Miracle-dz/UniFAR-FTUs huggingface dataset:
Do the determinants of food security differ in improved rice variety adoption? Evidence from Sierra Leone

Abstract The Sierra Leonean government has implemented the improved rice varieties directed at enhancing more rice production to reduce food insecurity. This paper evaluates the food security effect of improved rice variety adoption using cross-sectional data collected in 2017 from a randomly selected sample of 624 rice farmers in Sierra Leone. The analysis uses the endogenous switching regression and propensity score matching (PSM) approach. The results revealed that the adoption of improved rice varieties has a significant positive effect on food security. That confirms the crucial role of improved rice variety adoption in increasing food production and food security. Therefore, the study recommended the intensification of policies that promote improved rice variety adoption, if more food production and food security are to be realized. Further, the government should continue the lead in rice variety promotion and dissemination and in enhancing an enabling environment for the effective adoption of farmers. Given the preponderant evidence of the different factors of food security, appropriate policies that seek to promote formal education, more income generation for farmers, and easy and credible access to farmland for landless farmers would enhance food security.
The role of alpha Na,K-ATPase isoforms in mediating cardiac hypertrophy in response to endogenous cardiotonic steroids

Chronic heart failure (CHF) remains a major public health problem, despite advances in the prevention and treatment of cardiovascular diseases. Cardiotonic steroids (CS), such as digoxin, are used therapeutically to treat CHF, and are mechanistically based on altered activity of its target receptor, the alpha subunit of the Na,K-ATPase pump. In addition to its conventional role as an ion transporter, recent evidence suggests that the sodium pump may also act as a signal transducer upon CS binding and induce non-proliferative cardiac growth, implicating a role in the development of cardiac hypertrophy and progressive failure of the heart. Moreover, elevated concentrations of endogenous CS have been detected in diverse clinical situations such as chronic renal failure, congestive heart failure and essential hypertension, however, the exact (patho) physiological role of endogenous CS is not fully understood. The hypothesis of this dissertation was that hypertrophic response to pressure overload would be aggravated in mutant mice with an ouabain-sensitive 1 Na,KATPase (NKA) isoform in response to endogenous CS. To test this hypothesis, transverse aortic constriction (TAC) surgery was performed in 1-resistant/ 2-resistant ( 1 2 ), 1-sensitive/ 2-resistant ( 1 2 ), and 1-resistant/ 2-sensitive mice ( 1 2 , wild type) to experimentally produce chronic pressure overload induced left ventricular (LV) hypertrophy. We have evidence that mutant mice with altered “ouabain-sensitivity” of the various -subunits of the Na,KATPase represent useful models for investigating the (patho)physiological effects of endogenous caredenolides and bufadienolides and the underlying molecular mechanisms that involve specific Na,K-ATPase -subunits. Echocardiography was performed after TAC to assess post-TAC cardiac structure and function. At 4 weeks 1 S/S 2 R/R mice
Importance of Crosstalk Between Chronic Lymphocytic Leukemia Cells and the Stromal Microenvironment: Direct Contact, Soluble Factors, and Extracellular Vesicles

Chronic lymphocytic leukemia (CLL) is caused by the accumulation of malignant B cells due to a defect in apoptosis and the presence of small population of proliferating cells principally in the lymph nodes. The abnormal survival of CLL B cells is explained by a plethora of supportive stimuli produced by the surrounding cells of the microenvironment, including follicular dendritic cells (FDCs), and mesenchymal stromal cells (MSCs). This crosstalk between malignant cells and normal cells can take place directly by cell-to-cell contact (assisted by adhesion molecules such as VLA-4 or CD100), indirectly by soluble factors (chemokines such as CXCL12, CXCL13, or CCL2) interacting with their receptors or by the exchange of material (protein, microRNAs or long non-coding RNAs) via extracellular vesicles. These different communication methods lead to different activation pathways (including BCR and NFκB pathways), gene expression modifications (chemokines, antiapoptotic protein increase, prognostic biomarkers), chemotaxis, homing in lymphoid tissues and survival of leukemic cells. In addition, these interactions are bidirectional, and CLL cells can manipulate the normal surrounding stromal cells in different ways to establish a supportive microenvironment. Here, we review this complex crosstalk between CLL cells and stromal cells, focusing on the different types of interactions, activated pathways, treatment strategies to disrupt this bidirectional communication, and the prognostic impact of these induced modifications.
A novel entropy proposition for spherical fuzzy sets and its application in multiple attribute decision‐making

The inherent vagueness and uncertainty in reaching decisions are effectively coped with fuzzy logic and the concept of spherical fuzzy sets is one of the latest developments in this area. The hesitancy of decision‐maker(s) about an attribute can be represented more extensively since the squared sum of membership, nonmembership, and hesitancy degrees should be between 0 and 1 while each degree should be defined in [0, 1]. In this study, we propose a novel entropy measure for spherical fuzzy sets and show its ability to provide the required properties. Then its usage in determining objective attribute weights is shown in an application of SF‐WASPAS (Spherical Fuzzy extension of Weighted Aggregated Sum Product Assessment) on an illustrative problem. The robustness of the novel entropy's usage as an objective weighting tool is demonstrated by comparing the ranking solution of the proposed SF‐WASPAS with the rankings obtained by SF‐TOPSIS, SF‐VIKOR, and SF‐CODAS.
Probabilistic Answers over Inconsistent Knowledge Bases

Consistent query answering is a generally accepted approach for querying inconsistent knowledge bases. A consistent answer to a query is a tuple entailed by every repair, where a repair is a consistent database that “minimally” differs from the original (possibly inconsistent) one. This is a somewhat coarse-grained classiﬁcation of tuples into consistent and non-consistent does not provide much information about the non-consistent tuples (e.g., a tuple entailed by 99 out of 100 repairs might be considered “almost consistent”). To overcome this limitation, we propose a probabilistic approach to querying inconsistent knowledge bases, which provides more informative query answers by associating a degree of consistency with each query answer by associating a probability to each repair, depending on the changes needed to obtain it.
Efficient and low‐cost defense against distributed denial‐of‐service attacks in SDN‐based networks

Distributed are common threats in many networks, where attackers attempt to make victim servers unavailable to other users by flooding them with worthless requests. These attacks cannot be easily stopped by firewalls, since they forge lots of connections to victims with various IP addresses. The paper aims to exploit the software‐defined networking (SDN) technique to defend against DDoS attacks. However, the controller has to handle lots of connections launched by DDoS attacks, which burdens it with a heavy load and degrades SDN's performance. Therefore, the paper proposes an efficient and low‐cost DDoS defense (ELD) mechanism for SDN. It adopts a nested reverse‐exponential data storage scheme to help the controller efficiently record the information of packets in the limited memory. Once there are many packets with high IP variability sent to a certain server and this situation lasts for a while, then a DDoS attack is likely happening. In this case, the controller asks switches to block malicious connections by installing flow rules. Experimental results verify that the ELD mechanism rapidly recognizes protocol‐based DDoS attacks and stops them in time, including TCP SYN flood, UDP flood, and ICMP flood, and also greatly reduces the overhead for the controller to defend against attacks. Moreover, ELD can distinguish DDoS flows from legitimate ones with similar features such as elephant flows and impulse flows, thereby eliminating false alarms.
Matrix Stiffness-Upregulated MicroRNA-17-5p Attenuates the Intervention Effects of Metformin on HCC Invasion and Metastasis by Targeting the PTEN/PI3K/Akt Pathway

Background Metformin, a traditional first-line anti-hyperglycemic agent for diabetes, recently exhibits better antitumor effect in hepatocellular carcinoma (HCC). However, its resistance and tolerance mechanism in HCC remains largely unknown. Here, we investigated whether increased matrix stiffness attenuated the intervention effects of metformin on HCC invasion and metastasis, and explored its underlying molecular mechanism. Methods FN-coated gel substrates with 6, 10, and 16 kPa, which simulated the stiffness of normal, fibrotic, and cirrhotic liver tissues respectively, were established to evaluate matrix stiffness-mediated effects on HCC cells. Alterations in morphology, proliferation, motility, and invasive/metastatic-associated genes (PTEN, MMP2, MMP9) of HCC cells grown on different-stiffness substrates were comparatively analyzed before and after metformin intervention. Subsequently, the underlying molecular mechanism by which higher matrix stiffness attenuates antitumor effects of metformin in HCC was further elucidated. Results Metformin significantly inhibited proliferation, migration, and invasion of HCC cells. Compared with the controls on lower-stiffness substrate, HCC cells grown on higher-stiffness substrate exhibited an obvious resistance to intervention effects of metformin on proliferation, migration, invasion and metastasis. High stiffness stimulation significantly activated the miR-17-5p/PTEN/PI3K/Akt signaling pathway in HCC cells via integrin β1 and in turn resulted in MMP2 and MMP9 upregulation. Meanwhile, integrin β1 knockdown or PI3K inhibitor partially reversed the activation of the above signaling molecules. For HCC cells grown on the same-stiffness substrate, metformin remarkably upregulated PTEN expression and suppressed the activation of the PI3K/Akt/MMP pathway, but no effect on integrin β1 expression. Importantly, the increase in fold of PTEN expression and decrease in folds of Akt phosphorylation level and MMP2 and MMP9 expressions in the treated HCC cells with metformin on 16-kPa stiffness substrate were evidently weakened compared with those in the controls on the 6-kPa stiffness substrate. Conclusions: Increased matrix stiffness significantly attenuates the inhibitory effect of metformin on HCC invasion and metastasis, and a common pathway of PTEN/PI3K/Akt/MMPs activated by mechanical stiffness signal and inactivated by metformin contributes to matrix stiffness-caused metformin resistance. To the best of our knowledge, this is the first report to clarify the mechanism of metformin intervention resistance from the perspective of tumor biophysical microenvironment.
Voltage Stability Analysis of Kosovo Power System Local Area

This paper presents voltage stability analysis of Kosovo power system local area including the 35kV overhead power line from Substation(SS) Prizreni 110/35/10kV to SS Dragashi 35/10kV. In this work, the maximum load capability limit is determined using several static analysis methods. Initially, the maximum limit is calculated using the P-V and Q-V curve methods. Then in order to find out the causes of voltage instability a modal analysis is performed to identify the critical buses and branches using their nodal and branch participation factors.
Gathering Malware Data through High-Interaction Honeypots

The widespread and ever increasing number of services and devices which expose their interfaces to the Internet make the cyberspace a fertile ground for malware activities. Hence there is a strong demand for cybersecurity solutions ensuring their safe operation. Honeypots are networked computer systems purposely designed and crafted to mimic regular services, operating systems and devices with the goal of capturing and storing information about the interactions with attacking entities and we repute them a crucial technology in the study of cyber threats and attacks. We presents the main features of EMPHAsis, a data streaming analytics system based on high-interaction honeypots, which enables the collection and analysis of relevant data about intercepted malware.
Randomization in Non-Uniform Finite Automata

The non-uniform version of Turing machines with an extra advice input tape that depends on the length of the input but not the input itself is a well-studied model in complexity theory. We investigate the same notion of non-uniformity in weaker models, namely one-way ﬁnite automata. In particular, we are interested in the power of two-sided bounded-error randomization, and how it compares to determinism and non-determinism. We show that for unlimited advice, randomization is strictly stronger than determinism, and strictly weaker than non-determinism. However, when the advice is restricted to polynomial length, the landscape changes: the expressive power of determinism and randomization does not change, but the power of non-determinism is reduced to the extent that it becomes incomparable with randomization.
How to estimate community energy flux? A comparison of approaches reveals that size-abundance trade-offs alter the scaling of community energy flux

Size and metabolism are highly correlated, so that community energy flux might be predicted from size distributions alone. However, the accuracy of predictions based on interspecific energy–size relationships relative to approaches not based on size distributions is unknown. We compare six approaches to predict energy flux in phytoplankton communities across succession: assuming a constant energy use among species (per cell or unit biomass), using energy–size interspecific scaling relationships and species-specific rates (both with or without accounting for density effects). Except for the per cell approach, all others explained some variation in energy flux but their accuracy varied considerably. Surprisingly, the best approach overall was based on mean biomass-specific rates, followed by the most complex (species-specific rates with density). We show that biomass-specific rates alone predict community energy flux because the allometric scaling of energy use with size measured for species in isolation does not reflect the isometric scaling of these species in communities. We also find energy equivalence throughout succession, even when communities are not at carrying capacity. Finally, we discuss that species assembly can alter energy–size relationships, and that metabolic suppression in response to density might drive the allometry of community energy flux as biomass accumulates.
Utilization of Galectins by Pathogens for Infection

Galectins are glycan-binding proteins which are expressed by many different cell types and secreted extracellularly. These molecules are well-known regulators of immune responses and involved in a broad range of cellular and pathophysiological functions. During infections, host galectins can either avoid or facilitate infections by interacting with host cells- and/or pathogen-derived glycoconjugates and less commonly, with proteins. Some pathogens also express self-produced galectins to interfere with host immune responses. This review summarizes pathogens which take advantage of host- or pathogen-produced galectins to establish the infection.
RESGCN: RESidual Graph Convolutional Network based Free Dock Prediction in Bike Sharing System

As an environment-friendly public transport, shared bikes have become an important urban transport, providing cheap and convenient services for urban residents. However, the number of docks of a station in a bike sharing system is fixed when it is built, and there exists imbalance between bike usage and supply in reality. An accurate real-time free dock prediction can help guide users to choose a proper station (with free bikes/docks) to rent or return a bike. Many earlier efforts are paid to do bike sharing prediction based on model-based approaches. Recently, deep neural networks (DNN), like convolutional neural networks (CNN) and recurrent neural networks (RNN), have been introduced to solve traffic prediction problems. However, three are some unsolved issues to make accurate real-time free dock prediction, such as learning complicated temporal variation and periodicity of bike usage, spatial correlations of free docks among different stations, and the impact of external factors like weather. To overcome these challenges, we propose a novel deep neural network model, which combines graph convolution and a residual structure together. We first model a bike sharing system as a weighted graph, and the non-Euclidean spatial correlations among stations (represented by weighted edges in the graph) are extracted by random walk operation in graph convolution layers. Moreover, periodic patterns of free docks in different time scales are captured by a residual structure, and external factors are considered to improve the accuracy of prediction. We also conduct comprehensive experiments based on a public real-world dataset of riding trips from Boston, and the results show that our method outperforms state-of-the-art baselines.
Sources of variation in the foraging efficiency of grazing ruminants

1. The relationships between foraging behaviour, incisor arcade breadth and body mass were described for sheep and goats grazing from swards of sown grass at two sward surface heights. 2. variation in bite mass was related to incisor arcade breadth more closely than to body mass. 3. Bite area scaled as the square of the incisor arcade breadth and was smaller on the Tall sward height treatment. 4. Sheep had greater bite masses than did goats but goats had a higher bite rate than sheep and as a consequence there was little difference in short-term intake rate between the species. 5. Young animals of both species had smaller bite masses and intake rates than did mature animals. However, when expressed relative to body mass there were no significant differences in bite mass between maturity classes. 6. The results of the current study confirm that the size of the incisor arcade is a direct determinant of bite dimensions and a driving variable for intake rate in grazing ruminants.
Genetic Polymorphism and Correlation Analysis with Growth Traits of ACTA1 Gene in Yanbian Yellow Cattle

To search for differentially expressed genes and analysis its genetic effections,in this study,actin alpha 1(ACTA1) gene was found differentially expressed in longissimus dorsi of Yanbian Yellow cattle,which had significant difference on intramuscular fat(IMF) content,and its full-length cDNA sequence was obtained from Yanbian Yellow cattle.The tissue expression analysis indicated that the ACTA1 gene was highly expressed in muscle and longissimus dorsi.One SNP was detected by sequence comparison in 5′ non-coding region.The polymorphisms of ACTA1 were detected in a 100 Yanbian Yellow cattles population by PCR-RFLP and the association of genotype with growth traits,which result in 3 genotypes named AA,AG and GG respectively,the genotype frequency of AA,AG and GG were 0.24,0.50 and 0.26;Allele frequencies of A and G were 0.37 and 0.63. Variance analysis showed: some indexes of the individuals with GG genotype were higher than that with genotype AA and AG,such as the indexes of body length,body weight and average day gain(P0.05);the heart girth of individuals with GG genotype was extremely higher than AG genotype(P0.05);there were not significantly differences in the wither height individuals with different genotype.The GG genotype may have positive effect on growth traits.This indicated that ACTA1 gene as marker can be used to selection of growth trait in Yanbian Yellow cattle.
Machine Learning Enabling Analog Beam Selection for Concurrent Transmissions in Millimeter-Wave V2V Communications

With the development of millimeter-wave (mmWave) technology and vehicle-to-vehicle (V2V) communications, the mmWave vehicular ad hoc networks (VANETs) is envisioned to support rapidly growing number of vehicles. Against this background, each V2V user (VUE) is expected to employ large-scale array to form directional analog beams for improving spatial spectrum reuse, and it is capable of achieving concurrent transmissions from multiple other VUEs simultaneously. However, due to the high dynamics of V2V links, it can be challenging for each VUE to quickly select an effective analog beam. In this paper, we propose a machine learning (ML) approach to achieve an efficient and fast analog beam selection for mmWave V2V communications. Specifically, we first derive the probabilities that multiple V2V transmitters (TXs) serve one VUE to obtain the average sum rate (ASR) for mmWave V2V communications. On that basis, we develop an ML approach to maximize the ASR, whereby the support vector machine (SVM) classifier is utilized for optimizing the analog beam selection. Besides, we further proposed an iteration sequential minimal optimization training algorithm to train data samples of all V2V links, and convergence of the proposed solution is also discussed. Finally, an extensive sample training and simulations are evaluated by Google TensorFlow. The results verified that our proposed ML approach is capable of achieving a higher ASR yet substantially lower computational complexity than traditional solutions based on explicitly estimated channels.
Smart Data Exchange

The problem of exchanging data, even considering incomplete and heterogeneous data, has been deeply investigated in the last years. The approaches proposed so far are quite rigid as they refer to ﬁxed schema and/or are based on a deductive approach consisting in the use of a ﬁxed set of (mapping) rules. In this paper we describe a smart data exchange framework integrating deductive and inductive techniques to obtain new knowledge. The use of graph-based representation of source and target data, together with the midway relational database and the extraction of new knowledge allow us to manage dynamic databases where also features of data may change over the time.
Lactobacillus rhamnosus Strains Relieve Loperamide-Induced Constipation via Different Pathways Independent of Short-Chain Fatty Acids

Increasing researches have confirmed the relationship between slow-transit constipation and gut microbiota dysbiosis. Many population and animal experiments have identified probiotics as effectors for the relief of constipation symptoms, but the specific mechanism remains unclear. In this intervention study, Lactobacillus rhamnosus strains isolated from five different sources were administered to mice with loperamide-induced constipation, and the impacts of these strains on constipation-related indicators were evaluated. All five strains of L. rhamnosus were found to improve constipation to various degrees. However, contrary to previous studies, the abilities of L. rhamnosus strains to improve constipation symptoms were not associated with the levels of short-chain fatty acids (SCFAs) in the colon. The effects of different strains of L. rhamnosus on constipation relief were associated with different aspects of the GI tract, including gastrointestinal regulatory peptides, neurotransmitters, neurotrophic factors, and gut microbiota. The findings of this study demonstrate that L. rhamnosus strains can alleviate constipation-related symptoms via different pathways independent of SCFAs regulation. This study yields a new perspective for clinical use of probiotics to better improve constipation symptoms, by combining strains with different mechanisms for alleviation of constipation.