Systematic investigation of the Borrelia miyamotoi spirochetes in ticks, wildlife and domestic animal hosts in Yunnan province, Southwest China.

Background: Borrelia miyamotoi is a spirochete species transmitted via hard ticks. Following its discovery in Japan, this pathogen has been detected around the world, and is increasingly confirmed as a human pathogen causing febrile disease, namely relapsing fever. Its presence has been confirmed in the Northeast China. However, there is little information regarding the presence of B. miyamotoi and other hard-tick-borne relapsing fever spirochetes in southern China including Yunnan province, where tick and animal species are abundant and many people both inhabit and visit for recreation. Methods: For the present study, we collected samples of ticks, wildlife, and domestic animal hosts from different counties in Yunnan province. Nucleic acids from samples were extracted, and the presence of B. miyamotoi and other relapsing fever spirochetes was confirmed using polymerase chain reaction (PCR) for the 16S rRNA specific target gene fragment. The positive samples were then amplified for partial genome of the flaB and glpQ genes. Statistical differences in its distribution were analyzed by SPSS 20 software. Sequence of partial 16S rRNA, flaB and glpQ genome were analyzed and phylogenetic trees were constructed. Results: A total of 8260 samples including 2304 ticks, 4120 small mammals and 1836 blood of domestic animal hosts were collected for screening for infection of B. miyamotoi and other relapsing fever spirochetes. Cattle and sheep act as the main hosts and Rhipicephalus microplus, Haemaphysalis nepalensis, H. kolonini and Ixodes ovatus were identified as the important vector host with high prevalence or wide distribution. Only one Mus caroli (mouse) and one Sorex alpinus (shrew) were confirmed positive for relapsing fever spirochetes. Evidence of vertical transmission in ticks was also confirmed. Two known strains of B. miyamotoi and one novel relapsing fever spirochetes, B. theileri-like agent, were confirmed and described with their host adaptation, mutation, and potential risk of spreading and spillover for human beings. Conclusions: Our results provide new evidence of relapsing fever spirochetes in vector and animal hosts in Yunnan province based on large sample sizes, and offer guidance on further investigation, surveillance and monitoring of this pathogen.

Recycling drinking water treatment sludge in construction and building materials: A review.

Drinking water treatment sludge (DWTS) is a by-product of water treatment, and it is difficult to recycle to high value and poses potential environmental risks. Recycling DWTS into cement-based materials is an effective measure to achieve its high-volume utilization and reduce its environmental load. DWTS is rich in silica-alumina phases and has potential pozzolanic activity after drying, grinding and calcination, giving it similar properties to traditional supplementary cementitious materials. Adjusting the sludge production process and coagulant type will change its physical and chemical properties. Adding a small amount of DWTS can generate additional hydration products and refine the pore structure of the cement sample, thus improving the mechanical properties and durability of the sample. However, adding high-volume DWTS to concrete causes microstructural deterioration, but it is feasible to use high-volume DWTS to produce artificial aggregates, lightweight concrete, and sintered bricks. Meanwhile, calcined DWTS has similar compositions to clay, which makes it a potential raw material for cement clinker production. Cement-based materials can effectively solidify heavy metal ions in DWTS, and alkali-activated binders, magnesium-based cement, and carbon curing technology can further reduce the risk of heavy metal leaching. This review provides support for the high-value utilization of DWTS in cement-based materials and the reduction of its potential environmental risks.

Upcycling textile sludge into magnesium oxychloride cement: Physical properties, microstructure, and leaching behavior.

Textile sludge is a by-product produced during the wastewater treatment process in the textile printing and dyeing industry. Textile sludge is rich in heavy metal elements, which makes it a potential risk to the surrounding environment. This study designs a magnesium oxychloride cement (MOC) components to solidify harmful substances in textile sludge and studies the influence of textile sludge ash (TSA) on the mechanical properties and microstructure of MOC samples. The results indicated that adding 5 %-20 % TSA is beneficial for increasing the compressive strength of air-cured MOC paste and improving its water resistance. Meanwhile, the MOC sample shows volume expansion in 168 h, which is related to the further hydration of residual MgO. Incorporating 10 %-20 % TSA substantially increased the volume expansion ratio of the mixture compared to plain MOC sample. In addition, the porosity of TSA-modified MOC after water curing did not change significantly compared to the sample before water curing, while the pore structure of plain MOC after water curing significantly coarsened. This is mainly because TSA reacts with MOC and generates Mg-Al-Cl-Si-H and Mg-Cl-Si-H gels, consequently improving the water stability of MOC sample. At the nanoscale, the 3/5-phase crystal and unreacted MgO content in the 15 % TSA-modified MOC sample is relatively reduced by 7.79 % and 25 %, respectively, compared to the plain sample, but the 13 % gel phase is detected. In addition, the MOC component can effectively solidify heavy metal elements in textile sludge. For the leachate of 20 % TSA-modified MOC paste, the Ni element is not detected, and its solidifying effect on heavy elements such as Zn and Mn exceeded 99 %.

Twist-Induced Modification in the Electronic Structure of Bilayer WSe2.

The recent discovery of strongly correlated phases in twisted transition-metal dichalcogenides (TMDs) highlights the significant impact of twist-induced modifications on electronic structures. In this study, we employed angle-resolved photoemission spectroscopy with submicrometer spatial resolution (µ-ARPES) to investigate these modifications by comparing valence band structures of twisted (5.3°) and nontwisted (AB-stacked) bilayer regions within the same WSe2 device. Relative to the nontwisted region, the twisted area exhibits pronounced moiré bands and ∼90 meV renormalization at the Γ-valley, substantial momentum separation between different layers, and an absence of flat bands at the K-valley. We further simulated the effects of lattice relaxation, which can flatten the Γ-valley edge but not the K-valley edge. Our results provide a direct visualization of twist-induced modifications in the electronic structures of twisted TMDs and elucidate their valley-dependent responses to lattice relaxation.

Recycling of water treatment sludge in concrete: The role of water-binder ratio from a nanoscale perspective.

For eutrophic water bodies, potassium permanganate is an effective pre-oxidant to remove algae and its residue in water treatment sludge. Recycling water treatment sludge in concrete is an environmentally friendly and high-value utilization measure. However, little research has been done on the effect of manganese-rich drinking water sludge ash (DWSA) on concrete. The effect of water-binder ratio (w/b) on strength, shrinkage and microstructural characteristics of concrete containing DWSA was investigated, and the structural behavior was explained from a nanoscale perspective. The results show that recycling 10 % DWSA in concrete improved the strength and shrinkage resistance of the samples. Reducing the w/b effectively increased the strength of DWSA-modified concrete and reduced the shrinkage deformation. The paste with high w/b had higher contents of non-evaporated water and calcium hydroxide, as well as higher reaction degree of DWSA. Nanoscale characterization shows that reducing the w/b reduced the volume fraction of pore and unhydrated phases in the matrix and increased the proportion of high-density C-S-H. Meanwhile, reducing the w/b also reduced the interfacial transition zone width of DWSA-modified concrete. Recycling DWSA in concrete effectively reduced the total carbon footprint and cost of the mixture. The combined application of reducing the w/b and incorporating DWSA effectively improved the economic and environmental benefits of concrete material. For the concrete modified with 10 % DWSA (w/b = 0.3), its cost and carbon emissions are reduced by 14 %-21 % and 19 %-25 % compared with the reference sample, respectively. Overall, this study reveals the action mechanism of DWSA in cement system at different w/b from nanoscale perspective, and gives a new insight on determining the optimal w/b in full-scale application of DWSA concrete.

Contrastive Bayesian Analysis for Deep Metric Learning.

Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other. In this work, we recognize that there is a significant semantic gap between features at the intermediate feature layer and class labels at the final output layer. To bridge this gap, we develop a contrastive Bayesian analysis to characterize and model the posterior probabilities of image labels conditioned by their features similarity in a contrastive learning setting. This contrastive Bayesian analysis leads to a new loss function for deep metric learning. To improve the generalization capability of the proposed method onto new classes, we further extend the contrastive Bayesian loss with a metric variance constraint. Our experimental results and ablation studies demonstrate that the proposed contrastive Bayesian metric learning method significantly improves the performance of deep metric learning in both supervised and pseudo-supervised scenarios, outperforming existing methods by a large margin.

Coinfection of Two Rickettsia Species in a Single Tick Species Provides New Insight into Rickettsia-Rickettsia and Rickettsia-Vector Interactions.

Rickettsiae are obligate intracellular bacteria that can cause life-threatening illnesses. There is an ongoing debate as to whether established infections by one Rickettsia species preclude the maintenance of the second species in ticks. Here, we identified two Rickettsia species in inoculum from Haemaphysalis montgomeryi ticks and subsequently obtained pure isolates of each species by plaque selection. The two isolates were classified as a transitional group and spotted fever group rickettsiae and named Rickettsia hoogstraalii str CS and Rickettsia rhipicephalii str EH, respectively. The coinfection of these two Rickettsia species was detected in 25.6% of individual field-collected H. montgomeryi. In cell culture infection models, R. hoogstraalii str CS overwhelmed R. rhipicephalii str EH with more obvious cytopathic effects, faster plaque formation, and increased cellular growth when cocultured, and R. hoogstraalii str CS seemed to polymerize actin tails differently from R. rhipicephalii str EH in vitro. This work provides a model to investigate the mechanisms of both Rickettsia-Rickettsia and Rickettsia-vector interactions. IMPORTANCE The rickettsiae are a group of obligate intracellular Gram-negative bacteria that include human pathogens causing an array of clinical symptoms and even death. There is an important question in the field, that is whether one infection can block the superinfection of other rickettsiae. This work demonstrated the coinfection of two Rickettsia species in individual ticks and further highlighted that testing the rickettsial competitive exclusion hypothesis will undoubtedly be a promising area as methods for bioengineering and pathogen biocontrol become amenable for rickettsiae.

Fabrication of Loose Nanofiltration Membranes with High Rejection Selectivity between Natural Organic Matter and Salts for Drinking Water Treatment.

Loose nanofiltration (LNF) membranes with a molecular weight cut-off (MWCO) of about 1000 Da and high surface negative charge density have great application potential for drinking water treatment pursuing high rejection selectivity between natural organic matter (NOM) and mineral salts. This study was conducted to exploit the novel method coupling non-solvent induced phase separation (NIPS) and interfacial polymerization (IP) for the preparation of high-performance LNF membranes. A number of LNF membranes were synthesized by varying the polyethersulfone (PES) and piperazine (PIP) concentrations in the cast solution for the PES support layer preparation. Results showed that these two conditions could greatly affect the membrane water permeance, MWCO and surface charge. One LNF membrane, with a water permeance as high as 23.0 ± 1.8 L/m2/h/bar, when used for the filtration of conventional process-treated natural water, demonstrated a rejection of NOM higher than 70% and a low rejection of mineral salts at about 20%. Both the mineral salts/NOM selectivity and permselectivity were superior to the currently available LNF membranes as far as the authors know. This study demonstrated the great advantage of the NIPS-IP method for the fabrication of LNF membranes, particularly for the advanced treatment of drinking water.

Mechanical, water resistance and environmental benefits of magnesium oxychloride cement incorporating rice husk ash.

Magnesium oxychloride cement (MOC) has received extensive attention as an eco-friendly cement, but its poor water resistance limits its engineering applications. In this study, MOC mixture (MOCM) was modified with 10-50 % rice husk ash (RHA) (wt% of MgO), and the development of their fresh properties, mechanical strength and microstructure was investigated. The results show that the incorporation of RHA to MOCM increases the setting time of the mixture and reduces its flowability. Due to the fine particle size and high reactivity of RHA, the incorporation of an appropriate amount of RHA to MOCM improves the matrix compactness, thereby enhancing the compressive strength of the samples. Although the microstructure of MOCM deteriorates and the strength decreases after immersion in water, the strength retention coefficient of MOCM with 50 % RHA increases by 24.57 % compared with that of plain MOCM. The incorporation of RHA not only reduces the relative content of magnesium oxide in MOCM, but also generates Mg-Cl-Si-H gel, which is beneficial to improve the water resistance of MOCM. Meanwhile, with the increase of RHA content, the carbon emission of MOCM also decreases. Compared with other modification methods, RHA-modified MOCM performs better in terms of water resistance, environmental benefits and strength enhancement.

High Diversity and Prevalence of Borrelia burgdorferi sensu lato in Wildlife Hosts, Domestic Animals, and Ticks in Yunnan Province, Southwestern China.

Borrelia burgdorferi sensu lato (BBSL), the causative agent of Lyme disease, is commonly found in wild and domestic mammals and ticks worldwide. In China, human cases of Borrelia burgdorferi infections have been identified across a wide geographic range including Yunnan Province, but few studies have examined BBSL in reservoirs and vectors in southwestern China. Here we conducted a thorough and broad-range investigation of BBSL in small mammals, domestic mammals, and ticks collected from 159 sample sites across 42 counties in Yunnan Province. DNA was extracted from spleen tissue of small mammals, blood from domestic mammals, and homogenized ticks. Nested PCR targeting the 5S-23S rRNA intergenic spacer gene of BBSL was used for screening, with amplicons sequenced directly and analyzed using a BLAST algorithm. A total of 8,478 samples were collected, which were composed of 5,044 mammals belonging to 68 species, 1,927 livestock belonging to five species, and 1, 507 ticks belonging to 14 species. BBSL was detected in 147 mammals (2.9%) from 30 different species, 20 of which represent the first reported detection in that species. A total of 52 (2.7%) livestock samples were positive for BBSL, with dogs having the highest detection rate (6.3%, 43/687), and 103 ticks (6.8%) tested positive with high prevalence in Ixodes granulatus (44.2%, 23/52), Haemaphysalis nepalensi (33.3%, 3/9) and Haemaphysalis kolonini (19.0%, 31/163). Sequence analysis revealed six genospecies of BBSL including B. afzelii, B. burgdorferi sensu stricto, B. japonica, B. garinii, B. sinica, and B. valaisiana. Significant differences in prevalence rates of BBSL were observed by species, landscape types, altitude, and season. Our findings indicate a wide distribution of multiple endemic BBSL genospecies based on a large-scale survey within Yunnan, which underline the need to expand surveillance efforts for human in southwestern China.

Recycling coral waste into eco-friendly UHPC: Mechanical strength, microstructure, and environmental benefits.

On islands far away from the mainland, the raw materials for concrete production are often more difficult to obtain. Converting the coral waste generated during the island construction process into a marine ultra-high performance concrete (UHPC) mixture is an eco-friendly strategy. Coral powder (CP) is used to partially replace cement and silica fume (SF), and its mechanical strength, microstructure and environmental benefits are evaluated. Results show that using a small amount of CP (5%) to replace cement can improve the mechanical properties of UHPC, but the strength of UHPC decreases with the further increase of CP content. From the perspective of nanoindentation test, an appropriate amount of CP refines the pore structure of the UHPC matrix and increases the content of C-S-H, especially the proportion of high-density C-S-H. When 15% of SF is replaced by CP (SF15), the strength of UHPC decreases due to the decrease of C-S-H phase and the deterioration of microstructure. In terms of the width of the interface transition zone, the width of the C5 sample (CP replace 5% cement) is decreased by 16.7% compared with the control group, while the width of the SF15 group is increased by 38.9%. Compared with conventional UHPC, CP-based UHPC has lower carbon emission and non-renewable energy consumption, which effectively utilizes waste and promotes sustainability.

Local Semantic Correlation Modeling Over Graph Neural Networks for Deep Feature Embedding and Image Retrieval.

Deep feature embedding aims to learn discriminative features or feature embeddings for image samples which can minimize their intra-class distance while maximizing their inter-class distance. Recent state-of-the-art methods have been focusing on learning deep neural networks with carefully designed loss functions. In this work, we propose to explore a new approach to deep feature embedding. We learn a graph neural network to characterize and predict the local correlation structure of images in the feature space. Based on this correlation structure, neighboring images collaborate with each other to generate and refine their embedded features based on local linear combination. Graph edges learn a correlation prediction network to predict the correlation scores between neighboring images. Graph nodes learn a feature embedding network to generate the embedded feature for a given image based on a weighted summation of neighboring image features with the correlation scores as weights. Our extensive experimental results under the image retrieval settings demonstrate that our proposed method outperforms the state-of-the-art methods by a large margin, especially for top-1 recalls.

Bio-cement-modified construction materials and their performances.

The microbial induced mineral precipitation can be used to modify and improve the performance of construction materials and can partially replace ordinary Portland cement. Microbially induced carbonate precipitation (MICP) mainly uses the urease secreted during the growth of urease-producing bacteria (UPB) to hydrolyze urea produce CO32- and reacts with Ca2+ to form CaCO3. Microbially induced struvite precipitation (MISP) mainly uses the urease to decompose urea to produce NH4+. In the presence of hydrogen phosphate and magnesium ions, the struvite can be precipitated. The elemental composition and chemical composition of the precipitates produced by the MICP and MISP processes are analyzed by energy dispersive X-ray spectroscopy (EDS) and powder X-ray diffraction analysis (XRD). The morphology of the precipitates can be observed by scanning electron microscope (SEM). Compared with the initial porosity, the MICP method can reduce the initial porosity of the sand column by 2.98% within 90 min. However, the MISP is only 1.45%. The permeability coefficient of the sand column can be effectively reduced in the MICP process. The total content of cementitious materials is 27.71g and 13.16g in MICP- and MISP-cemented sand columns, respectively. The MICP technology can improve the strength of alkali-activated mortars under different pH values of the UPB solution.

Deformable adversarial registration network with multiple loss constraints.

Deformable medical image registration has the necessary value of theoretical research and clinical application. Traditional methods cannot meet clinical application standards in terms of registration accuracy and efficiency. This article proposes a deformable generate adversarial registration framework, which avoids the dependence on ground-truth deformation. The proposed residual registration network based on Nested U-Net has excellent feature extraction ability and robustness. Multiple constraints that incorporate the potential information of anatomical segmentation extracted by the discriminator can help the model adapt to different modal registration tasks. Through interpatient X-ray chest registration, the deep-supervised training method, and the proposed loss constraint are proved to improve the model's performance and training stability. The experimental results show that our model, compared with state-of-the-art methods, provides a more accurate spatial alignment relationship between different patients' lung organs while ensuring the displacement field's authenticity. Finally, we explored the relationship between the accuracy and validity of the model.

Removing Adversarial Noise via Low-Rank Completion of High-Sensitivity Points.

Deep neural networks are fragile under adversarial attacks. In this work, we propose to develop a new defense method based on image restoration to remove adversarial attack noise. Using the gradient information back-propagated over the network to the input image, we identify high-sensitivity keypoints which have significant contributions to the image classification performance. We then partition the image pixels into the two groups: high-sensitivity and low-sensitivity points. For low-sensitivity pixels, we use a total variation (TV) norm-based image smoothing method to remove adversarial attack noise. For those high-sensitivity keypoints, we develop a structure-preserving low-rank image completion method. Based on matrix analysis and optimization, we derive an iterative solution for this optimization problem. Our extensive experimental results on the CIFAR-10, SVHN, and Tiny-ImageNet datasets have demonstrated that our method significantly outperforms other defense methods which are based on image de-noising or restoration, especially under powerful adversarial attacks.

Experimental study on full-volume slag alkali-activated mortars: Air-cooled blast furnace slag versus machine-made sand as fine aggregates.

As the industrial waste from blast furnace ironmaking, air-cooled blast furnace slag (ACBFS) puts a lot of pressure on the environment. It is becoming more and more urgent to deal with the increasing ACBFS. In this study, the concept of "full-volume slag alkali-activated mortars (FSAM)" is proposed using ground granulated water-cooled blast furnace slag (GGBS) as aluminosilicate material and ACBFS to replace machine-made sand, aiming to solve the current situation of increasing scarcity of natural resources. The characteristics of ACBFS are investigated, and its stability and heavy metal leaching all meet the requirements as a building material. The results show that the flowability and mechanical properties of FSAM are significantly enhanced with the substitution rate of ACBFS increases. Meanwhile, the incorporation of ACBFS is also beneficial to improve the compactness of the microstructure of the mortar, thereby improving the impermeability (Water, ion and gas) of FSAM. In addition, the specimen mixed with ACBFS showed good high temperature resistance due to the porous feature of the aggregate. Furthermore, using a small amount of limestone powder to replace GGBS can slightly improve the performance of FSAM. Therefore, ACBFS is recommended to be used in FSAM, which meets safety, cost and environmental benefits.

Zero-Shot Learning to Index on Semantic Trees for Scalable Image Retrieval.

In this study, we develop a new approach, called zero-shot learning to index on semantic trees (LTI-ST), for efficient image indexing and scalable image retrieval. Our method learns to model the inherent correlation structure between visual representations using a binary semantic tree from training images which can be effectively transferred to new test images from unknown classes. Based on predicted correlation structure, we construct an efficient indexing scheme for the whole test image set. Unlike existing image index methods, our proposed LTI-ST method has the following two unique characteristics. First, it does not need to analyze the test images in the query database to construct the index structure. Instead, it is directly predicted by a network learnt from the training set. This zero-shot capability is critical for flexible, distributed, and scalable implementation and deployment of the image indexing and retrieval services at large scales. Second, unlike the existing distance-based index methods, our index structure is learnt using the LTI-ST deep neural network with binary encoding and decoding on a hierarchical semantic tree. Our extensive experimental results on benchmark datasets and ablation studies demonstrate that the proposed LTI-ST method outperforms existing index methods by a large margin while providing the above new capabilities which are highly desirable in practice.

Complete genome sequence of a novel bovine hepacivirus from Yunnan, China.

We detected a novel bovine hepacivirus N (HNV) subtype, IME_BovHep_01, in the serum of cattle in Tengchong, Yunnan, China, by high-throughput sequencing. The complete genome of IME_BovHep_01, was sequenced using an Illumina MiSeq sequencer and found to be 8850 nt in length, encoding one hypothetical protein. BLASTn analysis showed that the genome sequence shared similarity with the bovine hepacivirus isolate BovHepV_209/Ger/2014, with 88% query coverage and 70.8% identity. However, the highest similarity was to bovine hepacivirus N strain BRBovHep_RS963, for which only a partial genome sequence is available, with 68% query coverage and 81.5% identity. Sequence comparisons and phylogenetic analysis suggested that IME_BovHep_01 is a novel HNV subtype. Importantly, IME_BovHep_01 is the first member of this new genotype for which the complete genome sequence was determined.

Intercalated Iridium Diselenide Electrocatalysts for Efficient pH-Universal Water Splitting.

Developing bifunctional catalysts for both hydrogen and oxygen evolution reactions is a promising approach to the practical implementation of electrocatalytic water splitting. However, most of the reported bifunctional catalysts are only applicable to alkaline electrolyzer, although a few are effective in acidic or neutral media that appeals more to industrial applications. Here, a lithium-intercalated iridium diselenide (Li-IrSe2 ) is developed that outperformed other reported catalysts toward overall water splitting in both acidic and neutral environments. Li intercalation activated the inert pristine IrSe2 via bringing high porosities and abundant Se vacancies for efficient hydrogen and oxygen evolution reactions. When Li-IrSe2 was assembled into two-electrode electrolyzers for overall water splitting, the cell voltages at 10 mA cm-2 were 1.44 and 1.50 V under pH 0 and 7, respectively, being record-low values in both conditions.

Animal Scanner: Software for classifying humans, animals, and empty frames in camera trap images.

Camera traps are a popular tool to sample animal populations because they are noninvasive, detect a variety of species, and can record many thousands of animal detections per deployment. Cameras are typically set to take bursts of multiple photographs for each detection and are deployed in arrays of dozens or hundreds of sites, often resulting in millions of photographs per study. The task of converting photographs to animal detection records from such large image collections is daunting, and made worse by situations that generate copious empty pictures from false triggers (e.g., camera malfunction or moving vegetation) or pictures of humans. We developed computer vision algorithms to detect and classify moving objects to aid the first step of camera trap image filtering-separating the animal detections from the empty frames and pictures of humans. Our new work couples foreground object segmentation through background subtraction with deep learning classification to provide a fast and accurate scheme for human-animal detection. We provide these programs as both Matlab GUI and command prompt developed with C++. The software reads folders of camera trap images and outputs images annotated with bounding boxes around moving objects and a text file summary of results. This software maintains high accuracy while reducing the execution time by 14 times. It takes about 6 seconds to process a sequence of ten frames (on a 2.6 GHZ CPU computer). For those cameras with excessive empty frames due to camera malfunction or blowing vegetation automatically removes 54% of the false-triggers sequences without influencing the human/animal sequences. We achieve 99.58% on image-level empty versus object classification of Serengeti dataset. We offer the first computer vision tool for processing camera trap images providing substantial time savings for processing large image datasets, thus improving our ability to monitor wildlife across large scales with camera traps.