A Deep Learning Model for Predicting Molecular Subtype of Breast Cancer by Fusing Multiple Sequences of DCE-MRI From Two Institutes.

RATIONALE AND OBJECTIVES: To evaluate the performance of deep learning (DL) in predicting different breast cancer molecular subtypes using DCE-MRI from two institutes. MATERIALS AND METHODS: This retrospective study included 366 breast cancer patients from two institutes, divided into training (n = 292), validation (n = 49) and testing (n = 25) sets. We first transformed the public DCE-MRI appearance to ours to alleviate small-data-size and class-imbalance issues. Second, we developed a multi-branch convolutional-neural-network (MBCNN) to perform molecular subtype prediction. Third, we assessed the MBCNN with different regions of interest (ROIs) and fusion strategies, and compared it to previous DL models. Area under the curve (AUC) and accuracy (ACC) were used to assess different models. Delong-test was used for the comparison of different groups. RESULTS: MBCNN achieved the optimal performance under intermediate fusion and ROI size of 80 pixels with appearance transformation. It outperformed CNN and convolutional long-short-term-memory (CLSTM) in predicting luminal B, HER2-enriched and TN subtypes, but without demonstrating statistical significance except against CNN in TN subtypes, with testing AUCs of 0.8182 vs. [0.7208, 0.7922] (p=0.44, 0.80), 0.8500 vs. [0.7300, 0.8200] (p=0.36, 0.70) and 0.8900 vs. [0.7600, 0.8300] (p=0.03, 0.63), respectively. When predicting luminal A, MBCNN outperformed CNN with AUCs of 0.8571 vs. 0.7619 (p=0.08) without achieving statistical significance, and is comparable to CLSTM. For four-subtype prediction, MBCNN achieved an ACC of 0.64, better than CNN and CLSTM models with ACCs of 0.48 and 0.52, respectively. CONCLUSION: Developed DL model with the feature extraction and fusion of DCE-MRI from two institutes enabled preoperative prediction of breast cancer molecular subtypes with high diagnostic performance.

Distribution and bulk flow analyses of the intraflagellar transport (IFT) motor kinesin-2 support an "on-demand" model for Chlamydomonas ciliary length control.

Most cells tightly control the length of their cilia. The regulation likely involves intraflagellar transport (IFT), a bidirectional motility of multi-subunit particles organized into trains that deliver building blocks into the organelle. In Chlamydomonas, the anterograde IFT motor kinesin-2 consists of the motor subunits FLA8 and FLA10 and the nonmotor subunit KAP. KAP dissociates from IFT at the ciliary tip and diffuses back to the cell body. This observation led to the diffusion-as-a-ruler model of ciliary length control, which postulates that KAP is progressively sequestered into elongating cilia because its return to the cell body will require increasingly more time, limiting motor availability at the ciliary base, train assembly, building block supply, and ciliary growth. Here, we show that Chlamydomonas FLA8 also returns to the cell body by diffusion. However, more than 95% of KAP and FLA8 are present in the cell body and, at a given time, just ~1% of the motor participates in IFT. After repeated photobleaching of both cilia, IFT of fluorescent kinesin subunits continued indicating that kinesin-2 cycles from the large cell-body pool through the cilia and back. Furthermore, growing and full-length cilia contained similar amounts of kinesin-2 subunits and the size of the motor pool at the base changed only slightly with ciliary length. These observations are incompatible with the diffusion-as-a-ruler model, but rather support an "on-demand model," in which the cargo load of the trains is regulated to assemble cilia of the desired length.

[Research Process on the Combined Pollution of Microplastics and Typical Pollutants in Agricultural Soils].

As a new type of environmental persistent pollutant， microplastics can not only have adverse effects on the ecosystem but also form complex pollution with co-existing pollutants in the surrounding environment， resulting in higher ecological and health risks. Based on the perspective of agroecosystems， this study focused on the combined pollution of heavy metals， pesticides， and antibiotics， which are three typical pollutants of farmland soil， as well as microplastics and discussed the adsorption-desorption behavior of heavy metals， pesticides， and antibiotics on microplastics. The influence of the structure and properties of microplastics， the physicochemical properties of pollutants， and environmental conditions on the adsorption and desorption behavior of heavy metals， pesticides， and antibiotics on microplastics was discussed. The influence of microplastics on the bioavailability of heavy metals， pesticides， and antibiotics in farmland soil and the internal mechanism were expounded. The existing problems and shortcomings of current research were pointed out， and the future research direction was proposed. This study can provide a scientific reference for ecological risk assessment of the combined pollution of microplastics and typical pollutants in farmland soil.

Overexpression and knockdown of cotton GhdadD gene reveals its drought and salt stress tolerance role.

The 5'-deoxyadenosine deaminase (DADD), a member of the amidohydrolase family regulates biological purine metabolism. In this study, bioinformatic analysis, overexpression and knockdown of GhdadD gene were detected to identify its potential role in drought and salt stress tolerance. The results revealed that GhdadD was induced by ABA, Auxin, MBS and light responsive elements. In transgenic Arabidopsis, seed germination rate and root length were increased under drought or salt stress. GhdadD overexpressed seedlings resulted in higher plant height, less leaf damage and lower ion permeability. The expression of osmotic stress and ABA-responsive genes were up regulated. While in GhdadD-silenced cotton seedlings, CAT, SOD activity and soluble sugar content were reduced, MDA content was increased, and the stoma opening was depressed under drought or salt stress. Some osmics stress marker genes were also up regulated. These data indicating that GhdadD enhanced plant resistance to drought and salt stress through ABA pathways.

CORONet: A Cross-Sequence Joint Representation and Hypergraph Convolutional Network for Classifying Molecular Subtypes of Breast Cancer Using Incomplete DCE-MRI.

Breast cancer, the predominant malignancy among women, is characterized by significant heterogeneity, leading to the emergence of distinct molecular subtypes. Accurate differentiation of these molecular subtypes holds paramount clinical significance, owing to substantial variations in prognosis, therapeutic strategies, and survival outcomes. In this study, we propose a cross-sequence joint representation and hypergraph convolution network (CORONet) for classifying molecular subtypes of breast cancer using incomplete DCE-MRI. Specifically, we first build a cross-sequence joint representation (COR) module to integrate image imputation and feature representation into a unified framework, encouraging effective feature extraction for subsequent classification. Then, we fuse multiple COR features and applied feature selection to reduce the redundant information between sequences. Finally, we deploy hypergraph structures to model high-order correlation among different subjects and extracted high-level semantic features by hypergraph convolutions for molecular subtyping. Extensive experiments on incomplete DCE-MRIs of 395 patients from the TCIA repository showed a significant improvement of our CORONet over state of the arts, with the area under the curve (AUC) of 0.891 and 0.903 for luminal and triple-negative (TN) subtype prediction, respectively. Similar advantages of CORONet were also confirmed in partial complete DCE-MRIs of 144 patients, achieving an AUC of 0.858 and 0.832 for predicting luminal and TN subtypes of breast cancer, respectively. Nevertheless, both of these values were lower compared to the scenario where DCE-MRIs from all 395 patients were utilized. Our study contributes to the precise molecular subtyping using incomplete multi-sequence DCE-MRI, thereby offering promising prospects for future risk stratification of breast cancer patients.

Widespread incomplete lineage sorting and introgression shaped adaptive radiation in the Gossypium genus.

Cotton (Gossypium) stands as a crucial economic crop, serving as the primary source of natural fiber for the textile sector. However, the evolutionary mechanisms driving speciation within the Gossypium genus remain unresolved. In this investigation, we leveraged 25 Gossypium genomes and introduced four novel assemblies-G. harknessii, G. gossypioides, G. trilobum, and G. klotzschianum (Gklo)-to delve into the speciation history of this genus. Notably, we encountered intricate phylogenies potentially stemming from introgression. These complexities are further compounded by incomplete lineage sorting (ILS), a factor likely to have been instrumental in shaping the swift diversification of cotton. Our focus subsequently shifted to the rapid radiation episode during a concise period in Gossypium evolution. For a recently diverged lineage comprising G. davidsonii, Gklo, and G. raimondii, we constructed a finely detailed ILS map. Intriguingly, this analysis revealed the non-random distribution of ILS regions across the reference Gklo genome. Moreover, we identified signs of robust natural selection influencing specific ILS regions. Noteworthy variations pertaining to speciation emerged between the closely related sister species Gklo and G. davidsonii. Approximately 15.74% of speciation structural variation genes and 12.04% of speciation-associated genes were estimated to intersect with ILS signatures. These findings enrich our understanding of the role of ILS in adaptive radiation, shedding fresh light on the intricate speciation history of the Gossypium genus.

Regularization parameter based on incomplete variables for X-ray luminescence computed tomography.

X-ray luminescence computed tomography (XLCT) is an emerging molecular imaging technique for biological application. However, it is still a challenge to get a stable and accurate solution of the reconstruction of XLCT. This paper presents a regularization parameter selection strategy based on incomplete variables frame for XLCT. A residual information, which is derived from Karush-Kuhn-Tucker (KKT) equivalent condition, is employed to determine the regularization parameter. This residual contains the relevant information about the solution norm and gradient norm, which improved the recovered results. Simulation and phantom experiments are designed to test the performance of the algorithm.Clinical Relevance- The results have not yet been used in clinical relevance currently, we believed that this strategy will facilitate the development of the preclinical applications in FMT.

Adaptive Sparsity Orthogonal Least Square with Neighbor Strategy for Fluorescence Molecular Tomography.

Fluorescence molecular tomography (FMT) is a highly sensitive and noninvasive optical imaging technique which has been widely applied to disease diagnosis and drug discovery. However, FMT reconstruction is a highly ill-posed problem. In this work, L0-norm regularization is employed to construct the mathematical model of the inverse problem of FMT. And an adaptive sparsity orthogonal least square with a neighbor strategy (ASOLS-NS) is proposed to solve this model. This algorithm can provide an adaptive sparsity and can establish the candidate sets by a novel neighbor expansion strategy for the orthogonal least square (OLS) algorithm. Numerical simulation experiments have shown that the ASOLS-NS improves the reconstruction of images, especially for the double targets reconstruction.Clinical relevance- The purpose of this work is to improve the reconstruction results of FMT. Current experiments are focused on simulation experiments, and the proposed algorithm will be applied to the clinical tumor detection in the future.

Assessing the Role of Different Heterogeneous Regions in DCE-MRI for Predicting Molecular Subtypes of Breast Cancer based on Network Architecture Search and Vision Transformer.

Breast cancer, the most common female malignancy, is highly heterogeneous, manifesting as different molecular subtypes. It is clinically important to distinguish between these molecular subtypes due to marked differences in prognosis, treatment and survival outcomes. In this study, we first performed convex analysis of mixtures (CAM) analysis on both intratumoral and peritumoral regions in DCE-MRI to generate multiple heterogeneous regions. Then, we developed a vision transformer (ViT)-based DL model and performed network architecture search (NAS) to evaluate all the combination of different heterogeneous regions for predicting molecular subtypes of breast cancer. Experimental results showed that the input plasma from both peritumoral and intratumoral regions, and the fast-flow kinetics from intratumoral regions were critical for predicting different molecular subtypes, achieving an area under receiver operating characteristic curve (AUROC) value of 0.66-0.68.Clinical Relevance- This study reduces the redundancy in multiple heterogeneous subregions and supports the precise prediction of molecular subtypes, which is of potential importance for the medicine care and treatment planning of patients with breast cancer.

One-dimensional convolutional neural network for Jacobian in Diffuse Optical Tomography.

Non-linear least square minimization algorithms are often employed to solve Diffuse Optical Tomography (DOT) inverse problem. However, it is time-consuming to calculate the Jacobian matrix. This work has proposed a data-driven neural network method to improve computational efficiency. The singular value decomposition is employed to compute the updated Jacobian and a mapping from boundary measurements to the singular values based on a convolutional neural network (CNN) is learned to obtain the singular values. The method is validated with 3D numerical simulation data. We have demonstrated that the approach can save computation time compared to Adjoint method, and reconstructed absorption coefficient close to Adjoint method.Clinical Relevance- These results are not focused on clinical relevance currently, but in the future may be helpful to accelerant DOT reconstruction in clinic.

A hybrid spatiotemporal deep belief network and sparse representation-based framework reveals multilevel core functional components in decoding multitask fMRI signals.

Decoding human brain activity on various task-based functional brain imaging data is of great significance for uncovering the functioning mechanism of the human mind. Currently, most feature extraction model-based methods for brain state decoding are shallow machine learning models, which may struggle to capture complex and precise spatiotemporal patterns of brain activity from the highly noisy fMRI raw data. Moreover, although decoding models based on deep learning methods benefit from their multilayer structure that could extract spatiotemporal features at multiscale, the relatively large populations of fMRI datasets are indispensable, and the explainability of their results is elusive. To address the above problems, we proposed a computational framework based on hybrid spatiotemporal deep belief network and sparse representations to differentiate multitask fMRI (tfMRI) signals. Using a relatively small cohort of tfMRI data as a test bed, our framework can achieve an average classification accuracy of 97.86% and define the multilevel temporal and spatial patterns of multiple cognitive tasks. Intriguingly, our model can characterize the key components for differentiating the multitask fMRI signals. Overall, the proposed framework can identify the interpretable and discriminative fMRI composition patterns at multiple scales, offering an effective methodology for basic neuroscience and clinical research with relatively small cohorts.

Decoding the guardians of cotton resilience: A comprehensive exploration of the ßCA genes and its role in Verticillium dahliae resistance.

Plant Carbonic anhydrases (Cas) have been shown to be stress-responsive enzymes that may play a role in adapting to adverse conditions. Cotton is a significant economic crop in China, with upland cotton (Gossypium hirsutum) being the most widely cultivated species. We conducted genome-wide identification of the ßCA gene in six cotton species and preliminary analysis of the ßCA gene in upland cotton. In total, 73 ßCA genes from six cotton species were identified, with phylogenetic analysis dividing them into five subgroups. GHßCA proteins were predominantly localized in the chloroplast and cytoplasm. The genes exhibited conserved motifs, with motifs 1, 2, and 3 being prominent. GHßCA genes were unevenly distributed across chromosomes and were associated with stress-responsive cis-regulatory elements, including those responding to light, MeJA, salicylic acid, abscisic acid, cell cycle regulation, and defence/stress. Expression analysis indicated that GHßCA6, GHßCA7, GHßCA10, GHßCA15, and GHßCA16 were highly expressed under various abiotic stress conditions, whereas GHßCA3, GHßCA9, GHßCA10, and GHßCA18 had higher expression patterns under Verticillium dahliae infection at different time intervals. In Gossypium thurberi, GthßCA1, GthßCA2, and GthßCA4 showed elevated expression across stress conditions and tissues. Silencing GHßCA10 through VIGS increased Verticillium wilt severity and reduced lignin deposition compared to non-silenced plants. GHßCA10 is crucial for cotton's defense against Verticillium dahliae. Further research is needed to understand the underlying mechanisms and develop strategies to enhance resistance against Verticillium wilt.

Genome-wide association study for boll weight in Gossypium hirsutum races.

High yield has always been an essential target in almost all of the cotton breeding programs. Boll weight (BW) is a key component of cotton yield. Numerous linkage mapping and genome-wide association studies (GWAS) have been performed to understand the genetic mechanism of BW, but information on the markers/genes controlling BW remains limited. In this study, we conducted a GWAS for BW using 51,268 high-quality single-nucleotide polymorphisms (SNPs) and 189 Gossypium hirsutum accessions across five different environments. A total of 55 SNPs significantly associated with BW were detected, of which 29 and 26 were distributed in the A and D subgenomes, respectively. Five SNPs were simultaneously detected in two environments. For TM5655, TM8662, TM36371, and TM50258, the BW grouped by alleles of each SNP was significantly different. The ± 550 kb regions around these four key SNPs contained 262 genes. Of them, Gh_A02G1473, Gh_A10G1765, and Gh_A02G1442 were expressed highly at 0 to 1 days post-anthesis (dpa), - 3 to 0 dpa, and - 3 to 0 dpa in ovule of TM-1, respectively. They were presumed as the candidate genes for fiber cell differentiation, initiation, or elongation based on gene annotation of their homologs. Overall, these results supplemented valuable information for dissecting the genetic architecture of BW and might help to improve cotton yield through molecular marker-assisted selection breeding and molecular design breeding.

Dynamic characteristics and functional analysis provide new insights into the role of GauERF105 for resistance against Verticillium dahliae in cotton.

BACKGROUND: The cotton industry suffers significant yield losses annually due to Verticillium wilt, which is considered the most destructive disease affecting the crop. However, the precise mechanisms behind this disease in cotton remain largely unexplored. METHODS: Our approach involved utilizing transcriptome data from G. australe which was exposed to Verticillium dahliae infection. From this data, we identified ethylene-responsive factors and further investigated their potential role in resistance through functional validations via Virus-induced gene silencing (VIGS) in cotton and overexpression in Arabidopsis. RESULTS: A total of 23 ethylene response factors (ERFs) were identified and their expression was analyzed at different time intervals (24 h, 48 h, and 72 h post-inoculation). Among them, GauERF105 was selected based on qRT-PCR expression analysis for further investigation. To demonstrate the significance of GauERF105, VIGS was utilized, revealing that suppressing GauERF105 leads to more severe infections in cotton plants compared to the wild-type. Additionally, the silenced plants exhibited reduced lignin deposition in the stems compared to the WT plants, indicating that the silencing of GauERF105 also impacts lignin content. The overexpression of GauERF105 in Arabidopsis confirmed its pivotal role in conferring resistance against Verticillium dahliae infection. Our results suggest that WT possesses higher levels of the oxidative stress markers MDA and H2O2 as compared to the overexpressed lines. In contrast, the activities of the antioxidant enzymes SOD and POD were higher in the overexpressed lines compared to the WT. Furthermore, DAB and trypan staining of the overexpressed lines suggested a greater impact of the disease in the wild-type compared to the transgenic lines. CONCLUSIONS: Our findings provide confirmation that GauERF105 is a crucial candidate in the defense mechanism of cotton against Verticillium dahliae invasion, and plays a pivotal role in this process. These results have the potential to facilitate the development of germplasm resistance in cotton.

Neighbor-based adaptive sparsity orthogonal least square for fluorescence molecular tomography.

Significance: Fluorescence molecular tomography (FMT) is a promising imaging modality, which has played a key role in disease progression and treatment response. However, the quality of FMT reconstruction is limited by the strong scattering and inadequate surface measurements, which makes it a highly ill-posed problem. Improving the quality of FMT reconstruction is crucial to meet the actual clinical application requirements. Aim: We propose an algorithm, neighbor-based adaptive sparsity orthogonal least square (NASOLS), to improve the quality of FMT reconstruction. Approach: The proposed NASOLS does not require sparsity prior information and is designed to efficiently establish a support set using a neighbor expansion strategy based on the orthogonal least squares algorithm. The performance of the algorithm was tested through numerical simulations, physical phantom experiments, and small animal experiments. Results: The results of the experiments demonstrated that the NASOLS significantly improves the reconstruction of images according to indicators, especially for double-target reconstruction. Conclusion: NASOLS can recover the fluorescence target with a good location error according to simulation experiments, phantom experiments and small mice experiments. This method is suitable for sparsity target reconstruction, and it would be applied to early detection of tumors.

Mapping the time-varying functional brain networks in response to naturalistic movie stimuli.

One of human brain's remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain's dynamic changes during the processing of visual and auditory stimuli.

Genome-Wide Association Study of Lint Percentage in Gossypium hirsutum L. Races.

Lint percentage is one of the most essential yield components and an important economic index for cotton planting. Improving lint percentage is an effective way to achieve high-yield in cotton breeding worldwide, especially upland cotton (Gossypium hirsutum L.). However, the genetic basis controlling lint percentage has not yet been systematically understood. Here, we performed a genome-wide association mapping for lint percentage using a natural population consisting of 189 G. hirsutum accessions (188 accessions of G. hirsutum races and one cultivar TM-1). The results showed that 274 single-nucleotide polymorphisms (SNPs) significantly associated with lint percentage were detected, and they were distributed on 24 chromosomes. Forty-five SNPs were detected at least by two models or at least in two environments, and their 5 Mb up- and downstream regions included 584 makers related to lint percentage identified in previous studies. In total, 11 out of 45 SNPs were detected at least in two environments, and their 550 Kb up- and downstream region contained 335 genes. Through RNA sequencing, gene annotation, qRT-PCR, protein-protein interaction analysis, the cis-elements of the promotor region, and related miRNA prediction, Gh_D12G0934 and Gh_A08G0526 were selected as key candidate genes for fiber initiation and elongation, respectively. These excavated SNPs and candidate genes could supplement marker and gene information for deciphering the genetic basis of lint percentage and facilitate high-yield breeding programs of G. hirsutum ultimately.

Aerodynamic performance of a ventilation system for droplet control by coughing in a hospital isolation ward.

Over 766 million people have been infected by coronavirus disease 2019 (COVID-19) in the past 3 years, resulting in 7 million deaths. The virus is primarily transmitted through droplets or aerosols produced by coughing, sneezing, and talking. A full-scale isolation ward in Wuhan Pulmonary Hospital is modeled in this work, and water droplet diffusion is simulated using computational fluid dynamics (CFD). In an isolation ward, a local exhaust ventilation system is intended to avoid cross-infection. The existence of a local exhaust system increases turbulent movement, leading to a complete breakup of the droplet cluster and improved droplet dispersion inside the ward. When the outlet negative pressure is 4.5 Pa, the number of moving droplets in the ward decreases by approximately 30% compared to the original ward. The local exhaust system could minimize the number of droplets evaporated in the ward; however, the formation of aerosols cannot be avoided. Furthermore, 60.83%, 62.04%, 61.03%, 60.22%, 62.97%, and 61.52% of droplets produced through coughing reached patients in six different scenarios. However, the local exhaust ventilation system has no apparent influence on the control of surface contamination. In this study, several suggestions with regards to the optimization of ventilation in wards and scientific evidence are provided to ensure the air quality of hospital isolation wards.

Diagnostic performance of angiography-derived fractional flow reserve analysis based on bifurcation fractal law for assessing hemodynamic significance of coronary stenosis.

OBJECTIVES: Blood flow into the side branch affects the calculation of coronary angiography-derived fractional flow reserve (FFR), called Angio-FFR. Neglecting or improperly compensating for the side branch flow may decrease the diagnostic accuracy of Angio-FFR. This study aims to evaluate the diagnostic accuracy of a novel Angio-FFR analysis that considers the side branch flow based on the bifurcation fractal law. METHODS: A one-dimensional reduced-order model based on the vessel segment was used to perform Angio-FFR analysis. The main epicardial coronary artery was divided into several segments according to the bifurcation nodes. Side branch flow was quantified using the bifurcation fractal law to correct the blood flow in each vessel segment. In order to verify the diagnostic performance of our Angio-FFR analysis, two other computational methods were taken as control groups: (i) FFR_s: FFR calculated by delineating the coronary artery tree to consider side branch flow, (ii) FFR_n: FFR calculated by just delineating the main epicardial coronary artery and neglecting the side branch flow. RESULTS: The analysis of 159 vessels from 119 patients showed that our Anio-FFR calculation method had comparable diagnostic accuracy to FFR_s and provided significantly higher diagnostic accuracy than that of FFR_n. In addition, using invasive FFR as a reference, the Pearson correlation coefficients of Angio-FFR and FFRs were 0.92 and 0.91, respectively, while that of FFR_n was only 0.85. CONCLUSIONS: Our Angio-FFR analysis has demonstrated good diagnostic performance in assessing the hemodynamic significance of coronary stenosis by using the bifurcation fractal law to compensate for side branch flow. CLINICAL RELEVANCE STATEMENT: Bifurcation fractal law can be used to compensate for side branch flow during the Angio-FFR calculation of the main epicardial vessel. Compensating for side branch flow can improve the ability of Angio-FFR to diagnose stenosis functional severity. KEY POINTS: • The bifurcation fractal law could accurately estimate the blood flow from the proximal main vessel into the main branch, thus compensating for the side branch flow. • Angiography-derived FFR based on the bifurcation fractal law is feasible to evaluate the target diseased coronary artery without delineating the side branch.

An adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction.

To alleviate the ill-posed of the inverse problem in fluorescent molecular tomography (FMT), many regularization methods based on L2 or L1 norm have been proposed. Whereas, the quality of regularization parameters affects the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L2 or L1 norm and achieve good reconstruction performance.