Miracle-dz/UniFAR-FTUs huggingface dataset:
Maize Kernel set at low water potential. II, Sensitivity to reduced assimilates at pollination

Plant water deficits at pollination decrease kernel set in maize. An important component of this kernel loss is developmental failure after fertilization. This study was undertaken to determine if xygotic abortion in water-deficient plants results primarily from a decrease in assimilate supply during the critical period around pollination. Plants were grown in soil/sand media in a controlled environment, and photosynthesis was inhibited by water-deficit or low-light treatments
Feeding Value of Extracted Coconut Meal for White Leghorn Layers

An experiment was conducted to study the effect of extracted coconut meal (ECM) on egg production performance, egg quality, carcass characteristics and biochemical parameters from 21 to 52 weeks in one hundred and eighty Single Comb White Leghorn (SCWL) layers. These birds were randomly grouped into five treatments with three replicates of twelve birds each. The treatment groups consisted of 0 (T ), 5 (T ), 10 (T ), 15 (T ) and 20 percent (T ) coconut meal inclusion in the egg type chicken diet. 1 2 3 4 5 The results revealed that five percent coconut meal fed group showed significantly (P<0.01) higher body weight when compared to other coconut meal fed groups but it was comparable to control at 52 weeks of age. No significant difference was observed in overall mean feed consumption, feed conversion ratio and livability during the experimental period. Both overall hen housed and hen day egg production significantly (P<0.05) reduced in T compared to other treatment groups. Based upon this study, it is recommended to 5 include the coconut meal up to 10 percent in egg type ration for better egg production.
Gender Differences in Upper Elementary Students' Regulation of Learning while Pair Programming

Collaborative learning has demonstrated benefits for girls in computer science [7] and this may be a way to help address the gender gap in CS. Research indicates that while collaborating, boys often express more individualistic ideas whereas girls tend to be more supportive [1]. It is important for students to regulate their learning in collaborative learning environments because they need to negotiate group goals and diverse approaches to the task [3], and the use of open-ended tasks with multiple solution paths are common [4]. There is minimal research in CS education on regulation of learning (e.g., [5,6,]). Co-regulated learning is the process in which an other helps regulate the learning of a student [2] self such as by asking questions that prompt the student to monitor and evaluate (e.g., "What do you already know about 'if' blocks that would help here? ''). In this way, thinking and reasoning through the problem is shared by the group members [2] self. Our work lies in the context of upper elementary students pair programming. We investigated how four 5th grade dyads differentially regulated their learning and explored the co-occurrence of students' coded discourse by gender. Initial findings indicate that boys monitored their learning and engaged in individualistic, disagreement, and collaborative-coded statements. Girls engaged in all three components of the regulation cycle, while offering collaborative, disagreement, confusion, and tutoring-coded statements. Confusion was often expressed as not understanding the partner or the task such as "Wait! What are you talking about? '' Individualistic utterances reflect no clear attempt to involve the partner, such as "You can trust me to handle it. '' Both occur far more than desired for collaborative work. Our findings can be used to inform the personalization of human and AI support in ways that will help both groups of students co-regulate and reduce the gender gap in computer science.
Enabling efficient ReRAM-based neural network computing via crossbar structure adaptive optimization

Resistive random-access memory (ReRAM) based accelerators have been widely studied to achieve efficient neural network computing in speed and energy. Neural network optimization algorithms such as sparsity are developed to achieve efficient neural network computing on traditional computer architectures such as CPU and GPU. However, such computing efficiency improvement is hindered when deploying these algorithms on the ReRAM-based accelerator because of its unique crossbar-structural computations. And a specific algorithm and hardware co-optimization for the ReRAM-based architecture is still in a lack. In this work, we propose an efficient neural network computing framework that is specialized for the crossbar-structural computations on the ReRAM-based accelerators. The proposed framework includes a crossbar specific feature map pruning and an adaptive neural network deployment. Experimental results show our design can improve the computing accuracy by 9.1% compared with the state-of-the-art sparse neural networks. Based on a famous ReRAM-based DNN accelerator, the proposed framework demonstrates up to 1.4× speedup, 4.3× power efficiency, and 4.4× area saving.
Using the fast impact of anthropogenic aerosols on regional land temperature to constrain aerosol forcing

Atmospheric simulations of regional land temperature suggest a powerful approach for constraining historical aerosol forcing. Anthropogenic aerosols have been postulated to have a cooling effect on climate, but its magnitude remains uncertain. Using atmospheric general circulation model simulations, we separate the land temperature response into a fast response to radiative forcings and a slow response to changing oceanic conditions and find that the former accounts for about one fifth of the observed warming of the Northern Hemisphere land during summer and autumn since the 1960s. While small, this fast response can be constrained by observations. Spatially varying aerosol effects can be detected on the regional scale, specifically warming over Europe and cooling over Asia. These results provide empirical evidence for the important role of aerosols in setting regional land temperature trends and point to an emergent constraint that suggests strong global aerosol forcing and high transient climate response.
Revisiting Self-Efficacy in Introductory Programming

For many years, the C++-based Computer Programming Self-Efficacy Scale by Ramalingam and Wiedenbeck has been the de facto standard for assessing self-efficacy in introductory programming. Since the development of this instrument, however, both the landscape as well as the intended audience of introductory programming courses has changed beyond the use of a particular programming language. We revisit this instrument and its factorization in light of curricular developments and research results regarding concepts and competences taught in introductory courses. We report on the development and validation of a new instrument that covers most paradigms and languages used in CS1 and present exploratory and confirmatory factor analyses across different populations. Our validation and factor analyses suggest that the new instrument indeed measures self-efficacy with an acceptable fit of the model. In contrast, the factorization of the Computer Programming Self-Efficacy Scale was found to be less robust. Nonetheless, and in line with self-efficacy theory, our analyses suggest that researchers should take into account the educational context of the study population when reporting or comparing results at the level of factors.
Self RNA Sensing by RIG-I–like Receptors in Viral Infection and Sterile Inflammation

The innate immune system uses pattern recognition receptors to survey the intracellular and extracellular environment for signs of infection. Viral infection is detected through the presence of viral nucleic acids in infected cells. Pattern recognition receptor activation by viral nucleic acids induces the expression and secretion of type I IFNs (IFN-Is), important mediators of antiviral immunity. RIG-I–like receptors (RLRs) are RNA sensors that detect viral RNA in the cytosol and induce an IFN-I response. Viral RNAs contain features that set them apart from host RNAs, allowing RLRs to discriminate between cellular/self and viral/non-self RNA. The notion emerged that self RNAs can also engage RLRs and modulate the IFN-I response, indicating that the distinction between self and non-self RNA is not watertight. We review how self RNAs regulate RLR activation and the IFN-I response during viral infection and how recognition of self RNAs by RLRs is implicated in autoinflammatory disorders and cancer.
Bi-particle Adverbs, PoS-Tagging and the Recognition of German Separable Prefix Verbs

In this paper we propose an algorithm for computing the full lemma of German verbs that occur in sentences with a separated prefix. The algorithm is meant for large-scale corpus annotation. It relies on Part-of-Speech tags and works with 97% precision when the tags are correct. Unfortunately there are multi-word adverbs with particles that are homographs with separated verb particles and prepositions. Since the usage as separated particle and preposition is much more frequent, these multi-word adverbs are often incorrectly tagged. We show that special treatment of these bi-particle adverbs improves the re-attachment of separated verb particles.
Tablet computers in Philippine public schools: school-level factors that influence technology management and use

ABSTRACT Many developing countries are investing in large-scale initiatives to deploy information and communications technology in schools. However, merely equipping schools with hardware and software does not guarantee that the technology is used effectively to improve learning outcomes. This study aims to identify factors that influence the use of technology in schools in a developing-country context. It investigates this through a multiple-case study of the Australian AID tablet computer programme in the Philippines, in which 1000 tablets were given to nine public schools for student use. Focusing specifically on the impact at a whole-school level, the study revealed factors affecting outcomes that were specific to a developing-country context, as well as confirming the relevance of more general factors identified in the literature. The study also brings to light tablet-specific benefits and issues in this context. These results have implications for the effective school-level implementation of technology programmes in developing countries. Recommendations that proceed from these are presented for policy-makers and school administrators looking to use these devices in schools.
Sound event detection with binary neural networks on tightly power-constrained IoT devices

Sound event detection (SED) is a hot topic in consumer and smart city applications. Existing approaches based on deep neural networks (DNNs) are very effective, but highly demanding in terms of memory, power, and throughput when targeting ultra-low power always-on devices. Latency, availability, cost, and privacy requirements are pushing recent IoT systems to process the data on the node, close to the sensor, with a very limited energy supply, and tight constraints on the memory size and processing capabilities precluding to run state-of-the-art DNNs. In this paper, we explore the combination of extreme quantization to a small-footprint binary neural network (BNN) with the highly energy-efficient, RISC-V-based (8+1)-core GAP8 microcontroller. Starting from an existing CNN for SED whose footprint (815 kB) exceeds the 512 kB of memory available on our platform, we retrain the network using binary filters and activations to match these memory constraints. (Fully) binary neural networks come with a natural drop in accuracy of 12-18% on the challenging ImageNet object recognition challenge compared to their equivalent full-precision baselines. This BNN reaches a 77.9% accuracy, just 7% lower than the full-precision version, with 58 kB (7.2× less) for the weights and 262 kB (2.4× less) memory in total. With our BNN implementation, we reach a peak throughput of 4.6 GMAC/s and 1.5 GMAC/s over the full network, including preprocessing with Mel bins, which corresponds to an efficiency of 67.1 GMAC/s/W and 31.3 GMAC/s/W, respectively. Compared to the performance of an ARM Cortex-M4 implementation, our system has a 10.3× faster execution time and a 51.1× higher energy-efficiency.
Single-atom doping of MoS2 with manganese enables ultrasensitive detection of dopamine: Experimental and computational approach

We describe a flexible biosensor, based on Mn-doped MoS2, for low-level detection of dopamine in sweat and serum solution. Two-dimensional transition metal dichalcogenides (TMDs) emerged as a promising platform to construct sensitive biosensors. We report an ultrasensitive electrochemical dopamine sensor based on manganese-doped MoS2 synthesized via a scalable two-step approach (with Mn ~2.15 atomic %). Selective dopamine detection is achieved with a detection limit of 50 pM in buffer solution, 5 nM in 10% serum, and 50 nM in artificial sweat. Density functional theory calculations and scanning transmission electron microscopy show that two types of Mn defects are dominant: Mn on top of a Mo atom (MntopMo) and Mn substituting a Mo atom (MnMo). At low dopamine concentrations, physisorption on MnMo dominates. At higher concentrations, dopamine chemisorbs on MntopMo, which is consistent with calculations of the dopamine binding energy (2.91 eV for MntopMo versus 0.65 eV for MnMo). Our results demonstrate that metal-doped layered materials, such as TMDs, constitute an emergent platform to construct ultrasensitive and tunable biosensors.
Complexity and Chimera States in a Ring-Coupled Fractional-Order Memristor Neural Network

At present, dynamics and coupled control of fractional-order non-linear systems are arousing much interest from researchers. In this paper, the fractional-order derivative is introduced into an improved memristor neural system. The dynamics of the fractional-order memristor neural model are investigated by means of bifurcation diagrams, Lyapunov exponents, and phase diagrams. To discuss the dynamical behavior of a fractional-order memristor neuron in a network, we construct a ring network of neurons and capture the spatiotemporal patterns of the neurons in the network in the presence of different excitations. Finally, the chimera state is observed, and the complexity of the network is analyzed. The analysis shows that the complexity algorithm provides a new approach for the dynamical analysis of the network.
Convergently Emergent: Ecological and Enactive Approaches to the Texture of Agency

Enactive and ecological approaches to cognitive science both claim a “mutuality” between agents and their environments – that they have a complementary nature and should be addressed as a single whole system. Despite this apparent agreement, each offers criticisms of the other on precisely this point – enactivists claiming that ecological psychologists over-emphasize the environment, while the complementary criticism, of agent-centered constructivism, is leveled by ecological psychologists at enactivists. In this paper I suggest that underlying the confusion between the two approaches is the complexity of agency, which comes in different forms, at different scales or levels of analysis. Cognitive science has not theorized the relationship between these different forms in a sufficiently disciplined manner, and a task therefore remains of finding a way to map the complex territory of agency.
Evidence for anoxic zones in 2-3 mm tips of aerenchymatous maize roots under low O2 supply

Root elongation and the production of the end-products of anaerobic catabolism, ethanol, alanine and lactate, were measured in intact maize roots and excised tips exposed to a variety of oxygen regimes. Elongation was retarded by 56 and 44% respectively in intact aerenchymatous primary roots of maize incubated in 0.1% stagnant agar, or exposed to 0.06 mol m-3 external O2 in gas-sparged solution. This growth reduction was accompanied by a 3-5-fold increase in alanine as a percentage of total soluble amino acids in the 0-2 mm root tips. The increase in this value was not in response to ethylene or translocation of alanine from other parts of the root. Moreover, in excised tips exposed to 0.06 mol O2 m-3, net production of ethanol, alanine and lactate occurred. Even so, these root tips continued to elongate at 30% of the rate observed in aerated excised root tips. It appears that adaptation of maize to O2 deficiency may involve a combination of aerenchyma formation and tolerance to anoxia. We suggest that metabolic cooperation, in the form of symplastic transport of energy-rich compounds, might exist between cortical cells receiving adequate oxygen supply and cells in anoxic zones.
Alphaherpesvirus Major Tegument Protein VP22: Its Precise Function in the Viral Life Cycle

Alphaherpesviruses are zoonotic pathogens that can cause a variety of diseases in humans and animals and severely damage health. Alphaherpesvirus infection is a slow and orderly process that can lie dormant for the lifetime of the host but may be reactivated when the immune system is compromised. All alphaherpesviruses feature a protein layer called the tegument that lies between the capsid and the envelope. Virus protein (VP) 22 is one of the most highly expressed tegument proteins; there are more than 2,000 copies of this protein in each viral particle. VP22 can interact with viral proteins, cellular proteins, and chromatin, and these interactions play important roles. This review summarizes the latest literature and discusses the roles of VP22 in viral gene transcription, protein synthesis, virion assembly, and viral cell-to-cell spread with the purpose of enhancing understanding of the life cycle of herpesviruses and other pathogens in host cells. The molecular interaction information herein provides important reference data.
Recent advances in milk clotting enzymes

Coagulating enzymes are an absolute necessity for the production of ripened cheese varieties. The objective of this review is to summarise and interpret the latest findings for the most important types of enzymes, which are animal rennet, genetically engineered chymosin, coagulants of microbial origin, and plant-derived clotting enzymes. Special emphasis has been placed on aspects of enzyme chemistry and technology, selected methods for the analysis of coagulants, and the impact of the enzymes on proteolysis, cheese yield and cheese quality.
Effects of Arbuscular Mycorrhizae on the Establishment of the Alien Plant Oenothera laciniata (Onagraceae) on a Japanese Coastal Sand Dune

Abstract We conducted field and culture experiments to study the effects of arbuscular mycorrhizal (AM) fungi on the establishment of the alien plant Oenothera laciniata (Onagraceae) in a coastal sand dune in Japan. We examined the distribution of plants, AM fungal colonization of their roots, and fungal spore density in the soil of a coastal sand dune at Keinomatsubara, Hyogo Prefecture, western Japan. Of the nine dominant plant species at the study site, six species, including O. laciniata, were heavily colonized by AM fungi. The spore density tended to decrease from inland toward the shoreline, but there were no significant relationships between the level of AM fungal colonization and spore density. Seedlings of O. laciniata inoculated with AM fungi, as well as uninoculated seedlings, were transplanted to the study site. No significant difference in survival rates was detected between the two groups of seedlings. We also examined the effects of AM infection and nutrient (N and P) application on plant growth in a pot culture experiment. The results suggested that the growth of O. laciniata was limited mainly by N availability and that AM mycorrhizae had little effect on the establishment of the plant on the coastal sand dune.
Neurogenesis From Neural Crest Cells: Molecular Mechanisms in the Formation of Cranial Nerves and Ganglia

The neural crest (NC) is a transient multipotent cell population that originates in the dorsal neural tube. Cells of the NC are highly migratory, as they travel considerable distances through the body to reach their final sites. Derivatives of the NC are neurons and glia of the peripheral nervous system (PNS) and the enteric nervous system as well as non-neural cells. Different signaling pathways triggered by Bone Morphogenetic Proteins (BMPs), Fibroblast Growth Factors (FGFs), Wnt proteins, Notch ligands, retinoic acid (RA), and Receptor Tyrosine Kinases (RTKs) participate in the processes of induction, specification, cell migration and neural differentiation of the NC. A specific set of signaling pathways and transcription factors are initially expressed in the neural plate border and then in the NC cell precursors to the formation of cranial nerves. The molecular mechanisms of control during embryonic development have been gradually elucidated, pointing to an important role of transcriptional regulators when neural differentiation occurs. However, some of these proteins have an important participation in malformations of the cranial portion and their mutation results in aberrant neurogenesis. This review aims to give an overview of the role of cell signaling and of the function of transcription factors involved in the specification of ganglia precursors and neurogenesis to form the NC-derived cranial nerves during organogenesis.
Interferon‐γ induced cell death in a cultured human salivary gland cell line

Increased levels of several cytokines, including interferon‐γ (IFN‐γ) and tumor necrosis factor‐α (TNF‐α), have been demonstrated in the salivary gland microenvironment of patients with Sjögren's syndrome (SS). How these cytokines may be contributing to the pathogenesis of the disease is not well understood. This study examined the role of IFN‐γ ± TNF‐α on cellular death in a cultured human salivary gland cell line (HSG). Cells treated long‐term with IFN‐γ ± TNF‐α demonstrate a profound antiproliferative effect with a decrease in cell number to below that initially plated. Treatment of HSG cells with TNF‐α alone did not have any significant effects on growth but did increase the expression of the IFN‐γ receptor. Cells labelled with propidium iodide and anti‐digoxigenin dUTP/dATP were examined by flow cytometry to determine the percentage of cells exhibiting low DNA content and DNA strand breaks. The percentage of cells exhibiting subdiploid DNA and DNA strand breaks increased with increased time of exposure to the cytokines. The maximum percentage of cells exhibiting DNA degradation at 12 days was 58% for cells treated with IFN‐γ + TNF‐α, 31% for IFN‐γ treated cells, and <5% for TNF‐α‐treated and untreated cells. The cells with subdiploid (<2n) DNA were subsequently demonstrated to represent two populations, both with evidence of increased DNA strand breaks but with differing light scatter characteristics. One population had features of cells undergoing necrosis, whereas the second population exhibited features of apoptosis. These findings were confirmed by transmission electron microscopy. Cells not exposed to cytokines did not exhibit significant evidence of either death process. We conclude that long‐term exposure of a human salivary gland epithelial cell line to IFN‐γ ± TNF‐α leads to increased DNA degradation and subsequent cell death. This suggests a potential SS disease mechanism and implicates the role of the epithelial cell in this disease as an important area for future study. © 1996 Wiley‐Liss, Inc.