Miracle-dz/UniFAR-FTUs huggingface dataset:
Ocean Acidification State in the Highly Eutrophic Tokyo Bay, Japan: Controls on Seasonal and Interannual Variability

Seasonal and interannual variabilities in the partial pressure of CO2 (pCO2), pH, and calcium carbonate saturation state (Ω) were investigated in the highly eutrophicated Tokyo Bay, Japan, based on monthly observations that were conducted from 2011 to 2017. There were large variabilities in these parameters for surface and bottom waters due to photosynthesis and respiration, respectively. Warming/cooling and freshwater input also altered the surface Ω. During the observation period, calcium carbonate undersaturation was observed twice in the anoxic bottom waters in summer. The data indicate that anaerobic remineralization under anoxic conditions lowers the Ω, causing undersaturation. These findings suggest that de-eutrophication can decelerate ocean acidification in the bottom waters of Tokyo Bay. However, if atmospheric CO2 exceeds 650 ppm, aragonite undersaturation will be a common feature in the summer bottom water, even if hypoxia/anoxia are alleviated by de-eutrophication.
Cardiovascular Risk Is Increased in Miner’s Chronic Intermittent Hypobaric Hypoxia Exposure From 0 to 2,500 m?

Over the past 40 years, mining activities in Chile have relocated miners who normally live at sea level to work at high altitudes. This results in a form of chronic intermittent hypobaric hypoxia (CIHH) characterized by alternating periods of work at high altitude and rest periods at sea level. Previous studies performed in our laboratory showed that aerobic capacity is reduced at 3,800 m, even when oxygen content is maintained. Our study aimed to determine the corporal composition, food intake, maximum oxygen uptake, and concentration of high sensitivity C reactive protein (hsCRP) in an acclimatized miner population that work from 0 to 2,500 m with CIHH exposure over 4 years. All miners recruited for our study were operators of heavy trucks with CIHH for over 4 years (shiftwork 7*7 days), and our experimental population was composed of 54 miners at sea level, 61 at 1,600 m, and 38 at 2,500 m. All evaluations were performed on the 3rd or 4th day of diurnal shiftwork. To determine corporal composition, we measured weight and height (to calculate body mass index, BMI), skinfolds (to calculate body fatty, BF), and waist circumference (WC); maximal aerobic capacity was evaluated using a ramp-incremental cycling to exhaustion protocol and a venous blood sample before the exercise test to measure (hsCRP) via an ELISA test. We found higher values of BMI, BF, and WC, in the miners’ population but observed no significant difference between populations. We found a decrease in VO2 of 11.6% at 1,600 m and 25.9% at 2,500 m compared to miners at sea level. An increase in (hsCRP) at 1,600 and 2,500 m regards sea level. We observed a high prevalence of overweight and obese subjects, which was related to the ad libitum availability of food and low physical activity (sedentarism). We found that work capacity was maintained despite a decreased VO2 max at moderate altitude. However, overweight and obesity support an increased risk of cardiometabolic disease in miner’s which is unrelated to altitude. In contrast, an increased hsCRP level could be associated with increased inflammatory mechanisms at 1,600 and 2,500 m.
Epistemic Injustice in Brain Studies of (Trans)Gender Identity

This study undertakes an analysis of the conceptualization of gender identity in neuroscientific studies of (trans)gender identity that contrast the brains of cisgender and transgender participants. The analysis focuses on instances of epistemic injustice that combine scientific deficiencies and the exclusion of relevant bodies of knowledge. The results of a content analysis show how the ignoring of biosocial, developmental, mosaicist, contextualist, and depathologizing approaches leads to internal conceptual inconsistencies, hermeneutical deficiencies and the upholding of questionable paradigms in the research field. Interviews with researchers involved in these brain studies reveal targeted and diffuse forms of testimonial injustice against alternative approaches, promoted by the hierarchical arrangements of research teams in combination with the careerist and economic logic of research. The analysis points to the exclusion of critical epistemologies of science and the historical oppression of trans people as epistemic agents as the underlying hermeneutical deficiencies.
Manipulation of EGFR-Induced Signaling for the Recruitment of Quiescent Neural Stem Cells in the Adult Mouse Forebrain

The ventricular-subventricular zone (V-SVZ) is the principal neurogenic niche in the adult mammalian forebrain. Neural stem/progenitor cell (NSPC) activity within the V-SVZ is controlled by numerous of extrinsic factors, whose downstream effects on NSPC proliferation, survival and differentiation are transduced via a limited number of intracellular signaling pathways. Here, we investigated the relationship between age-related changes in NSPC output and activity of signaling pathways downstream of the epidermal growth factor receptor (EGFR), a major regulator of NSPC activity. Biochemical experiments indicated that age-related decline of NSPC activity in vivo is accompanied by selective deficits amongst various EGFR-induced signal pathways within the V-SVZ niche. Pharmacological loss-of-function signaling experiments with cultured NSPCs revealed both overlap and selectivity in the biological functions modulated by the EGFR-induced PI3K/AKT, MEK/ERK and mTOR signaling modules. Specifically, while all three modules promoted EGFR-mediated NSPC proliferation, only mTOR contributed to NSPC survival and only MEK/ERK repressed NSPC differentiation. Using a gain-of-function in vivo genetic approach, we electroporated a constitutively active EGFR construct into a subpopulation of quiescent, EGFR-negative neural stem cells (qNSCs); this ectopic activation of EGFR signaling enabled qNSCs to divide in 3-month-old early adult mice, but not in mice at middle-age or carrying familial Alzheimer disease mutations. Thus, (i) individual EGFR-induced signaling pathways have dissociable effects on NSPC proliferation, survival, and differentiation, (ii) activation of EGFR signaling is sufficient to stimulate qNSC cell cycle entry during early adulthood, and (iii) the proliferative effects of EGFR-induced signaling are dominantly overridden by anti-proliferative signals associated with aging and Alzheimer’s disease.
Horizontal Transfer of Different erm(B)-Carrying Mobile Elements Among Streptococcus suis Strains With Different Serotypes

Macrolide-resistant Streptococcus suis is highly prevalent worldwide. The acquisition of the erm(B) gene mediated by mobile genetic elements (MGEs) in particular integrative and conjugative elements (ICEs) is recognized as the main reason for the rapid spread of macrolide-resistant streptococcal strains. However, knowledge about different erm(B)-carrying elements responsible for the widespread of macrolide resistance and their transferability in S. suis remains poorly understood. In the present study, two erm(B)- and tet(O)-harboring putative ICEs, designated as ICESsuYSB17_rplL and ICESsuYSJ15_rplL, and a novel erm(B)- and aadE-spw-like-carrying genomic island (GI), named GISsuJHJ17_rpsI, were identified to be excised from the chromosome and transferred among S. suis strains with different serotypes. ICESsuYSB17_rplL and ICESsuYSJ15_rplL were integrated downstream the rplL gene, a conserve locus of the ICESa2603 family. GISsuJHJ17_rpsI, with no genes belonging to the conjugation module, was integrated into the site of rpsI. All transconjugants did not exhibit obvious fitness cost by growth curve and competition assays when compared with the recipient. The results demonstrate that different erm(B)-carrying elements were presented and highlight the role of these elements in the dissemination of macrolide resistance in S. suis.
Adversarial Discriminative Active Deep Learning for Domain Adaptation in Hyperspectral Images Classification

ABSTRACT Active deep learning (ADL) presents an appropriate solution for hyperspectral images (HSIs) classification based on domain adaptation (DA) with limited labelled samples in the target domain. But some challenges still exist. First, traditional ADL methods only match the feature distributions between the source and target domains globally without considering the decision boundaries between classes, which makes the ambiguous features near land-cover class boundaries and reduces the classification accuracy. Second, in previous ADL settings, a trained classiﬁer is ﬁrst used to obtain the predictions for the unlabelled data and then a measure is applied to achieve the uncertainty of such a classifier prediction. This two-step approach does not consider unlabelled data in the classifier training, which ignores dealing with noisy and complex data in the target domain. To overcome these issues, we propose adversarial discriminative active deep learning (ADADL), which presents an adversarial model and incorporates two different land-cover classiﬁers as a discriminator to consider the class boundaries in aligning feature distributions. Furthermore, ADADL combines the entropy measure along with the cross-entropy loss during training to use the information on the unlabelled data of the target domain. The experimental results with two benchmark HSIs show that the proposed ADADL creates robust transferable features far from the original class boundaries and improves the classiﬁcation accuracy significantly compared to the state-of-art ADL methods even in complex and noisy data.
Chemical Compositions, Antioxidant and Anti-diabetic Activities of Root Wood and Root Bark Essential Oils from Pterocarpus soyauxii TAUB.

Abstract The chemical composition of essential oils (EOs) isolated by hydrodistillation from Pterocarpus soyauxii Taub. root wood and root bark were determined for the first time by GC/MS analysis. The EOs gave a percentage yield (w/w) of 0.34 % and 0.31 % for root wood and root bark respectively. A total of 60 compounds accounting for 86.1 % of the root wood EO composition were identified with monoterpenes (45.4 %) and sesquiterpenes (14.7 %) as predominant constituents. Root bark EO gave 53 identified constituents which make up about 78.5 % of the EO dominated by non-terpenes (23.4 %) and alkanes (14.1 %). The EOs are good sources of limonene (16.9 %), γ-terpinene (9.8 %) and p-cymene (6.70 %). The anti-diabetic (α-amylase and α-glucosidase models) and antioxidant activities (DPPH and H2O2 models) of EOs were evaluated in comparison with standard drugs. Alpha-amylase inhibition gave IC50 values (mg/mL) of 0.0493 and 0.0471 for root wood and root bark EOs respectively, compared to acarbose the standard anti-diabetic drug (0.0401) while α-glucosidase assay gave IC50 values (mg/mL) of 0.0517 and 0.0486 for root wood and root bark EOs respectively, compared with acarbose (0.0418). In the DPPH antioxidant assay, root bark EO showed higher activity (0.157 mg/mL) compared to root wood EO (0.159 mg/mL) while Root wood and root bark EOs had IC50 values (mg/mL) of 0.157 and 0.171 respectively using the H2O2 model. Results revealed the presence of chemical constituents in the EOs which could be responsible for activities expressed by the plant.
Replacing human interpretation of agricultural land in Afghanistan with a deep convolutional neural network

ABSTRACT Afghanistan’s annual opium survey relies upon time-consuming human interpretation of satellite images to map the area of potential poppy cultivation for statistical sample design. Deep Convolutional Neural Networks (CNNs) have shown ground-breaking performance for image classification tasks by encoding local contextual information, in some cases outperforming trained analysts. In this study, we investigate the development of a CNN to automate the classification of agriculture from medium-resolution satellite imagery as an alternative to manual interpretation. The residual network (ResNet50) CNN architecture was trained and validated for delineating the agricultural area using labelled multi-seasonal Disaster Monitoring Constellation (DMC) satellite imagery (32 m) of Helmand and Kandahar provinces. The effect of input image chip size, training sampling strategy, elevation data, and multi-seasonal imagery were investigated. The best-performing single-year classification used an input chip size of 33 × 33 pixels, a targeted sampling strategy and transfer learning, resulting in high overall accuracy (94%). The inclusion of elevation data marginally lowered performance (93%). Multi-seasonal classification achieved an overall accuracy of 89% using the previous two years’ data. Only 25% of the target year’s training samples were necessary to update the model to achieve >94% overall accuracy. A data-driven approach to automate agricultural mask production using CNNs is proposed to reduce the burden of human interpretation. The ability to continually update CNN models with new data has the potential to significantly improve automatic classification of vegetation across years.
A hierarchical object detection method in large-scale optical remote sensing satellite imagery using saliency detection and CNN

ABSTRACT Detecting geospatial objects, especially small, time-sensitive targets such as airplanes and ships in cluttered scenes, is a substantial challenge in large-scale, high-resolution optical satellite images. Directly detecting targets in countless image blocks results in higher false alarms and is also inefficient. In this paper, we introduce a hierarchical architecture to quickly locate related areas and detect these targets effectively. In the coarse layer, we use an improved saliency detection model that utilizes geospatial priors and multi-level saliency features to probe suspected regions in broad and complicated remote sensing images. Then, in the fine layer of each region, an efficacious end-to-end neural network that predicts the categories and locations of the objects is adopted. To improve the detection performance, an enhanced network, adaptive multi-scale anchors, and an improved loss function are designed to overcome the great diversity and complexity of backgrounds and targets. The experimental results obtained for both a public dataset and our collected images validated the eﬀectiveness of our proposed method. In particular, for large-scale images (more than 500 km2), the adopted method far surpasses most unsupervised saliency models in terms of the performance in region saliency detection and can quickly detect targets within 1 minute, with 95.0% recall and 93.2% precision rates on average.
Multiscale denoising autoencoder for improvement of target detection

ABSTRACT Target detection is one of the most important applications of hyperspectral technology. However, due to spectral variations caused by noise or environment, the within-class variation is enlarged which degrades the performance of detectors, especially when the target size is small. Therefore, improving the detection performance of small targets and noisy targets is a key task. Considering the great feature extraction and representation ability of deep learning models, denoising autoencoder (DAE) is introduced to reconstruct spectrums and exploit the invariant information for target detection. To fully extract the features from the original spectrums, a multiscale denoising autoencoder (MSDAE) model is designed to incorporate complementary informationin in this paper. The final spectrum is fused by reconstructed spectrums from different scales representations, which provides more complex information and more robust features for subsequent spectral identification. Results on simulated hyperspectral images (HSIs) and real-world HSIs demonstrate that the proposed MSDAE model can effectively remove noise interference and lead to great improvements of the target detection. In addition, the proposed method shows significant potential in preserving small targets.
Hardware-Software Co-Design for Decimal Multiplication

Decimal arithmetic using software is slow for very large-scale applications. On the other hand, when hardware is employed, extra area overhead is required. A balanced strategy can overcome both issues. Our proposed methods are compliant with the IEEE 754-2008 standard for decimal floating-point arithmetic and combinations of software and hardware. In our methods, software with some area-efficient decimal component (hardware) is used to design the multiplication process. Analysis in a RISC-V-based integrated co-design evaluation framework reveals that the proposed methods provide several Pareto points for decimal multiplication solutions. The total execution process is sped up by 1.43× to 2.37× compared with a full software solution. In addition, 7–97% less hardware is required compared with an area-efficient full hardware solution.
Anodic-biased titania nanotube growth in low-dielectric viscous media

The influence was investigated of aqueous electrolytes and organic-based electrolytes on nanopore growth. To create well-defined nanotubes with a high aspect ratio, it is important to maintain equilibrium between field-assisted metal oxide dissolution and field-assisted water dissociation, which influence the temperature and pH levels in TiO2 nanotubes (TiO2 NTs). This sought after balance in the net reactions of the TiO2 NT growth can be achieved by choosing the appropriate electrolytes that are a relatively low dielectric viscous media. Viscosity (η) and dielectrics (ϵ) can be a priority in designing the experiment since it is closely related to joule-heating, diffusion-driven metal ions dissolution and chemical etching of the metal oxide. Solvation and the hydrogen bonding ability are inter-correlated to the stability of the [TiF6]2– complex. In order to examine various electrolyte effects on the anodic-biased nanotube, different categories of electrolytes, each with different viscosity and dielectric properties, were incorporated in this study.
A survey of script learning

Script is the structured knowledge representation of prototypical real-life event sequences. Learning the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensible inferences. Script learning is an interesting and promising research direction, in which a trained script learning system can process narrative texts to capture script knowledge and draw inferences. However, there are currently no survey articles on script learning, so we are providing this comprehensive survey to deeply investigate the standard framework and the major research topics on script learning. This research field contains three main topics: event representations, script learning models, and evaluation approaches. For each topic, we systematically summarize and categorize the existing script learning systems, and carefully analyze and compare the advantages and disadvantages of the representative systems. We also discuss the current state of the research and possible future directions.
A three-tiered intrusion detection system for industrial control systems

This article presents three-tiered intrusion detection systems, which uses a supervised approach to detect cyber-attacks in industrial control systems networks. The proposed approach does not only aim to identify malicious packets on the network but also attempts to identify the general and finer grain attack type occurring on the network. This is key in the industrial control systems environment as the ability to identify exact attack types will lead to an increased response rate to the incident and the defence of the infrastructure. More specifically, the proposed system consists of three stages that aim to classify: (i) whether packets are malicious; (ii) the general attack type of malicious packets (e.g. Denial of Service); and (iii) finer-grained cyber-attacks (e.g. bad cyclic redundancy check, attack). The effectiveness of the proposed intrusion detection systems is evaluated on network data collected from a real industrial gas pipeline system. In addition, an insight is provided as to which features are most relevant in detecting such malicious behaviour. The performance of the system results in an F-measure of: (i) 87.4%, (ii) 74.5% and (iii) 41.2%, for each of the layers, respectively. This demonstrates that the proposed architecture can successfully distinguish whether network activity is malicious and detect which general attack was deployed.
Network Analysis of Local Gene Regulators in Arabidopsis thaliana under Spaceflight Stress

Spaceflight microgravity affects normal plant growth in several ways. The transcriptional dataset of the plant model organism Arabidopsis thaliana grown in the international space station is mined using graph-theoretic network analysis approaches to identify significant gene transcriptions in microgravity essential for the plant’s survival and growth in altered environments. The photosynthesis process is critical for the survival of the plants in spaceflight under different environmentally stressful conditions such as lower levels of gravity, lesser oxygen availability, low atmospheric pressure, and the presence of cosmic radiation. Lasso regression method is used for gene regulatory network inferencing from gene expressions of four different ecotypes of Arabidopsis in spaceflight microgravity related to the photosynthetic process. The individual behavior of hub-genes and stress response genes in the photosynthetic process and their impact on the whole network is analyzed. Logistic regression on centrality measures computed from the networks, including average shortest path, betweenness centrality, closeness centrality, and eccentricity, and the HITS algorithm is used to rank genes and identify interactor or target genes from the networks. Through the hub and authority gene interactions, several biological processes associated with photosynthesis and carbon fixation genes are identified. The altered conditions in spaceflight have made all the ecotypes of Arabidopsis sensitive to dehydration-and-salt stress. The oxidative and heat-shock stress-response genes regulate the photosynthesis genes that are involved in the oxidation-reduction process in spaceflight microgravity, enabling the plant to adapt successfully to the spaceflight environment.
Nanocasting Route to Ordered Mesoporous Carbon with FePt Nanoparticles and Its Phenol Adsorption Property

Magnetic ordered mesoporous carbon with superparamagnetic FePt nanoparticles has been successfully prepared via a simple nanocasting route. Polyfurfuryl alcohol was formed in the channels of mesoporous silica; FePt sources (iron(III) acetylacetonate (Fe(acac)3 and platinum(II) acetylacetonate (Pt(acac)2) were located in the framework of polyfurfuryl alcohol. After the carbonization process in argon, cubic FePt nanoparticles were formed in the carbon matrix. The FePt@C composites were characterized by X-ray diffraction, transmission electron microscopy, N2 adsorption−desorption technique, and SQUID magnetometer. The superparamagnetic FePt@C sample can be obtained at the lower FePt loading level; while at higher FePt loading levels, the FePt@C sample exhibits the ferromagnetic property. By use of phenol as a model pollutant, the adsorption capacities of FePt@C-L-700 and FePt@C-H-700 can reach 139 and 114 mg/g, respectively. The magnetic separation can also be realized using an external magnetic field. There.