Miracle-dz/UniFAR-FTUs huggingface dataset:
Conference on Computer Vision and Pattern Recognition (CVPR) 2022

devoted to presentations by sponsors and exhibitors. Over the final three days, presentations of scientific technologies, products, services and platforms will be by academics. (4) Innovation track. This will consist of: (i) a mapathon, where participants will make online map improvements from geospatial imagery; and (ii) a hackathon, where geospatial scientists and developers will collaboratively create usable software. (5) Youth Forum. Over the final three days and organised by the ISPRS Student Consortium. Designed to link students, young researchers and professionals from different countries and provide a platform for the exchange of information. (6) General Assembly. Held over three days. Restricted to delegates and representatives of the Ordinary, Associate and Regional Member organisations plus the Council, Financial Commission and Honorary Members of ISPRS.
Automatic Differentiable Procedural Modeling

Procedural modeling allows for an automatic generation of large amounts of similar assets, but there is limited control over the generated output. We address this problem by introducing Automatic Differentiable Procedural Modeling (ADPM). The forward procedural model generates a final editable model. The user modifies the output interactively, and the modifications are transferred back to the procedural model as its parameters by solving an inverse procedural modeling problem. We present an auto‐differentiable representation of the procedural model that significantly accelerates optimization. In ADPM the procedural model is always available, all changes are non‐destructive, and the user can interactively model the 3D object while keeping the procedural representation. ADPM provides the user with precise control over the resulting model comparable to non‐procedural interactive modeling. ADPM is node‐based, and it generates hierarchical 3D scene geometry converted to a differentiable computational graph. Our formulation focuses on the differentiability of high‐level primitives and bounding volumes of components of the procedural model rather than the detailed mesh geometry. Although this high‐level formulation limits the expressiveness of user edits, it allows for efficient derivative computation and enables interactivity. We designed a new optimizer to solve for inverse procedural modeling. It can detect that an edit is under‐determined and has degrees of freedom. Leveraging cheap derivative evaluation, it can explore the region of optimality of edits and suggest various configurations, all of which achieve the requested edit differently. We show our system's efficiency on several examples, and we validate it by a user study.
High-throughput simulation studies of angiogenesis - Reverse engineering the role of tip cells and pericytes in vascular development

Angiogenesis is the process by which new blood vessels develop by splitting of or by sprouting from existing vessels. In sprouting angiogenesis vessels branch out and connect with other sprouts to form a new network. This process involves both the endothelial cells, which make up the inner lining of a vessel, and the perivascular cells, which surround the vessel. The collective behavior of these cells results in the formation of sprouts and eventually vascular networks. The cells involved in angiogenesis differ in shape and behavior, which affects their collective behavior. Furthermore, the cells also affect one another via diffusive and membrane bound signaling molecules. In this thesis we aim to understand how interactions between multiple cell-types exhibiting subtle differences in behavior change the resulting collective angiogenic sprouting. To this end, we developed cell-based, computational models of angiogenesis, based on the cellular Potts model. The inputs of these models are the observed or hypothesized behavior of individual cells and the output is the resulting collective cell behavior: e.g., the formation of angiogenic sprouts or vascular networks. By assigning different behavior to a subset of the cells, these models can be used to study the interplay between cell types exhibiting different behavior.
Design Principles for Neurorobotics

In their book “How the Body Shapes the Way We Think: A New View of Intelligence,” Pfeifer and Bongard put forth an embodied approach to cognition. Because of this position, many of their robot examples demonstrated “intelligent” behavior despite limited neural processing. It is our belief that neurorobots should attempt to follow many of these principles. In this article, we discuss a number of principles to consider when designing neurorobots and experiments using robots to test brain theories. These principles are strongly inspired by Pfeifer and Bongard, but build on their design principles by grounding them in neuroscience and by adding principles based on neuroscience research. Our design principles fall into three categories. First, organisms must react quickly and appropriately to events. Second, organisms must have the ability to learn and remember over their lifetimes. Third, organisms must weigh options that are crucial for survival. We believe that by following these design principles a robot's behavior will be more naturalistic and more successful.
Toward Understanding El Niño Southern-Oscillation’s Spatiotemporal Pattern Diversity

The El Niño Southern Oscillation (ENSO) phenomenon, manifested by the great swings of large-scale sea surface temperature (SST) anomalies over the equatorial central to eastern Pacific oceans, is a major source of interannual global shifts in climate patterns and weather activities. ENSO’s SST anomalies exhibit remarkable spatiotemporal pattern diversity (STPD), with their spatial pattern diversity dominated by Central Pacific (CP) and Eastern Pacific (EP) El Niño events and their temporal diversity marked by different timescales and intermittency in these types of events. By affecting various Earth system components, ENSO and its STPD yield significant environmental, ecological, economic, and societal impacts over the globe. The basic dynamics of ENSO as a canonical oscillator generated by coupled ocean–atmosphere interactions in the tropical Pacific have been largely understood. A minimal simple conceptual model such as the recharge oscillator paradigm provides means for quantifying the linear and nonlinear seasonally modulated growth rate and frequency together with ENSO’s state-dependent noise forcing for understanding ENSO’s amplitude and periodicity, boreal winter-time phase locking, and warm/cold phase asymmetry. However, the dynamical mechanisms explaining the key features of ENSO STPD associated with CP and EP events remain to be better understood. This article provides a summary of the recent active research on the dynamics of ENSO STPD together with discussions on challenges and outlooks for theoretical, diagnostic, and numerical modeling approaches to advance our understanding and modeling of ENSO, its STPD, and their broad impacts.
Detection and Genetic Correlation Analysis of Diarrhea Cases and Norovirus in Oysters in Yantai, China

Background This study aimed to assess the correlation between Norovirus (NoV), diarrhea, and raw oysters from the eastern coastal areas of Yantai, Shandong, China. Methods Marine oysters were selected from the three aquatic markets in Laishan district, Yantai City, in March 2019. Meanwhile, 100 fecal samples were collected from patients with diarrhea from the same areas during the same period. Nucleic acids were extracted from these samples and detected by employing reverse transcription polymerase chain reaction (RT-PCR) for NoV GI/GII. The VP1 gene of the coat protein of NoV was amplified by semi-nested RT-PCR and sequenced. Sequence comparison of VP1 was performed with BioEdit software, and the evolutionary tree was constructed with Mega7.0 software. Results Of the 151 oysters, 42 (27.8%) were positive for NoV. Among them, 32 (21.2%) were GII-positive, 10 (6.6%) were GI-positive, and one GI VP1 sequence was obtained in the oyster samples. Of 100 fecal samples from patients with diarrhea, 38 were GII-positive and 17 were GI-positive. Totally, 19 GII VP 1 sequences and eight GI VP 1 sequences were obtained. Two G1 VP 1 sequences in two fecal samples showed 98.7% nucleotide sequence identity and 99.1% amino acid sequence identity G1 VP 1 acquired in the oyster sample. Conclusions The results suggest that oysters may be responsible for the spread of NoV in Yantai, Shandong province, China.
Kappa Opioid Receptor Blockade in the Amygdala Mitigates Pain Like-Behaviors by Inhibiting Corticotropin Releasing Factor Neurons in a Rat Model of Functional Pain

Functional pain syndromes (FPS) occur in the absence of identifiable tissue injury or noxious events and include conditions such as migraine, fibromyalgia, and others. Stressors are very common triggers of pain attacks in various FPS conditions. It has been recently demonstrated that kappa opioid receptors (KOR) in the central nucleus of amygdala (CeA) contribute to FPS conditions, but underlying mechanisms remain unclear. The CeA is rich in KOR and encompasses major output pathways involving extra-amygdalar projections of corticotropin releasing factor (CRF) expressing neurons. Here we tested the hypothesis that KOR blockade in the CeA in a rat model of FPS reduces pain-like and nocifensive behaviors by restoring inhibition of CeA-CRF neurons. Intra-CeA administration of a KOR antagonist (nor-BNI) decreased mechanical hypersensitivity and affective and anxiety-like behaviors in a stress-induced FPS model. In systems electrophysiology experiments in anesthetized rats, intra-CeA application of nor-BNI reduced spontaneous firing and responsiveness of CeA neurons to peripheral stimulation. In brain slice whole-cell patch-clamp recordings, nor-BNI increased feedforward inhibitory transmission evoked by optogenetic and electrical stimulation of parabrachial afferents, but had no effect on monosynaptic excitatory transmission. Nor-BNI decreased frequency, but not amplitude, of spontaneous inhibitory synaptic currents, suggesting a presynaptic action. Blocking KOR receptors in stress-induced FPS conditions may therefore represent a novel therapeutic strategy.
Genetically Predicted Obesity Causally Increased the Risk of Hypertension Disorders in Pregnancy

Aims This study aimed to evaluate the causal association between obesity and hypertension disorders in pregnancy. Methods Two-sample Mendelian randomization (MR) study was conducted based on the data obtained from the GIANT (n = 98,697 participants) consortium and FinnGen (n = 96,449 participants) consortium to determine the causal effect of obesity on the risk of hypertension disorders in pregnancy. Based on a genome-wide significance, 14 single-nucleotide polymorphisms (SNPs) associated with obesity-related databases were used as instrumental variables. The random-effects inverse-variance weighted (IVW) method was adopted as the main analysis with a supplemented sensitive analysis of the MR-Egger and weighted median approaches. Results All three MR methods showed that genetically predicted obesity causally increased the risk of hypertension disorders in pregnancy. IVW analysis provided obesity as a risk factor for hypertension disorders in pregnancy with an odds ratio (OR) of 1.39 [95% confidence interval (CI) 1.21–1.59; P = 2.46 × 10−6]. Weighted median and MR Egger regression also showed directionally similar results [weighted median OR = 1.49 (95% CI, 1.24–1.79), P = 2.45 × 10−5; MR-Egger OR = 1.95 (95% CI, 1.35–2.82), P = 3.84 × 10−3]. No directional pleiotropic effects were found between obesity and hypertension disorders in pregnancy with both MR-Egger intercepts and funnel plots. Conclusions Our findings provided directed evidence that obesity was causally associated with a higher risk of hypertension disorders in pregnancy. Taking measures to reduce the proportion of obesity may help reduce the incidence of hypertension disorders in pregnancy.
Myocardial-Treg Crosstalk: How to Tame a Wolf

The immune system plays a vital role in maintaining tissue integrity and organismal homeostasis. The sudden stress caused by myocardial infarction (MI) poses a significant challenge for the immune system: it must quickly substitute dead myocardial with fibrotic tissue while controlling overt inflammatory responses. In this review, we will discuss the central role of myocardial regulatory T-cells (Tregs) in orchestrating tissue repair processes and controlling local inflammation in the context of MI. We herein compile recent advances enabled by the use of transgenic mouse models with defined cardiac antigen specificity, explore whole-heart imaging techniques, outline clinical studies and summarize deep-phenotyping conducted by independent labs using single-cell transcriptomics and T-cell repertoire analysis. Furthermore, we point to multiple mechanisms and cell types targeted by Tregs in the infarcted heart, ranging from pro-fibrotic responses in mesenchymal cells to local immune modulation in myeloid and lymphoid lineages. We also discuss how both cardiac-specific and polyclonal Tregs participate in MI repair. In addition, we consider intriguing novel evidence on how the myocardial milieu takes control of potentially auto-aggressive local immune reactions by shaping myosin-specific T-cell development towards a regulatory phenotype. Finally, we examine the potential use of Treg manipulating drugs in the clinic after MI.
Impact of Climate Change on Hydrochemical Processes at Two High-Elevation Forested Watersheds in the Southern Appalachians, United States

Climate change increasingly affects primary productivity and biogeochemical cycles in forest ecosystems at local and global scales. To predict change in vegetation, soil, and hydrologic processes, we applied an integrated biogeochemical model Photosynthesis-EvapoTranspration and BioGeoChemistry (PnET-BGC) to two high-elevation forested watersheds in the southern Appalachians in the US under representative (or radiative) concentration pathway (RCP)4.5 and RCP8.5 scenarios. We investigated seasonal variability of the changes from current (1986–2015) to future climate scenarios (2071–2100) for important biogeochemical processes/states; identified change points for biogeochemical variables from 1931 to 2100 that indicate potential regime shifts; and compared the climate change impacts of a lower-elevation watershed (WS18) with a higher-elevation watershed (WS27) at the Coweeta Hydrologic Laboratory, North Carolina, United States. We find that gross primary productivity (GPP), net primary productivity (NPP), transpiration, nitrogen mineralization, and streamflow are projected to increase, while soil base saturation, and base cation concentration and ANC of streamwater are projected to decrease at the annual scale but with strong seasonal variability under a changing climate, showing the general trend of acidification of soil and streamwater despite an increase in primary productivity. The predicted changes show distinct contrasts between lower and higher elevations. Climate change is predicted to have larger impact on soil processes at the lower elevation watershed and on vegetation processes at the higher elevation watershed. We also detect five change points of the first principal component of 17 key biogeochemical variables simulated with PnET-BGC between 1931 and 2100, with the last change point projected to occur 20 years earlier under RCP8.5 (2059 at WS18 and WS27) than under RCP4.5 (2079 at WS18 and 2074 at WS27) at both watersheds. The change points occurred earlier at WS18 than at WS27 in the 1980s and 2010s but in the future are projected to occur earlier in WS27 (2074) than WS18 (2079) under RCP4.5, implying that changes in biogeochemical cycles in vegetation, soil, and streams may be accelerating at higher-elevation WS27.
A Cys2His2 Zinc Finger Transcription Factor BpSZA1 Positively Modulates Salt Stress in Betula platyphylla

Zinc finger proteins (ZFPs) are widely involved in plant growth and abiotic stress responses, however, few of these proteins have been functionally characterized in tree species. In this study, we cloned and characterized the BpSZA1 gene encoding a C2H2-type ZFP from Betula platyphylla. BpSZA1 is a transcription factor localized in the nucleus, with a transcription activation domain located at the N-terminus. BpSZA1 was predominantly expressed in stems and was induced by salt. We generated transgenic birch lines displaying overexpression (OE) or RNAi silencing (Ri) of BpSZA1 and exposed these along with wild-type birch seedlings to salinity. Phenotypic and physiological parameters such as superoxide dismutase, peroxisome, H2O2 content, proline content, water loss rate, and malondialdehyde content were examined. Overexpression of BpSZA1 in birch conferred increased salt tolerance. Chromatin immunoprecipitation-qPCR and RNA-seq showed that BpSZA1 binds to the GAGA-motif in the promoter of downstream target genes including BpAPX1, BpAPX2, BpCAT, and Bp6PGDH to activate their transcription. BpSZA1 also participates in abscisic acid (ABA) biosynthesis, proline biosynthesis, and the ABA/jasmonic acid pathway to enhance the salt stress of B. platyphylla.
Decomposition-Based Correlation Learning for Multi-Modal MRI-Based Classification of Neuropsychiatric Disorders

Multi-modal magnetic resonance imaging (MRI) is widely used for diagnosing brain disease in clinical practice. However, the high-dimensionality of MRI images is challenging when training a convolution neural network. In addition, utilizing multiple MRI modalities jointly is even more challenging. We developed a method using decomposition-based correlation learning (DCL). To overcome the above challenges, we used a strategy to capture the complex relationship between structural MRI and functional MRI data. Under the guidance of matrix decomposition, DCL takes into account the spike magnitude of leading eigenvalues, the number of samples, and the dimensionality of the matrix. A canonical correlation analysis (CCA) was used to analyze the correlation and construct matrices. We evaluated DCL in the classification of multiple neuropsychiatric disorders listed in the Consortium for Neuropsychiatric Phenomics (CNP) dataset. In experiments, our method had a higher accuracy than several existing methods. Moreover, we found interesting feature connections from brain matrices based on DCL that can differentiate disease and normal cases and different subtypes of the disease. Furthermore, we extended experiments on a large sample size dataset and a small sample size dataset, compared with several other well-established methods that were designed for the multi neuropsychiatric disorder classification; our proposed method achieved state-of-the-art performance on all three datasets.
Integrated Behavioral, Genetic and Brain Circuit Visualization Methods to Unravel Functional Anatomy of Zebrafish Amygdala

The mammalian amygdala is a complex forebrain structure consisting of a heterogeneous group of nuclei derived from the pallial and subpallial telencephalon. It plays a critical role in a broad range of behaviors such as emotion, cognition, and social behavior; within the amygdala each nucleus has a distinct role in these behavioral processes. Topological, hodological, molecular, and functional studies suggest the presence of an amygdala-like structure in the zebrafish brain. It has been suggested that the pallial amygdala homolog corresponds to the medial zone of the dorsal telencephalon (Dm) and the subpallial amygdala homolog corresponds to the nuclei in the ventral telencephalon located close to and topographically basal to Dm. However, these brain regions are broad and understanding the functional anatomy of the zebrafish amygdala requires investigating the role of specific populations of neurons in brain function and behavior. In zebrafish, the highly efficient Tol2 transposon-mediated transgenesis method together with the targeted gene expression by the Gal4-UAS system has been a powerful tool in labeling, visualizing, and manipulating the function of specific cell types in the brain. The transgenic resource combined with neuronal activity imaging, optogenetics, pharmacology, and quantitative behavioral analyses enables functional analyses of neuronal circuits. Here, we review earlier studies focused on teleost amygdala anatomy and function and discuss how the transgenic resource and tools can help unravel the functional anatomy of the zebrafish amygdala.
Fast, Simple, and Highly Specific Molecular Detection of Porphyromonas gingivalis Using Isothermal Amplification and Lateral Flow Strip Methods

Porphyromonas gingivalis is an important oral pathogen that causes periodontal disease and is difficult to culture under conventional conditions. Therefore, a reliable technique for detecting this pathogenic bacterium is required. Here, isothermal recombinase polymerase amplification (RPA), a new nucleic acid amplification method, was combined with a visualization method based on nanoparticle-based lateral flow strips (LFS) for the rapid detection of P. gingivalis. The species-specific 16S rRNA sequence of P. gingivalis was used as the target for RPA, and a set of specific primer–probe combinations were designed and screened to amplify the target sequences. As a thermostatic amplification method, the RPA reaction, under optimized conditions, takes only 30 min to complete at a constant temperature (37°C). The amplification reaction products can be detected visually by LFS without any need for special equipment. The RPA-LFS method established for the detection of P. gingivalis was shown to be highly specific in distinguishing P. gingivalis from other pathogenic organisms by using 20 clinical isolates of P. gingivalis and 23 common pathogenic microorganisms. Susceptibility measurements and probit regression analysis were performed with gradient dilutions of P. gingivalis genomic DNA. The method was obtained to be highly sensitive, with a detection limit of 9.27 CFU per reaction at 95% probability. By analyzing the gingival sulcus fluid specimens from 130 patients with chronic periodontitis, the results showed that the RPA-LFS method detected 118 positive cases and 12 negative cases of P. gingivalis, and the results obtained were consistent with those of a conventional PCR assay. The RPA–LFS method is an efficient, rapid, and convenient diagnostic method that simplifies the tedious process of detecting P. gingivalis.
Protecting Our Oceans: Why the Definition of Biodiversity Matters

Biodiversity is one of the most-used terms in biology, but there are many ways of measuring and interpreting it. Understanding the meaning of the different measures of biodiversity is vitally important for managing and conserving endangered ecosystems. Marine ecosystems are degrading quickly due to human impacts like overfishing, habitat destruction, or climate change. In this article, we introduce and explain three aspects of biodiversity: taxonomic, functional, and food web biodiversity. All three focus on individual species and their roles in ecosystems and food chains. Each aspect of biodiversity is useful in measuring or achieving conservation goals, depending on the type of habitat or environment that is considered. The examples we provide show that finding the best way to protect an ecosystem requires careful consideration of biodiversity from different perspectives, which will remain a challenge for current and future conservationists.