Nonreciprocal P T-symmetric magnon laser in spinning cavity optomagnonics.

We propose a scheme to achieve nonreciprocal parity-time (P T)-symmetric magnon laser in a P T-symmetric cavity optomagnonical system. The system consists of active and passive optical spinning resonators. We demonstrate that the Fizeau light-dragging effect induced by the spinning of a resonator results in significant variations in magnon gain and stimulated emitted magnon numbers for different driving directions. We find that utilizing the Fizeau light-dragging effect allows the system to operate at ultra-low thresholds even without reaching gain-loss balance. A one-way magnon laser can also be realized across a range of parameters. High tunability of the magnon laser is achieved by changing the spinning speed of the resonators and driving direction. Our work provides a new way to explore various nonreciprocal effects in non-Hermitian magnonic systems, which may be applied to manipulate photons and magnons in multi-body non-Hermitian coupled systems.

Synthesis of higher-B0 CEST Z-spectra from lower-B0 data via deep learning and singular value decomposition.

Chemical exchange saturation transfer (CEST) MRI at 3 T suffers from low specificity due to overlapping CEST effects from multiple metabolites, while higher field strengths (B0) allow for better separation of Z-spectral "peaks," aiding signal interpretation and quantification. However, data acquisition at higher B0 is restricted by equipment access, field inhomogeneity and safety issues. Herein, we aim to synthesize higher-B0 Z-spectra from readily available data acquired with 3 T clinical scanners using a deep learning framework. Trained with simulation data using models based on Bloch-McConnell equations, this framework comprised two deep neural networks (DNNs) and a singular value decomposition (SVD) module. The first DNN identified B0 shifts in Z-spectra and aligned them to correct frequencies. After B0 correction, the lower-B0 Z-spectra were streamlined to the second DNN, casting into the key feature representations of higher-B0 Z-spectra, obtained through SVD truncation. Finally, the complete higher-B0 Z-spectra were recovered from inverse SVD, given the low-rank property of Z-spectra. This study constructed and validated two models, a phosphocreatine (PCr) model and a pseudo-in-vivo one. Each experimental dataset, including PCr phantoms, egg white phantoms, and in vivo rat brains, was sequentially acquired on a 3 T human and a 9.4 T animal scanner. Results demonstrated that the synthetic 9.4 T Z-spectra were almost identical to the experimental ground truth, showing low RMSE (0.11% ± 0.0013% for seven PCr tubes, 1.8% ± 0.2% for three egg white tubes, and 0.79% ± 0.54% for three rat slices) and high R2 (>0.99). The synthesized amide and NOE contrast maps, calculated using the Lorentzian difference, were also well matched with the experiments. Additionally, the synthesis model exhibited robustness to B0 inhomogeneities, noise, and other acquisition imperfections. In conclusion, the proposed framework enables synthesis of higher-B0 Z-spectra from lower-B0 ones, which may facilitate CEST MRI quantification and applications.

FSMN-Net: a free space matching network based on manifold convolution for optical molecular tomography.

Optical molecular tomography (OMT) can monitor glioblastomas in small animals non-invasively. Although deep learning (DL) methods have made remarkable achievements in this field, improving its generalization against diverse reconstruction systems remains a formidable challenge. In this Letter, a free space matching network (FSMN-Net) was presented to overcome the parameter mismatch problem in different reconstruction systems. Specifically, a novel, to the best of our knowledge, manifold convolution operator was designed by considering the mathematical model of OMT as a space matching process. Based on the dynamic domain expansion concept, an end-to-end fully convolutional codec further integrates this operator to realize robust reconstruction with voxel-level accuracy. The results of numerical simulations and in vivo experiments demonstrate that the FSMN-Net can stably generate high-resolution reconstruction volumetric images under different reconstruction systems.

Porous FeNi Prussian blue cubes derived carbon-based phosphides as superior sulfur hosts for high-performance lithium-sulfur batteries.

In contrast to lithium-ion batteries, lithium-sulfur batteries have higher theoretical energy density and lower cost, so they would become competitive in the practical application. However, the shuttle effect of polysulfides and slow oxidation-reduction kinetics can degrade their electrochemical performance and cycle life. In this work, we have first developed the porous FeNi Prussian blue cubes as precursors. The calcination in different atmospheres was employed to make precursors convert into common pyrolysis products or novel carbon-based phosphides, and sulfides, labeled as FeNiP/A-C, FeNiP/A-P, and FeNiP/A-S. When these products serve as host materials in the sulfur cathode, the electrochemical performance of lithium-sulfur batteries is in the order of S@FeNiP/A-P > S@FeNiP/A-S > S@FeNiP/A-C. Specifically, the initial discharge capacity of S@FeNiP/A-P can reach 679.1 mAh g-1at 1 C, and the capacity would maintain 594.6 mAh g-1after 300 cycles. That is because the combination of carbon-based porous structure and numerous well-dispersed Ni2P/Fe2P active sites contribute FeNiP/A-P to obtain larger lithium-ion diffusion, lower resistance, stronger chemisorption, and more excellent catalytic effect than other samples. This work may deliver that metal-organic framework-derived carbon-based phosphides are more suitable to serve as sulfur hosts than carbon-based sulfides or common pyrolysis products for enhancing Li-S batteries' performance.

The complex world of kefir: Structural insights and symbiotic relationships.

Kefir milk, known for its high nutritional value and health benefits, is traditionally produced by fermenting milk with kefir grains. These grains are a complex symbiotic community of lactic acid bacteria, acetic acid bacteria, yeasts, and other microorganisms. However, the intricate coexistence mechanisms within these microbial colonies remain a mystery, posing challenges in predicting their biological and functional traits. This uncertainty often leads to variability in kefir milk's quality and safety. This review delves into the unique structural characteristics of kefir grains, particularly their distinctive hollow structure. We propose hypotheses on their formation, which appears to be influenced by the aggregation behaviors of the community members and their alliances. In kefir milk, a systematic colonization process is driven by metabolite release, orchestrating the spatiotemporal rearrangement of ecological niches. We place special emphasis on the dynamic spatiotemporal changes within the kefir microbial community. Spatially, we observe variations in species morphology and distribution across different locations within the grain structure. Temporally, the review highlights the succession patterns of the microbial community, shedding light on their evolving interactions.Furthermore, we explore the ecological mechanisms underpinning the formation of a stable community composition. The interplay of cooperative and competitive species within these microorganisms ensures a dynamic balance, contributing to the community's richness and stability. In kefir community, competitive species foster diversity and stability, whereas cooperative species bolster mutualistic symbiosis. By deepening our understanding of the behaviors of these complex microbial communities, we can pave the way for future advancements in the development and diversification of starter cultures for food fermentation processes.

Acquisition of Mandarin long passives by children with developmental language disorder.

This study investigated the comprehension and production of long passives (i.e. bei-constructions with an overt agent) in Mandarin-speaking children with developmental language disorder (DLD). Seventeen preschool children with DLD (1 female; mean age: 61 months old) and 23 typically developing (TD) children (6 females; mean age: 62 months old) participated in a sentence-picture matching task (for comprehension) and an elicited production task. Their nonverbal working memory (NVWM) was measured with the fourth edition of the Wechsler Preschool and Primary Scale of Intelligence. Results showed that children with DLD were less accurate and more likely to choose the picture with reversed thematic roles than their TD peers on passives in the sentence-picture matching task; in the elicited production task, they produced fewer target responses than TD children in passives. For NVWM, although that of the DLD group was lower than that of TD children, most children in the DLD group were within the average range. Furthermore, their performance on passives in the comprehension and production tasks was significantly correlated with their NVWM, which adds to the body of work suggesting links between complex syntax and working memory. However, the fact that NVWM could be preserved in the face of difficulties with passives suggests that this link may be due to NVWM enhancing performance during tasks with a high visual component, while it may not be underlyingly responsible for syntactic impairments in children with DLD.

Characteristics and sources of peroxyacetyl nitrate (PAN) in the rural North China Plain: Results from 1-year continuous observations.

Peroxyacetyl nitrate (PAN) is an important photochemical pollutant in the troposphere, whereas long-term measurements are scarce in rural areas in North China Plain (NCP), resulting in unclear seasonal variations and sources of PAN in rural NCP. In this study, we conducted a 1-year observation of PAN during 2021-2022 at the rural NCP site. The average concentrations of PAN were 1.10, 0.75, 0.65, and 0.88 ppbv in spring, summer, autumn, and winter, respectively, with a 1-year average of 0.81 ± 0.60 ppbv. Calculations indicate that the loss of PAN through thermal decomposition in summer accounts for 43.2% of the total formed PAN, which is an important reason for the low concentration of PAN in summer. We speculate that since the correlation between PAN and O3 in winter is significantly lower than that in other seasons, the observed regional transport of PAN cannot be ignored in winter. Through budget analysis, regional transport accounted for 12.8% and 55.9% of the observed PAN on the spring and winter pollution days, respectively, which showed that regional transport played key roles during the photochemical pollution of the rural NCP in winter. The potential source contribution function revealed that the transported PAN mainly comes from southern Hebei in spring. In winter, the transported PAN was mainly from Langfang, Hengshui, and southern Beijing. Our findings may aid in understanding PAN variations in different seasons in rural areas and highlight the impact of regional transport on the PAN budget.

Reassembled saturation transfer (REST) MR images at 2 B1 values for in vivo exchange-dependent imaging of amide and nuclear Overhauser enhancement.

PURPOSE: Design an efficient CEST scheme for exchange-dependent images with high contrast-to-noise ratio. THEORY: Reassembled saturation transfer (REST) signals were defined as Δ $$ \Delta $$ r.Z = r.Zref - r.ZCEST and the reassembled exchange-dependen magnetization transfer ratio r.MTRRex  = r.1/Zref - r.1/ZCEST , utilizing the averages over loosely sampled reference frequency offsets as Zref and over densely sampled target offsets as ZCEST . Using r.MTRRex measured under 2 B1,sat values, exchange rate could be estimated. METHODS: The REST approach was optimized and assessed quantitatively by simulations for various exchange rates, pool concentration, and water T1 . In vivo evaluation was performed on ischemic rat brains at 7 Tesla and human brains at 3 Tesla, in comparison with conventional asymmetrical analysis, Lorentzian difference (LD), an MTRRex_ LD. RESULTS: For a broad choice of Δ ω ref $$ \Delta {\omega}_{ref} $$ ranges and numbers, Δr.Z and r.MTRRex exhibited comparable quantification features with conventional LD and MTRRex _LD, respectively, when B1,sat  ≤ 1 µT. The subtraction of 2 REST values under distinct B1,sat values showed linear relationships with exchange rate and obtained immunity to field inhomogeneity and variation in MT and water T1 . For both rat and human studies, REST images exhibited similar contrast distribution to MTRRex _LD, with superiority in contrast-to-noise ratio and acquisition efficiency. Compared with MTRRex _LD, 2-B1,sat subtraction REST images displayed better resistance to B1 inhomogeneity, with more specific enhanced regions. They also showed higher signals for amide than for nuclear Overhauser enhancement effect in human brain, presumably reflecting the higher increment from faster-exchanging species as B1,sat increased. CONCLUSION: Featuring high contrast-to-noise ratio efficiency, REST could be a practical exchange-dependent approach readily applicable to either retrospective Z-spectra analysis or perspective 6-offset acquisition.

Niche-Specific Restructuring of Bacterial Communities Associated with Submerged Macrophyte under Ammonium Stress.

Submerged macrophytes and their epiphytic microbes form a "holobiont" that plays crucial roles in regulating the biogeochemical cycles of aquatic ecosystems but is sensitive to environmental disturbances such as ammonium loadings. Increasingly more studies suggest that plants may actively seek help from surrounding microbial communities whereby conferring benefits in responding to particular abiotic stresses. However, empirical evidence is scarce regarding how aquatic plants reconstruct their microbiomes as a "cry-for-help" against acute ammonium stress. Here, we investigated the temporal dynamics of the phyllosphere and rhizosphere bacterial communities of Vallisneria natans following ammonium stress and recovery periods. The bacterial community diversity of different plant niches exhibited opposite patterns with ammonium stress, that is, decreasing in the phyllosphere while increasing in the rhizosphere. Furthermore, both phyllosphere and rhizosphere bacterial communities underwent large compositional changes at the end of ammonium stress, significantly enriching of several nitrifiers and denitrifiers. Meanwhile, bacterial legacies wrought by ammonium stress were detected for weeks; some plant growth-promoting and stress-relieving bacteria remained enriched even after stress disappeared. Structural equation model analysis showed that the reshaped bacterial communities in plant niches collectively had a positive effect on maintaining plant biomass. Additionally, we applied an age-prediction model to predict the bacterial community's successional trajectory, and the results revealed a persistent change in bacterial community development under ammonium treatment. Our findings highlight the importance of plant-microbe interactions in mitigating plant stress and fostering a better understanding of the assembly of plant-beneficial microbes under ammonium stress in aquatic ecosystems. IMPORTANCE Increasing anthropogenic input of ammonium is accelerating the decline of submerged macrophytes in aquatic ecosystems. Finding efficient ways to release submerged macrophytes from ammonium stress is crucial to maintain their ecological benefits. Microbial symbioses can alleviate abiotic stress in plants, but harnessing these beneficial interactions requires a detailed understanding of plant microbiome responses to ammonium stress, especially over a continuous time course. Here, we tracked the temporal changes in bacterial communities associated with the phyllosphere and rhizosphere of Vallisneria natans during ammonium stress and recovery periods. Our results showed that severe ammonium stress triggers a plant-driven timely reshaping of the associated bacterial community in a niche-specific strategy. The reassembled bacterial communities could potentially benefit the plant by positively contributing to nitrogen transformation and plant growth promotion. These findings provide empirical evidence regarding the adaptive strategy of aquatic plants whereby they recruit beneficial microbes against ammonium stress.

Parametrically amplified nonreciprocal magnon laser in a hybrid cavity optomagnonical system.

We propose a scheme to achieve a tunable nonreciprocal magnon laser with parametric amplification in a hybrid cavity optomagnonical system, which consists a yttrium iron garnet (YIG) sphere and a spinning resonator. We demonstrate the control of magnon laser can be enhanced via parametric amplification, which make easier and more convenient to control the magnon laser. Moreover, we analyze and evaluate the effects of pump light input direction and amplification amplitude on the magnon gain and laser threshold power. The results indicate that we can obtian a higher magnon gain and a broader range of threshold power of the magnon laser. In our scheme both the nonreciprocity and magnon gain of the magnon laser can be increased significantly. Our proposal provides a way to obtain a novel nonreciprocal magnon laser and offers new possibilities for both nonreciprocal optics and spin-electronics applications.

3D-deep optical learning: a multimodal and multitask reconstruction framework for optical molecular tomography.

Optical molecular tomography (OMT) is an emerging imaging technique. To date, the poor universality of reconstruction algorithms based on deep learning for various imaged objects and optical probes limits the development and application of OMT. In this study, based on a new mapping representation, a multimodal and multitask reconstruction framework-3D deep optical learning (3DOL), was presented to overcome the limitations of OMT in universality by decomposing it into two tasks, optical field recovery and luminous source reconstruction. Specifically, slices of the original anatomy (provided by computed tomography) and boundary optical measurement of imaged objects serve as inputs of a recurrent convolutional neural network encoded parallel to extract multimodal features, and 2D information from a few axial planes within the samples is explicitly incorporated, which enables 3DOL to recognize different imaged objects. Subsequently, the optical field is recovered under the constraint of the object geometry, and then the luminous source is segmented by a learnable Laplace operator from the recovered optical field, which obtains stable and high-quality reconstruction results with extremely few parameters. This strategy enable 3DOL to better understand the relationship between the boundary optical measurement, optical field, and luminous source to improve 3DOL's ability to work in a wide range of spectra. The results of numerical simulations, physical phantoms, and in vivo experiments demonstrate that 3DOL is a compatible deep-learning approach to tomographic imaging diverse objects. Moreover, the fully trained 3DOL under specific wavelengths can be generalized to other spectra in the 620-900 nm NIR-I window.

Frequency importance analysis for chemical exchange saturation transfer magnetic resonance imaging using permuted random forest.

Chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) is a promising molecular imaging tool that allows sensitive detection of endogenous metabolic changes. However, because the CEST spectrum does not display a clear peak like MR spectroscopy, its signal interpretation is challenging, especially under 3-T field strength or with a large saturation B1 . Herein, as an alternative to conventional Z-spectral fitting approaches, a permuted random forest (PRF) method is developed to determine featured saturation frequencies for lesion identification, so-called CEST frequency importance analysis. Briefly, voxels in the CEST dataset were labeled as lesion and control according to multicontrast MR images. Then, by considering each voxel's saturation signal series as a sample, a permutation importance algorithm was employed to rank the contribution of saturation frequency offsets in the differentiation of lesion and normal tissue. Simulations demonstrated that PRF could correctly determine the frequency offsets (3.5 or -3.5 ppm) for classifying two groups of Z-spectra, under a range of B0 , B1 conditions and sample sizes. For ischemic rat brains, PRF only displayed high feature importance around amide frequency at 2 h postischemia, reflecting that the pH changes occurred at an early stage. By contrast, the data acquired at 24 h postischemia exhibited high feature importance at multiple frequencies (amide, water, and lipids), which suggested the complex tissue changes that occur during the later stages. Finally, PRF was assessed using 3-T CEST data from four brain tumor patients. By defining the tumor region on amide proton transfer-weighted images, PRF analysis identified different CEST frequency importance for two types of tumors (glioblastoma and metastatic tumor) (p < 0.05, with each image slice as a subject). In conclusion, the PRF method was able to rank and interpret the contribution of all acquired saturation offsets to lesion identification; this may facilitate CEST analysis in clinical applications, and open up new doors for comprehensive CEST analysis tools other than model-based approaches.

MRI-Based Radiomics Nomogram for Preoperative Differentiation Between Ocular Adnexal Lymphoma and Idiopathic Orbital Inflammation.

BACKGROUND: Ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI) are malignant and benign lesions for which radiotherapy and corticosteroids are indicated, but similar clinical manifestations make their differentiation difficult. PURPOSE: To develop and validate an MRI-based radiomics nomogram for individual diagnosis of OAL vs. IOI. STUDY TYPE: Retrospective. POPULATION: A total of 103 patients (46.6% female) with mean age of 56.4 ± 16.3 years having OAL (n = 58) or IOI (n = 45) were divided into an independent training (n = 82) and a testing dataset (n = 21). FIELD STRENGTH/SEQUENCE: A 3-T, precontrast T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and postcontrast T1WI (T1 + C). ASSESSMENT: Radiomics features were extracted and selected from segmented tumors and peritumoral regions in MRI before-and-after filtering. These features, alone or combined with clinical characteristics, were used to construct a radiomics or joint signature to differentiate OAL from IOI, respectively. A joint nomogram was built to show the impact of the radiomics signature and clinical characteristics on individual risk of developing OAL. STATISTICAL TESTS: Area under the curve (AUC) and accuracy (ACC) were used for performance evaluation. Mann-Whitney U and Chi-square tests were used to analyze continuous and categorical variables. Decision curve analysis, kappa statistics, DeLong and Hosmer-Lemeshow tests were also conducted. P < 0.05 was considered statistically significant. RESULTS: The joint signature achieved an AUC of 0.833 (95% confidence interval [CI]: 0.806-0.870), slightly better than the radiomics signature with an AUC of 0.806 (95% CI: 0.767-0.838) (P = 0.778). The joint and radiomics signatures were comparable to experienced radiologists referencing to clinical characteristics (ACC = 0.810 vs. 0.796-0.806, P > 0.05) or not (AUC = 0.806 vs. 0.753-0.791, P > 0.05), respectively. The joint nomogram gained more net benefits than the radiomics nomogram, despite both showing good calibration and discriminatory efficiency (P > 0.05). DATA CONCLUSION: The developed radiomics-based analysis might help to improve the diagnostic performance and reveal the association between radiomics features and individual risk of developing OAL. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: 3.

Evaluating hs-cTnI serum levels and cTnI gene expression compared with cardiac nuclear scan in patients with angina pectoris.

Exercise stress can cause reversible myocardial ischemia in people with coronary artery disease (CAD). On the other hand, the new troponin biomarker with high sensitivity can detect faster and small amounts of troponin in blood circulation. The present study aimed to investigate the serum troponin level following exercise stress and the result of nuclear heart scans as the gold standard. For this purpose, 93 patients with stable angina and no history of known CAD and organic disease were included in this cross-sectional study. The serum level of the highly sensitive cardiac troponin I (hs-cTnI) was measured 75 minutes after the peak of the exercise test and reached at least 85% of the maximum heart rate. It was compared with the rate of reversible myocardial ischemia based on the nuclear heart scan, the three-month prognosis and the persistence of chest pain were investigated. Also, the expression level of the cTnI gene was evaluated by real-time PCR technique. The results showed that the average age of the patients was 58.9+12.4 years, and 62 (66.66%) patients were female. Reversible myocardial ischemia was observed in 31 patients. The relationship between hs-cTI level and the rate of reversible ischemia cases was significant (p = 0.0041). Also, the cTnI gene expression showed the same results as the serum level. According to the heart nuclear scan report, the hs-cTnI value above 1.6ng/dl had a specificity of 72% and sensitivity of 66%, a positive predictive value of 53%, and a negative predictive value of 78%. There was no significant relationship between hs-cTnI level and prognosis and the continuation of chest pain in patients after three months. Generally, the serum level of high-sensitivity cardiac troponin was higher after exercise in the group with reversible myocardial ischemia.

Efficient FMT reconstruction based on L1-αL2 regularization via half-quadratic splitting and a two-probe separation light source strategy.

Fluorescence molecular tomography (FMT) can achieve noninvasive, high-contrast, high-sensitivity three-dimensional imaging in vivo by relying on a variety of fluorescent molecular probes, and has excellent clinical transformation prospects in the detection of tumors in vivo. However, the limited surface fluorescence makes the FMT reconstruction have some ill-posedness, and it is difficult to obtain the ideal reconstruction effect. In this paper, two different emission fluorescent probes and L 1-L 2 regularization are combined to improve the temporal and spatial resolution of FMT visual reconstruction by introducing the weighting factor α and a half-quadratic splitting alternating optimization (HQSAO) iterative algorithm. By introducing an auxiliary variable, the HQSAO method breaks the sparse FMT reconstruction task into two subproblems that can be solved in turn: simple reconstruction and image denoising. The weight factor α (α>1) can increase the weight of nonconvex terms to further promote the sparsity of the algorithm. Importantly, this paper combines two different dominant fluorescent probes to achieve high-quality reconstruction of dual light sources. The performance of the proposed reconstruction strategy was evaluated by digital mouse and nude mouse single/dual light source models. The simulation results show that the HQSAO iterative algorithm can achieve more excellent positioning accuracy and morphology distribution in a shorter time. In vivo experiments also further prove that the HQSAO algorithm has advantages in light source information preservation and artifact suppression. In particular, the introduction of two main emission fluorescent probes makes it easy to separate and reconstruct the dual light sources. When it comes to localization and three-dimensional morphology, the results of the reconstruction are much better than those using a fluorescent probe, which further facilitates the clinical transformation of FMT.

Acquisition of grammatical aspect by Mandarin-speaking preschool children with autism spectrum disorder.

BACKGROUND: Mandarin-speaking children with autism spectrum disorder (ASD) have difficulties producing aspect markers. The difficulties were explained in terms of pragmatic deficits since these children demonstrated strength in the comprehension of aspect markers using the Intermodal Preferential Looking (IPL) task. AIMS: To verify whether this dissociation between production and comprehension could be replicated using another technique to the IPL, and if all children with ASD show difficulties in the production of aspect markers. METHODS & PROCEDURES: A total of 34 children with ASD without cognitive delay, half with language impairment (ALI; mean age = 61.25 months old) and half with normal language (ALN, mean age = 61.52 months old), as well as 17 age-matched typically developing (TD) children (mean age = 61.38 months old) participated in a sentence-picture-matching task and a priming picture-description task to investigate their comprehension and production of Mandarin aspect markers zai-, -le and -zhe. OUTCOMES & RESULTS: In the comprehension task, children in the ALN group performed similarly to their TD peers, but those in the ALI group were less accurate on zai- and -le than TD children; children in all groups received higher accuracy when zai- was combined with Activity rather than Accomplishment verbs, and those in the ALI group was also more accurate when -le occurred with Achievement verbs, in contrast to Activity verbs. In the production task, children in the ALI group produced fewer targets and more irrelevant sentences with zai- than their TD peers, and they tended to produce bare verbs for -le and -zhe than TD children; children in all groups tended to combine zai- with Activity verbs, and those in the ALN group also tended to combine -le with Achievement verbs. CONCLUSIONS & IMPLICATIONS: The comprehension and production of Mandarin aspect markers by children with ASD are linked to general language abilities, and interactions between lexical and grammatical aspect. Patterns of performance are similar to those of TD peers only for the subgroup with spared global language, while pragmatic deficits are pervasive throughout the spectrum. Therefore, training on formal language, with a specific emphasis on aspectual rather than pragmatic abilities, may be more effective at enhancing the production of aspect markers. WHAT THIS PAPER ADDS: What is already known on this subject Mandarin-speaking children with ASD have difficulties producing aspect markers but demonstrate strengths in aspectual comprehension via the IPL task. Therefore, it has been proposed that their 'specific' difficulties in aspectual production should be ascribed to their pragmatic deficits. However, pragmatic deficits are highly pervasive in children with ASD while only a subgroup of children with ASD who are impaired in language development (children with ALI) show difficulties in producing tense/aspect morphology. Pursuing this reasoning, pragmatic deficits might not be the critical factor impacting the performance of children with ASD in aspectual production. What this study adds Children with ASD were divided into one group with ALI and the other with normal language (ALN). Results of a sentence-picture-matching and a priming picture-description task illustrated that both groups preserved the comprehension of Mandarin aspect markers zai-, -le and -zhe. However, children with ALI performed worse than age-matched TD children, while children with ALN demonstrated similar performance to TD children in aspectual production. These findings, coupled with the fact that pragmatic challenges affect individuals throughout the spectrum, suggest that general language abilities rather than pragmatics better explain the performance of children with ASD on aspectual production. What are the potential or actual clinical implications of this work? Since general language abilities rather than pragmatic deficits of children with ASD determine their performance on the production of aspect markers, direct training on the use of aspect markers or more global language therapy could benefit children with ASD in the production of aspect markers.

A multitask deep learning radiomics model for predicting the macrotrabecular-massive subtype and prognosis of hepatocellular carcinoma after hepatic arterial infusion chemotherapy.

BACKGROUND: The macrotrabecular-massive (MTM) is a special subtype of hepatocellular carcinoma (HCC), which has commonly a dismal prognosis. This study aimed to develop a multitask deep learning radiomics (MDLR) model for predicting MTM and HCC patients' prognosis after hepatic arterial infusion chemotherapy (HAIC). METHODS: From June 2018 to March 2020, 158 eligible patients with HCC who underwent surgery were retrospectively enrolled in MTM related cohorts, and 752 HCC patients who underwent HAIC were included in HAIC related cohorts during the same period. DLR features were extracted from dual-phase (arterial phase and venous phase) contrast-enhanced computed tomography (CECT) of the entire liver region. Then, an MDLR model was used for the simultaneous prediction of the MTM subtype and patient prognosis after HAIC. The MDLR model for prognostic risk stratification incorporated DLR signatures, clinical variables and MTM subtype. FINDINGS: The predictive performance of the DLR model for the MTM subtype was 0.968 in the training cohort [TC], 0.912 in the internal test cohort [ITC] and 0.773 in the external test cohort [ETC], respectively. Multivariable analysis identified portal vein tumor thrombus (PVTT) (p = 0.012), HAIC response (p < 0.001), HAIC sessions (p < 0.001) and MTM subtype (p < 0.001) as indicators of poor prognosis. After incorporating DLR signatures, the MDLR model yielded the best performance among all models (AUC, 0.855 in the TC, 0.805 in the ITC and 0.792 in the ETC). With these variables, the MDLR model provided two risk strata for overall survival (OS) in the TC: low risk (5-year OS, 44.9%) and high risk (5-year OS, 4.9%). INTERPRETATION: A tool based on MDLR was developed to consider that the MTM is an important prognosis factor for HCC patients. MDLR showed outstanding performance for the prognostic risk stratification of HCC patients who underwent HAIC and may help physicians with therapeutic decision making and surveillance strategy selection in clinical practice.

The comprehension of relative clauses in Mandarin Children with suspected specific language impairment.

This paper investigates the comprehension of Relative Clauses (RCs) in 15 Mandarin children with suspected Specific Language Impairment (SLI) (aged between 4; 5 and 6; 0) and 29 typically developing (TD) controls. Results from a Character Picture Matching Task indicate that (i) the subject RC was better understood than the object RC in children with SLI, but there was no asymmetry in the comprehension of the two RCs in TD children; (ii) the performance of children with SLI was significantly worse than that of their TD peers; (iii) children with SLI were prone to committing thematic role reversal errors and middle errors. In order to overcome the shortcomings of previous accounts, we therefore put forward the Edge Feature Underspecification Hypothesis, which can not only explain the asymmetry of comprehension seen in children with SLI but also shed light on the nature of errors committed by them in the task.

Total Variation Constrained Graph Manifold Learning Strategy for Cerenkov Luminescence Tomography.

Harnessing the power and flexibility of radiolabeled molecules, Cerenkov luminescence tomography (CLT) provides a novel technique for non-invasive visualisation and quantification of viable tumour cells in a living organism. However, owing to the photon scattering effect and the ill-posed inverse problem, CLT still suffers from insufficient spatial resolution and shape recovery in various preclinical applications. In this study, we proposed a total variation constrained graph manifold learning (TV-GML) strategy for achieving accurate spatial location, dual-source resolution, and tumour morphology. TV-GML integrates the isotropic total variation term and dynamic graph Laplacian constraint to make a trade-off between edge preservation and piecewise smooth region reconstruction. Meanwhile, the tetrahedral mesh-Cartesian grid pair method based on the k-nearest neighbour, and the adaptive and composite Barzilai-Borwein method, were proposed to ensure global super linear convergence of the solution of TV-GML. The comparison results of both simulation experiments and in vivo experiments further indicated that TV-GML achieved superior reconstruction performance in terms of location accuracy, dual-source resolution, shape recovery capability, robustness, and in vivo practicability. Significance: We believe that this novel method will be beneficial to the application of CLT for quantitative analysis and morphological observation of various preclinical applications and facilitate the development of the theory of solving inverse problem.

VoxDMRN: a voxelwise deep max-pooling residual network for bioluminescence tomography reconstruction.

Bioluminescence tomography (BLT) has extensive applications in preclinical studies for cancer research and drug development. However, the spatial resolution of BLT is inadequate because the numerical methods are limited for solving the physical models of photon propagation and the restriction of using tetrahedral meshes for reconstruction. We conducted a series of theoretical derivations and divided the BLT reconstruction process into two steps: feature extraction and nonlinear mapping. Inspired by deep learning, a voxelwise deep max-pooling residual network (VoxDMRN) is proposed to establish the nonlinear relationship between the internal bioluminescent source and surface boundary density to improve the spatial resolution in BLT reconstruction. The numerical simulation and in vivo experiments both demonstrated that VoxDMRN greatly improves the reconstruction performance regarding location accuracy, shape recovery capability, dual-source resolution, robustness, and in vivo practicability.