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Abstract Background Traditional chemotherapeutic agents suffer from a lack of selectivity, poor targeting ability, and drug resistance. Developing tumor-specific therapies is crucial for precisely eliminating tumors while circumventing toxicity to normal tissues. Disulfiram (DSF), an FDA-approved drug for treating alcohol dependence, exhibits antitumor effect by forming complexes with copper ions (Cu(DDC)2). Here, we developed a Cu-doped polydopamine-based nanosystem (DSF@CuPDA-PEGM) to achieve in situ generation of toxic Cu(DDC)2. Results In cancer cells with elevated H2O2 contents, CuPDA responsively degrades to release Cu ions and DSF, allowing on-site synthesis of Cu(DDC)2 with potent antitumor activity. DSF@CuPDA-PEGM exhibits excellent therapeutic efficacy against both drug-sensitive and drug-resistant cancer cells while minimizing toxicity to noncancerous cells. Moreover, DSF@CuPDA-PEGM promotes the immune response by inducing cancer cell immunogenic death, thereby augmenting anti-PD-1-based immune checkpoint blockade therapy. Conclusion A tumor-specifically degradable Cu-doped polydopamine-based nanosystem is developed to achieve in situ synthesis of antitumor compounds, providing a promising approach to precisely eliminate tumors and heighten chemo-immunotherapy. Graphical Abstract

Abstract Background Mountain ecosystems are critical hotspots of global biodiversity, yet the dynamics of community assembly along their elevational gradients are not well understood. This gap is primarily due to the complexity of environmental and biotic interactions that influence species distribution and community structure. Although extensive research has been conducted on certain taxa, such as small mammals and bats, comprehensive studies encompassing entire mammal assemblages are lacking. Our research aims to bridge this gap by examining the taxonomic, phylogenetic, and functional diversity, as well as the assemblage structures of mammal communities across elevational gradients in the Central and East Himalayas, exploring how diverse ecological and evolutionary processes impact community assembly. Methods We analyzed mammal elevational diversity patterns using species richness, functional diversity (FD), and phylogenetic diversity (PD). We compared the observed values of community structure, such as mean pairwise phylogenetic distance (MPD) and mean pairwise functional distance (MFD), with null-model corrected effect sizes to identify patterns and processes of community assembly. Using structural equation modeling and hierarchical partitioning, we investigated the relationships between climate, productivity, and various facets of diversity, describing the organization of each component across different elevations. Results Taxonomic, functional, and phylogenetic diversity generally decreased with elevation but showed varied patterns such as mid-elevation peaks, low-elevation plateaus, or monotonic declines across different regions. Richness-controlled functional diversity increased towards mid-low elevations and decreased at higher elevations in both regions, whereas richness-controlled phylogenetic diversity lacked consistent patterns. Phylogenetic structures tended to cluster from mid to high elevations, indicating closer relationships than those observed in random communities, likely due to significant environmental turnover near tree lines. Functional structure showed greater clustering at high elevations and increased over-dispersion at lower elevations, suggesting that species are more functionally similar than expected at higher elevations and more diverse at lower elevations. Our results revealed that environmental factors, evolutionary histories, and trait-driven ecological processes collectively shape species richness along these gradients. Conclusions Our results showed incongruent community structures across phylogenetic and functional diversity. Generally, functional traits are closely linked to environmental conditions, reducing the chance of observing traits that are misaligned with their surroundings. Species with similar ecological roles or distinct evolutionary lineages often show convergent adaptations to highland environments. Additionally, our findings emphasize that community assembly varies with the biogeography and diversification history of individual mountain ranges, complicating the development of a generalized theory. Using multiple measures is important for accurate community structure assessments and effective conservation planning, as variable elevational patterns exist across different diversity dimensions.

Abstract Plasmonic materials can generate strong electromagnetic fields to boost the Raman scattering of surrounding molecules, known as surface-enhanced Raman scattering. However, these electromagnetic fields are heterogeneous, with only molecules located at the ‘hotspots’, which account for ≈ 1% of the surface area, experiencing efficient enhancement. Herein, we propose patterned plasmonic trimers, consisting of a pair of plasmonic dimers at the bilateral sides and a trap particle positioned in between, to address this challenge. The trimer configuration selectively directs probe molecules to the central traps where ‘hotspots’ are located through chemical affinity, ensuring a precise spatial overlap between the probes and the location of maximum field enhancement. We investigate the Raman enhancement of the Au@Al2O3-Au-Au@Al2O3 trimers, achieving a detection limit of 10−14 M of 4-methylbenzenethiol, 4-mercaptopyridine, and 4-aminothiophenol. Moreover, single-molecule SERS sensitivity is demonstrated by a bi-analyte method. Benefiting from this sensitivity, our approach is employed for the early detection of lung tumors using fresh tissues. Our findings suggest that this approach is sensitive to adenocarcinoma but not to squamous carcinoma or benign cases, offering insights into the differentiation between lung tumor subtypes.

Species range size reflects the underlying niche of species and directly affects the spatial pattern of species richness. However, the exploration of community mean range size has received relatively limited attention compared to the emphasis on species richness patterns. Additionally, few studies have tested how the rescue effect (the proximity of the distribution limits of potentially interacting species) relates to species richness. In this study, we used a two-year dataset of breeding birds along an elevational gradient in the Lebu Valley, Eastern Himalayas of China, aiming to explore (a) the association between the mean elevational range size of bird community and elevation, using three different ways to calculate the mean elevational range size (Stevens' method, midpoint method and specimen method), (b) examine the influence of drivers (including temperature seasonality, precipitation seasonality, net primary productivity, habitat diversity, geometric constraint, and interspecific competition) on the mean elevational range size pattern, and (c) investigate the effect of rescue effect on species richness pattern. Our results showed that the pattern of mean elevational range size and its underlying drivers vary significantly depending on the assessment method used. Using Stevens' method, which were calculated based on species occurrences, we observed a low-plateau pattern in mean elevational range size. In contrast, the midpoint and specimen methods, which calculate based on the species' distributing midpoint and abundance center, respectively, revealed a mid-peak pattern. Besides, our study supports several ecological hypotheses, including the ambient energy hypothesis, mid-domain hypothesis, and interspecific competition hypothesis. Furthermore, we found that geometric constraints and interspecific competition were the most influential predictors of mean elevational range size patterns, irrespective of the calculation method. Additionally, a significantly positive correlation was observed between the overlap of range margins and species richness, suggesting a contribution of the rescue effect to species richness patterns. The significance of the correlation is associated with the definition of species range size boundaries. Remarkably, this correlation becomes significant only when species range margins reach their 43 % threshold; beyond the 78 % range margin, the increase in correlation strength becomes minimal. Moreover, the rescue effect from low-elevation species was significantly more pronounced than that from high-elevation species, resulting in a species richness hump skewed towards lower elevations. Our findings contribute to validating the applicability and limitations of hypotheses related to species distribution, enhancing both theoretical and empirical aspects of research. This provides deeper insights for a more comprehensive and profound understanding of the local dynamics and interactions within the ecosystem.

Soil salinization is one of the primary factors contributing to land degradation in arid areas, severely restricting the sustainable development of agriculture and the economy. Satellite remote sensing is essential for real-time, large-scale soil salinity content (SSC) evaluation. However, some satellite images have low temporal resolution and are affected by weather conditions, leading to the absence of satellite images synchronized with ground observations. Additionally, some high-temporal-resolution satellite images have overly coarse spatial resolution compared to ground features. Therefore, the limitations of these spatiotemporal features may affect the accuracy of SSC evaluation. This study focuses on the arable land in the Manas River Basin, located in the arid areas of northwest China, to explore the potential of integrated spatiotemporal data fusion and deep learning algorithms for evaluating SSC. We used the flexible spatiotemporal data fusion (FSDAF) model to merge Landsat and MODIS images, obtaining satellite fused images synchronized with ground sampling times. Using support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models, we evaluated the differences in SSC evaluation results between synchronized and unsynchronized satellite images with ground sampling times. The results showed that the FSDAF model’s fused image was highly similar to the original image in spectral reflectance, with a coefficient of determination (R2) exceeding 0.8 and a root mean square error (RMSE) below 0.029. This model effectively compensates for the missing fine-resolution satellite images synchronized with ground sampling times. The optimal salinity indices for evaluating the SSC of arable land in arid areas are S3, S5, SI, SI1, SI3, SI4, and Int1. These indices show a high correlation with SSC based on both synchronized and unsynchronized satellite images with ground sampling times. SSC evaluation models based on synchronized satellite images with ground sampling times were more accurate than those based on unsynchronized images. This indicates that synchronizing satellite images with ground sampling times significantly impacts SSC evaluation accuracy. Among the three models, the CNN model demonstrates the highest predictive accuracy in SSC evaluation based on synchronized and unsynchronized satellite images with ground sampling times, indicating its significant potential in image prediction. The optimal evaluation scheme is the CNN model based on satellite image synchronized with ground sampling times, with an R2 of 0.767 and an RMSE of 1.677 g·kg−1. Therefore, we proposed a framework for integrated spatiotemporal data fusion and CNN algorithms for evaluating soil salinity, which improves the accuracy of soil salinity evaluation. The results provide a valuable reference for the real-time, rapid, and accurate evaluation of soil salinity of arable land in arid areas.

Abstract Self-adaptability is highly envisioned for artificial devices such as robots with chemical noses. For this goal, seeking catalysts with multiple and modulable reaction pathways is promising but generally hampered by inconsistent reaction conditions and negative internal interferences. Herein, we report an adaptable graphitic C6N6-based copper single-atom catalyst. It drives the basic oxidation of peroxidase substrates by a bound copper-oxo pathway, and undertakes a second gain reaction triggered by light via a free hydroxyl radical pathway. Such multiformity of reactive oxygen-related intermediates for the same oxidation reaction makes the reaction conditions capable to be the same. Moreover, the unique topological structure of CuSAC6N6 along with the specialized donor-π-acceptor linker promotes intramolecular charge separation and migration, thus inhibiting negative interferences of the above two reaction pathways. As a result, a sound basic activity and a superb gain of up to 3.6 times under household lights are observed, superior to that of the controls, including peroxidase-like catalysts, photocatalysts, or their mixtures. CuSAC6N6 is further applied to a glucose biosensor, which can intelligently switch sensitivity and linear detection range in vitro.

Ultrasonic wireless power transfer technology (UWPT) represents a key technology employed for energizing implantable medical devices (IMDs). In recent years, aluminum nitride (AlN) has gained significant attention due to its biocompatibility and compatibility with complementary metal-oxide-semiconductor (CMOS) technology. In the meantime, the integration of scandium-doped aluminum nitride (Al90.4%Sc9.6%N) is an effective solution to address the sensitivity limitations of AlN material for both receiving and transmission capabilities. This study focuses on developing a miniaturized UWPT receiver device based on AlScN piezoelectric micro-electromechanical transducers (PMUTs). The proposed receiver features a PMUT array of 2.8 × 2.8 mm2 comprising 13 × 13 square elements. An acoustic matching gel is applied to address acoustic impedance mismatch when operating in liquid environments. Experimental evaluations in deionized water demonstrated that the power transfer efficiency (PTE) is up to 2.33%. The back-end signal processing circuitry includes voltage-doubling rectification, energy storage, and voltage regulation conversion sections, which effectively transform the generated AC signal into a stable 3.3 V DC voltage output and successfully light a commercial LED. This research extends the scope of wireless charging applications and paves the way for further device miniaturization by integrating all system components into a single chip in future implementations.

In this paper, we propose a novel method for temperature measurement using surface acoustic wave (SAW) temperature sensors on curved or irregular surfaces. We integrate SAW resonators onto flexible printed circuit boards (FPCBs) to ensure better conformity of the temperature sensor with the surface of the object under test. Compared to traditional rigid PCBs, FPCBs offer greater dynamic flexibility, lighter weight, and thinner thickness, which make them an ideal choice for making SAW devices working for temperature measurements under curved surfaces. We design a temperature sensor array consisting of three devices with different operating frequencies to measure the temperature at multiple points on the surface of the object. To distinguish between different target points in the sensor array, each sensor operates at a different frequency, and the operating frequency bands do not overlap. This differentiation is achieved using Frequency Division Multiple Access (FDMA) technology. Experimental results indicate that the frequency temperature coefficients of these sensors are −30.248 ppm/°C, −30.195 ppm/°C, and −30.115 ppm/°C, respectively. In addition, the sensor array enables wireless communication via antenna and transceiver circuits. This innovation heralds enhanced adaptability and applicability for SAW temperature sensor applications.

The surface acoustic wave (SAW) temperature sensor has received significant attention due to its wirelessly powered, battery-free, and chipless capabilities. This paper proposes a wireless sensing system comprising a one-port SAW resonator, helix antenna, and transceiver circuit. The SAW resonator used in this system is based on aluminum nitride (AlN) thin film, which exhibits high velocity and excellent piezoelectric properties. Simulations and experiments were conducted to investigate the performance of the designed SAW resonator. A helix antenna was also designed using finite element simulation to facilitate signal transmission between the SAW temperature sensor and the transceiver. An impedance-matching network was introduced between the helix antenna and the SAW resonator to optimize signal transmission. When the wireless SAW temperature sensor was placed within a certain distance of the mother antenna, the reflection peak of the SAW resonator was observed in the spectrum of the return signal. The frequency of the echo signal increased almost linearly as the temperature increased during the temperature tests. The fitted temperature coefficient of frequency (TCF) was −31.34 ppm/°C, indicating that the wireless temperature sensing system has high-temperature sensitivity.

The how and why of the spatial variation in biodiversity is one of the fundamental topics in biogeography research, but accurate insights require the incorporation of phylogenetic information. Here, to provide a better understanding of the origin and mechanism of biodiversity in montane ecosystems and thus support conservation planning, we explored the elevational patterns of phylogenetic diversity and structure of seed plants in the Gyirong Valley, the longest valley in China’s central Himalayas. The observed patterns were related to climatic, historical, geographical, and disturbance-related factors, and the relative contributions of these factors in explaining them were assessed. Our results showed that phylogenetic diversity of seed plants in the central Himalayas were described by a strongly hump-shaped pattern, peaking at 2700 m around. The phylogenetic structure of the plants was characterized by overdispersion at lower elevations and clustering at higher elevations, reflecting the cooperation between interspecies competition and environmental filtering in community assembly. Among the examined environmental factors, climatic factors showed the highest explanatory power, suggesting environmental filtering was largely driven by climatic rather than other factors. In summary, our finding indicating that mild climate at lower elevations presumably encourage phylogenetic differentiation by promoting interspecies competition, whereas increasingly harsh climatic condition with elevation may lead to closer phylogenetic relatedness through environmental filtering. Overall, our findings improve our knowledge about the elevational gradient of biodiversity in the Himalayas, and highlight the need for intensified conservation efforts at middle elevations as well as greater scientific concern regarding the impact of climate change on the biodiversity.

Due to toxicities, capacities for long-range transportation, bioaccumulation, and biomagnification, mercury (Hg) presents a unique concern to wildlife in remote ecosystems, including “the roof of the world”. Large carnivorous predators are thought to be exposed to elevated Hg due to their high trophic positions, but the direct assessment for Hg contamination is a challenge. Given the poorly understood Hg exposure in these carnivores, establishing a reliable and straightforward assessment would be essential to identify targeted species at Hg exposure risk for effective conservation, particularly in fragile biodiversity hotspots. Small mammals are abundant and serve as prey for large top predators. Combined with local observations, we provided an assessment to estimate the daily Hg exposure via consumption of small mammals for large carnivorous mammals recorded in the Hengduan Mountains, a world biodiversity hotspot, China. Within an altitude span from 2043 to 4251 m a.s.l., the average topsoil total mercury concentration (hereafter [THg]) was 44.65 ± 25.80 μg/kg (mean ± sd; 10.54 – 135.15 μg/kg, n = 41), while the hair [THg] in small mammals was 104.66 ± 91.96 μg/kg (mean ± sd; 7.73 to 385.70 μg/kg, n = 13). Furthermore, the daily intake of Hg was calculated among the 22 investigated/historical-recorded carnivore mammals belonging to 5 families. We found a large variance in daily intake of Hg via small mammals: Felidae (median: 205.93 μg/day) > Ursidae (135.02 μg/day) > Canidae (92.92 μg/day) > Viverridae (19.88 μg/day) > Mustelidae (7.18 μg/day). Specifically, Tiger Panthera tigris was found with the highest Hg daily intake (1701.39 μg/day) via consuming small mammals, while species belonging to Mustelidae generally have low Hg daily intake (

Ultrasonic nondestructive testing (NDT) usually utilizes conventional bulk piezoelectric transducers as transceivers. However, the complicated preparation and assembly process of bulk piezoelectric ceramics limits the development of NDT probes toward miniaturization and high frequency. In this paper, a 4.4 mm × 4.4 mm aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducer (PMUT) array is designed, fabricated, characterized, and packaged for ultrasonic pulse–echo NDT of solids for the first time. The PMUT array is prepared based on the cavity silicon-on-insulator (CSOI) process and packaged using polyurethane (PU) material with acoustic properties similar to water. The fabricated PMUT array resonates at 2.183 MHz in air and at around 1.25 MHz after PU encapsulation. The bandwidth of the packaged PMUT receiver (244 kHz) is wider than that of a bulk piezoelectric transducer (179 kHz), which is good for axis resolution improvement. In this work, a hybrid ultrasonic NDT probe is designed using two packaged PMUT receivers and one 1.25 MHz bulk transmitter. The bulk transmitter radiates an ultrasonic wave into the sample, and the defect echo is received by two PMUT receivers. The 2D position of the defect could be figured out by time-of-flight (TOF) difference, and a 30 mm × 65 mm detection area is acquired. This work demonstrates the feasibility of applying AlN PMUTs to ultrasonic NDT of solids and paves the way toward a miniaturized NDT probe using AlN PMUT technology.

Fetal distress is a symptom of fetal intrauterine hypoxia, which is seriously harmful to both the fetus and the pregnant woman. The current primary clinical tool for the assessment of fetal distress is Cardiotocography (CTG). Due to subjective variability, physicians often interpret CTG results inconsistently, hence the need to develop an auxiliary diagnostic system for fetal distress. Although the deep learning-based fetal distress-assisted diagnosis model has a high classification accuracy, the model not only has a large number of parameters but also requires a large number of computational resources, which is difficult to deploy to practical end-use scenarios. Therefore, this paper proposes a lightweight fetal distress-assisted diagnosis network, LW-FHRNet, based on a cross-channel interactive attention mechanism. The wavelet packet decomposition technique is used to convert the one-dimensional fetal heart rate (FHR) signal into a two-dimensional wavelet packet coefficient matrix map as the network input layer to fully obtain the feature information of the FHR signal. With ShuffleNet-v2 as the core, a local cross-channel interactive attention mechanism is introduced to enhance the model’s ability to extract features and achieve effective fusion of multichannel features without dimensionality reduction. In this paper, the publicly available database CTU-UHB is used for the network performance evaluation. LW-FHRNet achieves 95.24% accuracy, which meets or exceeds the classification results of deep learning-based models. Additionally, the number of model parameters is reduced many times compared with the deep learning model, and the size of the model parameters is only 0.33 M. The results show that the lightweight model proposed in this paper can effectively aid in fetal distress diagnosis.

The endoplasmic reticulum (ER) can sense a wide variety of external and internal perturbations and responds by mounting stress coping responses, such as the unfolded protein response (UPR). The UPR is composed of three stress sensors, namely IRE1α, PERK, and ATF6 that are activated to re-establish ER homeostasis. IRE1α represents the most ancient branch of the UPR affecting many cellular processes in plant and animal cells. IRE1α is a type I transmembrane protein with kinase/nuclease activities in response to ER stress. Both the ER luminal and cytosolic IRE1α interactomes have been identified revealing a multifunctional role of the ER stress sensor. IRE1α is also associated with organellar membrane contacts to promote rapid communication between intracellular organelles under stress conditions.

Cardiotocography (CTG) monitoring is an important medical diagnostic tool for fetal well-being evaluation in late pregnancy. In this regard, intelligent CTG classification based on Fetal Heart Rate (FHR) signals is a challenging research area that can assist obstetricians in making clinical decisions, thereby improving the efficiency and accuracy of pregnancy management. Most existing methods focus on one specific modality, that is, they only detect one type of modality and inevitably have limitations such as incomplete or redundant source domain feature extraction, and poor repeatability. This study focuses on modeling multimodal learning for Fetal Distress Diagnosis (FDD); however, exists three major challenges: unaligned multimodalities; failure to learn and fuse the causality and inclusion between multimodal biomedical data; modality sensitivity, that is, difficulty in implementing a task in the absence of modalities. To address these three issues, we propose a Multimodal Medical Information Fusion framework named MMIF, where the Category Constrained-Parallel ViT model (CCPViT) was first proposed to explore multimodal learning tasks and address the misalignment between multimodalities. Based on CCPViT, a cross-attention-based image-text joint component is introduced to establish a Multimodal Representation Alignment Network model (MRAN), explore the deep-level interactive representation between cross-modal data, and assist multimodal learning. Furthermore, we designed a simple-structured FDD test model based on the highly modal alignment MMIF, realizing task delegation from multimodal model training (image and text) to unimodal pathological diagnosis (image). Extensive experiments, including model parameter sensitivity analysis, cross-modal alignment assessment, and pathological diagnostic accuracy evaluation, were conducted to show our models’ superior performance and effectiveness.

Abstract A fundamental yet controversial topic in biogeography is how and why species range sizes vary along spatial gradients. To advance our understanding of these questions and to provide insights into biological conservation, we assessed elevational variations in the range sizes of vascular plants with different life forms and biogeographical affinities and explored the main drivers underlying these variations in the longest valley in China's Himalayas, the Gyirong Valley. Elevational range sizes of vascular plants were documented in 96 sampling plots along an elevational gradient ranging from 1,800 to 5,400 m above sea level. We assessed the elevational variations in range size by averaging the range sizes of all recorded species within each sampling plot. We then related the range size to climate, disturbance, and the mid‐domain effect and explored the relative importance of these factors in explaining the range size variations using the Random Forest model. A total of 545 vascular plants were recorded in the sampling plots along the elevational gradient. Of these, 158, 387, 337, and 112 were woody, herbaceous, temperate, and tropical species, respectively. The range size of each group of vascular plants exhibited uniform increasing trends along the elevational gradient, which was consistent with the prediction of Rapoport's rule. Climate was the main driver of the increasing trends of vascular plant range sizes in the Gyirong Valley. The climate variability hypothesis and mean climate condition hypothesis could both explain the elevation–range size relationships. Our results reinforce the previous notion that Rapoport's rule applies to regions where the influence of climate is the most pronounced, and call for close attention to the impact of climate change to prevent species range contraction and even extinction due to global warming.

Spatiotemporal patterning of hydrogel matrices has been used to control cell behavior. Here the authors present photoresponsive DNA-based hydrogels and demonstrate patterning of physical and biochemical properties to tune local cell responses.

In this paper, the temperature performance of AlN-SAW resonators and AlScN-SAW resonators is studied. They are simulated by COMSOL Multiphysics, and their modes and the S11 curve are analyzed. The two devices were fabricated using MEMS technology and tested using VNA, and the test results were consistent with the simulation results. Temperature experiments were carried out with temperature control equipment. With the change in temperature, the changes in S11 parameters, TCF coefficient, phase velocity, and quality factor Q were analyzed. The results show that the temperature performance of the AlN-SAW resonator and the AlScN-SAW resonator is very good, and both have good linearity. At the same time, the sensitivity of the AlScN-SAW resonator is greater by 9.5%, the linearity is greater by 15%, and the TCF coefficient is greater by 11.1%. The temperature performance is excellent, and it is more suitable as a temperature sensor.

Abstract Elusive species often use latrines which also serves as communication and information hubs. Thus, studying behavior at latrines may provide critical insights into the species’ ecology and behavior. While it is established that musk deer use latrines for defecating, very little is known about the endangered Himalayan musk deer (Moschus leucogaster) and their latrines. We examined musk deer behavior from the various video clips lasting 238 min altogether, captured at latrine sites during both breeding and non‐breeding seasons in the Annapurna Conservation Area of Nepal. A total of 428 visits by musk deer and 479 behavioral events by them were captured. We constructed an ethogram to describe musk deer behavior and compared behavior across individuals and sex using parametric and non‐parametric tests. We found that musk deer are crepuscular and nocturnal animals. Both male and female musk deer repeatedly and independently visited shared latrine sites as well as exclusively used latrine sites. The proportion of male musk deer visited latrine sites were significantly higher than that of female musk deer. Hence, male musk deer were found more active (>2 times) than females during both seasons. The most frequently observed behavioral activities at the latrine sites were defecating, sniffing and browsing, followed by scrapping and covering, and ignoring the latrine sites. The defecating and sniffing activity were performed throughout breeding and non‐breeding seasons to establish communication among peers and to claim territory. Based on the behaviors observed at the latrine sites, we can presume that Himalayan musk deer likely use latrines to convey various messages, including personality, maturity, sexual status, and territory marking among conspecifics. These findings of this study can be used for the conservation of musk deer in its natural habitat and replicated in captivity to enhance breeding performance that improves long‐term conservation prospects for this species.

Utility tunnels are an important part of smart cities. This research focuses on the application of wireless sensors networks (WSNs) in utility tunnels. The utility tunnel space and Chinese utility tunnel building standard are considered in the design process. An adaptive subunit clustering multi-hop protocol (ASCMP) is proposed. In this protocol, some improvements are applied to increase the WSN's performance, such as cluster head (CH) election, network transmission, and node energy setting. Finally, ASCMP is compared with three other classic WSNs, namely, LEACH, SEP, and M-LEACH. Simulation results demonstrate that ASCMP's data transfer rate and lifetime increase by 71.76% and 200%, respectively, as shown by MATLAB 2012a simulations.

How and why biodiversity varies along the elevational gradients is fundamental yet controversial topics in ecology. To advance our understanding on these questions and provide insight into biological conservation, we explore the elevational patterns of vascular plant richness and their drivers in the Gyirong Valley, the longest valley in China’s Himalayas. Elevational richness patterns of vascular plant were documented by 96 sampling plots along 12 elevational bands of 300-m ranging from 1800 to 5400 m above sea level. We related the elevational patterns of vascular plant richness to the climate, spatial, historical, and disturbance factors, and assessed the relative importance of these factors in explaining above patterns using the Random Forest model. Overall, the species richness of vascular plant showed a hump-shaped pattern along the elevational gradient in the Gyirong Valley, although the life form and endemism of species in combination with the scale effect modified the patterns observed. Climate factors played a greater role than other environmental factors in explaining the elevational pattern of vascular plant richness. Such strong climate-species relationship was nicely interpreted by the water-energy dynamic hypothesis, indicating the combined effect of energy and water is the major driver that shaped vascular plant richness along elevational gradient. The climate-driven hump-shaped pattern of vascular plant in the Gyirong Valley is aligned with most of previous report from different part of the Himalayas, highlighting the need for more conservation effort at the intermediate elevations of this region, and calling for more scientific concern on the impact of climate change on elevational pattern of biodiversity.

Detecting off-nadir objects is a well-known challenge in remote sensing due to the distortion and mutable representation. Existing methods mainly focus on a narrow range of view angles, and they ignore broad-view pantoscopic remote sensing imagery. To address the off-nadir object detection problem in remote sensing, a new nadir-like generative adversarial network (NaGAN) is proposed in this paper by narrowing the representation differences between the off-nadir and nadir object. NaGAN consists of a generator and a discriminator, in which the generator learns to transform the off-nadir object to a nadir-like one so that they are difficult to discriminate by the discriminator, and the discriminator competes with the generator to learn more nadir-like features. With the progressive competition between the generator and discriminator, the performances of off-nadir object detection are improved significantly. Extensive evaluations on the challenging SpaceNet benchmark for remote sensing demonstrate the superiority of NaGAN to the well-established state-of-the-art in detecting off-nadir objects.

Given their recent worldwide declines and extinctions, characterization of species-level diversity is of critical importance for large-scale biodiversity assessments and conservation of amphibians. This task is made difficult by the existence of cryptic species complexes, species groups comprising closely related and morphologically analogous species. The combination of morphology, genetic, and bioacoustic analyses permits robust and accurate species identification. Using these methods, we discovered two undescribed Xenophrys species, namely Xenophrys lini sp. nov. and Xenophrys cheni sp. nov. from the middle range of Luoxiao Mountains, southeast China. These two new species can be reliably distinguished from other known congeners by morphological and morphometric differences, distinctness in male advertisement calls, and substantial genetic distances (>3.6%) based on the mitochondrial 16s and 12s rRNA genes. The two new species, together with X. jinggangensis, are sympatric in the middle range of Luoxiao Mountains but may be isolated altitudinally and ecologically. Our study provides a first step to help resolve previously unrecognized cryptic biodiversity and provides insights into the understanding of Xenophrys diversification in the mountain complexes of southeast China.