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Dendritic cells (DCs) are crucial in initiating and shaping both innate and adaptive immune responses. Clinical studies and experimental models have highlighted their significant involvement in various autoimmune diseases, positioning them as promising therapeutic targets. Nicotinamide (NAM), a form of vitamin B3, with its anti-inflammatory properties, has been suggested, while the involvement of NAM in DCs regulation remains elusive. Here, through analyzing publicly available databases, we observe substantial alterations in NAM levels and NAM metabolic pathways during DCs activation. Furthermore, we discover that NAM, but not Nicotinamide Mononucleotide (NMN), significantly inhibits DCs over-activation in vitro and in vivo. The suppression of DCs hyperactivation effectively alleviates symptoms of psoriasis. Mechanistically, NAM impairs DCs activation through a Poly (ADP-ribose) polymerases (PARPs)-NF-κB dependent manner. Notably, phosphoribosyl transferase (NAMPT) and PARPs are significantly upregulated in lipopolysaccharide (LPS)-stimulated DCs and psoriasis patients; elevated NAMPT and PARPs expression in psoriasis patients correlates with higher psoriasis area and severity index (PASI) scores. In summary, our findings underscore the pivotal role of NAM in modulating DCs functions and autoimmune disorders. Targeting the NAMPT-PARP axis emerges as a promising therapeutic approach for DC-related diseases. [ABSTRACT FROM AUTHOR]

In traditional cemented carbides, Co is mainly used as a binder. Recently, replacing Co with medium- to high-entropy alloys has shown significant improvements in hardness, fracture toughness, high-temperature oxidation resistance, and corrosion resistance, making it a research focus globally. Both the typical grain refiner Cr3C2 and medium- to high-entropy alloy binders affect the WC grain size in cemented carbides. This study investigates the synergistic grain refinement mechanism of Cr3C2 and medium- to high-entropy alloy binders on WC grains and their impact on the microstructure and mechanical properties of cemented carbides. The results show that increasing the Cr3C2 addition refined WC grains in the WC-CoNiFe alloy, increased coercivity, and enhanced hardness, with transverse rupture strength first increasing and then decreasing. The alloy achieved optimal performance at 0.6 wt.%Cr3C2, with a hardness of 91.25 HRA and transverse rupture strength of 3883.2 MPa. [ABSTRACT FROM AUTHOR]

In supporting the stable operation of high-penetration renewable energy grids, flywheel energy storage systems undergo frequent charge–discharge cycles, resulting in significant stress fluctuations in the rotor core. This paper investigates the fatigue life of flywheel energy storage rotors fabricated from 30Cr2Ni4MoV alloy steel, attempting to elucidate the material's mechanical properties, crack propagation behavior, and impact of internal defects on fatigue life. Tensile tests reveal that the material exhibited high yield (992 MPa) and tensile strengths (1130 MPa). The Paris formula is used to model crack growth rates, ending in good agreement with the experimental data. Fatigue tests at various stress conditions highlight the material's significant variability in fatigue life and emphasize the need for reliable design approaches. This paper also demonstrates that internal defect size and location critically affect fatigue life, calling for improvements in forging inspection standards. Overall, the present study provides a comprehensive analysis of 30Cr2Ni4MoV steel's suitability for flywheel rotors, balancing safety, and operational efficiency. [ABSTRACT FROM AUTHOR]

3He is a splendid neutron detection material due to its high neutron reaction cross section, gaseous state, and nonelectronegative and nonpoisonous nature. With the worldwide problem of the "3He supply crisis" arising, boron-lined gaseous neutron detectors are being widely used in neutron detection to replace 3He neutron detectors. In this work, to reduce the scattering neutron background coming from the substrate of a boron-lined neutron detector in the application of neutron scattering, a new design of the boron-lined gaseous neutron detector composed of a boron-lined multichip converter and a multiwire proportional chamber was proposed. The electron drift efficiency matrix simulated by Garfield++ (Version 2023.4) and the values and positions of electron energy deposition simulated by Geant4 were obtained. The α, 7Li, and total charged particle energy deposition spectra were acquired via coupling calculations of the electron drift efficiency matrix and the values and positions of electron energy deposition, and the width of the slit was selected as 3 mm. The boron-lined multilayer converter neutron detector (BMCND) was tested using a 241Am–239Pu mixture α source, and the total count rate of α charged particles was measured as 599.5 s−1, which is 89% of the theoretical α particle emission rate of 672.9 s−1. The drift voltage experiments showed that 1200 V is enough to acquire a relatively ideal count, and a 2500 V drift voltage was confirmed, considering the higher count and instrument safety. We also performed the neutron detection experiments using a photo-neutron source, and a characteristic spectrum shape of "two stairs" was measured. When borated polyethylene was used to shield the BMCND, the detected total count decreased while keeping the characteristic spectrum shape, demonstrating that the BMCND was equipped with the ability to detect neurons, indicating that BMCNDs have the potential to be an outstanding 3He alternative neutron detector. [ABSTRACT FROM AUTHOR]

Detecting transportation pipeline leakage points within chemical plants is difficult due to complex pathways, multi-dimensional survey points, and highly dynamic scenarios. However, hexapod robots' maneuverability and adaptability make it an ideal candidate for conducting surveys across different planes. The path-planning problem of hexapod robots in multi-dimensional environments is a significant challenge, especially when identifying suitable transition points and planning shorter paths to reach survey points while traversing multi-level environments. This study proposes a Particle Swarm Optimization (PSO)-guided Double Deep Q-Network (DDQN) approach, namely, the PSO-guided DDQN (PG-DDQN) algorithm, for solving this problem. The proposed algorithm incorporates the PSO algorithm to supplant the traditional random selection strategy, and the data obtained from this guided approach are subsequently employed to train the DDQN neural network. The multi-dimensional random environment is abstracted into localized maps comprising current and next level planes. Comparative experiments were performed with PG-DDQN, standard DQN, and standard DDQN to evaluate the algorithm's performance by using multiple randomly generated localized maps. After testing each iteration, each algorithm obtained the total reward values and completion times. The results demonstrate that PG-DDQN exhibited faster convergence under an equivalent iteration count. Compared with standard DQN and standard DDQN, reductions in path-planning time of at least 33.94% and 42.60%, respectively, were observed, significantly improving the robot's mobility. Finally, the PG-DDQN algorithm was integrated with sensors onto a hexapod robot, and validation was performed through Gazebo simulations and Experiment. The results show that controlling hexapod robots by applying PG-DDQN provides valuable insights for path planning to reach transportation pipeline leakage points within chemical plants. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) are important tools for assessing the historical characteristics and predicting the future characteristics of precipitation, yet the quantitative understanding of how these land–atmospheric coupling models perform in simulating precipitation characteristics remains limited. This study conducts a comprehensive evaluation of precipitation changes simulated by 43 ESMs in CMIP5 and 32 ESMs in CMIP6 in Arid Central Asia (ALL) and its two sub-regions for 1959–2005 with reference to Climate Research Unit (CRU) data, and predicts precipitation changes for 2054–2100. Our analyses suggest the following: (a) no single model consistently outperformed the others in all aspects of simulated precipitation variability (annual averages, long-term trends, and climatological monthly patterns); (b) the CMIP5 and CMIP6 model simulations tended to overestimate average annual precipitation for most of the ALL region, especially in the Xinjiang Uygur Autonomous Region of China (XJ); (c) most model simulations projected a stronger increasing trend in average annual precipitation; (d) although all the model simulations reasonably captured the climatological monthly precipitation, there was an underestimation; (e) compared to CMIP5, most CMIP6 model simulations exhibited an enhanced capacity to simulate precipitation across all aspects, although discrepancies persisted in individual sub-regions; (f) it was confirmed that the multi-model ensemble mean (MME) provides a more accurate representation of the three aspects of precipitation compared to the majority of single-model simulations. Lastly, the values of precipitation predicted by the more efficient models across the ALL region and its sub-regions under the different scenarios showed an increasing trend in most seasons. Notably, the strongest increasing trend in precipitation was seen under the high-emission scenarios. [ABSTRACT FROM AUTHOR]

A series of five-element CoCrFeNi-Nbx (x = 0, 1, 3, 5, 7, and 9 wt%) high-entropy alloys were prepared using high-energy ball milling and discharge plasma sintering methods. Then, the effects of doping with Nb elements on the organization and properties of the CoCrFeNi HEAs were systematically investigated by tensile testing, hardness testing, and examining their micro-morphologies. The results show that with the addition of the Nb element, the lattice distortion of the alloy due to the large size of the Nb atoms causes the microstructure of CoCrFeNi HEAs to change from a single-phase FCC structure to a dual-phase structure of FCC and Laves. With the increase in the Nb content, the increase in the volume fraction of the hard and brittle Laves phase leads to the enhancement of the HEA's tensile strength, yield strength, and hardness, and a decrease in plasticity. The Nb5 alloy showed the most excellent comprehensive performance, with a tensile strength, yield strength, and plasticity of 879.1 MPa, 491.8 MPa, and 39.8%, respectively, and all the properties were improved compared with those of the HEAs obtained by the arc melting method. The increase in the hardness of the HEAs was nearly proportional to the increase in the volume fraction of the Laves phase, which was the direct cause of the increase in the hardness of the HEA. Therefore, since the Laves phase is the direct cause of the increase in HEA hardness, the doping of CoCrFeNi HEAs with Nb can significantly improve the properties of HEAs. [ABSTRACT FROM AUTHOR]

Simple Summary: The dietary habits of animals that inhabit the same region can reveal valuable information about their food composition, nutritional strategies, and competition for resources. Analysis of their dietary habits can provide insight into differences in food consumption, thus identifying the potential overlap and competition for resources. In this study, we used DNA metabarcoding to investigate the winter dietary habits of Tibetan macaques and grey snub-nosed monkeys through an analysis of 40 fecal samples. The results showed that Tibetan macaques consumed plants from 117 families and 184 genera, while grey snub-nosed monkeys consumed plants from 109 families and 165 genera. The aim of the research was to assess the winter dietary habits of both monkey species living in the same area, to increase our knowledge of their food preferences and its composition, and to reveal the possible relationship between the overlap of their food niche and interspecific relations, providing useful information for the conservation of the resources in their natural habitat. Assessing the trophic niche and interspecific relationships between related species and determining how the species maintain differences in nutritional niches while coexisting in the same area are important topics in ecological research. Therefore, exploring the mechanism of food resource utilization, competition and coexistence among species distributed in the same region is important. In this study, we used fecal samples and metagenome sequencing technology to study the plant feeding habits and coexistence mechanisms of Tibetan macaques (Macaca thibetana) and grey snub-nosed monkeys (Rhinopithecus brelichi) within the same area. In the winter of 2020, we collected a total of 40 fecal samples from Tibetan macaques and grey snub-nosed monkeys; of those, 29 samples were considered valid and were analyzed using DNA metabarcoding. The results showed that in winter, Tibetan macaques consumed plants from 117 families and 184 genera, whereas grey snub-nosed monkeys consumed plants from 109 families and 165 genera. Diversity analysis revealed that there was a significant difference in the food composition of Tibetan macaques and grey snub-nosed monkeys. Tibetan macaques had a broader food niche width than grey snub-nosed monkeys at the family and genus levels. In winter, the food niches of Tibetan macaques and grey snub-nosed monkeys almost entirely overlapped (0.99). Our research provides detailed dietary data for Tibetan macaques and grey snub-nosed monkeys and valuable information that can aid in conservation efforts targeting these species. [ABSTRACT FROM AUTHOR]

With the upgrading of people's consumption patterns, the omni-channel supply chain becomes the mainstream form of e-commerce platform enterprise development. Aiming at two different e-commerce enterprises, we construct an evolutionary game model for enterprises' "online+offline" omni-channel construction strategy by self-build or cooperating with brick-and-mortar stores. It is based on the Stackelberg and Cournot competition model, combined with the omni-channel pricing strategy, using the theory of perfect rationality and bounded rationality, and combing the non-cooperative game and evolutionary game to realize. Moreover, the evolutionary game process is simulated. Through the dynamic changes of the system, the strategy selection behavior mechanism of the retail channel subjects is deeply analyzed. It is found that enterprises' strategy choices are influenced by both competitors and profits, and evolutionary stabilization strategies are not unique. In addition, changes in consumer loyalty, physical feelings, and sharing ratio during the evolutionary process will affect the stability rate of enterprises' behavioral choices. [ABSTRACT FROM AUTHOR]

The emission of greenhouse gases poses enormous pressure on current carbon emissions and carbon reduction. Accurate quantification of carbon emissions from coal-fired power plants is of great significance for achieving the dual carbon goal. To enable enterprises to better understand their carbon emissions, this study constructs a carbon emission model and carbon emission data accounting model for coal-fired power plants. Case data calculations and a carbon emission reduction analysis were conducted. The experiment showcases that the carbon sensitivity of the inner side of the boiler under control conditions is higher than that of the operating parameters controlled on the inner side of the steam turbine, with a maximum total value of 16.67 g/MJ; the annual average low calorific value of coal remains between 16,000 kJ/kg; the activity level of coal remains between 30,000 TJ; and the oxidation probability of coal char during combustion fluctuates, with a maximum of 99.8%. In the calculation of coal-fired carbon emissions, the fitting difference between the emissions of generator unit 1 and generator unit 2 is maintained within 2%. Overall, the CO2 emissions of power plants involved in the study are generally high. The model built through this study has well analyzed the carbon emissions of power plants. It is of great significance for the actual carbon emission reduction of coal-fired power plants. [ABSTRACT FROM AUTHOR]

Nighttime light data is a mainstay method in confirming and supporting other traditional economic data indicators, which in turn influence business and policy-making decisions. Accuracy in and clear definition of economic data and its related indicators are thus of great importance not only for analysis of urban development and related policies seeking sustainable development, but also plays a key role in whether or not these policies are successful. Discovering and recognizing the applications and limitations of nighttime light and other peripheral data could prove helpful in future data analysis and sustainable development policy. One possible limitation could exist in GDP decrease, and whether or not nighttime light would decrease accordingly, as most studies show nighttime light increase confirms economic growth, thus affecting the reliability of the data's correlation with economic data. This study utilizes nighttime light data in a cross-reference with GDP data during instances of global GDP shrinkage over the years of 2007–2017, split between 2007–2012 for the DMSP dataset and 2013–2017 for the VIIRs dataset. It seeks to establish through linear regression whether or not yearly average nighttime light data products show a positive correlation even during periods of economic decline, thereby providing a clearer understanding of the strengths and limitations of NTL as an economic validation indicator. Analysis shows that both years of global GDP decrease in turn also displayed global nighttime light decrease, in addition to linear regression giving satisfactory results pointing to a positive correlation over the timespan. The VIIRS data series resulted in higher regression coefficients, which is in line with the results of previous literature. [ABSTRACT FROM AUTHOR]

Fine-grained recognition has many applications in many fields and aims to identify targets from subcategories. This is a highly challenging task due to the minor differences between subcategories. Both modal missing and adversarial sample attacks are easily encountered in fine-grained recognition tasks based on multimodal data. These situations can easily lead to the model needing to be fixed. An Enhanced Framework for the Complementarity of Multimodal Features (EFCMF) is proposed in this study to solve this problem. The model's learning of multimodal data complementarity is enhanced by randomly deactivating modal features in the constructed multimodal fine-grained recognition model. The results show that the model gains the ability to handle modal missing without additional training of the model and can achieve 91.14% and 99.31% accuracy on Birds and Flowers datasets. The average accuracy of EFCMF on the two datasets is 52.85%, which is 27.13% higher than that of Bi-modal PMA when facing four adversarial example attacks, namely FGSM, BIM, PGD and C&W. In the face of missing modal cases, the average accuracy of EFCMF is 76.33% on both datasets respectively, which is 32.63% higher than that of Bi-modal PMA. Compared with existing methods, EFCMF is robust in the face of modal missing and adversarial example attacks in multimodal fine-grained recognition tasks. The source code is available at https://github.com/RPZ97/EFCMF (accessed on 8 January 2023). [ABSTRACT FROM AUTHOR]

In this paper, an all-Si resonant pressure microsensor based on eutectic bonding was developed, which can eliminate thermal expansion coefficient mismatches and residual thermal stresses during the bonding process. More specifically, the resonant pressure microsensor included an SOI wafer with a pressure-sensitive film embedded with resonators, which was eutectically bonded with a silicon cap for vacuum encapsulation. The all-Si resonant pressure microsensor was carefully designed and simulated numerically, where the use of the silicon cap was shown to effectively address temperature disturbances of the microsensor. The microsensor was then fabricated based on MEMS processes where eutectic bonding was adopted to link the SOI wafer and the silicon cap. The characterization results showed that the temperature disturbances of the resonant pressure microsensor encapsulated with the silicon cap were quantified as −0.82 Hz/°C of the central resonator and −2.36 Hz/°C of the side resonator within a temperature range from −40 °C to 80 °C, which were at least eight times lower than that of the microsensor encapsulated with the glass cap. Compared with the microsensor using the glass cap, the all-silicon microsensor demonstrated an accuracy improvement from 0.03% FS to 0.01% FS and a reduction in short-term frequency fluctuations from 3.2 Hz to 1.5 Hz. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common type of dementia, but its pathogenesis is not fully understood, and effective drugs to treat or reverse the progression of the disease are lacking. Long noncoding RNAs (lncRNAs) are abnormally expressed and deregulated in AD and are closely related to the occurrence and development of AD. In addition, the high tissue specificity and spatiotemporal specificity make lncRNAs particularly attractive as diagnostic biomarkers and specific therapeutic targets. Therefore, an in-depth understanding of the regulatory mechanisms of lncRNAs in AD is essential for developing new treatment strategies. In this review, we discuss the unique regulatory functions of lncRNAs in AD, ranging from Aβ production to clearance, with a focus on their interaction with critical molecules. Additionally, we highlight the advantages and challenges of using lncRNAs as biomarkers for diagnosis or therapeutic targets in AD and present future perspectives in clinical practice. [ABSTRACT FROM AUTHOR]

Simple Summary: Pangolins are one of the world's most trafficked mammals. Their numbers have decreased sharply due to their economic and assumed medicinal value in some parts of the world. Effective ex situ conservation requires appropriate nutrition to maintain a healthy population. However, due to the special feeding traits of pangolins and their high dependence on a natural ecosystem, many technical obstacles still limit the success of captive pangolin breeding. Therefore, based on the existing literature and practical experience, this review aims to compare the natural diet and successful diet of pangolins under human care, to outline the key factors of successful ex situ maintenance, and the strategies to improve their conservation success in animal care centers and in the wild. Pangolins are one of the world's most trafficked mammals. Since pangolins are highly adapted to ants and termites, they are important for controlling forest termite infestations. In addition to their ecological value, pangolins have economic and medicinal value. Currently, poaching and habitat destruction have radically reduced the number of pangolins, and Manis pentadactyla, Manis javanica, and Manis culionensis are now considered the most threatened pangolin species. In addition to the control of hunting and illegal trade, ex situ breeding is also a useful conservation method. However, many technical obstacles still limit the success of ex situ pangolin breeding. The special feeding traits of pangolins require a diet that meets nutritional and ethological needs. Based on the existing literature and practical experience, this review aims to compare the natural diet and successful diet in the human care of pangolins, to outline the key factors of successful ex situ maintenance from a dietary perspective, and the strategies to improve their conservation success in animal care centers and in the wild. The type of food used in successful pangolin protection agencies is quite variable in nutritional composition. In the diet of pangolins in the wild, the nutrient profile of different species of termites and ants and even the same species of termites and ants but different types (queens, soldiers, etc.) also displays differences. The crude protein content of some ants is higher than that of other foods, such as eggs, milk, and common cat food. The mineral and vitamin concentrations of ants also exceed many common food items, such as oil, meat, and eggs. However, not much is known about the bioavailability of minerals from ants and termites. Based on comparisons between foods, it is clear that the main difference between diets in the wild and in human care of pangolins is that the latter contains fewer insects and vitamins, such as vitamin E, vitamin A, and vitamin B2, and more carbohydrates and non-protein substances than the former. Although many successful dietary formulae have been developed, the pangolin's nutritional needs are still less well studied. A diet with the nutrient concentrations observed in the wild may add to successful ex situ conservation. [ABSTRACT FROM AUTHOR]

The combination of natural environment changes and human activities affects the growth of grasslands. In order to quantitatively assess the causes of spatial and temporal variation of grasslands in Xilingol, this study assessed the spatial and temporal evolution patterns of grassland health based on MOD13A1 long time series Normalized Difference Vegetation Index (NDVI) data from 2000–2019 using trend analysis. The geodetector model was used to explore the dominant drivers of spatial variation in grassland NDVI, combined with 34 factors covering natural environmental changes and human disturbances over the same period. The results show that the grasslands of Xilingol showed an overall recovery trend from 2000 to 2019, with an average annual NDVI growth rate of 0.0028/a, a monthly increasing rate of 0.0005/month, and 68.06% of the grassland at an average recovery level. Moisture-dominated natural climate change factors, such as Growing Season Precipitation (Prep2), Annual Mean Water Vapor Pressure (WVP), and Annual Mean Relative Humidity (RH), were the underlying cause of grassland health changes during the study period, with the highest explanatory factor being growing season precipitation (q value of 0.59 on a multi-year average). The influence of primary production value among human activities was greater, and the explanatory factor of tertiary production value showed an increasing trend. The interactions among natural and anthropogenic factors significantly enhances their explanatory credibility for NDVI, with the type of interaction dominated by the two-factor enhancement. Risk detection of the top 10 dominant drivers in terms of q statistic were carried out to obtain the threshold range of each driver in the high zone of grassland NDVI, which can provide a scientific reference for the sustainable restoration of grassland. [ABSTRACT FROM AUTHOR]

A systematic method is employed for the design and analysis of a small size eddy current (EC) displacement sensor. Simulations are first performed to determine the optimal winding structure and dimensions of the sensor. A linear-fitting approach is then developed for converting the AC displacement signal of the sensor to a DC signal. Finally, a compensation method is proposed for mitigating the temperature drift of the EC sensor under different working temperatures. The experimental results show that the proposed sensor has a sensitivity of approximately 3 μm, a working temperature range of 25–55 °C, and a linearity of ±1.025%. [ABSTRACT FROM AUTHOR]

Recycling channel construction plays an important role in the development of closed-loop supply chains. In particular, the emergence of online recycling channels has made up for the shortcomings of traditional recycling channels with poor information and limited markets. This paper constructs an evolutionary game model to investigate the cooperation between manufacturers and e-commerce platforms with government intervention or not. The result shows that whether an enterprise actively participates in the cooperative recycling depends on the actual cost of establishing the cooperative recycling system. Additionally, the government support and supervision will affect the actual cost of cooperation. When the actual cost of establishing a collaborative recovery system is very large, under the influence of government interventions, there will be two kinds of evolutionary results for enterprises, either with cooperation or not at the same time. On the contrary, when the actual cost is small or medium, both manufacturers and the platforms will choose to cooperate. Thus, government participation in a cooperative recovery system is the best strategic option. [ABSTRACT FROM AUTHOR]

A global challenge to sustainable development is land degradation, and to achieve land degradation neutrality, monitoring, mapping, and impact assessment of ongoing ecological restoration efforts is necessary. Here, we analyze the desertification process and role of restoration projects at a spatial and temporal scale in Mu Us Desert from 2001 to 2018. We used 17 years of data to (1) assess the vegetation trend including its significance and map land degradation based on Sustainable Development Goal Indicator 15.3.1 (2) address how vegetation activity has changed under the influence of restoration programs and climate change (3) estimate how successful are the positive human interventions to achieve Land degradation neutrality. Results showed an overall increasing vegetation trend (85.69% significant increasing) and a partial decreasing vegetation trend (1.33% significant decreasing) in Mu Us desert. Ecological restoration activities are found to be one of the key driving forces of vegetation restoration in the desert, however, limited impact of climatic factors on vegetation cover change was observed. Results revealed that 41.42% of total significant restoration is attributed to ecological restoration programs out of which 40.42% area has shown improvement in all three sub-indicators of land degradation. [ABSTRACT FROM AUTHOR]

Fertilization is a key event for sexually reproducing plants. Pollen–stigma adhesion, which is the first step in male–female interaction during fertilization, requires proper pollen wall patterning. Callose, which is a β-1.3-glucan, is an essential polysaccharide that is required for pollen development and pollen wall formation. Mutations in CALLOSE SYNTHASE 5 (CalS5) disrupt male meiotic callose accumulation; however, how CalS5 activity and callose synthesis are regulated is not fully understood. In this paper, we report the isolation of a kompeito-1 (kom-1) mutant defective in pollen wall patterning and pollen–stigma adhesion in Arabidopsis thaliana. Callose was not accumulated in kom-1 meiocytes or microspores, which was very similar to the cals5 mutant. The KOM gene encoded a member of a subclass of Rhomboid serine protease proteins that lacked active site residues. KOM was localized to the Golgi apparatus, and both KOM and CalS5 genes were highly expressed in meiocytes. A 220 kDa CalS5 protein was detected in wild-type (Col-0) floral buds but was dramatically reduced in kom-1. These results suggested that KOM was required for CalS5 protein accumulation, leading to the regulation of meiocyte-specific callose accumulation and pollen wall formation. [ABSTRACT FROM AUTHOR]

The large area estimation of forest canopy closure (FCC) using remotely sensed data is of high interest in monitoring forest changes and forest health, as well as in assessing forest ecological services. The accurate estimation of FCC over the regional or global scale is challenging due to the difficulty of sample acquisition and the slow processing efficiency of large amounts of remote sensing data. To address this issue, we developed a novel bounding envelope methodology based on vegetation indices (BEVIs) for determining vegetation and bare soil endmembers using the normalized differences vegetation index (NDVI), modified bare soil index (MBSI), and bare soil index (BSI) derived from Landsat 8 OLI and Sentinel-2 image within the Google Earth Engine (GEE) platform, then combined the NDVI with the dimidiate pixel model (DPM), one of the most commonly used spectral-based unmixing methods, to map the FCC distribution over an area of more than 90,000 km2. The key processing was the determination of the threshold parameter in BEVIs that characterizes the spectral boundary of vegetation and soil endmembers. The results demonstrated that when the threshold equals 0.1, the extraction accuracy of vegetation and bare soil endmembers is the highest with the threshold range given as (0, 0.3), and the estimated spatial distribution of FCC using both Landsat 8 and Sentinel-2 images were consistent, that is, the area with high canopy density was mainly distributed in the western mountainous region of Chifeng city. The verification was carried out using independent field plots. The proposed approach yielded reliable results when the Landsat 8 data were used (R2 = 0.6, RMSE = 0.13, and 1-rRMSE = 80%), and the accuracy was further improved using Sentinel-2 images with higher spatial resolution (R2 = 0.81, RMSE = 0.09, and 1-rRMSE = 86%). The findings demonstrate that the proposed method is portable among sensors with similar spectral wavebands, and can assist in mapping FCC at a regional scale while using multispectral satellite imagery. [ABSTRACT FROM AUTHOR]

Laser detection technology has manypromising applications in the field of motor speed and position measurement. Accurate and fast measurement of position information of spherical rotor is very important for motor control. In this paper, we propose a method for non-contact measurement of the angular velocity of a multi-DOF spherical motor using the Doppler effect of the laser, and further obtain the position information of the motor rotor. The horizontal laser beam from the laser generator is divided into a reference beam I and a measurement beam II through a beam splitter, and the measurement beam II reflects and undergoes Doppler effect after irradiating the rotating motor. The two beams pass through the photoelectric conversion module to obtain the corresponding frequency difference signals to derive the angular velocity and position information of the motor rotor. The correctness of the method is verified experimentally. The results show that the coordinate error of Z and Y axes is less than 2 mm, thatthe error of Z-axes is less than 0.2 mm, and that the method can better measure the spherical rotor position information of the motor. [ABSTRACT FROM AUTHOR]

A closed-loop supply chain operation is an effective way to improve the dual benefits of economy and environment. Inspired by the practice of closed-loop supply chain coordination, this paper attempts to investigate the supply chain operation strategies of different recycling modes and patent licensing strategies and consider the impact of government subsidies. We construct a multi-player game model of an original manufacturer, a remanufacturer, and a retailer under a waste product recycling mode, patent licensing strategy, and government subsidy system. We provide the operation strategies under different strategy combinations in the closed-loop supply chain, and then analyze their differences and the interests of various subjects. We further analyze the impact of government subsidies on the operation strategies of the supply chain. The results illustrate that when the original manufacturer adopts the fixed fee patent licensing strategy, the recycling price of waste products is higher. When the original manufacturer adopts the unit fee patent licensing strategy and the retailer and the remanufacturer participate in the recycling of waste products simultaneously, the original manufacturer will increase the unit patent licensing fee. When the remanufacturer recycles waste products alone, consumers can obtain greater unit income in the waste product recycling market. Different government subsidies have different effects on the wholesale price and the retail price of new products and remanufactured products. [ABSTRACT FROM AUTHOR]

The simple and robust log-likelihood ratio (LLR) computation of coded Multiple Phase Shift Keying (MPSK) signals in Wireless Sensor Networks (WSNs) is considered under both phase noncoherent and Rayleigh fading channels for healthcare applications. We first simplify the optimal LLR for phase noncoherent channel, the estimation of the instantaneous channel state information (CSI) for both the fading amplitude and the additive white Gaussian noise (AWGN) is successfully avoided, and the complexity-intensive process for zero-order Bessel function of the first kind is also perfectly eliminated. Furthermore, we also develop the simplified LLR under Rayleigh fading channel. Correspondingly, the variance estimation for both AWGN and the statistical characteristic of the fading amplitude is no longer required, and the complicated process for implementation of the exponential function is also successfully avoided. Compared to the calculation of optimal LLR with full complexity, the proposed method is implementation-friendly, which is practically desired for energy-limited WSNs. The simulations are developed in the context of low-density parity-check (LDPC) codes, and the corresponding results show that the detection performance is extremely close to that of the full-complexity LLR metrics. That is, the performance degradation is efficiently prevented, whereas complexity reduction is also successfully achieved. [ABSTRACT FROM AUTHOR]

Evaluating the regional trends of air pollution disaster risk in areas of heavy industry and economically developed cities is vital for regional sustainable development. Until now, previous studies have mainly adopted a traditional weighted comprehensive evaluation method to analyze the air pollution disaster risk. This research has integrated principal component analysis (PCA), a genetic algorithm (GA) and a backpropagation (BP) neural network to evaluate the regional disaster risk. Hazard risk, hazard-laden environment sensitivity, hazard-bearing body vulnerability and disaster resilience were used to measure the degree of disaster risk. The main findings were: (1) the air pollution disaster risk index of Liaoning Province, Beijing, Shanghai and Guangdong Province increased year by year from 2010 to 2019; (2) the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) of each regional air pollution disaster risk index in 2019, as predicted by the PCA-GA-BP neural network, were 0.607, 0.317 and 20.3%, respectively; (3) the predicted results were more accurate than those using a PCA-BP neural network, GA-BP neural network, traditional BP neural network, support vector regression (SVR) or extreme gradient boosting (XGBoost), which verified that machine learning could be used as a method of air pollution disaster risk assessment to a considerable extent. [ABSTRACT FROM AUTHOR]

In this study, forchlorfenuron (CPPU) was coupled with succinic anhydride to yield a CPPU hapten (CPPU-COOH), and a high-affinity monoclonal antibody (mAb) that can specifically recognize CPPU was produced. Using this mAb as a recognition reagent, a sensitive indirect competitive enzyme-linked immunosorbent assay (icELISA) for CPPU was optimized, which exhibits an IC50 of 1.04 ng/mL, a limit of detection of 0.16 ng/mL, and a linear range of 0.31–3.43 ng/mL for CPPU. Cross-reactivity percentages with six analogues were all below 6%. The average recovery rates for cucumber and orange samples were from 85.23% to 119.14%. The analysis results of this icELISA showed good consistency with those from liquid chromatography mass spectrometry. These results suggest that the proposed icELISA provides a sensitive, specific, and reliable strategy for CPPU detection in food samples. [ABSTRACT FROM AUTHOR]

This paper presents a mechanical control method for precise weeding based on deep learning. Deep convolutional neural network was used to identify and locate weeds. A special modular weeder was designed, which can be installed on the rear of a mobile platform. An inverted pyramid-shaped weeding tool equipped in the modular weeder can shovel out weeds without being contaminated by soil. The weed detection and control method was implemented on an embedded system with a high-speed graphics processing unit and integrated with the weeder. The experimental results showed that even if the speed of the mobile platform reaches 20 cm/s, the weeds can still be accurately detected and the position of the weeds can be located by the system. Moreover, the weeding mechanism can successfully shovel out the roots of the weeds. The proposed weeder has been tested in the field, and its performance and weed coverage have been verified to be precise for weeding. [ABSTRACT FROM AUTHOR]

Accurate information on tree species is in high demand for forestry management and further investigations on biodiversity and environmental monitoring. Over regional or large areas, distinguishing tree species at high resolutions faces the challenges of a lack of representative features and computational power. A novel methodology was proposed to delineate the explicit spatial distribution of six dominant tree species (Pinus tabulaeformis, Quercus mongolia, Betula spp., Populus spp., Larix spp., and Armeniaca sibirica) and one residual class at 10 m resolution. Their spatial patterns were analyzed over an area covering over 90,000 km2 using the analysis-ready large volume of multisensor imagery within the Google Earth engine (GEE) platform afterwards. Random forest algorithm built into GEE was used together with the 20th and 80th percentiles of multitemporal features extracted from Sentinel-1/2, and topographic features. The composition of tree species in natural forests and plantations at the city and county-level were performed in detail afterwards. The classification achieved a reliable accuracy (77.5% overall accuracy, 0.71 kappa), and the spatial distribution revealed that plantations (Pinus tabulaeformis, Populus spp., Larix spp., and Armeniaca sibirica) outnumber natural forests (Quercus mongolia and Betula spp.) by 6% and were mainly concentrated in the northern and southern regions. Arhorchin had the largest forest area of over 4500 km2, while Hexingten and Aohan ranked first in natural forest and plantation area. Additionally, the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. We suggest focusing more on the suitable areas modeling for tree species using species' distribution models and environmental factors based on the classification results rather than field survey plots in further studies. [ABSTRACT FROM AUTHOR]

As international trade and freight volumes increase, there is a growing port congestion problem, leading to the long truck queues at US marine terminal gates. To address this problem, some countermeasures have been proposed and implemented for reducing truck queue length at marine terminals. To assess the effectiveness of these countermeasures, a method for accurately estimating terminal gate truck queue length is needed. This study developed a new method, named the state-dependent approximation method, for estimating the truck queue length at marine terminals. Based on the simulation of the truck queuing system, it was found that it takes several hours for the truck queue length to reach its steady state, and neglecting the queue formation (queue dispersion) processes will cause overestimation (underestimation) of truck queue length. The developed model can take into account the queue formation and dispersion processes, and it can be used to estimate the truck queue length caused by short-term oversaturation at marine terminals. For model evaluation, a simulation-based case study was conducted to evaluate the prediction accuracy of the developed model by comparing its results with the simulated queue lengths and the results of other four existing methods, including the fluid flow model, the M/M/S queuing model, and a simulation-based regression model developed a previous study. The evaluation results indicate that the developed model outperformed the other four modeling methods for different states of queue formation and dispersion processes. In addition, this new method can accurately estimate the truck queue length caused by the short-term system oversaturation during peak hours. Therefore, it will be useful for assessing the effectiveness of the countermeasures that are targeted at reducing the peak-hour congestion at marine terminals. [ABSTRACT FROM AUTHOR]

Free-range chicken farming allows egg-laying hens to move freely through their environment and perform their natural behavior, including laying her eggs. However, it takes time to gather these eggs manually, giving rise to high labor costs. This study proposes a smart mobile robot for poultry farms that can recognize eggs of two different colors on free-range farms. The robot can also pick up and sort eggs without damaging them. An egg feature extraction method with automatic thresholding is employed to detect both white and brown eggs, and a behavior-based navigation method is applied to allow the robot to reach the eggs while avoiding obstacles. The robot can move towards the position of each egg via visual tracking. Once the egg is within the collection area of the robot, it is gathered, sorted and stored in the tank inside the robot. Experiments are carried out in an outdoor field of size 5 m × 5 m under different climatic conditions, and the results showed that the average egg recognition rate is between 94.7% and 97.6%. The proposed mobile poultry robot is low in production cost and simple in operation. It can provide chicken farmers with automatic egg gathering on free-range farms. [ABSTRACT FROM AUTHOR]

Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research. [ABSTRACT FROM AUTHOR]

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10−4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models. [ABSTRACT FROM AUTHOR]

Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after the Greater Hinggan Mountain forest fire in the year 1987. The influence of phenology on vegetation monitoring was analyzed through three aspects: band characteristics, normalized difference vegetation index (NDVI) and disturbance index (DI) values. The comparison of the band characteristics shows that in the blue band and the red band, the average reflectance values of the study area after eliminating phenological influence is lower than that without eliminating the phenological influence in each year. In the infrared band, the average reflectance value after eliminating the influence of phenology is greater than the value with phenological influence in almost every year. In the second shortwave infrared band, the average reflectance value without phenological influence is lower than that with phenological influence in almost every year. The analysis results of NDVI and DI values in the study area of each year show that the NDVI and DI curves vary considerably without eliminating the phenological influence, and there is no obvious trend. After eliminating the phenological influence, the changing trend of the NDVI and DI values in each year is more stable and shows that the forest in the region was impacted by other factors in some years and also the recovery trend. The results show that the spatio-temporal data fusion approach used in this study can eliminate vegetation phenology effectively and the elimination of the phenology impact provides more reliable information about changes in vegetation regions affected by the forest fires. The results will be useful as a reference for future monitoring and management of forest resources. [ABSTRACT FROM AUTHOR]

This paper presents resonant pressure micro sensors based on dual double ended tuning fork (DETF) resonators, which are electrostatically excited and piezoresistively detected. In operation, the barometric pressure under measurement bends the pressure sensitive diaphragm functioning as the anchor of DETF resonators and therefore produces eigenfrequency shifts of the resonators. Theoretical analyses and finite element analyses (FEA) were conducted to optimize the key geometries of the DETF resonators with enhanced signal to noise ratios (SNRs). In fabrications, key steps including deep reactive ion etching (DRIE) and anodic bonding were used, where sleeve holes were adopted to form electrical connections, leading to high-efficiency structure layout. Experimental results indicate that the presented micro sensors produced SNRs of 63.70 ± 3.46 dB in the open-loop characterizations and differential sensitivities of 101.3 ± 1.2 Hz/kPa, in the closed-loop characterizations. In addition, pressure cycling tests with a pressure range of 5 to 155 kPa were conducted, revealing that the developed micro sensors demonstrated pressure shifts of 83 ± 2 ppm, pressure hysteresis of 67 ± 3 ppm, and repeatability errors of 39 ± 2 ppm. Thus, the developed resonant pressure micro sensors may potentially function as an enabling tool for barometric pressure measurements. [ABSTRACT FROM AUTHOR]

This study proposes a microfabricated resonant pressure sensor in which a pair of double-ended tuning forks were utilized as resonators where comb electrodes and single-crystal silicon-based piezoresistors were used for electrostatic excitation and piezoresistive detection, respectively. In operations, pressures under measurements deform the pressure-sensitive diaphragm to cause stress variations of two resonators distributed on the central and side positions of the pressure-sensitive diaphragm, where the corresponding changes of the intrinsic resonant frequencies are then captured piezoresistively. The developed resonant pressure sensors were fabricated based on MEMS with open-loop and closed-loop characterizations conducted. Key sensing parameters including quality factors, differential pressure/temperature sensitivities and fitting errors were quantified as higher than 17,000, 48.24 Hz/kPa, 0.15 Hz/°C and better than 0.01% F.S. (140 kpa), respectively. In comparison to previously reported resonant pressure sensors driven by parallel-plate electrodes, the developed sensor in this study is featured with a lower temperature sensitivity and a higher stability. [ABSTRACT FROM AUTHOR]

The use of engineered cementitious composite (ECC) has attracted extensive attention in recent years because of the highly enhanced ductility owing to its unique strain-hardening behavior. In this paper, an electromechanical impedance-based technique is used to monitor the structural damage of RC beams strengthened with an ECC layer at the tensile zone. To achieve this purpose, three specimens are tested under bending loads to evaluate the proposed damage detection methodology. Five externally bonded PZT transducers are uniformly distributed at the surface of the ECC layer of the beams to measure the output conductance signatures in a healthy state and in different damage scenarios induced by different load levels. Test results showed that discrepancies exist between the signals measured in the intact state and each damage state, which can be used to evaluate the structural integrity changes. To assess the damage of ECC-concrete composite beams quantitatively, the statistical scalar index-root mean square deviation (RMSD) is used as the index, which can be calculated from the variations of conductance measurements of PZT sensors. The damage index values of the uniformly distributed PZT sensors provided cogent evidence of damage and revealed the evolution of structural damage. The crack patterns of beams at different damage levels are compared with the damage index values, and it shows the damage location can be derived from the measured conductance signatures of an array of PZT transducers. [ABSTRACT FROM AUTHOR]

This paper presents a resonant pressure microsensor with the measurement range of 1 MPa suitable for the soaring demands of industrial gas pressure calibration equipment. The proposed microsensor consists of an SOI layer as a sensing element and a glass cap for vacuum packaging. The sensing elements include a pressure-sensitive diaphragm and two resonators embedded in the diaphragm by anchor structures. The resonators are excited by a convenient Lorentz force and detected by electromagnetic induction, which can maintain high signal outputs. In operation, the pressure under measurement bends the pressure-sensitive diaphragm of the microsensor, producing frequency shifts of the two underlining resonators. The microsensor structures were designed and optimized using finite element analyses and a 4" SOI wafer was employed in fabrications, which requires only one photolithographic step. Experimental results indicate that the Q-factors of the resonators are higher than 25,000 with a differential temperature sensitivity of 0.22 Hz/°C, pressure sensitivities of 6.6 Hz/kPa, and −6.5 Hz/kPa, which match the simulation results of differential temperature sensitivity of 0.2 Hz/°C and pressure sensitivities of ±6.5 Hz/kPa. In addition, characterizations based on a closed-loop manner indicate that the presented sensor demonstrates low fitting errors within 0.01% FS, high accuracy of 0.01% FS in the pressure range of 20 kPa to 1 MPa and temperature range of −55 to 85 °C, and the long-term stability within 0.01% FS in a 156-day period under the room temperature. [ABSTRACT FROM AUTHOR]

Recombination among porcine reproductive and respiratory syndrome viruses (PRRSVs), coupled with point mutations, insertions, and deletions occurring in the genome, is considered to contribute to the emergence of new variants. Here, we report the complete genome sequences of a PRRSV field strain, designated SCN17, isolated from a RespPRRS MLV-vaccinated piglet in China in 2017. Sequence alignment revealed that SCN17 had discontinuous 131-amino acid (111 + 1 + 19-aa) deletion in the NSP2-coding region identical to that of NADC30 when compared to VR-2332. Notably, the strain, SCN17, contained an additional 1-aa deletion in NSP2, a 1-aa deletion in ORF5, and a unique 3-nt deletion in the 3′-UTR. Phylogenetic analysis showed that SCN17 clustered into NADC30-like lineage based on ORF5 genotyping, whereas it belonged to an inter-lineage between the NADC30-like and VR-2332-like lineages as established based on the full-length genome. Importantly, the SCN17 was identified as a novel virus recombined between a NADC30-like (moderately pathogenic), a JXA1-like (highly pathogenic), and an attenuated vaccine strain, RespPRRS MLV (parental strain VR-2332). Furthermore, we tested its pathogenicity in piglets. SCN17 infection caused a persistent fever, moderate interstitial pneumonia, and increased the viremia and antibody levels in the inoculated piglets. Of note, all SCN17-infected piglets survived throughout the study. The new virus was showed to be a moderately virulent isolate and have lower pathogenicity than HP-PRRSV strain, SCwhn09CD. Our results provide evidence for the continuing evolution of PRRSV field strain by genetic recombination and mutation leading to outbreaks in the vaccinated pig populations in China. [ABSTRACT FROM AUTHOR]

This paper presents a resonant pressure microsensor relying on electrostatic excitation and piezoresistive detection where two double-ended tuning forks were used as resonators, enabling differential outputs. Pressure under measurement caused the deformation of the pressure sensitive membrane, leading to stress buildup of the resonator under electrostatic excitation with a corresponding shift of the resonant frequency detected piezoresistively. The proposed microsensor was fabricated by simplified SOI-MEMS technologies and characterized by both open-loop and closed-loop circuits, producing a quality factor higher than 10,000, a sensitivity of 79.44 Hz/kPa and an accuracy rate of over 0.01% F.S. In comparison to the previously reported resonant piezoresistive sensors, the proposed device used single-crystal silicon as piezoresistors, which was featured with low DC biased voltages, simple sensing structures and fabrication steps. In addition, the two double-ended tuning forks were used as resonators, producing high quality factors and differential outputs, which further improved the sensor performances. [ABSTRACT FROM AUTHOR]

Recombination among porcine reproductive and respiratory syndrome viruses (PRRSVs) is thought to contribute to the emergence of new PRRSV variants. In this study, two newly emerged PRRSV strains, designated SCcd16 and SCya17, are isolated from lung tissues of piglets in Southwestern China. Genome comparative analysis reveals that SCcd16/SCya17 exhibit 93.1%/93.2%, 86.9%/87.0%, 85.3%/85.7%, and 83.6%/82.0% nucleotide similarity to PRRSVs JXA1, VR-2332, QYYZ and NADC30, respectively. They only exhibit 44.8%/45.1% sequence identity with LV (PRRSV-1), indicating that both emergent strains belong to the PRRSV-2 genotype. Genomic sequence alignment shows that SCcd16 and SCya17 have the same discontinuous 30-amino acid (aa) deletion in Nsp2 of the highly pathogenic Chinese PRRSV strain JXA1, when compared to strain VR-2332. Notably, SCya17 shows a unique 5-nt deletion in its 3’-UTR. Phylogenetic analysis shows that both of the isolates are classified in the QYYZ-like lineage based on ORF5 genotyping, whereas they appear to constitute an inter-lineage between JXA1-like and QYYZ-like lineages based on their genomic sequences. Furthermore, recombination analyses reveal that the two newly emerged PRRSV isolates share the same novel recombination pattern. They have both likely originated from multiple recombination events between lineage 8 (JXA1-like), lineage 1 (NADC30-like), and lineage 3 (QYYZ-like) strains that have circulated in China recently. The genomic data from SCcd16 and SCya17 indicate that there is on going evolution of PRRSV field strains through genetic recombination, leading to outbreaks in the pig populations in Southwestern China. [ABSTRACT FROM AUTHOR]

Since the emergence of NADC30-like porcine reproductive and respiratory syndrome virus (PRRSV) in China in 2013, PRRSVs have undergone rapid evolution. In this study, a novel variant of PRRSV strain (designated SCcd17) was successfully isolated from piglets with clinical signs in Sichuan Province in China in 2017, and the complete genomic sequence was determined. The genome of this new isolate was 15,015 nucleotides (nt) long, and comparative analysis revealed that SCcd17 exhibited 90.2%, 85.2%, 84.9%, and 84.0% nucleotide similarity to PRRSVs NADC30, JXA1, CH-1a, and VR-2332, respectively. Phylogenetic analysis indicated that the SCcd17 strain was classified into the NADC30-like sub-genotype, in which all the strains contained the unique discontinuous 131-amino acid deletion in nonstructural protein 2 (nsp2) when compared to VR-2332-like viruses. Notably, extensive amino acid substitutions were observed in nsp2 and a unique single amino acid deletion at position 33 of the GP5 is being described for the first time. Strikingly, recombination analysis revealed that SCcd17 was the result of recombination between the NADC30-like, JXA1-like, and VR-2332-like strains at five recombination breakpoints: nsp1α (nt 641), nsp3 (nt 5141), nsp10 (nt 9521), open reading frame 3 (ORF3) (nt 12,581), and ORF4 (nt 13,021). The genomic data of SCcd17 will be helpful for understanding the role of genomic recombination in the evolution of PRRSV. [ABSTRACT FROM AUTHOR]

Exposure to Formaldehyde (FA) results in many pathophysiological symptoms, however the underlying mechanisms are not well understood. Given the complicated modulatory role of intestinal microbiota on human health, we hypothesized that interactions between FA and the gut microbiome may account for FA’s toxicity. Balb/c mice were allocated randomly to three groups: a control group, a methanol group (0.1 and 0.3 ng/mL MeOH subgroups), and an FA group (1 and 3 ng/mL FA subgroups). Groups of either three or six mice were used for the control or experiment. We applied high-throughput sequencing of 16S ribosomal RNA (rRNA) gene approaches and investigated possible alterations in the composition of mouse gut microbiota induced by FA. Changes in bacterial genera induced by FA exposure were identified. By analyzing KEGG metabolic pathways predicted by PICRUSt software, we also explored the potential metabolic changes, such as alpha-Linolenic acid metabolism and pathways in cancer, associated with FA exposure in mice. To the best of our knowledge, this preliminary study is the first to identify changes in the mouse gut microbiome after FA exposure, and to analyze the relevant potential metabolisms. The limitation of this study: this study is relatively small and needs to be further confirmed through a larger study. [ABSTRACT FROM AUTHOR]

Habitat quality (HQ) is widely considered to be a proxy indicator for biodiversity. Assessing the dynamic changes in HQ induced by land-use and land-cover (LULC) changes could provide a scientific perspective for regional sustainable development, especially in the ecologically fragile karst plateau mountainous areas. We selected nine landscape metrics to examine LULC dynamics in the Key Biodiversity Conservation Project Area of Wuling Mountains in Guizhou province, China, based on the land-use data for the 1990-2018 period. HQ was evaluated using the InVEST model and the topographic gradient effect on HQ was analyzed using the topographic position index. The results showed that the dominant land categories in the study area were arable land, grassland, and forestland. Land transformation was mainly characterized by a shift from cultivated land to construction land and forestland, and a mutual conversion between grassland and forestland. The changes improved landscape heterogeneity and the degree of fragmentation. The HQ of the study area was high, although exhibited a declining trend from 1990 to 2018. The eastern and western parts had relatively high HQ, whereas the southern and northern parts had low HQ. The topographic gradient had a significant effect on spatial patterns of HQ. High HQ distribution was consistent with that of forestland and was dominant along the topographic gradient, while low HQ distribution was consistent with that of construction land and cultivated land and was dominant along the topographic gradient.

This paper presents the fabrication and characterization of a resonant pressure microsensor based on SOI-glass wafer-level vacuum packaging. The SOI-based pressure microsensor consists of a pressure-sensitive diaphragm at the handle layer and two lateral resonators (electrostatic excitation and capacitive detection) on the device layer as a differential setup. The resonators were vacuum packaged with a glass cap using anodic bonding and the wire interconnection was realized using a mask-free electrochemical etching approach by selectively patterning an Au film on highly topographic surfaces. The fabricated resonant pressure microsensor with dual resonators was characterized in a systematic manner, producing a quality factor higher than 10,000 (~6 months), a sensitivity of about 166 Hz/kPa and a reduced nonlinear error of 0.033% F.S. Based on the differential output, the sensitivity was increased to two times and the temperature-caused frequency drift was decreased to 25%.