Milk fat globule epithelial growth factorⅧ(MFG-E8)sustains survival of cancer cells by prompting tumor angiogenesis and suppressing host immunities

Milk fat globule epithelial growth factor VIII(MFG-E8) is a novel adhesion protein mainly produced by macrophages and dendritic cells; it is expressed in most of the human tissues and functions to prompt cancer progression and survival. MFG-E8 contains a signal sequence for secretion, two epidermal growth factor(EGF)-like domains at the NH2 terminus and two discoidin domains with blood-clotting factor V/factor Ⅷ(C1 and C2) at the COOH terminus. The second EGF domain contains an arginine-glycine-aspartic(RGD) integrin-binding motif that engages α_vβ_5 integrins to facilitate cell adhesion and induce integrinmediated signal transduction. Integrin α_vβ_3 associates with VEGF receptor 2, engagement of integrins can promote angiogenesis, which plays key roles in growth, proliferation, and survival of cancer cells. VEGF stimulates the expression of α_vβ_3 and α_vβ_5 integrins on angiogenic vasculature, thereby potentiating effects of VEGF receptor engagement. Mice expressing a mutant form of α_vβ_3 integrin are unable to undergo tyrosine phosphorylation, confirming the important role that this integrin plays in pathological angiogenesis and providing important mechanistic insights. The C-terminus discoidin-like domains promote binding to membrane phospholipids, functioning close to VEGF like angiogenesis. MFG-E8 is an opsonin for apoptotic cells, and it acts as a bridging protein between apoptotic cells and phagocytes. It also influences cell immunities by altering CD4^+ and/or CD8^+ cells. Antibody or small peptide works with MFG-E8 at different functional sites or interacts with EGF-like domains and/or discoidin-like domains may play an important role in anti-angiogenesis or immune restoration. Altering the structures and/or functions of MFG-E8 and/or its domains is promising for development of novel anti-cancer strategies.

Treatment of Skin Reaction Induced by Nivolumab Combined with Radiotherapy in Non-small Cell Lung Cancer:A Case Report

Skin reaction or dermatological toxicities induced by immunotherapy is common.It usually manifests skin rash or erythema and can be cured by skin lotion or steroid.Nivolumab,a human IgG4 programmed cell death protein 1(PD-1)inhibitor,blocks T cells activation preventing signal and allows the immune system to clear cancer cells.Nivolumab was approved in the second-line therapy in squamous cell lung cancer by FDA,with less than 10%unusual skin reaction,like sensory neuropathy,peeling skin,erythema multiforme,vitiligo,and psoriasis.Radiotherapy could aggravate this skin reaction through inflammatory response and promotion of immunity.The combined treatment of anti-PD-1 and radiotherapy represented a new promising therapeutic approach in many studies,but the risk of side effects may be high.We reported a patient with advanced squamous cell lung cancer who suffered from serious skin immune-related adverse events when he was treated with nivolumab and radiotherapy.The immune overreaction of the treatment of anti-PD-1 treatment and radiotherapy might cause these serious skin adverse events.Our report warranted careful workup to reduce the risk of side effects by combinative therapy with anti-PD-1 and radiotherapy.

Growth and mineral nutrient analysis of teak (Tectona grandis) grown on acidic soils in south China

Teak （Tectona grandis L.f.） is widely planted in the world due to its high market demand, economic, ecological and social value. Its plantations have mostly been established and expanded into sites that are acidic to severely acidic in southern China. But, there are no available and specific evidence-based nutrient management techniques. To better recognize and understand the relationship between teak tree growth and nutrient content in the foliage and soil and establish nutrient norms are critical to optimally manage these young plantations. We studied the foliar nutrient and soil chemistry in 19 representative teak plantations aged 5-8 years. Regression analysis indicated that the mean annual increment of teak volume was linearly and positively correlated with foliar N, Ca, Fe and B concentrations, with soil base saturation percentage, available P and Zn concentrations, and negatively correlated with soil Al concentration. Only if the Ca and Mg contents in soil were enhanced, could the increase in soil base saturation percentage benefit teak growth. A revised classification of low-and high-yielding stands was established by using a sorting method of principal components over 6 foliar macro and 8 micro elements in a Diagnosis and Recommendation Integrated System （DRIS）. Specific DRIS norms for teak plantations in acid soils were derived. The nutrient balance of N, P, K Ca, Mg, Zn, B with Fe or A1, Ca with Mg, and Fe with AI provided a key to promote the growth of teak in acid soils. Meanwhile, soil Zn was also found as a primary trace element that affected teak growth in this study.

A Core Leader Based Label Propagation Algorithm for Community Detection

A large number of community discovery algorithms have been proposed in the last decade. Recently, the sharp increase of network scale has become a great challenge for traditional community discovery algorithms. Label propagation algorithm is a semi-supervised machine learning method, which has linear time complexity when coping with large scale networks. However, the output result has less stability and the quality of the output communities still remains to be improved. Therefore, we propose a novel coreleader based label propagation algorithm for community detection called CLBLPA. Firstly, we find core leaders of potential community by using a greedy method. Then we utilize the label influence potential to guide the process of label propagation. Thus we can accelerate the convergence of algorithm and improve the stability of the output. Experimental results on synthetic datasets and real networks show that CLBLPA can significantly improve the quality of the output communities.

Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network

Storm surges pose significant danger and havoc to the coastal residents’safety,property,and lives,particularly at offshore locations with shallow water levels.Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans.In addition to experienced predictions and numerical models,artificial intelligence(AI)techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations.Convolutional neural network(CNN)and long short-term memory(LSTM)are two of the most important models among AI techniques.However,they have been scarcely utilised for surge level(SL)forecasting,and combinations of the two models are even rarer.This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information.The architectures of the CNN,LSTM,and two sequential techniques of combining the models(LSTM–CNN and CNN–LSTM)were constructed via a trial-and-error approach and knowledge obtained from previous studies.As a case study,11 a of hourly observed SL and wind data of the Xiuying Station,Hainan Province,China,were organised as inputs for training to verify the feasibility and superiority of the proposed models.The results show that CNN and LSTM had evident advantages over support vector regression(SVR)and multilayer perceptron(MLP),and the combined models outperformed the individual models(CNN and LSTM),mostly by 4%–6%.However,on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges,the accuracy was found to improve by over 10%at all forecasting steps.

多酸基共价有机骨架材料的合成与氧化脱硫性能研究在实验教学中的设计与探究

化石燃料燃烧带来的硫排放将导致酸雨的产生,因而实现燃油深度脱硫显得至关重要。多酸(POM)基共价有机骨架材料具有氧化还原性、可调的亲疏水性、可循环再生等特点,被认为是燃油氧化脱硫的一种优异催化材料。本实验在科研成果的基础上创新设计了一个综合实验。通过离子交换法将强氧化型多酸分子(NH_(4))_(5)H_(6)PMo_(4)V_(8)O_(40)(简写为PMo_(4)V_(8))引入共价有机骨架(COF),合成了一种多酸基共价有机骨架材料(POM@EB-COF,EB代表溴化乙啶)。利用X射线衍射、红外光谱、元素分析等表征手段对材料结构进行解析。以含有二苯并噻吩的十氢萘作为模型燃油,体系中通入氧气,在PMo_(4)V_(8)@EB-COF固体材料表面进行二苯并噻吩的氧化反应,考察其氧化脱硫性能。研究结果表明,以PMo_(4)V_(8)@EB-COF作为氧化脱硫催化剂,反应温度为100℃,反应时间2.5 h,二苯并噻吩(DBT)的转化率达96.6%,DBT氧化产物砜的选择性达100%,PMo_(4)V_(8)@EB-COF展现出优异的催化性能。本实验涉及多个化学二级学科的知识要点,普及了合成化学与绿色化学的理念,也适用于模块化实验教学以满足不同教学要求,提高了学生的创新意识和综合能力。

Early rehabilitation and neuroprotective drug therapy outcomes in elderly patients with acute stroke

Sixty elderly patients, viro cluffered from acute stroke and were admitted within a 1-year period to the Department of Geriatrics in the First Affiliated Hospital of School of Medicine, Zhejiang University, China, underwent early rehabilitation in combination with neuroprotective drug therapy. Limb movement, cognitive functions and daily life self-care ability in elderly patients upon admission and discharge were assessed using the Hunt-Hess scale, functional independence measures and mini-mental state examination. The mean duration of hospital stay among the 60 patients was 35 days. Upon discharge, 42 （75%） of the patients exhibited cognitive impairment to varying degrees, and 25 （45%） of the 56 stroke patients who underwent rehabilitation evaluation attained independence in daily living activities, 11 （20%） required intermittent supervision, and 20 （36%） required 24-hour constant supervision during performance of these activities. Results demonstrated that early rehabilitation treatment in combination with neuroprotective therapy for acute stroke was effective.

Post-stroke seizures in consecutive elderly stroke patients

This prospective study sought to investigate the clinical, radiological and electroencephalographic （EEG） characteristics of seizures in elderly stroke patients, and their outcomes. Over a 2-year study period, 158 consecutive elderly patients with stroke were examined and followed up. Of these patients, 32 （20%） developed seizures, primarily related to stroke, within a follow up period between 5 months and 2 years. Of these 32 cases, 20 experienced infarctions, and 12 experienced hemorrhages. Involvement of cortical regions was detected in most of the patients exhibiting seizures. In these patients, 44% of the lesions involved cortical areas exclusively or in addition to subcortical areas observed on computed tomography （CT） images. Twenty-five patients （78%） developed early seizures （within 2 weeks after stroke）, and half exhibited immediate post-stroke seizures. None of the patients exhibiting early onset seizures developed recurrent seizures or epilepsy, while 57% of late onset seizures （four cases） developed epilepsy. No specific EEG pattems were apparent in those who later developed epilepsy. Overall, early onset seizures after stroke were found to be relatively common, and did not affect outcome. Late onset seizures were less common, but were associated with chronic epilepsy.

Nutritional status in chronically hospitalized stroke patients complicated with pulmonary infection

Complicated pulmonary infection following stroke has traditionally been considered an aspirated infection by many physicians, and little attention has been paid to concomitant protein-energy malnutrition. In the present study, we hypothesized that protein-energy malnutrition may be present in hospitalized chronic stroke patients complicated with pulmonary infection. The results revealed that body protein and fat stores were significantly depleted in stroke patients with pulmonary infection. Protein-energy malnutrition was present in 12 of 27 patients with pulmonary infection. In comparison, only eight of 42 stroke patients without pulmonary infection exhibited protein-energy malnutrition. A significantly higher prevalence of protein-energy malnutrition was found in the pulmonary infection group, suggesting that protein-energy malnutrition is more likely to be present in hospitalized chronic stroke patients with pulmonary infection.

Effects of V Addition on Microstructural Evolution and Mechanical Properties of AlCrFe_(2)Ni_(2) High‑Entropy Alloys

The effects of vanadium addition on the microstructural evolution and mechanical properties of AlCrFe_(2)Ni_(2) high-entropy alloy(HEA)were investigated.The results showed that the AlCrFe_(2)Ni_(2)V_(0.2) HEA was composed of FCC phase,disordered BCC phase,and ordered BCC(B2)phase.With the increase in vanadium content,the formation of FCC phase was inhibited,and a transition from FCC phase to BCC phase occurred.The FCC phase disappeared completely when the value of x exceeds 0.4 in AlCrFe_(2)Ni_(2)V_(x) HEAs.Besides,the amplitude-modulated microstructure morphology transformed from a B2 phase matrix with dispersed BCC nano-phase into an alternating interconnected B2 and BCC phases.Vanadium element has the function of stabilizing BCC phase and B2 phase in AlCrFe_(2)Ni_(2)V_(x) alloys.The hardness of AlCrFe_(2)Ni_(2)V_(x) alloys increased from HV 332.4 to HV 590.7,while the yield strength increased from 765 to 1744.6 MPa with increasing vanadium content,which was mainly due to the decreasing content of FCC phase and the solid solution strengthening of vanadium element.At the same time,the compression ratio of the alloys decreased with the disappearance of the FCC phase.Among the alloys,the AlCrFe_(2)Ni_(2)V_(0.2) alloy possessed the most excellent comprehensive mechanical properties with yield strength,fracture strength,and compressive ratio 1231.1,2861.9 MPa,and 44.5%,respectively.

Impact of phase-locked loop on stability of active damped LCL-filter-based grid-connected inverters with capacitor voltage feedback

Active damped LCL-filter-based inverters have been widely used for grid-connected distributed generation(DG) systems. In weak grids, however, the phase-locked loop(PLL) dynamics may detrimentally affect the stability of grid-connected inverters due to interaction between the PLL and the controller. In order to solve the problem, the impact of PLL dynamics on small-signal stability is investigated for the active damped LCL-filtered grid-connected inverters with capacitor voltage feedback. The system closed-loop transfer function is established based on the Norton equivalent model by taking the PLL dynamics into account. Using an established model, the system stability boundary is identified from the viewpoint of PLL bandwidth and current regulator gain. The accuracy of the ranges of stability for the PLL bandwidth and current regulator gain is verified by both simulation and experimental results.

Growth mechanism of primary Ti_(5)Si_(3) phases in special brasses and their effect on wear resistance

In the present work, in-situ Ti_(5)Si_(3) reinforced special brasses were prepared by melt reaction method. The synthesized Ti_(5)Si_(3) phase shows various morphologies in brasses with different Ti_(5)Si_(3) content, and the3 D morphological evolution of primary Ti_(5)Si_(3)and its growth mechanism were investigated. The Ti_(5)Si_(3) crystal, which bears D8_(8) hexagonal crystal structure, grows along <0001> direction and is revealed by{1010} faces during growth. With the increase of Ti_(5)Si_(3) content in the brasses, the morphology of primary Ti_(5)Si_(3)significantly changes from fibers to hexagonal prisms to short-rods with hollow. In addition,the influence of Ti_(5)Si_(3) volume fraction and morphology on the wear behavior of special brass was also revealed. It was substantiated that the wear resistance increases with the increasing volume fraction of Ti_(5)Si_(3), and the corresponding wear mechanism changes from delamination to slight adhesive wear and abrasive wear. However, the friction coefficient shows an abnormal increase when most of the Ti_(5)Si_(3) containing hollows appears in the brass. That is mainly due to the fact that the Ti_(5)Si_(3) is easier to break and fall off resulted by the hollow as a crack source, which makes it unable to resist the plastic deformation of the contact surface during the sliding.

Free-standing ZnO nanorod arrays modified with single-walled carbon nanotubes for betavoltaics and photovoltaics

A full-duplex radiant energy converter based on both betavoltaic and photovoltaic effects in an easyto-implement way is an attractive alternative for the autonomous wireless sensor microsystem.Here,we report a novel beta/photovoltaic cell based on free-standing Zn O nanorod arrays(ZNRAs)modified with metallic single-walled carbon nanotubes(m-SWCNTs),using radioisotope63 Ni as beta-emitting source.The ZNRAs were grown on Al-doped Zn O(AZO)conductive glass using hydrothermal method.The optimum length and diameter of Zn O nanorods were determined by Monte Carlo simulation for beta energy deposition in ZNRAs.The m-SWCNTs were anchored into the ZNRAs to form a three-dimensional(3-D)Schottky junction structure for effectively separating the beta/photo-excited electron-hole pairs.Experimentally,the betavoltaic and photovoltaic effects were confirmed through the I-V measurements of beta/photovoltaic cells under beta/UV/Vis irradiations,respectively.It is suggested that the m-SWCNTs play key role for the enhancement of beta/photovoltaic performance through the formation of extensive3-D Schottky junction,the conductive network for hole transport,and the surface plasmon resonance exciton absorption for visible light.

Semisolid-rolling and annealing process of woven carbon fibers reinforced Al-matrix composites

Semisolid-rolling method was successfully developed to prepare the Ni-coated woven carbon fibers reinforced Al-matrix composite. Due to the appropriate matrix flowability and rolling pressure, the Al-matrix could infiltrate into the woven fibers sufficiently and attach to the reinforcements closely forming a smooth interface. The rolling speed of 4 rad/min offered a subtle equilibrium between the heat transfer and the material deformation. The covering matrix should be controlled at semisolid state to provide a better infiltration behavior and a protective effect on the carbon fibers. With the addition of fibers, an improvement for more than 25% was obtained in the bending strength of the materials. Furthermore, the woven carbon fibers could strengthen the composite in multiple directions, rather than only along the fiber longitudinal directions. The annealing process promoted the Ni coating to react with and to diffuse into the matrix, resulted in an obvious increase of the bending strength.

A study of the initial adhesive force of cells on silk fibroin-based materials using micropipette aspiration

With the development of biomaterials,more attention is paid to the adhesion characteristics between cells and materials.It is necessary to study the adhesive force with a suitable method.Silk fibroin(SF)is widely investigated in biomedical application due to its novel biocompatibility and mechanical properties.In this article,the micropipette aspiration method and measurement pattern of uniform cells in round shape(UCR)was used to study the initial adhesive force of three types of cells on pure silk fibroin films(SFFs).We also compared the adhesive forces of modified SFFs with that of pure SFFs.The results of adhesive force in the initial adhesive stage were in concordance with the results of MTT assay andmicroscope observation,which were confirmed by the above three cell lines and four kinds of SFFs.The results indicated UCR was an efficient and quantitative measurement pattern in initial adhesion stage.This article also provides a useful method in identifying initial cell-materials interactions.

A fully abstract semantics for value-passing CCS for trees

We propose a fully abstract semantics for valuepassing CCS for trees (VCCTS) with the feature that processes are located at the vertices of a graph whose edges describe possible interaction capabilities. The operational semantics is given both in terms of a reduction semantics and in terms of a labelled transition semantics. We develop a theory of behavioral equivalences by introducing both weak barbed congruence and weak bisimilarity. In particular, we show that, on image-finite processes, weak barbed congruence coincides with weak bisimilarity. To illustrate potential applications and the powerful expressiveness of VCCTS, we formally compare VCCTS with some well-known models, e.g., dynamic pushdown networks, top-down tree automata and value-passing CCS.

High performance solid-state thermoelectric energy conversion via inorganic metal halide perovskites under tailored mechanical deformation

Solid-state thermoelectric energy conversion devices attract broad research interests because of their great promises in waste heat recycling,space power generation,deep water power generation,and temperature control,but the search for essential thermoelectric materials with high performance still remains a great challenge.As an emerging low cost,solution-processed thermoelectric material,inorganic metal halide perovskites CsPb(I_(1–x)Br_(x))_(3) under mechanical deformation is systematically investigated using the first-principle calculations and the Boltzmann transport theory.It is demonstrated that halogen mixing and mechanical deformation are efficient methods to tailor electronic structures and charge transport properties in CsPb(I_(1–x)Br_(x))_(3) synergistically.Halogen mixing leads to band splitting and anisotropic charge transport due to symmetry-breakinginduced intrinsic strains.Such band splitting reconstructs the band edge and can decrease the charge carrier effective mass,leading to excellent charge transport properties.Mechanical deformation can further push the orbital energies apart from each other in a more controllable manner,surpassing the impact from intrinsic strains.Both anisotropic charge transport properties and ZT values are sensitive to the direction and magnitude of strain,showing a wide range of variation from 20%to 400%(with a ZT value of up to 1.85)compared with unstrained cases.The power generation efficiency of the thermoelectric device can reach as high as approximately 12%using mixed halide perovskites under tailored mechanical deformation when the heat-source is at 500 K and the cold side is maintained at 300 K,surpassing the performance of many existing bulk thermoelectric materials.

Adaptive output regulation for cyber-physical systems under time-delay attacks

In this paper,we present an output regulation method for unknown cyber-physical systems(CPSs)under time-delay attacks in both the sensor-to-controller(S-C)channel and the controller-to-actuator(C-A)channel.The proposed approach is designed using control inputs and tracking errors which are accessible data.Reinforcement learning is leveraged to update the control gains in real time using policy or value iterations.A thorough stability analysis is conducted and it is found that the proposed controller can sustain the convergence and asymptotic stability even when two channels are attacked.Finally,comparison results with a simulated CPS verify the effectiveness of the proposed output regulation method.

Cooperation-based sperm clusters mediate sperm oviduct entry and fertilization

Dear Editor,Sperm cooperation has been observed in multiple species(Pizzari and Foster,2008),yet its existenee and benefit for reproductive success in mammals remains underexplored.Here,combining tissue-clearing with deep three-dimensional(3D)imagi ng,we dem on strate that postcopulatory mouse sperm congregate into unidirectional sperm cooperative clusters at the utero-tubal junction(UTJ),a key physical barrier for passage into the oviduct.

Deep learning for drug-drug interaction prediction:A comprehensive review

The prediction of drug-drug interactions(DDIs)is a crucial task for drug safety research,and identifying potential DDIs helps us to explore the mechanism behind combinatorial therapy.Traditional wet chemical experiments for DDI are cumbersome and time-consuming,and are too small in scale,limiting the efficiency of DDI predictions.Therefore,it is particularly crucial to develop improved computational methods for detecting drug interactions.With the development of deep learning,several computational models based on deep learning have been proposed for DDI prediction.In this review,we summarized the high-quality DDI prediction methods based on deep learning in recent years,and divided them into four categories:neural network-based methods,graph neural network-based methods,knowledge graph-based methods,and multimodal-based methods.Furthermore,we discuss the challenges of existing methods and future potential perspectives.This review reveals that deep learning can significantly improve DDI prediction performance compared to traditional machine learning.Deep learning models can scale to large-scale datasets and accept multiple data types as input,thus making DDI predictions more efficient and accurate.