Risk Factors of Ventilator-Associated Pneumonia in Critically III Patients

Ventilator-associated pneumonia (VAP), a hospital acquired pneumonia that occurs more than 48 h after mechanical ventilation, is a common complication of mechanical ventilation with a high mortality rate. VAP can cause patients to have difficulty weaning off the ventilator and to stay in the hospital longer, which results in a huge financial burden to patients and a huge demand for medical resources. Several strategies, such as drugs including chlorhexidine, β-lactam antibiotics and probiotics, have been used to prevent VAP in clinic. The incidence and the mortality rate of VAP have been decreased with the development of preventative strategies in the past decades, but VAP remains one of the most common causes of nosocomial infections and death in the intensive care unit. Current challenges in the management of VAP involved the lack of a gold standard for diagnosis, the absence of effective preventative strategies, and the rise in antibiotic resistance. Therefore, in order to reduce the incidence of VAP and improve the outcome of patients with mechanical ventilation, it is necessary to clarify the risk factors of VAP for clinical prevention and control of VAP. This paper reviews the international risk factors of VAP occurrence reported in recent years, including patient characteristics, increased mechanical ventilation time and prolonged length of hospital stay, disorders of consciousness, burns, comorbidities, prior antibiotic therapy, invasive operations, gene polymorphisms, and mentions the corresponding preventive measures. Each factor is not only an independent risk factor of VAP, but also has an influence on each other. A better understanding of risk factors for VAP is helpful for predicting the occurrence of VAP, improving the prevention and control of VAP, and reducing the morbidity and mortality rates of patients with VAP

Recommending Analogical APIs via Knowledge Graph Embedding

Library migration, which re-implements the same software behavior by using a different library instead of using the current one, has been widely observed in software evolution. One essential part of library migration is to find an analogical API that could provide the same functionality as current ones. However, given the large number of libraries/APIs, manually finding an analogical API could be very time-consuming and error-prone. Researchers have developed multiple automated analogical API recommendation techniques. Documentation-based methods have particularly attracted significant interest. Despite their potential, these methods have limitations, such as a lack of comprehensive semantic understanding in documentation and scalability challenges. In this work, we propose KGE4AR, a novel documentation-based approach that leverages knowledge graph (KG) embedding to recommend analogical APIs during library migration. Specifically, KGE4AR proposes a novel unified API KG to comprehensively and structurally represent three types of knowledge in documentation, which can better capture the high-level semantics. Moreover, KGE4AR then proposes to embed the unified API KG into vectors, enabling more effective and scalable similarity calculation. We build KGE4AR' s unified API KG for 35,773 Java libraries and assess it in two API recommendation scenarios: with and without target libraries. Our results show that KGE4AR substantially outperforms state-of-the-art documentation-based techniques in both evaluation scenarios in terms of all metrics (e.g., 47.1%-143.0% and 11.7%-80.6% MRR improvements in each scenario). Additionally, we explore KGE4AR' s scalability, confirming its effective scaling with the growing number of libraries.Comment: Accepted by FSE 202

Masked Language Model Based Textual Adversarial Example Detection

Adversarial attacks are a serious threat to the reliable deployment of machine learning models in safety-critical applications. They can misguide current models to predict incorrectly by slightly modifying the inputs. Recently, substantial work has shown that adversarial examples tend to deviate from the underlying data manifold of normal examples, whereas pre-trained masked language models can fit the manifold of normal NLP data. To explore how to use the masked language model in adversarial detection, we propose a novel textual adversarial example detection method, namely Masked Language Model-based Detection (MLMD), which can produce clearly distinguishable signals between normal examples and adversarial examples by exploring the changes in manifolds induced by the masked language model. MLMD features a plug and play usage (i.e., no need to retrain the victim model) for adversarial defense and it is agnostic to classification tasks, victim model's architectures, and to-be-defended attack methods. We evaluate MLMD on various benchmark textual datasets, widely studied machine learning models, and state-of-the-art (SOTA) adversarial attacks (in total $3*4*4 = 48$ settings). Experimental results show that MLMD can achieve strong performance, with detection accuracy up to 0.984, 0.967, and 0.901 on AG-NEWS, IMDB, and SST-2 datasets, respectively. Additionally, MLMD is superior, or at least comparable to, the SOTA detection defenses in detection accuracy and F1 score. Among many defenses based on the off-manifold assumption of adversarial examples, this work offers a new angle for capturing the manifold change. The code for this work is openly accessible at \url{https://github.com/mlmddetection/MLMDdetection}.Comment: 13 pages,3 figure

Formation Flight in Dense Environments

Formation flight has a vast potential for aerial robot swarms in various applications. However, existing methods lack the capability to achieve fully autonomous large-scale formation flight in dense environments. To bridge the gap, we present a complete formation flight system that effectively integrates real-world constraints into aerial formation navigation. This paper proposes a differentiable graph-based metric to quantify the overall similarity error between formations. This metric is invariant to rotation, translation, and scaling, providing more freedom for formation coordination. We design a distributed trajectory optimization framework that considers formation similarity, obstacle avoidance, and dynamic feasibility. The optimization is decoupled to make large-scale formation flights computationally feasible. To improve the elasticity of formation navigation in highly constrained scenes, we present a swarm reorganization method which adaptively adjusts the formation parameters and task assignments by generating local navigation goals. A novel swarm agreement strategy called global-remap-local-replan and a formation-level path planner is proposed in this work to coordinate the swarm global planning and local trajectory optimizations efficiently. To validate the proposed method, we design comprehensive benchmarks and simulations with other cutting-edge works in terms of adaptability, predictability, elasticity, resilience, and efficiency. Finally, integrated with palm-sized swarm platforms with onboard computers and sensors, the proposed method demonstrates its efficiency and robustness by achieving the largest scale formation flight in dense outdoor environments.Comment: Submitted for IEEE Transactions on Robotic

Identification of new classical Be stars from the LAMOST MRS survey

Be stars are B-type main-sequence stars that display broad Balmer emission lines in their spectra. Identification of Be population is essential to further examine the formation and evolutionary models. We report the detection of classical Be (CBe) stars from observations with the Large sky Area Multi-Object fiber Spectroscopic Telescope Medium Resolution Survey of Date Release 7 (LAMOST MRS DR7). We used a deep convolutional neural network, the ResNet, with an 18-layer module to examine the morphology of the H alpha profile. We identified 1,162 candidate Be stars from the collection of 2,260,387 spectra for 789,918 stars in the database. The ResNet network achieves a Be star classification accuracy of 99.5%. Among the detections, 151 of these are prior known Be stars cross-matched from the literature. By applying a three-step test, we identified 183 new CBe stars. We find that 41 CBe stars are members of known open clusters. Based upon an investigation of the kinematics of the identified CBe stars from the Gaia EDR3 astrometric solutions, we identified 16 new runaways. These new identifications will provide a reference for future follow-ups to further investigate their physical properties.Comment: 34 pages, 12 figures, 11 table

Soybean \u3ci\u3eGm\u3c/i\u3eSAUL1, a Bona Fide U-Box E3 Ligase, Negatively Regulates Immunity Likely through Repressing the Activation of \u3ci\u3eGm\u3c/i\u3eMPK3

E3 ubiquitin ligases play important roles in plant immunity, but their role in soybean has not been investigated previously. Here, we used Bean pod mottle virus (BPMV)-mediated virusinduced gene silencing (VIGS) to investigate the function of GmSAUL1 (Senescence-Associated E3 Ubiquitin Ligase 1) homologs in soybean. When two closely related SAUL1 homologs were silenced simultaneously, the soybean plants displayed autoimmune phenotypes, which were significantly alleviated by high temperature, suggesting that GmSAUL1a/1b might be guarded by an R protein. Interestingly, silencing GmSAUL1a/1b resulted in the decreased activation of GmMPK6, but increased activation of GmMPK3 in response to flg22, suggesting that the activation of GmMPK3 is most likely responsible for the activated immunity observed in the GmSAUL1a/1b-silenced plants. Furthermore, we provided evidence that GmSAUL1a is a bona fide E3 ligase. Collectively, our results indicated that GmSAUL1 plays a negative role in regulating cell death and immunity in soybean

Enabling privacy-preserving sharing of genomic data for GWASs in decentralized networks

The human genome can reveal sensitive information and is potentially re-identifiable, which raises privacy and security concerns about sharing such data on wide scales. In this work, we propose a preventive approach for privacy-preserving sharing of genomic data in decentralized networks for Genome-wide association studies (GWASs), which have been widely used in discovering the association between genotypes and phenotypes. The key components of this work are: a decentralized secure network, with a privacy-preserving sharing protocol, and a gene fragmentation framework that is trainable in an end-to-end manner. Our experiments on real datasets show the effectiveness of our privacy-preserving approaches as well as significant improvements in efficiency when compared with recent, related algorithms

Identification of lactate regulation pattern on tumor immune infiltration, therapy response, and DNA methylation in diffuse large B-cell lymphoma

BackgroundLactate, produced through glycolytic metabolism in the tumor microenvironment (TME), is implicated in tumorigenesis and progression in diverse cancers. However, the impact of lactate on the remodeling of the TME in diffuse large B-cell lymphoma (DLBCL) and its implications for therapy options remain unclear.MethodA lactate-related (LAR) scoring model was constructed in DLBCL patients using bioinformatic methods. CIBERSORT, XCELL, and ssGSEA algorithms were used to determine the correlation between LAR score and immune cell infiltration. Tumor Immune Dysfunction and Exclusion (TIDE), rituximab, cyclophosphamide, adriamycin, vincristine, and prednisone (R-CHOP) cohorts, and Genomics of Drug Sensitivity in Cancer (GDSC) were utilized to predict the therapeutic response of DLBCL patients. The impact of the hub gene STAT4 on tumor biological behavior and DNA methylation was experimentally validated or accessed by the TSIDE database.ResultsThe LAR scoring model was developed based on 20 prognosis-related lactate genes, which enabled the division of DLBCL patients into high- and low-risk groups based on the median LAR score. Patients with high-risk DLBCL exhibited significantly worse survival outcomes in both the training cohorts (GSE181063) and the validation cohorts (GSE10846, GSE32918, and GSE69053), as indicated by statistically significant differences (all P<0.05) and area under the curve (AUC) values exceeding 0.6. Immune analyses revealed that low-risk DLBCL patients had higher levels of immune cell infiltration and antitumor immune activation compared to high-risk DLBCL patients. Furthermore, DLBCL patients with high LAR scores were associated with a lower TIDE value and poor therapeutic efficacy of the R-CHOP regimen. GDSC analysis identified 18 drugs that exhibited significant response sensitivity in low-risk DLBCL patients. Moreover, in vitro experiments demonstrated that overexpression of the lactate key gene STAT4 could suppress proliferation and migration, induce cell cycle arrest, and promote cell apoptosis in DLBCL cells. Transcriptional expression and methylation of the STAT4 gene were found to be associated with immunomodulators and chemokines.ConclusionThe lactate-based gene signature effectively predicts the prognosis and regulates TME in DLBCL. Our study underscores the role of lactate gene, STAT4, as an important tumor suppressor in DLBCL. Modulating STAT4 could be a promising strategy for DLBCL in clinical practice

A novel method for in vivo measurement of dynamic ischiofemoral space based on MRI and motion capture

Purpose: To use a novel in vivo method to simulate a moving hip model. Then, measure the dynamic bone-to-bone distance, and analyze the ischiofemoral space (IFS) of patients diagnosed with ischiofemoral impingement syndrome (IFI) during dynamic activities.Methods: Nine healthy subjects and 9 patients with IFI were recruited to collect MRI images and motion capture data. The motion trail of the hip during motion capture was matched to a personalized 3D hip model reconstructed from MRI images to get a dynamic bone model. This personalized dynamic in vivo method was then used to simulate the bone motion in dynamic activities. Validation was conducted on a 3D-printed sphere by comparing the calculated data using this novel method with the actual measured moving data using motion capture. Moreover, the novel method was used to analyze the in vivo dynamic IFS between healthy subjects and IFI patients during normal and long stride walking.Results: The validation results show that the root mean square error (RMSE) of slide and rotation was 1.42 mm/1.84° and 1.58 mm/2.19°, respectively. During normal walking, the in vivo dynamic IFS was significantly larger in healthy hips (ranged between 15.09 and 50.24 mm) compared with affected hips (between 10.16 and 39.74 mm) in 40.27%–83.81% of the gait cycle (p = 0.027). During long stride walking, the in vivo dynamic IFS was also significantly larger in healthy hips (ranged between 13.02 and 51.99 mm) than affected hips (between 9.63 and 44.22 mm) in 0%–5.85% of the gait cycle (p = 0.049). Additionally, the IFS of normal walking was significantly smaller than long stride walking during 0%–14.05% and 85.07%–100% of the gait cycle (p = 0.033, 0.033) in healthy hips. However, there was no difference between the two methods of walking among the patients.Conclusions: This study established a novel in vivo method to measure the dynamic bone-to-bone distance and was well validated. This method was used to measure the IFS of patients diagnosed with IFI, and the results showed that the IFS of patients is smaller compared with healthy subjects, whether in normal or long stride walking. Meanwhile, IFI eliminated the difference between normal and long stride walking

A global monthly field of seawater pH over 3 decades: a machine learning approach

The continuous uptake of anthropogenic CO2 by the ocean leads to ocean acidification, which is an ongoing threat to the marine ecosystem. The ocean acidification rate was globally documented in the surface ocean but limited below the surface. Here, we present a monthly four-dimensional 1°×1° gridded product of global seawater pH, derived from a machine learning algorithm trained on pH observations at total scale and in-situ temperature from the Global Ocean Data Analysis Project (GLODAP). The constructed pH product covers the years 1992–2020 and depths from the surface to 2 km on 41 levels. Three types of machine learning algorithms were used in the pH product construction, including self-organizing map neural networks for region dividing, a stepwise algorithm for predictor selection, and feed-forward neural networks (FFNN) for non-linear relationship regression. The performance of the machine learning algorithm was validated using real observations by a cross validation method, where four repeating iterations were carried out with 25 % varied observations for each evaluation and 75 % for training. The constructed pH product is evaluated through comparisons to time series observations and the GLODAP pH climatology. The overall root mean square error between the FFNN constructed pH and the GLODAP measurements is 0.028, ranging from 0.044 in the surface to 0.013 at 2000 m. The pH product is distributed through the data repository of the Marine Science Data Center of the Chinese Academy of Sciences at http://dx.doi.org/10.12157/IOCAS.20230720.001 (Zhong et al., 2023)