Molecular-Sieving Separation of Methanol/Benzene Azeotrope by a Flexible Metal-Organic Framework.

Separation of methanol/benzene azeotrope mixtures is very challenging not only by the conventional distillation technique but also by adsorbents. In this work, we design and synthesize a flexible Ca-based metal-organic framework MAF-58 consisting of cheap raw materials. MAF-58 shows selective methanol-induced pore-opening flexibility. Although the opened pores are large enough to accommodate benzene molecules, MAF-58 shows methanol/benzene molecular sieving with ultrahigh experimental selectivity, giving 5.1 mmol g-1 high-purity (99.99%+) methanol and 2.0 mmol g-1 high-purity (99.97%+) benzene in a single adsorption/desorption cycle. Computational simulations reveal that the preferentially adsorbed, coordinated methanol molecules act as the gating component to selectively block the diffusion of benzene, offering a new gating adsorption mechanism.

Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma.

Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients.

Mining multi-center heterogeneous medical data with distributed synthetic learning.

Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and ensure the protection of sensitive personal information. DSL enables the building of a homogeneous dataset with entirely synthetic medical images via a form of GAN-based synthetic learning. The proposed DSL architecture has the following key functionalities: multi-modality learning, missing modality completion learning, and continual learning. We systematically evaluate the performance of DSL on different medical applications using cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets. Extensive experiments demonstrate the superior performance of DSL as a high-quality synthetic medical image provider by the use of an ideal synthetic quality metric called Dist-FID. We show that DSL can be adapted to heterogeneous data and remarkably outperforms the real misaligned modalities segmentation model by 55% and the temporal datasets segmentation model by 8%.

Association between post-stroke smoking and stroke recurrence in first-ever ischemic stroke survivors: based on a 10-year prospective cohort.

BACKGROUND: Whether smoking is a risk factor for ischemic stroke (IS) recurrence in IS survivors is still uncovered, and evidences are sparse. Meanwhile, an add-on effect of clopidogrel was observed in myocardial infarction patients who smoked, but whether the paradox exists in IS patients is still unsolved. The objectives of this study are to explore the association between smoking behavior after index stroke and IS recurrence and to explore whether the paradox exists. METHODS: A prospective cohort of first-ever IS patients was conducted between 2010 and 2019. The prognosis and smoking features of enrolled patients were obtained via telephone follow-up every 3 months. Fine-gray model with interaction terms was applied to measure the relationships between stroke recurrence and smoking behaviors after index stroke and to explore the add-on effect of clopidogrel in smoking patients. RESULTS: There were 171 (24.26%) recurrences and 129 (18.30%) deaths during follow-up in 705 enrolled IS patients. One hundred forty-six (20.71%) patients smoked after index stroke. The hazard ratios (HRs) and 95% confidence intervals (CIs) of interaction terms between antiplatelet drug and follow-up smoking (smoking status and daily smoking amount) were 1.092 (95% CI: 0.524, 2.276) and 0.985 (95% CI: 0.941, 1.031), respectively. A significantly higher risk of recurrence was observed in patients with a higher daily smoking amount during follow-up (per cigarette), with HR being 1.027 (95% CI: 1.003, 1.052). CONCLUSIONS: Smoking could elevate the risk of IS recurrence, and IS survivor should be advised to quit or smoke less. Add-on effect of clopidogrel may not exist in smoking strokers taking clopidogrel.

Heat-driven molecule gatekeepers in MOF membrane for record-high H2 selectivity.

Hydrogen/carbon dioxide (H2/CO2) separation for sustainable energy is in desperate need of reliable membranes at high temperatures. Molecular sieve membranes take their nanopores to differentiate sizes between H2 and CO2 but have compromised at a marked loss of selectivity at high temperatures owing to diffusion activation of CO2. We used molecule gatekeepers that were locked in the cavities of the metal-organic framework membrane to meet this challenge. Ab initio calculations and in situ characterizations demonstrate that the molecule gatekeepers make a notable move at high temperatures to dynamically reshape the sieving apertures as being extremely tight for CO2 and restitute with cool conditions. The H2/CO2 selectivity was improved by an order of magnitude at 513 kelvin (K) relative to that at the ambient temperature.

Water-Stable Metal Azolate Frameworks Showing Interesting Flexibilities for Highly Effective Bioethanol Dehydration.

The ethanol/water separation challenge highlights the adsorption capacity/selectivity trade-off problem. We show that the target guest can serve as a gating component of the host to block the undesired guest, giving molecular sieving effect for the adsorbent possessing large pores. Two hydrophilic/water-stable metal azolate frameworks were designed to compare the effects of gating and pore-opening flexibility. Large amounts (up to 28.7 mmol g-1 ) of ethanol with fuel-grade (99.5 %+) and even higher purities (99.9999 %+) can be produced in a single adsorption process from not only 95 : 5 but also 10 : 90 ethanol/water mixtures. More interestingly, the pore-opening adsorbent possessing large pore apertures showed not only high water adsorption capacity but also exceptionally high water/ethanol selectivity characteristic of molecular sieving. Computational simulations demonstrated the critical role of guest-anchoring aperture for the guest-dominated gating process.

A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer.

The success of training computer-vision models heavily relies on the support of large-scale, real-world images with annotations. Yet such an annotation-ready dataset is difficult to curate in pathology due to the privacy protection and excessive annotation burden. To aid in computational pathology, synthetic data generation, curation, and annotation present a cost-effective means to quickly enable data diversity that is required to boost model performance at different stages. In this study, we introduce a large-scale synthetic pathological image dataset paired with the annotation for nuclei semantic segmentation, termed as Synthetic Nuclei and annOtation Wizard (SNOW). The proposed SNOW is developed via a standardized workflow by applying the off-the-shelf image generator and nuclei annotator. The dataset contains overall 20k image tiles and 1,448,522 annotated nuclei with the CC-BY license. We show that SNOW can be used in both supervised and semi-supervised training scenarios. Extensive results suggest that synthetic-data-trained models are competitive under a variety of model training settings, expanding the scope of better using synthetic images for enhancing downstream data-driven clinical tasks.

Distinguishing multiple primary lung cancers from intrapulmonary metastasis using CT-based radiomics.

PURPOSE: To develop CT-based radiomics models that can efficiently distinguish between multiple primary lung cancers (MPLCs) and intrapulmonary metastasis (IPMs). METHOD: This retrospective study included 127 patients with 254 lung tumors pathologically proved as MPLCs or IPMs between May 2009 and January 2020. Radiomics features of lung tumors were extracted from baseline CT scans. Particularly, we incorporated tumor-focused, refined radiomics by calculating relative radiomics differences from paired tumors of individual patients. We applied the L1-norm regularization and analysis of variance to select informative radiomics features for constructing radiomics model (RM) and refined radiomics model (RRM). The performance was assessed by the area under the receiver operating characteristic curve (AUC-ROC). The two radiomics models were compared with the clinical-CT model (CCM, including clinical and CT semantic features). We incorporated both radiomics features to construct fusion model1 (FM1). We also, build fusion model2 (FM2) by combing both radiomics, clinical and CT semantic features. The performance of the FM1 and FM2 were further compared with that of the RRM. RESULTS: On the validation set, the RM achieved an AUC of 0.857. The RRM demonstrated improved performance (validation set AUC, 0.870) than the RM, and showed significant differences compared with the CCM (validation set AUC, 0.782). Fusion models further led prediction performance (validation set AUC, FM1:0.885; FM2:0.889). There were no significant differences among the performance of the FM1, the FM2 and the RRM. CONCLUSIONS: The CT-based radiomics models presented good performance on the discrimination between MPLCs and IPMs, demonstrating the potential for early diagnosis and treatment guidance for MPLCs and IPMs.

Signal Processing and Machine Learning for Smart Sensing Applications.

The Special Issue "Signal Processing and Machine Learning for Smart Sensing Applications" focused on the publication of advanced signal processing methods by means of state-of-the-art machine learning technologies for smart sensing applications [...].

Deep generative modeling and clustering of single cell Hi-C data.

Deciphering 3D genome conformation is important for understanding gene regulation and cellular function at a spatial level. The recent advances of single cell Hi-C technologies have enabled the profiling of the 3D architecture of DNA within individual cell, which allows us to study the cell-to-cell variability of 3D chromatin organization. Computational approaches are in urgent need to comprehensively analyze the sparse and heterogeneous single cell Hi-C data. Here, we proposed scDEC-Hi-C, a new framework for single cell Hi-C analysis with deep generative neural networks. scDEC-Hi-C outperforms existing methods in terms of single cell Hi-C data clustering and imputation. Moreover, the generative power of scDEC-Hi-C could help unveil the differences of chromatin architecture across cell types. We expect that scDEC-Hi-C could shed light on deepening our understanding of the complex mechanism underlying the formation of chromatin contacts.

RSSI Fingerprint Height Based Empirical Model Prediction for Smart Indoor Localization.

Smart indoor living advances in the recent decade, such as home indoor localization and positioning, has seen a significant need for low-cost localization systems based on freely available resources such as Received Signal Strength Indicator by the dense deployment of Wireless Local Area Networks (WLAN). The off-the-shelf user equipment (UE's) available at an affordable price across the globe are well equipped with the functionality to scan the radio access network for hearable single strength; in complex indoor environments, multiple signals can be received at a particular reference point with no consideration of the height of the transmitter and possible broadcasting coverage. Most effective fingerprinting algorithm solutions require specialized labor, are time-consuming to carry out site surveys, training of the data, big data analysis, and in most cases, additional hardware requirements relatively increase energy consumption and cost, not forgetting that in case of changes in the indoor environment will highly affect the fingerprint due to interferences. This paper experimentally evaluates and proposes a novel technique for Received Signal Indicator (RSSI) distance prediction, leveraging transceiver height, and Fresnel ranging in a complex indoor environment to better suit the path loss of RSSI at a particular Reference Point (RP) and time, which further contributes greatly to indoor localization. The experimentation in different complex indoor environments of the corridor and office lab during work hours to ascertain real-life and time feasibility shows that the technique's accuracy is greatly improved in the office room and the corridor, achieving lower average prediction errors at low-cost than the comparison prediction algorithms. Compared with the conventional prediction techniques, for example, with Access Point 1 (AP1), the proposed Height Dependence Path-Loss (HEM) model at 0 dBm error attains a confidence probability of 10.98%, higher than the 2.65% for the distance dependence of Path-Loss New Empirical Model (NEM), 4.2% for the Multi-Wall dependence on Path-Loss (MWM) model, and 0% for the Conventional one-slope Path-Loss (OSM) model, respectively. Online localization, amongst the hearable APs, it is seen the proposed HEM fingerprint localization based on the proposed HEM prediction model attains a confidence probability of 31% at 3 m, 55% at 6 m, 78% at 9 m, outperforming the NEM with 26%, 43%, 62%, 62%, the MWM with 23%, 43%, 66%, respectively. The robustness of the HEM fingerprint using diverse predicted test samples by the NEM and MWM models indicates better localization of 13% than comparison fingerprints.

Autocollimation angle-measurement method with a large range based on spot deformation.

A novel hollow cylindrical cube-corner reflector (HCCCR) for the autocollimator (AC) is proposed. The angle measuring range of AC will be effectively increased by using the parallel propagation characteristics of the reflected light and the incident light in local area of this reflector. And the yaw and pitch angles of HCCCR will be measured through the morphological changes of the reflected beam. The experimental results show that the measuring range of the autocollimation angle measurement method is extended from ±30' to ±30°, and the dynamic measurement distance is 0.2∼5m, the measurement accuracy of pitch angle and yaw angle is better than 69" and 51", respectively.

Spatially aware graph neural networks and cross-level molecular profile prediction in colon cancer histopathology: a retrospective multi-cohort study.

BACKGROUND: Digital whole-slide images are a unique way to assess the spatial context of the cancer microenvironment. Exploring these spatial characteristics will enable us to better identify cross-level molecular markers that could deepen our understanding of cancer biology and related patient outcomes. METHODS: We proposed a graph neural network approach that emphasises spatialisation of tumour tiles towards a comprehensive evaluation of predicting cross-level molecular profiles of genetic mutations, copy number alterations, and functional protein expressions from whole-slide images. We introduced a transformation strategy that converts whole-slide image scans into graph-structured data to address the spatial heterogeneity of colon cancer. We developed and assessed the performance of the model on The Cancer Genome Atlas colon adenocarcinoma (TCGA-COAD) and validated it on two external datasets (ie, The Cancer Genome Atlas rectum adenocarcinoma [TCGA-READ] and Clinical Proteomic Tumor Analysis Consortium colon adenocarcinoma [CPTAC-COAD]). We also predicted microsatellite instability and result interpretability. FINDINGS: The model was developed on 459 colon tumour whole-slide images from TCGA-COAD, and externally validated on 165 rectum tumour whole-slide images from TCGA-READ and 161 colon tumour whole-slide images from CPTAC-COAD. For TCGA cohorts, our method accurately predicted the molecular classes of the gene mutations (area under the curve [AUCs] from 82·54 [95% CI 77·41-87·14] to 87·08 [83·28-90·82] on TCGA-COAD, and AUCs from 70·46 [61·37-79·61] to 81·80 [72·20-89·70] on TCGA-READ), along with genes with copy number alterations (AUCs from 81·98 [73·34-89·68] to 90·55 [86·02-94·89] on TCGA-COAD, and AUCs from 62·05 [48·94-73·46] to 76·48 [64·78-86·71] on TCGA-READ), microsatellite instability (MSI) status classification (AUC 83·92 [77·41-87·59] on TCGA-COAD, and AUC 61·28 [53·28-67·93] on TCGA-READ), and protein expressions (AUCs from 85·57 [81·16-89·44] to 89·64 [86·29-93·19] on TCGA-COAD, and AUCs from 51·77 [42·53-61·83] to 59·79 [50·79-68·57] on TCGA-READ). For the CPTAC-COAD cohort, our model predicted a panel of gene mutations with AUC values from 63·74 (95% CI 52·92-75·37) to 82·90 (73·69-90·71), genes with copy number alterations with AUC values from 62·39 (51·37-73·76) to 86·08 (79·67-91·74), and MSI status prediction with AUC value of 73·15 (63·21-83·13). INTERPRETATION: We showed that spatially connected graph models enable molecular profile predictions in colon cancer and are generalised to rectum cancer. After further validation, our method could be used to infer the prognostic value of multiscale molecular biomarkers and identify targeted therapies for patients with colon cancer. FUNDING: This research has been partially funded by ARO MURI 805491, NSF IIS-1793883, NSF CNS-1747778, NSF IIS 1763523, DOD-ARO ACC-W911NF, and NSF OIA-2040638 to Dimitri N Metaxas.

Enhanced Photoelectrochemical Water Splitting of Black Silicon Photoanode with pH-Dependent Copper-Bipyridine Catalysts.

Since the water oxidation half-reaction requires the transfer of multi-electrons and the formation of O-O bond, it's crucial to investigate the catalytic behaviours of semiconductor photoanodes. In this work, a bio-inspired copper-bipyridine catalyst of Cu(dcbpy) is decorated on the nanoporous Si photoanode (black Si, b-Si). Under AM1.5G illumination, the b-Si/Cu(dcbpy) photoanode exhibits a high photocurrent density of 6.31 mA cm-2 at 1.5 VRHE at pH 11.0, which is dramatically improved from the b-Si photoanode (1.03 mA cm-2 ) and f-Si photoanode (0.0087 mA cm-2 ). Mechanism studies demonstrate that b-Si/Cu(dcbpy) has improved light-harvesting, interfacial charge-transfer, and surface area for water splitting. More interestingly, b-Si/Cu(dcbpy) exhibits a pH-dependent water oxidation behaviour with a minimum Tafel slope of 241 mV/dec and the lowest overpotential of 0.19 V at pH 11.0, which is due to the monomer/dimer equilibrium of copper catalyst. At pH ∼11, the formation of dimeric hydroxyl-complex could form O-O bond through a redox isomerization (RI) mechanism, which decreases the required potential for water oxidation. This in-depth understanding of pH-dependent water oxidation catalyst brings insights into the design of dimer water oxidation catalysts and efficient photoanodes for solar energy conversion.

Multi-animal pose estimation, identification and tracking with DeepLabCut.

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking-features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal's identity to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.

DualGCN: a dual graph convolutional network model to predict cancer drug response.

BACKGROUND: Drug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future. RESULTS: We present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein-protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data. CONCLUSIONS: The proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine.

Treatment of a genetic brain disease by CNS-wide microglia replacement.

Hematopoietic cell transplantation after myeloablative conditioning has been used to treat various genetic metabolic syndromes but is largely ineffective in diseases affecting the brain presumably due to poor and variable myeloid cell incorporation into the central nervous system. Here, we developed and characterized a near-complete and homogeneous replacement of microglia with bone marrow cells in mice without the need for genetic manipulation of donor or host. The high chimerism resulted from a competitive advantage of scarce donor cells during microglia repopulation rather than enhanced recruitment from the periphery. Hematopoietic stem cells, but not immediate myeloid or monocyte progenitor cells, contained full microglia replacement potency equivalent to whole bone marrow. To explore its therapeutic potential, we applied microglia replacement to a mouse model for Prosaposin deficiency, which is characterized by a progressive neurodegeneration phenotype. We found a reduction of cerebellar neurodegeneration and gliosis in treated brains, improvement of motor and balance impairment, and life span extension even with treatment started in young adulthood. This proof-of-concept study suggests that efficient microglia replacement may have therapeutic efficacy for a variety of neurological diseases.

Single-crystal superprotonic conductivity in an interpenetrated hydrogen-bonded quadruplex framework.

A proton-transporting pathway is crucial to the conduction mechanism in fuel cells and biological systems. Here, we report a novel 5-fold interpenetrated three-dimensional (3D) hydrogen-bonded quadruplex framework, which exhibits an ultrahigh single-crystal proton conductivity of 1.2(1) × 10-2 S cm-1 at 95 °C and 98% relative humidity, benefitting from the spiral H3O+/H2O chains in 1D pore channels studded with COOH/COO- groups.

Altered diversity and composition of gut microbiota in patients with allergic rhinitis.

BACKGROUND: Recently, multiple studies have suggested an association between gut dysbiosis and allergic rhinitis (AR) development. However, the role of gut microbiota in AR development remains obscure. METHODS: The goal of this study was to compare the gut microbiota composition and short-chain fatty acid (SCFAs) differences associated with AR (N = 18) and HCs (healthy controls, N = 17). Gut microbiota 16SrRNA gene sequences were analyzed based on next-generation sequencing. SCFAs in stool samples were analyzed by gas chromatography-mass spectrometry (GC-MS). RESULTS: Compared with HCs, the gut microbiota composition of AR was significantly different in diversity and richness. At the phylum level, the abundance of Firmicutes in the AR group were significantly lower than those in the HCs group. At the genus level, the abundance of Blautia, Eubacterium_hallii_group, Romboutsia, Collinsella, Dorea, Subdoligranulum and Fusicatenibacter in the AR group were significantly lower than that in the HCs group. The concentrations of SCFAs were significantly lower in the AR group compared with the HCs group. Correlation analysis showed that the Eubacterium-hallii-group and Blautia correlated positively with SCFAs. CONCLUSION: Our results demonstrate compositional and functional alterations of the gut microbiome in AR.