How incubation temperature affects hatchling performance in reptiles: an integrative insight based on plasticity in metabolic enzyme.

Evaluating the effects of temperature variations on animals plays an important role in understanding the threat of climate warming. The effects of developmental temperature on offspring performance are critical in evaluating the effects of warming temperatures on the fitness of oviparous species, but the physiological and biochemical basis of this developmental plasticity is largely unknown. In this study, we incubated eggs of the turtle Pelodiscus sinensis at low (24 °C), medium (28 °C), and high (32 °C) temperatures, and evaluated the effects of developmental temperature on offspring fitness, and metabolic enzymes in the neck and limb muscles of hatchlings. The hatchlings from eggs incubated at the medium temperature showed better fitness-related performance (righting response and swimming capacity) and higher activities of metabolic enzymes (hexokinase, HK; lactate dehydrogenase, LDH) than hatchlings from the eggs incubated at high or low temperatures. In addition, the swimming speed and righting response were significantly correlated with the HK activities in limb (swimming speed) and neck (righting response) muscles, suggesting that the developmental plasticity of energy metabolic pathway might play a role in determining the way incubation temperature affects offspring phenotypes. Integrating the fitness-related performance and the activities of metabolic enzymes, we predict that the P. sinensis from high latitude would not face the detrimental effects of climate warming until the average nest temperatures reach 32 °C.

Cross-View Approximation on Grassmann Manifold for Multiview Clustering.

In existing multiview clustering research, the comprehensive learning from multiview graph and feature spaces simultaneously remains insufficient when achieving a consistent clustering structure. In addition, a postprocessing step is often required. In light of these considerations, a cross-view approximation on Grassman manifold (CAGM) model is proposed to address inconsistencies within multiview adjacency matrices, feature matrices, and cross-view combinations from the two sources. The model uses a ratio-formed objective function, enabling parameter-free bidirectional fusion. Furthermore, the CAGM model incorporates a paired encoding mechanism to generate low-dimensional and orthogonal cross-view embeddings. Through the approximation of two measurable subspaces on the Grassmann manifold, the direct acquisition of the indicator matrix is realized. Furthermore, an effective optimization algorithm corresponding to the CAGM model is derived. Comprehensive experiments on four real-world datasets are conducted to substantiate the effectiveness of our proposed method.

EBMGC-GNF: Efficient Balanced Multi-view Graph Clustering via Good Neighbor Fusion.

Exploiting consistent structure from multiple graphs is vital for multi-view graph clustering. To achieve this goal, we propose an Efficient Balanced Multi-view Graph Clustering via Good Neighbor Fusion (EBMGC-GNF) model which comprehensively extracts credible consistent neighbor information from multiple views by designing a Cross-view Good Neighbors Voting module. Moreover, a novel balanced regularization term based on p-power function is introduced to adjust the balance property of clusters, which helps the model adapt to data with different distributions. To solve the optimization problem of EBMGC-GNF, we transform EBMGC-GNF into an efficient form with graph coarsening method and optimize it based on accelareted coordinate descent algorithm. In experiments, extensive results demonstrate that, in the majority of scenarios, our proposals outperform state-of-the-art methods in terms of both effectiveness and efficiency.

Joint Structured Bipartite Graph and Row-Sparse Projection for Large-Scale Feature Selection.

Feature selection plays an important role in data analysis, yet traditional graph-based methods often produce suboptimal results. These methods typically follow a two-stage process: constructing a graph with data-to-data affinities or a bipartite graph with data-to-anchor affinities and independently selecting features based on their scores. In this article, a large-scale feature selection approach based on structured bipartite graph and row-sparse projection (RS 2 BLFS) is proposed to overcome this limitation. RS 2 BLFS integrates the construction of a structured bipartite graph consisting of c connected components into row-sparse projection learning with k nonzero rows. This integration allows for the joint selection of an optimal feature subset in an unsupervised manner. Notably, the c connected components of the structured bipartite graph correspond to c clusters, each with multiple subcluster centers. This feature makes RS 2 BLFS particularly effective for feature selection and clustering on nonspherical large-scale data. An algorithm with theoretical analysis is developed to solve the optimization problem involved in RS 2 BLFS. Experimental results on synthetic and real-world datasets confirm its effectiveness in feature selection tasks.

Collective effects of rising average temperatures and heat events on oviparous embryos.

Survival of the immobile embryo in response to rising temperature is important to determine a species' vulnerability to climate change. However, the collective effects of 2 key thermal characteristics associated with climate change (i.e., rising average temperature and acute heat events) on embryonic survival remain largely unexplored. We used empirical measurements and niche modeling to investigate how chronic and acute heat stress independently and collectively influence the embryonic survival of lizards across latitudes. We collected and bred lizards from 5 latitudes and incubated their eggs across a range of temperatures to quantify population-specific responses to chronic and acute heat stress. Using an embryonic development model parameterized with measured embryonic heat tolerances, we further identified a collective impact of embryonic chronic and acute heat tolerances on embryonic survival. We also incorporated embryonic chronic and acute heat tolerance in hybrid species distribution models to determine species' range shifts under climate change. Embryos' tolerance of chronic heat (T-chronic) remained consistent across latitudes, whereas their tolerance of acute heat (T-acute) was higher at high latitudes than at low latitudes. Tolerance of acute heat exerted a more pronounced influence than tolerance of chronic heat. In species distribution models, climate change led to the most significant habitat loss for each population and species in its low-latitude distribution. Consequently, habitat for populations across all latitudes will shift toward high latitudes. Our study also highlights the importance of considering embryonic survival under chronic and acute heat stresses to predict species' vulnerability to climate change.

Efectos colectivos del aumento de las temperaturas promedio y los eventos de calor en embriones ovíparos Resumen La supervivencia de los embriones inmóviles en respuesta al incremento de temperatura es importante para determinar la vulnerabilidad de las especies al cambio climático. Sin embargo, los efectos colectivos de dos características térmicas claves asociadas con el cambio climático (i. e., aumento de temperatura promedio y eventos de calor agudo) sobre la supervivencia embrionaria permanecen en gran parte inexplorados. Utilizamos mediciones empíricas y modelos de nicho para investigar cómo el estrés térmico crónico y agudo influye de forma independiente y colectiva en la supervivencia embrionaria de los lagartos en todas las latitudes. Recolectamos y criamos lagartos de cinco latitudes e incubamos sus huevos en un rango de temperaturas para cuantificar las respuestas específicas de la población al estrés por calor crónico y agudo. Posteriormente, mediante un modelo de desarrollo embrionario parametrizado con mediciones de tolerancia embrionaria al calor, identificamos un impacto colectivo de las tolerancias embrionarias al calor agudo y crónico en la supervivencia embrionaria. También incorporamos la tolerancia embrionaria crónica y aguda al calor en modelos de distribución de especies híbridas para determinar los cambios de distribución de las especies bajo el cambio climático. La tolerancia embrionaria al calor crónico (T­crónico) permaneció constante, mientras que la tolerancia al calor agudo (T­agudo) fue mayor en latitudes altas que en latitudes bajas. La tolerancia al calor agudo ejerció una influencia más pronunciada que la tolerancia al calor crónico. En los modelos de distribución de especies, el cambio climático provocó la pérdida de hábitat más significativa para cada población y especie en su distribución de latitudes bajas. En consecuencia, el hábitat para poblaciones en todas las latitudes se desplazará a latitudes altas. Nuestro estudio también resalta la importancia de considerar la supervivencia embrionaria bajo estrés térmico crónico y agudo para predecir la vulnerabilidad de las especies al cambio climático.

Molecular Characterization and Expression Changes of the bcl2l13 Gene in Response to Hypoxia in Megalobrama amblycephala.

Hypoxia is a unique environmental stress, which not only reflects the insufficient oxygen supply of cells and tissues, but also occurs in various physiological and pathological environments. Mitophagy as a selective autophagy can recover and utilize damaged organelles and misfolded proteins to ensure normal cell functions and promote cell survival. Bcl2l13 (B-cell lymphoma-2 like 13) is reported to induce mitophagy as a functional mammalian homolog of Atg32. However, the function of the bcl2l13 gene is still unclear in fish. Here the sequence and structure of the bcl2l13 gene in Megalobrama amblycephala were identified and showed that bcl2l13 contained an open reading frame (ORF) of 1458 bp for encoding 485 aa. Amino acid sequence analysis indicated that Bcl2l13, as a typical anti-apoptotic protein of the Bcl2 family, contained four BH domains, one BHNo domain, and one TM domain. Further study showed that Bcl2l13 was mainly located in the mitochondria, while its localization was changed within the whole cell after the TM domain was deleted. Real-time PCR analysis revealed that bcl2l13 showed higher expression levels in early embryos. After hypoxia treatment, the mRNA levels of the bcl2l13 and autophagy-related genes were significantly up-regulated in most detected tissues, and the bcl2l13 transcription was regulated by Hif-1α mediated pathway. Additionally, the transcription activity of the bcl2l13 promoter was further analyzed using luciferase reporter assays and showed the highest activity in the promoter region from -475 to +111. These results indicated that bcl2l13 may play important roles in embryogenesis and hypoxia mediated autophagy in fish.

Genetically Encoded Lizard Color Divergence for Camouflage and Thermoregulation.

Local adaptation is critical in speciation and evolution, yet comprehensive studies on proximate and ultimate causes of local adaptation are generally scarce. Here, we integrated field ecological experiments, genome sequencing, and genetic verification to demonstrate both driving forces and molecular mechanisms governing local adaptation of body coloration in a lizard from the Qinghai-Tibet Plateau. We found dark lizards from the cold meadow population had lower spectrum reflectance but higher melanin contents than light counterparts from the warm dune population. Additionally, the colorations of both dark and light lizards facilitated the camouflage and thermoregulation in their respective microhabitat simultaneously. More importantly, by genome resequencing analysis, we detected a novel mutation in Tyrp1 that underpinned this color adaptation. The allele frequencies at the site of SNP 459# in the gene of Tyrp1 are 22.22% G/C and 77.78% C/C in dark lizards and 100% G/G in light lizards. Model-predicted structure and catalytic activity showed that this mutation increased structure flexibility and catalytic activity in enzyme TYRP1, and thereby facilitated the generation of eumelanin in dark lizards. The function of the mutation in Tyrp1 was further verified by more melanin contents and darker coloration detected in the zebrafish injected with the genotype of Tyrp1 from dark lizards. Therefore, our study demonstrates that a novel mutation of a major melanin-generating gene underpins skin color variation co-selected by camouflage and thermoregulation in a lizard. The resulting strong selection may reinforce adaptive genetic divergence and enable the persistence of adjacent populations with distinct body coloration.

Sparse Trace Ratio LDA for Supervised Feature Selection.

Classification is a fundamental task in the field of data mining. Unfortunately, high-dimensional data often degrade the performance of classification. To solve this problem, dimensionality reduction is usually adopted as an essential preprocessing technique, which can be divided into feature extraction and feature selection. Due to the ability to obtain category discrimination, linear discriminant analysis (LDA) is recognized as a classic feature extraction method for classification. Compared with feature extraction, feature selection has plenty of advantages in many applications. If we can integrate the discrimination of LDA and the advantages of feature selection, it is bound to play an important role in the classification of high-dimensional data. Motivated by the idea, we propose a supervised feature selection method for classification. It combines trace ratio LDA with l2,p -norm regularization and imposes the orthogonal constraint on the projection matrix. The learned row-sparse projection matrix can be used to select discriminative features. Then, we present an optimization algorithm to solve the proposed method. Finally, the extensive experiments on both synthetic and real-world datasets indicate the effectiveness of the proposed method.

Radiologic grading scores enhance clinical model's prognostic ability for Guillain-Barré syndrome.

OBJECTIVE: To assess the value of magnetic resonance imaging (MRI) grading scores based on lumbosacral muscle denervation edema in predicting the course of Guillain-Barré syndrome (GBS). METHODS: We collected data from 354 GBS patients and developed MRI grading criteria (5-point scale) based on the transverse area and longitudinal length of lumbosacral edema. Univariate and multivariate logistic regression analyses were conducted to identify factors associated with GBS prognosis among 12 demographic and radiological features. Clinical models and clinical-MRI models were separately trained and validated by data from Institution 1. External test was performed using data from Institution 2. Differences between the models were assessed using the z-test. RESULTS: Four clinical factors (sex, albumin cytological dissociation in cerebrospinal fluid, medical research council [MRC] sum score at admission, and MRC sum score at discharge [odds ratio, 0.24-5.15; all p < 0.001]) and MRI grading scores (odds ratio, 2.44; p < 0.001) are independent prognostic factors for GBS patients. The shallow neural network achieved the best prognostic performance both clinical model (accuracy of external test cohort, 83.96%) and clinical-MRI model (accuracy of external test cohort, 90.56%). A significant difference between clinical and clinical-MRI model was also found (clinical model vs. clinical-MRI model, area under the receiver operating curve, 0.84 (95% CI: [0.71, 0.91]) vs. 0.97 (95% CI: [0.86, 0.99]), p < 0.001). INTERPRETATION: The MRI grading scores for muscle denervation edema may serve as a potential prognostic risk factor for GBS. Furthermore, they significantly improve the prognostic performance of standalone clinical model in predicting GBS prognosis.

Surgical management of giant pituitary adenomas: institutional experience and clinical outcomes of 94 patients.

Background: Giant (with a diameter of at least 40 mm and a volume of at least 10 cm3) pituitary adenomas (GPAs) are intricate tumors that pose considerable difficulty for surgical removal. While endoscopic transsphenoidal surgery (ETS) is a commonly employed technique for these destructive tumors, its effectiveness may be restricted in cases where invasion into multiple compartments is present, leading to limited resection. Methods: A retrospective review was conducted on the clinical records of 94 patients diagnosed with GPAs who had undergone surgical resection from 2014 to 2022. An analysis was conducted on the outcomes of the surgical and clinical procedures. Results: In this group, the average size of the tumor before surgery was 44.6 ± 5.6 mm (range, 40-73 mm), and the volume was 25. 5± 16.6 cm3 (range, 10-20.67 cm3). Of the total number of patients, 72 (76.6%) underwent a single ETS, 12 (12.8%) opted for transcranial surgery (TCS), and 10 (10.6%) chose a combined method. Gross total resection (GTR) was successfully performed in 49 (68.1%), 3 (25.0%), and 8 (80.0%) patients who underwent each surgical approach. Seventy-four (78.7%) patients had improved vision, 20 (21.3%) were unchanged, and none had deterioration. Twenty-two patients (23.4%) experienced a total of 43 complications, which comprised hormonal insufficiency (11/94, 11.7%), diabetic insipidus (6/88, 6.8%), electrolyte disorders (7/94, 7.4%), cerebrospinal fluid leakage (5/94, 5.3%), meningitis (8/94, 8.5%), and hydrocephalus (6/94, 6.4%). The GTR, subtotal resection (STR), and partial resection (PTR) rates were 63.8% (60/94), 21.3% (20/94), and 14.9% (14/94), respectively. Throughout the follow-up duration, 18.1% (17/94) of patients required reoperation and/or adjuvant radiation treatment as a result of tumor regrowth or inadequate biochemical remission of functioning GPAs. Conclusion: ETS remains the optimal surgical option for most GPAs and generally offers safe and efficient tumor resection. However, a combined approach with TCS remains a requirement in cases that are not suitable for treatment with a single ETS. To achieve optimal tumor removal and minimize the occurrence of surgical complications, a flexible combination of ETS and TCS is recommended based on the characteristics of the tumor.

A facile mixed complex synthesis method for perovskite oxides toward electrocatalytic oxygen reduction.

The perovskite-type La(0.5+x)Sr(0.5-x)FeO3-δ (x = 0.00, 0.10, 0.20) oxides for the electrocatalytic oxygen reduction reaction (ORR) were synthesized by a facile reaction-EDTA/citric acid mixed complex sol-gel method. The cubic single-phase perovskite structure of the as-prepared oxides is demonstrated using powder X-ray diffraction (XRD). Scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM-EDX), transmission electron microscopy/selected area electron diffraction (TEM-SAED), and X-ray photoelectron spectroscopy (XPS) characterizations were also conducted for the perovskite-type La(0.5+x)Sr(0.5-x)FeO3-δ (x = 0.00, 0.10, 0.20) oxides. Furthermore, the electrochemical ORR properties of the as-prepared oxides in alkaline media were studied, with the oxides exhibiting good electrocatalytic ORR performance.

Multi-View and Multi-Order Structured Graph Learning.

Recently, graph-based multi-view clustering (GMC) has attracted extensive attention from researchers, in which multi-view clustering based on structured graph learning (SGL) can be considered as one of the most interesting branches, achieving promising performance. However, most of the existing SGL methods suffer from sparse graphs lacking useful information, which normally appears in practice. To alleviate this problem, we propose a novel multi-view and multi-order SGL (M 2 SGL) model which introduces multiple different orders (multi-order) graphs into the SGL procedure reasonably. To be more specific, M 2 SGL designs a two-layer weighted-learning mechanism, in which the first layer truncatedly selects part of views in different orders to retain the most useful information, and the second layer assigns smooth weights into retained multi-order graphs to fuse them attentively. Moreover, an iterative optimization algorithm is derived to solve the optimization problem involved in M 2 SGL, and the corresponding theoretical analyses are provided. In experiments, extensive empirical results demonstrate that the proposed M 2 SGL model achieves the state-of-the-art performance in several benchmarks.

Acute myeloid leukemia with NUP98::RARG resembling acute promyelocytic leukemia accompanying ARID1B gene mutation.

In this study, we present a case of acute myeloid leukemia characterized by the t(11;12)(p15;q13) translocation, exhibiting clinical, immunophenotypical, and morphological features consistent with acute promyelocytic leukemia (APL). The RNA sequencing analysis of the patient's bone marrow samples revealed the presence of the NUP98-retinoic acid receptor gamma (RARG) (NUP98::RARG) gene resulting from the translocation. Furthermore, the presence of a mutation in the ARID1B gene in the patient under study indicates a potential association with resistance to all-trans retinoic acid (ATRA).

An attempt to confirm the contribution to ORR activity of different N-species in M-NC (M = Fe, Co, Ni) catalysts with XPS analysis.

M-NC catalysts were prepared by a combination of the electrospinning method and thermal treatment. For the first time, the contribution of N-species to the ORR (oxygen reduction reaction) of the M-NC was analysed using XPS (X-ray photoelectron spectroscopy). The obtained relations were verified by VASP (Vienna Ab-initio Simulation Package).

USP13 reduces septic mediated cardiomyocyte oxidative stress and inflammation by inducing Nrf2.

BACKGROUND: Sepsis is a common cardiovascular complication that can cause heart damage. The regulatory role of ubiquitin-specific peptidase 13 (USP13) on erythroid 2-related factor 2 (Nrf2) has been reported, but its regulatory role in septic cardiomyopathy remains unclear. METHODS: The Sprague Dawley (SD) rat model of septic myocardial injury was constructed by lipopolysaccharides (LPS). The serum lactate dehydrogenase (LDH) and creatine kinase (CK) levels were detected, the mRNA and protein expression levels of Nrf2 and USP13 in tissues were detected by real-time quantitative reverse transcription PCR (qRT-PCR) and western blot (WB), and the expression of USP13 at the treatment time of 3 h, 6 h, and 12 h was also detected. The cell viability and USP13, Nrf-2 and heme oxygenase-1 (HO-1) expression levels of H9C2-treated cells by LPS and the oxidative stress level and inflammatory response of H9C2 cells were detected by enzyme-linked immunosorbent assay (ELISA) and WB. RESULTS: The results showed that USP13 was downregulated in septic myocardial injury tissues, and the Nrf2 level was increased in vitro after the cells were treated with LPS. Overexpression of USP13 further induced Nrf2 to reduce apoptosis, oxidative stress, and expression of inflammatory factors. CONCLUSION: In conclusion, this study demonstrated that USP13 was downregulated in septic myocardial injury tissues, and USP13 overexpression increased Nrf2 levels and reduced apoptosis. Further studies showed that USP13 reduced LPS-induced oxidative stress and inflammation by inducing Nrf2.

Sparse PCA via l2,p-Norm Regularization for Unsupervised Feature Selection.

In the field of data mining, how to deal with high-dimensional data is an inevitable topic. Since it does not rely on labels, unsupervised feature selection has attracted a lot of attention. The performance of spectral-based unsupervised methods depends on the quality of the constructed similarity matrix, which is used to depict the intrinsic structure of data. However, real-world data often contain plenty of noise features, making the similarity matrix constructed by original data cannot be completely reliable. Worse still, the size of a similarity matrix expands rapidly as the number of samples rises, making the computational cost increase significantly. To solve this problem, a simple and efficient unsupervised model is proposed to perform feature selection. We formulate PCA as a reconstruction error minimization problem, and incorporate a l2,p-norm regularization term to make the projection matrix sparse. The learned row-sparse and orthogonal projection matrix is used to select discriminative features. Then, we present an efficient optimization algorithm to solve the proposed unsupervised model, and analyse the convergence and computational complexity of the algorithm theoretically. Finally, experiments on both synthetic and real-world data sets demonstrate the effectiveness of our proposed method.

Unsupervised Feature Selection With Weighted and Projected Adaptive Neighbors.

In the field of data mining, how to deal with high-dimensional data is a fundamental problem. If they are used directly, it is not only computationally expensive but also difficult to obtain satisfactory results. Unsupervised feature selection is designed to reduce the dimension of data by finding a subset of features in the absence of labels. Many unsupervised methods perform feature selection by exploring spectral analysis and manifold learning, such that the intrinsic structure of data can be preserved. However, most of these methods ignore a fact: due to the existence of noise features, the intrinsic structure directly built from original data may be unreliable. To solve this problem, a new unsupervised feature selection model is proposed. The graph structure, feature weights, and projection matrix are learned simultaneously, such that the intrinsic structure is constructed by the data that have been feature weighted and projected. For each data point, its nearest neighbors are acquired in the process of graph construction. Therefore, we call them adaptive neighbors. Besides, an additional constraint is added to the proposed model. It requires that a graph, corresponding to a similarity matrix, should contain exactly c connected components. Then, we present an optimization algorithm to solve the proposed model. Next, we discuss the method of determining the regularization parameter γ in our proposed method and analyze the computational complexity of the optimization algorithm. Finally, experiments are implemented on both synthetic and real-world datasets to demonstrate the effectiveness of the proposed method.

Sequence Analysis of the Plastomes of Two Tibetan Medicinal Plants of the Family Papaveraceae.

BACKGROUND: With the rapid development of next-generation sequencing technology, more plants plastomes have been sequenced, further advancing species identification and phylogenetic studies. However, there are a few studies on the genetic and phylogenetic analysis of the plastomes of Dicranostigma lactucoides Hook. f. et Thoms. and Hypecoum leptocarpum Hook. f. et Thoms. METHODS: In this study, we sequenced and analyzed the plastomes of Dicranostigma lactucoides Hook. f. et Thoms. and Hypecoum leptocarpum Hook. f. et Thoms., and conducted a phylogenetic analysis using 13 related species. RESULTS: The results showed that the plastomes of both D. lactucoides and H. leptocarpum had a typical tetrad structure, with sizes of 166,819 bp and 163,282 bp, respectively. We annotated 133 genes for D. lactucoides and 120 genes for H. leptocarpum. A total of 72 and 43 simple repetitive sequences were detected in D. lactucoides and H. leptocarpum, respectively. Codon preference analysis showed that the relative usage frequency of codons and the relative abundance of synonymous codons used were the same for both plastomes. Nucleotide polymorphism analysis identified seven variant loci with high nucleotide diversity (Pi) values, all located in the large single copy (LSC) region. Inverted repeat (IR) boundary analysis revealed differences in gene types and locations on both sides of the boundary, except for the small single copy/inverted repeat a (SSC/IRa) boundary. The phylogenetic analysis showed the species clustered into two major groups, one with five genera (Hypecoum, Corydalis, Papaver, Meconopsis, and Dicranostigma) and the other with two genera (Coreanomecon; and Hylomecon). CONCLUSIONS: Comparative analysis of the plastome genomic characteristics and phylogeny of D. lactucoides and H. leptocarpum laid the foundation for identifying the above two species and the phylogenetic study and comprehensive exploitation of the Papaveraceae.

The divergent effects of moderate climate warming on the gut microbiota and energetic state of cold-climate lizards from open and semi-closed microhabitats.

Introduction: Understanding the physiological responses to warming temperatures is critical for evaluating the vulnerabilities of animals to climate warming. The physiological responses are increasingly affected by gut microbiota. However, the interactions between physiological responses and the gut microbiota of sympatric animals from various microhabitats in the face of climate change remain largely unknown. Methods: To evaluate the effects of warming temperatures on animals from different microhabitats, we compared locomotor performance, metabolic rate, growth, survival, and gut microbiota of two sympatric ectothermic species (Eremias argus and Takydromus amurensis) from open and semi-closed microhabitats under present and moderate warming climate conditions, respectively. Results and discussion: We found that locomotor performance and growth rates of snout-vent length (SVL) were enhanced in both lizard species by warming climate. Interestingly, warming temperatures enhanced resting metabolic rates (RMR) in the open-habitat lizard, E. argus, but depressed them in the semi-closed habitat lizard, T. amurensis. Reversely, the metabolism-related gut microbiota was not affected by warming in E. argus, whereas it was significantly enhanced by warming in T. amurensis, indicating a plausible compensatory effect of the gut microbiota on the metabolic regulation of T. amurensis. Furthermore, warming likely improved immunity in both lizard species by significantly reducing pathogenic bacteria while increasing probiotics. This study found that high-latitude sympatric lizards from both open and semi-closed habitats were beneficial to warming temperatures by physiological modification and regulation of the gut microbiota and highlighted the importance of integrating the physiology and gut microbiota in evaluating the vulnerability of animals to climate warming.

Bidirectional Probabilistic Subspaces Approximation for Multiview Clustering.

The existing multiview clustering models learn a consistent low-dimensional embedding either from multiple feature matrices or multiple similarity matrices, which ignores the interaction between the two procedures and limits the improvement of clustering performance on multiview data. To address this issue, a bidirectional probabilistic subspaces approximation (BPSA) model is developed in this article to learn a consistently orthogonal embedding from multiple feature matrices and multiple similarity matrices simultaneously via the disturbed probabilistic subspace modeling and approximation. A skillful bidirectional fusion strategy is designed to guarantee the parameter-free property of the BPSA model. Two adaptively weighted learning mechanisms are introduced to ensure the inconsistencies among multiple views and the inconsistencies between bidirectional learning processes. To solve the optimization problem involved in the BPSA model, an iterative solver is derived, and a rigorous convergence guarantee is provided. Extensive experimental results on both toy and real-world datasets demonstrate that our BPSA model achieves state-of-the-art performance even if it is parameter-free.