Miracle-dz/UniFAR-FTUs huggingface dataset:
An Overview of the Biology of Reaction Wood Formation

Reaction wood possesses altered properties and performs the function of regulating a tree's form, but it is a serious defect in wood utility. Trees usually develop reaction wood in response to a gravistimulus. Reaction wood in gymnosperms is referred to as compression wood and develops on the lower side of leaning stems or branches. In arboreal, dicotyledonous angiosperms, however, it is called tension wood and is formed on the upper side of the leaning. Exploring the biology of reaction wood formation is of great value for the understanding of the wood differentiation mechanisms, cambial activity, gravitropism, and the systematics and evolution of plants. After giving an outline of the variety of wood and properties of reaction wood, this review lays emphasis on various stimuli for reaction wood induction and the extensive studies carried out so far on the roles of plant hormones in reaction wood formation. Inconsistent results have been reported for the effects of plant hormones. Both auxin and ethylene regulate the formation of compression wood in gymnosperms. However, the role of ethylene may be indirect as exogenous ethylene cannot induce compression wood formation. Tension wood formation is mainly regulated by auxin and gibberellin. Interactions among hormones and other substances may play important parts in the regulation of reaction wood formation.
Geographical extent of Arctic char (Salvelinus alpinus) mtDNA introgression in brook char populations (S. fontinalis) from eastern Québec, Canada

The geographical extent of Arctic char (Salvelinus alpinus) mitochondrial DNA introgression into brook char (Salvelinus fontinalis) populations found in eastern Québec was determined by analysing a total of 598 fish from 29 lakes. The nuclear genome was analysed by protein electrophoresis, whereas the ND‐5,6 portion of the mitochondrial genome was analysed by restriction fragment length polymorphism. This survey revealed that introgressed S. fontinalis populations are restricted to only one river subdrainage of the Portneuf basin, where Arctic char is completely absent. Elsewhere, nonintrogressed pure S. fontinalis populations populate the lakes. These findings suggest that the initial hybridization event between the species is ancient and probably occurred shortly after recolonization of the area. At that time, the species would have been in contact and the chances of reproductive isolation mechanisms breaking down would have been high. We discuss the possibility that a combination of biogeographical conditions coupled with positive selection for mtDNA introgression led to the present‐day distribution of introgressed S. fontinalis in northeastern North America.
Control of leaf expansion in sunflower (Helianthus annuus L.) by nitrogen nutrition.

Seedlings of Helianthus annuus L. were grown at an initially high relative nitrate supply rate (0.27 mol N mol N–1 d–1). The supply was subsequently reduced to a low rate (0.04 mol N mol N–1 d–1). The response of leaf area development to this abrupt decrease in nitrate availability was characterized by following the expansion of the primary and secondary leaf pairs. The timing of the drop in nitrate supply was when cell division in the epidermis of the primary leaf pair was largely complete. Reducing the availability of nitrate had a strong effect on leaf area expansion. The final leaf size of the primary leaf pair was affected indicating an effect of nitrate availability on cell expansion. By the end of the experiment the secondary leaf pair was only one-third the area of that on control seedlings. The role of epidermal cell turgor pressure in this growth response was assessed by direct measurements with a miniature cell pressure probe. No reduction in cell turgor pressure following the decrease in nitrate availability was detected. It is concluded that a reduction in turgor pressure was not responsible for the reduction in leaf area expansion and it is suggested that reduced cell expansion was due to changes in cell wall properties. Concentrations of leaf and root abscisic acid increased following the reduction in nitrate availability.
The effect of soil warming on bulk soil vs. rhizosphere respiration

There has been considerable debate on whether root/rhizosphere respiration or bulk soil respiration is more sensitive to long‐term temperature changes. We investigated the response of belowground respiration to soil warming by 3 °C above ambient in bare soil plots and plots planted with wheat and maize. Initially, belowground respiration responded more to the soil warming in bare soil plots than in planted plots. However, as the growing season progressed, a greater soil‐warming response developed in the planted plots as the contribution of root/rhizosphere respiration to belowground respiration declined. A negative correlation was observed between the contribution of root/rhizosphere respiration to total belowground respiration and the magnitude of the soil‐warming response indicating that bulk soil respiration is more temperature sensitive than root/rhizosphere respiration. The dependence of root/rhizosphere respiration on substrate provision from photosynthesis is the most probable explanation for the observed lower temperature sensitivity of root/rhizosphere respiration. At harvest in late September, final crop biomass did not differ between the two soil temperature treatments in either the maize or wheat plots. Postharvest, flux measurements during the winter months indicated that the response of belowground respiration to the soil‐warming treatment increased in magnitude (response equated to a Q10 value of 5.7 compared with ∼2.3 during the growing season). However, it appeared that this response was partly caused by a strong indirect effect of soil warming. When measurements were made at a common temperature, belowground respiration remained higher in the warmed subplots suggesting soil warming had maintained a more active microbial community through the winter months. It is proposed that any changes in winter temperatures, resulting from global warming, could alter the sink strength of terrestrial ecosystems considerably.
Genetic and morphological variation in a Mediterranean glacial refugium: evidence from Italian pygmy shrews, Sorex minutus (Mammalia: Soricomorpha)

At the Last Glacial Maximum (LGM), the southern European peninsulas were important refugia for temperate species. Current genetic subdivision of species within these peninsulas may reflect past population subdivision at the LGM, as in ‘refugia within refugia’, and/or at other time periods. In the present study, we assess whether pygmy shrew populations from different regions within Italy are genetically and morphologically distinct. One maternally and two paternally inherited molecular markers (cytochrome b and Y-chromosome introns, respectively) were analysed using several phylogenetic methods. A geometric morphometric analysis was performed on mandibles to evaluate size and shape variability between populations. Mandible shape was also explored with a functional approach that considered the mandible as a first-order lever affecting bite force. We found genetically and morphologically distinct European, Italian, and southern Italian groups. Mandible size increased with decreasing latitude and southern Italian pygmy shrews exhibited mandibles with the strongest bite force. It is not clear whether or not the southern Italian and Italian groups of pygmy shrews occupied different refugia within the Italian peninsula at the LGM. It is likely, however, that geographic isolation earlier than the LGM on islands at the site of present-day Calabria was important in generating the distinctive southern Italian group of pygmy shrews, and also the genetic groups in other small vertebrates that we review here. Calabria is an important hotspot for genetic diversity, and is worthy of conservation attention. © 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 774–787.
Mice Deficient in the α7 Neuronal Nicotinic Acetylcholine Receptor Lack α-Bungarotoxin Binding Sites and Hippocampal Fast Nicotinic Currents

The α7 subunit of the neuronal nicotinic acetylcholine receptor (nAChR) is abundantly expressed in hippocampus and is implicated in modulating neurotransmitter release and in binding α-bungarotoxin (α-BGT). A null mutation for the α7 subunit was prepared by deleting the last three exons of the gene. Mice homozygous for the null mutation lack detectable mRNA, but the mice are viable and anatomically normal. Neuropathological examination of the brain revealed normal structure and cell layering, including normal cortical barrel fields; histochemical assessment of the hippocampus was also normal. Autoradiography with [3H]nicotine revealed no detectable abnormalities of high-affinity nicotine binding sites, but there was an absence of high-affinity [125I]α-BGT sites. Null mice also lack rapidly desensitizing, methyllycaconitine-sensitive, nicotinic currents that are present in hippocampal neurons. The results of this study indicate that the α-BGT binding sites are equivalent to the α7-containing nAChRs that mediate fast, desensitizing nicotinic currents in the hippocampus. These mice demonstrate that the α7 subunit is not essential for normal development or for apparently normal neurological function, but the mice may prove to have subtle phenotypic abnormalities and will be valuable in defining the functional role of this gene product in vivo.
PRODUCTION OF CHIMERIC TIR-INTIMIN PROTEIN FROM E. COLI O157: H7 IN THE TOBACCO (NICOTIANA TOBBACUM) PLANT AND ITS IMMUNOLOGICAL EVALUATION IN AN ANIMAL MODEL

Objective: Escherichia coli (E.coli) O157:H7 is one of the most important pathogenic causes of hemorrhagic colitis in humans. Cattle are the main reservoirs of this bacteria and vaccination is a key mechanism for its control. The intimin, translocated intimin receptor (tir), and EspA proteins are virulence factors expressed by the LEE locus of enterohemorrhagic E. coli. EspA protein is a member of the type III secretion system (TTSS) needle complexes that delivers the tir protein into the host cell. Surface arrayed intimin docks the bacterium to the translocated intimin receptor (Tir). This intimate linkage is the starting point for attachment and effacing lesions. We hypothesize that the chimeric recombinant forms of two of these three effectors, as edible-based immunogens, would reduce colonization of E. coli O157:H7 in the mice model. Methods: We constructed a synthetic gene (it) composed of eae (i) and tir (t) attached together by a peptide linker. The synthetic gene (it) was codon optimized based on the tobacco (Nicotiana tobbacum) plant and cloned into plant expression vectors adjacent to CaMV35S promoters for expression in transgenic tobacco plants. The antigen produced in this plant was orally fed to mice. Results: Immunization of the mice model by the transgenic plant that contained the divalent immunogen showed the presence of IgG antibodies against E. coli O157:H7. Conclusion: This method could be an effective tool for protecting against E. coli O157:H7 hemorrhagic colitis.
Intimate Partner Violence Perpetration Following Status Disclosure: Narrative Literature Review

Men’s experience of perpetration of violence of intimate partners against female partners is a social and public health issue globally. The impact of IPV and the effects it has on victims are enormous. The factors that cause perpetrators to engage in IPV is an important aspect that needs attention. Men who perpetrate violence also go through violence which is normally neglected by researchers and also the fact that the disclosure of HIV infected female partners to their male partners serve as factors that create risk for perpetration of IPV also needs to be reviewed to find out if enough research work has been done in this area. The review was conducted on this phenomenon of perpetration of IPV against HIV infected female partners following seropositive status disclosure by the women. The review was conducted as part of a study that sought to explore and explain the experiences of intimae partner violence against HIV positive partners by men in Ghana.
Full Digital Implementation of an Optimized Modulation Strategy for Series-Parallel Resonant Converter

The series-parallel resonant converter is one of the most popular topologies among resonant converters. In order to minimize its switching losses, an optimized modulation strategy was proposed and implemented using a hybrid hardware structure. In this contribution the working principles of the state-of-the-art hybrid solution and its disadvantages are analyzed first. In order to overcome those drawbacks, a full digital optimized modulation control unit is developed. Aiming to ensure zero-voltage switching in the converter start-up phase, a novel operation strategy is proposed and implemented. Experimental results are given to prove the feasibility of the novel digital optimized modulation control unit.
Self-interacting dark matter from late decays and the 
H0
 tension

We study a dark matter production mechanism based on decays of a messenger WIMP-like state into a pair of dark matter particles that are self-interacting via exchange of a light mediator. Its distinctive thermal history allows the mediator to be stable and therefore avoid strong limits from the cosmic microwave background and indirect detection. A natural by-product of this mechanism is a possibility of a late time, i.e., after recombination, transition to subdominant dark radiation component through three-body and one-loop decays to states containing the light mediator. We examine to what extent such a process can help to alleviate the $H_0$ tension. Additionally, the mechanism can provide a natural way of constructing dark matter models with ultra-strong self-interactions that may positively affect the supermassive black hole formation rate. We provide a simple realization of the mechanism in a Higgs portal dark matter model and find a significant region of the parameter space that leads to a mild relaxation of the Hubble tension while simultaneously having the potential of addressing small-scale structure problems of $\Lambda$CDM.
Application of variational policy gradient to atomic-scale materials synthesis

Atomic-scale materials synthesis via layer deposition techniques present a unique opportunity to control material structures and yield systems that display unique functional properties that cannot be stabilized using traditional bulk synthetic routes. However, the deposition process itself presents a large, multidimensional space that is traditionally optimized via intuition and trial and error, slowing down progress. Here, we present an application of deep reinforcement learning to a simulated materials synthesis problem, utilizing the Stein variational policy gradient (SVPG) approach to train multiple agents to optimize a stochastic policy to yield desired functional properties. Our contributions are (1) A fully open source simulation environment for layered materials synthesis problems, utilizing a kinetic Monte-Carlo engine and implemented in the OpenAI Gym framework, (2) Extension of the Stein variational policy gradient approach to deal with both image and tabular input, and (3) Developing a parallel (synchronous) implementation of SVPG using Horovod, distributing multiple agents across GPUs and individual simulation environments on CPUs. We demonstrate the utility of this approach in optimizing for a material surface characteristic, surface roughness, and explore the strategies used by the agents as compared with a traditional actor-critic (A2C) baseline. Further, we find that SVPG stabilizes the training process over traditional A2C. Such trained agents can be useful to a variety of atomic-scale deposition techniques, including pulsed laser deposition and molecular beam epitaxy, if the implementation challenges are addressed.
Specular- and Diffuse-reflection-based Face Spoofing Detection for Mobile Devices

In light of the rising demand for biometric-authentication systems, preventing face spoofing attacks is a critical issue for the safe deployment of face recognition systems. Here, we propose an efficient face presentation attack detection (PAD) algorithm that requires minimal hardware and only a small database, making it suitable for resource-constrained devices such as mobile phones. Utilizing one monocular visible light camera, the proposed algorithm takes two facial photos, one taken with a flash, the other without a flash. The proposed SpecDiff descriptor is constructed by leveraging two types of reflection: (i) specular reflections from the iris region that have a specific intensity distribution depending on liveness, and (ii) diffuse reflections from the entire face region that represents the 3D structure of a subject's face. Classifiers trained with SpecDiff descriptor outperforms other flash-based PAD algorithms on both an in-house database and on publicly available NUAA, Replay-Attack, and SiW databases. Moreover, the proposed algorithm achieves statistically significantly better accuracy to that of an end-to-end, deep neural network classifier, while being approximately six-times faster execution speed. The code is publicly available at https://github.com/Akinori-F-Ebihara/SpecDiff-spoofing-detector.
Variational Online Learning of Neural Dynamics

New technologies for recording the activity of large neural populations during complex behavior provide exciting opportunities for investigating the neural computations that underlie perception, cognition, and decision-making. Non-linear state space models provide an interpretable signal processing framework by combining an intuitive dynamical system with a probabilistic observation model, which can provide insights into neural dynamics, neural computation, and development of neural prosthetics and treatment through feedback control. This brings with it the challenge of learning both latent neural state and the underlying dynamical system because neither are known for neural systems a priori. We developed a flexible online learning framework for latent non-linear state dynamics and filtered latent states. Using the stochastic gradient variational Bayes approach, our method jointly optimizes the parameters of the non-linear dynamical system, the observation model, and the black-box recognition model. Unlike previous approaches, our framework can incorporate non-trivial distributions of observation noise and has constant time and space complexity. These features make our approach amenable to real-time applications and the potential to automate analysis and experimental design in ways that testably track and modify behavior using stimuli designed to influence learning.
The effect of pH and temperature on initiation of growth of Listeria monocytogenes

The ability of 16 strains of Listeria monocytogenes, including two serotype 1 and 14 serotype 4 strains, to grow in a nutrient medium acidified with HC1 to pH values between 4·2 and 7·0 at intervals of 0·2 units has been investigated. The minimum pH values at which growth was detected at 30, 20, 10, 7 and 4°C were, respectively, 4·39, 4·39, 4·62, 4·62 and 5·23.
Fast and Private Submodular and k-Submodular Functions Maximization with Matroid Constraints

The problem of maximizing nonnegative monotone submodular functions under a certain constraint has been intensively studied in the last decade, and a wide range of efficient approximation algorithms have been developed for this problem. Many machine learning problems, including data summarization and influence maximization, can be naturally modeled as the problem of maximizing monotone submodular functions. However, when such applications involve sensitive data about individuals, their privacy concerns should be addressed. In this paper, we study the problem of maximizing monotone submodular functions subject to matroid constraints in the framework of differential privacy. We provide $(1-\frac{1}{\mathrm{e}})$-approximation algorithm which improves upon the previous results in terms of approximation guarantee. This is done with an almost cubic number of function evaluations in our algorithm. Moreover, we study $k$-submodularity, a natural generalization of submodularity. We give the first $\frac{1}{2}$-approximation algorithm that preserves differential privacy for maximizing monotone $k$-submodular functions subject to matroid constraints. The approximation ratio is asymptotically tight and is obtained with an almost linear number of function evaluations.
Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs

Many systems have been developed in recent years to mine logical rules from large-scale Knowledge Graphs (KGs), on the grounds that representing regularities as rules enables both the interpretable inference of new facts, and the explanation of known facts. Among these systems, the walk-based methods that generate the instantiated rules containing constants by abstracting sampled paths in KGs demonstrate strong predictive performance and expressivity. However, due to the large volume of possible rules, these systems do not scale well where computational resources are often wasted on generating and evaluating unpromising rules. In this work, we address such scalability issues by proposing new methods for pruning unpromising rules using rule hierarchies. The approach consists of two phases. Firstly, since rule hierarchies are not readily available in walk-based methods, we have built a Rule Hierarchy Framework (RHF), which leverages a collection of subsumption frameworks to build a proper rule hierarchy from a set of learned rules. And secondly, we adapt RHF to an existing rule learner where we design and implement two methods for Hierarchical Pruning (HPMs), which utilize the generated hierarchies to remove irrelevant and redundant rules. Through experiments over four public benchmark datasets, we show that the application of HPMs is effective in removing unpromising rules, which leads to significant reductions in the runtime as well as in the number of learned rules, without compromising the predictive performance.
Iterative Machine Teaching without Teachers

Iterative machine teaching is a method for selecting an optimal teaching example that enables a student to efficiently learn a target concept at each iteration. Existing studies on iterative machine teaching are based on supervised machine learning and assume that there are teachers who know the true answers of all teaching examples. In this study, we consider an unsupervised case where such teachers do not exist; that is, we cannot access the true answer of any teaching example. Students are given a teaching example at each iteration, but there is no guarantee if the corresponding label is correct. Recent studies on crowdsourcing have developed methods for estimating the true answers from crowdsourcing responses. In this study, we apply these to iterative machine teaching for estimating the true labels of teaching examples along with student models that are used for teaching. Our method supports the collaborative learning of students without teachers. The experimental results show that the teaching performance of our method is particularly effective for low-level students in particular.