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.

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.

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.

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.

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 [...].

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.

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.

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.

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.

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.

Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing.

MOTIVATION: Mining drug-disease association and related interactions are essential for developing in silico drug repurposing (DR) methods and understanding underlying biological mechanisms. Recently, large-scale biological databases are increasingly available for pharmaceutical research, allowing for deep characterization for molecular informatics and drug discovery. However, DR is challenging due to the molecular heterogeneity of disease and diverse drug-disease associations. Importantly, the complexity of molecular target interactions, such as protein-protein interaction (PPI), remains to be elucidated. DR thus requires deep exploration of a multimodal biological network in an integrative context. RESULTS: In this study, we propose BiFusion, a bipartite graph convolution network model for DR through heterogeneous information fusion. Our approach combines insights of multiscale pharmaceutical information by constructing a multirelational graph of drug-protein, disease-protein and PPIs. Especially, our model introduces protein nodes as a bridge for message passing among diverse biological domains, which provides insights into utilizing PPI for improved DR assessment. Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation. We offered an exploratory analysis for finding novel drug-disease associations. Extensive experiments showed that our approach achieved improved performance than multiple baselines for DR analysis. AVAILABILITY AND IMPLEMENTATION: Source code and preprocessed datasets are at: https://github.com/zcwang0702/BiFusion.

DeepCDR: a hybrid graph convolutional network for predicting cancer drug response.

MOTIVATION: Accurate prediction of cancer drug response (CDR) is challenging due to the uncertainty of drug efficacy and heterogeneity of cancer patients. Strong evidences have implicated the high dependence of CDR on tumor genomic and transcriptomic profiles of individual patients. Precise identification of CDR is crucial in both guiding anti-cancer drug design and understanding cancer biology. RESULTS: In this study, we present DeepCDR which integrates multi-omics profiles of cancer cells and explores intrinsic chemical structures of drugs for predicting CDR. Specifically, DeepCDR is a hybrid graph convolutional network consisting of a uniform graph convolutional network and multiple subnetworks. Unlike prior studies modeling hand-crafted features of drugs, DeepCDR automatically learns the latent representation of topological structures among atoms and bonds of drugs. Extensive experiments showed that DeepCDR outperformed state-of-the-art methods in both classification and regression settings under various data settings. We also evaluated the contribution of different types of omics profiles for assessing drug response. Furthermore, we provided an exploratory strategy for identifying potential cancer-associated genes concerning specific cancer types. Our results highlighted the predictive power of DeepCDR and its potential translational value in guiding disease-specific drug design. AVAILABILITY AND IMPLEMENTATION: DeepCDR is freely available at https://github.com/kimmo1019/DeepCDR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Sparse Representation in Awake Auditory Cortex: Cell-type Dependence, Synaptic Mechanisms, Developmental Emergence, and Modulation.

Sparse representation is considered an important coding strategy for cortical processing in various sensory modalities. It remains unclear how cortical sparseness arises and is being regulated. Here, unbiased recordings from primary auditory cortex of awake adult mice revealed salient sparseness in layer (L)2/3, with a majority of excitatory neurons exhibiting no increased spiking in response to each of sound types tested. Sparse representation was not observed in parvalbumin (PV) inhibitory neurons. The nonresponding neurons did receive auditory-evoked synaptic inputs, marked by weaker excitation and lower excitation/inhibition (E/I) ratios than responding cells. Sparse representation arises during development in an experience-dependent manner, accompanied by differential changes of excitatory input strength and a transition from unimodal to bimodal distribution of E/I ratios. Sparseness level could be reduced by suppressing PV or L1 inhibitory neurons. Thus, sparse representation may be dynamically regulated via modulating E/I balance, optimizing cortical representation of the external sensory world.

Remote Monitoring of Human Vital Signs Based on 77-GHz mm-Wave FMCW Radar.

In recent years, non-contact radar detection technology has been able to achieve long-term and long-range detection for the breathing and heartbeat signals. Compared with contact-based detection methods, it brings a more comfortable and a faster experience to the human body, and it has gradually received attention in the field of radar sensing. Therefore, this paper extends the application of millimeter-wave radar to the field of health care. The millimeter-wave radar first transmits the frequency-modulated continuous wave (FMCW) and collects the echo signals of the human body. Then, the phase information of the intermediate frequency (IF) signals including the breathing and heartbeat signals are extracted, and the Direct Current (DC) offset of the phase information is corrected using the circle center dynamic tracking algorithm. The extended differential and cross-multiply (DACM) is further applied for phase unwrapping. We propose two algorithms, namely the compressive sensing based on orthogonal matching pursuit (CS-OMP) algorithm and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) algorithm, to separate and reconstruct the breathing and heartbeat signals. Then, a frequency-domain fast Fourier transform and a time-domain autocorrelation estimation algorithm are proposed to calculate the respiratory and heartbeat rates. The proposed algorithms are compared with the contact-based detection ones. The results demonstrate that the proposed algorithms effectively suppress the noise and harmonic interference, and the accuracies of the proposed algorithms for both respiratory rate and heartbeat rate reach about 93%.

Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.

Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocarcinoma. Conventional identification of EGFR genotype requires biopsy and sequence testing which is invasive and may suffer from the difficulty of accessing tissue samples. Here, we propose a deep learning model to predict EGFR mutation status in lung adenocarcinoma using non-invasive computed tomography (CT).We retrospectively collected data from 844 lung adenocarcinoma patients with pre-operative CT images, EGFR mutation and clinical information from two hospitals. An end-to-end deep learning model was proposed to predict the EGFR mutation status by CT scanning.By training in 14 926 CT images, the deep learning model achieved encouraging predictive performance in both the primary cohort (n=603; AUC 0.85, 95% CI 0.83-0.88) and the independent validation cohort (n=241; AUC 0.81, 95% CI 0.79-0.83), which showed significant improvement over previous studies using hand-crafted CT features or clinical characteristics (p<0.001). The deep learning score demonstrated significant differences in EGFR-mutant and EGFR-wild type tumours (p<0.001).Since CT is routinely used in lung cancer diagnosis, the deep learning model provides a non-invasive and easy-to-use method for EGFR mutation status prediction.

Development and validation of a prognostic index for efficacy evaluation and prognosis of first-line chemotherapy in stage III-IV lung squamous cell carcinoma.

OBJECTIVES: To establish a pre-therapy prognostic index model (PIM) of the first-line chemotherapy aiming to achieve accurate prediction of time to progression (TTP) and overall survival among the patients diagnosed with locally advanced (stage III) or distant metastasis (stage IV) lung squamous cell carcinoma (LSCC). METHODS: Ninety-six LSCC patients treated with first-line chemotherapy were retrospectively enrolled to build the model. Fourteen epidermal growth factor receptor (EGFR)-mutant LSCC patients treated with first-line EGFR-tyrosine kinase inhibitor (TKI) therapy were enrolled for validation dataset. From CT images, 56,000 phenotype features were initially computed. PIM was constructed by integrating a CT phenotype signature selected by the least absolute shrinkage and selection operator and the significant blood-based biomarkers selected by multivariate Cox regression. PIM was then compared with other four prognostic models constructed by the CT phenotype signature, clinical factors, post-therapy tumor response, and Glasgow Prognostic Score. RESULTS: The signature includes eight optimal features extracted from co-occurrence, run length, and Gabor features. By using PIM, chemotherapy efficacy of patients categorized in the low-risk, intermediate-risk, and high-risk progression subgroups (median TTP = 7.2 months, 3.4 months, and 1.8 months, respectively) was significantly different (p < 0.0001, log-rank test). Chemotherapy efficacy of the low-risk progression subgroup was comparable with EGFR-TKI therapy (p = 0.835, log-rank test). Prognostic prediction of chemotherapy efficacy by PIM was significantly higher than other models (p < 0.05, z test). CONCLUSION: The study demonstrated that the PIM yielded significantly higher performance to identify individual stage III-IV LSCC patients who can potentially benefit most from first-line chemotherapy, and predict the risk of failure from chemotherapy for individual patients. KEY POINTS: • TTP and OS of first-line chemotherapy in individual stage III-IV LSCC patients could be predicted by pre-therapy blood-based biomarkers and image-based signatures. • Risk status of pre-therapy indicators affected the efficacy of first-line chemotherapy in stage III-IV LSCC patients. • Those stage III-IV LSCC patients who were able to achieve similar efficacy to EGFR-TKI therapy through chemotherapy were identified.

Stereoselective Assembly of Multifunctional Spirocyclohexene Pyrazolones That Induce Autophagy-Dependent Apoptosis in Colorectal Cancer Cells.

Enantio- and diastereoselective synthesis of multifunctional spiropyrazolone scaffolds has been achieved using secondary amine-catalyzed [4 + 2] annulations of α,ß,γ,δ-unsaturated pyrazolones with aldehydes. The pyrazolone substrates serve as C4 synthons to produce 6-membered, carbocycle-based, chiral spiropyrazolone derivatives. The synthesized chiral compounds showed potent toxicity against a panel of cancer cell lines. The most potent compound 3h-induced cell cycle arrest and macroautophagy in HCT116 colorectal cancer cells, triggering autophagy-dependent apoptotic cell death.

[Altered Functional Connectivity in Patients with Transient Ischemic Attack by Resting-state Functional MRI].

OBJECTIVE: To investigate longitudinal changes in functional connectivity during resting-state in patients with transient ischemic attack (TIA). METHODS: 35 patients first suffering TIA in the right hemisphere were recruited, with 35 healthy volunteers were recruited as control. At 1 week and 3 months after TIA attack, functional magnetic resonance imaging (fMRI) scans were performed, then resting-state functional connectivity was assessed and compared with that of healthy subjects. Right inferior prefrontal cortex (iPFC) and its mirror region was used as region of interest (ROI) in this analysis. RESULTS: Compared with controls, higher functional connectivity with the left cerebellum, right superior temporal gyrus (STG) and insula, and lower functional connectivity with the right middle frontal gyrus (MFG) was demonstrated in patients at 1 week after TIA; while decreased functional connectivity in right STG, left insula and bilateral thalamus was shown in patients at 3 month after TIA. Correlation analysis found that functional connectivity of right iPFC with the cerebellum and insula was positively correlated with 2-back reaction time at 1 week after TIA. CONCLUSION: Although the nervous system signs of TIA can be quickly recovered, abnormal activation of working memory-related brain regions will occur for a long time.

Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications.

Purpose To create a radiogenomic map linking computed tomographic (CT) image features and gene expression profiles generated by RNA sequencing for patients with non-small cell lung cancer (NSCLC). Materials and Methods A cohort of 113 patients with NSCLC diagnosed between April 2008 and September 2014 who had preoperative CT data and tumor tissue available was studied. For each tumor, a thoracic radiologist recorded 87 semantic image features, selected to reflect radiologic characteristics of nodule shape, margin, texture, tumor environment, and overall lung characteristics. Next, total RNA was extracted from the tissue and analyzed with RNA sequencing technology. Ten highly coexpressed gene clusters, termed metagenes, were identified, validated in publicly available gene-expression cohorts, and correlated with prognosis. Next, a radiogenomics map was built that linked semantic image features to metagenes by using the t statistic and the Spearman correlation metric with multiple testing correction. Results RNA sequencing analysis resulted in 10 metagenes that capture a variety of molecular pathways, including the epidermal growth factor (EGF) pathway. A radiogenomic map was created with 32 statistically significant correlations between semantic image features and metagenes. For example, nodule attenuation and margins are associated with the late cell-cycle genes, and a metagene that represents the EGF pathway was significantly correlated with the presence of ground-glass opacity and irregular nodules or nodules with poorly defined margins. Conclusion Radiogenomic analysis of NSCLC showed multiple associations between semantic image features and metagenes that represented canonical molecular pathways, and it can result in noninvasive identification of molecular properties of NSCLC. Online supplemental material is available for this article.