Unveiling the micronutrient-immunity puzzle in inactivated COVID-19 vaccination: A comprehensive analysis of circulating micronutrient levels and humoral responses in healthy adults.

While micronutrients are crucial for immune function, their impact on humoral responses to inactivated COVID-19 vaccination remains unclear. We investigated the associations between seven key micronutrients and antibody responses in 44 healthy adults with two doses of an inactivated COVID-19 vaccine. Blood samples were collected pre-vaccination and 28 days post-booster. We measured circulating minerals (iron, zinc, copper, and selenium) and vitamins (A, D, and E) concentrations alongside antibody responses and assessed their associations using linear regression analyses. Our analysis revealed inverse associations between blood iron and zinc concentrations and anti-SARS-CoV-2 IgM antibody binding affinity (AUC for iron: ß = -258.21, p < 0.0001; zinc: ß = -17.25, p = 0.0004). Notably, antibody quality presented complex relationships. Blood selenium was positively associated (ß = 18.61, p = 0.0030), while copper/selenium ratio was inversely associated (ß = -1.36, p = 0.0055) with the neutralizing ability against SARS-CoV-2 virus at a 1:10 plasma dilution. There was no significant association between circulating micronutrient concentrations and anti-SARS-CoV-2 IgG binding affinity. These findings suggest that circulating iron, zinc, and selenium concentrations and copper/selenium ratio, may serve as potential biomarkers for both quantity (binding affinity) and quality (neutralization) of humoral responses after inactivated COVID-19 vaccination. Furthermore, they hint at the potential of pre-vaccination dietary interventions, such as selenium supplementation, to improve vaccine efficacy. However, larger, diverse studies are needed to validate these findings. This research advances the understanding of the impact of micronutrients on vaccine response, offering the potential for personalized vaccination strategies.

Assessing quality assurance of multi-leaf collimator using the structural similarity index.

PURPOSE: This study aims to evaluate the viability of utilizing the Structural Similarity Index (SSI*) as an innovative imaging metric for quality assurance (QA) of the multi-leaf collimator (MLC). Additionally, we compared the results obtained through SSI* with those derived from a conventional Gamma index test for three types of Varian machines (Trilogy, Truebeam, and Edge) over a 12-week period of MLC QA in our clinic. METHOD: To assess sensitivity to MLC positioning errors, we designed a 1 cm slit on the reference MLC, subsequently shifted by 0.5-5 mm on the target MLC. For evaluating sensitivity to output error, we irradiated five 25 cm × 25 cm open fields on the portal image with varying Monitor Units (MUs) of 96-100. We compared SSI* and Gamma index tests using three linear accelerator (LINAC) machines: Varian Trilogy, Truebeam, and Edge, with MLC leaf widths of 1, 0.5, and 0.25 mm. Weekly QA included VMAT and static field modes, with Picket fence test images acquired. Mechanical uncertainties related to the LINAC head, electronic portal imaging device (EPID), and MLC during gantry rotation and leaf motion were monitored. RESULTS: The Gamma index test started detecting the MLC shift at a threshold of 4 mm, whereas the SSI* metric showed sensitivity to shifts as small as 2 mm. Moreover, the Gamma index test identified dose changes at 95MUs, indicating a 5% dose difference based on the distance to agreement (DTA)/dose difference (DD) criteria of 1 mm/3%. In contrast, the SSI* metric alerted to dose differences starting from 97MUs, corresponding to a 3% dose difference. The Gamma index test passed all measurements conducted on each machine. However, the SSI* metric rejected all measurements from the Edge and Trilogy machines and two from the Truebeam. CONCLUSIONS: Our findings demonstrate that the SSI* exhibits greater sensitivity than the Gamma index test in detecting MLC positioning errors and dose changes between static and VMAT modes. The SSI* metric outperformed the Gamma index test regarding sensitivity across these parameters.

An analysis of differences in Carbapenem-resistant Enterobacterales in different regions: a multicenter cross-sectional study.

OBJECTIVE: This study aimed to explore the characteristics of carbapenem-resistant Enterobacterales (CRE) patients in the intensive care unit (ICU) in different regions of Henan Province to provide evidence for the targeted prevention and treatment of CRE. METHODS: This was a cross-sectional study. CRE screening was conducted in the ICUs of 78 hospitals in Henan Province, China, on March 10, 2021. The patients were divided into provincial capital hospitals and nonprovincial capital hospitals for comparative analysis. RESULTS: This study involved 1009 patients in total, of whom 241 were CRE-positive patients, 92 were in the provincial capital hospital and 149 were in the nonprovincial capital hospital. Provincial capital hospitals had a higher rate of CRE positivity, and there was a significant difference in the rate of CRE positivity between the two groups. The body temperature; immunosuppressed state; transfer from the ICU to other hospitals; and use of enemas, arterial catheters, carbapenems, or tigecycline at the provincial capital hospital were greater than those at the nonprovincial capital hospital (P < 0.05). However, there was no significant difference in the distribution of carbapenemase strains or enzymes between the two groups. CONCLUSIONS: The detection rate of CRE was significantly greater in provincial capital hospitals than in nonprovincial capital hospitals. The source of the patients, invasive procedures, and use of advanced antibiotics may account for the differences. Carbapenem-resistant Klebsiella pneumoniae (CR-KPN) was the most prevalent strain. Klebsiella pneumoniae carbapenemase (KPC) was the predominant carbapenemase enzyme. The distributions of carbapenemase strains and enzymes were similar in different regions.

V4D: Voxel for 4D Novel View Synthesis.

Neural radiance fields have made a remarkable breakthrough in the novel view synthesis task at the 3D static scene. However, for the 4D circumstance (e.g., dynamic scene), the performance of the existing method is still limited by the capacity of the neural network, typically in a multilayer perceptron network (MLP). In this article, we utilize 3D Voxel to model the 4D neural radiance field, short as V4D, where the 3D voxel has two formats. The first one is to regularly model the 3D space and then use the sampled local 3D feature with the time index to model the density field and the texture field by a tiny MLP. The second one is in look-up tables (LUTs) format that is for the pixel-level refinement, where the pseudo-surface produced by the volume rendering is utilized as the guidance information to learn a 2D pixel-level refinement mapping. The proposed LUTs-based refinement module achieves the performance gain with little computational cost and could serve as the plug-and-play module in the novel view synthesis task. Moreover, we propose a more effective conditional positional encoding toward the 4D data that achieves performance gain with negligible computational burdens. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance at a low computational cost.

Technical note: Determination of proton linear energy transfer from the integral depth dose.

BACKGROUND: Proton linear energy transfer (LET) is associated with the relative biological effectiveness of radiation on tissues. Monte Carlo (MC) simulations have been known to be the preferred method to calculate LET. Detectors have also been built to measure LET, but they need to be calibrated with MC simulations. PURPOSE: To propose and test a MC-free method for determining LET from the measured integral depth dose (LFI) of the protons of interest. METHOD AND MATERIALS: LFI consists of three steps: (1) IDD measurements, (2) extraction of energy spectrum (ES) from the IDD, and (3) LET determination from the extracted ES and the stopping power of each energy. To validate the accuracy of the extraction of ES, we use Gaussian ES to synthesize IDD, extract ES from the synthesized IDD, and then compare the original (ground truth) and extracted ES. LETs calculated from the original and extracted ES are also compared. To obtain the LET of protons of interest, we measure IDDs by a large-area plane-parallel ionization chamber in water. Finally, TOPAS MC is employed to simulate IDDs, ES, and LETs. From the simulated IDD, the extracted ES and LET are compared with the simulations from TOPAS MC. RESULTS: From the synthesized IDDs, the LETs agreed excellently when the peak energies ≥10 and 1.25 MeV with depth resolutions 0.1 and 0.01 mm, respectively. For energy <1.25 MeV, even higher depth resolution than 0.01 mm is required. From the MC simulated IDDs, our track-averaged LET excellently agreed with MC simulation, but not the LETd . Our LETd was smaller than MC simulated LETd in the shallow region but larger in the distal Bragg peak region. CONCLUSION: LET can be accurately determined from the IDD. This method can be used in the clinic to commission or validate LETs from other measurement methods or a treatment planning system.

RBD class 1 and 2 antibody epitopes elicit around 70% neutralizing capacity against SARS-CoV-2 virus following boosting with inactivated virus vaccine.

A third dose of inactivated virus vaccine (IVV) boosts neutralizing antibodies, reducing SARS-CoV-2 transmission rate and COVID-19 severity. However, the impact of RBD-elicited antibodies and their neutralizing activity by the boost of IVV is unknown. We investigated the impact of IVV's boost shot on RBD-elicited antibodies and their neutralizing activity in 18 subjects receiving the second and third IVV doses. Using an RBD antibodies depletion assay, we assessed the neutralizing activity of RBD-elicited antibodies. After the second dose, RBD-antigen elicitation accounted for ∼60% of neutralizing activity, which increased to 82% after the IVV boost against ancestral SARS-CoV-2. Depleting class 3 and class 4-specific antibodies with the Beta-RBD protein revealed that NAbs targeting RBD class 1 and class 2 subdomains increased from 57% to 75% post-boost. These findings highlight the significant enhancement of RBD-specific antibodies, especially against RBD class 1 and class 2, with IVV booster doses. Our study offers valuable insights for optimizing COVID-19 vaccine strategies.

Non-linear links between human capital, educational inequality and income inequality, evidence from China.

This study aims to reveal short-run and long-run asymmetries among human capital, educational inequality, and income inequality in China over the period 1975-2020 using a nonlinear autoregressive distributed lag (NARDL) model. The estimated long-run asymmetry parameters reflect that positive shocks to secondary education (SSE) and higher education (HE) are negatively correlated with income Gini coefficient. The adverse shocks of secondary education (SSE) and higher education (HE) stimulate the Gini coefficient of income, but the effect of secondary education (SSE) on the Gini coefficient of income is not significant, while that of higher education (HE) is significant. The results also highlight that, in the long run, there is a significant asymptotic effect of the education Gini coefficient (educational inequality) and economic growth on the income Gini coefficient (income inequality). However, physical capital stock has a significant adverse effect on income inequality in the long run. Higher education significantly promotes educational inequality, while the square of higher education significantly reduces educational inequality, thus verifying the inverted U-shaped Kuznets curve hypothesis between higher education and educational inequality. Strategically, this study suggests higher education as a powerful tool for mitigating income inequality by emphasizing educational equity.

Spike substitution T813S increases Sarbecovirus fusogenicity by enhancing the usage of TMPRSS2.

SARS-CoV Spike (S) protein shares considerable homology with SARS-CoV-2 S, especially in the conserved S2 subunit (S2). S protein mediates coronavirus receptor binding and membrane fusion, and the latter activity can greatly influence coronavirus infection. We observed that SARS-CoV S is less effective in inducing membrane fusion compared with SARS-CoV-2 S. We identify that S813T mutation is sufficient in S2 interfering with the cleavage of SARS-CoV-2 S by TMPRSS2, reducing spike fusogenicity and pseudoparticle entry. Conversely, the mutation of T813S in SARS-CoV S increased fusion ability and viral replication. Our data suggested that residue 813 in the S was critical for the proteolytic activation, and the change from threonine to serine at 813 position might be an evolutionary feature adopted by SARS-2-related viruses. This finding deepened the understanding of Spike fusogenicity and could provide a new perspective for exploring Sarbecovirus' evolution.

Semi-Supervised Domain Alignment Learning for Single Image Dehazing.

Convolutional neural networks (CNNs) have attracted much research attention and achieved great improvements in single-image dehazing. However, previous learning-based dehazing methods are mainly trained on synthetic data, which greatly degrades their generalization capability on natural hazy images. To address this issue, this article proposes a semi-supervised learning approach for single-image dehazing, where both synthetic and realistic images are leveraged during training. Considering the situation that it is hard to obtain the realistic pairs of hazy and haze-free images, how to utilize the realistic data is not a trivial work. In this article, a domain alignment module is introduced to narrow the distribution distance between synthetic data and realistic hazy images in a latent feature space. Meanwhile, a haze-aware attention module is designed to describe haze densities of different regions in the image, thus adaptively responds for different hazy areas. Furthermore, the dark channel prior is introduced to the framework to improve the quality of the unsupervised learning results by considering the statistical characters of haze-free images. Such a semi-supervised design can significantly address the domain shift issue between the synthetic and realistic data, and improve generalization performance in the real world. Experiments indicate that the proposed method obtains state-of-the-art performance on both public synthetic and realistic hazy images with better visual results.

Density Distillation for Fast Nonparametric Density Estimation.

Nonparametric density estimation has been extensively used in various application scenarios and theoretical models. However, the modeling of these powerful methods is inseparable from the sample data and comes at the cost of repeated and intensive kernel calculations, which makes their efficiency greatly affected by the sample scale, data dimension, and evaluation scale. Inspired by the knowledge distillation method, a student-teacher paradigm model named density convolutional neural network (DCNN) is proposed in this article. The method extracts the density knowledge of the samples based on the density convolution rule and transfers it to a compact and small deep neural network, in order to separate the sample data from the modeling and avoid the cumbersome kernel calculations. Experimental results show the superiority of the proposed method to various nonparametric estimation methods in terms of accuracy, stability, processing efficiency, and low-storage advantage. Especially, for the estimation speed, a univariate density estimation on 1.0E + 08 evaluation points using GPU only takes 1.57 s, and a 10-D multivariate density estimation on 1.0E + 08 evaluation points only takes 10.50 s, which makes our method very suitable for real-time and large-scale repetitive density estimation tasks.

Evaluation of intrafraction couch shifts for proton treatment delivery in head-and-neck cancer patients: Toward optimal imaging frequency.

PURPOSE: Treatment planning for head-and-neck (H&N) cancer, in particular oropharynx, nasopharynx, and paranasal sinus cases, at our center requires noncoplanar proton beams due to the complexity of the anatomy and target location. Targeting accuracy for all beams is carefully evaluated by using image guidance before delivering proton beam therapy (PBT). In this study, we analyzed couch shifts to evaluate whether imaging is required before delivering each field with different couch angles. METHODS: After the Institutional Review Board approval, a retrospective analysis was performed on data from 28 H&N patients treated with PBT. Each plan was made with two-to-three noncoplanar and two-to-three coplanar fields. Cone-beam computed tomography and orthogonal kilovoltage (kV) images were acquired for setup and before delivering each field, respectively. The Cartesian (longitudinal, vertical, and lateral) and angular (pitch and roll) shifts for each field were recorded from the treatment summary on the first two fractions and every subsequent fifth fraction. A net magnitude of the three-dimensional (3D) shift in Cartesian coordinates was calculated, and a 3D vector was created from the 6 degrees of freedom coordinates for transforming couch shifts in the system coordinate to the beam's-eye view. RESULTS: A total of 3219 Cartesian and 2146 angular shift values were recorded for 28 patients. Of the Cartesian shifts, 2069 were zero (64.3%), and 1150 (35.7%) were nonzero (range, -7 to 11 mm). Of the angular shifts, 1034 (48.2%) were zero, and 1112 (51.8%) were nonzero (range, -3.0° to 3.2°). For 17 patients, the couch shifts increased toward the end of the treatment course. We also found that patients with higher body mass index (BMI) presented increased net couch shifts (p < 0.001). With BMI < 27, all overall net shift averages were <2 mm, and overall maximum net shifts were <6 mm. CONCLUSIONS: These results confirm the need for orthogonal kV imaging before delivering each field of H&N PBT at our center, where a couch rotation is involved.

Readiness for Radiation Treatment Continuity: Survey on Contingency Plans Against Cyberattacks.

Purpose: Cyberattacks on health care systems have been on the rise over the past 5 years. Formulation and implementation of a robust postattack business continuity plan and/or contingency plan (CP) is essential for minimal disruption to patient care. The level of awareness and planning within the radiation oncology community for cyberattacks is not clear. This study was undertaken to survey and assess cyberattack CP awareness and preparedness. Methods and Materials: A survey instrument comprising 5 questions on awareness and preparedness of cyberattack CPs was e-mailed to 150 radiation oncology departments. Recipients included 105 institutions with residency programs in therapeutic medical physics, as listed by the Commission on Accreditation of Medical Physics Education Program (usually either school-based or large institutional settings), and 45 additional smaller settings within the United States, representing community practices. Results: Forty-three responses were deemed evaluable for analysis. Forty-two percent (18 respondents) of respondents responded that they are well-aware of the concept of a cyberattack CP. A large discrepancy in awareness exists between larger hospitals (LH) that have 5 or more treatment machines and smaller hospitals (SH) that have 4 or fewer, 54% versus 24 % (P < .05). Fifty-eight percent of respondents considered it "essential" to have such a plan in place, and 28% considered it "desirable" to do so but not practical. Nine percent regarded a cyberattack CP as unnecessary. No significant differences in responses were noted among different types or sizes of institutions on this issue. Sixty-two percent of LH responded that they were either preparing or evaluating a CP, compared with only 29% of SH (P = .03). However, no respondents explicitly replied that they already had a CP in place in their practices. Conclusions: The importance of cyberattack preparedness and implementation does not seem to be well-recognized in radiation oncology. Both the awareness and the preparedness of SH are substantially less than those of LH. Specific and ongoing education efforts in parallel with development of appropriate programs are needed to counter the increasingly pervasive and complex threat of cyberattacks.

A study of skin marker alignment using different diamond-shaped light fields for prone breast external-beam radiation therapy.

For breast cancer patients treated in the prone position with tangential fields, a diamond-shaped light field (DSLF) can be used to align with corresponding skin markers for image-guided radiation therapy (IGRT). This study evaluates and compares the benefits of different DSLF setups. Seventy-one patients who underwent daily tangential kilovoltage (kV) IGRT were categorized retrospectively into four groups: (1) DSLF field size (FS) = 10 × 10 cm2 , gantry angle = 90° (right breast)/270° (left breast), with the same isocenter as treatment tangential beams; (2) same as group 1, except DSLF FS = 4 × 4 cm2 ; (3) DSLF FS = 4 × 4-6 × 8 cm2 , gantry angle = tangential treatment beam, off-isocenter so that the DSLF was at the approximate breast center; and (4) No-DSLF. We compared their total setup time (including any DSLF/marker-based alignment and IGRT) and relative kV-based couch shift corrections. For groups 1-3, DSLF-only dose distributions (excluding kV-based correction) were simulated by reversely shifting the couch positions from the computed tomography plans, which were assumed equivalent to the delivered dose when both DSLF and IGRT were used. For patient groups 1-4, the average daily setup time was 2.6, 2.5, 5.0, and 8.3 min, respectively. Their mean and standard deviations of daily kV-based couch shifts were 0.64 ± 0.4, 0.68 ± 0.3, 0.8 ± 0.6, and 1.0 ± 0.6 cm. The average target dose changes after excluding kV-IGRT for groups 1-3 were-0.2%, -0.1%, and +0.4%, respectively, whereas DSLF-1 was most efficient in sparing heart and chest wall, DSLF-2 had lowest lung Dmax ; and DSLF-3 maintained the highest target coverage at the cost of highest OAR dose. In general, the use of DSLF greatly reduces patient setup time and may result in smaller IGRT corrections. If IGRT is limited, different DSLF setups yield different target coverage and OAR dose sparing. Our findings will help DSLF setup optimization in the prone breast treatment setting.

Comparative global B cell receptor repertoire difference induced by SARS-CoV-2 infection or vaccination via single-cell V(D)J sequencing.

Dynamic changes of the paired heavy and light chain B cell receptor (BCR) repertoire provide an essential insight into understanding the humoral immune response post-SARS-CoV-2 infection and vaccination. However, differences between the endogenous paired BCR repertoire kinetics in SARS-CoV-2 infection and previously recovered/naïve subjects treated with the inactivated vaccine remain largely unknown. We performed single-cell V(D)J sequencing of B cells from six healthy donors with three shots of inactivated SARS-CoV-2 vaccine (BBIBP-CorV), five people who received the BBIBP-CorV vaccine after having recovered from COVID-19, five unvaccinated COVID-19 recovered patients and then integrated with public data of B cells from four SARS-CoV-2-infected subjects. We discovered that BCR variable (V) genes were more prominently used in the SARS-CoV-2 exposed groups (both in the group with active infection and in the group that had recovered) than in the vaccinated groups. The VH gene that expanded the most after SARS-CoV-2 infection was IGHV3-33, while IGHV3-23 in the vaccinated groups. SARS-CoV-2-infected group enhanced more BCR clonal expansion and somatic hypermutation than the vaccinated healthy group. A small proportion of public clonotypes were shared between the SARS-CoV-2 infected, vaccinated healthy, and recovered groups. Moreover, several public antibodies had been identified against SARS-CoV-2 spike protein. We comprehensively characterize the paired heavy and light chain BCR repertoire from SARS-CoV-2 infection to vaccination, providing further guidance for the development of the next-generation precision vaccine.

Is a weekly qualitative picket fence test sufficient? A proposed alternate EPID-based weekly MLC QA program.

PURPOSE: Well-designed routine multileaf collimator (MLC) quality assurance (QA) is important to assure external-beam radiation treatment delivery accuracy. This study evaluates the clinical necessity of a comprehensive weekly (C-Weekly) MLC QA program compared to the American Association of Physics in Medicinerecommended weekly picket fence test (PF-Weekly), based on our seven-year experience with weekly MLC QA. METHODS: The C-Weekly MLC QA program used in this study includes 5 tests to analyze: (1) absolute MLC leaf position; (2) interdigitation MLC leaf position; (3) picket fence MLC leaf positions at static gantry angle; (4) minimum leaf-gap setting; and (5) volumetric-modulated arc therapy delivery. A total of 20,226 QA images from 16,855 tests (3,371 tests × 5) for 11 linacs at 5 photon clinical sites from May 2014 to June 2021 were analyzed. Failure mode and effects analysis was performed with 5 failure modes related to the 5 tests. For each failure mode, a risk probability number (RPN) was calculated for a C-Weekly and a PF-Weekly MLC QA program. The probability of occurrence was evaluated from statistical analyses of the C-Weekly MLC QA. RESULTS: The total number of failures for these 16,855 tests was 143 (0.9%): 39 (27.3%) for absolute MLC leaf position, 13 (9.1%) for interdigitation position, 9 (6.3%) for static gantry picket fence, 2 (1.4%) for minimum leaf-gap setting, and 80 (55.9%) for VMAT delivery. RPN scores for PF-Weekly MLC QA ranged from 60 to 192 and from 48 to 96 for C-Weekly MLC QA. CONCLUSION: RPNs for the 5 failure modes of MLC QA tests were quantitatively determined and analyzed. A comprehensive weekly MLC QA is imperative to lower the RPNs of the 5 failure modes to the desired level (<125); those from the PF-Weekly MLC QA program were found to be higher (>125). This supports the clinical necessity for comprehensive weekly MLC QA.

Development of an Electronic Stethoscope and a Classification Algorithm for Cardiopulmonary Sounds.

With conventional stethoscopes, the auscultation results may vary from one doctor to another due to a decline in his/her hearing ability with age or his/her different professional training, and the problematic cardiopulmonary sound cannot be recorded for analysis. In this paper, to resolve the above-mentioned issues, an electronic stethoscope was developed consisting of a traditional stethoscope with a condenser microphone embedded in the head to collect cardiopulmonary sounds and an AI-based classifier for cardiopulmonary sounds was proposed. Different deployments of the microphone in the stethoscope head with amplification and filter circuits were explored and analyzed using fast Fourier transform (FFT) to evaluate the effects of noise reduction. After testing, the microphone placed in the stethoscope head surrounded by cork is found to have better noise reduction. For classifying normal (healthy) and abnormal (pathological) cardiopulmonary sounds, each sample of cardiopulmonary sound is first segmented into several small frames and then a principal component analysis is performed on each small frame. The difference signal is obtained by subtracting PCA from the original signal. MFCC (Mel-frequency cepstral coefficients) and statistics are used for feature extraction based on the difference signal, and ensemble learning is used as the classifier. The final results are determined by voting based on the classification results of each small frame. After the testing, two distinct classifiers, one for heart sounds and one for lung sounds, are proposed. The best voting for heart sounds falls at 5-45% and the best voting for lung sounds falls at 5-65%. The best accuracy of 86.9%, sensitivity of 81.9%, specificity of 91.8%, and F1 score of 86.1% are obtained for heart sounds using 2 s frame segmentation with a 20% overlap, whereas the best accuracy of 73.3%, sensitivity of 66.7%, specificity of 80%, and F1 score of 71.5% are yielded for lung sounds using 5 s frame segmentation with a 50% overlap.

Analysis of genetic characteristics of 436 children with dysplasia and detailed analysis of rare karyotype.

Chromosomal abnormality is one of the important causes of dysplasia in children. However, due to regional and ethnic differences, the reported rates of chromosomal abnormalities in patients with dysplasia vary greatly. Moreover, the clinical manifestations in children with rare chromosomal diseases were heterogeneous. So, we retrospectively analyzed the karyotype results of 436 children with dysplasia and conducted a detailed analysis of rare chromosomal diseases. The results showed that chromosomal abnormalities were present in 181 of 436 cases. Intellectual disability, dysmorphology, congenital malformations, the disorder of sexual development, and short stature were the main five clinical symptoms in children with chromosomal abnormalities. Moreover, 136 cases of Trisomy 21 (Tri21) were detected, of which 130 were standard Tri21, 5 were robertsonian Tri21, and 1 was chimera type. In addition, 16 cases of rare abnormal karyotype, including complex Tri21, complex Turner syndrome, 4p-syndrome, 18q-syndrome, and 5p-syndrome, were also detected. In summary, chromosome abnormality is one of the important causes of dysplasia in children. Furthermore, prenatal screening and diagnosis could play a great significance in preventing dysplasia in children. In addition, the retrospective analysis of rare cases is valuable for clinical diagnosis and risk assessment of recurrence.

Insight into atmospheric deposition and spatial distribution of bioavailable iron in the glaciers of northeastern Tibetan Plateau.

Iron (Fe) is an essential micronutrient in glacial ecosystems and modulates global biogeochemical cycles. To find out the deposition concentration, multiple origins and release form of iron in various glacier areas of central Asia, this study investigated the total Fe (TFe) and dissolved-Fe (dFe, diameter < 0.45 or <0.2 µm) deposition in glaciers and snowpack of northeast Tibetan Plateau, based on snow and meltwater sampling in ablation period of 2014-2017. The composition and concentration of dFe in the samples were measured, and the spatial distribution and temporal variations of dFe in glacial surface snow and meltwater runoff were investigated. Results showed that average TFe and dFe contents exhibited a generally heterogeneous geographic distribution that varied from north to south. The northern locations in eastern Tianshan Mountains (e.g. Miaoergou Glacier) showed the highest TFe and dFe values, followed by Yuzhufeng Glacier of eastern Kunlun Mountains, whereas the Qilian Mountains locations displayed relatively lower TFe and dFe contents spanning a wide range. Based on the good correlation between TFe and dFe, we infer that aeolian dust and anthropogenic aerosols, and their chemical interactions are likely the important origins for dFe deposition. In meltwater runoff the peak values of dFe release flux appeared in July, with maximum appeared earlier (the early of July) than TFe (the end of July). Moreover, the annual dFe release flux from Laohugou glacier terminus meltwater runoff is estimated to be 1740 kg yr-1 (with 9256 kg yr-1 for TFe), and meltwater showed higher mean concentration of dFe than that of glacier snowpack. We also provided a conceptual framework showing the multiple origins and transport dynamics of dissolved Fe along the atmosphere-glacier-meltwater runoff path. Compared to Fe release in other global glacier/ice-sheet, the TP glacier is an important potential dFe reservoir and may have a profound effect on regional downstream ecosystem through Fe biochemistry cycle.

Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis From Lung CT Scans With Multi-Scale Guided Dense Attention.

Computed Tomography (CT) plays an important role in monitoring radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the PF lesions is highly desired for diagnosis and treatment follow-up. However, the task is challenged by ambiguous boundary, irregular shape, various position and size of the lesions, as well as the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a novel convolutional neural network called PF-Net and incorporate it into a semi-supervised learning framework based on Iterative Confidence-based Refinement And Weighting of pseudo Labels (I-CRAWL). Our PF-Net combines 2D and 3D convolutions to deal with CT volumes with large inter-slice spacing, and uses multi-scale guided dense attention to segment complex PF lesions. For semi-supervised learning, our I-CRAWL employs pixel-level uncertainty-based confidence-aware refinement to improve the accuracy of pseudo labels of unannotated images, and uses image-level uncertainty for confidence-based image weighting to suppress low-quality pseudo labels in an iterative training process. Extensive experiments with CT scans of Rhesus Macaques with radiation-induced PF showed that: 1) PF-Net achieved higher segmentation accuracy than existing 2D, 3D and 2.5D neural networks, and 2) I-CRAWL outperformed state-of-the-art semi-supervised learning methods for the PF lesion segmentation task. Our method has a potential to improve the diagnosis of PF and clinical assessment of side effects of radiotherapy for lung cancers.

Deep Network Quantization via Error Compensation.

For portable devices with limited resources, it is often difficult to deploy deep networks due to the prohibitive computational overhead. Numerous approaches have been proposed to quantize weights and/or activations to speed up the inference. Loss-aware quantization has been proposed to directly formulate the impact of weight quantization on the model's final loss. However, we discover that, under certain circumstances, such a method may not converge and end up oscillating. To tackle this issue, we introduce a novel loss-aware quantization algorithm to efficiently compress deep networks with low bit-width model weights. We provide a more accurate estimation of gradients by leveraging the Taylor expansion to compensate for the quantization error, which leads to better convergence behavior. Our theoretical analysis indicates that the gradient mismatch issue can be fixed by the newly introduced quantization error compensation term. Experimental results for both linear models and convolutional networks verify the effectiveness of our proposed method.