Homogenizing The Low-Dimensional Phases for Stable 2D-3D Tin Perovskite Solar Cells.

2D-3D tin-based perovskites are considered as promising candidates for achieving efficient lead-free perovskite solar cells (PSCs). However, the existence of multiple low-dimensional phases formed during the film preparation hinders the efficient transport of charge carriers. In addition, the non-homogeneous distribution of low-dimensional phases leads to lattice distortion and increases the defect density, which are undesirable for the stability of tin-based PSCs. Here, mixed spacer cations [diethylamine (DEA+) and phenethylamine (PEA+)] are introduced into tin perovskite films to modulate the distribution of the 2D phases. It is found that compared to the film with only PEA+, the combination of DEA+ and PEA+ favors the formation of homogeneous low-dimensional perovskite phases with three octahedral monolayers (n = 3), especially near the bottom interface between perovskite and hole transport layer. The homogenization of 2D phases help improve the film quality with reduced lattice distortion and released strain. With these merits, the tin PSC shows significantly improved stability with 94% of its initial efficiency retained after storing in a nitrogen atmosphere for over 4600 h, and over 80% efficiency maintained after continuous illumination for 400 h.

Ambient Air Processed Inverted Inorganic Perovskite Solar Cells with over 21% Efficiency Enabled by Multifunctional Ethacridine Lactate.

All-inorganic cesium lead triiodide perovskites (CsPbI3) have attracted increasing attention due to their good thermal stability, remarkable optoelectronic properties, and adaptability in tandem solar cells. However, N2-filled glovebox is generally required to strictly control the humidity during film fabrication due to the moisture-induced black-to-yellow phase transition, which remains a great hinderance for further commercialization. Herein, we report an effective approach via incorporating multifunctional ethacridine lactate (EAL) to mitigate moisture invasion and enable the fabrication of efficient inverted (p-i-n) CsPbI3 perovskite solar cells (PSCs) under ambient condition. It is revealed that the lactate anions accelerate the crystallization of CsPbI3, shortening the exposure time to moisture during film fabrication. Meanwhile, the conjugated backbone and multiple functional groups in the ethacridine cations can interact with I- and Pb2+ to reduce the undesired defects, stabilize the perovskite structure and facilitate the charge transport in the film. Moreover, EAL incorporation also leads to better energy alignment, thus favoring charge extraction at both upper and bottom interfaces. Consequently, the device efficiency and stability are enormously enhanced, with the champion efficiency reaching 21.08%. This even surpasses the highest value reported for the devices fabricated in glovebox, representing a record efficiency of inverted all-inorganic PSCs.

Phosphonic Chloride Assisted Fabrication of Highly Emissive Mixed Halide Perovskite Films in Ambient Air for Blue Light-Emitting Diodes.

Blue perovskite light-emitting diodes (LEDs) have emerged as promising candidates for full-color display and lighting applications. However, the fabrication of blue-emitting perovskite films typically requires an inert environment, leading to increased complexity and cost in the manufacturing process, which is undesirable for applications of perovskite LEDs. Herein, we report a strategy to fabricate bright blue-emitting perovskite films in ambient air by incorporating phosphonic chlorides in a perovskite precursor solution. We used two different phosphonic chlorides, diphenylphosphonic chloride (DPPC) and phenylphosphonic dichloride (PPDC), and comparatively studied their effects on the properties of perovskite films and the blue LEDs. It is found that PPDC possesses a stronger chlorination ability due to higher hydrolysis reactivity; meanwhile, it has a stronger interaction with the perovskite compared to DPPC, resulting in an improved film quality and enhanced blue emission with a photoluminescence quantum yield of 45%, which represents the record value for the air-processed blue perovskite films. Blue perovskite LEDs are fabricated, and the emission wavelengths are effectively tuned by controlling the concentration of phosphonic chlorides. Benefiting from the optimized perovskite films with reduced nonradiative recombination and promoted charge injection and transport, the PPDC-derived blue perovskite LEDs exhibit improved performance with an external quantum efficiency of 3.3% and 1.2% for the 490 and 480 nm emission wavelength, respectively.

Modulating Nucleation and Crystal Growth of Tin Perovskite Films for Efficient Solar Cells.

The performance of tin halide perovskite solar cells (PSCs) has been severely limited by the rapid crystallization of tin perovskites, which usually leads to an undesirable film quality. In this work, we tackle this issue by regulating the nucleation and crystal growth of tin perovskite films using a small Lewis base additive, urea. The urea-SnI2 interaction facilitates the formation of larger and more uniform clusters, thus accelerating the nucleation process. Additionally, the crystal growth process is extended, resulting in a high-quality tin perovskite film with compact morphology, increased crystallinity, and reduced defects. Consequently, the efficiency of tin PSCs is significantly increased from 10.42% to 14.22%. This work highlights the importance of manipulating the nucleation and crystal growth of tin perovskites to realize efficient tin PSCs.

Relationship Between Mindfulness and Affiliate Stigma in Parents of Children with Autism Spectrum Disorder in China: The Mediating Role of Coping Styles.

PURPOSE: This study aims to investigate the current status of affiliate stigma among parents of autistic children, analyze the influencing factors, explore the relationship among mindfulness, coping styles, and affiliate stigma, and verify the mediating role of coping styles between mindfulness and affiliate stigma in parents of children with autism in China. METHOD: Between February and April 2023, the Child Development Behaviour Centre of a public hospital in China recruited 345 parents of children with autism. These parents completed the general information questionnaire, the Mindful Attention Awareness Scale, the Affiliate Stigma Scale, and the Simple Coping Style Questionnaire. We then adapted the Hayes Process Macro and Bootstrap methods to examine the mediating effects of coping styles between mindfulness and affiliate stigma. RESULTS: (1) The total affiliate stigma score of parents of children with autism was 48.53 (standard deviation:: 10.74). Parents' age, monthly family income, duration of care, mindfulness, and coping styles were the influencing factors of parental affiliate stigma. (2) Mindfulness was positively correlated with positive coping style (r = 0.33, p < .01) and negatively correlated with negative coping style, affiliate stigma (r = -0.38, -0.39, p < .01), whereas affiliate stigma was negatively correlated with positive coping style (r = -0.34, p < .01) and positively correlated with negative coping style (r = 0.41, p < .01). (3) Positive coping style and negative coping style play a parallel mediating role between mindfulness and affiliate stigma of parents of autistic children. CONCLUSIONS: Parents of children with autism experience significant levels of affiliate stigma. Mindfulness has a direct impact on associated stigma in parents of children with autism and also indirectly predicts associated stigma through the intermediary influence of positive and negative coping styles. Healthcare professionals could perform mindfulness interventions from an optimistic psychology viewpoint to boost parents' mindfulness and coping abilities, thereby accomplishing the objective of mitigating affiliate stigma.

Automated segmentation of cell organelles in volume electron microscopy using deep learning.

Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of producing accurate instance segmentation results that will then need to be re-assembled into the original dataset: the entire process requires substantial expertise and time to achieve quantifiable results. To speed-up the process, from cell organelle detection to quantification across electron microscopy modalities, we propose a deep-learning based approach for fast automatic outline segmentation (FAMOUS), that involves organelle detection combined with image morphology, and 3D meshing to automatically segment, visualize and quantify cell organelles within volume electron microscopy datasets. From start to finish, FAMOUS provides full segmentation results within a week on previously unseen datasets. FAMOUS was showcased on a HeLa cell dataset acquired using a focused ion beam scanning electron microscope, and on yeast cells acquired by transmission electron tomography. RESEARCH HIGHLIGHTS: Introducing a rapid, multimodal machine-learning workflow for the automatic segmentation of 3D cell organelles. Successfully applied to a variety of volume electron microscopy datasets and cell lines. Outperforming manual segmentation methods in time and accuracy. Enabling high-throughput quantitative cell biology.

Zabramski classification in predicting the occurrence of symptomatic intracerebral hemorrhage in sporadic cerebral cavernous malformations.

OBJECTIVE: The authors aimed to investigate the evolutionary characteristics of the Zabramski classification of cerebral cavernous malformations (CCMs) and the value of the Zabramski classification in predicting clinical outcome in patients with sporadic CCM. METHODS: This retrospective study consecutively included cases of sporadic CCM that had been untreated from January 2001 through December 2021. Baseline and follow-up patient information was recorded. The evolution of the Zabramski classification of a sporadic CCM was defined as the initial lesion type changing into another type for the first time on MRI follow-up. The primary outcome was the occurrence of a hemorrhage event, which was defined as a symptomatic event with radiological evidence of overt intracerebral hemorrhage. RESULTS: Among the 255 included cases, 55 (21.6%) were classified as type I CCM, 129 (50.6%) as type II CCM, and 71 (27.8%) as type III CCM, based on initial MRI. During a mean follow-up of 58.8 ± 33.6 months, 51 (20.0%) patients had lesion classification transformation, whereas 204 (80.0%) patients maintained their initial type. Among the 51 transformed lesions, 29 (56.9%) were type I, 11 (21.6%) were type II, and 11 (21.6%) were type III. Based on all follow-up imaging, of the initial 55 type I lesions, 26 (47.3%) remained type I and 27 (49.1%) regressed to type III because of hematoma absorption; 91.5% of type II and 84.5% of type III lesions maintained their initial type during MRI follow-up. The classification change rate of type I lesions was statistically significantly higher than those of type II and III lesions. After a total follow-up of 1157.7 patient-years, new clinical hemorrhage events occurred in 40 (15.7%) patients. The annual cumulative incidence rate for symptomatic hemorrhage in all patients was 3.4 (95% CI 2.5-4.7) per 100 person-years. Kaplan-Meier survival analysis showed that the annual cumulative incidence rate for symptomatic hemorrhage of type I CCM (15.3 per 100 patient-years) was significantly higher than those of type II (0.6 per 100 patient-years) and type III (2.3 per 100 patient-years). CONCLUSIONS: This study suggests that the Zabramski classification is helpful in estimating clinical outcome and can assist with surgical decision-making in patients with sporadic CCM.

Multifunctional Buried Interface Modification Enables Efficient Tin Perovskite Solar Cells.

Tin perovskite solar cells (PSCs) are considered promising candidates to promote lead-free perovskite photovoltaics. However, their power conversion efficiency (PCE) is limited by the easy oxidation of Sn2+ and low quality of tin perovskite film. Herein, an ultra-thin 1-carboxymethyl-3-methylimidazolium chloride (ImAcCl) layer is used to modify the buried interface in tin PSCs, which can induce multifunctional improvements and remarkably enhance the PCE. The carboxylate group (CO) and the hydrogen bond donor (NH) in ImAcCl can interact with tin perovskites, thus significantly suppressing the oxidation of Sn2+ and reducing the trap density in perovskite films. The interfacial roughness is reduced, which contributes to a high-quality tin perovskite film with increased crystallinity and compactness. In addition, the buried interface modification can modulate the crystal dimensionality, favoring the formation of large bulk-like crystals instead of low-dimensional ones in tin perovskite films. Therefore, the charge carrier transport is effectively promoted and the charge carrier recombination is suppressed. Eventually, tin PSCs show a remarkably enhanced PCE from 10.12% to 12.08%. This work highlights the importance of buried interface engineering and provides an effective way to realize efficient tin PSCs.

Fabrication of Highly Luminescent Quasi Two-Dimensional CsPbBr3 Perovskite Films in High Humidity Air for Light-Emitting Diodes.

Perovskite light-emitting diodes (LEDs) have attracted extensive attention in recent years due to their outstanding performance and promise in lighting and display applications. However, the fabrication of perovskite LEDs usually requires a low-humidity atmosphere, which is unfavorable for industrial production. Herein, we report an effective strategy to fabricate highly luminescent quasi two-dimensional CsPbBr3 perovskite films in an ambient atmosphere with a humidity up to 60%. We reveal that the hole transport layer (HTL) plays a significant role in the morphology and optical properties of the perovskite films. Using hydrophobic self-assembled monolayer materials as HTLs can remarkably improve the quality of the perovskite films processed in high humidity air. The resultant perovskite LEDs show reduced leakage current and significantly enhanced performance. Furthermore, surface treatment is conducted to prevent water invasion and promote radiative recombination in perovskite films and LEDs. Eventually, the perovskite LEDs exhibit bright green emission with an external quantum efficiency of 4.87%. The present work provides a feasible pathway to overcome the humidity limitation for obtaining bright perovskite films and LEDs, which would contribute to further reducing the fabrication cost of perovskite LEDs and promoting their applications.

Object Detection Based on Swin Deformable Transformer-BiPAFPN-YOLOX.

Object detection technology plays a crucial role in people's everyday lives, as well as enterprise production and modern national defense. Most current object detection networks, such as YOLOX, employ convolutional neural networks instead of a Transformer as a backbone. However, these techniques lack a global understanding of the images and may lose meaningful information, such as the precise location of the most active feature detector. Recently, a Transformer with larger receptive fields showed superior performance to corresponding convolutional neural networks in computer vision tasks. The Transformer splits the image into patches and subsequently feeds them to the Transformer in a sequence structure similar to word embeddings. This makes it capable of global modeling of entire images and implies global understanding of images. However, simply using a Transformer with a larger receptive field raises several concerns. For example, self-attention in the Swin Transformer backbone will limit its ability to model long range relations, resulting in poor feature extraction results and low convergence speed during training. To address the above problems, first, we propose an important region-based Reconstructed Deformable Self-Attention that shifts attention to important regions for efficient global modeling. Second, based on the Reconstructed Deformable Self-Attention, we propose the Swin Deformable Transformer backbone, which improves the feature extraction ability and convergence speed. Finally, based on the Swin Deformable Transformer backbone, we propose a novel object detection network, namely, Swin Deformable Transformer-BiPAFPN-YOLOX. experimental results on the COCO dataset show that the training period is reduced by 55.4%, average precision is increased by 2.4%, average precision of small objects is increased by 3.7%, and inference speed is increased by 35%.

Control of n-Phase Distribution in Quasi Two-Dimensional Perovskite for Efficient Blue Light-Emitting Diodes.

Pure-bromide quasi-2D perovskite (PBQ-2DP) promises high-performance light-emitting diodes (LEDs), while a challenge remains on control over its n-phase distribution for bright true-blue emission. Present work addresses the challenge through exploring the passivation molecule of amino acid with reinforced binding energy, which generates narrow n-phase distribution preferentially at n = 3 with true blue emission at 478 nm. Consequently, a peak external quantum efficiency of 5.52% and a record brightness of 512 cd m-2 are achieved on the PBQ-2DP-based true blue PeLED, these both values located among the top in the records of similar devices. We further reveal that the electron-phonon coupling results in the red-shifted emission in the PBQ-2DP film, suggesting that the view of n-phase distribution dominated true-blue emission in PBQ-2DP needs to be revisited, pointing out a guideline of electron-phonon coupling suppression to relieve the strait of realizing true blue or even deep blue emission in the PBQ-2DP film.

Cascading effects of Pb on the environmental and symbiotic microbiota and tadpoles' physiology based on field data and laboratory validation.

Heavy metal pollution poses a serious threat to ecosystems. Currently, there is a lack of field data that would enable us to gain a systematic understanding of the influences of heavy metals on aquatic ecosystems, especially the interactions between environments and animals. We studied the relationships between the variations in heavy metal concentrations (10 species including Pb in sediments and surface water), the community structure of environmental and symbiotic microbiota, and the gut traits of Bufo gargarizans tadpoles across 16 sampling sites on the Chengdu Plain through rigorous statistical analysis and laboratory validation. The results show that heavy metal concentrations, especially the Pb concentration of the sediment, are linked to the variations in sediment and tadpoles' gut microbiomes but not to water microbiota. For the sediment microbiota, Pb causes a trade-off between the proportions of Burkholderiales and Verrucomicrobiae and affects the methane, sulfide, and nitrate metabolisms. For tadpoles, a high sediment Pb content leads to a low abundance of gut aerobic bacteria and a large relative gut weight under both field and laboratory conditions. In addition, Pb promotes the growth of B. gargarizans tadpoles under laboratory conditions. These effects seem to be beneficial to tadpoles. However, a high Pb content leads to a low abundance of probiotic bacteria (e.g., Verrucomicrobiae, Eubacteriaceae, and Cetobacterium) and a high abundance of pathogenic bacteria in the gut and environment, suggesting potential health risks posed by Pb. Interestingly, there is a causal relationship between Pb-induced variations in sediment and symbiotic microbiotas, and the latter is further linked to the variation in relative gut weight of tadpoles. This suggests a cascading effect of Pb on the ecosystem. In conclusion, our results indicate that among the heavy metals, the Pb in sediment is a critical factor affecting the aquatic ecosystem through an environment-gut-physiology pathway mediated by microbiota.

Simplex cerebral cavernous malformations with MAP3K3 mutation have distinct clinical characteristics.

Objectives: To investigate the clinical characteristics of cerebral cavernous malformations (CCMs) with MAP3K3 somatic mutation. Methods: We performed a retrospective review of our CCMs database between May 2017 and December 2019. The patients with simplex CCMs identified to harbor a MAP3K3 or CCM gene somatic mutation were included. Clinical characteristics were recorded. Univariate and multivariate logistic analyses were used to assess the risk factors associated with hemorrhage events of CCMs. To explore the underlying mechanism, we transfected MEKK3-I441M-overexpressing and CCM2-knockdown lentiviruses into human umbilical vein endothelial cells (HUVECs) and investigated thrombomodulin (TM) and tight junctions (TJs) protein expression by western blotting and immunofluorescence. Finally, immunohistochemistry was used to validate TM and TJs protein expression in surgical samples. Results: Fifty simplex CCMs patients were included, comprising 38 MAP3K3 mutations and 12 CCM gene mutations. Nine (23.7%) patients with MAP3K3 mutations and 11(91.7%) patients with CCM gene mutations exhibited overt hemorrhage, respectively. Multivariate logistic analyses revealed that MAP3K3 mutation was associated with a lower risk of hemorrhage events. In the vitro experiments, ZO-1 expression was not reduced in MEKK3-I441M-overexpressing HUVECs compared with wild type, whereas it was significantly decreased in CCM2-knockdown HUVECs compared with control. In the MEKK3-I441M-overexpressing HUVECs, TM expression was increased, and the NF-κB pathway was significantly activated. After treatment with an NF-κB signaling inhibitor, TM expression was further upregulated. Meanwhile, TM expression was increased, but the NF-κB pathway was not activated in CCM2-knockdown HUVECs. Accordingly, immunohistochemistry showed that ZO-1 expression in the MAP3K3-mutant samples was significantly higher than that in the CCM-mutant samples. TM expression in the MAP3K3-mutant lesions was significantly lower than that in the CCM-mutant samples. Conclusion: Simplex CCMs with MAP3K3 mutation occasionally present with overt hemorrhage, which is associated with the biological function of MAP3K3 mutation.

Mitigating Voc Loss in Tin Perovskite Solar Cells via Simultaneous Suppression of Bulk and Interface Nonradiative Recombination.

Tin-based perovskite solar cells (PSCs) have recently attracted extensive attention as a promising alternative to lead-based counterparts due to their low toxicity and narrow band gap. However, the severe open-circuit voltage (Voc) loss remains one of the most significant obstacles to further improving photovoltaic performance. Herein, we report an effective approach to reducing the Voc loss of tin-based PSCs. We find that introducing ethylammonium bromide (EABr) as an additive into the tin perovskite film can effectively reduce defect density both in the tin perovskite film and at the surface as well as optimize the energy level alignment between the perovskite layer and [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) transport material, thereby suppressing nonradiative recombination both in the bulk film and at the interface. Furthermore, it is demonstrated that the Voc loss is gradually mitigated along with increasing storage duration due to the slow passivation effect. As a result, a remarkable Voc of 0.83 V is achieved in the devices optimized with the EABr additive, which shows a significantly improved power conversion efficiency (PCE) of 10.80% and good stability.

How to examine anastomotic integrity intraoperatively in totally laparoscopic radical gastrectomy? Methylene blue testing prevents technical defect-related anastomotic leaks.

BACKGROUND: Intraoperative methylene blue testing (IMBT), air leak testing, or endoscopy is used to assess the anastomotic integrity of esophagojejunostomy during open total gastrectomy for gastric cancer. Totally laparoscopic radical gastrectomy has been widely used to treat gastric cancer in the last few decades. However, reports on testing anastomotic integrity in totally laparoscopic radical gastrectomy are limited. AIM: To explore the effects of IMBT on the incidence of postoperative anastomotic leaks (PALs) and identify the risk factors for PALs in totally laparoscopic radical gastrectomy. METHODS: From January 2017 to December 2019, patients who underwent totally laparoscopic radical gastrectomy at the Shaanxi Provincial People's Hospital were retrospectively analyzed. According to whether or not they experienced an IMBT, the patients were divided into an IMBT group and a control group. If the IMBT was positive, an intraoperative suture was required to reinforce the anastomosis. The difference in the incidence of PALs was compared, and the risk factors were investigated. RESULTS: This study consisted of 513 patients, 211 in the IMBT group and 302 in the control group. Positive IMBT was shown in seven patients (3.3%) in the IMBT group, and no PAL occurred in these patients after suture reinforcement. Multivariate analysis showed that risk factors for predicting positive IMBT were body mass index (BMI) > 25 kg/m2 (hazard ratio [HR] = 8.357, P = 0.009), operation time > 4 h (HR = 55.881, P = 0.002), and insufficient surgical experience (HR = 15.286, P = 0.010). Moreover, 15 patients (2.9%) developed PALs in 513 patients, and the rates of PALs were significantly lower in the IMBT group than in the control group [2 of 211 patients (0.9%) vs 13 of 302 patients (4.3%), P = 0.0026]. Further analysis demonstrated that preoperative complications (HR = 13.128, P = 0.017), totally laparoscopic total gastrectomy (HR = 9.075, P = 0.043), and neoadjuvant chemotherapy (HR = 7.150, P = 0.008) were independent risk factors for PALs. CONCLUSION: IMBT is an effective method to evaluate the integrity of anastomosis during totally laparoscopic radical gastrectomy, thus preventing technical defect-related anastomotic leaks. Preoperative complications, totally laparoscopic total gastrectomy, and neoadjuvant chemotherapy are independent risk factors for PALs.

A High Time-Efficient Missing Tag Detection Scheme for Integrated RFID Systems.

Missing tag incidents are common in RFID-enabled supply-chain and warehousing scenarios due to cargo theft and employee error operations, which may lead to serious economic losses or potential safety hazards. On the premise of ensuring the accuracy of missing tag detection, this paper aims to improve the time efficiency in an integrated RFID system. Unlike prior work focusing on detecting missing items from a large number of homogeneous tags that are monitored by a single reader, one integrated RFID system possesses multiple readers to communicate with the heterogeneous tags, which have different categorical attributes. In addition, the prior work required repeating the execution several times to capture the missing tags in assorted categories, which is of low time efficiency. Thus, a protocol called Multi-reader Missing Tag Detection (MMTD) is proposed to capture the missing tag quickly and reliably, which can detect missing tags from different categories in a parallel manner and is much more time-efficient than previous work. MMTD has two major advantages compared to prior work: (i) It leverages the knowledge of the spatial distribution of tags to divide up a difficult detection task into several lightweight tasks, which are shared by multiple readers. (ii) It personalizes the time frame of the reader based on the tag population to optimize the utilization of the communication channel. The final simulation results reveal that MMTD is the best in time-efficiency among the comparison protocols, and MMTD outperforms the other missing tag detection protocols by at least 1.5× in the Integrated RFID scenarios.

Low Serum Superoxide Dismutase Is Associated With a High Risk of Cognitive Impairment After Mild Acute Ischemic Stroke.

Background: Post-stroke cognitive impairment (PSCI) is a common complication after stroke, but effective therapy is limited. Identifying potential risk factors for effective intervention is warranted. We investigated whether serum superoxide dismutase (SOD) levels were related to cognitive impairment after mild acute ischemic stroke (AIS) by using a prospective cohort design. Methods: A total of 187 patients diagnosed with mild AIS (National Institutes of Health Stroke Scale ≤ 8) were recruited. Serum SOD, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and interleukin-6 (IL-6) levels were measured, and cognitive assessments (Mini-Mental State Examination, MMSE; Montreal Cognitive Assessment, MoCA) were performed in the early phase (within 2 weeks). These indexes and assessments were repeated at 3 months after onset. MoCA < 22 was defined as early cognitive impairment (CI-E) within 2 weeks and late cognitive impairment (CI-L) at 3 months after stroke. Results: In a survey, 105 of 187 (56.1%) patients were identified as CI-E after mild AIS. Lower serum SOD associated with higher inflammatory biomarkers (ESR, CRP, and IL-6) and worse cognitive scores was observed in CI-E patients. In a survey, 39 of 103 (37.9%) stroke patients who completed the 3-month follow-up were identified as CI-L. Serum SOD was consistently lower in CI-L patients at baseline and 3 months and positively associated with cognitive scores. In adjusted analyses, low serum SOD at baseline was independently associated with high risks of CI-E and CI-L, with odds ratios (ORs) of 0.64 and 0.33 per standard deviation increase in serum SOD, respectively. Multiple-adjusted spline regression models showed linear associations between serum SOD and CI-E (P = 0.044 for linearity) and CI-L (P = 0.006 for linearity). Moreover, 35.2% (19/54) of CI-E patients cognitively recovered during the 3-month follow-up. In multivariable analysis, SOD was identified as a protective factor for cognitive recovery after stroke (OR 1.04, 95% CI: 1.01-1.08, P = 0.024). Conclusion: We demonstrate that low serum SOD is associated with a high risk of cognitive impairment after mild AIS, indicating SOD may be a potential modifiable factor for PSCI.

Scalable Distributed Hashing for Approximate Nearest Neighbor Search.

Hashing has been widely applied to the large-scale approximate nearest neighbor search problem owing to its high efficiency and low storage requirement. Most investigations concentrate on learning hashing methods in a centralized setting. However, in existing big data systems, data is often stored across different nodes. In some situations, data is even collected in a distributed manner. A straightforward way to solve this problem is to aggregate all the data into the fusion center to obtain the search result (aggregating method). However, this strategy is not feasible because of the prohibitive communication cost. Although a few distributed hashing methods have been proposed to reduce this cost, they only focus on designing a distributed algorithm for a specific global optimization objective without considering scalability. Moreover, existing distributed hashing methods aim at finding a distributed solution to hashing, meanwhile avoiding accuracy loss, rather than improving accuracy. To address these challenges, we propose a Scalable Distributed Hashing (SDisH) model in which most existing hashing methods can be extended to process distributed data with no changes. Furthermore, to improve accuracy, we utilize the search radius as a global variable across different nodes to achieve a global optimum search result for every iteration. In addition, a voting algorithm is presented based on the results produced by multiple iterations to further reduce search errors. Theoretical analyses of communication, computation, and accuracy demonstrate the superiority of the proposed model. Numerical simulations on three large-scale and two relatively small benchmark datasets also show that the SDisH model achieves up to 44.75% and 10.23% accuracy gains compared to the aggregating method and state-of-the-art distributed hashing methods, respectively.

Dysbiosis of Gut Microbiota Is an Independent Risk Factor of Stroke-Associated Pneumonia: A Chinese Pilot Study.

Background and Purpose: Identifying risks of stroke-associated pneumonia (SAP) is important for clinical management. We aimed to evaluate the association between gut microbiome composition and SAP in patients with acute ischemic stroke (AIS). Methods: A prospective observational study was conducted, and 188 AIS patients were enrolled as the training cohort. Fecal and serum samples were collected at admission. SAP was diagnosed by specialized physicians, and disease severity scores were recorded. Fecal samples were subjected to 16S rRNA V4 tag sequencing and analysed with QIIME and LEfSe. Associations between the most relevant taxa and SAP were analysed and validated with an independent cohort. Fecal short-chain fatty acid (SCFA), serum D-lactate (D-LA), intestinal fatty acid-binding protein (iFABP) and lipopolysaccharide binding protein (LBP) levels were measured. Results: Overall, 52 patients (27.7%) had SAP in the training cohort. The gut microbiome differed between SAP and non-SAP patients; specifically, Roseburia depletion and opportunistic pathogen enrichment were noted in SAP patients, as confirmed in the validation cohort (n=144, 28 SAP [19.4%]). Based on multivariate analysis, Roseburia was identified as a protective factor against SAP in both cohorts (training, aOR 0.52; 95% CI, 0.30-0.90; validation, aOR 0.44; 95% CI, 0.23-0.85). The combination of these taxa into a microbial dysbiosis index (MDI) revealed that dysbiosis increased nearly 2 times risk of SAP (training, aOR 1.95; 95% CI, 1.19-3.20; validation, aOR 2.22; 95% CI, 1.15-4.26). Lower fecal SCFA levels and higher serum D-LA levels were observed in SAP patients. Furthermore, SAP was an independent risk factor of 30-day death and 90-day unfavorable outcome. Conclusion: We demonstrate that a microbial community with depleted Roseburia and enriched opportunistic pathogens is associated with increased risk of SAP among AIS patients. Gut microbiota screening might be useful for identifying patients at high risk for SAP and provide clues for stroke treatment.

Fast kNN Search in Weighted Hamming Space With Multiple Tables.

Hashing methods have been widely used in Approximate Nearest Neighbor (ANN) search for big data due to low storage requirements and high search efficiency. These methods usually map the ANN search for big data into the k -Nearest Neighbor ( k NN) search problem in Hamming space. However, Hamming distance calculation ignores the bit-level distinction, leading to confusing ranking. In order to further increase search accuracy, various bit-level weights have been proposed to rank hash codes in weighted Hamming space. Nevertheless, existing ranking methods in weighted Hamming space are almost based on exhaustive linear scan, which is time consuming and not suitable for large datasets. Although Multi-Index hashing that is a sub-linear search method has been proposed, it relies on Hamming distance rather than weighted Hamming distance. To address this issue, we propose an exact k NN search approach with Multiple Tables in Weighted Hamming space named WHMT, in which the distribution of bit-level weights is incorporated into the multi-index building. By WHMT, we can get the optimal candidate set for exact k NN search in weighted Hamming space without exhaustive linear scan. Experimental results show that WHMT can achieve dramatic speedup up to 69.8 times over linear scan baseline without losing accuracy in weighted Hamming space.