GNNPipe: Scaling Deep GNN Training with Pipelined Model Parallelism

Communication is a key bottleneck for distributed graph neural network (GNN) training. This paper proposes GNNPipe, a new approach that scales the distributed full-graph deep GNN training. Being the first to use layer-level model parallelism for GNN training, GNNPipe partitions GNN layers among GPUs, each device performs the computation for a disjoint subset of consecutive GNN layers on the whole graph. Compared to graph parallelism with each GPU handling a graph partition, GNNPipe reduces the communication volume by a factor of the number of GNN layers. GNNPipe overcomes the unique challenges for pipelined layer-level model parallelism on the whole graph by partitioning it into dependent chunks, allowing the use of historical vertex embeddings, and applying specific training techniques to ensure convergence. We also propose a hybrid approach by combining GNNPipe with graph parallelism to handle large graphs, achieve better computer resource utilization and ensure model convergence. We build a general GNN training system supporting all three parallelism setting. Extensive experiments show that our method reduces the per-epoch training time by up to 2.45x (on average 1.58x) and reduces the communication volume and overhead by up to 22.89x and 27.21x (on average 8.69x and 11.60x), respectively, while achieving a comparable level of model accuracy and convergence speed compared to graph parallelism

Quark: A Gradient-Free Quantum Learning Framework for Classification Tasks

As more practical and scalable quantum computers emerge, much attention has been focused on realizing quantum supremacy in machine learning. Existing quantum ML methods either (1) embed a classical model into a target Hamiltonian to enable quantum optimization or (2) represent a quantum model using variational quantum circuits and apply classical gradient-based optimization. The former method leverages the power of quantum optimization but only supports simple ML models, while the latter provides flexibility in model design but relies on gradient calculation, resulting in barren plateau (i.e., gradient vanishing) and frequent classical-quantum interactions. To address the limitations of existing quantum ML methods, we introduce Quark, a gradient-free quantum learning framework that optimizes quantum ML models using quantum optimization. Quark does not rely on gradient computation and therefore avoids barren plateau and frequent classical-quantum interactions. In addition, Quark can support more general ML models than prior quantum ML methods and achieves a dataset-size-independent optimization complexity. Theoretically, we prove that Quark can outperform classical gradient-based methods by reducing model query complexity for highly non-convex problems; empirically, evaluations on the Edge Detection and Tiny-MNIST tasks show that Quark can support complex ML models and significantly reduce the number of measurements needed for discovering near-optimal weights for these tasks.Comment: under revie

Preparation of antibacterial microfibre

Three different kinds of antibacterial microfibres (270D, 300D and 330D) have been developed by adding 2-4 wt % nano silver masterbatch in the melt spinning process. The mechanical properties, silver content and morphology have been examined with tensile tester, inductively coupled plasma-optical emission spectrometer and scanning electron microscope respectively. Their antibacterial abilities are also studied with KS K 0693:2011. The results show that the added nano-particles have little influence on mechanical properties of antibacterial microfibres and their max strain and tenacity are similar to that of common manmade fibre. The fineness of the 270D, 300D and 330D samples are found to be 0.23, 0.26 and 0.30 den, and the corresponding added silver contents are 265.5, 231 and 259 ppm respectively. It is also observed that all samples bacteriostatic reduction rates are about 99.9% for both Staphylococcus aureus and Klebsiella pneumonia before washing. But after washing, it drops to 65.4%/75%, 91.9%/97.7% and 94.8%/99.9% respectively for both the bacteria in case of 270D, 300D and 330D samples. It is concluded that 300D and 330D microfibre samples have good antibacterial ability before and after washing

Radix Astragali

A previous study conducted by our group demonstrated that Radix Astragali compounded with Codonopsis pilosula and Plastrum testudinis was effective in treating pediatric β-thalassemia in a randomized, controlled clinical trial. However, the mechanism of action that underpins this treatment remains to be elucidated. Blood was collected from patients participating in this clinical trial and nucleated red blood cell-enriched mononuclear cells were isolated to facilitate the extraction of RNA and protein. RT-PCR was used to monitor the expression of globin genes and p38 MAPK, and total and phosphorylated p38 MAPK expression was assessed using Western blot analysis. Expression of α-, β-, and Aγ-globin mRNAs was not significantly affected following treatment with R. Astragali or the compounded formulation. However, Gγ-globin mRNA levels increased significantly in both treatment groups (when compared with pretreatment levels) following 12 weeks of treatment. Moreover, posttreatment Gγ-globin expression was significantly higher in both treatment groups compared with the control group. Although neither p38 MAPK mRNA nor protein levels were affected by the treatments, posttreatment phosphorylation of p38 MAPK was significantly increased in the R. Astragali and compounded formulation groups compared with the control group. These data suggest that the molecular mechanisms that underpin the efficacious use of R. Astragali (and its compounded formulation) in pediatric β-thalassemia treatment facilitate the induction of Gγ-globin expression following activation of p38 MAPK

Machine learning for the prediction of cognitive impairment in older adults

ObjectiveThe purpose of this study was to develop and validate a predictive model of cognitive impairment in older adults based on a novel machine learning (ML) algorithm.MethodsThe complete data of 2,226 participants aged 60–80 years were extracted from the 2011–2014 National Health and Nutrition Examination Survey database. Cognitive abilities were assessed using a composite cognitive functioning score (Z-score) calculated using a correlation test among the Consortium to Establish a Registry for Alzheimer's Disease Word Learning and Delayed Recall tests, Animal Fluency Test, and the Digit Symbol Substitution Test. Thirteen demographic characteristics and risk factors associated with cognitive impairment were considered: age, sex, race, body mass index (BMI), drink, smoke, direct HDL-cholesterol level, stroke history, dietary inflammatory index (DII), glycated hemoglobin (HbA1c), Patient Health Questionnaire-9 (PHQ-9) score, sleep duration, and albumin level. Feature selection is performed using the Boruta algorithm. Model building is performed using ten-fold cross-validation, machine learning (ML) algorithms such as generalized linear model (GLM), random forest (RF), support vector machine (SVM), artificial neural network (ANN), and stochastic gradient boosting (SGB). The performance of these models was evaluated in terms of discriminatory power and clinical application.ResultsThe study ultimately included 2,226 older adults for analysis, of whom 384 (17.25%) had cognitive impairment. After random assignment, 1,559 and 667 older adults were included in the training and test sets, respectively. A total of 10 variables such as age, race, BMI, direct HDL-cholesterol level, stroke history, DII, HbA1c, PHQ-9 score, sleep duration, and albumin level were selected to construct the model. GLM, RF, SVM, ANN, and SGB were established to obtain the area under the working characteristic curve of the test set subjects 0.779, 0.754, 0.726, 0.776, and 0.754. Among all models, the GLM model had the best predictive performance in terms of discriminatory power and clinical application.ConclusionsML models can be a reliable tool to predict the occurrence of cognitive impairment in older adults. This study used machine learning methods to develop and validate a well performing risk prediction model for the development of cognitive impairment in the elderly

Does sacubitril/valsartan work in children with heart failure?—a pilot study

BackgroundSacubitril/valsartan is an angiotensin receptor neprilysin antagonist (ARNI) approved for adult heart failure (HF). Its safety and efficacy in pediatric HF patients with cardiomyopathy or congenital heart disease are poorly understood. A pilot study was conducted to assess the clinical response, efficacy and safety of sacubitril/valsartan in this population at a tertiary care hospital in China.MethodsClinical parameters of patients who received sacubitril/valsartan from January 2019 to March 2023 were retrospectively collected and analyzed. Children over 1 month with a left ventricular ejection fraction (LVEF) <45% were included. Clinical efficacy was evaluated by echocardiographic LVEF, N-terminal pro-brain natriuretic peptide (NT-proBNP), New York Heart Association (NYHA) HF classification, HF re-admission, and death or transplantation. The initial dose was either 0.2 mg/kg bid or 0.4 mg/kg bid, with a target dose of 2.3 mg/kg bid or 3.1 mg/kg bid.ResultsForty-five patients (60% male) with a median age of 7.86 years were enrolled. Among them, 23 had congenital heart disease and 22 had cardiomyopathies. The median maintenance dose was 0.76 mg/kg. The primary endpoint of LVEF up to 45% was reached by 24 patients (53.3%). The median NT-proBNP was significantly decreased from 5,501.5 pg/ml to 2,241.5 pg/ml (P < 0.001), more in congenital heart disease than in cardiomyopathies (P = 0.032). The NYHA HF class was improved or remained stable in 42 cases (93.3%). During a median follow-up of 1.23 years, 13 patients (28.9%) were re-hospitalized due to HF, and 9 patients (20%) died or underwent transplantation. Hypotension was the main adverse event, occurring in 8 patients.ConclusionsSacubitril/valsartan may be effective in children with HF, but its safety and outcomes may differ depending on the etiology and anatomy of HF. Early post-operative congenital heart disease patients had less tolerance, more hypotension but better recovery and outcomes, while mid- and late- post-operative congenital heart disease patients and cardiomyopathy patients had less side effects but poorer clinical outcomes

Transcutaneous auricular vagus nerve stimulation improves social deficits through the inhibition of IL-17a signaling in a mouse model of autism

BackgroundMaternal exposure to inflammation is one of the causes of autism spectrum disorder (ASD). Electrical stimulation of the vagus nerve exerts a neuroprotective effect via its anti-inflammatory action. We thus investigated whether transcutaneous auricular vagus nerve stimulation (taVNS) can enhance social abilities in a mouse model of ASD induced by maternal immune activation (MIA).MethodsASD mouse model were constructed by intraperitoneal injection of polyinosinic:polycytidylic acid (poly (I:C)). TaVNS with different parameters were tested in ASD mouse model and in C57BL/6 mice, then various behavioral tests and biochemical analyses related to autism were conducted. ASD model mice were injected with an interleukin (IL)-17a antibody into the brain, followed by behavioral testing and biochemical analyses.ResultsTaVNS reduced anxiety, improved social function, decreased the number of microglia, and inhibited M1 polarization of microglia. Additionally, taVNS attenuated the expression of the IL-17a protein in the prefrontal cortex and blood of ASD model mice. To examine the possible involvement of IL-17a in taVNS-induced neuroprotection, we injected an IL-17a antibody into the prefrontal cortex of ASD model mice and found that neutralizing IL-17a decreased the number of microglia and inhibited M1 polarization. Furthermore, neutralizing IL-17a improved social function in autism model mice.ConclusionOur study revealed that reduced neuroinflammation is an important mechanism of taVNS-mediated social improvement and neuroprotection against autism. This effect of taVNS could be attributed to the inhibition of the IL-17a pathway

A broadly reactive antibody targeting the N-terminal domain of SARS-CoV-2 spike confers Fc-mediated protection

Most neutralizing anti-SARS-CoV-2 monoclonal antibodies (mAbs) target the receptor binding domain (RBD) of the spike (S) protein. Here, we characterize a panel of mAbs targeting the N-terminal domain (NTD) or other non-RBD epitopes of S. A subset of NTD mAbs inhibits SARS-CoV-2 entry at a post-attachment step and avidly binds the surface of infected cells. One neutralizing NTD mAb, SARS2-57, protects K18-hACE2 mice against SARS-CoV-2 infection in an Fc-dependent manner. Structural analysis demonstrates that SARS2-57 engages an antigenic supersite that is remodeled by deletions common to emerging variants. In neutralization escape studies with SARS2-57, this NTD site accumulates mutations, including a similar deletion, but the addition of an anti-RBD mAb prevents such escape. Thus, our study highlights a common strategy of immune evasion by SARS-CoV-2 variants and how targeting spatially distinct epitopes, including those in the NTD, may limit such escape