Search Results (225)

This paper presents a column-parallel adaptive-gain single-slope (SS) analog-to-digital converter (ADC) for high-speed high-dynamic-range (HDR) CMOS image sensors. Conventional adaptive-gain approaches often rely on dual-ramp generation or duplicated column circuits, which increase area and power overhead. In contrast, the proposed architecture achieves adaptive-gain operation using a single global ramp shared across all columns. A reconfigurable capacitive attenuation network embedded inside each column comparator locally scales the ramp at the comparator input, enabling seamless transition between high-gain operation for low-level signals and unity-gain operation for large signals within a single exposure and readout cycle. To suppress mode-dependent offsets while maintaining low noise, a configurable dual-source-follower ramp buffer symmetrically buffers the ramp and reference voltages during auto-zeroing and is reconfigured as a full-sized buffer during unity-gain conversion. Switching-induced column offsets are compensated using optical black pixels and lightweight digital processing. The ADC is implemented in a 110 nm CMOS image sensor process and validated through post-layout simulations including extracted parasitics and Monte Carlo mismatch analysis. The core ADC consumes 36.8 µW per column. Simulation results demonstrate linearity error below 1% without missing codes and show that the proposed AG_x8-to-AG_x1 configuration extends the effective dynamic range up to 78.3 dB. Full article

Cell-based immunotherapies require noninvasive tools that can quantify the migration, biodistribution, and persistence of administered immune cells. This review focuses primarily on oncologic immune cell therapy, while also considering selected inflammatory disease models in which immune-cell trafficking is biologically relevant. We critically compare direct radionuclide labeling, sodium iodide symporter (NIS)-based reporter gene imaging, radionuclide-integrated nanoplatforms, and Cerenkov-based hybrid optical conversion strategies. Direct labeling with agents such as [⁸⁹Zr]Zr-oxine, [¹¹¹In]In-oxine, and [⁹⁹ᵐTc]Tc-HMPAO enables early positron emission tomography (PET)/single-photon emission computed tomography (SPECT) biodistribution assessment, usually within hours to several days after cell administration. NIS reporter imaging with [¹²⁴I]NaI, [¹²³I]NaI, [⁹⁹ᵐTc]TcO₄⁻, or [¹⁸F]TFB supports repeated viability-dependent imaging, because signal generation depends on active transporter expression in living engineered cells. Radionuclide-integrated gold nanoplatforms can improve intracellular retention and offer theranostic potential through combined imaging, photothermal, radiotherapeutic, or immunomodulatory functions. We further discuss PET/SPECT balance, radiopharmaceutical nomenclature, nanoparticle stabilization, ethical aspects of genetic modification, tumor-on-a-chip systems for preclinical testing, and limitations of narrative evidence synthesis. Together, these platforms provide complementary strategies for image-guided immune cell therapy, with translational relevance for patient selection, treatment optimization, safety monitoring, and oncology practice. In conclusion, NIS-centered nuclear imaging and radionuclide-integrated nanoplatforms represent complementary, clinically actionable tools for quantitative immune-cell tracking, therapeutic optimization, and safety monitoring in translational oncology and inflammatory disease research. Full article

Children increasingly consume online video content, creating a growing need for scalable approaches to support content moderation workflows. However, directly identifying harmful or policy-violating content, such as violence, sexual content, or self-harm, remains a complex task that typically requires specialized classifiers and domain-specific annotations. In this context, sentiment analysis can provide complementary information by capturing affective signals expressed through language and visual cues. This study does not treat sentiment polarity as a direct indicator of unsafe or policy-violating content. Instead, it explores multimodal sentiment analysis as an auxiliary triage signal that may help prioritize content for human review or identify segments requiring further inspection. This paper investigates the feasibility of using large vision–language models (LVLMs) for zero-shot multimodal sentiment analysis on utterance-aligned video segments. We evaluate two LVLMs, LLaVA-OneVision-7B and Qwen2.5-VL-7B, under three input settings: text-only, vision-only, and multimodal, using a conversational TV-series dataset consisting of short utterance-level video segments and transcripts. The results show that multimodal sentiment inference can provide useful screening signals without task-specific fine-tuning, although the benefits are model-dependent. LLaVA-OneVision-7B consistently outperforms Qwen2.5-VL-7B and benefits more clearly from combining textual and visual inputs, whereas Qwen2.5-VL-7B shows limited improvement across modality settings. We also analyze the trade-off between frame sampling and image resolution. Finally, we discuss limitations related to dataset scope, annotation subjectivity, class imbalance, and the need for broader validation before real-world deployment. Full article

This study proposes the Hydroponic Plant Growth Analysis System (HPGAS), a public-data-based preliminary framework for multimodal plant growth state analysis toward future filter-free aquaponic validation. The HPGAS integrates plant images, water quality signals, and environmental signals to estimate an image-centered growth index, growth stage, and proxy abnormal state probability. Because no public dataset jointly provides plant images, direct growth labels, fish metabolic variables, suspended solids, and nitrification-related measurements from a real filter-free aquaponic system, this study is not a direct operational validation. A two-stage evaluation was conducted using the Autonomous Greenhouse Challenge (AGC), HydroGrowNet, and two aquaponic Internet of Things (IoT) water quality datasets. Stage 1 implemented dataset loaders, image–sensor alignment, proxy label generation, and unimodal and fusion baselines. Stage 2 expanded handcrafted image and sensor-context features and adopted month-wise hold-out evaluation. The image-only model achieved the best growth index regression performance, with a root mean square error (RMSE) of 0.0492 ± 0.0187, whereas the fusion model showed a RMSE of 0.0837 ± 0.0196. Conversely, the fusion model achieved the best proxy abnormal state classification performance, with a F1 score of 0.9695 ± 0.0057 under the clean condition, decreasing to 0.9232 ± 0.0263 under sensor dropout and 0.9132 ± 0.0169 under image noise. Under sensor dropout, the fusion model was more stable than the sensor-only model, whereas under image noise it degraded more than the image-only model. These results indicate that multimodal fusion is most useful for proxy abnormal state classification and robust state interpretation, rather than universally superior scalar growth regression. The HPGAS provides a reproducible baseline for future real filter-free aquaponic experiments, while its operational validity remains to be tested using real filter-free aquaponic data. Full article

Alpha-ketoglutarate (aKG) is a central intermediate of cerebral energy metabolism and a precursor for glutamate synthesis in the brain. Alterations in aKG metabolism occur in pathological contexts, including isocitrate dehydrogenase (IDH) mutant astrocytomas and oligodendrogliomas, in which mutant IDH converts aKG to the oncometabolite 2-hydroxyglutarate. Given its central role in brain metabolism, non-invasive interrogation of aKG-dependent metabolic flux is needed. Hyperpolarized (HP) ¹³C MR enables real-time visualization of metabolic conversion by transiently enhancing signal intensity by several orders of magnitude. Leveraging this approach, we report the first-in-human feasibility and safety study of HP [1-¹³C]aKG MR spectroscopy in the healthy brain (n = 3). A standard operating procedure (SOP) was developed for sterile [1-¹³C]aKG dose production, achieving reproducible polarization levels averaging 30.5 ± 2.2%. Following intravenous administration, time-resolved ¹³C spectra in healthy volunteers demonstrated the detection of HP aKG resonance and a measurable downstream glutamate signal, consistent across repeat acquisitions, with a delayed temporal profile relative to aKG observed in a representative dataset. Although performed in healthy volunteers, these results establish feasibility for HP [1-¹³C]aKG metabolic imaging to open a new window into normal and pathological brain cellular metabolism. Full article

Background: Glioblastoma (GBM) exhibits marked cellular heterogeneity and resistance to therapy. Calcium (Ca²⁺) signaling at endoplasmic reticulum (ER)–mitochondria contact sites has emerged as a key regulator of mitochondrial function and cell fate; however, its lineage-specific role and therapeutic relevance in GBM remain unclear. Methods: ITPR1 expression was analyzed using single-cell and bulk RNA sequencing (RNA-seq) datasets and validated by immunohistochemistry and survival analyses. Functional studies were conducted using genetic silencing or CRISPR-mediated activation of ITPR1, combined with DRP1 knockdown, Ca²⁺ imaging, transmission electron microscopy, co-immunoprecipitation, mitochondrial fractionation, and mitochondrial functional assays. Therapeutic efficacy was evaluated in orthotopic GBM xenograft models treated with 2-aminoethoxydiphenyl borate (2-APB), temozolomide (TMZ), or their combination. Results: ITPR1 was enriched in mesenchymal-like malignant cell states and associated with higher tumor grade, recurrence, and poor prognosis. ITPR1 knockdown suppressed GBM cell proliferation and tumor growth while promoting intrinsic apoptosis. Mechanistically, loss of ITPR1 impaired ER-to-mitochondria Ca²⁺ transfer, disrupted ER–mitochondria contacts, and altered mitochondrial ultrastructure. This was accompanied by reduced DRP1 Ser616 phosphorylation and mitochondrial recruitment, as well as decreased autophagy and mitophagy activity. Consequently, ITPR1 knockdown led to mitochondrial depolarization, increased mitochondrial reactive oxygen species (ROS) accumulation, and activation of mitochondria-dependent apoptosis. Conversely, DRP1 knockdown attenuated the mitochondrial and pro-survival effects induced by ITPR1 overexpression. In vivo, combined treatment with 2-APB and TMZ resulted in greater tumor suppression and prolonged survival compared with either treatment alone, accompanied by increased apoptosis and reduced proliferation in tumor tissues. Conclusions: ITPR1 promotes GBM progression by sustaining ER–mitochondria Ca²⁺ coupling and DRP1-dependent mitochondrial quality control, thereby maintaining mitochondrial homeostasis and cell survival. Targeting inositol 1,4,5-trisphosphate receptor (IP₃R)-mediated Ca²⁺ signaling with 2-APB enhances the therapeutic efficacy of TMZ, suggesting that ITPR1-centered Ca²⁺ signaling may represent a potential therapeutic vulnerability in aggressive GBM. Full article

Preoperative risk stratification for laparoscopic cholecystectomy (LC) remains imperfect, particularly in patients with chronic inflammatory remodeling and biliary anatomic variants. Existing tools often focus on acute presentations or intraoperative variables, resulting in uncertainty on how congenital anatomy, recurrent biliary colic, and cystic pediculitis interact. We synthesize a hypothesis-generating conceptual framework and propose an illustrative candidate preoperative rubric for future validation. We performed a structured narrative review of PubMed, Scopus, and Web of Science (January 1990–December 2024; last search: 15 December 2024). Eligible primary studies evaluated clinical history, imaging-defined anatomy, inflammatory biomarkers, and/or operative outcomes (conversion, intraoperative complications, or operative difficulty) in the setting of LC. Acute cholecystitis and chronic/elective cohorts were interpreted separately during the narrative synthesis. Two reviewers screened titles/abstracts and assessed full texts using predefined inclusion/exclusion criteria; due to heterogeneity, no meta-analysis and no formal risk-of-bias tool were applied. The literature supports a plausible vicious cycle in which biliary anatomic variants may impair drainage and promote stasis, recurrent biliary colic, and chronic inflammation, ultimately leading to fibrosis/pediculitis and a “frozen” Calot’s triangle. We translate these signals into an illustrative candidate rubric (0–16 points) spanning three domains: clinical history (0–6), imaging (0–6), and inflammatory biomarkers (0–4). Weights and cut-offs (low: 0–4; moderate: 5–9; high: 10–16) were chosen a priori for conceptual clarity and are not data-derived. This review provides a conceptual map and a candidate variable set to support hypothesis generation, standardized data collection, and staged validation. The rubric is not validated and must not be used for clinical decision-making. Planned next steps include feasibility-oriented derivation, followed by prospective multicenter external validation and impact assessment. Full article

Bearings serve as vital components in rotating machinery. Fault diagnosis of bearings constitutes an essential area within mechanical health monitoring. However, most existing methods rely solely on single-modal data or employ a single signal-to-image conversion technique, leading to insufficient information dimensionality and inadequate feature representation, which ultimately limits diagnostic accuracy. To address these challenges, this paper proposes a bearing fault diagnosis method (GADF-MTF-RP-VGG16, GMRVGG) based on tri-modal image feature fusion. Specifically, three image conversion techniques—Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP)—are utilized to first convert 1D vibration signals into 2D images. Subsequently, shallow to deep features are extracted and fused through the VGG16 backbone network. Finally, fault diagnosis is achieved by integrating a fully connected classifier layer. The proposed methodology was comprehensively validated on both the Case Western Reserve University (CWRU) and the University of Ottawa datasets, which were augmented with severe 6 dB Gaussian white noise and 6 dB pink noise to simulate complex industrial environments. Under these harsh conditions, the proposed method achieved superior overall accuracies (up to 96.9% on the CWRU dataset and consistently 95.8% on the Ottawa dataset), significantly surpassing conventional single-modal approaches. This effectively addresses the limitations of insufficient feature dimensionality and inadequate representation, establishing a highly reliable and robust solution for intelligent bearing fault diagnosis. Full article

High quality factor (Q factor) resonant metasurfaces enable efficient mid-infrared (MIR) upconversion, yet their narrow operating bandwidths severely limit practical broadband detection and imaging applications. Although high Q magnetic toroidal dipole (MTD) modes exhibit outstanding momentum space ($k$-space) stability in linear optics, their application in nonlinear processes has primarily been confined to degenerate second-harmonic generation (SHG), leaving complex non-degenerate processes such as sum-frequency generation (SFG) largely unexplored. Here, we propose a tunable MIR upconversion platform based on an all-dielectric gallium phosphide (GaP) dimer metasurface. Breaking the in-plane symmetry to trigger Brillouin zone folding excites robust MTD quasi-guided modes (MTD-QGM), tightly confining the locally enhanced optical fields within the highly nonlinear GaP nanostructure. Synchronizing this high Q resonance with a spatially overlapping pump mode yields an exceptional SFG conversion efficiency of $7.9\times {10}^{-4}$, successfully translating a 3101.8 nm MIR signal to the 903 nm near-infrared band. Crucially, the intrinsic $k$-space stability of the MTD-QGM enables continuous, broadband upconversion through simple angle tuning. This mechanism effectively overcomes the narrow-band limitations characteristic of typical symmetry-protected resonators, establishing a robust paradigm for room-temperature MIR detection. Full article

This study presents an all-optical artificial synapse based on 2D perovskite materials for neuromorphic visual simulation. While conventional optoelectronic synapses, which integrate memory and processing, are prevalent in this field, their inherent optical-to-electrical conversion during signal processing incurs significant energy costs. In contrast, our proposed device operates purely in the optical domain. Under ultraviolet–visible light control, the change in light transmittance of this device can simulate various key biological synaptic plasticity behaviors, including paired-pulse facilitation and learning ability. By integrating these devices into a 28 × 28 synaptic array, we constructed an artificial neural network that mimics the experience-driven enhancement characteristic of human visual perceptual learning. Under light-responsive regulation, the system optimized image recognition learning behavior, and after multiple training sessions, the recognition accuracy stabilized above 97%. This study is based on two-dimensional perovskite materials and provides a new material platform for realizing intelligent visual systems with adaptive learning capabilities. Full article

In classic communication systems, signals and data were mostly continuous in time, such as voice (fixed and mobile telephony, and radio systems) and video signals (Television services), Conversely, in modern communication systems, most signals are packet-based (text and images in messaging services and social media) and even continuous-time data has to be converted into a discrete-time nature data, such as video and voice services that are now discretized to be sent in packet-based communication systems. However, these classic communication systems were analyzed, studied, and designed using continuous-time analysis, such as the classic Erlang-B formula. This classic analysis can still be used in modern systems, but a discrete-based framework provides a seamless analysis and yields more accurate results. In this work, the effect of the system’s elementary time step is analyzed, and guidelines for its selection are provided to adequately analyze continuous-time systems within a discrete-time framework. To demonstrate the utility of the discretization and to consider these guidelines, we developed a mathematical analysis based on a discrete-time Markov chain to study a system with a buffer capacity under conventional and bursty traffic, which is commonly found in an Internet of Things application. The derived formulas allow us to quantify system performance under a discrete framework. This, in turn, allows us to provide some relevant guidelines for the elementary time step selection to adequately analyze continuous-time systems under a discrete-time framework. Full article

A capsule endoscope (CE) provides noninvasive access to the gastrointestinal tract, offering diagnostic information that cannot be obtained through external imaging alone. However, during the examination inside the stomach, the CE’s posture may change rapidly as it moves within a dynamically deforming organ, making it difficult to determine its orientation using only the onboard camera feedback. To address this problem, this study proposes a method that employs an external array of Hall Effect Sensors (HES) to estimate the capsule’s position and orientation in real time, based on the magnetic field generated by a permanent magnet (PM) embedded inside the capsule, without the need for any additional internal sensors. This approach introduces a unified magnetic actuation and localization framework that enables real-time 5-degree-of-freedom posture estimation using only the internal PM of the capsule. Furthermore, the proposed system features an integrated architecture capable of simultaneous actuation and localization. To enhance system practicality, the sensor module and communication board were combined into a single unit that employs a digital serial communication scheme, eliminating the need for analog to digital conversion of sensing signals. By avoiding additional onboard sensors and employing a PM-based actuation system, the proposed system simplifies hardware configuration by preserving capsule miniaturization and by eliminating the high power consumption and thermal issues associated with electromagnet-based actuation, while maintaining accurate real-time tracking performance. Through an optimization process, the system achieved a position error of less than 2 mm and an angular error within 2° over a sensing range of up to 60 mm. Repeated experiments further validated the system’s effectiveness and reliability under realistic operating conditions, demonstrating its feasibility for compact and clinically applicable active capsule endoscopy systems. Full article

Background: Imaging-transcriptomic analysis, through the integration of multimodal magnetic resonance imaging (MRI) and transcriptomic data, provides complementary structural, functional, and molecular information that is crucial for the early detection and mechanistic exploration of Alzheimer’s disease (AD). However, effectively extracting features from heterogeneous multimodal data and capturing the associations between microscopic molecular variations and macroscopic brain alterations remain key challenges. Recent advances in deep learning and multimodal integration have enhanced the ability to model nonlinear cross-modal relationships, enabling more accurate identification of imaging-transcriptomic biomarkers and subtypes. Developing robust multimodal frameworks is therefore essential for early AD detection, subtype identification, and advancing precision medicine in neurodegenerative diseases. Methods: In this study, a two-stage method of multimodal Feature Extraction based on Association Analysis and Graph Convolutional Network with Self-Attention and Self-Expression framework (MFEAA-GCNSASE) for early diagnosis of AD and effective identification of subtypes of MCI with different progression to AD is proposed. In the first stage, the MFEAA model is applied to integrate multiple association analysis methods on sMRI, PET, and transcriptomic data to identify key multimodal biomarkers for AD and mild cognitive impairment (MCI). In the second stage, the GCNSASE model enhances classification accuracy between AD and MCI patients through self-attention and self-expression layers. Additionally, unsupervised clustering was performed on MCI samples using top multimodal biomarkers to explore subtype heterogeneity and conversion risk. Reliable MCI subtypes were also identified through a consensus clustering approach. Results: The proposed algorithm integrates sMRI, PET, and transcriptomic data, identifying robust biomarkers including the Left Hippocampus, Left Angular Gyrus, and key genes such as SLC25A5 and GABARAP. To ensure statistical robustness given the extreme class imbalance, we employed a rigorous repeated stratified cross-validation (RSCV) framework. GCNSASE achieved state-of-the-art discrimination performance with mean AUC values ranging from 0.946 to 0.961 across feature subsets (10–50%), significantly outperforming MOGONET (mean AUC: 0.844–0.875, p < 0.001) and conventional machine learning models with tighter 95% confidence intervals, indicating superior stability despite the limited AD sample size. Clustering analysis revealed two distinct MCI subtypes with divergent molecular landscapes: Subtype A was enriched in energy metabolism and cellular maintenance pathways, whereas Subtype B was enriched in neuroinflammatory and aberrant signaling pathways. Notably, the majority of MCI patients who subsequently converted to AD were concentrated in the immune-inflammatory Subtype B. These findings highlight that neuroinflammation coupled with bioenergetic failure constitutes a critical mechanism driving the conversion from MCI to AD. Conclusions: The proposed methods not only provide the key multimodal biomarkers and enhance the accuracy of the classification model for early AD diagnosis but also identify biologically and clinically meaningful MCI subtypes with distinct molecular signatures and conversion risks. Exploring these associated multimodal biomarkers and MCI subtypes is of great significance, as they help elucidate the heterogeneous mechanisms underlying AD onset and progression, enable the identification of high-risk individuals likely to convert to AD, and provide a foundation for targeted therapeutic strategies and individualized clinical management. These findings have important implications for understanding disease heterogeneity, discovering potential intervention targets, and advancing precision medicine in neurodegenerative diseases. Full article

For sensing applications, a complementary metal oxide semiconductor (CMOS) image sensor (CIS) with a lateral overflow integration capacitor (LOFIC) is in high demand. The LOFIC CIS can achieve high-dynamic-range (HDR) imaging by combining a low-conversion-gain (LCG) signal for large maximum signal electrons and a high-conversion-gain (HCG) signal for a low electron-referred noise floor. However, the LOFIC CIS faces challenges regarding the power consumption and circuit area when reading both HCG and LCG signals. To address these issues, this study proposes a readout circuit composed of area-efficient MOS capacitors using a folding DC operating point technique and an in-column signal selector for an on-chip HDR merger of HCG and LCG signals. A 10-bit test chip was fabricated with a $0.18$ µm CMOS process with MOS capacitors. The fabricated chip maintains high linearity, achieving an integral nonlinearity (INL) of +7.17/−6.93 LSB for the HCG signal and +7.95/−7.41 LSB for the LCG signal. Furthermore, the proposed design achieves a $14.92\%$ reduction in the average power consumption of the total readout circuit and a $36.5\%$ reduction in the readout circuit area. Full article

Aboveground storage tanks are used to store various fluids and chemicals for many industrial purposes. According to API standard 653, the structural integrity of these tanks must be regularly assessed. The U.S. EPA requires each operator to have a Spill Prevention, Control and Countermeasure Plan (SPCC) for aboveground storage containers. The accepted practice for inspection of these tanks, particularly the tank bottoms, requires removing the tank from service, emptying the tank, and interior entry for direct inspection of the structure. The required inspection operations are hazardous due to the chemicals themselves as well as the requirement to operate within confined spaces. An inspection from outside the tank would have significant cost and time benefits and would provide a large reduction in the risks faced by inspection personnel. Guided wave (GW) testing is a promising candidate for screening of storage tank walls and bottoms from the tank exterior due to the ability of GWs to propagate over long distances from a fixed probe location. The lowest-order transverse-motion guided wave modes (e.g., torsional vibrations in pipes) are a good choice for long-range inspection because this mode is not dispersive; therefore, the wave packets do not spread out in time. A common weakness of guided wave inspection is the complexity of report generation in the presence of multiple geometry features in the structure, such as welds, welded plate corners, attachments and so on. In some cases, these features cause generation of non-relevant indications caused by mode conversion. Another significant challenge in applying GW testing is development of probes with high-enough signal amplitudes and relatively small footprints to allow them to be mounted on short tank bottom extensions. In this paper, a new generation of magnetostrictive transducers will be presented. The transducers are based on the reversed Wiedemann effect and can generate shear horizontal mode guided waves over a wide frequency range (20–150 kHz) with SNRs in excess of 50 dB. The recently developed SwRI MST 8 × 8 probe contains an array of eight pairs of individual magnetostrictive transducers (MsTs). The data acquisition hardware allows acquisition using Full Matrix Capture (FMC) and analysis software reporting of anomalies based on Total Focusing Method (TFM) image reconstruction. This novel inspection package allows generation of reports that map out corrosion locations and provide estimates of defect widths. Case studies of this technology on actual storage tank walls and bottoms will be presented together with validation of processing methods on mockups with known anomalies and geometry features. Full article