Miracle-dz/UniFAR-FTUs huggingface dataset:
Grazing alters species relative abundance by affecting plant functional traits in a Tibetan subalpine meadow

Abstract Domestic livestock grazing has caused dramatic changes in plant community composition across the globe. However, the response of plant species abundance in communities subject to grazing has not often been investigated through a functional lens, especially for belowground traits. Grazing directly impacts aboveground plant tissues, but the relationships between above‐ and belowground traits, and their influence on species abundance are also not well known. We collected plant trait and species relative abundance data in the grazed and nongrazed meadow plant communities in a species‐rich subalpine ecosystem of the Qinghai–Tibet Plateau. We measured three aboveground traits (leaf photosynthesis rate, specific leaf area, and maximum height) and five belowground traits (root average diameter, root biomass, specific root length, root tissue density, and specific root area). We tested for shifts in the relationship between species relative abundance and among all measured traits under grazing compared with the nongrazed meadow. We also compared the power of above‐ and belowground traits to predict species relative abundance. We observed a significant shift from a resource conservation strategy to a resource acquisition strategy. Moreover, this resource conservation versus resource acquisition trade‐off can also determine species relative abundance in the grazed and nongrazed plant communities. Specifically, abundant species in the nongrazed meadow had aboveground and belowground traits that are associated with high resource conservation, whereas aboveground and belowground traits that are correlated with high resource acquisition determined species relative abundance in the grazed meadow. However, belowground traits were found to explain more variances in species relative abundance than aboveground traits in the nongrazed meadow, while aboveground and belowground traits had comparable predictive power in the grazed meadow. We show that species relative abundance in both the grazed and the nongrazed meadows can be predicted by both aboveground traits and belowground traits associated with a resource acquisition versus conservation trade‐off. More importantly, we show that belowground traits have higher predictive power of species relative abundance than aboveground traits in the nongrazed meadow, whereas in the grazed meadows, above‐ and belowground traits had comparable high predictive power.
Numerical Security Proof for Decoy-State BB84 and Measurement-Device-Independent QKD Resistant against Large Basis Misalignment

In this work, we incorporate decoy-state analysis into a well-established numerical framework for key rate calculation, and apply the numerical framework to decoy-state BB84 and measurementdevice-independent (MDI) QKD protocols as examples. Additionally, we combine with these decoystate protocols what is called “fine-grained statistics”, which is a variation of existing QKD protocols that makes use of originally discarded data to get a better key rate. We show that such variations can grant protocols resilience against any unknown and slowly changing rotation along one axis, similar to reference-frame-independent QKD, but without the need for encoding physically in an additional rotation-invariant basis. Such an analysis can easily be applied to existing systems, or even data already recorded in previous experiments, to gain significantly higher key rate when considerable misalignment is present, extending the maximum distance for BB84 and MDI-QKD and reducing the need for manual alignment in an experiment.
On a Low-Frequency and Contrast Stabilized Full-Wave Volume Integral Equation Solver for Lossy Media

In this paper we present a new regularized electric flux volume integral equation (D-VIE) for modeling high-contrast conductive dielectric objects in a broad frequency range. This new formulation is particularly suitable for modeling biological tissues at low frequencies, as it is required by brain epileptogenic area imaging, but also at higher ones, as it is required by several applications including, but not limited to, deep brain stimulation (DBS). When modeling inhomogeneous objects with high complex permittivities at low frequencies, the traditional D-VIE is ill-conditioned and suffers from numerical instabilities that result in slower convergence and in less accurate solutions. In this work we address these shortcomings by leveraging a new set of volume quasi-Helmholtz projectors. Their scaling by the material permittivity matrix allows for the re-balancing of the equation when applied to inhomogeneous scatterers and thereby makes the proposed method accurate and stable even for high complex permittivity objects until arbitrarily low frequencies. Numerical results, canonical and realistic, corroborate the theory and confirm the stability and the accuracy of this new method both in the quasi-static regime and at higher frequencies.
Potential local adaptation in populations of invasive reed canary grass (Phalaris arundinacea) across an urbanization gradient

Abstract Urban stressors represent strong selective gradients that can elicit evolutionary change, especially in non‐native species that may harbor substantial within‐population variability. To test whether urban stressors drive phenotypic differentiation and influence local adaptation, we compared stress responses of populations of a ubiquitous invader, reed canary grass (Phalaris arundinacea). Specifically, we quantified responses to salt, copper, and zinc additions by reed canary grass collected from four populations spanning an urbanization gradient (natural, rural, moderate urban, and intense urban). We measured ten phenotypic traits and trait plasticities, because reed canary grass is known to be highly plastic and because plasticity may enhance invasion success. We tested the following hypotheses: (a) Source populations vary systematically in their stress response, with the intense urban population least sensitive and the natural population most sensitive, and (b) plastic responses are adaptive under stressful conditions. We found clear trait variation among populations, with the greatest divergence in traits and trait plasticities between the natural and intense urban populations. The intense urban population showed stress tolerator characteristics for resource acquisition traits including leaf dry matter content and specific root length. Trait plasticity varied among populations for over half the traits measured, highlighting that plasticity differences were as common as trait differences. Plasticity in root mass ratio and specific root length were adaptive in some contexts, suggesting that natural selection by anthropogenic stressors may have contributed to root trait differences. Reed canary grass populations in highly urbanized wetlands may therefore be evolving enhanced tolerance to urban stressors, suggesting a mechanism by which invasive species may proliferate across urban wetland systems generally.
Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision

Depth estimation is a long-lasting yet important task in computer vision. Most of the previous works try to estimate depth from input images and assume images are all-in-focus (AiF), which is less common in real-world applications. On the other hand, a few works take defocus blur into account and consider it as another cue for depth estimation. In this paper, we propose a method to estimate not only a depth map but an AiF image from a set of images with different focus positions (known as a focal stack). We design a shared architecture to exploit the relationship between depth and AiF estimation. As a result, the proposed method can be trained either supervisedly with ground truth depth, or unsupervisedly with AiF images as supervisory signals. We show in various experiments that our method outperforms the state-of-the-art methods both quantitatively and qualitatively, and also has higher efficiency in inference time.
The complete chloroplast genome of Keteleeria evelyniana

Abstract Here, we report the complete chloroplast genome of Keteleeria evelyniana. The genome is 116,940 bp in size, which is comprised of a large single-copy (LSC) region of 74,075 bp, a small single-copy (SSC) region of 40,425 bp, and two short inverted repeat (IR) regions of 1,220 bp. The overall GC content of the plastome was 38.5%. The new sequence comprised 103 unique genes, including 74 protein-coding genes, 4 rRNA genes, and 25 tRNA genes. Phylogenetic analysis showed that K.evelyniana was close to Keteleeria hainanensis and Keteleeria davidiana.
Asymptotic additivity of the Turaev–Viro invariants for a family of 3‐manifolds

In this paper, we show that the Turaev–Viro invariant volume conjecture posed by Chen and Yang is preserved under gluings of toroidal boundary components for a family of 3‐manifolds. In particular, we show that the asymptotics of the Turaev–Viro invariants are additive under certain gluings of elementary pieces arising from a construction of hyperbolic cusped 3‐manifolds due to Agol. The gluings of the elementary pieces are known to be additive with respect to the simplicial volume. This allows us to construct families of manifolds which have an arbitrary number of hyperbolic pieces and satisfy an extended version of the Turaev–Viro invariant volume conjecture.
Northward geographic diversification of a kleptoparasitic spider Argyrodes lanyuensis (Araneae, Theridiidae) from the Philippine Archipelago to Orchid Island

Abstract Oceanic islands are unique geographic systems that promote local adaptations and allopatric speciation in many of their highly endemic taxa. This is a common case in the Philippine Archipelago, where numerous unrelated taxa on islands have been inferred to have diversified in isolation. However, few cases have been reported in invertebrates especially among parasitic organisms. Here, we tested for biogeographical structure in novel populations of the “generalist" kleptoparasitic spider, Argyrodes lanyuensis Yoshida, Tso & Severinghaus, 1998 in the Philippines. Results showed that, in addition to Orchid/Lanyu Island, this species has a wide geographic distribution in the Philippine Archipelago. The estimated divergence time of this lineage using the mitochondrial cytochrome oxidase 1 (mt‐CO1) suggests that this species diverged ca 3.12 MYA, during the Pliocene. Two reciprocal monophyletic clades were elucidated in A. lanyuensis, but with limited differentiation across Pleistocene Aggregate Island Complex (PAIC) boundaries and modern‐day islands. However, in our analyses of morphological variation, we identified two phenotypically differentiated units in males (Orchid Island, Taiwan + Luzon, Philippine PAIC populations vs. Palawan + West Visayan + Mindanao PAIC populations). We infer that this species diverged in the southern portion of the Philippine Archipelago and only recently colonized Orchid Island. Our study provides new information on the extensive distribution of A. lanyuensis outside Orchid Island, Taiwan, but we documented a very limited geographically associated genetic variation. Our study points to behavioral phenomena such as foraging behavior as essential contributor to the evolutionary process of species diversification, in contrast to the traditionally invoked geographic drivers of divergence.
Population Genetic Difference of Pharmacogenomic VIP Variants in the Tibetan Population

Background Genetic variation influences drug reaction or adverse prognosis. The purpose of this research was to genotype very important pharmacogenetic (VIP) variants in the Tibetan population. Methods and Materials Blood samples from 200 Tibetans were randomly collected and 59 VIP variants were genotyped, and then compared our data to 26 other populations in the 1000 project to further analyze and identify significant difference. Results The results showed that on comparing with most of the 26 populations from the 1000 project, rs4291 (ACE), rs1051296 (SLC19A1) and rs1065852 (CYP2D6) significantly differed in the Tibetan population. Furthermore, three significant loci were related to drug response. In addition, the allele frequency of Tibetans least differed from that of East Asian populations, and most differed from that of Americans. Conclusion Three significant loci of variation ACE rs4291, SLC19A1 rs1051296 and CYP2D6 rs1065852 were associated with drug response. This result will contribute to improving the information of the Tibetan in the pharmacogenomics database, and providing a theoretical basis for clinical individualised drug use in Tibetans.
Non-invasive multi-channel deep learning convolutional neural networks for localization and classification of common hepatic lesions

Purpose Machine learning techniques, especially convolutional neural networks (CNN), have revolutionized the spectrum of computer vision tasks with a primary focus on supervised and labelled image datasets. We aimed to assess a novel method to segment the liver from the abdomen computed tomography (CT) image using the CNN network, and to train a unique method to locate and classify liver lesion pre-histological findings using multi-channel deep learning CNN (MDL-CNN). Material and methods The post-contrast CT images of the liver with a resolution of 0.625 mm were chosen for the study. In a random method, 50 examples of each hepatocellular carcinomas, metastases tumours, haemangiomas, hepatic cysts were chosen and evaluated. Results The dice score quantitatively analyses the similarity of segmentation results with the training dataset. In the first CNN model for segmenting the liver, the dice score was 96.18%. The MDL-CNN model yielded 98.78% accuracy in classification, and the dice score for locating liver lesions was 95.70%. Additionally, the performance of this model was compared to various other existing models. Conclusions According to our study, the machine learning approach can be successfully implemented to segment the liver and classify lesions, which will help radiologists impart better diagnosis.
Inverse Kinematics of Concentric Tube Robots in the Presence of Environmental Constraints

Inverse kinematics (IK) of concentric tube continuum robots (CTRs) is associated with two main problems. First, the robot model (e.g., the relationship between the configuration space parameters and the robot end-effector) is not linear. Second, multiple solutions for the IK are available. This paper presents a general approach to solve the IK of CTRs in the presence of constrained environments. It is assumed that the distal tube of the CTR is inserted into a cavity while its proximal end is placed inside a tube resembling the vessel enabling the entry to the organ cavity. The robot-tissue interaction at the beginning of the organ-cavity imposed displacement and force constraints to the IK problem to secure a safe interaction between the robot and tissue. The IK in CTRs has been carried out by treating the problem as an optimization problem. To find the optimized IK of the CTR, the cost function is defined to be the minimization of input force into the body cavity and the occupied area by the robot shaft body. The optimization results show that CTRs can keep the safe force range in interaction with tissue for the specified trajectories of the distal tube. Various simulation scenarios are conducted to validate the approach. Using the IK obtained from the presented approach, the tracking accuracy is achieved as 0.01 mm which is acceptable for the application.
(Mis)leading the COVID-19 Vaccination Discourse on Twitter: An Exploratory Study of Infodemic Around the Pandemic

In this work, we collect a moderate-sized representative corpus of tweets (over 200 000) pertaining to COVID-19 vaccination spanning for a period of seven months (September 2020–March 2021). Following a transfer learning approach, we utilize a pretrained transformer-based XLNet model to classify tweets as misleading or nonmisleading and manually validate the results with random subsets of samples. We leverage this to study and contrast the characteristics of tweets in the corpus that are misleading in nature against non-misleading ones. This exploratory analysis enables us to design features such as sentiments, hashtags, nouns, and pronouns which can, in turn, be exploited for classifying tweets as (non-)misleading using various machine learning (ML) models in an explainable manner. Specifically, several ML models are employed for prediction, with up to 90% accuracy, with the importance of each feature is explained using SHAP Explainable AI (XAI) tool. While the thrust of this work is principally exploratory in nature to obtain insight on the online discourse on COVID-19 vaccination, we conclude the article by outlining how these insights provide the foundations for a more actionable approach to mitigate misinformation. We have made the curated data as well as the accompanying code available so that the research community at large can reproduce, compare against, or build upon this work.
The Potential Effects of Oxidative Stress-Related Plasma Abnormal Protein Aggregate Levels on Brain Volume and Its Neuropsychiatric Consequences in Parkinson's Disease

Background Oxidative stress has been implicated in the pathogenesis of many diseases, including Parkinson's disease. Large protein aggregates may be produced after the breakdown of the proteostasis network due to overt oxidative stress. Meanwhile, brain volume loss and neuropsychiatric deficits are common comorbidities in Parkinson's disease patients. In this study, we applied a mediation model to determine the potential influences of oxidative stress-related plasma abnormal protein aggregate levels on brain volume and neuropsychiatric consequences in Parkinson's disease. Method 31 patients with PD and 24 healthy controls participated in this study. The PD patients were further grouped according to the presentation of cognitive decline or not. All participants received complete examinations to determine plasma abnormal protein aggregates levels, brain volume, and neuropsychiatric performance. The results were collected and analyzed in a single-level three-variable mediation model. Results Patients with PD cognitive decline exhibited higher plasma NfL levels, decreased regional brain volume, and poor neuropsychiatric subtest results compared with PD patients with normal cognition, with several correlations among these clinical presentations. The mediation model showed that the superior temporal gyrus completely mediated the effects of elevated plasma NfL levels due to the poor psychiatric performance of picture completion and digit span. Conclusion This study provides insight into the effects of oxidative stress-related plasma abnormal protein aggregate levels on regional brain volume and neuropsychiatric consequences in Parkinson's disease patients.
Apexoconnection: A Novel Technique to Determine Working Length

Purpose The aim of this study was to evaluate and compare the accuracy of working length (WL) determination when using the conventional electronic WL (EWL) technique versus using the novel Apexoconnection technique involving EWL with a connector (EWLc). Materials and Methods Sixty extracted posterior teeth with a total of 118 root canals were selected for the study. The real WL (RWL) of each canal was verified using a dental microscope under 25.6x magnification. Root canal lengths were measured with an apex locator using the attachment device directly attached to the hand file in the root canal and then adding another hand file as a connector between the attachment device and the file in the root canal. The distance from the file’s stopper to its tip was measured using a digital caliper. Statistical analysis was carried out to analyze the data between experimental groups. Results The results of the statistical test showed that the differences in the WL determined by either technique and the proper length as determined under the microscope were not statistically significant. The results of the statistical test comparing the groups were also not statistically significant. Conclusion In this in vitro study, both the novel and conventional techniques were equally accurate for determining WL when compared with the RWL.
A nuclear polymorphism at the 8q24 region is associated with improved survival time and chemo-response in high-grade serous ovarian cancer

The 8q24 chromosomal region is strongly associated with an increased risk of ovarian cancer. One single nucleotide polymorphism that is associated with ovarian cancer in this region is rs6983267, located within the long non-coding RNA colon cancer associated transcript 2 (CCAT2). The aim of the present study was to assess the association between rs6983267 and clinical outcomes in patients with high-grade serous ovarian cancer (HGSOC). The present retrospective genetic association study utilized Sanger sequencing to determine the genotype at the rs6983267 locus (GG, GT, TT) in 98 patients with HGSOC. Survival time and chemotherapy responses between patients were compared with the TT genotype and patients with a genotype containing a G allele (GT, GG). Survival analyses were performed using Cox proportional hazard ratio analysis. Association with chemo-response was performed using a logistic regression. The results revealed that patients with HGSOC and the TT genotype at the rs6983267 locus had improved survival time compared with patients with genotypes containing a G allele [hazard ratio=0.59; 95% confidence interval (CI), 0.36–0.97; P=0.039] and were significantly associated with International Federation of Gynecology and Obstetrics stage [odds ratio (OR)=5.34; 95% CI, 1.50–22.62; P=0.014] and positive chemo-response (OR=4.51; 95% CI, 1.40–18.00; P=0.018). In summary, patients with HGSOC and the TT genotype at the rs6983267 locus had improved survival time compared with those with a G allele, despite being associated with more advanced disease; this was possibly due to an improved response to chemotherapy.
Investigating the Role of Vanadium-Dependent Haloperoxidase Enzymology in Microbial Secondary Metabolism and Chemical Ecology

The chemical diversity of natural products is established by an elegant network of biosynthetic machinery and controlled by a suite of intracellular and environmental cues. Advances in genomics, transcriptomics, and metabolomics have provided useful insight to understand how organisms respond to abiotic and biotic factors to adjust their chemical output; this has permitted researchers to begin asking bigger-picture questions regarding the ecological significance of these molecules to the producing organism and its community. ABSTRACT The chemical diversity of natural products is established by an elegant network of biosynthetic machinery and controlled by a suite of intracellular and environmental cues. Advances in genomics, transcriptomics, and metabolomics have provided useful insight to understand how organisms respond to abiotic and biotic factors to adjust their chemical output; this has permitted researchers to begin asking bigger-picture questions regarding the ecological significance of these molecules to the producing organism and its community. Our lab is motivated by understanding how select microbes construct and manipulate bioactive molecules by utilizing vanadium-dependent haloperoxidase (VHPO) enzymology. This commentary will give perspective into our efforts to understand the unique VHPO-catalyzed conversions which modulate the activities within two ecologically relevant natural product families. Through enhancing our knowledge of microbial natural product biosynthesis, we can understand how and why these bioactive molecules are created.