IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI

Parallel imaging is a commonly used technique to accelerate magnetic resonance imaging (MRI) data acquisition. Mathematically, parallel MRI reconstruction can be formulated as an inverse problem relating the sparsely sampled k-space measurements to the desired MRI image. Despite the success of many existing reconstruction algorithms, it remains a challenge to reliably reconstruct a high-quality image from highly reduced k-space measurements. Recently, implicit neural representation has emerged as a powerful paradigm to exploit the internal information and the physics of partially acquired data to generate the desired object. In this study, we introduced IMJENSE, a scan-specific implicit neural representation-based method for improving parallel MRI reconstruction. Specifically, the underlying MRI image and coil sensitivities were modeled as continuous functions of spatial coordinates, parameterized by neural networks and polynomials, respectively. The weights in the networks and coefficients in the polynomials were simultaneously learned directly from sparsely acquired k-space measurements, without fully sampled ground truth data for training. Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms. With extremely limited calibration data, IMJENSE is more stable than supervised calibrationless and calibration-based deep-learning methods. Results show that IMJENSE robustly reconstructs the images acquired at 5$\mathbf{\times}$ and 6$\mathbf{\times}$ accelerations with only 4 or 8 calibration lines in 2D Cartesian acquisitions, corresponding to 22.0% and 19.5% undersampling rates. The high-quality results and scanning specificity make the proposed method hold the potential for further accelerating the data acquisition of parallel MRI

Improved SST-precipitation intraseasonal relationships in the ECMWF coupled climate reanalysis

The European Centre for Medium-range Weather Forecasts (ECMWF) has produced the ocean-atmosphere coupled reanalysis for the 20th century CERA-20C, following on from the similar, but atmosphere-only, reanalysis ERA-20C. Here we demonstrate the capability of CERA-20C in producing more physically consistent ocean and atmosphere boundary conditions, by focusing on sea surface temperature (SST)-precipitation intra-seasonal relationships. CERA-20C reproduces well the observed SST-precipitation correlations, while these relationships are poorly represented in ERA-20C, with the greatest discrepancies in the early 1900s. The improved relationships in CERA-20C are due to intra-seasonal improvements in SST that are not present in the external HadISST2 product. In CERA-20C, SST-precipitation relationships are slightly weaker in the 1900s than in the 2000s, mainly due to differences in the assimilated observation density. We also find that the coupled model initialized from CERA-20C in the 2000s realistically simulates these relationships, while relaxing SST towards HadISST2 tends to damp these relationships. CERA-20C has improved mean and variance in precipitation over ERA-20C, but these are mostly due to improvements in the atmospheric model and not due to coupled feedbacks

OV-VG: A Benchmark for Open-Vocabulary Visual Grounding

Open-vocabulary learning has emerged as a cutting-edge research area, particularly in light of the widespread adoption of vision-based foundational models. Its primary objective is to comprehend novel concepts that are not encompassed within a predefined vocabulary. One key facet of this endeavor is Visual Grounding, which entails locating a specific region within an image based on a corresponding language description. While current foundational models excel at various visual language tasks, there's a noticeable absence of models specifically tailored for open-vocabulary visual grounding. This research endeavor introduces novel and challenging OV tasks, namely Open-Vocabulary Visual Grounding and Open-Vocabulary Phrase Localization. The overarching aim is to establish connections between language descriptions and the localization of novel objects. To facilitate this, we have curated a comprehensive annotated benchmark, encompassing 7,272 OV-VG images and 1,000 OV-PL images. In our pursuit of addressing these challenges, we delved into various baseline methodologies rooted in existing open-vocabulary object detection, VG, and phrase localization frameworks. Surprisingly, we discovered that state-of-the-art methods often falter in diverse scenarios. Consequently, we developed a novel framework that integrates two critical components: Text-Image Query Selection and Language-Guided Feature Attention. These modules are designed to bolster the recognition of novel categories and enhance the alignment between visual and linguistic information. Extensive experiments demonstrate the efficacy of our proposed framework, which consistently attains SOTA performance across the OV-VG task. Additionally, ablation studies provide further evidence of the effectiveness of our innovative models. Codes and datasets will be made publicly available at https://github.com/cv516Buaa/OV-VG

Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision

Visual Grounding (VG) aims at localizing target objects from an image based on given expressions and has made significant progress with the development of detection and vision transformer. However, existing VG methods tend to generate false-alarm objects when presented with inaccurate or irrelevant descriptions, which commonly occur in practical applications. Moreover, existing methods fail to capture fine-grained features, accurate localization, and sufficient context comprehension from the whole image and textual descriptions. To address both issues, we propose an Iterative Robust Visual Grounding (IR-VG) framework with Masked Reference based Centerpoint Supervision (MRCS). The framework introduces iterative multi-level vision-language fusion (IMVF) for better alignment. We use MRCS to ahieve more accurate localization with point-wised feature supervision. Then, to improve the robustness of VG, we also present a multi-stage false-alarm sensitive decoder (MFSD) to prevent the generation of false-alarm objects when presented with inaccurate expressions. The proposed framework is evaluated on five regular VG datasets and two newly constructed robust VG datasets. Extensive experiments demonstrate that IR-VG achieves new state-of-the-art (SOTA) results, with improvements of 25\% and 10\% compared to existing SOTA approaches on the two newly proposed robust VG datasets. Moreover, the proposed framework is also verified effective on five regular VG datasets. Codes and models will be publicly at https://github.com/cv516Buaa/IR-VG

An experimental study and axial tensile constitutive model of the toughness of PP-SACC for rapid repairs

To improve the economic benefits of engineered cementitious composites and control the repair cycle, repair materials were designed, with the key components of the mixture being low-cost polypropylene (PP) fibers and fast-setting sulfoaluminate cement. The effects of water/binder ratio, fiber content, and aggregate particle size on the flowability, mechanical properties, and toughness of the polypropylene fiber-reinforced sulfoaluminate cementitious composite (PP-SACC) were explored. Based on experimentally measured axial tensile stress–strain curves, a constitutive model of PP-SACC was derived in terms of fiber content and water/binder ratio. Additionally, the correlation coefficients representing the relationships of the mixture indices with the tensile properties were explored based on revised gray relational analysis. Test results indicated that fiber content and water/binder ratio were the most important factors affecting the mechanical properties, toughness, and fluidity of the material; in contrast, the influence of aggregate size was slight. The PP-SACC mixture with an aggregate size of 75 µm, a water/binder ratio of 0.30, and a fiber content of 3.0% demonstrated an excellent degree of toughness and exhibited a flexural hardening phenomenon under bending load

Seroprevalence and Genetic Characterization of Toxoplasma Gondii in Three Species of Pet Birds in China

Background Toxoplasmosis, caused by the protozoan parasite Toxoplasma gondii, is one of the most common zoonosis worldwide, affecting a wide range of warm-blooded mammals and birds worldwide. However, no information on T. gondii infection in pet birds in China is available. Therefore, this study was performed to determine the prevalence of T. gondii infection in pet birds in Gansu province, China. Methods A total of 687 blood samples were collected from pet birds (Carduelis spinus, Alauda gulgula, Cocothraustes migratorlus) in three representative administrative regions in Gansu province, northwest China between August 2011 and September 2012 T. gondii antibodies were determined using the modified agglutination test (MAT). Genomic DNA was extracted from the brain tissues of seropositive pet birds and T. gondii B1 gene was amplified using a semi-nested PCR.DNA samples giving positive B1 amplification were then genetically characterized using multi-locus PCR-RFLP. Results The overall T. gondii seroprevalence was 11.21% (77/687). C. spinus had the highest T. gondii seroprevalence (11.65%), followed by A. arvensis (11.39%) and C. migratorlus (5.26%), these differences were not statistically significant (P \u3e 0.05). Of 77 DNA samples, 8 were positive for the T. gondii B1 gene, four showed complete genotyping results. Only one genotype (the Type II variant: ToxoDB genotype #3) was identified. Conclusions The results of the present survey indicated the presence of T. gondii infection in pet birds in Gansu province, China. These data provide base-line information for the execution of control strategies against T. gondii infection in pet birds. To our knowledge, this is the first report documenting the occurrence of T. gondii prevalence and genotype in pet birds in China

Characteristics of prefrontal activity during emotional and cognitive processing in patients with bipolar disorder: A multi-channel functional near-infrared spectroscopy study

Bipolar disorder (BD) is a recurrent chronic mental disorder with a broad profile of functional deficits including disturbed emotional processing and cognitive impairments. The goal of the current study was to further explore the underlying neural mechanism of dysfunction in patients with BD from a comprehensive perspective of both cognition and emotion. Forty-six clinical patients with BD and forty-five healthy controls performed emotion induction task and verbal fluency task (VFT), with frontal activity measured by functional near-infrared spectroscopy (fNIRS). Our results show distinct hemodynamic activity in the prefrontal region during emotional and cognitive processing between patients with BD and healthy controls. Patients with BD exhibit valence-dependent prefrontal cortex (PFC) hemodynamic response to emotional stimuli, with bilateral frontal hypoactivity indicating decreased positive reactivity and left frontal hyperactivity indicating increased negative reactivity. On the other hand, patients with BD showed impaired performance with bilateral frontal hypoactivity during VFT. Taken together, frontal dysfunction of cognition and emotionality in patients with BD probed by fNIRS would be a potential biomarker in clinical assessment

Manipulating refractive index, homogeneity and spectroscopy of Yb3+^{3+}-doped silica-core glass towards high-power large mode area photonic crystal fiber lasers

Output power scaling of single mode large mode area (LMA) photonic crystal fiber (PCF) amplifiers urgently requires the low refractive index of Yb³⁺-doped silica glasses whilst maintaining high optical homogeneity. In this paper, we report on a promising alternative Yb³⁺/Al³⁺/F¯/P⁵⁺-co-doped silica core-glass (YAFP), which is prepared by modified sol-gel method developed by our group and highly suitable for fabricating high power LMA PCF amplifiers. By controlling the doping combinations of Al³⁺/F¯/P⁵⁺ in Yb³⁺- doped silica glass,it not only ensures low refractive index (RI) but also maintains the excellent optical homogeneity and spectroscopic properties of Yb³⁺. The spectroscopic properties of Yb³⁺ ions have not deteriorated by the co-doping of F¯ and P⁵⁺ in YAFP glass compared with that of Yb³⁺/Al³⁺ co-doped silica glass. A large-size (⌀5 mm × 90 mm) YAFP silica-core glass rod with low average RI difference of 2.6 × 10¯⁴ (with respect to pure silica glass), and low radial and axial RI fluctuations of ~2 × 10¯⁴, was prepared. A LMA PCF with 50 μm core diameter was obtained by stack-capillary-draw techniques using YAFP core glass. Its core NA is 0.027. An average amplified power of 97 W peaking at 1030 nm and light-light efficiency of 54% are achieved from a 6.5 m long PCF in the pulse amplification laser experiment. Meanwhile, quasi-single-mode transmission is obtained with laser beam quality factor M² of 1.4

Intra-tumoural heterogeneity characterization through texture and colour analysis for differentiation of non-small cell lung carcinoma subtypes

Radiomics has shown potential in disease diagnosis, but its feasibility for non-small cell lung carcinoma (NSCLC) subtype classification is unclear. This study aims to explore the diagnosis value of texture and colour features from positron emission tomography computed tomography (PET-CT) images in differentiation of NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). Two patient cohorts were retrospectively collected into a dataset of 341 18F-labeled 2-deoxy-2fluoro-d-glucose ([18F] FDG) PET-CT images of NSCLC tumours (125 ADC, 174 SqCC, and 42 cases with unknown subtype). Quantification of texture and colour features was performed using freehand regions of interest. The relation between extracted features and commonly used parameters such as age, gender, tumour size, and standard uptake value (SUVmax) was explored. To classify NSCLC subtypes, support vector machine algorithm was applied on these features and the classification performance was evaluated by receiver operating characteristic curve analysis. There was a significant difference between ADC and SqCC subtypes in texture and colour features (P  <  0.05); this showed that imaging features were significantly correlated to both SUVmax and tumour diameter (P  <  0.05). When evaluating classification performance, features combining texture and colour showed an AUC of 0.89 (95% CI, 0.78–1.00), colour features showed an AUC of 0.85 (95% CI, 0.71–0.99), and texture features showed an AUC of 0.68 (95% CI, 0.48–0.88). DeLong's test showed that AUC was higher for features combining texture and colour than that for texture features only (P  =  0.010), but not significantly different from that for colour features only (P  =  0.328). HSV colour features showed a similar performance to RGB colour features (P  =  0.473). The colour features are promising in the refinement of NSCLC subtype differentiation, and features combining texture and colour of PET-CT images could result in better classification performance