Miracle-dz/UniFAR-FTUs huggingface dataset:
Wheat inositol pyrophosphate kinase TaVIH2-3B modulates cell-wall composition for drought tolerance in Arabidopsis

Background Inositol pyrophosphates (PP-InsPs) are high-energy cellular molecules involved in different signalling and regulatory responses. Two distinct classes of inositol phosphate kinases responsible for the synthesis of PP-InsPs have been identified in Arabidopsis i.e. ITPKinase (inositol 1,3,4 trisphosphate 5/6 kinase) and PP-IP5Kinase (diphosphoinositol pentakisphosphate kinases). Plant PP-IP5Ks are capable of synthesizing InsP8 and were shown to control pathogenic defence and phosphate response signals. However, other roles offered by plant PP-IP5Ks, especially towards abiotic stress, remain poorly understood. Results Here, we characterized two Triticum aestivum L. (hexaploid wheat) PPIP5K homologs, TaVIH1 and TaVIH2, for their physiological functions. We demonstrated that wheat VIH proteins could utilize InsP7 as the substrate to produce InsP8, a process that requires the functional VIH-kinase domains. At the transcriptional level, both TaVIH1 and TaVIH2 are expressed in different wheat tissues, including developing grains, but show selective response to abiotic stresses during drought-mimic experiments. Overexpression of TaVIH2-3B homolog in Arabidopsis conferred tolerance to drought stress and rescued the sensitivity of Atvih2 mutants. RNAseq analysis of TaVIH2-3B transgenic lines of Arabidopsis showed a genome-wide reprogramming with remarkable effects on cell-wall biosynthesis genes with enhanced the accumulation of polysaccharides (arabinogalactan, cellulose and arabinoxylan). Conclusions Overall, this work identifies a novel function of VIH proteins, implying their roles in modulating cell-wall homeostasis genes and providing water-deficit stress tolerance. This work suggests that the plant VIH enzymes could be linked to drought tolerance and also opens up investigations to address the roles of plant VIH derived products in generating drought resistant plants.
Highly suppressed solar absorption in a daytime radiative cooler designed by genetic algorithm

Abstract Here, we report a selective multilayer emitter for eco-friendly daytime passive radiative cooling. The types of materials and thickness of up to 10 layers of the multilayer structure are optimized by a genetic algorithm. The passive radiative cooler is designed to mainly target low solar absorption, which allows sub-ambient cooling under direct sunlight. We used a custom objective function in the solar region to achieve high-performance daytime radiative cooling to minimize solar absorption. The designed structure minimizes solar absorption with an average absorptivity of 5.0% in the solar region (0.3–2.5 μm) while strongly emitting thermal radiation with an average emissivity of 86.0% in the atmospheric transparency window (8–13 μm). The designed and fabricated structure achieves daytime net cooling flux of 84.8 W m−2 and 70.6 W m−2, respectively, under the direct AM 1.5 solar irradiation (SI) (total heat flux of 892 W m−2 in the 0.3–2.5 μm wavelength region). Finally, we experimentally demonstrate a passive radiative cooling of the fabricated selective emitter through a 72-hour day-night cycle, showing an average and maximum temperature reduction of 3.1 °C and 6.0 °C, respectively. Our approach provides additional degrees of freedom by designing both materials and thickness and thereby is expected to allow high-performance daytime radiative cooling.
Comparison of APACHE II scores and mortality with CRP/albumin, neutrophil/lymphocyte and thrombocyte/lymphocyte ratios in patients admitted to internal medicine and anesthesia reanimation intensive care unit

Abstract Objectives This study aimed to evaluate the relationship between C-reactive protein/albumin (CRP/Alb), neutrophil/lymphocyte (NLR), platelet/lymphocyte (PLR) ratios and the Acute Physiology And Chronic Health Evaluation II (APACHE II) score and 28-day mortality among 400 patients admitted to internal medicine and anesthesia reanimation intensive care unit (ICU). Methods This prospective study included a total of 400 patients who were admitted to hospital internal medicine and anesthesia reanimation ICUs. Results The most common reasons for ICU admission were pneumonia (29.3%), gastrointestinal bleeding (10.3%), acute exacerbation of chronic kidney disease (CKD) (10.3%), and acute kidney injury (7.5%). The comparison of the laboratory findings with survival outcomes revealed that among the patients with acute exacerbation of CKD, the median NLR (p=0.043) and median CRP/Alb (p=0.021) were significantly higher in patients who died. For all of the patients, the APACHE II score was positively correlated with CRP (p<0.001) and CRP/Alb (p<0.001), negatively correlated with Alb (p<0.001), positively correlated with the NLR (p<0.001), and positively correlated with the PLR. Conclusions The APACHE II score was significantly correlated with the CRP/Alb ratio, NLR, and PLR. The NLR and CRP/Alb ratio were statistically associated with mortality in patients hospitalized for acute exacerbation of CKD.
Inverting and/or non-inverting biquad circuit using second-generation current conveyors

A configuration for realizing high input impedance inverting and/or non-inverting voltage-mode filters using second-generation current conveyors (CCIIs) is presented. This configuration uses only three second-generation current conveyors, four (or three) grounded resistors and two (or three) grounded capacitors and can simultaneously realize bandpass and lowpass or bandpass and highpass responses. The resonance angular frequency and the quality factor can be orthogonally controllable. PSPICE simulation results are also included. current conveyors (CCIIs) is
Low Cost Wear Leveling for High-Density SPI NAND Flash in Memory Constrained Embedded System

SPI NOR Flash is widely used in embedded system for its compact package and simple interface. It is suitable to medium scale data storage. Now, industry provides higher density NAND Flash with the same interface and package as SPI NOR Flash. But the erase/program circles of NAND Flash is less than that of NOR Flash. The lifetime of NAND Flash is becoming the critical issue. Wear leveling algorithms prevent NAND Flash from prematurely retiring by mapping the logical block to different physical blocks. To our knowledge, most wear leveling algorithms need several kilobytes of RAM for keeping the mapping data structures, which is costly for the resource constrained micro controller. This study first proposes a no RAM required design with presetting lifetime, and then presents an adaptive design which requires only 2 kilobytes RAM with elasticity lifetime. The proposed design can be adapted in most of embedded systems based on low cost micro controller.
The ‘landscape scale’ in planning: Recent experience of bio-geographic planning units in Britain

Abstract Landscape-scale mapping is becoming more widely used as an approach to identifying ‘characteristic’ units of countryside for information gathering and policy delivery purposes. A number of innovative approaches currently being developed and applied in Britain are reviewed. The justifications for producing these methods, as reflected in published sources, are analysed. Interviews with selected professionals, who identify various benefits for more effective policy delivery, integrated planning, partnership formation and public engagement, are reported on. Most of the approaches also override administrative boundaries, which enables a more naturalistic reflection of environmental systems, whilst also creating some political difficulties. Other problems are associated with cost and subjectivity. Overall, the methods are likely to improve joined-up rural decision making, but further research into their effectiveness is needed.
Classi ﬁ cation of wood knots using arti ﬁ cial neural networks with texture and local feature-based image descriptors

: This paper describes feature-based techniques for wood knot classification. For automated classification of macroscopic wood knot images, models were established using artificial neural networks with texture and local feature descriptors, and the performances of feature extraction algorithms were compared. Classi ﬁ cation models trained with texture descriptors, gray-level co-occurrence matrix and local binary pattern, achieved better performance than those trained with local feature descriptors, scale-invariant feature transform and dense scale-invariant feature transform. Hence, it was con ﬁ rmed that wood knot classi ﬁ cationwasmore appropriatefortexture classi ﬁ cation rather than an approach based on morphological classi ﬁ - cation. The gray-level co-occurrence matrix produced the highest F1 score despite representing images with relatively low-dimensionalfeaturevectors.The scale-invariantfeature transform algorithm could not detect a suf ﬁ cient number of features from the knot images; hence, the histogram of oriented gradients and dense scale-invariant feature transform algorithms that describe the entire image were better for wood knot classi ﬁ cation. The arti ﬁ cial neural network model provided better classi ﬁ cation performance than the support vector machine and k -nearest neighbor models, which suggests the suitability of the nonlinear classi ﬁ cation model for wood knot classi ﬁ cation.
Small Recuperated Gas Turbine APU Concept to Abate Concern About Emissions, High Fuel Cost, and Noise

During the last decade microturbines in the 30 to 250 kW power range have been in service, but they have not been produced in significant enough quantities to impact the DG market due to a combination of factors including their high cost, modest efficiency, utility institutional concerns, and low interest in CHP use in the USA, although the latter could change as a result of fuel cost escalation. Very small gas turbines (ie. less than 10 kW) have been investigated periodically over the years, mainly for potential military use, but to date have not found a niche. This could well change with over 20 states now having instigated idling regulations that limit or prohibit heavy duty truck diesel engine idling. With emphasis on reducing emissions and noise levels this essentially implies that in the future a small APU will be required to provide the truck’s stationary electrical needs and cabin climate control. While there will be several power source types vying for this potentially large market, a small gas turbine APU is viewed as having attractive features which include low emissions, low acoustic signature, vibration-free operation, compact and light weight package, obviates the need for oil lubrication and liquid coolant systems, ease of cold weather starting, immediate response, and the use of fuel from the truck tank. In this paper a small recuperated gas turbine APU concept rated at 5kW is discussed including component design considerations, layout features, engine performance, and target cost. The conservative design of the APU is based on the use of existing materials and state-of-the-art gas turbine technology, and is amenable to high volume automated manufacturing processes. A competitive cost is projected if the proposed APU of modular construction was fabricated in large quantities like the production of vehicular turbochargers in Europe.Copyright © 2007 by ASME
Simple but Effective Knowledge-Based Query Reformulations for Precision Medicine Retrieval

In Information Retrieval (IR), the semantic gap represents the mismatch between users’ queries and how retrieval models answer to these queries. In this paper, we explore how to use external knowledge resources to enhance bag-of-words representations and reduce the effect of the semantic gap between queries and documents. In this regard, we propose several simple but effective knowledge-based query expansion and reduction techniques, and we evaluate them for the medical domain. The query reformulations proposed are used to increase the probability of retrieving relevant documents through the addition to, or the removal from, the original query of highly specific terms. The experimental analyses on different test collections for Precision Medicine IR show the effectiveness of the developed techniques. In particular, a specific subset of query reformulations allow retrieval models to achieve top performing results in all the considered test collections.
Fiber Optic Displacement Sensor with New Reflectivity Compensation Method

In this paper, a fiber optic displacement sensor with a new reflectivity compensation method is presented. The proposed compensation method is based on two light receiving channels with characteristic displacement sensitivities. The sensitivity characteristic for each channel is achieved by using fibers with different numerical apertures. The ratio of the intensity values of the two receiving channels is a function of the object displacement and fairly independent from the reflectivity of the measured object. The sensor is characterized by a well-defined measurement spot. By use of a focus lens mounted onto the fiber optics probe head, the object displacement range can be extended. The sensor is suitable for measurements with changing object reflectivity and demanding distance ranges.
Pear Defect Detection Method Based on ResNet and DCGAN

To address the current situation, in which pear defect detection is still based on a workforce with low efficiency, we propose the use of the CNN model to detect pear defects. Since it is challenging to obtain defect images in the implementation process, a deep convolutional adversarial generation network was used to augment the defect images. As the experimental results indicated, the detection accuracy of the proposed method on the 3000 validation set was as high as 97.35%. Variant mainstream CNNs were compared to evaluate the model’s performance thoroughly, and the top performer was selected to conduct further comparative experiments with traditional machine learning methods, such as support vector machine algorithm, random forest algorithm, and k-nearest neighbor clustering algorithm. Moreover, the other two varieties of pears that have not been trained were chosen to validate the robustness and generalization capability of the model. The validation results illustrated that the proposed method is more accurate than the commonly used algorithms for pear defect detection. It is robust enough to be generalized well to other datasets. In order to allow the method proposed in this paper to be applied in agriculture, an intelligent pear defect detection system was built based on an iOS device.
Verb-Mediated Prediction in Bilingual Toddlers

Prediction is an important mechanism for efficient language processing. It has been shown that as a part of sentence processing, both children and adults predict nouns based on semantically constraining verbs. Language proficiency is said to modulate prediction: the higher proficiency, the better the predictive skill. Children growing up acquiring two languages are often more proficient in one of them, and as such, investigation of the predictive ability in young bilingual children can shed light on the role of language proficiency. Furthermore, according to production-based models, the language production system drives the predictive ability. The present study investigates whether bilingual toddlers predict upcoming nouns based on verb meanings in both their languages, and whether this ability is associated with expressive vocabulary. Seventeen Norwegian-English bilingual toddlers (aged 2;5–3;3), dominant in Norwegian, participated in the study. Verb-mediated predictive ability was measured via a visual world paradigm (VWP) experiment, including sentences with semantically constraining and neutral verbs. Expressive vocabulary was measured by MacArthur-Bates CDI II. The results suggested that the toddler group predicted upcoming noun arguments in both their dominant and non-dominant languages, but were faster in their dominant language. This finding highlights the importance of language dominance for predictive processing. There was no significant relationship between predictive ability and expressive vocabulary in either language.
Tissue Type Differences in ABCB1 Expression and Paclitaxel Tissue Pharmacokinetics in Patients With Esophageal Cancer

Background: Data from previous work suggests that there is no correlation between systemic (plasma) paclitaxel exposure and efficacy in patients treated for esophageal cancer. In this trial, we investigated ATP-binding cassette efflux transporter expression and intratumoral pharmacokinetics of paclitaxel to identify changes which could be a first sign of chemoresistance. Methods: Patients with esophageal cancer treated with paclitaxel and carboplatin (± concomitant radiotherapy) were included. During the first and last cycle of weekly paclitaxel, blood samples and biopsies of esophageal mucosa and tumor tissue were taken. Changes in paclitaxel exposure and expression of ABCB1 (P-glycoprotein) over time were studied in both tumor tissue and normal appearing esophageal mucosa. Results: ABCB1 was significantly higher expressed in tumor tissue compared to esophageal tissue, during both the first and last cycle of paclitaxel (cycle 1: p < 0.01; cycle 5/6: p = 0.01). Interestingly, ABCB1 expression was significantly higher in adenocarcinoma than in squamous cell carcinoma (p < 0.01). During the first cycle, a trend towards a higher intratumoral paclitaxel concentration was observed compared to the esophageal mucosa concentration (RD:43%; 95%CI: −3% to 111% p = 0.07). Intratumoral and plasma paclitaxel concentrations were significantly correlated during the first cycle (AUC0–48 h: r = 0.72; p < 0.01). Conclusion: Higher ABCB1 expression in tumor tissue, and differences between histological tumor types might partly explain why tumors respond differently to systemic treatment. Resistance by altered intratumoral paclitaxel concentrations could not be demonstrated because the majority of the biopsies taken at the last cycle of paclitaxel did contain a low amount of tumor cells or no tumor.
Mechanisms of Cell Entry by dsRNA Viruses: Insights for Efficient Delivery of dsRNA and Tools for Improved RNAi-Based Pest Control

While RNAi is often heralded as a promising new strategy for insect pest control, a major obstacle that still remains is the efficient delivery of dsRNA molecules within the cells of the targeted insects. However, it seems overlooked that dsRNA viruses already have developed efficient strategies for transport of dsRNA molecules across tissue barriers and cellular membranes. Besides protecting their dsRNA genomes in a protective shell, dsRNA viruses also display outer capsid layers that incorporate sophisticated mechanisms to disrupt the plasma membrane layer and to translocate core particles (with linear dsRNA genome fragments) within the cytoplasm. Because of the perceived efficiency of the translocation mechanism, it is well worth analyzing in detail the molecular processes that are used to achieve this feat. In this review, the mechanism of cell entry by dsRNA viruses belonging to the Reoviridae family is discussed in detail. Because of the large amount of progress in mammalian versus insect models, the mechanism of infections of reoviruses in mammals (orthoreoviruses, rotaviruses, orbiviruses) will be treated as a point of reference against which infections of reoviruses in insects (orbiviruses in midges, plant viruses in hemipterans, insect-specific cypoviruses in lepidopterans) will be compared. The goal of this discussion is to uncover the basic principles by which dsRNA viruses cross tissue barriers and translocate their cargo to the cellular cytoplasm; such knowledge subsequently can be incorporated into the design of dsRNA virus-based viral-like particles for optimal delivery of RNAi triggers in targeted insect pests.
Mindfulness-Based Stress Reduction Increases Mental Wellbeing and Emotion Regulation During the First Wave of the COVID-19 Pandemic: A Synchronous Online Intervention Study

The COVID-19 pandemic imposed extreme living conditions of social distancing, which triggered negative mental health problems and created challenges in seeking mental health support. Mindfulness-based interventions (MBIs) have been found to enhance wellbeing and mental health by reducing stress and anxiety and improving emotion regulation. Preliminary evidence suggests that online, synchronous MBIs may produce beneficial effects similar to face-to-face programs. However, the effectiveness of such online-MBIs to support mental health in highly stressful times, such as a global pandemic, requires further study. To this end, we investigated the effect of an online 8-week Mindfulness-Based Stress Reduction (MBSR) program on aspects of mental health during the first wave of the COVID-19 pandemic. Participants (N=92) who expressed interest in discounted online-MBSR programs were recruited for the study. The division into experimental and control groups was based on actual enrollment to the courses. Those who enrolled in a program were assigned to the experimental condition and those who decided not to enroll served as controls. Participants were assessed pre-intervention, post-intervention, and 1-month post-intervention for levels of mindfulness, perceived stress, anxiety, emotion regulation, and intolerance of uncertainty. Differences between the groups were tested using the general linear mixed effects model (GLMM) and Individual Growth Curve Models (IGCM) in intent to treat analysis. The findings indicated that, relative to the control group, MBSR improved mindfulness abilities (p <0.001), decreased anxiety (p <0.001), and stress (p <0.001) and increased emotion regulation (p <0.001). These effects were found to persist 1 month after the end of the program, despite the increased governmental public-health restrictions due to COVID-19 at that time. The ability to tolerate uncertainty, a central characteristic of the pandemic, was not found to be affected by the program. A mediation analysis revealed that the effect of the intervention on mental health improvement was partially mediated by the improvement in emotion regulation. Overall, the findings provide positive evidence for the feasibility of an online-MBSR program to support the mental health of individuals from the general population through the mediation of emotion regulation in challenging times, such as a global pandemic.