Phase 1 study of high-dose DFMO, celecoxib, cyclophosphamide and topotecan for patients with relapsed neuroblastoma: a New Approaches to Neuroblastoma Therapy trial.

BACKGROUND: MYC genes regulate ornithine decarboxylase (Odc) to increase intratumoral polyamines. We conducted a Phase I trial [NCT02030964] to determine the maximum tolerated dose (MTD) of DFMO, an Odc inhibitor, with celecoxib, cyclophosphamide and topotecan. METHODS: Patients 2-30 years of age with relapsed/refractory high-risk neuroblastoma received oral DFMO at doses up to 9000 mg/m2/day, with celecoxib (500 mg/m2 daily), cyclophosphamide (250 mg/m2/day) and topotecan (0.75 mg/m2/day) IV for 5 days, for up to one year with G-CSF support. RESULTS: Twenty-four patients (median age, 6.8 years) received 136 courses. Slow platelet recovery with 21-day courses (dose-levels 1 and 2) led to subsequent dose-levels using 28-day courses (dose-levels 2a-4a). There were three course-1 dose-limiting toxicities (DLTs; hematologic; anorexia; transaminases), and 23 serious adverse events (78% fever-related). Five patients (21%) completed 1-year of therapy. Nine stopped for PD, 2 for DLT, 8 by choice. Best overall response included two PR and four MR. Median time-to-progression was 19.8 months, and 3 patients remained progression-free at >4 years without receiving additional therapy. The MTD of DFMO with this regimen was 6750 mg/m2/day. CONCLUSION: High-dose DFMO is tolerable when added to chemotherapy in heavily pre-treated patients. A randomized Phase 2 trial of DFMO added to chemoimmunotherapy is ongoing [NCT03794349].

Reprogramming neuroblastoma by diet-enhanced polyamine depletion.

Neuroblastoma is a highly lethal childhood tumor derived from differentiation-arrested neural crest cells1,2. Like all cancers, its growth is fueled by metabolites obtained from either circulation or local biosynthesis3,4. Neuroblastomas depend on local polyamine biosynthesis, with the inhibitor difluoromethylornithine showing clinical activity5. Here we show that such inhibition can be augmented by dietary restriction of upstream amino acid substrates, leading to disruption of oncogenic protein translation, tumor differentiation, and profound survival gains in the TH-MYCN mouse model. Specifically, an arginine/proline-free diet decreases the polyamine precursor ornithine and augments tumor polyamine depletion by difluoromethylornithine. This polyamine depletion causes ribosome stalling, unexpectedly specifically at adenosine-ending codons. Such codons are selectively enriched in cell cycle genes and low in neuronal differentiation genes. Thus, impaired translation of these codons, induced by the diet-drug combination, favors a pro-differentiation proteome. These results suggest that the genes of specific cellular programs have evolved hallmark codon usage preferences that enable coherent translational rewiring in response to metabolic stresses, and that this process can be targeted to activate differentiation of pediatric cancers.

Flexible p-i-n perovskite solar cell with optimized performance by KBF4 additive.

Flexible perovskite solar cells (F-PSCs) prevail in the clean energy field for their light weight, easy fabrication and installation, but the power conversion efficiency of F-PSCs needs further improvement. In this work, we numerically simulate and experimentally demonstrate the effect of the perovskite trap defects density on the power conversion efficiency. The pseudo-halide KBF4 is employed as the additive to passivate the trap defects in the perovskite films. The high electrophilicity of BF4 - group ensures its entering into perovskite lattice, optimizing crystallinity and improving the qualities of perovskite films, K+ ions can effectively passivate grain boundaries and inhibit halide anion migrations. After KBF4 passivation, trap defect density of the perovskite film was decreased from 8.0 × 1015cm-3 to 3.9 × 1015cm-3, and also the carrier lifetime increased from 108.52 ns to 234.72 ns. Consequently, the power conversion efficiency (PCE) of the F-PSCs devices increased from 13.99% to 16.04%.

Rapid quantitative magnetization transfer imaging: Utilizing the hybrid state and the generalized Bloch model.

PURPOSE: To explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model parameters. THEORY AND METHODS: We combine two recently proposed models in a Bloch-McConnell equation: the dynamics of the free spin pool are confined to the hybrid state, and the dynamics of the semi-solid spin pool are described by the generalized Bloch model. We numerically optimize the flip angles and durations of a train of radio frequency pulses to enhance the encoding of three qMT parameters while accounting for all eight parameters of the two-pool model. We sparsely sample each time frame along this spin dynamics with a three-dimensional radial koosh-ball trajectory, reconstruct the data with subspace modeling, and fit the qMT model with a neural network for computational efficiency. RESULTS: We extracted qMT parameter maps of the whole brain with an effective resolution of 1.24 mm from a 12.6-min scan. In lesions of multiple sclerosis subjects, we observe a decreased size of the semi-solid spin pool and longer relaxation times, consistent with previous reports. CONCLUSION: The encoding power of the hybrid state, combined with regularized image reconstruction, and the accuracy of the generalized Bloch model provide an excellent basis for efficient quantitative magnetization transfer imaging with few constraints on model parameters.

Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects.

Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a novel framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors ($\rho = 0.845$, 95% CI [0.743,0.908]) and video ($\rho = 0.746$, 95% C.I [0.594, 0.847]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment ($\rho = 0.644$, 95% C.I [0.585,0.696]).

Entropy engineering of La-based perovskite for simultaneous photocatalytic CO2 reduction and biomass oxidation.

Herein, the high-entropy perovskite, i.e. La(FeCoNiCrMn)O3, was prepared for simultaneous CO2 reduction and biomass upgrading. Based on the synergistic effect between the elements in the high-entropy material, an excellent CO evolution rate of 131.8 µmol g-1 h-1 and a xylonic acid yield of 63.9% were gained.

Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data.

To meet the challenge of food security, it is necessary to obtain information about rice fields accurately, quickly and conveniently. In this study, based on the analysis of existing rice fields extraction methods and the characteristics of intra-annual variation of normalized difference vegetation index (NDVI) in the different types of ground features, the NDVI difference method is used to extract rice fields using Sentinel data based on the unique feature of rice fields having large differences in vegetation between the pre-harvest and post-harvest periods. Firstly, partial correlation analysis is used to study the influencing factors of the rice harvesting period, and a simulation model of the rice harvesting period is constructed by multiple regression analysis with data from 32 sample points. Sentinel data of the pre-harvest and post-harvest periods of rice fields are determined based on the selected rice harvesting period. The NDVI values of the rice fields are calculated for both the pre-harvest and post-harvest periods, and 33 samples of the rice fields are selected from the high-resolution image. The threshold value for rice field extraction is determined through statistical analysis of the NDVI difference in the sample area. This threshold was then utilized to extract the initial extent of rice fields. Secondly, to address the phenomenon of the "water edge effect" in the initial data, the water extraction method based on the normalized difference water index (NDWI) is used to remove the pixels of water edges. Finally, the extraction results are verified and analyzed for accuracy. The study results show that: (1) The rice harvesting period is significantly correlated with altitude and latitude, with coefficients of 0.978 and 0.922, respectively, and the simulation model of the harvesting period can effectively determine the best period of remote sensing images needed to extract rice fields; (2) The NDVI difference method based on sentinel data for rice fields extraction is excellent; (3) The mixed pixels have a large impact on the accuracy of rice fields extraction, due to the water edge effect. Combining NDWI can effectively reduce the water edge effect and significantly improve the accuracy of rice field extraction.

K2S Challenge: From Undersampled K-Space to Automatic Segmentation.

Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space. We curated the 300-patient K2S dataset of multicoil raw k-space and radiologist quality-checked segmentations. 87 teams registered for the challenge and there were 12 submissions, varying in methodologies from serial reconstruction and segmentation to end-to-end networks to another that eschewed a reconstruction algorithm altogether. Four teams produced strong submissions, with the winner having a weighted Dice Similarity Coefficient of 0.910 ± 0.021 across knee bones and cartilage. Interestingly, there was no correlation between reconstruction and segmentation metrics. Further analysis showed the top four submissions were suitable for downstream biomarker analysis, largely preserving cartilage thicknesses and key bone shape features with respect to ground truth. K2S thus showed the value in considering reconstruction and image analysis as end-to-end tasks, as this leaves room for optimization while more realistically reflecting the long-term use case of tools being developed by the MR community.

Impact of built environment on residential online car-hailing trips: Based on MGWR model.

With the development of smart mobile devices and global positioning technology, people's daily travel has become increasingly dependent on online car-hailing. Meanwhile, it has also become possible to use multi-source data to explore the factors influencing urban residents' car-hailing trips. Using online data on car-hailing trajectories, points of interest (POIs) data and other auxiliary data, the paper explores how the built environment impacts online car-hailing passengers. Within a 200 x 200m research grid, the unique spatiotemporal patterns of weekday car-hailing trips during a one-week period are analyzed, using statistics on pick-ups and drop-offs at different time of the day. By combining these data with built environment variables and various economic and traffic indicators, a multi-scale geographically weighted regression (MGWR) model is developed for different time scales. The MGWR model outperforms the classical geographically weighted regression (GWR) model and the ordinary least squares (OLS) regression model in terms of goodness of fit and all other aspects. More importantly, this study finds a high degree of temporal and spatial heterogeneity in the impact of built environment factors on local car-hailing trips across different regions, and the paper analyzes the business residence coefficient in detail. The study provides valuable insights to help improve the level of urban transportation services, as well as urban transportation planning and construction.

Research on Mechanical and Carbonization Properties of Hybrid Fiber Iron Tailings Concrete Based on Deep Learning.

Iron tailings sand is a kind of mineral waste, and open-air storage is a common treatment method for iron tailings, which not only has a huge impact on the ecological environment but also occupies a lot of land resources. Therefore, the preparation of high-ductility fiber reinforced iron tailings concrete and its application in practical engineering structures have good application prospects. This paper is based on the deep learning research on the mechanical and carbonization properties of hybrid fiber iron tailings concrete. Therefore, tailings sands with different substitution rates, single-mixed steel fiber, and mixed steel-PVA fiber concrete were prepared in this paper. Its compressive strength, split tensile strength, axial compressive strength, elastic modulus, strain, and carbonization depth were tested. Through the existing concrete compressive stress-strain curve equations, the nonlinear calculation of each group of concrete compressive stress-strain curve equations in this paper is carried out, some parameters are determined, and the carbonation depth equation is established. The results show that, with the increase of tailings content, the properties of concrete increase first and then decrease and the addition of fibers can effectively improve the strength, elastic modulus, peak strain, and carbonization depth of concrete. However, with the increase of PVA fiber content, its performance enhancement effect decreased.

Cramér-Rao bound-informed training of neural networks for quantitative MRI.

PURPOSE: To improve the performance of neural networks for parameter estimation in quantitative MRI, in particular when the noise propagation varies throughout the space of biophysical parameters. THEORY AND METHODS: A theoretically well-founded loss function is proposed that normalizes the squared error of each estimate with respective Cramér-Rao bound (CRB)-a theoretical lower bound for the variance of an unbiased estimator. This avoids a dominance of hard-to-estimate parameters and areas in parameter space, which are often of little interest. The normalization with corresponding CRB balances the large errors of fundamentally more noisy estimates and the small errors of fundamentally less noisy estimates, allowing the network to better learn to estimate the latter. Further, proposed loss function provides an absolute evaluation metric for performance: A network has an average loss of 1 if it is a maximally efficient unbiased estimator, which can be considered the ideal performance. The performance gain with proposed loss function is demonstrated at the example of an eight-parameter magnetization transfer model that is fitted to phantom and in vivo data. RESULTS: Networks trained with proposed loss function perform close to optimal, that is, their loss converges to approximately 1, and their performance is superior to networks trained with the standard mean-squared error (MSE). The proposed loss function reduces the bias of the estimates compared to the MSE loss, and improves the match of the noise variance to the CRB. This performance gain translates to in vivo maps that align better with the literature. CONCLUSION: Normalizing the squared error with the CRB during the training of neural networks improves their performance in estimating biophysical parameters.

Effect of Se on Structure and Electrical Properties of Ge-As-Te Glass.

The Ge-As-Te glass has a wide infrared transmission window range of 3-18 µm, but its crystallization tendency is severe due to the metallicity of the Te atom, which limits its development in the mid- and far-infrared fields. In this work, the Se element was introduced to stabilize the Ge-As-Te glass. Some glasses with ΔT ≥ 150 °C have excellent thermal stability, indicating these glasses can be prepared in large sizes for industrialization. The Ge-As-Se-Te (GAST) glasses still have a wide infrared transmission window (3-18 µm) and a high linear refractive index (3.2-3.6), indicating that the GAST glass is an ideal material for infrared optics. Raman spectra show that the main structural units for GAST glass are [GeTe4] tetrahedra, [AsTe3] pyramids, and [GeTe4Se4-x] tetrahedra, and with the decrease of Te content (≤50 mol%), As-As and Ge-Ge homopolar bonds appear in the glass due to the non-stoichiometric ratio. The conductivity σ of the studied GAST glasses decreases with the decrease of the Te content. The highest σ value of 1.55 × 10-5 S/cm is obtained in the glass with a high Te content. The activation energy Ea of the glass increases with the decrease of the Te content, indicating that the glass with a high Te content is more sensitive to temperature. This work provides a foundation for widening the application of GAST glass materials in the field of infrared optics.

Reduced ER-mitochondria connectivity promotes neuroblastoma multidrug resistance.

Most cancer deaths result from progression of therapy resistant disease, yet our understanding of this phenotype is limited. Cancer therapies generate stress signals that act upon mitochondria to initiate apoptosis. Mitochondria isolated from neuroblastoma cells were exposed to tBid or Bim, death effectors activated by therapeutic stress. Multidrug-resistant tumor cells obtained from children at relapse had markedly attenuated Bak and Bax oligomerization and cytochrome c release (surrogates for apoptotic commitment) in comparison with patient-matched tumor cells obtained at diagnosis. Electron microscopy identified reduced ER-mitochondria-associated membranes (MAMs; ER-mitochondria contacts, ERMCs) in therapy-resistant cells, and genetically or biochemically reducing MAMs in therapy-sensitive tumors phenocopied resistance. MAMs serve as platforms to transfer Ca2+ and bioactive lipids to mitochondria. Reduced Ca2+ transfer was found in some but not all resistant cells, and inhibiting transfer did not attenuate apoptotic signaling. In contrast, reduced ceramide synthesis and transfer was common to resistant cells and its inhibition induced stress resistance. We identify ER-mitochondria-associated membranes as physiologic regulators of apoptosis via ceramide transfer and uncover a previously unrecognized mechanism for cancer multidrug resistance.

StrokeRehab: A Benchmark Dataset for Sub-second Action Identification.

Automatic action identification from video and kinematic data is an important machine learning problem with applications ranging from robotics to smart health. Most existing works focus on identifying coarse actions such as running, climbing, or cutting vegetables, which have relatively long durations and a complex series of motions. This is an important limitation for applications that require identification of more elemental motions at high temporal resolution. For example, in the rehabilitation of arm impairment after stroke, quantifying the training dose (number of repetitions) requires differentiating motions with sub-second durations. Our goal is to bridge this gap. To this end, we introduce a large-scale, multimodal dataset, StrokeRehab, as a new action-recognition benchmark that includes elemental short-duration actions labeled at a high temporal resolution. StrokeRehab consists of high-quality inertial measurement unit sensor and video data of 51 stroke-impaired patients and 20 healthy subjects performing activities of daily living like feeding, brushing teeth, etc. Because it contains data from both healthy and impaired individuals, StrokeRehab can be used to study the influence of distribution shift in action-recognition tasks. When evaluated on StrokeRehab, current state-of-the-art models for action segmentation produce noisy predictions, which reduces their accuracy in identifying the corresponding sequence of actions. To address this, we propose a novel approach for high-resolution action identification, inspired by speech-recognition techniques, which is based on a sequence-to-sequence model that directly predicts the sequence of actions. This approach outperforms current state-of-the-art methods on StrokeRehab, as well as on the standard benchmark datasets 50Salads, Breakfast, and Jigsaws.

Study on Collapse Resistance of RC Frame under the Corner Column Removal Scenario.

The progressive collapse of buildings induces a variety of catastrophic consequences, such as casualties and property loss over the past few decades. The corner column is more prone to abnormal load events compared to the inner column and outer column; thus, it is easier to trigger progressive collapse. By considering the effects of floor slabs and adjacent bays on progressive collapse behavior, the pseudo-static loading method was used to study the progressive collapse test of a 1/3 scaled, one story, 2 × 2-bay cast-in-place reinforced concrete frame substructure under the removal condition of a corner column. The test results show that the flexural deformation principally concentrates upon the components of a directly affected part (DAP), and compressive arch actions are observed in members of the indirectly affected part (IAP). Moreover, the slab adjacent to the removed column and periphery elements contributes great resistance to a progressive collapse.

Recent Advances and Challenges in Photoreforming of Biomass-Derived Feedstocks into Hydrogen, Biofuels, or Chemicals by Using Functional Carbon Nitride Photocatalysts.

Photoreforming of biomass into hydrogen, biofuels, and chemicals is highly desired, yet this field of research is still in its infancy. Developing an efficient, novel, and environmentally friendly photocatalyst is key to achieving these goals. To date, the nonmetallic and eco-friendly material carbon nitride has found many uses in reactions such as water splitting, CO2 reduction, N2 fixation, and biorefinery, owing to its outstanding photocatalytic activity. However, a narrow light absorption range and fast charge recombination are often encountered in the pristine carbon nitride photocatalytic system, which resulted in unsatisfying photocatalytic activity. To improve the photocatalytic performance of pure carbon nitride in biomass reforming, modification is needed. In this Review, the design and preparation of functional carbon nitride, as well as its photocatalytic properties for the synthesis of hydrogen, biofuels, and chemicals through biomass reforming, are discussed alongside potential avenues for its future development.

Anlotinib, a novel TKI, as a third-line or further-line treatment in patients with advanced non-small cell lung cancer in China: A systemic review and meta-analysis of its efficacy and safety.

PURPOSE: In this meta-analysis and systemic review, we focused on the effectiveness and safety of anlotinib in patients with advanced non-small cell lung cancer(NSCLC). METHODS: The databases of PubMed, EMBASE, Cochrane Library, CNKI, Wanfang, and CBM were searched by 2 investigators up to April 2020. Titles and abstracts of all records were screened and eligible publications were retrieved in full. Review Manager (version 5.2, Cochrane Library) was used for data analysis. The outcomes of interest were disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and treatment-related adverse event (TRAE). Data was pooled for quantitative analysis and the effect size was reported as hazard ratio for survival outcomes and odds ratio (OR) for safety outcomes, both with a random-effects model. RESULTS: A sum of 1480 patients were included in 11 trials ranging from 2018 to 2020. Substantial improvements of PFS, OS, and DCR were observed in patients treated with anlotinib alone or in combination with other conventional treatment. Accompanied TRAE included statistically significant higher risk for hypertension (OR = 11.05, 95% confidence interval [CI] = 7.85-15.55, P < .001), hepatic dysfunction (OR = 1.96, 95% CI = 1.29-2.68, P < .001), diarrhea (OR = 2.20, 95% CI = 1.17-4.16, P < .05), and hemoptysis (OR = 2.59, 95% CI = 1.71-3.93, P < .01). CONCLUSIONS: Our study suggested that anlotinib as maintenance therapy for advanced NSCLC patients is associated with prolonged PFS and OS as well as DCR improvement, but it was accompanied by increased risk of TRAE, such as hypertension, hepatic dysfunction, diarrhea and hemoptysis. Although much effort has been made to clinical trials of anlotinib, further studies are warranted to provide more convincing evidence.

Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis.

In the last few years, deep learning classifiers have shown promising results in image-based medical diagnosis. However, interpreting the outputs of these models remains a challenge. In cancer diagnosis, interpretability can be achieved by localizing the region of the input image responsible for the output, i.e. the location of a lesion. Alternatively, segmentation or detection models can be trained with pixel-wise annotations indicating the locations of malignant lesions. Unfortunately, acquiring such labels is labor-intensive and requires medical expertise. To overcome this difficulty, weakly-supervised localization can be utilized. These methods allow neural network classifiers to output saliency maps highlighting the regions of the input most relevant to the classification task (e.g. malignant lesions in mammograms) using only image-level labels (e.g. whether the patient has cancer or not) during training. When applied to high-resolution images, existing methods produce low-resolution saliency maps. This is problematic in applications in which suspicious lesions are small in relation to the image size. In this work, we introduce a novel neural network architecture to perform weakly-supervised segmentation of high-resolution images. The proposed model selects regions of interest via coarse-level localization, and then performs fine-grained segmentation of those regions. We apply this model to breast cancer diagnosis with screening mammography, and validate it on a large clinically-realistic dataset. Measured by Dice similarity score, our approach outperforms existing methods by a large margin in terms of localization performance of benign and malignant lesions, relatively improving the performance by 39.6% and 20.0%, respectively. Code and the weights of some of the models are available at https://github.com/nyukat/GLAM.

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization.

Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be applicable to medical image analysis. In this work, we propose a novel neural network model to address these unique properties of medical images. This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions. It then applies another higher-capacity network to collect details from chosen regions. Finally, it employs a fusion module that aggregates global and local information to make a prediction. While existing methods often require lesion segmentation during training, our model is trained with only image-level labels and can generate pixel-level saliency maps indicating possible malignant findings. We apply the model to screening mammography interpretation: predicting the presence or absence of benign and malignant lesions. On the NYU Breast Cancer Screening Dataset, our model outperforms (AUC = 0.93) ResNet-34 and Faster R-CNN in classifying breasts with malignant findings. On the CBIS-DDSM dataset, our model achieves performance (AUC = 0.858) on par with state-of-the-art approaches. Compared to ResNet-34, our model is 4.1x faster for inference while using 78.4% less GPU memory. Furthermore, we demonstrate, in a reader study, that our model surpasses radiologist-level AUC by a margin of 0.11.

Polyaniline Nanofiber Wrapped Fabric for High Performance Flexible Pressure Sensors.

The rational design of high-performance flexible pressure sensors with both high sensitivity and wide linear range attracts great attention because of their potential applications in wearable electronics and human-machine interfaces. Here, polyaniline nanofiber wrapped nonwoven fabric was used as the active material to construct high performance, flexible, all fabric pressure sensors with a bottom interdigitated textile electrode. Due to the unique hierarchical structures, large surface roughness of the polyaniline coated fabric and high conductivity of the interdigitated textile electrodes, the obtained pressure sensor shows superior performance, including ultrahigh sensitivity of 46.48 kPa-1 in a wide linear range (<4.5 kPa), rapid response/relaxation time (7/16 ms) and low detection limit (0.46 Pa). Based on these merits, the practical applications in monitoring human physiological signals and detecting spatial distribution of subtle pressure are demonstrated, showing its potential for health monitoring as wearable electronics.