Miracle-dz/UniFAR-FTUs huggingface dataset:
Variation in symbiotic N2 fixation rates among Sphagnum mosses

Biological nitrogen (N) fixation is an important process supporting primary production in ecosystems, especially in those where N availability is limiting growth, such as peatlands and boreal forests. In many peatlands, peat mosses (genus Sphagnum) are the prime ecosystem engineers, and like feather mosses in boreal forests, they are associated with a diverse community of diazotrophs (N2-fixing microorganisms) that live in and on their tissue. The large variation in N2 fixation rates reported in literature remains, however, to be explained. To assess the potential roles of habitat (including nutrient concentration) and species traits (in particular litter decomposability and photosynthetic capacity) on the variability in N2 fixation rates, we compared rates associated with various Sphagnum moss species in a bog, the surrounding forest and a fen in Sweden. We found appreciable variation in N2 fixation rates among moss species and habitats, and showed that both species and habitat conditions strongly influenced N2 fixation. We here show that higher decomposition rates, as explained by lower levels of decomposition-inhibiting compounds, and higher phosphorous (P) levels, are related with higher diazotrophic activity. Combining our findings with those of other studies, we propose a conceptual model in which both species-specific traits of mosses (as related to the trade-off between rapid photosynthesis and resistance to decomposition) and P availability, explain N2 fixation rates. This is expected to result in a tight coupling between P and N cycling in peatlands.
Locoregional recurrence risk after lipofilling in breast cancer patients.

BACKGROUND Lipofilling has been indicated for postmastectomy and postlumpectomy breast reconstruction. The clinical literatures underline its technical efficacy but experimental studies raise important questions about the potential detrimental effect of adipocytes on the stimulation of cancer growth and reappearance. DESIGN We collected 321 consecutive patients operated for a primary breast cancer between 1997 and 2008 who subsequently underwent lipofilling for reconstructive purpose. For each patient, we selected two matched patients with similar characteristics who did not undergo a lipofilling. RESULTS Eighty-nine percent of the tumors were invasive. Median follow-up was 56 months from the primary surgery and 26 months from the lipofilling. Eight and 19 patients had a local event in the lipofilling and control group, respectively, leading to comparable cumulative incidence curves [P = 0.792; Hazard RatioLipovs No lipo = 1.11 (95% confidence interval 0.47-2.64)]. These results were confirmed when patients undergoing quadrantectomy and mastectomy were analyzed separately and when the analysis was limited to invasive tumors. Based on 37 cases, the lipofilling group resulted at higher risk of local events when the analysis was limited to intraepithelial neoplasia. CONCLUSIONS Lipofilling seems to be a safe procedure in breast cancer patients. Longer follow-up and further experiences from oncological series are urgently required to confirm these findings.
Automated Diagnosis of Meniscus Tears from MRI of the Knee

Meniscus tears are serious knee abnormalities that can cause knee osteoarthritis disorder. Therefore, early detection and treatment of meniscus tears that may occur in the knee with computer-aided systems will prevent the progression of these disorders. In this study, an approach which can detect the meniscus tears automatically by using and comparing two different feature extraction methods have been presented. With these methods, features of the knee MR images were obtained and automatic meniscus tear classification was performed by such features. Four different classifiers have been used to model the features in the classification phase. The most successful classification results were obtained from the support vector machines (SVM) with a success rate of 90.13% and the extreme learning machines (ELM) with a success rate of 87.85% via the LBP feature extraction method. It is observed that better results are obtained than the ones in similar studies in the literature. It is aimed to improve the existing success with the use of deep feature extraction methods in the future.
Mapping Urban Spatial Structure Based on POI (Point of Interest) Data: A Case Study of the Central City of Lanzhou, China

The study of urban spatial structure is currently one of the most popular research fields in urban geography. This study uses Lanzhou, one of the major cities in Northwest China, as a case area. Using the industry classification of POI data, the nearest-neighbor index, kernel density estimation, and location entropy are adopted to analyze the spatial clustering-discrete distribution characteristics of the overall economic geographical elements of the city center, the spatial distribution characteristics of the various industry elements, and the overall spatial structure characteristics of the city. All of these can provide a scientific reference for the sustainable optimization of urban space. The urban economic geographical elements generally present the distribution trend of center agglomeration. In respect of spatial distribution, the economic geographical elements in the central urban area of Lanzhou have obvious characteristics of central agglomeration. Many industrial elements have large-scale agglomeration centers, which have formed specialized functional areas. There is a clear “central–peripheral” difference distribution in space, with an obvious circular structure. Generally, tertiary industry is distributed in the central area, and secondary industry is distributed in the peripheral areas. In general, a strip-shaped urban spatial structure with a strong main center, weak subcenter and multiple groups is present. Improving the complexity of urban functional space is an important goal of spatial structure optimization.
Apparent evidence for unconscious sound symbolism is probably artifactual: Commentary on Heyman, Maerten, Vankrunkelsven, Voorspoels and Moors (in press).

Sound symbolism refers to the intuition that a word’s sound should match the characteristics of its referents – e.g., kiki should label something spiky – and its prevalence and systematicity provide compelling evidence for an intuitive mapping between linguistic form and meaning. Striking recent work (Hung, Styles, & Hsieh, 2017) suggests that these mappings may have an unconscious basis, such that participants can compute the fit between a word’s sound and an object’s shape when both are masked from awareness. This surprising finding replicated in the pre-registered report by Heyman, Maerten, Vankrunkelsven, Voorspoels and Moors (2019), with potentially far-reaching implications for the role of awareness in language processing (Hassin, 2013; Rabagliati, Robertson, & Carmel, 2018). However, as I demonstrate, it is an artifact of the stimuli used. Once item effects are accounted for, these data provide no evidence that sound symbolism, and language more generally, can be processed without awareness.
Surveillance and Eradication of Classical Swine Fever in China

Classical swine fever(CSF), a highly contagious disease of pigs caused by the classical swine fever virus(CSFV), can lead to important economic losses in the pig industry. The disease can manifest as acute hemorrhagic fever with respiratory, gastrointestinal and neurological symptoms resulting in high mortality rates. This paper reviews the control and eradication techniques of CSF, and summarizes the results of eradication of CSF implemented by National Swine Industry Technology System in recent years.
Efficient content caching for named data network nodes

Named Data Networking (NDN) is a promising Content-Centric Network (CCN) architecture that supports data distribution and data sharing by in-network ubiquitous content caching. In NDN, each router has content store to cache data packets passed by and, therefore, frequently requested content by consumers (e.g., end hosts) is cached at multiple routers in the network. Content caching at routers enables data delivery to consumers from a nearest location with minimal latency and thereby enhances overall network performance. Content store at nodes should have sufficient space to hold the current frame of locality of reference for attaining a good hit rate. The content store size requirement for each node is different due to their topological characteristics. Homogeneous caching mechanisms distribute the total cache budget equally among the nodes irrespective of their topological characteristics. In contrast, heterogeneous caching allocates cache to the nodes based on their topological importance. In this paper, a heterogeneous on-path cache budget distribution approach is proposed that distributes cache to the content stores based on reference locality of the nodes. The proposed cache distribution algorithm is evaluated for structured and unstructured network topology using the ndnSIM simulator. The results are compared with the homogeneous cache distribution mechanism and 14% improvement in cache hit rate is achieved.
AugToAct: scaling complex human activity recognition with few labels

Human activity recognition (HAR) from wearable sensor data has recently gained widespread adoption in a number of fields. However, recognizing complex human activities, postural and rhythmic body movements (e.g. dance, sports) is challenging due to the lack of domain-specific labeling information, the perpetual variability in human movement kinematics profiles due to age, sex, dexterity and the level of professional training. In this paper, we propose a deep activity recognition model to work with limited labeled data, both for simple and complex human activities. To mitigate the intra and inter-user spatio-temporal variability of movements, we posit novel data augmentation and domain normalization techniques. We depict a semi-supervised technique that learns noise and transformation invariant feature representation from sparsely labeled data to accommodate intra-personal and inter-user variations of human movement kinematics. We also postulate a transfer learning approach to learn domain invariant feature representations by minimizing the feature distribution distance between the source and target domains. We showcase the improved performance of our proposed framework, AugToAct, using a public HAR dataset. We also design our own data collection, annotation and experimental setup on complex dance activity recognition steps and kinematics movements where we achieved higher performance metrics with limited label data compared to simple activity recognition tasks.
Understanding the impact of number of CPU cores on user satisfaction in smartphones

Understanding user experience/satisfaction with mobile systems in order to manage computational resources has become a popular approach in recent years. One of the key challenges in this area is how to gauge user satisfaction. In this paper, we study the impact of CPU configuration on user satisfaction and power consumption with real users. Specifically, we propose a system to save energy by altering active CPU core count and frequency while keeping users satisfied. The system utilizes user-facing metrics such as frame rate and input lag to predict user satisfaction and then configure CPU core count and frequency in real-time to maximize satisfaction while minimizing power consumption. We first study a set of applications in-the-lab and show that we can accurately model satisfaction with the collected user-facing metrics. We then go into-the-wild in order to evaluate the proposed system in real environments. In the wild, we build a user-independent (user-oblivious) and user-dependent (personal) model. Users test the two models and the default scheme for one-week duration, which composes 140 days of worth of data. When compared to default scheme, our results show that, without impacting satisfaction, user-independent and user-dependent models save 12.3% and 11.8% of total system energy on average, respectively.
An abstract argumentation approach for the prediction of analysts' recommendations following earnings conference calls

Financial analysts constitute an important element of financial decision-making in stock exchanges throughout the world. By leveraging on argumentative reasoning, we develop a method to predict financial analysts' recommendations in earnings conference calls (ECCs), an important type of financial communication. We elaborate an analysis to select those reliable arguments in the Questions Answers (QA) part of ECCs that analysts evaluate to estimate their recommendation. The observation date of stock recommendation update may variate during the next quarter: it can be either the day after the ECC or it can take weeks. Our objective is to anticipate analysts' recommendations by predicting their judgment with the help of abstract argumentation. In this paper, we devise our approach to the analysis of ECCs, by designing a general processing framework which combines natural language processing along with abstract argumentation evaluation techniques to produce a final scoring function, representing the analysts' prediction about the company's trend. Then, we evaluate the performance of our approach by specifying a strategy to predict analysts recommendations starting from the evaluation of the argumentation graph properly instantiated from an ECC transcript. We also provide the experimental setting in which we perform the predictions of recommendations as a machine learning classification task. The method is shown to outperform approaches based only on sentiment analysis.
Salinity tolerance in barley during germination—homologs and potential genes

Salinity affects more than 6% of the world’s total land area, causing massive losses in crop yield. Salinity inhibits plant growth and development through osmotic and ionic stresses; however, some plants exhibit adaptations through osmotic regulation, exclusion, and translocation of accumulated Na+ or Cl-. Currently, there are no practical, economically viable methods for managing salinity, so the best practice is to grow crops with improved tolerance. Germination is the stage in a plant’s life cycle most adversely affected by salinity. Barley, the fourth most important cereal crop in the world, has outstanding salinity tolerance, relative to other cereal crops. Here, we review the genetics of salinity tolerance in barley during germination by summarizing reported quantitative trait loci (QTLs) and functional genes. The homologs of candidate genes for salinity tolerance in Arabidopsis, soybean, maize, wheat, and rice have been blasted and mapped on the barley reference genome. The genetic diversity of three reported functional gene families for salt tolerance during barley germination, namely dehydration-responsive element-binding (DREB) protein, somatic embryogenesis receptor-like kinase and aquaporin genes, is discussed. While all three gene families show great diversity in most plant species, the DREB gene family is more diverse in barley than in wheat and rice. Further to this review, a convenient method for screening for salinity tolerance at germination is needed, and the mechanisms of action of the genes involved in salt tolerance need to be identified, validated, and transferred to commercial cultivars for field production in saline soil.
SmartPaint: a co-creative drawing system based on generative adversarial networks

Artificial intelligence (AI) has played a significant role in imitating and producing large-scale designs such as e-commerce banners. However, it is less successful at creative and collaborative design outputs. Most humans express their ideas as rough sketches, and lack the professional skills to complete pleasing paintings. Existing AI approaches have failed to convert varied user sketches into artistically beautiful paintings while preserving their semantic concepts. To bridge this gap, we have developed SmartPaint, a co-creative drawing system based on generative adversarial networks (GANs), enabling a machine and a human being to collaborate in cartoon landscape painting. SmartPaint trains a GAN using triples of cartoon images, their corresponding semantic label maps, and edge detection maps. The machine can then simultaneously understand the cartoon style and semantics, along with the spatial relationships among the objects in the landscape images. The trained system receives a sketch as a semantic label map input, and automatically synthesizes its edge map for stable handling of varied sketches. It then outputs a creative and fine painting with the appropriate style corresponding to the human’s sketch. Experiments confirmed that the proposed SmartPaint system successfully generates high-quality cartoon paintings.
Examining the Effect of Caffeine and Technology on Adolescent Sleep

The current study expands upon existing sleep research by assessing adolescent caffeine consumption and technology use around bedtime and group differences in caffeine consumption and technology use in regard to sleep onset time, duration and quality, as well as symptoms of delayed sleep phase syndrome (DSPS). Participants, ranging in age from 1 3 to 1 8 years, were recruited from two Midwestern high schools and one junior high school. Online survey software was used to capture adolescents' self­ reported caffeine consumption, technology use, and sleep. Adolescents who drank more caffeine did not spend significantly more time using technology at night, nor did they report significantly more sleep problems. However, students who spent more time using technology reported significantly less sleep than those who spent less time using technology. Students with a parent-set bedtime reported significantly longer sleep duration than students without a parent-set bedtime and students who used technology in their bedroom spent more time using it after 9 p.m. than students who used it elsewhere. Limitations and recommendations for future research are discussed.
COMPREHENSIVENESS IN THE CARE NETWORK REGARDING THE CARE OF THE DISABLED PERSON

Objective: to analyze the implications of the Care Network for the Disabled Person with Disabilities within the Unified Health System. Method: it is a reflection study based on recent policies and literature related to the theme. Results: the structural changes change the care flow, as well as point out abilities for the professionals who work in the care points. The articulation of these care points is considered the principle for the system to function in an integrated way, in order to ensure the comprehensiveness of the care provided for people with disabilities. On the other hand, in the fields of practice, the persistent disarticulation of these components is still observed because they are limited to caring for people with disabilities in their own areas of activity. It was based on the National Policy on the Health of People with Disabilities, guiding the institutional and home rehabilitation actions, which assures these people access to comprehensive, interdisciplinary and intersectoral care and assistance, essential to the process of rehabilitation and social inclusion. Conclusion: it is concluded that this reflection represents a contribution so that the professionals who work in the area are better guided regarding their competencies, responsibilities and fundamental actions with the users of the network. DESCRIPTORS: Primary health care. Disabled people. Rehabilitation services. Public health policies. Nursing care. INTEGRALIDADE NA REDE DE CUIDADOS DA PESSOA COM DEFICIÊNCIA
W3173: Impacts of Stress Factors on Performance, Health, and Wellbeing of Farm animals

By 2050, the world population will be 34% higher than today, reaching an estimated 9.1 billion people. Food animal production will need to increase by 1.7-fold to meet the growing world demand for animal protein (FAO, 2012). As a result, animal husbandry systems may become more intensified (concentrated animal feeding operations; CAFO) because fewer natural resources will be available to raise animals. In addition, new biotechnologies (e.g. gene-editing, growth promotants, feed-additives) and better housing systems may be needed to help increase the adaptation rates for domestic animals so that they can remain efficient and healthy, despite a variable climate. However, CAFOs and biotechnologies are often viewed by consumers with animosity over the concern of animal welfare and food safety, therefore, producers have to balance these concerns with productivity despite an increase in climate variability and limited resources. Nonetheless, animals in both intensive and extensive production systems are faced with potential macroand micro-environmental stressors (Canario et al., 2013). In order to balance these challenges, producers will need to raise and manage farm animals so that they are resilient to environmental stressors. Stress physiology plays a significant role in objectively improving animal welfare, productivity, and ultimately, food security. When animals are not able to cope with macroand micro-environmental stressors, US livestock producers can lose billions of dollars. For example, St. Pierre et al., et al. (2003) reported that without heat stress abatement measures, total losses in poultry and livestock costs $2.4 billion each year. Dairy cattle were the most affected in this calculation, with a yearly loss of $897 to $1500 million annually (St.-Pierre et al., 2003). In 2003, Mader predicted that the extreme climate variation will cost beef cattle industry members $10-20 million annually.
Sustainable Supply Chain of Enterprises: Value Analysis

An enterprise carrying out operations according to the sustainable development (SD) concept should maximize its business goals without affecting interest groups. It requires not only a rethinking, but also a partial allocation of profit to social goals—used to contribute to and create a positive image of the enterprise and improve its competitiveness—over the long-term. Operations should result in an increase in profit—indirectly—by way of demand. This paper explores the sustainable supply chain (SSC) concept and the interplay of corporate social responsibility (CSR) within business objectives. The concept of SSC-compliant CSR is primarily stressed as an executive, top-down centric role. Businesses and societies at large that foster an SD continuum are considered healthy economies. The SD of enterprises and CSR in business are explored in relation to social investment and considered a viable long-term strategy for doing business. A pilot case study of Poland is carried out as a value analysis of the SSCs of enterprises. The key findings illustrate the benefits and problems of SSCs applied at the enterprise level. The problems of applying the principles of SSC within the enterprise and the connection between its levels of strategy and social value in terms of CSR and entrepreneurial operations are examined. Comparative studies indicate a number of overlapping trends in which positive SSC is cross-functional toward business partners, suppliers, customers, investors, and local communities.