Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM.

With the emergence of high-throughput technologies, computational screening based on gene expression profiles has become one of the most effective methods for drug discovery. More importantly, profile-based approaches remarkably enhance novel drug-disease pair discovery without relying on drug- or disease-specific prior knowledge, which has been widely used in modern medicine. However, profile-based systematic screening of active ingredients of traditional Chinese medicine (TCM) has been scarcely performed due to inadequate pharmacotranscriptomic data. Here, we develop the largest-to-date online TCM active ingredients-based pharmacotranscriptomic platform integrated traditional Chinese medicine (ITCM) for the effective screening of active ingredients. First, we performed unified high-throughput experiments and constructed the largest data repository of 496 representative active ingredients, which was five times larger than the previous one built by our team. The transcriptome-based multi-scale analysis was also performed to elucidate their mechanism. Then, we developed six state-of-art signature search methods to screen active ingredients and determine the optimal signature size for all methods. Moreover, we integrated them into a screening strategy, TCM-Query, to identify the potential active ingredients for the special disease. In addition, we also comprehensively collected the TCM-related resource by literature mining. Finally, we applied ITCM to an active ingredient bavachinin, and two diseases, including prostate cancer and COVID-19, to demonstrate the power of drug discovery. ITCM was aimed to comprehensively explore the active ingredients of TCM and boost studies of pharmacological action and drug discovery. ITCM is available at http://itcm.biotcm.net.

COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming.

Immune evasion and metabolism reprogramming have been regarded as two vital hallmarks of the mechanism of carcinogenesis. Thus, targeting the immune microenvironment and the reprogrammed metabolic processes will aid in developing novel anti-cancer drugs. In recent decades, herbal medicine has been widely utilized to treat cancer through the modulation of the immune microenvironment and reprogrammed metabolic processes. However, labor-based herbal ingredient screening is time consuming, laborious and costly. Luckily, some computational approaches have been proposed to screen candidates for drug discovery rapidly. Yet, it has been challenging to develop methods to screen drug candidates exclusively targeting specific pathways, especially for herbal ingredients which exert anti-cancer effects by multiple targets, multiple pathways and synergistic ways. Meanwhile, currently employed approaches cannot quantify the contribution of the specific pathway to the overall curative effect of herbal ingredients. Hence, to address this problem, this study proposes a new computational framework to infer the contribution of the immune microenvironment and metabolic reprogramming (COIMMR) in herbal ingredients against human cancer and specifically screen herbal ingredients targeting the immune microenvironment and metabolic reprogramming. Finally, COIMMR was applied to identify isoliquiritigenin that specifically regulates the T cells in stomach adenocarcinoma and cephaelin hydrochloride that specifically targets metabolic reprogramming in low-grade glioma. The in silico results were further verified using in vitro experiments. Taken together, our approach opens new possibilities for repositioning drugs targeting immune and metabolic dysfunction in human cancer and provides new insights for drug development in other diseases. COIMMR is available at https://github.com/LYN2323/COIMMR.

Root rot destabilizes the Sanqi rhizosphere core fungal microbiome by reducing the negative connectivity of beneficial microbes.

The stability of microbial communities, especially among core taxa, is essential for supporting plant health. However, the impacts of disease infection on the stability of rhizosphere fungal core microbiome remain largely unexplored. In this study, we delved into the effects of root rot infestation on the community structure, function, network complexity, and stability of Sanqi fungal core microbiomes, employing amplicon sequencing combined with co-occurrence network and cohesion analyses. Our investigation revealed that root rot disease led to a decrease in the α-diversity but an increase in the ß-diversity of the Sanqi fungal core microbiomes in the rhizosphere. Notably, Ilyonectria, Plectosphaerella, and Fusarium emerged as indicator species in the rhizosphere core microbiome of root rot-infected Sanqi plants, while Mortierella predominated as the dominant biomarker taxa in healthy soils. Additionally, root rot diminished the complexity and modularity of the rhizosphere networks by reducing the metrics associated with nodes, edges, degrees, and modularity. Furthermore, root rot resulted in a reduction in the proportion of negative connections in the network and the negative/positive cohesion of the entire core fungal microbiome. Particularly noteworthy was the observation that root rot infection destabilized the rhizosphere core fungal microbiome by weakening the negative connectivity associated with beneficial agents. Collectively, these results highlight the significance of the negative connectivity of beneficial agents in ensuring the stability of core microbial community.IMPORTANCERoot rot disease has been reported as the most devastating disease in the production process of artificial cultivated Sanqi ginseng, which seriously threatens the Sanqi industry. This study provides valuable insights into how root rot influences microbial relationships within the community. These findings open up opportunities for disease prevention and the promotion of plant health by regulating microbial interactions. In summary, the research sheds light on the ecological consequences of root rot on rhizosphere fungal microbiomes and offers potential strategies for managing soil-borne diseases and enhancing plant health.

Soil recalcitrant but not labile organic nitrogen mineralization contributes to microbial nitrogen immobilization and plant nitrogen uptake.

Soil organic nitrogen (N) mineralization not only supports ecosystem productivity but also weakens carbon and N accumulation in soils. Recalcitrant (mainly mineral-associated organic matter) and labile (mainly particulate organic matter) organic materials differ dramatically in nature. Yet, the patterns and drivers of recalcitrant (MNrec) and labile (MNlab) organic N mineralization rates and their consequences on ecosystem N retention are still unclear. By collecting MNrec (299 observations) and MNlab (299 observations) from 57 15N tracing studies, we found that soil pH and total N were the master factors controlling MNrec and MNlab, respectively. This was consistent with the significantly higher rates of MNrec in alkaline soils and of MNlab in natural ecosystems. Interestingly, our analysis revealed that MNrec directly stimulated microbial N immobilization and plant N uptake, while MNlab stimulated the soil gross autotrophic nitrification which discouraged ammonium immobilization and accelerated nitrate production. We also noted that MNrec was more efficient at lower precipitation and higher temperatures due to increased soil pH. In contrast, MNlab was more efficient at higher precipitation and lower temperatures due to increased soil total N. Overall, we suggest that increasing MNrec may lead to a conservative N cycle, improving the ecosystem services and functions, while increasing MNlab may stimulate the potential risk of soil N loss.

Aridity creates global thresholds in soil nitrogen retention and availability.

Identifying tipping points in the relationship between aridity and gross nitrogen (N) cycling rates could show critical vulnerabilities of terrestrial ecosystems to climate change. Yet, the global pattern of gross N cycling response to aridity across terrestrial ecosystems remains unknown. Here, we collected 14,144 observations from 451 15 N-labeled studies and used segmented regression to identify the global threshold responses of soil gross N cycling rates and soil process-related variables to aridity index (AI), which decreases as aridity increases. We found on a global scale that increasing aridity reduced soil gross nitrate consumption but increased soil nitrification capacity, mainly due to reduced soil microbial biomass carbon (MBC) and N (MBN) and increased soil pH. Threshold response of gross N production and retention to aridity was observed across terrestrial ecosystems. In croplands, gross nitrification and extractable nitrate were inhibited with increasing aridity below the threshold AI ~0.8-0.9 due to inhibited ammonia-oxidizing archaea and bacteria, while the opposite was favored above this threshold. In grasslands, gross N mineralization and immobilization decreased with increasing aridity below the threshold AI ~0.5 due to decreased MBN, but the opposite was true above this threshold. In forests, increased aridity stimulated nitrate immobilization below the threshold AI ~1.0 due to increased soil C/N ratio, but inhibited ammonium immobilization above the threshold AI ~1.3 due to decreased soil total N and increased MBC/MBN ratio. Soil dissimilatory nitrate reduction to ammonium decreased with increasing aridity globally and in forests when the threshold AI ~1.4 was passed. Overall, we suggest that any projected increase in aridity in response to climate change is likely to reduce plant N availability in arid regions while enhancing it in humid regions, affecting the provision of ecosystem services and functions.

Direct transmission of severe fever with thrombocytopenia syndrome virus from farm-raised fur animals to workers in Weihai, China.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease. SFTS virus (SFTSV) is transmitted by tick bites and contact with the blood or body fluids of SFTS patients. Animal-to-human transmission of SFTS has been reported in Japan, but not in China. In this study, the possible transmission route of two patients who fed and cared for farm-raised fur animals in a mink farm was explored. METHOD: An epidemiological investigation and a genetic analysis of patients, animals and working environment were carried out. RESULTS: It was found that two patients had not been bitten by ticks and had no contact with patients infected with SFTS virus, but both of them had skinned the dying animals. 54.55% (12/22) of the farm workers were positive for SFTS virus antibody. By analyzing the large, medium and small segments sequences, the viral sequences from the two patients, animals and environments showed 99.9% homology. CONCLUSION: It is suspected that the two patients may be directly infected by farm-raised animals, and that the virus may have been transmitted by aerosols when skinning dying animals. Transmission by direct blood contacts or animal bites cannot be ignored.

Warming-Induced Stimulation of Soil N2O Emissions Counteracted by Elevated CO2 from Nine-Year Agroecosystem Temperature and Free Air Carbon Dioxide Enrichment.

Globally, agricultural soils account for approximately one-third of anthropogenic emissions of the potent greenhouse gas and stratospheric ozone-depleting substance nitrous oxide (N2O). Emissions of N2O from agricultural soils are affected by a number of global change factors, such as elevated air temperatures and elevated atmospheric carbon dioxide (CO2). Yet, a mechanistic understanding of how these climatic factors affect N2O emissions in agricultural soils remains largely unresolved. Here, we investigate the soil N2O emission pathway using a 15N tracing approach in a nine-year field experiment using a combined temperature and free air carbon dioxide enrichment (T-FACE). We show that the effect of CO2 enrichment completely counteracts warming-induced stimulation of both nitrification- and denitrification-derived N2O emissions. The elevated CO2 induced decrease in pH and labile organic nitrogen (N) masked the stimulation of organic carbon and N by warming. Unexpectedly, both elevated CO2 and warming had little effect on the abundances of the nitrifying and denitrifying genes. Overall, our study confirms the importance of multifactorial experiments to understand N2O emission pathways from agricultural soils under climate change. This better understanding is a prerequisite for more accurate models and the development of effective options to combat climate change.

Land Use Change from Natural Tropical Forests to Managed Ecosystems Reduces Gross Nitrogen Production Rates and Increases the Soil Microbial Nitrogen Limitation.

Understanding the underlying mechanisms of soil microbial nitrogen (N) utilization under land use change is critical to evaluating soil N availability or limitation and its environmental consequences. A combination of soil gross N production and ecoenzymatic stoichiometry provides a promising avenue for nutrient limitation assessment in soil microbial metabolism. Gross N production via 15N tracing and ecoenzymatic stoichiometry through the vector and threshold element ratio (Vector-TER) model were quantified to evaluate the soil microbial N limitation in response to land use changes. We used tropical soil samples from a natural forest ecosystem and three managed ecosystems (paddy, rubber, and eucalyptus sites). Soil extracellular enzyme activities were significantly lower in managed ecosystems than in a natural forest. The Vector-TER model results indicated microbial carbon (C) and N limitations in the natural forest soil, and land use change from the natural forest to managed ecosystems increased the soil microbial N limitation. The soil microbial N limitation was positively related to gross N mineralization (GNM) and nitrification (GN) rates. The decrease in microbial biomass C and N as well as hydrolyzable ammonium N in managed ecosystems led to the decrease in N-acquiring enzymes, inhibiting GNM and GN rates and ultimately increasing the microbial N limitation. Soil GNM was also positively correlated with leucine aminopeptidase and ß-N-acetylglucosaminidase. The results highlight that converting tropical natural forests to managed ecosystems can increase the soil microbial N limitation through reducing the soil microbial biomass and gross N production.

Antimicrobial peptide cecropin B functions in pathogen resistance of Mythimna separata.

Mythimna separata (Lepidoptera: Noctuidae) is an omnivorous pest that poses a great threat to food security. Insect antimicrobial peptides (AMPs) are small peptides that are important effector molecules of innate immunity. Here, we investigated the role of the AMP cecropin B in the growth, development, and immunity of M. separata. The gene encoding M. separata cecropin B (MscecropinB) was cloned. The expression of MscecropinB was determined in different developmental stages and tissues of M. separata. It was highest in the prepupal stage, followed by the pupal stage. Among larval stages, the highest expression was observed in the fourth instar. Tissue expression analysis of fourth instar larvae showed that MscecropinB was highly expressed in the fat body and haemolymph. An increase in population density led to upregulation of MscecropinB expression. MscecropinB expression was also upregulated by the infection of third and fourth instar M. separata with Beauveria bassiana or Bacillus thuringiensis (Bt). RNA interference (RNAi) targeting MscecropinB inhibited the emergence rate and fecundity of M. separata, and resulted in an increased sensitivity to B. bassiana and Bt. The mortality of M. separata larvae was significantly higher in pathogen plus RNAi-treated M. separata than in controls treated with pathogens only. Our findings indicate that MscecropinB functions in the eclosion and fecundity of M. separata and plays an important role in resistance to infection by B. bassiana and Bt.

High carbon resource diversity enhances the certainty of successful plant pathogen and disease control.

The host-associated microbiome highly determines plant health. Available organic resources, such as food for microbes, are important in shaping microbial community structure and multifunctionality. However, how using organic resources precisely manipulates the soil microbiome and makes it supportive of plant health remains unclear. Here, we experimentally tested the influence of carbon resource diversity on the microbial trophic network and pathogen invasion success in a microcosm study. We further explored how resource diversity affects microbial evenness, community functions, and plant disease outcomes in systems involving tomato plants and the in vivo soil microbiome. Increasing available resource diversity altered trophic network architecture, increased microbial evenness, and thus increased the certainty of successful pathogen control. By contrast, the invasion resistance effects of low resource diversity were less effective and highly varied. Accordingly, increases in the evenness and connection of dominant species induced by high resource diversity significantly contributed to plant disease suppression. Furthermore, high carbohydrate diversity upregulated plant immune system regulation-related microbial functions. Our results deepen the biodiversity-invasion resistance theory and provide practical guidance for the control of plant pathogens and diseases by using organic resource-mediated approaches, such as crop rotation, intercropping, and organic amendments.

Photonic millimeter-wave transfer with balanced dual-heterodyne phase noise detection and cancellation.

We report on the realization of long-haul and high-precision millimeter-wave (mm-wave) transfer through a fiber-optic link based on balanced dual-heterodyne phase noise detection. The balanced dual-heterodyne detection is achieved by detecting the fiber phase noise superimposed two intermediate frequency (IF) signals without requiring a local synchronization signal and its output is used to compensate the fiber-induced phase noise by actuating the frequency of the one optical carrier. The proposed scheme can effectively get rid of the effect of the local reference, largely simplifying the configuration at the local site. Additionally, we model and experimentally study the noise contribution coming from the out-of-band, which can be effectively suppressed to the below of the system noise floor with a fractional frequency instability of 1.9 × 10-17 at 10,000 s by designing and implementing a high-precision temperature control module with a peak-to-peak temperature fluctuation of no more than 0.002 K. We experimentally demonstrate that a 100 GHz mm-wave signal to be transmitted over a 150 km fiber-optic link can achieve the fractional frequency instabilities of less than 3.4 × 10-14 at 1 s and 3.5 × 10-17 at 10,000 s.

Excellent predictive-performances of photonic reservoir computers for chaotic time-series using the fusion-prediction approach.

In this work, based on two parallel reservoir computers realized by the two polarization components of the optically pumped spin-VCSEL with double optical feedbacks, we propose the fusion-prediction scheme for the Mackey-Glass (MG) and Lorenz (LZ) chaotic time series. Here, the direct prediction and iterative prediction results are fused in a weighted average way. Compared with the direct-prediction errors, the fusion-prediction errors appear great decrease. Their values are far less than the values of the direct-prediction errors when the iteration step-size are no more than 15. By the optimization of the temporal interval and the sampling period, under the iteration step-size of 3, the fusion-prediction errors for the MG and LZ chaotic time-series can be reduced to 0.00178 and 0.004627, which become 8.1% of the corresponding direct-prediction error and 28.68% of one, respectively. Even though the iteration step-size reaches to 15, the fusion-prediction errors for the MG and LZ chaotic time-series can be reduced to 55.61% of the corresponding direct-prediction error and 77.28% of one, respectively. In addition, the fusion-prediction errors have strong robustness on the perturbations of the system parameters. Our studied results can potentially apply in the improvement of prediction accuracy for some complex nonlinear time series.

Optical cascaded reservoir computing for realization of dual-channel high-speed OTDM chaotic secure communication via four optically pumped VCSEL.

In this work, we propose a chaotic secure communication system with optical time division multiplexing (OTDM), using two cascaded reservoir computing systems based on multi beams of chaotic polarization components emitted by four optically pumped VCSELs. Here, each level of reservoir layer includes four parallel reservoirs, and each parallel reservoir contains two sub-reservoirs. When the reservoirs in the first-level reservoir layer are well trained and the training errors are far less than 0.1, each group of chaotic masking signals can be effectively separated. When the reservoirs in the second reservoir layer are effectively trained and the training errors are far less than 0.1, the output for each reservoir can be well synchronized with the corresponding original delay chaotic carrier-wave. Here, the synchronization quality between them can be characterized by the correlation coefficients of more than 0.97 in different parameter spaces of the system. Under these high-quality synchronization conditions, we further discuss the performances of dual-channel OTDM with a rate of 4×60 Gb/s. By observing the eye diagram, bit error rate and time-waveform of each decoded message in detail, we find that there is a large eye-openings in the eye diagrams, low bit error rate and higher quality time-waveform for each decoded message. Except that the bit error rate of one decoded message is lower than 7 × 10-3 in different parameter spaces, and those of the other decoded messages are close to 0, indicating that high-quality data transmissions are expected to be realized in the system. The research results show that the multi-cascaded reservoir computing systems based on multiple optically pumped VCSELs provide an effective method for the realization of multi-channel OTDM chaotic secure communications with high-speed.

Global soil nitrogen cycle pattern and nitrogen enrichment effects: Tropical versus subtropical forests.

Tropical and subtropical forest biomes are a main hotspot for the global nitrogen (N) cycle. Yet, our understanding of global soil N cycle patterns and drivers and their response to N deposition in these biomes remains elusive. By a meta-analysis of 2426-single and 161-paired observations from 89 published 15 N pool dilution and tracing studies, we found that gross N mineralization (GNM), immobilization of ammonium ( I NH 4 ) and nitrate ( I NO 3 ), and dissimilatory nitrate reduction to ammonium (DNRA) were significantly higher in tropical forests than in subtropical forests. Soil N cycle was conservative in tropical forests with ratios of gross nitrification (GN) to I NH 4 (GN/ I NH 4 ) and of soil nitrate to ammonium (NO3 - /NH4 + ) less than one, but was leaky in subtropical forests with GN/ I NH 4 and NO3 - /NH4 + higher than one. Soil NH4 + dynamics were mainly controlled by soil substrate (e.g., total N), but climatic factors (e.g., precipitation and/or temperature) were more important in controlling soil NO3 - dynamics. Soil texture played a role, as GNM and I NH 4 were positively correlated with silt and clay contents, while I NO 3 and DNRA were positively correlated with sand and clay contents, respectively. The soil N cycle was more sensitive to N deposition in tropical forests than in subtropical forests. Nitrogen deposition leads to a leaky N cycle in tropical forests, as evidenced by the increase in GN/ I NH 4 , NO3 - /NH4 + , and nitrous oxide emissions and the decrease in I NO 3 and DNRA, mainly due to the decrease in soil microbial biomass and pH. Dominant tree species can also influence soil N cycle pattern, which has changed from conservative in deciduous forests to leaky in coniferous forests. We provide global evidence that tropical, but not subtropical, forests are characterized by soil N dynamics sustaining N availability and that N deposition inhibits soil N retention and stimulates N losses in these biomes.

Does auditory numerosity and non-numerical magnitude affect visual non-symbolic numerical representation?

In the present study, we investigated the effects of auditory numerosity and magnitude (loudness) on visual numerosity processing. Participants compared numerosities of two sequential dot arrays. The second dot array was paired with a tone array that was independent of visual comparison. The numerosity (One-tone vs. Multiple-tone) and the non-numerical magnitude of tones (loudness) were manipulated in Experiments 1 and 2, respectively. In Experiment 1, participants' inverse efficiency score (IES), that is, the quotient between response time and accuracy, was significantly smaller in the One-tone and Multiple-tone conditions than that in the No-tone condition, and linear trend analyses showed that the IES decreased with the number of tones. In Experiment 2, the IES in the Loud-tone condition was significantly smaller than that in the No-tone condition, and the IES decreased as the loudness of the tones increased. In Experiment 3, both auditory numerosity and magnitude were manipulated. For soft tones, the IES was smaller in the Multiple-tone condition than in the One-tone condition, whereas no significant difference was found between two conditions in loud tones. In sum, these findings suggest that the visual numerical representation can be spontaneously affected by the numerosity and non-numerical magnitude of stimuli from another modality.

Deep Learning-Aided Modulation Recognition for Non-Orthogonal Signals.

Automatic Modulation Recognition (AMR) can obtain the modulation mode of the received signal for subsequent processing without the assistance of the transmitter. Although the existing AMR methods have been mature for the orthogonal signals, these methods face challenges when deployed in non-orthogonal transmission systems due to the superimposed signals. In this paper, we aim to develop efficient AMR methods for both downlink and uplink non-orthogonal transmission signals using deep learning-based data-driven classification methodology. Specifically, for downlink non-orthogonal signals, we propose a Bi-directional Long Short-Term Memory (BiLSTM)-based AMR method that exploits long-term data dependence to automatically learn irregular signal constellation shapes. Transfer learning is further incorporated to improve recognition accuracy and robustness under varying transmission conditions. For uplink non-orthogonal signals, the combinatorial number of classification types explodes exponentially with the number of signal layers, which becomes the major obstacle to AMR. We develop a spatio-temporal fusion network based on the attention mechanism to efficiently extract spatio-temporal features, and network details are optimized according to the superposition characteristics of non-orthogonal signals. Experiments show that the proposed deep learning-based methods outperform their conventional counterparts in both downlink and uplink non-orthogonal systems. In a typical uplink scenario with three non-orthogonal signal layers, the recognition accuracy can approach 96.6% in the Gaussian channel, which is 19% higher than the vanilla Convolution Neural Network.

Adsorption and desorption of heavy metals at water sediment interface based on bayesian model.

Estuarine areas are not only the main gathering point for human sewage but also the place where one-way and two-way fluids interact, thus forming a complex and changeable geochemical physical field. Here, heavy metals (HMs) are adsorbed and desorbed due to physical, chemical, and biochemical processes. However, the adsorption and desorption behavior of HMs in the aquatic environment is complex, and physicochemical processes occurring in the estuarine sediment-water interface control the direction and boundaries of the system. This study analyzed the migration and distribution of HMs in rivers and lakes, and established a Bayesian network model to quantitatively understand the impact of nutrients and key environmental factors on the adsorption-desorption behavior of HMs in lake and estuaries, as well as the competitive relationship between environmental factors. The influence of environmental factors and the occurrence of HMs are both important model inputs. Our findings indicated that the migration risk of Cd in Qinghai Lake was high. Environmental factors such as Cation exchange capacity (CEC), Organic matter (OM), Soluble fluoride (SFL), and pH play the most important role in the adsorption and desorption of HMs. Our findings also indicated that the exchange and activity of HMs in sediments were much higher than in the overlying water. The organic matter content was the most complex environmental factor affecting HMS adsorption and desorption at the water-sediment interface. Additionally, the mass concentration of dissolved oxygen (DO) has a linear relationship with bioavailable HMs in river and lake sediments, but has no linear relationship with the concentration of water-soluble HMs. Interestingly, there are synergistic effects between environmental factors, which directly or indirectly affect the release of bioavailable HMs. However, it is important to determine whether the effects of different environmental factors on the exchange of bioavailable HMs are negative or positive. Our findings suggested that Bayesian network (BN) signals (positive or negative) could provide insights into the transfer direction of metals in the water-sediment interface.

Synthesis of Sequence-specific Poly(ester-carbonate) Copolymers via Chemoselective Terpolymerization Controlled by the Stoichiometric Ratio of Phosphazene/Triethylborane.

Chemoselective terpolymerization can produce polymer materials with diverse compositions and sequential structures, and thus have attracted considerable attention in the field of polymer synthesis. However, the intrinsic complexity of three-component system also brings great chanllenge, in regard to the reactivity and selectivity of different monomers. Herein, we report the terpolymerization of CO2 /epoxide/anhydride by a binary organocatalytic C3 N3 -Py-P3 /TEB (triethylborane) system. Both the activity and chemoselectivity were highly dependent upon the molar ratio of C3 N3 -Py-P3 to TEB, and sequence-controlled poly(ester-carbonate) copolymers were readily synthesized through one-pot/one-step methodology by tuning the stoichiometric ratio of phosphazene/TEB. In particular, C3 N3 -Py-P3 /TEB with a molar ratio of 1/0.5 exhibited an unprecedentedly high chemoselectivity for ring-opening alternating copolymerization (ROAC) of cyclohexene oxide (CHO) and phthalic anhydride (PA) first and then ROAC of CO2 /CHO. Thus, well-defined triblock polycarbonate-b-polyester-b-polycarbonate copolymers can be produced from the mixture of CO2 , CHO and PA using a bifunctional initiator. With C3 N3 -Py-P3 /TEB=1/1, tapered copolymers were obtained, while random copolymers with high content of polycarbonate (PC) were synthesized with further increasing the amount of TEB. The mechanism of the unexpected chemoselectivity was further investigated by DFT calculations.

Detections of the position-vectors of the multi targets located in a circular space based on an asymmetric coupling semiconductor lasers network.

We present a novel scheme for the detections of the position-vectors of the multi targets distributed in a circular space using multi channels of the probe chaotic waves emitted by the asymmetric coupling semiconductor lasers network (ACSLN), where these probe waves possess the attractive features of the time-space uncorrelation and wide bandwidth. Using these features, the accurate measurement for the position-vectors of the multi targets can be achieved by correlating the multi channels of the probe waves with their corresponding reference waves. The further research results show that the detections for the position-vectors of the multi targets possess very low relative errors that are no more than 0.22%. The ranging-resolutions for the multi targets located in a circular space can be achieved as high as 3 mm by optimizing some key parameters, such as injection current and injection strength. In addition, the ranging-resolutions exhibit excellent strong anti-noise performance even when the signal-to-noise ratio and relative noise intensity appear obvious enhancement. The detections for the position-vectors of the multi targets based on the ACSLN offers interesting perspectives for the potential applications in the driverless cars and the object tracking system with omnidirectional vision.

Multiple-access relay stations for long-haul fiber-optic radio frequency transfer.

We report on the realization of a long-haul radio frequency (RF) transfer scheme by using multiple-access relay stations (MARSs). The proposed scheme with independent link noise compensation for each fiber sub-link effectively solves the limitation of compensation bandwidth for long-haul transfer. The MARS can have the capability to share the same modulated optical signal for the front and rear fiber sub-links, simplifying the configuration at the repeater station and enabling the transfer system to have the multiple-access capability. At the same time, we for the first time theoretically model the effect of the MARS position on the fractional frequency instability of the fiber-optic RF transfer, demonstrating that the MARS position has little effect on system's performance when the ratio of the front and rear fiber sub-links is around 1:1. We experimentally demonstrate a 1 GHz signal transfer by using one MARS connecting 260 and 280 km fiber links with the fractional frequency instabilities of less than 5.9 × 10-14 at 1 s and 8.5 × 10-17 at 10,000 s at the remote site and of 5.6 × 10-14 and 6.6 × 10-17 at the integration times of 1 s and 10,000 s at the MARS. The proposed scalable technique can arbitrarily add the same MARSs in the fiber link, which has great potential in realizing ultra-long-haul RF transfer.