Diaqua­bis(4-methyl­benzoato-κO)zinc(II)

The Zn atom in the title mononuclear complex, [Zn(C8H7O2)2(H2O)2], lies on a special position of site symmetry 2. The carboxyl­ate group binds in a monodentate manner so that the geometry is best described as tetra­hedral. Adjacent mol­ecules are linked by O—H⋯O hydrogen bonds into a three-dimensional network

N′-(5-Bromo-2-hy­droxy­benzyl­idene)-4-nitro­benzohydrazide methanol monosolvate

In the title compound, C14H10BrN3O4·CH4O, the benzohydrazide mol­ecule is nearly planar [maximum deviation = 0.110 (2) Å]. The mean planes of the two benzene rings make a dihedral angle of 8.4 (3)°. In the benzohydrazide mol­ecule, there is an intra­molecular O—H⋯N hydrogen bond and the NH group is hydrogen bonded to the methanol solvent mol­ecule. In the crystal, inter­molecular O—H⋯O hydrogen bonds involving the methanol solvent mol­ecule link the benzohydrazide mol­ecules to form chains which propagate along the a axis

A study of instability in a miniature flying-wing aircraft in high-speed taxi

AbstractThis study investigates an instability that was observed during high-speed taxi tests of an experimental flying-wing aircraft. In order to resolve the reason of instability and probable solution of it, the instability was reproduced in simulations. An analysis revealed the unique stability characteristics of this aircraft. This aircraft has a rigid connection between the nose wheel steering mechanism and an electric servo, which is different from aircraft with a conventional tricycle landing gear system. The analysis based on simulation results suggests that there are two reasons for the instability. The first reason is a reversal of the lateral velocity of the nose wheel. The second reason is that the moment about the center of gravity created by the lateral friction force from the nose wheel is larger than that from the lateral friction force from the main wheels. These problems were corrected by changing the ground pitch angle. Simulations show that reducing the ground pitch angle can eliminate the instability in high-speed taxi

Poly[diaqua­(μ3-1H-benzimidazole-5,6-dicarboxyl­ato-κ4 N 3:O 5,O 6:O 6′)magnesium(II)]

In the title complex, [Mg(C9H4N2O4)(H2O)2]n, the MgII atom is six-coordinated by one N and three O atoms from three different 1H-benzimidazole-5,6-dicarboxyl­ate ligands and two O atoms from two water mol­ecules, forming a slightly distorted octa­hedral geometry. The ligand links the MgII centres into a three-dimensional network. Extensive N—H⋯O and O—H⋯O hydrogen bonds exist between the ligands and water mol­ecules, stabilizing the crystal structure

Heat Shock Protein 70 Protects the Heart from Ischemia/Reperfusion Injury through Inhibition of p38 MAPK Signaling.

BackgroundHeat shock protein 70 (Hsp70) has been shown to exert cardioprotection. Intracellular calcium ([Ca2+]i) overload induced by p38 mitogen-activated protein kinase (p38 MAPK) activation contributes to cardiac ischemia/reperfusion (I/R) injury. However, whether Hsp70 interacts with p38 MAPK signaling is unclear. Therefore, this study investigated the regulation of p38 MAPK by Hsp70 in I/R-induced cardiac injury.MethodsNeonatal rat cardiomyocytes were subjected to oxygen-glucose deprivation for 6 h followed by 2 h reoxygenation (OGD/R), and rats underwent left anterior artery ligation for 30 min followed by 30 min of reperfusion. The p38 MAPK inhibitor (SB203580), Hsp70 inhibitor (Quercetin), and Hsp70 short hairpin RNA (shRNA) were used prior to OGD/R or I/R. Cell viability, lactate dehydrogenase (LDH) release, serum cardiac troponin I (cTnI), [Ca2+]i levels, cell apoptosis, myocardial infarct size, mRNA level of IL-1β and IL-6, and protein expression of Hsp70, phosphorylated p38 MAPK (p-p38 MAPK), sarcoplasmic/endoplasmic reticulum Ca2+-ATPase2 (SERCA2), phosphorylated signal transducer and activator of transcription3 (p-STAT3), and cleaved caspase3 were assessed.ResultsPretreatment with a p38 MAPK inhibitor, SB203580, significantly attenuated OGD/R-induced cell injury or I/R-induced myocardial injury, as evidenced by improved cell viability and lower LDH release, resulted in lower serum cTnI and myocardial infarct size, alleviation of [Ca2+]i overload and cell apoptosis, inhibition of IL-1β and IL-6, and modulation of protein expressions of p-p38 MAPK, SERCA2, p-STAT3, and cleaved-caspase3. Knockdown of Hsp70 by shRNA exacerbated OGD/R-induced cell injury, which was effectively abolished by SB203580. Moreover, inhibition of Hsp70 by quercetin enhanced I/R-induced myocardial injury, while SB203580 pretreatment reversed the harmful effects caused by quercetin.ConclusionsInhibition of Hsp70 aggravates [Ca2+]i overload, inflammation, and apoptosis through regulating p38 MAPK signaling during cardiac I/R injury, which may help provide novel insight into cardioprotective strategies

Chrion: Optimizing Recurrent Neural Network Inference by Collaboratively Utilizing CPUs and GPUs

Deploying deep learning models in cloud clusters provides efficient and prompt inference services to accommodate the widespread application of deep learning. These clusters are usually equipped with host CPUs and accelerators with distinct responsibilities to handle serving requests, i.e. generalpurpose CPUs for input preprocessing and domain-specific GPUs for forward computation. Recurrent neural networks play an essential role in handling temporal inputs and display distinctive computation characteristics because of their high inter-operator parallelism. Hence, we propose Chrion to optimize recurrent neural network inference by collaboratively utilizing CPUs and GPUs. We formulate the model deployment in the CPU-GPU cluster as an NP-hard scheduling problem of directed acyclic graphs on heterogeneous devices. Given an input model in the ONNX format and user-defined SLO requirement, Chrion firstly preprocesses the model by model parsing and profiling, and then partitions the graph to select execution devices for each operator. When an online request arrives, Chrion performs forward computation according to the graph partition by executing the operators on the CPU and GPU in parallel. Our experimental results show that the execution time can be reduced by 19.4% at most in the latency-optimal pattern and GPU memory footprint by 67.5% in the memory-optimal pattern compared with the execution on the GPU

FedALA: Adaptive Local Aggregation for Personalized Federated Learning

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (ALA) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy.Comment: Accepted by AAAI 202

FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy

Recently, personalized federated learning (pFL) has attracted increasing attention in privacy protection, collaborative learning, and tackling statistical heterogeneity among clients, e.g., hospitals, mobile smartphones, etc. Most existing pFL methods focus on exploiting the global information and personalized information in the client-level model parameters while neglecting that data is the source of these two kinds of information. To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively. FedCP is more fine-grained to consider personalization in a sample-specific manner than existing pFL methods. Extensive experiments in computer vision and natural language processing domains show that FedCP outperforms eleven state-of-the-art methods by up to 6.69%. Furthermore, FedCP maintains its superiority when some clients accidentally drop out, which frequently happens in mobile settings. Our code is public at https://github.com/TsingZ0/FedCP.Comment: Accepted by KDD 202

Poly[[diaqua­(μ4-1H-benzimidazole-5,6-dicarboxyl­ato)strontium] monohydrate]

Each of the carboxyl­ate –CO2 fragments of the dianion ligand in the title compound, {[Sr(C9H4N2O4)(H2O)2]·H2O}n, chelates to a SrII atom and at the same time, one of the two O atoms coordinates to a third SrII atom. The μ4-bridging mode of the dianion generates a square-grid layer motif; adjacent layers are connected by O—H⋯O, O—H⋯N and N—H⋯O hydrogen bonds, forming a three-dimensional network. The eight-coordinate Sr atom exists in a distorted square-anti­prismatic geometry. The crystal studied was a non-merohedral twin with a minor twin component of 24%