single-shot precision was used for validation. A range of test signals—including 5 MHz sine, sawtooth, damped sine, and frequency-modulated chirp waveforms—were acquired using triangular, sinusoidal, and sawtooth sampling functions. Stroboscopic sampling was demonstrated using reference frequencies lower than the signal of interest, enabling effective undersampling of periodic radio frequency (RF) waveforms. The method achieved effective bandwidths approaching 100 MHz, with amplitude reconstruction errors of 0.05–0.30 RMS for sinusoidal signals and 0.15–0.40 RMS for sawtooth signals. Timing jitter showed strong dependence on the relative slope between the acquired waveform and sampling function: steep regions produced jitter near 5 ns, while shallow regions exhibited jitter up to 20 ns. The study has several limitations, including the bandwidth and dead-time constraints of the commercial TDC, the finite slew rate and noise of the comparator front-end, and the limited frequency range of the generated sampling functions. These factors influence the achievable timing precision and reconstruction accuracy, especially in low-gradient signal regions. Overall, the passive waveform-crossing method demonstrates strong potential for wideband, sparse, and rapidly varying signals, with natural scalability to multi-channel systems. Potential application domains include RF acquisition, ultra-wideband (UWB) radar, integrated sensing and communication (ISAC) systems, high-speed instrumentation, and wideband timed antenna arrays. Full article

Background/Objectives: Approved for treatment of acute leukemia, gemtuzumab ozogamicin (GO) and inotuzumab ozogamicin (InO) are antibody–drug conjugates (ADCs) that deliver a toxic calicheamicin (CLM) derivative. The resistance mechanisms to GO/InO remain incompletely understood. Methods: We performed a genome-wide clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 screen for CLM sensitivity genes, and then performed confirmatory cytotoxicity assays. Results: Several DNA damage pathway regulation genes were identified, most notably TP53. Across 13 acute leukemia cell lines, the six TP53-mutant cell lines (TP53^MUT) were indeed 10- to 1000-fold less sensitive to CLM than the seven TP53^WT cell lines. In five TP53^WT/KO syngeneic cell line pairs we generated, TP53^KO cells were significantly less sensitive to CLM than their TP53^WT counterparts. In TP53^WT but not TP53^MUT cells, the MDM2 inhibitor and p53 activator, idasanutlin, enhanced CLM cytotoxicity, demonstrating that decoupling of cells from MDM2-p53 regulation sensitizes leukemia cells to CLM. The ATM inhibitors AZD1390 and lartesertib also significantly enhanced CLM efficacy but did so independent of the TP53 status. In contrast, neither an ATR inhibitor, Chk1/Chk2 inhibitor, Chk2 inhibitor, or a PARP inhibitor significantly impacted CLM-induced cytotoxicity across the thirteen cell lines. Together, our studies identify ATM, MDM2, and TP53—which are in the same cellular response to DNA damage pathway—as key modulators of CLM-induced cytotoxicity in acute leukemia cells. Conclusions: These results support further evaluation of combination therapies with corresponding small-molecule inhibitors (currently pursued for therapy of other cancers) toward clinical testing as novel strategies to increase the efficacy of CLM-based ADCs such as GO and InO. Full article

This paper presents a precision, wide-dynamic-range leakage current sensor tailored for in-situ monitoring of aging mechanisms such as Time-Dependent Dielectric Breakdown (TDDB) in both active and passive components. The proposed architecture supports high-voltage stress and is fully monolithic, integrating a current-to-voltage front-end, tunable-gain amplifier, and a successive approximation register (SAR) analog-to-digital converter (ADC). To validate the concept, a discrete-component prototype was implemented and evaluated across a leakage current range of 1 nA to 1 $\mathsf{\mu }$A. The sensor achieves 12-bit resolution with measured integral non-linearity (INL) and differential non-linearity (DNL) within $\pm 1.5$ LSB and $\pm 0.3$ LSB, respectively. Compared to prior monitors, the design enables linear current digitization and supports high-voltage stress, features essential for accurate and scalable TDDB characterization. Applications include embedded reliability monitoring in power converters, analog building blocks, and large-scale aging test arrays. Full article

Background: Cancer epidemiology data for people living with human immunodeficiency virus (PLWH) in Thailand, particularly in the era of combination antiretroviral therapy (ART), remain limited. In this study, we describe the prevalence, temporal trends, clinical characteristics, and survival outcomes of patients with AIDS-defining cancers (ADCs) and non-AIDS-defining cancers (NADCs). Methods: We retrospectively reviewed adult PLWH diagnosed with malignancy at Songklanagarind Hospital in Thailand during 2003–2023. Demographic, human immunodeficiency virus (HIV)-related, and clinical data were analyzed using chi-square and Wilcoxon rank-sum tests and the Kaplan–Meier method. Results: Among 444 patients, 231 had NADCs and 213 had ADCs. The NADC proportion increased markedly over time. Common ADCs included non-Hodgkin lymphoma and cervical cancer; common NADCs included lung cancer, non-nasopharyngeal head and neck cancer, and hepatocellular carcinoma. Compared with patients with ADCs, those with NADCs were older, more often male, and had higher proportions of undetectable HIV viral load, CD4 counts ≥200 cells/µL, and ART use. Approximately one-third of patients presented with advanced-stage disease, and the median overall survival was 15.9 months. Conclusions: Over two decades, NADCs have become the predominant malignancy in Thai PLWH, associated with older age, male sex, and improved immune function. This reflects the evolving cancer risk in the era of combination ART. We suggest employing multidisciplinary approaches involving HIV and cancer care to improve survival outcomes and integrating age-appropriate screening for common NADCs into HIV care. Full article

Future space detectors for Ultra High Energy neutrinos and cosmic rays will utilize Cherenkov telescopes to detect forward-beamed Cherenkov light produced by charged particles in Extensive Air Showers (EASs). A Cherenkov detector can be equipped with an array of Silicon Photo-Multiplier (SiPM) pixels, which offer several advantages over traditional Photo-Multiplier Tubes (PMTs). SiPMs are compact and lightweight and operate at lower voltages, making them well-suited for space-based experiments. The SiSMUV (SiPM-based Space Monitor for UV-light) is developing a SiPM-based Cherenkov camera for PBR (POEMMA Baloon with Radio) at INFN Napoli. To understand the response of such an instrument, a comprehensive simulation of the response of individual SiPM pixels to incident light is needed. For the accurate simulation of a threshold trigger, this simulation must reproduce the current produced by a SiPM pixel as a function of time. Since a SiPM pixel is made of many individual Avalanche Photo-Diodes (APDs), saturation and pileup in APDs must also be simulated. A Gaussian mixture fit to ADC count spectrum of a SiPM pixel exposed to low levels of laser light at INFN Napoli shows a significant amount of samples between the expected PE (Photo Electron) peaks. Thus, noise sources such as dark counts and afterpulses, which result in partially integrated APD pulses, must be accounted for. With static, reasonable values for noise rates, the simulation chain presented in this work uses the characteristics of individual APDs to produce the aggregate current produced by a SiPM pixel. When many such pulses are simulated and integrated, the ADC spectra generated by low levels of laser light at the INFN Napoli SiSMUV test setup can be accurately reproduced. Full article