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Acknowledgements We are grateful to Dr. Minna Zhang and Dr. Yinong Li from Northeast Normal University, Dr. Xincheng Li from Fudan University, and Dr. Shengen Liu from China Three Gorges University for the valuable feedback and suggestions for the data analysis in the earlier version. M.D-B. is supported by a Ramón y Cajal grant (RYC2018-025483-I), a “Ayuda P.P. 2020. Desarrollo Lineas Investigación Propias (UPO), a project from the Spanish Ministry of Science and Innovation (PID2020-115813RA-I00), and a project PAIDI 2020 from the Junta de Andalucía (P20_00879). H.Y.C. is supported by National Natural Science Foundation of China (32101335), China Postdoctoral Science Foundation (2021M690589), Innovation Project of Young Technological Talents in Changchun City (21QC07), and Fundamental Research Funds for the Central Universities (2412021QD014). J.P.V. is thankful to DST and SERB (Science and Engineering Research Board), India for financial support for plant-microbe interaction research. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government., The environmental factors controlling soil biodiversity along resource gradients remain poorly understood in wet tropical ecosystems. Aboveground biodiversity is expected to be driven by changes in nutrient availability in these ecosystems, however, much less is known about the importance of nutrient availability in driving soil biodiversity. Here, we combined a cross-continental soil survey across tropical regions with a three decades' field experiment adding nitrogen (N) and phosphorus (P) (100 kg N ha−1y−1 and 100 kg P ha−1y−1) to Hawai'ian tropical forests with contrasting substrate ages (300 and 4,100,000 years) to investigate the influence of nutrient availability to explain the biodiversity of soil bacteria, fungi, protists, invertebrates and key functional genes. We found that soil biodiversity was driven by soil acidification during long-term pedogenesis and across environmental gradients, rather than by nutrient limitations. In fact, our results showed that experimental N additions caused substantial acidification in soils from Hawai'i. These declines in pH were related to large decreases in soil biodiversity from tropical ecosystems in four continents. Moreover, the microbial activity did not change in response to long-term N and P additions. We concluded that environmental filtering drives the biodiversity of multiple soil organisms, and that the acidification effects associated with N additions can further create substantial undesired net negative effects on overall soil biodiversity in naturally tropical acid soils. This knowledge is integral for the understanding and management of soil biodiversity in tropical ecosystems globally., Depto. de Biodiversidad, Ecología y Evolución, Fac. de Ciencias Biológicas, TRUE, pub

5G heterogeneous networks (HetNets) can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macro system. However, the large-scale deployment of small BSs significantly increases the complexity and workload of network maintenance and optimisation. The current handover (HO) triggering mechanism A3 event was designed only for mobility management in the macro system. Directly implementing A3 in 5G-HetNets may degrade the user mobility robustness. Motivated by the concept of self-organisation networks (SON), this study developed a self-optimised triggering mechanism to enable automated network maintenance and enhance user mobility robustness in 5G-HetNets. The proposed method integrates the advantages of subtractive clustering and Q-learning frameworks into the conventional fuzzy logic-based HO algorithm (FLHA). Subtractive clustering is first adopted to generate a membership function (MF) for the FLHA to enable FLHA with the self-configuration feature. Subsequently, Q-learning is utilised to learn the optimal HO policy from the environment as fuzzy rules that empower the FLHA with a self-optimisation function. The FLHA with SON functionality also overcomes the limitations of the conventional FLHA that must rely heavily on professional experience to design. The simulation results show that the proposed self-optimised FLHA can effectively generate MF and fuzzy rules for the FLHA. By comparing with conventional triggering mechanisms, the proposed approach can decrease the HO, ping-pong HO, and HO failure ratios by approximately 91%, 49%, and 97.5% while improving network throughput and latency by 8% and 35%, respectively.

Large-area hexagonal boron nitride (h-BN) can be grown on polycrystalline metallic substrates via chemical vapor deposition (CVD), but the impact of local inhomogeneities on the electrical properties of the h-BN and their effect in electronic devices is unknown. Conductive atomic force microscopy (CAFM) and probe station characterization show that the tunneling current across the h-BN stack fluctuates up to 3 orders of magnitude from one substrate (Pt) grain to another. Interestingly, the variability in the tunneling current across the h-BN within the same substrate grain is very low, which may enable the use of CVD-grown h-BN in ultra scaled technologies. Keywords: hexagonal boron nitride; chemical vapor deposition; electrical homogeneity; conductive AFM; polycrystalline, National Science Foundation (U.S.) (Grant DMR/ECCS–1509197)

Semimetallic-layered transition-metal dichalcogenides, such as TiS2, can serve as a platform material for exploring novel physics modulated by dimensionality, as well as for developing versatile applications in electronics and thermoelectrics. However, controlled synthesis of ultrathin TiS2 in a dry-chemistry way has yet to be realized because of the high oxophilicity of active Ti precursors. Here, we report the ambient pressure chemical vapor deposition (CVD) method to grow large-size, highly crystalline two-dimensional (2D) TiS2 nanosheets through in situ generating titanium chloride as the gaseous precursor. The addition of NH4Cl promoter can react with Ti powders and switch the solid-phase sulfurization reaction into a CVD process, thus enabling the controllability over the size, shape, and thickness of the TiS2 nanosheets via tuning the synthesis conditions. Interestingly, this semimetallic 2D material exhibits near-infrared surface plasmon resonance absorption and a memristor-like electrical behavior, both holding promise for further application developments. Our method hence opens a new avenue for the CVD growth of 2D metal dichalcogenides directly from metal powders and pave the way for exploring their intriguing properties and applications., National Science Foundation (U.S.) (Grant DMR-1231319)

The performance and reliability of large-area graphene grown by chemical vapor deposition are often limited by the presence of wrinkles and the transfer-process-induced polymer residue. Here, we report a transfer approach using paraffin as a support layer, whose thermal properties, low chemical reactivity and non-covalent affinity to graphene enable transfer of wrinkle-reduced and clean large-area graphene. The paraffin-transferred graphene has smooth morphology and high electrical reliability with uniform sheet resistance with ~1% deviation over a centimeter-scale area. Electronic devices fabricated on such smooth graphene exhibit electrical performance approaching that of intrinsic graphene with small Dirac points and high carrier mobility (hole mobility = 14,215 cm 2 V −1 s −1 ; electron mobility = 7438 cm 2 V −1 s −1 ), without the need of further annealing treatment. The paraffin-enabled transfer process could open realms for the development of high-performance ubiquitous electronics based on large-area two-dimensional materials., United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant no. FA9550-15-1-0514), National Science Foundation (U.S.). Division of Materials Research. Electrical, Communications and Cyber Systems (1509197), National Science Foundation (U.S.). Center for Energy Efficient Electronics Science (NSF Award 0939514), King Abdullah University of Science and Technology (No. OSR- 2015-CRG4-2634), United States. Army Research Office (through MIT Institute for Soldier Nanotechnologies (Grant No. 023674)), Korea Research Institute of Chemical Technology (project no. KK1801-G01), National Research Foundation of Korea. Basic Science Research Program (NRF-2017R1C1B2007153), United States. Office of Naval Research (N00014–16–1–233), National Science Foundation (U.S.) (grant number ACI-1053575), Singapore. Agency for Science, Technology and Research. Computational Resource Centre, Singapore. National Supercomputing Centre

Salmonella uses Type 3 secretion systems (T3SSs) to deliver virulence factors, called effectors, into host cells during infection. The T3SS effectors promote invasion into host cells and the generation of a replicative niche. SopB is a T3SS effector that plays an important role in Salmonella pathogenesis through its lipid phosphatase activity. Here, we show that SopB mediates the recruitment of Rho GTPases (RhoB, RhoD, RhoH, and RhoJ) to bacterial invasion sites. RhoJ contributes to Salmonella invasion, and RhoB and RhoH play an important role in Akt activation. R‐Ras1 also contributes to SopB‐dependent Akt activation by promoting the localised production of PI(3,4)P2/PI(3,4,5)P3. Our studies reveal new signalling factors involved in SopB‐dependent Salmonella infection.

An efficient one-pot, cyclocondensation of ammonium acetate, 1,2-diphenyl ethanedione, aromatic aldehyde or arylamine, and catalyzed by boric acid is achieved to form multisubstituted imidazole derivatives. Boric acid as green property is harmless to the environment and has improved the yield of multisubstituted imidazole derivatives. Moreover, we have tested the biological activities of imidazole derivatives by the four fungi. And the investigations showed most of imidazole derivatives have antifungal action on four fungus.

In this paper nickel oxide (NiO) nanoflower was synthesized and used for the realization of direct electrochemistry of horseradish peroxidase (HRP). By using carbon ionic liquid electrode (CILE) as the substrate electrode, NiO-HRP composite was casted on the surface of CILE with chitosan (CTS) as the film forming material and the modified electrode was denoted as CTS/NiO-HRP/CILE. UV-Vis absorption and FT-IR spectra confirmed that HRP retained its native structure after mixed with NiO nanoflower. Direct electron transfer of HRP on the modified electrode was investigated by cyclic voltammetry with a pair of quasi-reversible redox waves appeared, indicating that the presence of NiO nanoflower on the electrode surface could accelerate the electron transfer rate between the electroactive center of HRP and the substrate electrode. Electrochemical behaviors of HRP on the modified electrode were carefully investigated. The HRP modified electrode showed excellent electrocatalytic activity to the reduction of trichloroacetic acid with wider linear range and lower detection limit. Therefore the presence of NiO nanoflower could provide a friendly biocompatible interface for immobilizing biomolecules and keeping their native structure. The fabricated electrochemical biosensor displayed the advantages such as high sensitivity, good reproducibility and long-term stability. This work is licensed under a Creative Commons Attribution 4.0 International License.

Rhodotorula sp. USM-PSY62 ialah yis bersifat psikrofilik yang mampu hidup di persekitaran sejuk. Yis ini boleh menjadi organisma contoh yang baik untuk kajian mekanisme molekul intrinsik yang terlibat dalam penyesuaian dan adaptasi dalam keadaan sejuk. Rhodotorula sp. USM-PSY62 is a psychrophilic yeast that is competent in withstanding and surviving in extreme cold environment. It is a good model organism to study the intrinsic molecular mechanisms that are involved in cold adaptation. In this study, a robust RNA-seq approach was employed to study the global transcriptomic response of Rhodotorula sp.

Purpose: Most original equipment manufacturers (OEMs) are strategically involved in supplier base rationalization and increased consciousness of sustainable development thus, reinforcing need for accurately considering sustainability in supplier selection to improve organizational performance. In real industrial case, imprecise data, ambiguity of human judgment, uncertainty among sustainability factors and the need to capture all subjective and objective criteria are unavoidable and pose huge challenge to accurately incorporate sustainability factors into supplier selection. Methodology: This study develops a model based on integrated multi- criteria decision making (MCDM) methods to solve such problems. The developed model applies Fuzzy logic, DEMATEL and TOPSIS to effectively analyze the interdependencies between sustainability criteria and to select the best sustainable supplier in fuzzy environment while capturing all subjective and objective criteria. A case study is illustrated to test the proposed model in a gear manufacturing company, an OEM to provide insights and for practical applications. Findings: Results show that social factors of sustainability ranks as the most important in supplier selection. However, the most influential sustainability sub- criteria are work safety (WS) and quality. Originality/Value: The model is capable of capturing all subjective and objective criteria in fuzzy environment to accurately incorporate sustainability factors in supplier selection. It is decision support tool relevant for providing insights to managers while implementing sustainable supplier selection., Peer Reviewed

Photomultiplication and excess noise measurements have been undertaken on two 4H-SiC avalanche photodiodes (APDs) using 244-nm light and 325-nm light. The structures are APDs with separate absorption and multiplication regions having multiplication regions of 2.74 and 0.58 mu m, respectively. Pure injection conditions in the thicker device permit the measurement of pure-hole-initiated photomultiplication and an excess noise factor. Ionization coefficients for both carrier types have been extracted from these data using a local model. The use of the excess noise factor to infer the value of the less readily ionizing coefficient alpha from pure hole injection measurements is more robust than direct extraction from mixed injection measurements. This is because mixed injection introduces uncertainty in the generation profile. We report a significant reduction of the electron ionization coefficient a at low fields. The more readily ionizing hole coefficient beta remains very similar to prior work.

The study is related to the EU 7th Framework Programme CODICE (COmputationally Driven design of Innovative CEment-based materials) project. The main aim of the project is the development of a multi-scale model for the computer based simulation of mechanical and durability performance of cementitious materials. As part of the task to study the micromechanical properties of computationally driven designs and validate the model predictions, extensive work on micro/nano-mechanical characterisation of cement-based materials has been conducted, which cover synthetic C3S, C2S pastes, cement pastes hydrated at different ages and pastes subjected to accelerated calcium leaching, etc. Statistical nanoindentation and micro-mechanical property mapping technique was used to study intrinsic properties of different hydrate phases and microstructures down to approximately 1 µm. A new experimental technique – Peakforce QNM was also used to examine mechanical properties of cement paste micro/nano-structures down to approximately 10 nm. The importance of proper specimen preparation is highlighted, particularly for the early-aged and leached samples due to their weak and fragile microstructure. The results obtained from the two experimental techniques are presented and advantages/limitations for each technique discussed.

Objectives. Congenital diaphragmatic hernia (CDH) is one of the most common and serious congenital disorders seen in the neonatal intensive care unit (NICU) and it is associated with a high mortality. In order to determine the risk factors and outcomes of CDH, we summarized data from a 10 year period. Methods. A retrospective study was conducted on 38 CDH patients. Clinical characteristics and risk factors were compared and non-conditional logistic regression analysis was performed to determine independent predictors for mortality. Results. Thirty patients, from a total of 38, underwent surgery for CDH. The total survival rate in patients with CDH was 63.2% (24/38) and the overall operative mortality was 20.0% (6/30). There was a significant difference between CDH patients who survived (n=24) and those who died (n=14) in the age on admission, 5-minute Apgar score, onset of respiratory distress, cardiac malformations and presence of persistent pulmonary hypertension of newborn (PPHN). Using logistic regression analysis, the following factors independently predicted mortality: the age on admission (OR: 8.15, 95%CI: 1.43 to 46.41) and cardiac malformations (OR: 18.54, 95%CI: 1.32 to 259.62). Moreover, when we compared CDH patients who survived after surgery (n=24) with those who died (n=6), there was a significant difference in the admission age, 1-minute Apgar score, presence of PPHN, lung hypoplasia, time of stabilization prior to surgery, and highest oxygenation index after surgery. Conclusions. Mortality was very high in CDH patients and was associated with care procedures. Risk factors for mortality in neonatal CDH were the age on admission and associated malformations.

The precise investigation of the $W^+ W^-Z^0$ production at the $e^+e^-$ International Linear Collider(ILC) is of crucial importance in probing the couplings between massive vector gauge bosons and discovering the signature of new physics beyond the standard model(SM). We study the full one-loop EW effects on the observables, such as, the total cross section, the differential cross section of the invariant mass of $W$-pair, the distribution of the angle between $W$-pair, the production angle distributions of $W$- and $Z^0$-boson, the distributions of the transverse momenta of final $W$- and $Z^0$-boson, and the forward-backward charge asymmetry of $W^-$-boson. Our numerical results show that the EW relative correction to the total cross sections($\delta_{ew}$) varies from -18.9% to -5.7% when $m_H=120 GeV$ and $\sqrt{s}$ goes up from $300 GeV$ to $1 TeV$., Comment: 17 pages, 12 figures, accepted for publication in Physics Letters B

Cellular responses to inputs that vary both temporally and spatially are determined by complex relationships between the components of cell signaling networks. Analysis of these relationships requires access to a wide range of experimental reagents and techniques, including the ability to express the protein components of the model cells in a variety of contexts. As part of the Alliance for Cellular Signaling, we developed a robust method for cloning large numbers of signaling ORFs into Gateway® entry vectors, and we created a wide range of compatible expression platforms for proteomics applications. To date, we have generated over 3000 plasmids that are available to the scientific community via the American Type Culture Collection. We have established a website at www.signaling-gateway.org/data/plasmid/ that allows users to browse, search, and blast Alliance for Cellular Signaling plasmids. The collection primarily contains murine signaling ORFs with an emphasis on kinases and G protein signaling genes. Here we describe the cloning, databasing, and application of this proteomics resource for large scale subcellular localization screens in mammalian cell lines.

The scheduling of real-time tasks with fault-tolerant requirements has been an important problem in multiprocessor systems. Primary-backup (PB) approach is often used as a fault-tolerant technique to guarantee the deadlines of tasks despite the presence of faults. In this paper we propose a PB-based task scheduling approach, wherein an allocation parameter is used to search the available time slots for a newly arriving task, and the previously scheduled tasks can be rescheduled when there is no available time slot for the newly arriving task. In order to improve the schedulability we extend the existing PB-overloading and the Backup-backup (BB) overloading. Our proposed task scheduling algorithm is compared with some existing scheduling algorithms in the literature through simulation studies. The results have shown that the task rejection ratio of our real-time task scheduling algorithm is lower than the compared algorithms., Distributed Simulation and Real-Time Applications, 2007. DS-RT 2007. 11th IEEE International Symposium, identifier:https://dspace.jaist.ac.jp/dspace/handle/10119/7804

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.