Bhlhe40 protects cochlear hair cell-like HEI-OC1 cells against H_(2)O_(2) ‑triggered oxidative injury

Background:Cochlear hair cell injury is a common pathological feature of hearing loss.The basic helix-loop-helix family,member e40(Bhlhe40),a gene belonging to the basic helix-loop-helix(bHLH)family,exhibits strong transcriptional repression activity.Methods:Oxidative damage,in House Ear Institute-Organ of Corti 1(HEI-OC1)cells,was caused using hydrogen peroxide(H2O2).The Ad-Bhlhe40 particles were constructed to overexpress Bhlhe40 in HEI-OC1 cells.Various assays including cell counting kit-8(CCK-8),terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling assay(TUNEL),flow cytometry,immunofluorescence,and corresponding commercial kits were employed to investigate the impacts of Bhlhe40 on cell viability,apoptosis,oxidative stress levels,mitochondrial membrane potential and cellular senescence.Additionally,a dual-luciferase reporter assay was performed to confirm the targeting of the histone deacetylases 2(Hdac2)by Bhlhe40.Results:The results revealed that Bhlhe40 was downregulated in H_(2)O_(2)-treated HEI-OC1 cells,but its overexpression improved cell viability and mitigated H_(2)O_(2)-induced oxidative injury in HEI-OC1 cells with increase of superoxide dismutase(SOD),catalase(CAT)and glutathione peroxidase(GPx)activities and decrease of reactive oxygen species(ROS)levels.Besides,overexpression of Bhlhe40 suppressed H_(2)O_(2)-triggered cell senescence,as evidenced by the fact that the upregulation of P53,P21,and P16 in HEI-OC1 cells treated with H2O2 were all alleviated by Bhlhe40 overexpression.And we further verified that overexpression of Bhlhe40 could inhibit the expression of Hdac2,which may be related to the repression of Hdac2 transcription.Conclusion:This study suggests that Bhlhe40 plays a protective role against senescence and oxidative damage in cochlear hair cells exposed to H2O2.

FGW1, a protein containing DUF630 and DUF632 domains, regulates grain size and filling in Oryza sativa L.

Grain filling influences grain size and quality in cereal crops. The molecular mechanisms that regulate grain endosperm development remain elusive. In this study, we characterized a filling-defective and grain width mutant, fgw1, whose mutation increased rice seed width mainly via cell division and expansion in grains. Sucrose contents were higher but starch contents lower in the fgw1 mutant during the grainfilling stage, resulting in inferior endosperm of opaque, white appearance with loosely packed starch granules. Map-based cloning revealed that FGW1 encoded a protein containing DUF630/DUF632domains, localized in the plasma membrane with preferential expression in the panicle. RNA interference in FGW1 resulted in increased grain width and weight, whereas overexpression of FGW1 led to slightly narrower kernels and better grain filling. In a yeast two-hybrid assay, FGW1 interacted directly with the 14–3–3 protein GF14f, bimolecular fluorescence complementation verified that the site of interaction was the membrane, and the mutated FGW1 protein failed to interact with GF14f. The expression of GF14f was down-regulated in fgw1, and the activities of AGPase, StSase, and SuSase in the endosperm of fgw1increased similarly to those of a reported GF14f-RNAi. Transcriptome analysis indicated that FGW1 also regulates cellular processes and carbohydrate metabolism. Thus, FGW1 regulated grain formation via the GF14f pathway.

The auxin transporter OsAUX1 regulates tillering in rice(Oryza sativa)

Tillering is an important agronomic trait of rice(Oryza sativa)that affects the number of effective panicles,thereby affecting yields.The phytohormone auxin plays a key role in tillering.Here we identified the high tillering and semi-dwarf 1(htsd1)mutant with auxin-deficiency root characteristics,such as shortened lateral roots,reduced lateral root density,and enlarged root angles.htsd1 showed reduced sensitivity to auxin,but the external application of indole-3-acetic acid(IAA)inhibited its tillering.We identified the mutated gene in htsd1 as AUXIN1(OsAUX1,LOC_Os01g63770),which encodes an auxin influx transporter.The promoter sequence of OsAUX1 contains many SQUAMOSA PROMOTER BINDING PROTEIN-LIKE(SPL)binding sites,and we demonstrated that SPL7 binds to the OsAUX1 promoter.TEOSINTE BRANCHED1(OsTB1),a key gene that negatively regulates tillering,was significantly downregulated in htsd1.Tillering was enhanced in the OsTB1 knockout mutant,and the external application of IAA inhibited tiller elongation in this mutant.Overexpressing OsTB1 restored the multi-tiller phenotype of htsd1.These results suggest that SPL7 directly binds to the OsAUX1 promoter and regulates tillering in rice by altering OsTB1 expression to modulate auxin signaling.

A Heterogeneous Information Fusion Deep Reinforcement Learning for Intelligent Frequency Selection of HF Communication

The high-frequency(HF) communication is one of essential communication methods for military and emergency application. However, the selection of communication frequency channel is always a difficult problem as the crowded spectrum, the time-varying channels, and the malicious intelligent jamming. The existing frequency hopping, automatic link establishment and some new anti-jamming technologies can not completely solve the above problems. In this article, we adopt deep reinforcement learning to solve this intractable challenge. First, the combination of the spectrum state and the channel gain state is defined as the complex environmental state, and the Markov characteristic of defined state is analyzed and proved. Then, considering that the spectrum state and channel gain state are heterogeneous information, a new deep Q network(DQN) framework is designed, which contains multiple sub-networks to process different kinds of information. Finally, aiming to improve the learning speed and efficiency, the optimization targets of corresponding sub-networks are reasonably designed, and a heterogeneous information fusion deep reinforcement learning(HIF-DRL) algorithm is designed for the specific frequency selection. Simulation results show that the proposed algorithm performs well in channel prediction, jamming avoidance and frequency channel selection.

Recent advances in the development of in vitro liver models for hepatotoxicity testing

Liver injury is a common cause of drug approval withdrawal during drug development,pre-clinical research,and clinical treatment.If not properly treated,patients with severe liver injury can suffer from acute liver failure or even death.Thus,utilization of the convenient in vitro hepatotoxicity assessment model for early detection of drug-induced hepatotoxicity is vital for drug development and safe personalized medication.Biomaterials(e.g.,hydrogels,nanofibers,decellularized liver matrix)and bioengineering technologies(e.g.,microarrays,micropatterns,3D printing,and microfluidics)have been applied for in vitro hepatotoxicity assessment models.This review summarizes the structure and functions of the liver as well as the components of in vitro hepatotoxicity assessment models.In addition,it highlights the latest advances in developing hepatotoxicity models with the ultimate goal of further clinical translation.

Splicing Image and Its Localization:A Survey

With the rapid development of information technology,digital images have become an important medium for information transmission.However,manipulating images is becoming a common task with the powerful image editing tools and software,and people can tamper the images content without leaving any visible traces of splicing in order to gain personal goal.Images are easily spliced and distributed,and the situation will be a great threat to social security.The survey covers splicing image and its localization.The present status of splicing image localization approaches is discussed along with a recommendation for future research.

Workload Allocation Based on User Mobility in Mobile Edge Computing

Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all things.By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile devices.However,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in depth.In this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user.We obtain the next possible location through the user's past location information,and receive the next access server according to the grid matrix.Furthermore,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the task.In the experiments,the results demonstrate a high accuracy of our proposed model.

Securing Display Path for Security-Sensitive Applications on Mobile Devices

While smart devices based on ARM processor bring us a lot of convenience,they also become an attractive target of cyber-attacks.The threat is exaggerated as commodity OSes usually have a large code base and suffer from various software vulnerabilities.Nowadays,adversaries prefer to steal sensitive data by leaking the content of display output by a security-sensitive application.A promising solution is to exploit the hardware visualization extensions provided by modern ARM processors to construct a secure display path between the applications and the display device.In this work,we present a scheme named SecDisplay for trusted display service,it protects sensitive data displayed from being stolen or tampered surreptitiously by a compromised OS.The TCB of SecDisplay mainly consists of a tiny hypervisor and a super light-weight rendering painter,and has only^1400 lines of code.We implemented a prototype of SecDisplay and evaluated its performance overhead.The results show that SecDisplay only incurs an average drop of 3.4%.

Business Plan for Autonomous Delivery Robot

This paper introduces an autonomous robot (AR) cart to execute the last mile delivery task. We use navigation and intelligent avoidance algorithms to plan the path of the automatic robot. When AR encounters a new unrecognizable terrain, it will give control to the customer who can control the AR on its mobile app and navigate to the specified destination. We have initially designed an autonomous delivery robot with the cost of 2774 dollars.

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.

Geometry Flow-Based Deep Riemannian Metric Learning

Deep metric learning(DML)has achieved great results on visual understanding tasks by seamlessly integrating conventional metric learning with deep neural networks.Existing deep metric learning methods focus on designing pair-based distance loss to decrease intra-class distance while increasing interclass distance.However,these methods fail to preserve the geometric structure of data in the embedding space,which leads to the spatial structure shift across mini-batches and may slow down the convergence of embedding learning.To alleviate these issues,by assuming that the input data is embedded in a lower-dimensional sub-manifold,we propose a novel deep Riemannian metric learning(DRML)framework that exploits the non-Euclidean geometric structural information.Considering that the curvature information of data measures how much the Riemannian(nonEuclidean)metric deviates from the Euclidean metric,we leverage geometry flow,which is called a geometric evolution equation,to characterize the relation between the Riemannian metric and its curvature.Our DRML not only regularizes the local neighborhoods connection of the embeddings at the hidden layer but also adapts the embeddings to preserve the geometric structure of the data.On several benchmark datasets,the proposed DRML outperforms all existing methods and these results demonstrate its effectiveness.

Test Research on the Knock of a Common-Rail Diesel Engine Fueled with Diesel-Methanol Dual-Fuel

Experiments were conducted on a diesel-methanol dual-fuel(DMDF)engine modified by a six-cylinder,turbocharged,inter-cooled diesel engine.According to the number of diesel injection,the experiments are divided to two parts:the single injectionmode and double injectionmode.The results show that,at the double injectionmode,themaximumof pressure rise rate is small and the engine runs smoothly,however,knock still occurswhen the cocombustion ratio(CCR)is big enough.Under knock status,the power density of the block vibration concentrating at some special frequencies rises dramatically,and the special frequency of single injection mode(about 4.1 kHz)is lower than that of double injection mode(7–9 kHz).The cylinder pressure oscillations of knock status are very different fromthe non-knock status.Under knock status,cylinder pressure oscillations become more concentrated and fiercer at some special frequencies,and the same as the block vibration.The special frequency of single injection mode(3–6 kHz)is lower than that of double injection mode(above 9 kHz).

Guided Dropout: Improving Deep Networks Without Increased Computation

Deep convolution neural networks are going deeper and deeper.How-ever,the complexity of models is prone to overfitting in training.Dropout,one of the crucial tricks,prevents units from co-adapting too much by randomly drop-ping neurons during training.It effectively improves the performance of deep net-works but ignores the importance of the differences between neurons.To optimize this issue,this paper presents a new dropout method called guided dropout,which selects the neurons to switch off according to the differences between the convo-lution kernel and preserves the informative neurons.It uses an unsupervised clus-tering algorithm to cluster similar neurons in each hidden layer,and dropout uses a certain probability within each cluster.Thereby this would preserve the hidden layer neurons with different roles while maintaining the model’s scarcity and gen-eralization,which effectively improves the role of the hidden layer neurons in learning the features.We evaluated our approach compared with two standard dropout networks on three well-established public object detection datasets.Experimental results on multiple datasets show that the method proposed in this paper has been improved on false positives,precision-recall curve and average precision without increasing the amount of computation.It can be seen that the increased performance of guided dropout is thanks to shallow learning in the net-works.The concept of guided dropout would be beneficial to the other vision tasks.

A CRISPR/Cas9-based visual toolkit enabling multiplex integration at specific genomic loci in Aspergillus niger

Aspergillus niger is a highly versatile fungal strain utilized in industrial production.The expression levels of recombinant genes in A.niger can be enhanced by increasing the copy number.Nevertheless,given the prolonged gene editing cycle of A.niger,a“one-step”strategy facilitating the simultaneous integration of recombinant genes into multiple genomic loci would provide a definitive advantage.In our previous study,a visual multigene editing system(VMS)was designed to knock out five genes,employing a tRNA-sgRNA array that includes the pigment gene albA and the target genes.Building upon this system,hybrid donor DNAs(dDNAs)were introduced to establish a clustered regularly interspaced short palindromic repeats(CRISPR)-based multiplex integration toolkit.Firstly,a CRISPR-Cas9 homology-directed repair(CRISPR-HDR)system was constructed in A.niger by co-transforming the CRISPR-Cas9 plasmid(with a highly efficient sgRNA)and the dDNA,resulting in precise integration of recombinant xylanase gene xynA into the target loci(theβ-glucosidase gene bgl,the amylase gene amyA,and the acid amylase gene ammA).Subsequently,the length of homology arms in the dDNA was optimized to achieve 100%editing efficiency at each of the three gene loci.To achieve efficient multiplex integration in A.niger,the CRISPR plasmid pLM2 carrying a sgRNA-tRNA array was employed for concurrent double-strand breaks at multiple loci(bgl,amyA,ammA,and albA).Hybrid dDNAs were then employed for repair,including dDNA1-3(containing xynA expression cassettes without selection markers)and dDNAalbA(for albA knockout).Among the obtained white colonies(RLM2′),23.5%exhibited concurrent replacement of the bgl,amyA,and ammA genes with xynA(three copies).Notably,the xynA activity obtained by simultaneous insertion into three loci was 48.6%higher compared to that obtained by insertion into only the bgl locus.Furthermore,this multiple integration toolkit successfully enhanced the expression of endogenous pectinase pelA and Candida antarctica lipase CALB.Hence,the combined application of VMS and the CRISPR-HDR system enabled the simultaneous application of multiple selection markers,facilitating the rapid generation in the A.niger cell factories.

Two-Year Heart Failure Study with Allogeneic Myoblast Transplantation

Objectives: Allogeneic myoblast transplantation (AMT), cyclosporine immunosuppression and coronary artery bypass grafting (CABG) were used to treat end-stage heart failure (HF) subjects without hope of obtaining a heart transplant. Background: Severe myocardial infarction conveys serious complications such as ventricular aneurysm, wall thinning and rupture with fatal consequences. Methods: After meeting Inclusion/Exclusion criteria and signing Patient Informed Consents, 10 HF subjects having mean thinnest wall thickness of 2.21 ± 0.55 mm and ventricular aneurysms were admitted under intensive care. Each subject took daily cyclosporine for three weeks. On the third day of cyclosporine administration, approximately 1 billion myoblasts were implanted through 20 injections into the infarcted myocardium following CABG. Results: Safety No subject suffered death, viral infection, malignant arrhythmia, reduction in cardiac output, immune rejection, or aneurysm growth. No significant difference was found before versus after treatment in the mean levels of blood routine, liver and kidney enzymes, electrolytes and fibrinogen. Efficacy Emission computed tomography (ECT) and magnetic resonance (MR) demonstrated significant increases in viability and perfusion. Mean left ventricular ejection fraction (LVEF) significantly increased (P < 0.05) by 20.1% and 19.3% at 6 months and at 2 years postoperatively. New York Heart Association (NYHA) class improved by 2 grades, including 6-minute walk test (6 MWT) distance increase, and reductions in the number of episodes of angina pectoris, chest tightness, shortness of breath after exercise, and nighttime sit-up breathing. Conclusions: For the first time, AMT in adjunct use with CABG and cyclosporine demonstrated that cell survived and engrafted in patients with ischemic cardiomyopathy;in this small study the cell transplant was safe. The improvement in heart function and quality of life could be secondary to combined effect of bypass and cell transplant. A larger randomized clinical trial is required to confirm the efficacy.

In situ FTIR and ex situ XPS/HS-LEIS study of supported Cu/Al_(2)O_(3)and Cu/ZnO catalysts for CO_(2)hydrogenation

Cu-based catalysts are commonly used in industry for methanol synthesis.In this study,supported catalysts of 5 wt%Cu/Al_(2)O_(3)and 5 wt%Cu/ZnO were prepared,and their surface characteristics during H_(2) reduction and CO_(2)hydrogenation were investigated using in situ Fourier transform infrared spectroscopy(FTIR),ex situ X-ray photoelectron spectroscopy,and high sensitivity low energy ion scattering spectroscopy.During the H2 reduction and CO_(2)hydrogenation processes,it was found that Al_(2)O_(3)can stabilize Cu^(+).In situ FTIR spectra indicated that the 5 wt%Cu/Al_(2)O_(3)can adsorb large amounts of bicarbonate and carbonate species,which then convert into formate during CO_(2)hydrogenation.For the 5 wt%Cu/ZnO,it was found that Cu nanoparticles were gradually covered by a highly defective ZnOx overlayer during H2 reduction,which can effectively dissociate H2.During CO_(2)hydrogenation,the adsorbed bicarbonate or carbonate species can convert into formate and then into a methoxy species.Using these surface sensitive methods,a more in-depth understanding of the synergistic effect among the Cu,Al_(2)O_(3),and ZnO components of Cu-based catalysts was achieved.

Influence of higher-order ionospheric delay correction on GPS precise orbit determination and precise positioning

At present, Global Navigation Satellite Systems（GNSS） users usually eliminate the influence of ionospheric delay of the first order items by dual-frequency ionosphere-free combination. But there is still residual ionospheric delay error of higher order term. The influence of the higher-order ionospheric corrections on both GPS precision orbit determination and static Precise Point Positioning（PPP） are studied in this paper. The influence of higher-order corrections on GPS precision orbit determination, GPS observations and static PPP are analyzed by neglecting or considering the higher-order ionospheric corrections by using a globally distributed network which is composed of International GNSS Service（IGS） tracking stations. Numerical experimental results show that, the root mean square（RMS） in three dimensions of satellite orbit is 36.6 mme35.5 mm. The maximal second-order ionospheric correction is 9 cm, and the maximal third-order ionospheric correction is 1 cm. Higher-order corrections are influenced by latitude and station distribution. PPP is within 3 mm in the directions of east and up. Furthermore, the impact is mainly visible in the direction of north, showing a southward migration trend, especially at the lower latitudes where the influence value is likely to be bigger than 3 mm.

3D morphology and formation mechanism of fractures developed by true triaxial stress

As main part of underground rock mass,the three-dimensional(3D)morphology of natural fractures plays an important role in rock mass stability.Based on previous studies on 3D morphology,this study probes into the law and mechanism regarding the influence of the confining pressure constraints on 3D morphological features of natural fractures.First,fracture surfaces were obtained by true triaxial compression test and 3D laser scanning.Then 3D morphological parameters of fractures were calculated by using Grasselli’s model.The results show that the failure mode of granites developed by true triaxial stress can be categorized into tension failure and shear failure.Based on the spatial position of fractures,they can be divided into tension fracture surface,S-1 shear fracture surface,and S-2 shear fracture surface.Micro-failure of the tension fracture surface is dominated by mainly intergranular fracture;the maximum height of asperities on the fracture surface and the 3D roughness of fracture surfaces are influenced by σ_(3) only and they are greater than those of shear fracture surfaces,a lower overall uniformity than tension fracture surface.S-1 shear fracture surface and S-2 shear fracture surface are dominated by intragranular and intergranular coupling fracture.The maximum height of asperities on the fracture surface and 3D roughness of fracture surface are affected by σ_(1),σ_(2),and σ_(3).With the increase of σ_(2) or σ_(3),the cutting off of asperities on the fracture surface becomes more common,the maximum height of asperities and 3D roughness of fracture surface further decrease,and the overall uniformity gets further improved.The experimental results are favorable for selecting technical parameters of enhanced geothermal development and the safety of underground mine engineering.

An OpenFlow-Based Load Balancing Strategy in SDN

In today’s datacenter network,the quantity growth and complexity increment of traffic is unprecedented,which brings not only the booming of network development,but also the problem of network performance degradation,such as more chance of network congestion and serious load imbalance.Due to the dynamically changing traffic patterns,the state-of the-art approaches that do this all require forklift changes to data center networking gear.The root of problem is lack of distinct strategies for elephant and mice flows.Under this condition,it is essential to enforce accurate elephant flow detection and come up with a novel load balancing solution to alleviate the network congestion and achieve high bandwidth utilization.This paper proposed an OpenFlow-based load balancing strategy for datacenter networks that accurately detect elephant flows and enforce distinct routing schemes with different flow types so as to achieve high usage of network capacity.The prototype implemented in Mininet testbed with POX controller and verify the feasibility of our load-balancing strategy when dealing with flow confliction and network degradation.The results show the proposed strategy can adequately generate flow rules and significantly enhance the performance of the bandwidth usage compared against other solutions from the literature in terms of load balancing.

Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection

The extreme imbalanced data problem is the core issue in anomaly detection.The amount of abnormal data is so small that we cannot get adequate information to analyze it.The mainstream methods focus on taking fully advantages of the normal data,of which the discrimination method is that the data not belonging to normal data distribution is the anomaly.From the view of data science,we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method.In this kind of technologies,Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones,which generate synthetic examples randomly in selected line segments.In our work,we break the limitation of line segment and propose an Imbalanced Triangle Synthetic Data method.In theory,our method covers a wider range.In experiment with real world data,our method performs better than the SMOTE and its meliorations.