Miracle-dz/UniFAR-FTUs huggingface dataset:
Cleavage of p65 Subunit of NF-κB by Orientia tsutsugamushi

Orientia tsutsugamushi, a causative agent of scrub typhus, is an obligate intracellular parasite and usually propagates in the cytoplasm of host endothelial cells and macrophages. Macrophages are the first defense line against bacterial infection and NF-κB is activated upon contact with bacteria, resulting in the transcription of inflammatory cytokine to control bacterial infection. In this study, we investigated whether O. tsutsugamushi modulates NF-κB activation in the macrophages. We examined the changes of NF-κB proteins upon infection with O. tsutsugamushi and found that NF-κB is activated at a slow rate as judged with EMSA and immunoblot analysis. Interestingly, we found that p65 was cleaved generating a 45 kDa fragment. In addition, fragment of p65 is generated only by the virulent serotype strain of O. tsutsugamushi, suggesting this cleavage may be associated with the mouse virulence. It is still unknown whether this is a direct result of O. tsutsugamushi proteins or enzymes of host cell. Further exploration of the mechanism that modulates NF-κB activity by O. tsutsugamushi could contribute to a better understanding of the molecular pathogenesis of O. tsutsugamushi infection.
ATPγS Disrupts Human Immunodeficiency Virus Type 1 Virion Core Integrity

ABSTRACT Heat shock protein 70 (Hsp70) is incorporated within the membrane of primate lentiviral virions. Here we demonstrate that Hsp70 is also incorporated into oncoretroviral virions and that it remains associated with membrane-stripped human immunodeficiency virus type 1 (HIV-1) virion cores. To determine if Hsp70 promotes virion infectivity, we attempted to generate Hsp70-deficient virions with gag deletion mutations, Hsp70 transdominant mutants, or RNA interference, but these efforts were confounded, largely because they disrupt virion assembly. Given that polypeptide substrates are bound and released by Hsp70 in an ATP-hydrolytic reaction cycle, we supposed that incubation of HIV-1 virions with ATP would perturb Hsp70 interaction with substrates in the virion and thereby decrease infectivity. Treatment with ATP or ADP had no observable effect, but ATPγS and GTPγS, nucleotide triphosphate analogues resistant to Hsp70 hydrolysis, dramatically reduced the infectivity of HIV-1 and murine leukemia virus virions. ATPγS-treated virions were competent for fusion with susceptible target cells, but viral cDNA synthesis was inhibited to an extent that correlated with the magnitude of decrease in infectivity. Intravirion reverse transcription by HIV-1, simian immunodeficiency virus, or murine leukemia virus was also inhibited by ATPγS. The effects of ATPγS on HIV-1 reverse transcription appeared to be indirect, resulting from disruption of virion core morphology that was evident by transmission electron microscopy. Consistent with effects on capsid conformation, ATPγS-treated viruslike particles failed to saturate host antiviral restriction activity. Our observations support a model in which the catalytic activity of virion-associated Hsp70 is required to maintain structural integrity of the virion core.
The navigation problems and the curvature properties on conic Kropina manifolds

In this paper, we study navigation problems on conic Kropina manifolds. Let $F(x, y)$ be a conic Kropina metric on an $n$-dimensional manifold $M$ and $V$ be a conformal vector field on $(M, F)$ with $F(x, - V_{x})\leq 1$. Let $\widetilde{F}= \widetilde{F} (x,y)$ be the solution of the navigation problem with navigation data $(F, V)$. We prove that $\widetilde{F}$ must be either a Randers metric or a Kropina metric. Then we establish the relationships between some curvature properties of $F$ and the corresponding properties of the new metric $\widetilde{F}$, which involve S-curvature, flag curvature and Ricci curvature.
Contrastive learning of global and local features for medical image segmentation with limited annotations

A key requirement for the success of supervised deep learning is a large labeled dataset - a condition that is difficult to meet in medical image analysis. Self-supervised learning (SSL) can help in this regard by providing a strategy to pre-train a neural network with unlabeled data, followed by fine-tuning for a downstream task with limited annotations. Contrastive learning, a particular variant of SSL, is a powerful technique for learning image-level representations. In this work, we propose strategies for extending the contrastive learning framework for segmentation of volumetric medical images in the semi-supervised setting with limited annotations, by leveraging domain-specific and problem-specific cues. Specifically, we propose (1) novel contrasting strategies that leverage structural similarity across volumetric medical images (domain-specific cue) and (2) a local version of the contrastive loss to learn distinctive representations of local regions that are useful for per-pixel segmentation (problem-specific cue). We carry out an extensive evaluation on three Magnetic Resonance Imaging (MRI) datasets. In the limited annotation setting, the proposed method yields substantial improvements compared to other self-supervision and semi-supervised learning techniques. When combined with a simple data augmentation technique, the proposed method reaches within 8% of benchmark performance using only two labeled MRI volumes for training, corresponding to only 4% (for ACDC) of the training data used to train the benchmark.
Using a numerical model (PROTECH) to examine the impact of water transfers on phytoplankton dynamics in a Mediterranean reservoir

Using a numerical model (PROTECH) to examine the impact of water transfers on phytoplankton dynamics in a Mediterranean reservoir El Gergal is the last link in a system of reservoirs located in the Rivera de Huelva River, Seville (South-West Spain). Because of its position within the system and the different water transfer possibilities between reservoirs, the florisitic composition of phytoplankton in El Gergal can be strongly influenced by the concentration of cells present in the water transferred from other reservoirs in the series. In June 2000 a large Ceratium hirundinella inoculums was transferred from an upstream reservoir into El Gergal. The dinoflagellate inoculums entered in the receiving reservoir through the riverine zone depicting a marked horizontal structure, and inducing an intense summer bloom. Our results show that this Ceratium bloom was effective to compete and counteract the later development of the harmful cyanobacteria Aphanizomenon flos-aquae. Further, after the integration of a set of adaptations to the semi-arid Mediterranean region and subsequently validation, the PROTECH model (a 1D phytoplankton community model) was used to describe, assess and evaluate the potential impact on El Gergal phytoplankton dynamics of an alternative water transfer strategy. Finally, we encourage the use of dynamic modelling in lakes and reservoir management, as it constitutes a powerful tool that should offer reliable predictions and help managers in the usually difficult and complex task of adopting optimal decisions, in order to minimise water treatment costs and provide the best possible environmental quality.
Counting Richelot isogenies between superspecial abelian surfaces

Castryck, Decru, and Smith used superspecial genus-2 curves and their Richelot isogeny graph for basing genus-2 isogeny cryptography, and recently, Costello and Smith devised an improved isogeny path-finding algorithm in the genus-2 setting. In order to establish a firm ground for the cryptographic construction and analysis, we give a new characterization of {\em decomposed Richelot isogenies} in terms of {\em involutive reduced automorphisms} of genus-2 curves over a finite field, and explicitly count such decomposed (and non-decomposed) Richelot isogenies between {\em superspecial} principally polarized abelian surfaces. As a corollary, we give another algebraic geometric proof of Theorem 3 in the paper of Castryck et al.
Resistance of pasture production to projected climate changes in south-eastern Australia

Abstract. Climate change impact analysis relies largely on down-scaling climate projections to develop daily time-step, future climate scenarios for use in agricultural systems models. This process of climate down-scaling is complicated by differences in projections from greenhouse gas emission pathways and, in particular, the wide variation between global climate model outputs. In this study, a sensitivity analysis was used to test the resistance of pasture production to the incremental changes in climate predicted over the next 60 years in southern Australia. Twenty-five future climate scenarios were developed by scaling the historical climate by increments of 0, 1, 2, 3 and 4°C (with corresponding changes to atmospheric carbon dioxide concentrations and relative humidity) and rainfall by +10, 0, –10, –20 and –30%. The resistance of annual and seasonal pasture production to these climatic changes was simulated at six sites in south-eastern Australia. The sites spanned a range of climates from high rainfall, cool temperate in north-west Tasmania to the lower rainfall, temperate environment of Wagga Wagga in southern New South Wales. Local soil and pasture types were simulated at each site using the Sustainable Grazing Systems Pasture model. Little change or higher annual pasture production was simulated at all sites with 1°C warming, but varying responses were observed with further warming. In a pasture containing a C4 native grass at Wagga Wagga, annual pasture production increased with further warming, while production was stable or declined in pasture types based on C3 species in temperate environments. In a cool temperate region pasture production increased with up to 2°C warming. Compared with the historical baseline climate, warmer and drier climate scenarios led to lower pasture production, with summer and autumn growth being most affected, although there was some variation between sites. At all sites winter production was increased under all warming scenarios. Inter-annual variation in pasture production, expressed as the coefficient of variation, increased in the lower rainfall scenarios where production was simulated to decline, suggesting that changing rainfall patterns are likely to affect the variability in pasture production more than increasing temperatures. Together the results indicate that annual pasture production is resistant to climatic changes of up to 2°C warming. The approach used in this study can be used to test the sensitivity of agricultural production to climatic changes; however, it does not incorporate changes in seasonal and extreme climatic events that may also have significant impacts on these systems. Nonetheless, the approach can be used to identify strategies that may increase resilience of agricultural systems to climate change such as the incorporation of C4 species into the pasture base.
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

We introduce the SE(3)-Transformer, a variant of the self-attention module for 3D point clouds, which is equivariant under continuous 3D roto-translations. Equivariance is important to ensure stable and predictable performance in the presence of nuisance transformations of the data input. A positive corollary of equivariance is increased weight-tying within the model, leading to fewer trainable parameters and thus decreased sample complexity (i.e. we need less training data). The SE(3)-Transformer leverages the benefits of self-attention to operate on large point clouds with varying number of points, while guaranteeing SE(3)-equivariance for robustness. We evaluate our model on a toy $N$-body particle simulation dataset, showcasing the robustness of the predictions under rotations of the input. We further achieve competitive performance on two real-world datasets, ScanObjectNN and QM9. In all cases, our model outperforms a strong, non-equivariant attention baseline and an equivariant model without attention.
Cyclic Differentiable Architecture Search

Differentiable ARchiTecture Search, i.e., DARTS, has drawn great attention in neural architecture search. It tries to find the optimal architecture in a shallow search network and then measures its performance in a deep evaluation network. The independent optimization of the search and evaluation networks, however, leaves a room for potential improvement by allowing interaction between the two networks. To address the problematic optimization issue, we propose new joint optimization objectives and a novel Cyclic Differentiable ARchiTecture Search framework, dubbed CDARTS. Considering the structure difference, CDARTS builds a cyclic feedback mechanism between the search and evaluation networks with introspective distillation. First, the search network generates an initial architecture for evaluation, and the weights of the evaluation network are optimized. Second, the architecture weights in the search network are further optimized by the label supervision in classification, as well as the regularization from the evaluation network through feature distillation. Repeating the above cycle results in a joint optimization of the search and evaluation networks and thus enables the evolution of the architecture to fit the final evaluation network. The experiments and analysis on CIFAR, ImageNet and NATS-Bench [95] demonstrate the effectiveness of the proposed approach over the state-of-the-art ones. Specifically, in the DARTS search space, we achieve 97.52% top-1 accuracy on CIFAR10 and 76.3% top-1 accuracy on ImageNet. In the chain-structured search space, we achieve 78.2% top-1 accuracy on ImageNet, which is 1.1% higher than EfficientNet-B0. Our code and models are publicly available at https://github.com/microsoft/Cream.
Seasonal and spatial variability in egg production, abundance and production of small copepods in and near Jiaozhou Bay, China

Distribution, egg production rate (EPR) and abundances of four small copepods in genera Paracalanus and Acartia were studied monthly from October 2005 to September 2006 in and outside Jiaozhou Bay, China. Biomass and secondary production also were calculated to show the importance of these small copepods. As predominant species, Paracalanus parvus and Acartia bifilosa, occurred continuously in this bay throughout the year, with large abundance peaks from late spring to early summer. Acartia pacifica occurred sporadically in May, but in July, it reached higher maximum abundance than the other species. Parvocalanus crassirostris was relatively rare and occurred seasonally. Spatial distributions of these species differed according to the environmental variation inside and outside the bay. Egg production for each species mainly occurred in the warming period. The average female growth rates were higher for species with seasonal occurrence than those that were present continuously. The total secondary production rate of these four species was highest in June and lowest in April. These small copepods made greater contributions to the annual secondary production rate than did larger copepod species in Jiaozhou Bay. Comparisons are also made with other similar coastal waters worldwide.
CAPACITY FOR SOMATIC EMBRYOGENESIS IN DIFFERENT PEA CULTIVARS

Using the method described by GRIGA (1998), embryoids were obtained (through direct or indirect somatic embryogenesis) in cultures of shoot apical meristems of the following pea cultivars: Bankier, Dino, Hazard, Ilowiecki, Konserwowy IHAR, Kosynier, Makler, Oskar, Pegaz, as well as unregistered line HM-6. With cultivars Izolda and Lantra the efforts at somatic embryogenesis (SE) induction remained unsuccessful. The highest responsiveness to SE induction was observed (after 14 days of treatment with a relatively low concentration of picloram – 2.5 μM) in cultivars Oskar, Hazard and line HM-6, in which embryoids were formed with frequencies of 31, 15.9 and 12.5%, respectively. Increasing picloram level to 5 μM and extending period of induction to 28 days, it was possible to obtain SE efficiency above 10% in cultivars Konserwowy IHAR, Dino and Kosynier. Photoperiod affected SE efficiency and the degree and direction of this influence greatly depended on pea cultivar.
Growth of Juvenile Coho Salmon (Oncorhynchus kisutch) off Oregon and Washington, USA, in Years of Differing Coastal Upwelling

Estimated growth rates, condition, and stomach fullness of juvenile coho salmon (Oncorhynchus kisutch) caught in the ocean in early summer, when mortality was most variable, were as high in 1983 and 1984, years of very low survival and low early upwelling, as in 1981, 1982, and 1985, years of higher survival and higher early upwelling. Chronic food shortage leading to starvation, poor condition, or slow growth apparently was not the cause of the increased mortality of juvenile coho salmon in 1983 and 1984. Survival of juvenile coho salmon was positively correlated with purse seine catches of fish in June and with early summer upwelling, 1981–85. Hence, year-class success probably was determined early in the summer, soon after most juvenile coho salmon entered the ocean. Spacing of the first five ocean circuli, which was positively correlated with growth rate, was not significantly different for fish caught early in the summer and those caught late in the summer, suggesting that growth rate selective morta.
A Practical Online Method for Distributionally Deep Robust Optimization

In this paper, we propose a practical online method for solving a distributionally robust optimization (DRO) for deep learning, which has important applications in machine learning for improving the robustness of neural networks. In the literature, most methods for solving DRO are based on stochastic primal-dual methods. However, primal-dual methods for deep DRO suffer from several drawbacks: (1) manipulating a high-dimensional dual variable corresponding to the size of data is time expensive; (2) they are not friendly to online learning where data is coming sequentially. To address these issues, we transform the min-max formulation into a minimization formulation and propose a practical duality-free online stochastic method for solving deep DRO with KL divergence regularization. The proposed online stochastic method resembles the practical stochastic Nesterov's method in several perspectives that are widely used for learning deep neural networks. Under a Polyak-Lojasiewicz (PL) condition, we prove that the proposed method can enjoy an optimal sample complexity and a better round complexity (the number of gradient evaluations divided by a fixed mini-batch size) with a moderate mini-batch size than existing algorithms for solving the min-max or min formulation of DRO. Of independent interest, the proposed method can be also used for solving a family of stochastic compositional problems.
Chemical Composition and Antifungal Activity of Essential Oil from Cymbopogon nardus (Citronella Grass)

The objective of this study was to elucidate the chemical composition of essential oil from Cymbopogon nardus (citronella oil) and its antifungal activity. Chemical composition of the citronella oil was determined by capillary gas chromatography (GC) and GC/ mass spectrometry. Major constituents of the oil were geraniol (35.7% of total volatiles), trans-citral (22.7%), cis-citral (14.2%), geranyl acetate (9.7%), citronellal (5.8%) and citronellol (4.6%). The antifungal assay using the vapor-agar contact method showed that the crude essential oil markedly suppressed the growth of several species of Aspergillus, Penicillium and Eurotium at a dose of 250 mg/L in air. The most active compounds among the 16 examined volatiles, consisting of 6 major constituents of the essential oil and 10 other related monoterpenes were citronellal and linalool. Citronellal and linalool completely inhibited the growth of all tested fungal strains at a dose of 112 mg/L. Their minimum inhibitory doses ranged from 14 to 56 mg/L. The α- and β- pinenes showed an inhibitory activity against some fungi, whereas the other 8 volatile compounds lacked this property.
NH3 (1,1) hyperfine intensity anomalies in the Orion A molecular cloud

Ammonia (NH3) inversion lines, with their numerous hyperfine components, are a common tracer used in studies of molecular clouds (MCs). In local thermodynamical equilibrium, the two inner satellite lines (ISLs) and the two outer satellite lines (OSLs) of the NH3(J, K) = (1,1) transition are each predicted to have equal intensities. However, hyperfine intensity anomalies (HIAs) are observed to be omnipresent in star formation regions, a characteristic which is still not fully understood. In addressing this issue, we find that the computation method of the HIA by the ratio of the peak intensities may have defects, especially when used to process the spectra with low-velocity dispersions. Therefore, we defined the integrated HIAs of the ISLs (HIAIS) and OSLs (HIAOS) by the ratio of their redshifted to blueshifted integrated intensities (unity implies no anomaly) and developed a procedure to calculate them. Based on this procedure, we present a systematic study of the integrated HIAs in the northern part of the Orion A MC. We find that integrated HIAIS and HIAOS are commonly present in the Orion A MC and no clear distinction is found at different locations of the MC. The medians of the integrated HIAIS and HIAOS are 0.921 ± 0.003 and 1.422 ± 0.009, respectively, which is consistent with the HIA core model and inconsistent with the collapse or expansion (CE) model. In the selection of those 170 positions, where both integrated HIAs deviate by more than 3σ from unity, most (166) are characterized by HIAIS < 1 and HIAOS > 1, which suggests that the HIA core model plays a more significant role than the CE model. The remaining four positions are consistent with the CE model. We compare the integrated HIAs with the para-NH3 column density (N(para-NH3)), kinetic temperature (TK), total velocity dispersion (σv), non-thermal velocity dispersion (σNT), and the total opacity of the NH3(J, K) = (1,1) line (τ0). The integrated HIAIS and HIAOS are almost independent of N(para-NH3). The integrated HIAIS decreases slightly from unity (no anomaly) to about 0.7 with increasing TK, σv, and σNT. The integrated HIAOS is independent of TK and reaches values close to unity with increasing σv and σNT. The integrated HIAIS is almost independent of τ0, while the integrated HIAOS rises with τ0, thus showing higher anomalies. These correlations cannot be fully explained by either the HIA core nor the CE model.
Federated Learning With Quantized Global Model Updates

We study federated learning (FL), which enables mobile devices to utilize their local datasets to collaboratively train a global model with the help of a central server, while keeping data localized. At each iteration, the server broadcasts the current global model to the devices for local training, and aggregates the local model updates from the devices to update the global model. Previous work on the communication efficiency of FL has mainly focused on the aggregation of model updates from the devices, assuming perfect broadcasting of the global model. In this paper, we instead consider broadcasting a compressed version of the global model. This is to further reduce the communication cost of FL, which can be particularly limited when the global model is to be transmitted over a wireless medium. We introduce a lossy FL (LFL) algorithm, in which both the global model and the local model updates are quantized before being transmitted. We analyze the convergence behavior of the proposed LFL algorithm assuming the availability of accurate local model updates at the server. Numerical experiments show that the quantization of the global model can actually improve the performance for non-iid data distributions. This observation is corroborated with analytical convergence results.