Near-infrared Camera Research Articles

JWST/NIRCam Narrowband Survey of Paβ Emitters in the Spiderweb Protocluster at z = 2.16

We report the initial result of our Paβ narrowband imaging on a protocluster with the JWST Near Infrared Camera (NIRCam). As NIRCam enables deep narrowband imaging of rest-frame near-infrared lines at z > 1, we target one of the most studied protoclusters, the Spiderweb protocluster at z = 2.16, in which previous studies have confirmed more than a hundred member galaxies. The NIRCam F405N narrowband filter covers in the Paβ line the protocluster redshift given by known protocluster members, allowing for the search for new member candidates. The weight-corrected color–magnitude diagram obtained 57 sources showing narrowband excesses, 41 of which satisfy further color selection criteria for limiting the sample to Paβ emitter candidates at z ∼ 2.16, and 24 of them do not have Hα emitter counterparts. The Paβ emitter candidates appear to follow the spatial distribution of known protocluster members; however, follow-up spectroscopic confirmation is required. Only 17 out of 58 known Hα-emitting cluster members are selected as Paβ emitters in the current data, albeit the rest fall out of the narrowband selection owing to their small Paβ equivalent widths. We derive the Paβ luminosity function in the Spiderweb protocluster, showing a normalization density of logϕ*=−2.53±0.15 at a characteristic Paβ luminosity of logL*=42.33±0.17 . Furthermore, we examine the possibility of detecting faint line emitters visible only in the narrowband image but find no promising candidates.

A unique cuvette and near-infrared spectral imaging for fast and accurate quantification of milk compositions

Analyzing the nutritional compositions of milk using traditional methods is costly, time-consuming, and impractical for rapid field measurements, resulting in delays in quality control procedures. This paper presents a rapid, accurate, and easy-to-use method for quantitative analysis of finished milk compositions using a low-cost, portable, and environmentally friendly measurement system. The system uses a near-infrared (NIR) broadband digital camera to capture spectral images of milk after it is placed in a customized cuvette in the short-wave NIR region (700–1050 nm). A machine learning algorithm that combines image edge detection and gradient-boosted decision trees then regresses these images to predict the protein and fat content of the milk. Each measurement requires a maximum of 1.75 mL of milk sample with no additional consumables and takes 0.1 s to complete. The coefficient of determination (R2CV) for the fat detection model was 0.999 and the root mean square error (RMSECV) was 0.026 g/100 mL. For protein detection, the R2CV was 0.957 and the RMSECV was 0.058 g/100 mL. The experimental results show that the system achieves high precision and stability while realizing miniaturization and portability.

Impact of climate change on vegetation growth trends in a citrus farmland in the south-eastern Sicily

Abstract. The environmental repercussions of climate change triggered substantial alterations in existing ecosystem balances leading to a gradual reduction in biodiversity and the increasing degradation of soil and the built environment. This has a direct effect on agricultural production in particular, leading to increased crop water requirements, the spread of new pests and pathogens that are difficult to manage, and, thus, a general reduction in productivity. Through the analysis of multispectral data remotely sensed with latest generation of optical sensors (RGB, thermal, NIR and multi-spectral cameras), it is possible to recognize the health status and growth stages of crops. In fact, in relation to the amount of radiation reflected in the different bands it is possible to calculate spectral indices, or vegetation indices. The main vegetative indices are NDVI, SAVI, MSAVI2, GNDVI and NDRE, which are calculated by differentially combining the ultraviolet spectrum, the visible range, and the near and mid-infrared band.In this paper the outcomes of a multi-year monitoring of a citrus grove field near Rosolini (Syracuse), in south-eastern Sicily-Italy, are presented. This monitoring activity was conducted to highlight areas of productive greenery under stress, on which to act as a priority with specific agronomic interventions. In parallel with the direct monitoring phases of the citrus grove, the trend of average monthly and annual temperature and precipitation values over the past 24 years was also studied to more specifically contextualize the possible presence of significant variations in climatic trends and relate them to the results returned by the calculation of the indices.

Advances in Lanthanide-Based NIR-IIb Probes for In Vivo Biomedical Imaging.

The past decades have witnessed the significant development and practical interest of in vivo biomedical imaging technologies and optical materials in the second-near infrared (NIR-II, 1000-1700 nm) window. Imaging with the extended emission wavelength toward the long-wavelength end (NIR-IIb, 1500-1700 nm) further offers micrometer imaging resolution and centimeter tissue penetration depth by taking advantage of the much-reduced photon scattering and near-zero tissue autofluorescence background, which have become a very hot research area. This review focuses on the recent advances in the development of lanthanide-based NIR-IIb probes for in vivo biomedical applications. The progress including ratiometric imaging, multiplexed imaging for wide-field and microscopy, lifetime multiplexing and sensing, persistent luminescence, and multimodal imaging is summarized. Challenges and future directions concerning the investigation of the photophysical and photochemical properties of NIR-IIb probes, the selection of near-infrared cameras as well as the potential extension of the NIR-IIb imaging sub-window are pointed out. This review will inspire readers who have a strong interest in developing optical imaging technology and long-wavelength fluorescence probes for high-contrast in vivo biomedical applications.

An Empirical Calibration of the Tip of the Red Giant Branch Distance Method in the Near Infrared. II. JWST NIRCam Wide Filters

The tip of the red giant branch (TRGB) is a standardizable candle, identifiable as the discontinuity at the bright extreme of the red giant branch (RGB) stars in color–magnitude diagram space. The TRGB-based distance method has been used to measure distances to galaxies out to D ≤ 20 Mpc with the Hubble Space Telescope F814W filter, and is an important rung in the distance ladder to measure the Hubble constant, H 0. In the infrared (IR), the TRGB apparent magnitude ranges from 1–2 mag brighter than in the optical. Now with the James Webb Space Telescope (JWST), the feasible distance range of the TRGB method can reach ∼50 Mpc. However, the IR TRGB luminosity depends to varying degrees on stellar metallicity/age. Here we standardize the TRGB luminosity using stellar colors as a proxy for metallicity/age to derive color-based corrections for the JWST Near-Infrared Camera short-wavelength filters F090W, F115W, and F150W, and the long-wavelength filters F277W, F356W, and F444W. We provide recommended filters for distance measurements depending on the requisite precision. For science requiring high precision (≤1% in distance), we recommend measuring the TRGB in F090W versus F090W − F150W or F115W versus F115W − F277W with the caveat that even with JWST, long integration times will be necessary at farther distances. If lower precision (>1.5% in distance) can be tolerated, or if shorter integration times are desirable, we recommend measuring the TRGB in either F115W or F150W. We do not recommend F444W for precision TRGB measurements due to its lower angular resolution.

Off-Body Sleep Analysis for Predicting Adverse Behavior in Individuals With Autism Spectrum Disorder.

Poor sleep quality in Autism Spectrum Disorder (ASD) individuals is linked to severe daytime behaviors. This study explores the relationship between a prior night's sleep structure and its predictive power for next-day behavior in ASD individuals. The motion was extracted using a low-cost near-infrared camera in a privacy-preserving way. Over two years, we recorded overnight data from 14 individuals, spanning over 2000 nights, and tracked challenging daytime behaviors, including aggression, self-injury, and disruption. We developed an ensemble machine learning algorithm to predict next-day behavior in the morning and the afternoon. Our findings indicate that sleep quality is a more reliable predictor of morning behavior than afternoon behavior the next day. The proposed model attained an accuracy of 74% and a F1 score of 0.74in target-sensitive tasks and 67% accuracy and 0.69 F1 score in target-insensitive tasks. For 7 of the 14, better-than-chance balanced accuracy was obtained (p-value 0.05), with 3 showing significant trends (p-value 0.1). These results suggest off-body, privacy-preserving sleep monitoring as a viable method for predicting next-day adverse behavior in ASD individuals, with the potential for behavioral intervention and enhanced care in social and learning settings.

Sensing and Perception in Robotic Weeding: Innovations and Limitations for Digital Agriculture

The challenges and drawbacks of manual weeding and herbicide usage, such as inefficiency, high costs, time-consuming tasks, and environmental pollution, have led to a shift in the agricultural industry toward digital agriculture. The utilization of advanced robotic technologies in the process of weeding serves as prominent and symbolic proof of innovations under the umbrella of digital agriculture. Typically, robotic weeding consists of three primary phases: sensing, thinking, and acting. Among these stages, sensing has considerable significance, which has resulted in the development of sophisticated sensing technology. The present study specifically examines a variety of image-based sensing systems, such as RGB, NIR, spectral, and thermal cameras. Furthermore, it discusses non-imaging systems, including lasers, seed mapping, LIDAR, ToF, and ultrasonic systems. Regarding the benefits, we can highlight the reduced expenses and zero water and soil pollution. As for the obstacles, we can point out the significant initial investment, limited precision, unfavorable environmental circumstances, as well as the scarcity of professionals and subject knowledge. This study intends to address the advantages and challenges associated with each of these sensing technologies. Moreover, the technical remarks and solutions explored in this investigation provide a straightforward framework for future studies by both scholars and administrators in the context of robotic weeding.

Dual-labeled anti-GD2 targeted probe for intraoperative molecular imaging of neuroblastoma

BackgroundSurgical resection is integral for the treatment of neuroblastoma, the most common extracranial solid malignancy in children. Safely locating and resecting primary tumor and remote deposits of disease remains a significant challenge, resulting in high rates of complications and incomplete surgery, worsening outcomes. Intraoperative molecular imaging (IMI) uses targeted radioactive or fluorescent tracers to identify and visualize tumors intraoperatively. GD2 was selected as an IMI target, as it is highly overexpressed in neuroblastoma and minimally expressed in normal tissue.MethodsGD2 expression in neuroblastoma cell lines was measured by flow cytometry. DTPA and IRDye® 800CW were conjugated to anti-GD2 antibody to generate DTPA-αGD2-IR800. Binding affinity (Kd) of the antibody and the non-radiolabeled tracer were then measured by ELISA assay. Human neuroblastoma SK-N-BE(2) cells were surgically injected into the left adrenal gland of 3.5-5-week-old nude mice and the orthotopic xenograft tumors grew for 5 weeks. 111In-αGD2-IR800 or isotype control tracer was administered via tail vein injection. After 4 and 6 days, mice were euthanized and gamma and fluorescence biodistributions were measured using a gamma counter and ImageJ analysis of acquired SPY-PHI fluorescence images of resected organs (including tumor, contralateral adrenal, kidneys, liver, muscle, blood, and others). Organ uptake was compared by one-way ANOVA (with a separate analysis for each tracer/day combination), and if significant, Sidak’s multiple comparison test was used to compare the uptake of each organ to the tumor. Handheld tools were also used to detect and visualize tumor in situ, and to assess for residual disease following non-guided resection.Results111In-αGD2-IR800 was successfully synthesized with 0.75-2.0 DTPA and 2–3 IRDye® 800CW per antibody and retained adequate antigen-binding (Kd = 2.39 nM for aGD2 vs. 21.31 nM for DTPA-aGD2-IR800). The anti-GD2 tracer demonstrated antigen-specific uptake in mice with human neuroblastoma xenografts (gamma biodistribution tumor-to-blood ratios of 3.87 and 3.88 on days 4 and 6 with anti-GD2 tracer), while isotype control tracer did not accumulate (0.414 and 0.514 on days 4 and 6). Probe accumulation in xenografts was detected and visualized using widely available operative tools (Neoprobe® and SPY-PHI camera) and facilitated detection ofputative residual disease in the resection cavity following unguided resection.ConclusionsWe have developed a dual-labeled anti-GD2 antibody-based tracer that incorporates In-111 and IRDye® 800CW for radio- and fluorescence-guided surgery, respectively. The tracer adequately binds to GD2, specifically accumulates in GD2-expressing xenograft tumors, and enables tumor visualization with a hand-held NIR camera. These results encourage the development of 111In-αGD2-IR800 for future use in children with neuroblastoma, with the goal of improving patient safety, completeness of resection, and overall patient outcomes.

Realtime and noninvasive assessment of endotracheal tube displacement using near-infrared and visible cameras.

Endotracheal tube (ETT) intubation is a medical procedure routinely used for achieving mechanical ventilation in critically ill patients. Appropriate ETT placement is crucial as undetected tube migration may cause multiple complications or even fatalities. Therefore, prompt detection of unplanned movement of the ETT and immediate action to restore proper placement are essential to ensure patient safety. Despite this necessity, there is not a widely adopted tool for real-time assessment of ETT displacement. We have developed a device, a dual-camera endotracheal tube or DC-ETT, to address this unmet clinical need. This device uses a near-infrared (NIR) LED and a side-firing optical fiber embedded in the side of an ETT to light up the tracheal tissue and a visible and NIR camera module for the displacement detection. The NIR camera tracks the movement of the NIR pattern on the skin, while the visible camera is used to correct the body movements. The efficacy of the DC-ETT was assessed in two piglets with a linear displacement sensor as reference. A mean discrepancy of less than 0.5 mm between the DC-ETT and reference sensor was observed within a displacement range of ±15 mm. The results suggest that the DC-ETT can potentially provide a simple and cost-effective solution for real-time monitoring of ETT displacements in operating rooms, intensive care units, and emergency departments.

A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect Ratio.

Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this context, one solution to address the challenge of drowsiness detection is to anticipate drowsiness by alerting drivers in a timely and effective manner. Thus, this paper presents a Convolutional Neural Network (CNN)-based approach for drowsiness detection by analyzing the eye region and Mouth Aspect Ratio (MAR) for yawning detection. As part of this approach, endpoint delineation is optimized for extraction of the region of interest (ROI) around the eyes. An NVIDIA Jetson Nano-based device and near-infrared (NIR) camera are used for real-time applications. A Driver Drowsiness Artificial Intelligence (DD-AI) architecture is proposed for the eye state detection procedure. In a performance analysis, the results of the proposed approach were compared with architectures based on InceptionV3, VGG16, and ResNet50V2. Night-Time Yawning-Microsleep-Eyeblink-Driver Distraction (NITYMED) was used for training, validation, and testing of the architectures. The proposed DD-AI network achieved an accuracy of 99.88% with the NITYMED test data, proving superior to the other networks. In the hardware implementation, tests were conducted in a real environment, resulting in 96.55% and 14 fps on average for the DD-AI network, thereby confirming its superior performance.

The JWST Resolved Stellar Populations Early Release Science Program. VI. Identifying Evolved Stars in Nearby Galaxies

We present an investigation of evolved stars in the nearby star-forming galaxy Wolf–Lundmark–Melotte (WLM), using Near-Infrared Camera (NIRCam) imaging from the JWST Resolved Stellar Populations Early Release Science program. We find that various combinations of the F090W, F150W, F250M, and F430M filters can effectively isolate red supergiants and thermally pulsing asymptotic giant branch (TP-AGB) stars from one another, while also providing a reasonable separation of the primary TP-AGB subtypes: carbon-rich C-type stars and oxygen-rich M-type stars. The classification scheme we present here agrees very well with the well-established Hubble Space Telescope (HST) medium-band filter technique. The ratio of C to M-type stars is 0.8 ± 0.1 for both the new JWST and the HST classifications, which is within 1σ of empirical predictions from optical narrowband CN and TiO filters. The evolved star colors show good agreement with the predictions from the PARSEC + COLIBRI stellar evolutionary models, and the models indicate a strong metallicity dependence that makes stellar identification even more effective at higher metallicity. However, the models also indicate that evolved star identification with NIRCam may be more difficult at lower metallicities. We test every combination of NIRCam filters using the models and present additional filters that are also useful for evolved star studies. We also find that ≈90% of the dusty evolved stars are carbon rich, suggesting that carbonaceous dust dominates the present-day dust production in WLM, similar to the findings in the Magellanic Clouds. These results demonstrate the usefulness of NIRCam in identifying and classifying dust-producing stars without the need for mid-infrared data.

Efficient Point-spread Function Modeling with ShOpt.jl: A Point-spread Function Benchmarking Study with JWST NIRCam Imaging

With their high angular resolutions of 30–100 mas, large fields of view, and complex optical systems, imagers on next-generation optical/near-infrared space observatories, such as the Near-Infrared Camera (NIRCam) on the James Webb Space Telescope, present new opportunities for science and also new challenges for empirical point-spread function (PSF) characterization. In this context, we introduce ShOpt, a new PSF fitting tool developed in Julia and designed to bridge the advanced features of PSFs in the full field of view (PIFF) with the computational efficiency of PSF Extractor (PSFEx). Along with ShOpt, we propose a suite of nonparametric statistics suitable for evaluating PSF fit quality in space-based imaging. Our study benchmarks ShOpt against the established PSF fitters PSFEx and PIFF using real and simulated COSMOS-Web Survey imaging. We assess their respective PSF model fidelity with our proposed diagnostic statistics and investigate their computational efficiencies, focusing on their processing speed relative to the complexity and size of the PSF models. We find that ShOpt can already achieve PSF model fidelity comparable to PSFEx and PIFF while maintaining competitive processing speeds, constructing PSF models for large NIRCam mosaics within minutes.

A method to qualify image post-processing for thin wall thickness prediction from NIR camera image of aluminum WAAM process

A method to qualify image post-processing for thin wall thickness prediction from NIR camera image of aluminum WAAM process

Rice Leaf Nitrogen Content Estimation Through A Methodological Framework Using Single-Sensor Multispectral Images

Using non-destructive evaluation tools based on imaging techniques, including single-sensor multispectral cameras, provides a cost-effective solution for optimizing rice nitrogen fertilization through site-specific nutrient management. However, their accuracy and precision have been identified as areas for improvement. This study aims to develop a methodology to improve the accuracy of estimations through field experiments. It utilizes multispectral images captured by MAPIR Survey3W Orange Cyan Near-Infrared and MAPIR Survey3W Red Edge cameras. The Normalized Difference Vegetation Index and Red Edge values derived from these images are correlated with Soil Plant Analysis Development values to assess rice nitrogen levels. A prediction model is then built using the Support Vector Regression algorithm. Findings from the experiments underscore the importance of addressing shadow effects, integrating the dataset on light intensity and image capture time, conducting radiometric calibration, filtering outlier data, employing image segmentation, and utilizing nonlinear Canova tests to enhance estimation accuracy. By configuring the Support Vector Regression model with RBF kernel, gamma set to 1.24, and epsilon set to 0.1, the R2 of the train data and validation data reaches 0.851, and 0.840 respectively. Meanwhile, the R2 of the test data achieves 0.793 with a mean absolute percentage error of 3.49 % and a root mean square error of 1.70. These findings underscore the potential of the proposed methodology to improve the estimation of rice nitrogen status based on single-sensor multispectral images, paving the way for more effective nutrient management strategies in rice cultivation.

Study protocol for Near-infrared molecular imaging for lung cancer detection and treatment during mini-invasive surgery (phase II Trial) - (the RECOGNISE study)

Introduction. To date, radical surgery remains the best curative option in patients with early-stage lung cancer. In patients with small lung lesions, video-assisted thoracic surgery (VATS) should be increasingly chosen as a fundamental alternative to thoracotomy as it is associated with less postoperative pain and better quality of life. This scenario necessarily increases the need for thoracic surgeons to implement new localization techniques. The conventional near-infrared (NIR) indocyanine green (ICG) method demonstrated a significant limitation in deep cancer recognition, principally due to its intrinsic low-depth tissue penetration. Similarly, the lymph-node sentinel approach conducted by the ICG method was demonstrated to be inefficient, mainly due to the non-specificity of the tracker and the irregular pathway of pulmonary lymph node drainage. Our study aims to evaluate the effectiveness of Cetuximab- IRDye800CW in marking lung nodules and mediastinal lymph nodes. Methods and analysis. This study is defined as an open-label, single-arm, single-stage phase II trial evaluating the effectiveness of Cetuximab-IRDye800CW in detecting tumors and lymph-node metastases in patients with lung cancer who are undergoing video-assisted thoracic surgery (VATS). Cetuximab is a monoclonal antibody that binds, inhibits, and degrade the EGFR. The IRDye® 800CW, an indocyanine-type NIR fluorophore, demonstrated enhanced tissue penetration compared to other NIR dyes. The combination with the clinical approved monoclonal antibody anti-epidermal growth factor EGFR Cetuximab (Cetuximab-IRDye800) has shown promising results as a specific tracker in different cancer types (i.e., brain, pancreas, head, and neck). The study’s primary outcome is focused on the proportion of patients with lung nodules detected during surgery using an NIR camera. The secondary outcomes include a broad spectrum of items, including the proportion of patients with detection of unexpected cancer localization during surgery by NIR camera and the proportion of patients with negative surgical margins, the evaluation of the time spawns between the insertion of the NIR camera and the visualization of the nodule and the possible morbidity of the drug assessed during and after the drug infusion. Ethics and dissemination. This trial has been approved by the Ethical Committee of Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino (Torino, Italy) and by the Italian Medicines Agency (AIFA). Findings will be written as methodology papers for conference presentations and published in peer-reviewed journals. The Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, the University of Torino, and the AIRC Public Engagement Divisions will help identify how best to publicize the findings.Trial registration EudraCT 202,100,645,430. ClinicalTrials.gov NCT06101394 (October 23, 2023).

The progenitor star of SN 2023ixf: a massive red supergiant with enhanced, episodic pre-supernova mass loss

ABSTRACT We identify the progenitor star of SN 2023ixf in Messier 101 using Keck/NIRC2 adaptive optics imaging and pre-explosion Hubble Space Telescope (HST)/Advanced Camera for Surveys (ACS) images. The supernova, localized with diffraction spikes and high-precision astrometry, unambiguously coincides with a progenitor candidate of $m_\text{F814W}=24.87\pm 0.05$ (AB). Given its reported infrared excess and semiregular variability, we fit a time-dependent spectral energy distribution (SED) model of a dusty red supergiant (RSG) to a combined data set of HST optical, ground-based near-infrared, and Spitzer Infrared Array Camera (IRAC) [3.6], [4.5] photometry. The progenitor resembles an RSG of $T_\text{eff}=3488\pm 39$ K and $\log (L/\mathrm{L}_\odot)=5.15\pm 0.02$, with a $0.13\pm 0.01$ dex ($31.1\pm 1.7$ per cent) luminosity variation at a period of $P=1144.7\pm 4.8$ d, obscured by a dusty envelope of $\tau =2.92\pm 0.02$ at $1\, \mu \text{m}$ in optical depth (or $A_\text{V}=8.43\pm 0.11$ mag). The signatures match a post-main-sequence star of $18.2_{-0.6}^{+1.3}\, \mathrm{M}_\odot$ in zero-age main-sequence mass, among the most massive SN II progenitor, with a pulsation-enhanced mass-loss rate of $\dot{M}=(4.32\pm 0.26)\times 10^{-4} \, \mathrm{M}_\odot \, \text{yr}^{-1}$. The dense and confined circumstellar material is ejected during the last episode of radial pulsation before the explosion. Notably, we find strong evidence for variations of $\tau$ or $T_\text{eff}$ along with luminosity, a necessary assumption to reproduce the wavelength-dependent variability, which implies periodic dust sublimation and condensation. Given the observed SED, partial dust obscuration remains possible, but any unobstructed binary companion over $5.6\, \mathrm{ M}_\odot$ can be ruled out.

Cross-Modal Face Super-Resolution Based on Quasi-Siamese Domain Transfer Fusion Network

In this paper, we propose a Cross-Modal Face Super-Resolution (CMFSR) method to construct high-resolution (HR) facial images from low-resolution (LR) cross-modal facial images captured respectively by disjoint visible light (VIS) and near-infrared (NIR) cameras. Due to the coupling of modality transformation and information fusion, CMFSR is more difficult to obtain HR reconstructed results compared with traditional super-resolution. To solve this problem, a Quasi-Siamese Domain Transfer Fusion Network (QSDTFN) for CMFSR is proposed in this paper, whose two branches transfer two LR face modality to HR face modality by domain transfer respectively. Different from two completely independent branches in the traditional pseudo-siamese network, only the HR-to-LR face transfer processes of the two branches in our quasi-siamese network are independent, while the LR-to-HR face transfer processes are coupled. This coupled module called the Adaptive Weighted Domain Transfer Fusion Module (AWDTFM) disentangles the modality and identity information in the two LR faces, thus achieving modality transformation and identity information fusion simultaneously. In order to strengthen the optimization on the process of CMFSR, this method further introduces the backward QSDTFN to form a higher-level bidirectional structure with the forward QSDTFN, and specifically designs two types of losses: intra-network loss and inter-network loss, to constrain the modality and identity consistencies within one QSDTFN and between two QSDTFNs respectively. The experimental results on the challenging LR cross-modal face datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods.

The star-forming and ionizing properties of dwarf z ~ 6–9 galaxies in JADES: insights on bursty star formation and ionized bubble growth

ABSTRACT Reionization is thought to be driven by faint star-forming galaxies, but characterizing this population has long remained very challenging. Here, we utilize deep nine-band JADES (JWST Advanced Deep Extragalactic Survey)/NIRCam (Near-Infrared Camera) imaging to study the star-forming and ionizing properties of 756 $z\sim 6-9$ galaxies, including hundreds of very ultraviolet (UV)-faint objects ($M_\mathrm{UV}\gt -18$). The faintest ($m\sim 30$) galaxies in our sample typically have stellar masses of $M_\ast \sim (1-3)\times 10^7\ \mathrm{ M}_\odot$ and young light-weighted ages ($\sim$50 Myr), though some show strong Balmer breaks implying much older ages ($\sim$500 Myr). We find no evidence for extremely massive galaxies ($\gt 3\times 10^{10}\ \mathrm{ M}_\odot$) in our sample. We infer a strong (factor $\gt $2) decline in the typical [O iii]$+$H $\beta$ equivalent widths (EWs) towards very faint $z\sim 6-9$ galaxies, yet a weak UV luminosity dependence on the H $\alpha$ EWs at $z\sim 6$. We demonstrate that these EW trends can be explained if fainter galaxies have systematically lower metallicities as well as more recently declining star formation histories relative to the most UV-luminous galaxies. Our data provide evidence that the brightest galaxies are frequently experiencing a recent strong upturn in star formation rate. We also discuss how the EW trends may be influenced by a strong correlation between $M_\mathrm{UV}$ and Lyman continuum escape fraction. This alternative explanation has dramatically different implications for the contribution of galaxies along the luminosity function to cosmic reionization. Finally, we quantify the photometric overdensities around two $z\,\gt\,7$ strong Ly $\alpha$ emitters. One Ly $\alpha$ emitter lies close to a strong photometric overdensity, while the other shows no significant nearby overdensity, perhaps implying that not all strong $z\,\gt\, 7$ Ly $\alpha$ emitters reside in large ionized bubbles.

Fluorescence Intensity between Standard versus Diluted Indocyanine Green to Evaluate Flap Perfusion in Rats.

The ideal dose for indocyanine green (ICG) has not been established yet, although 5 mg per mL is widely accepted for free flap evaluation. Due to its high price and rarity in developing countries, this preliminary study aimed to find the lowest concentration of ICG without reducing the fluorescence quality read by near-infrared camera in animal models. An experimental study was conducted on 25 Wistar rats divided into five groups based on the injected ICG, which was in 5 mg per mL, 3.75 mg per mL, 2.5 mg per mL, 1.25 mg per mL, and 0.5 mg per mL concentrations. The epigastric flap was elevated and confirmed to be vital on the fifth day. Upon confirmation, bolus IV injection of ICG was given via the tail, and the flap was read using near-infrared camera. The 25 different videos are randomized and rated individually in a blind manner by five microsurgeons, chosen beforehand. The videos are evaluated with a scoring system ranging from 0 to 4, assessing fluorescence visibility and flap vasculature. Nonetheless, the intraclass correlation coefficient is 0.779. There was no difference between standard and diluted ICG concentrations to evaluate flap perfusion. The 2.5 mg per mL concentration of ICG was the most favorable. This finding is not clinically relevant for application in human subjects yet. However, this study shows promising results for further usage of ICG in daily practice at a lower cost.

Sulfur Dioxide and Other Molecular Species in the Atmosphere of the Sub-Neptune GJ 3470 b

We report observations of the atmospheric transmission spectrum of the sub-Neptune exoplanet GJ 3470 b taken using the Near-Infrared Camera on JWST. Combined with two archival Hubble Space Telescope/Wide-Field Camera 3 transit observations and 15 archival Spitzer transit observations, we detect water, methane, sulfur dioxide, and carbon dioxide in the atmosphere of GJ 3470 b, each with a significance of >3σ. GJ 3470 b is the lowest-mass—and coldest—exoplanet known to show a substantial sulfur dioxide feature in its spectrum, at M p = 11.2 M ⊕ and T eq = 600 K. This indicates that disequilibrium photochemistry drives sulfur dioxide production in exoplanet atmospheres over a wider range of masses and temperatures than has been reported or expected. The water, carbon dioxide, and sulfur dioxide abundances we measure indicate an atmospheric metallicity of approximately 100× solar. We see further evidence for disequilibrium chemistry in our inferred methane abundance, which is significantly lower than expected from equilibrium models consistent with our measured water and carbon dioxide abundances.