Single organ metastatic sites in non-small cell lung cancer: Patient characteristics, treatment patterns and outcomes from a large retrospective Canadian cohort

BACKGROUND

There is a paucity of information about the characteristics, treatment patterns, and outcomes of non-small cell lung cancer (NSCLC) patients with single organ metastasis (SOM).

METHODS

This retrospective cohort study includes all patients with a diagnosis of stage IV NSCLC diagnosed from 2014 to 2016 and treated at Princess Margaret Cancer Centre. We compared baseline characteristics and patterns of metastatic sites between patients with SOM versus multiple (M)OM. Additionally, we identified treatment modalities and outcomes for patients with SOM. Cox multivariable models (MVA) were utilized to evaluate differences in overall survival (OS) between the SOM and MOM cohorts.

RESULTS

Of 893 pts analyzed, 457 (51 %) had SOM, while 436 (49 %) had MOM at initial diagnosis. Demographics were comparable between the two groups. Brain was the most common site of metastasis for SOM patients. When compared to the MOM group, the SOM group had lower percentages of liver and adrenal metastases. Amongst SOM patients, 54 % received single modality treatment, and 20 % did not receive any treatment for their SOM. In MVA, patients with liver (HR 2.4), bone (HR 1.8), and pleural (HR 1.7) metastasis as their SOM site had the worst outcomes, with median OS of 6.8 months, 12.1 months, and 13.0 months respectively. Patients with SOM had a significantly improved median OS compared to those with MOM (15.9 months vs. 10.6 months; HR 0.56, 95 % CI 0.47-0.66, p < 0.001).

CONCLUSION

In NSCLC patients who presented with SOM, survival correlated with the initial organ involved and was better overall compared to patients with MOM. SOM NSCLC may benefit from specific management strategies and SOM patients could be considered as a specific subgroup for survival analyses in observational and non-randomized interventional studies. In clinical trials, SOM can be considered as a stratification factor in the future.

Neighborhood Perceptions Are Associated With Intrinsic Amygdala Activity and Resting-State Connectivity With Salience Network Nodes Among Older Adults

OBJECTIVE

Neighborhood perceptions are associated with physical and mental health outcomes; however, the biological associates of this relationship remain to be fully understood. Here, we evaluate the relationship between neighborhood perceptions and amygdala activity and connectivity with salience network (i.e., insula, anterior cingulate, thalamus) nodes.

METHODS

Forty-eight older adults (mean age = 68 [7] years, 52% female, 47% non-Hispanic Black, 2% Hispanic) without dementia or depression completed the Perceptions of Neighborhood Environment Scale. Lower scores indicated less favorable perceptions of aesthetic quality, walking environment, availability of healthy food, safety, violence (i.e., more perceived violence), social cohesion, and participation in activities with neighbors. Participants separately underwent resting-state functional magnetic resonance imaging.

RESULTS

Less favorable perceived safety ( β = -0.33, pFDR = .04) and participation in activities with neighbors ( β = -0.35, pFDR = .02) were associated with higher left amygdala activity, independent of covariates including psychosocial factors. Less favorable safety perceptions were also associated with enhanced left amygdala functional connectivity with the bilateral insular cortices and the left anterior insula ( β = -0.34, pFDR = .04). Less favorable perceived social cohesion was associated with enhanced left amygdala functional connectivity with the right thalamus ( β = -0.42, pFDR = .04), and less favorable perceptions about healthy food availability were associated with enhanced left amygdala functional connectivity with the bilateral anterior insula (right: β = -0.39, pFDR = .04; left: β = -0.42, pFDR = .02) and anterior cingulate gyrus ( β = -0.37, pFDR = .04).

CONCLUSIONS

Taken together, our findings document relationships between select neighborhood perceptions and amygdala activity as well as connectivity with salience network nodes; if confirmed, targeted community-level interventions and existing community strengths may promote brain-behavior relationships.

In-situ Bi-modified Pt towards glycerol and formic acid electro-oxidation: Effects of catalyst structure and surface microenvironment on activity and selectivity

The performances of glycerol electro-oxidation reaction (GOR) and formic acid electro-oxidation reaction (FAOR) catalyzed by Pt catalyst were dramatically improved by adding Bi3+ into the reaction solution. The dynamic structure and microenvironment of in-situ Bi-modified Pt and their impact on the catalytic performances were revealed. A strong correlation was established between the Bi coverage of Pt-based catalysts and their resistance to CO poisoning and performance in GOR and FAOR. When Bi3+ increased to a certain amount, a Bi-shell containing hydroxides was formed on Pt surfaces except the formation of Pt-Bi ensemble. On Pt catalyst covered with 43.9 % Bi, the peak mass-specific activities of GOR and FAOR in forward scans were 4.2 and 34.7 times that of Pt/NCNTs, respectively. The peak electrochemical active surface area (ECSA)-specific activity of FAOR in forward scan for Pt with 52.6 % Bi coverage was 80.6 times that of Pt/NCNTs. The dehydrogenation process in FAOR and the 4-electron pathway in GOR were improved for Bi-modified Pt. The experimental results and DFT calculations indicated that the positively charged Bi and structure of Pt-Bi ensemble improved the adsorption and interaction of negatively charged intermediates, and the enhanced hydroxides facilitated the oxidation and removal of toxic intermediates, such as CO.

Temporal Alterations in White Matter in An App Knock-In Mouse Model of Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia and results in neurodegeneration and cognitive impairment. White matter (WM) is affected in AD and has implications for neural circuitry and cognitive function. The trajectory of these changes across age, however, is still not well understood, especially at earlier stages in life. To address this, we used the AppNL-G-F/NL-G-F knock-in (APPKI) mouse model that harbors a single copy knock-in of the human amyloid precursor protein (APP) gene with three familial AD mutations. We performed in vivo diffusion tensor imaging (DTI) to study how the structural properties of the brain change across age in the context of AD. In late age APPKI mice, we observed reduced fractional anisotropy (FA), a proxy of WM integrity, in multiple brain regions, including the hippocampus, anterior commissure (AC), neocortex, and hypothalamus. At the cellular level, we observed greater numbers of oligodendrocytes in middle age (prior to observations in DTI) in both the AC, a major interhemispheric WM tract, and the hippocampus, which is involved in memory and heavily affected in AD, prior to observations in DTI. Proteomics analysis of the hippocampus also revealed altered expression of oligodendrocyte-related proteins with age and in APPKI mice. Together, these results help to improve our understanding of the development of AD pathology with age, and imply that middle age may be an important temporal window for potential therapeutic intervention.

Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes

The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activities between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, which was first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight coefficients, such that the simulated FC aligns well with the observed FC when compared with that simulated traditional structural connectome.

Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI

The Steady State Visual Evoked Potential (SSVEP) is a widely used component in BCIs due to its high noise resistance and low equipment requirements. Recently, a novel SSVEP-based paradigm has been introduced for direction detection, in which, unlike the common SSVEP paradigms that use several frequency stimuli, only one flickering stimulus is used and it makes direction detection very challenging. So far, only the CCA method has been used for direction detection using SSVEP component analysis. Since Canonical Correlation Analysis (CCA) has some limitations, a Task-Related Component Analysis (TRCA) based method has been introduced for feature extraction to improve the direction detection performance. Although these methods have been proven efficient, they do not utilize the latent frequency information in the EEG signal. Therefore, the performance of direction detection using SSVEP component analysis is still suboptimal. For further improvement, the TRCA-based algorithm is enhanced by incorporating frequency information and introducing Spectrum-Enhanced TRCA (SE-TRCA). SE-TRCA method can utilize frequency information in conjunction with spatial information by concatenating the EEG signal and its shifted version. Accordingly, the obtained spatio-spectral filters perform as a Finite Impulse Response (FIR) filter. To evaluate the proposed SE-TRCA method, two different sorts of datasets (1) a hybrid BCI dataset (including SSVEP component for direction detection) and (2) a pure benchmark SSVEP dataset (including SSVEP component for frequency detection) have been used. Our experiments showed that the accuracy of direction detection using the proposed SE-TRCA and TRCA approaches compared to CCA-based approach have been increased by 23.35% and 28.24%, respectively. Furthermore, the accuracy of character recognition obtained from integrating P300 and SSVEP components in CCA, TRCA, and SETRCA approaches are 54.01%, 56.02%, and 58.56%, on the hybrid dataset, respectively. The evaluation of the SE-TRCA method on the benchmark SSVEP dataset demonstrates that the SE-TRCA method outperforms both CCA and TRCA, particularly regarding frequency detection accuracy. In this specific dataset, the SE-TRCA method achieved an impressive frequency detection accuracy of 98.19% for a 3-s signal, surpassing the accuracies of TRCA and CCA, which were 97.91% and 90.47%, respectively. These results demonstrated that the TRCA-based approach is more efficient than the CCA approach to extracting spatial filters. Moreover, SE-TRCA, extracting both Spectral and spatial information from the EEG signal, can capture more discriminative features from the SSVEP component and increase the accuracy of classification. The results of this study emphasize the effectiveness of the proposed SE-TRCA approach across different SSVEP paradigms and tasks. These findings provide strong evidence for the method's ability to generalize well in SSVEP analysis.

Survival Outcomes of Extensive Stage Small Cell Lung Cancer Patients Treated with Consolidative Thoracic Radiotherapy at a Tertiary Cancer Center

PURPOSE/OBJECTIVE(S)

Most small cell lung cancer cases present as Stage IV (M1) or extensive stage (ES-SCLC), which are defined as tumor extending outside the hemithorax without a tolerable radiation portal. The CREST trial demonstrated improved local control with a modest overall survival (OS) benefit at the 2-year secondary endpoint of 14% survival with consolidative thoracic radiotherapy (CTRT) compared to 3% without CTRT. Low toxicity rates were also observed. This study reports our institutional ES-SCLC experience for patients treated with CTRT.

MATERIALS/METHODS

A retrospective review was conducted on ES-SCLC patients treated with CTRT at our institution between 2014 and 2019. CTRT was defined as ≥30 Gy of thoracic radiotherapy. OS and tolerability of treatment were assessed in this population. Chemotherapy details were also captured. OS rate was determined using the Kaplan-Meier method and the time from start of CTRT to last date of follow-up or death. CTRT tolerability was determined using incidence and grade of esophagitis and radiation pneumonitis as per CTCAE v5.

RESULTS

We identified 100 ES-SCLC patients treated with any thoracic RT at our institute, of which 45 received thoracic RT for palliative intent or with <30 Gy. The remaining 55 patients received ≥30 Gy CTRT and were included for analysis. The median age was 65.1 years (range 46.6-86.9) and 36 (65%) were male. The median follow-up for this sample was 0.8 (range 0.03-4.2) years. Median chemotherapy cycles were 6 (range 1-6), most receiving ≥4 cycles (87%) and completing chemotherapy prior to CTRT initiation (91%) with a minority concurrently receiving chemotherapy and CTRT (9%). Platinum chemotherapy was the most common (96%) with 2 patients receiving etoposide alone (4%). The most common CTRT regimens were 30 Gy in 10 fractions (80%) followed by 40 Gy in 15 fractions (9%) and 45 Gy in 30 twice-daily fractions (7%). Most patients (67%) were treated with IMRT/VMAT technique, while the remaining (33%) patients were treated with 3DCRT. The median survival time was 1.3 years with 1- and 2-year OS of 57.2% (CI 44.0 - 74.3%) and 26.1% (CI 12.9 - 52.7%), respectively. CTRT was well tolerated with no grade 4+ toxicities. The most common toxicity was esophagitis with 21 patients (39%), of which 15 were G1 (28%) and 6 were G2 (11%). Radiation pneumonitis was present in 5 patients (9.2%) with 1 G1 (2%), 3 G2 (6%), and 1 G3 (2%) cases.

CONCLUSION

Consolidative TRT for ES-SCLC in this institutional series was at least as good as the reported CREST outcome with modest acute toxicities in this cohort. Disease burden at diagnosis, chemotherapy response, patterns of failure, and subsequent therapies will be further investigated.

Disrupted excitation-inhibition balance in cognitively normal individuals at risk of Alzheimer's disease

Background

Sex differences impact Alzheimer's disease (AD) neuropathology, but cell-to-network level dysfunctions in the prodromal phase are unclear. Alterations in hippocampal excitation-inhibition balance (EIB) have recently been linked to early AD pathology.

Objective

Examine how AD risk factors (age, APOE-ɛ4, amyloid-β) relate to hippocampal EIB in cognitively normal males and females using connectome-level measures.

Methods

Individuals from the OASIS-3 cohort (age 42-95) were studied (N = 437), with a subset aged 65+ undergoing neuropsychological testing (N = 231).

Results

In absence of AD risk factors (APOE-ɛ4/Aβ+), whole-brain EIB decreases with age more significantly in males than females (p = 0.021, β = -0.007). Regression modeling including APOE-ɛ4 allele carriers (Aβ-) yielded a significant positive AGE-by-APOE interaction in the right hippocampus for females only (p = 0.013, β = 0.014), persisting with inclusion of Aβ+ individuals (p = 0.012, β = 0.014). Partial correlation analyses of neuropsychological testing showed significant associations with EIB in females: positive correlations between right hippocampal EIB with categorical fluency and whole-brain EIB with the trail-making test (p < 0.05).

Conclusion

Sex differences in EIB emerge during normal aging and progresses differently with AD risk. Results suggest APOE-ɛ4 disrupts hippocampal balance more than amyloid in females. Increased excitation correlates positively with neuropsychological performance in the female group, suggesting a duality in terms of potential beneficial effects prior to cognitive impairment. This underscores the translational relevance of APOE-ɛ4 related hyperexcitation in females, potentially informing therapeutic targets or early interventions to mitigate AD progression in this vulnerable population.

Recent Progress for Concurrent Realization of Shuttle-Inhibition and Dendrite-Free Lithium-Sulfur Batteries

Lithium-sulfur (Li-S) batteries have become one of the most promising new-generation energy storage systems owing to their ultrahigh energy density (2600 Wh kg-1 ), cost-effectiveness, and environmental friendliness. Nevertheless, their practical applications are seriously impeded by the shuttle effect of soluble lithium polysulfides (LiPSs), and the uncontrolled dendrite growth of metallic Li, which result in rapid capacity fading and battery safety problems. A systematic and comprehensive review of the cooperative combination effect and tackling the fundamental problems in terms of cathode and anode synchronously is still lacking. Herein, for the first time, the strategies for inhibiting shuttle behavior and dendrite-free Li-S batteries simultaneously are summarized and classified into three parts, including "two-in-one" S-cathode and Li-anode host materials toward Li-S full cell, "two birds with one stone" modified functional separators, and tailoring electrolyte for stabilizing sulfur and lithium electrodes. This review also emphasizes the fundamental Li-S chemistry mechanism and catalyst principles for improving electrochemical performance; advanced characterization technologies to monitor real-time LiPS evolution are also discussed in detail. The problems, perspectives, and challenges with respect to inhibiting the shuttle effect and dendrite growth issues as well as the practical application of Li-S batteries are also proposed.

Improved Measurement of the Evolution of the Reactor Antineutrino Flux and Spectrum at Daya Bay

Reactor neutrino experiments play a crucial role in advancing our knowledge of neutrinos. In this Letter, the evolution of the flux and spectrum as a function of the reactor isotopic content is reported in terms of the inverse-beta-decay yield at Daya Bay with 1958 days of data and improved systematic uncertainties. These measurements are compared with two signature model predictions: the Huber-Mueller model based on the conversion method and the SM2018 model based on the summation method. The measured average flux and spectrum, as well as the flux evolution with the ^{239}Pu isotopic fraction, are inconsistent with the predictions of the Huber-Mueller model. In contrast, the SM2018 model is shown to agree with the average flux and its evolution but fails to describe the energy spectrum. Altering the predicted inverse-beta-decay spectrum from ^{239}Pu fission does not improve the agreement with the measurement for either model. The models can be brought into better agreement with the measurements if either the predicted spectrum due to ^{235}U fission is changed or the predicted ^{235}U, ^{238}U, ^{239}Pu, and ^{241}Pu spectra are changed in equal measure.

Precision Measurement of Reactor Antineutrino Oscillation at Kilometer-Scale Baselines by Daya Bay

We present a new determination of the smallest neutrino mixing angle θ_{13} and the mass-squared difference Δm_{32}^{2} using a final sample of 5.55×10^{6} inverse beta-decay (IBD) candidates with the final-state neutron captured on gadolinium. This sample is selected from the complete dataset obtained by the Daya Bay reactor neutrino experiment in 3158 days of operation. Compared to the previous Daya Bay results, selection of IBD candidates has been optimized, energy calibration refined, and treatment of backgrounds further improved. The resulting oscillation parameters are sin^{2}2θ_{13}=0.0851±0.0024, Δm_{32}^{2}=(2.466±0.060)×10^{-3}  eV^{2} for the normal mass ordering or Δm_{32}^{2}=-(2.571±0.060)×10^{-3}  eV^{2} for the inverted mass ordering.

A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images

The COVID-19 pandemic has extremely threatened human health, and automated algorithms are needed to segment infected regions in the lung using computed tomography (CT). Although several deep convolutional neural networks (DCNNs) have proposed for this purpose, their performance on this task is suppressed due to the limited local receptive field and deficient global reasoning ability. To address these issues, we propose a segmentation network with a novel pixel-wise sparse graph reasoning (PSGR) module for the segmentation of COVID-19 infected regions in CT images. The PSGR module, which is inserted between the encoder and decoder of the network, can improve the modeling of global contextual information. In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the encoder. Then, we convert the graph into a sparsely-connected one by keeping K strongest connections to each uncertainly segmented pixel. Finally, the global reasoning is performed on the sparsely-connected graph. Our segmentation network was evaluated on three publicly available datasets and compared with a variety of widely-used segmentation models. Our results demonstrate that (1) the proposed PSGR module can capture the long-range dependencies effectively and (2) the segmentation model equipped with this PSGR module can accurately segment COVID-19 infected regions in CT images and outperform all other competing models.

Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes

The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activity between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight coefficients, such that the simulated FC well aligns with the observed FC when compared to that simulated with traditional structural connectome. Simulations were performed using the open source framework The Virtual Brain on High Performance Computing infrastructure.

BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks

Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by (1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and (2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we host the BrainGB website at https://braingb.us with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.

Hippocampal functional connectivity across age in an App knock-in mouse model of Alzheimer's disease

Introduction

Alzheimer's disease (AD) is a progressive neurodegenerative disease. The early processes of AD, however, are not fully understood and likely begin years before symptoms manifest. Importantly, disruption of the default mode network, including the hippocampus, has been implicated in AD.

Methods

To examine the role of functional network connectivity changes in the early stages of AD, we performed resting-state functional magnetic resonance imaging (rs-fMRI) using a mouse model harboring three familial AD mutations (App NL-G-F/NL-G-F knock-in, APPKI) in female mice in early, middle, and late age groups. The interhemispheric and intrahemispheric functional connectivity (FC) of the hippocampus was modeled across age.

Results

We observed higher interhemispheric functional connectivity (FC) in the hippocampus across age. This was reduced, however, in APPKI mice in later age. Further, we observed loss of hemispheric asymmetry in FC in APPKI mice.

Discussion

Together, this suggests that there are early changes in hippocampal FC prior to heavy onset of amyloid β plaques, and which may be clinically relevant as an early biomarker of AD.

3D bi-directional transformer U-Net for medical image segmentation

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics.

Ordered Mesoporous Carbon Grafted MXene Catalytic Heterostructure as Li-Ion Kinetic Pump toward High-Efficient Sulfur/Sulfide Conversions for Li-S Battery

Lithium-sulfur (Li-S) batteries exhibit unparalleled theoretical capacity and energy density than conventional lithium ion batteries, but they are hindered by the dissatisfactory "shuttle effect" and the sluggish conversion kinetics owing to the low lithium ion transport kinetics, resulting in rapid capacity fading. Herein, a catalytic two-dimensional heterostructure composite is prepared by evenly grafting mesoporous carbon on the MXene nanosheet (denoted as OMC-g-MXene), serving as interfacial kinetic accelerators in Li-S batteries. In this design, the grafted mesoporous carbon in the heterostructure can not only prevent the stack of MXene nanosheets with the enhanced mechanical property but also offer a facilitated pump for accelerating ion diffusion. Meanwhile, the exposed defect-rich OMC-g-MXene heterostructure inhibits the polysulfide shuttling with chemical interactions between OMC-g-MXene and polysulfides and thus simultaneously enhances the electrochemical conversion kinetics and efficiency, as fully investigated by in situ/ex situ characterizations. Consequently, the cells with OMC-g-MXene ion pumps achieve a high cycling capacity (966 mAh g-1 at 0.2 C after 200 cycles), a superior rate performance (537 mAh g-1 at 5 C), and an ultralow decaying rate of 0.047% per cycle after 800 cycles at 1 C. Even employed with a high sulfur loading of 7.08 mg cm-2 under lean electrolyte, an ultrahigh areal capacity of 4.5 mAh cm-2 is acquired, demonstrating a future practical application.

Detection of Mild Cognitive Impairment from Language Markers with Crossmodal Augmentation

Mild cognitive impairment is the prodromal stage of Alzheimer's disease. Its detection has been a critical task for establishing cohort studies and developing therapeutic interventions for Alzheimer's. Various types of markers have been developed for detection. For example, imaging markers from neuroimaging have shown great sensitivity, while its cost is still prohibitive for large-scale screening of early dementia. Recent advances from digital biomarkers, such as language markers, have provided an accessible and affordable alternative. While imaging markers give anatomical descriptions of the brain, language markers capture the behavior characteristics of early dementia subjects. Such differences suggest the benefits of auxiliary information from the imaging modality to improve the predictive power of unimodal predictive models based on language markers alone. However, one significant barrier to the joint analysis is that in typical cohorts, there are only very limited subjects that have both imaging and language modalities. To tackle this challenge, in this paper, we develop a novel crossmodal augmentation tool, which leverages auxiliary imaging information to improve the feature space of language markers so that a subject with only language markers can benefit from imaging information through the augmentation. Our experimental results show that the multi-modal predictive model trained with language markers and auxiliary imaging information significantly outperforms unimodal predictive models.

Disrupted Excitation-Inhibition Balance in Cognitively Normal Individuals at Risk of Alzheimer's Disease

BACKGROUND

Sex differences impact Alzheimer's disease (AD) neuropathology, but cell-to-network level dysfunctions in the prodromal phase are unclear. Alterations in hippocampal excitation-inhibition balance (EIB) have recently been linked to early AD pathology.

OBJECTIVE

Examine how AD risk factors (age, APOEɛ4, amyloid-β) relate to hippocampal EIB in cognitively normal males and females using connectome-level measures.

METHODS

Individuals from the OASIS-3 cohort (age 42-95) were studied (N = 437), with a subset aged 65+ undergoing neuropsychological testing (N = 231).

RESULTS

In absence of AD risk factors (APOEɛ4/Aβ+), whole-brain EIB decreases with age more significantly in males than females (p = 0.021, β= -0.007). Regression modeling including APOEɛ4 allele carriers (Aβ-) yielded a significant positive AGE-by-APOE interaction in the right hippocampus for females only (p = 0.013, β= 0.014), persisting with inclusion of Aβ+ individuals (p = 0.012, β= 0.014). Partial correlation analyses of neuropsychological testing showed significant associations with EIB in females: positive correlations between right hippocampal EIB with categorical fluency and whole-brain EIB with the Trail Making Test (p < 0.05).

CONCLUSIONS

Sex differences in EIB emerge during normal aging and progresses differently with AD risk. Results suggest APOEɛ4 disrupts hippocampal balance more than amyloid in females. Increased excitation correlates positively with neuropsychological performance in the female group, suggesting a duality in terms of potential beneficial effects prior to cognitive impairment. This underscores the translational relevance of APOEɛ4 related hyperexcitation in females, potentially informing therapeutic targets or early interventions to mitigate AD progression in this vulnerable population.

Signed graph representation learning for functional-to-structural brain network mapping

MRI-derived brain networks have been widely used to understand functional and structural interactions among brain regions, and factors that affect them, such as brain development and diseases. Graph mining on brain networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since brain functional and structural networks describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks has significant clinical implications. Most current studies aim to extract a fused representation by projecting the structural network to the functional counterpart. Since the functional network is dynamic and the structural network is static, mapping a static object to a dynamic object may not be optimal. However, mapping in the opposite direction (i.e., from functional to structural networks) are suffered from the challenges introduced by negative links within signed graphs. Here, we propose a novel graph learning framework, named as Deep Signed Brain Graph Mining or DSBGM, with a signed graph encoder that, from an opposite perspective, learns the cross-modality representations by projecting the functional network to the structural counterpart. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets (HCP and OASIS). Our experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.

Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model

Recently, brain networks have been widely adopted to study brain dynamics, brain development, and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. First, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes' predictions. Moreover, few of the current graph learning models are interpretable, which may not be capable of providing biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning (HSGPL) model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. To further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using data from human connectome project (HCP) and open access series of imaging studies (OASIS). Our results from extensive experiments demonstrate the superiority of the proposed model compared with several state-of-the-art techniques. In addition, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers.

Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM-MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22.

Dynamic Intercalation-Conversion Site Supported Ultrathin 2D Mesoporous SnO2/SnSe2 Hybrid as Bifunctional Polysulfide Immobilizer and Lithium Regulator for Lithium-Sulfur Chemistry

The practical application of lithium-sulfur batteries is impeded by the polysulfide shuttling and interfacial instability of the metallic lithium anode. In this work, a twinborn ultrathin two-dimensional graphene-based mesoporous SnO₂/SnSe₂ hybrid (denoted as G-mSnO₂/SnSe₂) is constructed as a polysulfide immobilizer and lithium regulator for Li-S chemistry. The as-designed G-mSnO₂/SnSe₂ hybrid possesses high conductivity, strong chemical affinity (SnO₂), and a dynamic intercalation-conversion site (Li_xSnSe₂), inhibits shuttle behavior, provides rapid Li-intercalative transport kinetics, accelerates LiPS conversion, and decreases the decomposition energy barrier for Li₂S, which is evidenced by the ex situ XAS spectra, in situ Raman, in situ XRD, and DFT calculations. Moreover, the mesoporous G-mSnO₂/SnSe₂ with lithiophilic characteristics enables homogeneous Li-ion deposition and inhibits Li dendrite growth. Therefore, Li-S batteries with a G-mSnO₂/SnSe₂ separator achieve a favorable electrochemical performance, including high sulfur utilization (1544 mAh g^-1 at 0.2 C), high-rate capability (794 mAh g^-1 at 8 C), and long cycle life (extremely low attenuation rate of 0.0144% each cycle at 5 C over 2000 cycles). Encouragingly, a 1.6 g S/Ah-level pouch cell realizes a high energy density of up to 359 Wh kg^-1 under a lean E/S usage of 3.0 μL mg^-1. This work sheds light on the design roadmap for tackling S-cathode and Li-anode challenges simultaneously toward long-durability Li-S chemistry.

First Measurement of High-Energy Reactor Antineutrinos at Daya Bay

This Letter reports the first measurement of high-energy reactor antineutrinos at Daya Bay, with nearly 9000 inverse beta decay candidates in the prompt energy region of 8-12 MeV observed over 1958 days of data collection. A multivariate analysis is used to separate 2500 signal events from background statistically. The hypothesis of no reactor antineutrinos with neutrino energy above 10 MeV is rejected with a significance of 6.2 standard deviations. A 29% antineutrino flux deficit in the prompt energy region of 8-11 MeV is observed compared to a recent model prediction. We provide the unfolded antineutrino spectrum above 7 MeV as a data-based reference for other experiments. This result provides the first direct observation of the production of antineutrinos from several high-Q_{β} isotopes in commercial reactors.

A Hierarchical Graph Learning Model for Brain Network Regression Analysis

Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency.

In suit constructing S-scheme FeOOH/MgIn2S4 heterojunction with boosted interfacial charge separation and redox activity for efficiently eliminating antibiotic pollutant

Photocatalytic elimination of antibiotic pollutant is an appealing avenue in response to the water contamination, but it still suffers from sluggish charge detachment, limited redox capacity as well as poor visible light utilization. Herein, a particular S-scheme FeOOH/MgIn₂S₄ heterojunction with wide visible light absorption was triumphantly constructed by in-situ growth of MgIn₂S₄ nanoparticles onto the surface of FeOOH nanorods, and employed as a high-efficiency visible light driven photocatalyst for removing tetracycline (TC). Conspicuously, the as-obtained FeOOH(15 wt%)/MgIn₂S₄ elucidated the optimal TC removal rate of 0.01258 min^-1 after 100 min of visible light illumination, which was almost 33.1 and 6.6 times larger than those of neat FeOOH and MgIn₂S₄, separately. The exceptional degradation performance was principally put down to the establishment of S-scheme heterojunction between FeOOH and MgIn₂S₄, which could not merely accelerate the detachment of photogenerated carriers, but also retain the powerful reducing ability of photoinduced electrons for MgIn₂S₄ and high oxidizing capacity of photoexcited holes for FeOOH, strongly driving the generation of plentiful active species including holes, superoxide and hydroxyl radicals. Additionally, the possible degradation mechanism and pathways of TC were also speculated. This work offers a valuable perspective for constructing high-efficiency S-scheme heterojunction photocatalysts for eradicating antibiotics.

Tislelizumab versus chemotherapy as second-line treatment of advanced or metastatic esophageal squamous cell carcinoma (RATIONALE 302): impact on health-related quality of life

Background

RATIONALE 302 (NCT03430843) an open-label, phase III study of second-line treatment of advanced/metastatic esophageal squamous cell carcinoma (ESCC), reported that tislelizumab, relative to investigator-chosen chemotherapy (ICC), was associated with improvements in overall survival and a favorable safety profile. This study assessed the health-related quality of life (HRQoL) and ESCC-related symptoms of patients in RATIONALE 302.

Methods

Adults with advanced/metastatic ESCC whose disease progressed following prior systemic therapy were randomized 1 : 1 to receive either tislelizumab or ICC (paclitaxel, docetaxel, or irinotecan). HRQoL was measured using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 items (EORTC QLQ-C30), the EORTC Quality of Life Questionnaire Oesophageal Cancer Module 18 items (QLQ-OES18), and the EuroQoL Five-Dimensions Five-Levels (EQ-5D-5L) visual analogue scale. Mixed effect modeling for repeated measurements examined changes from baseline to weeks 12 and 18. The Kaplan–Meier method was used to examine time to deterioration.

Results

Overall, 512 patients were randomized to tislelizumab (n = 256) or ICC (n = 256). The tislelizumab arm maintained QLQ-C30 global health status/quality whereas the ICC arm worsened at week 12 {difference in least square (LS) mean change: 5.8 [95% confidence interval (CI): 2.0-9.5], P = 0.0028} and week 18 [difference in LS mean change: 8.1 (95% CI: 3.4-12.8), P = 0.0008]. Physical functioning (week 18) and fatigue (weeks 12 and 18) worsened less in the tislelizumab compared with the ICC arm. The tislelizumab arm improved in reflux symptoms, whereas the ICC worsened at week 12 [difference in LS mean change: −4.1 (95% CI: −7.6 to −0.6), P = 0.0229]. The visual analogue scale remained consistent in the tislelizumab arm whereas it worsened in the ICC arm. The hazard of time to deterioration was lower in tislelizumab patients compared with ICC for physical functioning and reflux.

Conclusions

HRQoL, including fatigue symptoms and physical functioning, was maintained in patients with advanced or metastatic ESCC receiving tislelizumab compared with ICC-treated patients. These results provide additional support for the benefits of tislelizumab in this patient population.

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Global health status and HRQoL remained consistent in the tislelizumab arm whereas the ICC arm experienced worsening.

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Fatigue and physical functioning worsened in both arms; however, the worsening was greater in the ICC arm.

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The tislelizumab arm was at lower risk of reaching the threshold for worsening in physical functioning and reflux.

Manganese oxide nanorod catalysts for low-temperature selective catalytic reduction of NO with NH3

MnO_x nanorod catalysts were successfully synthesized by two different preparation methods using porous SiO₂ nanorods as the template and investigated for the low-temperature selective catalytic reduction (SCR) of NO with NH₃. The catalysts were characterized by scanning electron microscopy, transmission electron microscopy, nitrogen adsorption, X-ray diffraction, X-ray photoelectron spectroscopy, and NH₃ temperature-programmed desorption. The results show that the obtained MnO_x-P nanorod catalyst prepared by redox precipitation method exhibits higher NO removal activity than that prepared by the solvent evaporation method in the low temperature range of 100–180 °C, where about 98% NO conversion is achieved over MnO_x(0.36)-P nanorods. The reason is mainly attributed to MnO_x(0.36)-P nanorods possessing unique flower-like morphology and mesoporous structures with high pore volume, which facilitates the exposure of more active sites of MnO_x and the adsorption of reactant gas molecules. Furthermore, there is a lower crystallinity of MnO_x, higher percentage of Mn⁴⁺ species and a large amount of strong acid sites on the surface. These factors contribute to the excellent low-temperature SCR activity of MnO_x(0.36)-P nanorods.

Compared with MnO_x(0.36)-E nanorods, MnO_x(0.36)-P nanorods possess unique flower-like morphology and mesoporous structures with high pore volume, contributing to the excellent low-temperature SCR activity of MnO_x(0.36)-P nanorods.

Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function

Neural activity coordinated across different scales from neuronal circuits to large-scale brain networks gives rise to complex cognitive functions. Bridging the gap between micro- and macroscale processes, we present a novel framework based on the maximum entropy model to infer a hybrid resting-state structural connectome, representing functional interactions constrained by structural connectivity. We demonstrate that the structurally informed network outperforms the unconstrained model in simulating brain dynamics, wherein by constraining the inference model with the network structure we may improve the estimation of pairwise BOLD signal interactions. Further, we simulate brain network dynamics using Monte Carlo simulations with the new hybrid connectome to probe connectome-level differences in excitation-inhibition balance between apolipoprotein E (APOE)-ε4 carriers and noncarriers. Our results reveal sex differences among APOE-ε4 carriers in functional dynamics at criticality; specifically, female carriers appear to exhibit a lower tolerance to network disruptions resulting from increased excitatory interactions. In sum, the new multimodal network explored here enables analysis of brain dynamics through the integration of structure and function, providing insight into the complex interactions underlying neural activity such as the balance of excitation and inhibition.

HIERARCHICAL BRAIN EMBEDDING USING EXPLAINABLE GRAPH LEARNING

Brain networks have been extensively studied in neuroscience, to better understand human behavior, and to identify and characterize distributed brain abnormalities in neurological and psychiatric conditions. Several deep graph learning models have been proposed for brain network analysis, yet most current models lack interpretability, which makes it hard to gain any heuristic biological insights into the results. In this paper, we propose a new explainable graph learning model, named hierarchical brain embedding (HBE), to extract brain network representations based on the network community structure, yielding interpretable hierarchical patterns. We apply our new method to predict aggressivity, rule-breaking, and other standardized behavioral scores from functional brain networks derived using ICA from 1,000 young healthy subjects scanned by the Human Connectome Project. Our results show that the proposed HBE outperforms several state-of-the-art graph learning methods in predicting behavioral measures, and demonstrates similar hierarchical brain network patterns associated with clinical symptoms.

P-Doped NiTe2 with Te-Vacancies in Lithium-Sulfur Batteries Prevents Shuttling and Promotes Polysulfide Conversion

Lithium-sulfur (Li-S) batteries have been hindered by the shuttle effect and sluggish polysulfide conversion kinetics. Here, a P-doped nickel tellurium electrocatalyst with Te-vacancies (P⊂NiTe_{2- x} ) anchored on maize-straw carbon (MSC) nanosheets, served as a functional layer (MSC/P⊂NiTe_{2- x} ) on the separator of high-performance Li-S batteries. The P⊂NiTe_{2- x} electrocatalyst enhanced the intrinsic conductivity, strengthened the chemical affinity for polysulfides, and accelerated sulfur redox conversion. The MSC nanosheets enabled NiTe₂ nanoparticle dispersion and Li⁺ diffusion. In situ Raman and ex situ X-ray absorption spectra confirmed that the MSC/P⊂NiTe_{2- x} restrained the shuttle effect and accelerated the redox conversion. The MSC/P⊂NiTe_{2- x} -based cell has a cyclability of 637 mAh g^-1 at 4 C over 1800 cycles with a degradation rate of 0.0139% per cycle, high rate performance of 726 mAh g^-1 at 6 C, and a high areal capacity of 8.47 mAh cm^-2 under a sulfur configuration of 10.2 mg cm^-2 , and a low electrolyte/sulfur usage ratio of 3.9. This work demonstrates that vacancy-induced doping of heterogeneous atoms enables durable sulfur electrochemistry and can impact future electrocatalytic designs related to various energy-storage applications.

Joint Determination of Reactor Antineutrino Spectra from ^{235}U and ^{239}Pu Fission by Daya Bay and PROSPECT

A joint determination of the reactor antineutrino spectra resulting from the fission of ^{235}U and ^{239}Pu has been carried out by the Daya Bay and PROSPECT Collaborations. This Letter reports the level of consistency of ^{235}U spectrum measurements from the two experiments and presents new results from a joint analysis of both data sets. The measurements are found to be consistent. The combined analysis reduces the degeneracy between the dominant ^{235}U and ^{239}Pu isotopes and improves the uncertainty of the ^{235}U spectral shape to about 3%. The ^{235}U and ^{239}Pu antineutrino energy spectra are unfolded from the jointly deconvolved reactor spectra using the Wiener-SVD unfolding method, providing a data-based reference for other reactor antineutrino experiments and other applications. This is the first measurement of the ^{235}U and ^{239}Pu spectra based on the combination of experiments at low- and highly enriched uranium reactors.

Disentangled and Proportional Representation Learning for Multi-View Brain Connectomes

Diffusion MRI-derived brain structural connectomes or brain networks are widely used in the brain research. However, constructing brain networks is highly dependent on various tractography algorithms, which leads to difficulties in deciding the optimal view concerning the downstream analysis. In this paper, we propose to learn a unified representation from multi-view brain networks. Particularly, we expect the learned representations to convey the information from different views fairly and in a disentangled sense. We achieve the disentanglement via an approach using unsupervised variational graph auto-encoders. We achieve the view-wise fairness, i.e. proportionality, via an alternative training routine. More specifically, we construct an analogy between training the deep network and the network flow problem. Based on the analogy, the fair representations learning is attained via a network scheduling algorithm aware of proportionality. The experimental results demonstrate that the learned representations fit various downstream tasks well. They also show that the proposed approach effectively preserves the proportionality.

CommPOOL: An interpretable graph pooling framework for hierarchical graph representation learning

Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for graph level tasks such as graph classification. However, current HGPNNs do not take full advantage of the graph's intrinsic structures (e.g., community structure). Moreover, the pooling operations in existing HGPNNs are difficult to be interpreted. In this paper, we propose a new interpretable graph pooling framework - CommPOOL, that can capture and preserve the hierarchical community structure of graphs in the graph representation learning process. Specifically, the proposed community pooling mechanism in CommPOOL utilizes an unsupervised approach for capturing the inherent community structure of graphs in an interpretable manner. CommPOOL is a general and flexible framework for hierarchical graph representation learning that can further facilitate various graph-level tasks. Evaluations on five public benchmark datasets and one synthetic dataset demonstrate the superior performance of CommPOOL in graph representation learning for graph classification compared to the state-of-the-art baseline methods, and its effectiveness in capturing and preserving the community structure of graphs.

Phenotypic homogeneity of emetic Bacillus cereus isolates in China

Emetic Bacillus cereus strains produce a potent cereulide cytotoxin, which can cause acute and fatal cases of food poisoning. We isolated 18 emetic B. cereus strains from a food poisoning event, and from clinical and non-random food surveillance in China and phenotypic characteristics of haemolysis, starch hydrolysis, salicin fermentation, gelatin liquefaction, cytotoxicity, and susceptibility to antibiotics were assessed. All isolates were positive for haemolysis and gelatin liquefaction, and negative for starch hydrolysis and salicin fermentation. Their haemolytic potentials were intermediate to Bacillus anthracis and B. cereus ATCC 14579 (a non-emetic strain). All isolates were cytotoxic to CHO, Hep-2, and Vero cells, and were sensitive to ampicillin. The homogeneous phenotypes of emetic isolates from China are similar to the corresponding traits of European and Japanese isolates that have been characterized, suggesting highly similar phenotypes of emetic B. cereus worldwide.

AB0221 CURRENT IMPLEMENTATION OF TREAT-TO-TARGET APPROACH IN RHEUMATOID ARTHRITIS TREATMENT: THE PERSPECTIVE OF CHINESE RHEUMATOLOGISTS

Background:

Treat-to-target (T2T) approach is recommended as a standard management strategy in rheumatoid arthritis (RA) treatment by Chinese guideline for diagnosis and treatment of RA[1]. However, there is little known about its current implementation in China.

Objectives:

This study aimed to evaluate the implementation and achievement of T2T approach and explore their associated factors in Chinese RA cohort.

Methods:

A comprehensive cross-sectional survey of rheumatologists and their RA patients was conducted in China. Data were collected during May-Aug 2019 via physician-completed patient record forms. 60 rheumatologists provided data on demographic, clinical characteristics, treatments, and T2T approach implementation for 600 RA patients. Two logistic regressions were used to evaluate factors associated with T2T approach implementation and T2T goal achievement, respectively. Patients with missing data were not included in the models.

Results:

600 patients were included in this study (48.8±11.7 years, 70.3% female). 39.0% (N=234) of 600 patients were being treated with T2T approach, and 64.9% (N=366) of 564 patients had achieved T2T goal. Patients with longer disease duration (>2 years diagnosis) (odds ratio (OR) [95%CI]=1.61 [1.05, 2.49], vs. diagnosis ≤2 years), higher pain score (OR [95%CI]=1.26 [1.04, 1.51]), or receiving advanced therapy (OR [95%CI]=6.91 [3.64, 13.13]) were more likely to use T2T. Patients with BMI >23.9kg/m2 (OR [95%CI]=2.83 [1.59, 5.04], vs. BMI≤23.9kg/m2), or who worked full-time (OR [95%CI]=2.12 [1.26, 3.57]) were more likely to achieve T2T goal, while patients with more pain (OR [95%CI]=0.77 [0.64, 0.92]) were less likely to achieve T2T goal.

Conclusion:

Low implementation of T2T approach is observed in Chinese RA treatment. Longer disease duration, more pain, and receiving advanced therapy are associated with higher probability of T2T use, while higher BMI, full-time work and less pain are associated with higher probability of T2T goal achievement. Standard diagnosis and treatment according to guidelines may improve T2T approach implementation.

References:

[1]Association, C.R., 2018 Chinese guideline for the diagnosis and treatment of rheumatoid arthritis. Zhonghua nei ke za zhi, 2018. 57(4): p. 242.

Disclosure of Interests:

Jiu-liang Zhao: None declared, Xin Liu Employee of: Eli Lilly and Company, Lujing Zhan Employee of: Eli Lilly and Company, Hongyu Tang Employee of: Eli Lilly and Company (Intern), Jinnan Li Employee of: Eli Lilly and Company, Mengru Liu Employee of: Eli Lilly and Company, Elizabeth Holdsworth Consultant of: Eli Lilly and Company, Employee of: Adelphi Real World, Yan Zhao: None declared

Enhancement of the conversion of mechanical energy into chemical energy by using piezoelectric KNbO3−x with oxygen vacancies as a novel piezocatalyst

Synthesis of new piezoelectric materials to harness the vibrational and thermal energies may contribute to solve the current increasing energy demands. KNbO3 is a known piezo- electric material that exhibits poor piezocatalytic activity owing to the scarcity of charge carriers in it. In order to enhance the charge carrier density in KNbO3, extra electrons were added to KNbO3 lattice. Extrinsic piezoelectric KNbO3−x having extra electrons in the lattice was synthesized via the reaction between Nb2O5 and KBH4 at elevated temperatures. The KNbO3 nanostructures formed at 450 and 550 °C contained feebly piezoelectric KNbO3−x/Nb2O5−x and piezoelectric KNbO3−x respectively. The enhanced piezocatalytic activity of KNbO3−x is demonstrated by the production of hydrogen from water by harnessing the mechanical vibrations and the observed hydrogen production rates are 0.05 and 3.19 ml h−1 g1 for KNbO3−x/Nb2O5−x and KNbO3−x respectively. The enhanced piezocatalytic activity of KNbO3−x can be attributed to the enhancement of the charge carrier density resulting from the creation of oxygen vacancies in KNbO3 that lead to enhancing the electronic conductivity as well as charge carrier separation. It is demonstrated that the piezocatalytic activity can be boosted by augmenting the charge carrier density in piezoelectric materials by synthesizing them under highly reducing reaction conditions.