Deep placement of nitrogen fertilizer increases rice yield and nitrogen use efficiency with fewer greenhouse gas emissions in a mechanical direct-seeded cropping system

Deep placement of nitrogen fertilizer is a key strategy for improving nitrogen use efficiency. A two-year field experiment was conducted during the early rice growing seasons(March–July) of 2016 and 2017.The experimental treatments comprised two rice cultivars: Wufengyou 615(WFY 615) and Yuxiangyouzhan(YXYZ), and three N treatments: mechanical deep placement of all fertilizers as basal dose at 10 cm soil depth(one-time deep-placement fertilization, namely OTDP fertilization);manual surface broadcast(the common farmer practice) of 40% N fertilizer at one day before sowing(basal fertilizer)followed by broadcast application of 30% each at tillering and panicle initiation stages;and no fertilizer application at any growth stage as a control. One-time deep-placement fertilization increased grain yield of both rice cultivars by 11.8%–19.6%, total nitrogen accumulation by 10.3%–13.1%, nitrogen grain production efficiency by 29.7%–31.5%, nitrogen harvest index by 27.8%–30.0%, nitrogen agronomic efficiency by 71.3%–77.2%, and nitrogen recovery efficiency by 42.4%–56.7% for both rice cultivars, compared with the multiple-broadcast treatment. One-time deep-placement fertilization reduced CH4-induced global warming potential(GWP) by 20.7%–25.3%, N2O-induced GWP by 7.2%–12.3%, and total GWP by 14.7%–22.9% for both rice cultivars relative to the multiple-broadcast treatment. The activities of glutamine synthetase and nitrate reductase were increased at both panicle-initiation and heading stages in both rice cultivars following one-time deep-placement fertilization treatment. Larger leaf area index at heading stage and more favorable root morphological traits expressed as larger total root length, mean root diameter, and total root volume per hill were also observed. One-time deep-placement fertilization could be an effective strategy for increasing grain yield and nitrogen use efficiency and lowering greenhouse-gas emissions under mechanical direct-seeded cropping systems.

Soft Electronics for Health Monitoring Assisted by Machine Learning

Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft electronics.The soft electronics have huge potential in the physical sign monitoring and health care.One of the important advantages of soft electronics is forming good interface with skin,which can increase the user scale and improve the signal quality.Therefore,it is easy to build the specific dataset,which is important to improve the performance of machine learning algorithm.At the same time,with the assistance of machine learning algorithm,the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis.The soft electronics and machining learning algorithms complement each other very well.It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future.Therefore,in this review,we will give a careful introduction about the new soft material,physiological signal detected by soft devices,and the soft devices assisted by machine learning algorithm.Some soft materials will be discussed such as two-dimensional material,carbon nanotube,nanowire,nanomesh,and hydrogel.Then,soft sensors will be discussed according to the physiological signal types(pulse,respiration,human motion,intraocular pressure,phonation,etc.).After that,the soft electronics assisted by various algorithms will be reviewed,including some classical algorithms and powerful neural network algorithms.Especially,the soft device assisted by neural network will be introduced carefully.Finally,the outlook,challenge,and conclusion of soft system powered by machine learning algorithm will be discussed.

SNORA23 inhibits HCC tumorigenesis by impairing the 2'-O-ribose methylation level of 28S rRNA

Objective:The dysregulation of ribosome biogenesis is associated with the progression of numerous tumors,including hepatocellular carcinoma(HCC).Small nucleolar RNAs(sno RNAs)regulate ribosome biogenesis by guiding the modification of ribosomal RNAs(r RNAs).However,the underlying mechanism of this process in HCC remains elusive.Methods:RNA immunoprecipitation and sequencing were used to analyze RNAs targeted by ribosome proteins.The biological functions of SNORA23 were examined in HCC cells and a xenograft mouse model.To elucidate the underlying mechanisms,the 2′-O-ribose methylation level of r RNAs was evaluated by q PCR,and the key proteins in the PI3 K/Akt/m TOR pathway were detected using Western blot.Results:Twelve sno RNAs were found to co-exist in 4 cancer cell lines using RPS6 pull-down assays.SNORA23 was downregulated in HCC and correlated with the poor prognoses of HCC patients.SNORA23 inhibited the proliferation,migration,and invasion of HCC cells both in vitro and in vivo.We also found that SNORA23 regulated ribosome biogenesis by impairing 2′-O-ribose methylation of cytidine4506 of 28 S r RNA.Furthermore,SNORA23,which is regulated by the PI3 K/Akt/m TOR signaling pathway,significantly inhibited the phosphorylation of 4 E binding protein 1.SNORA23 and rapamycin blocked the PI3 K/AKT/m TOR signaling pathway and impaired HCC growth in vivo.Conclusions:SNORA23 exhibited antitumor effects in HCC and together with rapamycin,provided a promising therapeutic strategy for HCC treatment.

Rhodium-Catalyzed C(sp^(3))–H Arylation of 8-Methylquinolines with Arylsilanes

We herein describe a Cp^(*)Rh^(Ⅲ)-catalyzed C(sp^(3))–H mono-arylation of 8-methylquinolines with benign arylsilanes.The use of 1-adamantane carboxylic acid can benefit the efficiency in this transformation,and AgF was both activator and reoxidant.Control experiments indicated inability of C—H cleavage in determining the rate of the reaction.

Micromesh reinforced strain sensor with high stretchability and stability for full-range and periodic human motions monitoring

The development of strain sensors with high stretchability and stability is an inevitable requirement for achieving full-range and long-term use of wearable electronic devices.Herein,a resistive micromesh reinforced strain sensor(MRSS)with high stretchability and stability is prepared,consisting of a laser-scribed graphene(LSG)layer and two styrene-block-poly(ethylene-ran-butylene)-block-poly-styrene micromesh layers embedded in Ecoflex.The micromesh reinforced structure endows the MRSS with combined characteris-tics of a high stretchability(120%),excellent stability(with a repetition error of 0.8%after 11000 cycles),and outstanding sensitivity(gauge factor up to 2692 beyond 100%).Impressively,the MRSS can still be used continauously within the working range without damage,even if stretched to 300%.Furthermore,compared with different structure sensors,the mechanism of the MRSS with high stretchability and stability is elucidated.What's more,a multilayer finite element model,based on the layered structure of the LSG and the morphology of the cracks,is proposed to investigate the strain sensing behavior and failure mechanism of the MRSS.Finally,due to the outstanding performance,the MRSS not only performes well in monitoring full-range human motions,but also achieves intelligent recognitions of various respiratory activities and ges-tures assisted by neural network algorithms(the accuracy up to 94.29%and 100%,respectively).This work provides a new approach for designing high-performance resistive strain sensors and shows great potential in full-range and long-term intelligent health management and human-machine interac-tions applications.

Microcavity assisted graphene pressure sensor for single-vessel local blood pressure monitoring

Dynamic monitoring of blood pressure (BP) is beneficial to obtain comprehensive cardiovascular information of patients throughout the day. However, the clinical BP measurement method relies on wearing a bulky cuff, which limits the long-term monitoring and control of BP. In this work, a microcavity assisted graphene pressure sensor (MAGPS) for single-vessel local BP monitoring is designed to replace the cuff. The microcavity structure increases the working range of the sensor by gas pressure buffering. Therefore, the MAGPS achieves a wide linear response of 0–1050 kPa and sensitivity of 15.4 kPa^(−1). The large working range and the microcavity structure enable the sensor to fully meet the requirements of BP detection at the radial artery. A database of 228 BP data (60-s data fragment detected by MAGPS) and 11,804 pulse waves from 9 healthy subjects and 5 hypertensive subjects is built. Finally, the BP was detected and analyzed automatically by combining MAGPS and a two-stage convolutional neural network algorithm. For the BP detection method at local radial artery, the first stage algorithm first determines whether the subject has hypertension by the pulse wave. Then, the second stage algorithm can diagnose systolic and diastolic BP with the accuracy of 93.5% and 97.8% within a 10 mmHg error, respectively. This work demonstrates a new BP detection method based on single vessel, which greatly promotes the efficiency of BP detection.

Stochastic dynamics of the delayed chemostat with Levy noises

This paper formulates and studies a delayed ehemostat with Levy noises.Existence of the glohally positive solution is proved first by establishing suitable Lyapunov functions,and a further result on exact Lyapunov exponent shows the growth of the total concentration in the ehemostat.Then,we prove existence of the uniquely ergodic stationary distribution for a subsystem of the nutrient,based on this,a unique threshold is identified,which completely determines persistence or not of the microorganism in the ehemostat.Besides,recurrence is studied under special conditions in case that the microorganism persists.Results indicate that all the noises have negative effects on persistence of the microorganism,and the time delay has almost no effects on the sample Lyapunov exponent and the threshold value of the ehemostat.

JUNO sensitivity on proton decay p→νK^(+)searches

The Jiangmen Underground Neutrino Observatory(JUNO)is a large liquid scintillator detector designed to explore many topics in fundamental physics.In this study,the potential of searching for proton decay in the p→νK^(+)mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification.Moreover,the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals.Based on these advantages,the detection efficiency for the proton decay via p→νK^(+)is 36.9%±4.9%with a background level of 0.2±0.05(syst)±0.2(stat)events after 10 years of data collection.The estimated sensitivity based on 200 kton-years of exposure is 9.6×1033 years,which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies.

Feasibility and physics potential of detecting ^(8)B solar neutrinos at JUNO

The Jiangmen Underground Neutrino Observatory(JUNO)features a 20 kt multi-purpose underground liquid scintillator sphere as its main detector.Some of JUNO's features make it an excellent location for^8B solar neutrino measurements,such as its low-energy threshold,high energy resolution compared with water Cherenkov detectors,and much larger target mass compared with previous liquid scintillator detectors.In this paper,we present a comprehensive assessment of JUNO's potential for detecting^8B solar neutrinos via the neutrino-electron elastic scattering process.A reduced 2 MeV threshold for the recoil electron energy is found to be achievable,assuming that the intrinsic radioactive background^(238)U and^(232)Th in the liquid scintillator can be controlled to 10^(-17)g/g.With ten years of data acquisition,approximately 60,000 signal and 30,000 background events are expected.This large sample will enable an examination of the distortion of the recoil electron spectrum that is dominated by the neutrino flavor transformation in the dense solar matter,which will shed new light on the inconsistency between the measured electron spectra and the predictions of the standard three-flavor neutrino oscillation framework.IfDelta m^(2)_(21)=4.8times10^(-5);(7.5times10^(-5))eV^(2),JUNO can provide evidence of neutrino oscillation in the Earth at approximately the 3sigma(2sigma)level by measuring the non-zero signal rate variation with respect to the solar zenith angle.Moreover,JUNO can simultaneously measureDelta m^2_(21)using^8B solar neutrinos to a precision of 20% or better,depending on the central value,and to sub-percent precision using reactor antineutrinos.A comparison of these two measurements from the same detector will help understand the current mild inconsistency between the value of Delta m^2_(21)reported by solar neutrino experiments and the KamLAND experiment.

Sub-percent precision measurement of neutrino oscillation parameters with JUNO

JUNO is a multi-purpose neutrino observatory under construction in the south of China.This publication presents new sensitivity estimates for the measurement of the △m_(31)^(2),△m_(21)^(2),sin^(2)θ_(12),and sin^(2)θ_(13) oscillation parameters using reactor antineutrinos,which is one of the primary physics goals of the experiment.The sensitivities are obtained using the best knowledge available to date on the location and overburden of the experimental site,the nuclear reactors in the surrounding area and beyond,the detector response uncertainties,and the reactor antineutrino spectral shape constraints expected from the TAO satellite detector.It is found that the △m_(21)^(2) and sin^(2)θ_(12) oscillation parameters will be determined to 0.5%precision or better in six years of data collection.In the same period,the △m_(31)^(2) parameter will be determined to about 0.2%precision for each mass ordering hypothesis.The new precision represents approximately an order of magnitude improvement over existing constraints for these three parameters.

COL14A1 promotes self-renewal of human liver cancer stem cells through activation of ERK signaling

Objective:Liver cancer stem cells(CSCs)are the culprits of hepatocellular carcinoma metastasis and recurrence.Only by eliminating tumor stem cells can malignant tumors be fundamentally cured.This study aimed to identify the role and underlying mechanism of aberrant Collagen Type XIV Alpha 1 Chain(COL14A1)overexpression in liver CSCs,and improve understanding of the molecular basis of hepatocellular carcinoma metastasis and recurrence.Methods:First,quantitative real-time polymerase chain reaction was used to confirm aberrant high-expression of COL14A1 in liver CSCs.Next,interference experiments were performed to determine the key role of COL14A1.To explore the mechanism of COL14A1 overexpression in liver CSCs,putative microRNA(miRNAs)targeting COL14A1 were analyzed using the miRTarBase database.Next,quantitative real-time polymerase chain reaction,western blotting,and luciferase reporter assays were performed to verify the interaction between miR-7108-3p and COL14A1.Lastly,key target proteins of the COL14A1-extracellular-regulated signal kinase(ERK)signaling pathway were identified through western blotting analysis.This study was approved by the Ethics Committee of Shanghai Fourth People’s Hospital,Tongji University School of Medicine,China(approval No.2019tjdx17)on February 21,2019.Results:COL14A1 is abnormally highly expressed in liver CSCs,which is necessary for liver CSCs to maintain their self-renewal capability.Mechanistically,COL14A1 is post-transcriptionally regulated by miR-7108-3p in a negative manner.Low expression of miR-7108-3p increased translation of COL14A1,which subsequently activated ERK signaling,ultimately maintaining the self-renewal and stem cell-like properties of liver CSCs.Conclusion:COL14A1,which is negatively regulated by miR-7108-3p,was found to play a crucial role in maintaining the selfrenewal and stem cell-like properties of liver CSCs through activation of ERK signaling.

Trusted Virtual Machine Model Based on High-Performance Cipher Coprocessor

In the cloud computing environment,with the complex network environment,the virtualization platform faces many security problems.At the same time,trusted computing can greatly enhance the architecture security of virtualization platform systems,but there are many problems when trusted computing is deployed directly in the cloud environment.Therefore,this paper proposes a trusted virtual machine model based on high-performance cipher coprocessor to solve the security problems such as the isolation and insufficient performance of virtual TPM(vTPM)on the existing virtual platform.In this model,virtio technology was used to realize the virtualization of TPM,and a management architecture was designed to manage the life cycle of vTPM.The analysis shows that the model can complete the isolation of vTPM,and protect the security of vTPM during the migration process through the migration control server,and can strengthen the security of the virtualization platform.Finally,the simulation results show that the model is more feasible and suitable for cloud platform than hardware TPM.