Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Regularized Structured Output SVM for Early Expression Detection With Online Extension

In this study, a graph regularized algorithm for early expression detection (EED), called GraphEED, is proposed. EED is aimed at detecting the specified expression in the early stage of a video. Existing EED detectors fail to explicitly exploit the local geometrical structure of the data distribution, which may affect the prediction performance significantly. According to manifold learning, the data in real-world applications are likely to reside on a low-dimensional submanifold embedded in the high-dimensional ambient space. The proposed graph Laplacian consists of two parts: 1) a $k$ -nearest neighbor graph is first constructed to encode the geometrical information under the manifold assumption and 2) the entire expressions are regarded as the must-link constraints since they all contain the complete duration information and it is shown that this can also be formulated as a graph regularization. GraphEED is to have a detection function representing these graph structures. Even with the inclusion of the graph Laplacian, the proposed GraphEED has the same computational complexity as that of the max-margin EED, which is a well-known learning-based EED, but the detection performance has been largely improved. To further make the model appropriate in large-scale applications, with the technique of online learning, the proposed GraphEED is extended to the so-called online GraphEED (OGraphEED). In OGraphEED, the buffering technique is employed to make the optimization practical by reducing the computation and storage cost. Extensive experiments on three video-based datasets have demonstrated the superiority of the proposed methods in terms of both effectiveness and efficiency.
Novel approach to delivering pro-environmental messages significantly shifts norms and motivation, but children are not more effective spokespeople than adults

Three studies provided initial laboratory tests of the effectiveness of a novel form of community-based environmental messaging intended to be deployed on public digital signs. In all studies, adult participants watched a slideshow of “Community Voices,” a display that combines community images and quotes to celebrate and empower pro-environmental and pro-community thought and action. In addition to assessing the general efficacy of the approach, a central goal was to assess the impact of alternative messengers by comparing identical text associated with either adult or child messengers (Studies 1, 2, and 3). We also assessed the impact of alternative framing of the message itself by comparing: injunctive vs non-injunctive wording (Study 1), political vs non-political content (Study 1), and future vs. present-oriented framing (Study 2). Studies 1 and 2 were conducted on a national sample. In addition, to assess the impact of local vs. non-local messengers, Study 3 compared the response of a non-local sample to a local population in which subjects had personal connections with the people and places featured in the message content. Exposure to Community Voices messages resulted in significant increases in social norm perception, concern about environmental issues, commitment to action, and optimism, suggesting that this approach to messaging is potentially valuable for stimulating cultural change. However, messages attributed to child messengers were generally not more effective, and in some cases were less effective than the same message attributed to adults. We also found no significant difference in the impact of the alternative message frames studied.
Intra‐ and inter‐breed variation in immune response to acute and sub‐chronic Salmonella infection and commercial immune‐stimulant in two‐layer breeds

Abstract Salmonella is one of the most hazardous diseases in poultry farms. Markedly, the application of active immunostimulants is illustrated as potential protective agents against infection in poultry farms. Thus, this work aimed to explore inter‐ and intra‐breed variation in response to acute and subchronic Salmonella enteritidis infection in two‐layer breeds (one commercial [Hy‐line strain] and another native [Fayoumi breed]). Besides exploring the possible protective effect of a commercial immune modulator (STIMULAN) on the two breeds during the acute infection. The ELISA antibody titer in sub‐chronic infections and the expression analysis of some selected genes (IL‐1β, LITAF, TGF‐β, HSP90 and HSP70) are used as the clinical signs for acute infections to assess the possible protective role of a commercial immunomodulator (STIMULAN). Five groups were used during the acute experiment: G1—control; G2a—susceptible; G2b—resistant birds, G3—which received STIMULAN and G4—which received the infection + STIMULAN. The groups with sub‐chronic infections include G1 (control), G2 (high antibody titer) and G3 (low antibody titer). The gene expressions among the susceptible birds during acute infection of both breeds are nearly similar. They only differ in the expression of HSP90 in the Fayoumi breed. However, the resistant birds vary in their gene expression profile. The effect of STIMULAN as a feed additive in non‐infected birds was an up‐regulation of LITAF, TGF‐β, HSP90 in Fayoumi. Moreover, a powerful stimulatory role was observed when both breeds were infected. Both breeds were asymptomatic during the sub‐chronic infection. Although, the increased expression of inflammatory‐related genes in the Hy‐line was considered as an indication of infection persistence. Fayoumi is capable of immune clearance for this infection. Thus, the Fayoumi breed is more resistant to acute Salmonella infection. HSP90 plays a vital role in its resistance. We recommend the use of STIMULAN as an immunomodulator during Salmonella infection.
1 An HPLC-MS/MS Method Using a Multitoxin Clean-up Column for Analysis of Seven Mycotoxins in Aquafeeds.

BACKGROUND In Guangdong Province of China, the climate here is very wet, so there are many different fungus living in the aquatic feeds, which produce mycotoxins. These compounds contaminate agriculture products world-wide and represent a great threat to human health. It is necessary to determine their contamination level in aquatic feeds. OBJECTIVE A high performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) method was developed for the quantitative analysis of aflatoxin B1, aflatoxin M1, T-2 toxin, HT-2 toxin, deoxynivalenol, ochratoxin, and zearalenone in the fish and shrimp feed. METHODS Samples were extracted with acetonitrile-water (V: V = 3:1), and degreased with acetonitrile-saturated hexane. Such obtained extract was cleaned up with a multitoxin column. The target compounds were separated on a C18 chromatographic column and analyzed simultaneously by electrospray ionization mass spectrometry in both positive or negative ion mode. Detected compounds were quantified by using the matrix-matched external standard method. RESULTS Under the optimized conditions, good linearities for the analytes in corresponding concentration range were obtained with correlation coefficients (r2) higher than 0.9948. LOD ranged from 1.83 to 12.63 μg/kg, and LOQ ranged from 5.49 to 37.89 μg/kg. Average recoveries for the target mycotoxins at three spiked levels ranged from 80.5% to 116.5% with RSD ranging from 2.4% to 10.4%. 23 real aquafeed samples were determined by this method, and 7 kinds of toxins were all detected. CONCLUSIONS Obtained results showed that developed method could be successfully applied for the simultaneous determination of mycotoxins in aquatic feeds.
Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools

Objective To identify the key genes involved in prostate cancer and their regulatory network. Methods The dataset of mRNA/miRNA transcriptome sequencing was downloaded from The Cancer Genome Atlas/the Gene Expression Omnibus database for analysis. The “edgeR” package in the R environment was used to normalize and analyze differentially expressed genes (DEGs) and miRNAs (DEmiRNAs). First, the PANTHER online tool was used to analyze the function enrichment of DEGs. Next, a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape tools. Finally, miRNA-gene regulatory networks were constructed using the miRTarBase. Results We identified 4339 important DEGs, of which 2145 were upregulated (Up-DEGs) and 2194 were downregulated (Down-DEGs). Functional enrichment analysis showed that the Up-DEGs were related to the immune system and the cell cycle in prostate cancer, whereas the Down-DEGs were related to the nucleic acid metabolic process and metabolism pathways. Twelve core protein clusters were found in the PPI network. Further, the constructed miRNA-gene interaction network showed that 11 downregulated miRNAs regulated 16 Up-DEGs and 22 upregulated miRNAs regulated 22 Down-DEGs. Conclusion We identified 4339 genes and 70 miRNAs that may be involved in immune response, cell cycle, and other key pathways of the prostate cancer regulatory network. Genes such as BUB1B, ANX1A1, F5, HTR4, and MUC4 can be used as biomarkers to assist in the diagnosis and prognosis of prostate cancer.
The efficacy of acupuncture on the sleep structure of patients with insomnia: A systematic review and meta‐analysis

This study aims to evaluate the efficacy of acupuncture on the sleep structure of patients with insomnia, so as to provide a valuable basis for the effectiveness of acupuncture in the treatment of insomnia. We conducted searches based on MeSH terms and free words in Cochrane Library, PubMed, Web of science, CKNI (China Knowledge Resource Integrated Database), WanFang Database, and Chongqing VIP Information from the inception of these database until 10 July 2020 for randomized controlled trials (RCTs) that investigated acupuncture treatment in patients with insomnia, and pertinent details of the results were saved. Comprehensive analysis showed that: (1) compared with the Western medicine groups, the acupuncture groups showed significant advantages in reducing the percentage of N1 sleep stage and N2 sleep stage, as well as increasing that of N3 sleep stage and REM sleep stage. However, no significant difference was found in increasing the effective rate, reducing total PSQI score, improving the total sleep time, reducing sleep latency, and improving sleep efficiency between the Western medicine groups and the acupuncture groups. (2) Compared with the sham acupuncture groups, the acupuncture treatment showed advantages in increasing the effective rate, reducing Pittsburgh Sleep Quality Index (PSQI) score, increasing the total sleep time, and improving sleep efficiency. However, no significant difference was observed between the sham acupuncture groups and the acupuncture groups with regard to reducing sleep latency, the percentage of N1 sleep stage and N2 sleep stage, as well as increasing that of N3 sleep stage and REM sleep stage.
Epidemiology of snake envenomation from Mato Grosso do Sul, Brazil

Snake envenomation is considered a public health problem in tropical countries, where they occur in a high incidence. The present study reports the snake envenomation that occurred in Mato Grosso do Sul state (Brazil) between 2007 and 2017. Epidemiological data were obtained from the online platform of the Notification Disease Information System and were analyzed according to biome. A total of 5568 cases of snake envenomations were recorded during the study period, where the highest frequency was registered between October and April. The majority of envenomations occurred in working-age males (20 to 39 years), caused mainly by Bothrops snakes, and the duration of care after the envenomation in most cases took three hours. The municipalities that showed the highest snake envenomations case per 100,000 inhabitants presents low population density, and have their economy based on agricultural activity, which is a risk factor to snake envenomations. To the Mato Grosso do Sul state, the total number of snake envenomations had a positive relationship with the size of the municipality. Since this, larger areas usually have a mosaic of environments, which may harbor higher richness and abundance of snakes, and can cause more snake encounters with the population, resulting in more snake envenomations.
Chromosome-Scale Assembly of the Complete Genome Sequence of Leishmania (Mundinia) orientalis, Isolate LSCM4, Strain LV768

Leishmania (Mundinia) orientalis is a kinetoplastid parasite first isolated in 2014 in Thailand. We report the complete genome sequence of L. (M.) orientalis, sequenced using combined short-read and long-read technologies. This will facilitate greater understanding of this novel pathogen and its relationship to other members of the subgenus Mundinia. ABSTRACT Leishmania (Mundinia) orientalis is a kinetoplastid parasite first isolated in 2014 in Thailand. We report the complete genome sequence of L. (M.) orientalis, sequenced using combined short-read and long-read technologies. This will facilitate greater understanding of this novel pathogen and its relationship to other members of the subgenus Mundinia.
Characterization of the complete mitochondrial genome of the scale worm, Eunoe nodosa (Phyllodocida; Polynoidae) from the Beaufort Sea

Abstract To increase the mitogenome data available for robust phylogeny, we sequenced the complete mitochondrial DNA of the scale worm Eunoe nodosa (Sars, 1861) in the family Polynoidae of the order Phyllodocida. The complete mitogenome has 15,366 bp and has 28.9% A, 13.2% C, 19.0% G, and 38.8% T. Using MITOS and tRNAscan-SE, we identified the 13 typical protein-coding genes (PCGs), 2 ribosomal RNA (rRNA) genes, 22 transfer RNA (tRNA) genes, and a non-coding region. Phylogenomic analysis based on 27 in-group taxa belonging to five families of the subclass Errantia show congruence with the published phylogenetic relationship within the Polynoidae, in which E. nodosa lies in the clade of shallow water species.
Optimizing hepatitis B virus screening in the United States using a simple demographics‐based model

Chronic hepatitis B (CHB) affects >290 million persons globally, and only 10% have been diagnosed, presenting a severe gap that must be addressed. We developed logistic regression (LR) and machine learning (ML; random forest) models to accurately identify patients with HBV, using only easily obtained demographic data from a population‐based data set.
Magnetic and optical properties of green synthesized nickel ferrite nanoparticles and its application into photocatalysis

Spinel NiFe2O4 nanoparticles have been synthesized via hydrothermal route using Mangifera indica flower extract (MIFE) as a green surfactant and reducing agent. X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, and transmission electron microscopy techniques have been used to determine the structure and morphology. The formation of single-phase, monodispersed NiFe2O4 with mixed morphology, the predominant shape being of equi-axed nanoparticles having an average particle size ≲45 nm, is observed. The thermal magnetization of as-synthesized NiFe2O4 nanoparticles shows ferromagnetic to paramagnetic phase transition at T c ∼ 825 K. These nanoparticles show a very high saturation magnetization (M s) value of 55 emu g−1 close to the bulk material and amongst the highest reported values for green synthesized NiFe2O4 nanoparticles. This material has a coercivity (H c) of 0.15 kOe and remanent magnetization (M r) of 8.5 emu g−1. The as-synthesized NiFe2O4 nanoparticles show bandgap energy of 2.02 eV, derived from UV–vis absorption measurement, which is suitable for effective solar photocatalytic reactions. When exposed to sunlight in the presence of as-synthesized NiFe2O4 nanoparticles, 93% of MB-dye degradation is measured in 80 min, indicating excellent photocatalytic properties. Based on the as-synthesized NiFe2O4 nanoparticles’ observed properties, the effectiveness of MIFE as an environmentally friendly surfactant, and the low-cost dye-degradation prospects of green synthesized NiFe2O4 nanoparticles are affirmed.
IoT terminal security assessment system based on improved assessment method

The Internet of Things (IoT) technology is widely used and has been improved in research. However, due to the extensiveness of IoT technology, the heterogeneity and diversity of the device structure, the number of attacks against IoT has increased dramatically, so we need a method that can effectively and actively determine safety. Considering the diversity of the terminal structure of IoT, a security method for the IoT terminal based on structural balance, method objectivity, and reliability is currently a challenging task. This paper introduces the idea of rate of change in mathematics into trust analysis, and forms three attribute sets based on trust interval and rate of change: discrete interval, change range, and change frequency. By calculating the above attributes of the entity’s trust value, the entity’s trust situation is obtained, and an overall assessment of the terminal entity’s trust situation is made from the three levels of completeness, accuracy and objectivity. Under the premise of reducing encryption and other means, the above method can evaluate the trust state of the IoT terminal from the perspective of the data, and this evaluation method can provide a basis for the judgment of the IoT terminal more objectively and accurately.
On the use of Wasserstein metric in topological clustering of distributional data

This paper deals with a clustering algorithm for histogram data based on a Self-Organizing Map (SOM) learning. It combines a dimension reduction by SOM and the clustering of the data in a reduced space. Related to the kind of data, a suitable dissimilarity measure between distributions is introduced: the $L_2$ Wasserstein distance. Moreover, the number of clusters is not fixed in advance but it is automatically found according to a local data density estimation in the original space. Applications on synthetic and real data sets corroborate the proposed strategy.
AdjointNet: Constraining machine learning models with physics-based codes

Physics-informed Machine Learning has recently become attractive for learning physical parameters and features from simulation and observation data. However, most existing methods do not ensure that the physics, such as balance laws (e.g., mass, momentum, energy conservation), are constrained. Some recent works (e.g., physics-informed neural networks) softly enforce physics constraints by including partial differential equation (PDE)-based loss functions but need re-discretization of the PDEs using auto-differentiation. Training these neural nets on observational data showed that one could solve forward and inverse problems in one shot. They evaluate the state variables and the parameters in a PDE. This re-discretization of PDEs is not necessarily an attractive option for domain scientists that work with physics-based codes that have been developed for decades with sophisticated discretization techniques to solve complex process models and advanced equations of state. This paper proposes a physics constrained machine learning framework, AdjointNet, allowing domain scientists to embed their physics code in neural network training workflows. This embedding ensures that physics is constrained everywhere in the domain. Additionally, the mathematical properties such as consistency, stability, and convergence vital to the numerical solution of a PDE are still satisfied. We show that the proposed AdjointNet framework can be used for parameter estimation (and uncertainty quantification by extension) and experimental design using active learning. The applicability of our framework is demonstrated for four flow cases. Results show that AdjointNet-based inversion can estimate process model parameters with reasonable accuracy. These examples demonstrate the applicability of using existing software with no changes in source code to perform accurate and reliable inversion of model parameters.
Machine learning modeling of family wide enzyme-substrate specificity screens

Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates. While machine learning and in silico directed evolution are well-posed for this predictive modeling challenge, efforts to date have primarily aimed to increase activity against a single known substrate, rather than to identify enzymes capable of acting on new substrates of interest. To address this need, we curate 6 different high-quality enzyme family screens from the literature that each measure multiple enzymes against multiple substrates. We compare machine learning-based compound-protein interaction (CPI) modeling approaches from the literature used for predicting drug-target interactions. Surprisingly, comparing these interaction-based models against collections of independent (single task) enzyme-only or substrate-only models reveals that current CPI approaches are incapable of learning interactions between compounds and proteins in the current family level data regime. We further validate this observation by demonstrating that our no-interaction baseline can outperform CPI-based models from the literature used to guide the discovery of kinase inhibitors. Given the high performance of non-interaction based models, we introduce a new structure-based strategy for pooling residue representations across a protein sequence. Altogether, this work motivates a principled path forward in order to build and evaluate meaningful predictive models for biocatalysis and other drug discovery applications.
Relating Graph Neural Networks to Structural Causal Models

Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations. For most processes of interest the underlying SCM will only be partially observable, thus causal inference tries leveraging the exposed. Graph neural networks (GNN) as universal approximators on structured input pose a viable candidate for causal learning, suggesting a tighter integration with SCM. To this effect we present a theoretical analysis from first principles that establishes a more general view on neural-causal models, revealing several novel connections between GNN and SCM. We establish a new model class for GNN-based causal inference that is necessary and sufficient for causal effect identification. Our empirical illustration on simulations and standard benchmarks validate our theoretical proofs.
Gravitational memory effects in Chern-Simons modified gravity

The gravitational memory effects of Chern-Simons modified gravity are considered in the asymptotically flat spacetime. If the Chern-Simons scalar does not directly couple with the ordinary matter fields, there are also displacement, spin and center-of-mass memory effects as in general relativity. This is because the term of the action that violates the parity invariance is linear in the scalar field but quadratic in the curvature tensor. This results in the parity violation occuring at the higher orders in the inverse luminosity radius. Although there exists the Chern-Simons scalar field, interferometers and pulsar timing arrays are incapable of detecting its polarization. So the scalar field does not induce any new memory effects that can be detected by interferometers or pulsar timing arrays. The asymptotic symmetry is group is also the extended Bondi-Metzner-Sachs group. The constraints on the memory effects excited by the tensor modes are obtained. ar X iv :2 10 9. 04 23 8v 2 [ gr -q c] 1 0 Se p 20 21