Miracle-dz/UniFAR-FTUs huggingface dataset:
Gibberellins orchestrate panicle architecture mediated by DELLA–KNOX signalling in rice

Summary Panicle architecture is a key determinant of grain yield in cereals, but the mechanisms governing panicle morphogenesis and organ development remain elusive. Here, we have identified a quantitative trait locus (qPA1) associated with panicle architecture using chromosome segment substitution lines from parents Nipponbare and 9311. The panicle length, branch number and grain number of Nipponbare were significantly higher than CSSL‐9. Through map‐based cloning and complementation tests, we confirmed that qPA1 was identical to SD1 (Semi Dwarf1), which encodes a gibberellin 20‐oxidase enzyme participating in gibberellic acid (GA) biosynthesis. Transcript analysis revealed that SD1 was widely expressed during early panicle development. Analysis of sd1/osga20ox2 and gnp1/ osga20ox1 single and double mutants revealed that the two paralogous enzymes have non‐redundant functions during panicle development, likely due to differences in spatiotemporal expression; GNP1 expression under control of the SD1 promoter could rescue the sd1 phenotype. The DELLA protein SLR1, a component of the GA signalling pathway, accumulated more highly in sd1 plants. We have demonstrated that SLR1 physically interacts with the meristem identity class I KNOTTED1‐LIKE HOMEOBOX (KNOX) protein OSH1 to repress OSH1‐mediated activation of downstream genes related to panicle development, providing a mechanistic link between gibberellin and panicle architecture morphogenesis.
Positive abundance-elevational range size relationship weakened from temperate to subtropical ecosystems.

Describing the patterns and revealing the underlying mechanisms responsible for variations in community structure remains a central focus in ecology. However, important gaps remain, including our understanding of species abundance. Most studies on abundance-based relationships are from either temperate ecosystems or tropical ecosystems, and few have explicitly tested abundance-based relationships across a temperate to tropical ecotone. Here, we use a comprehensive dataset of breeding birds across elevation spanning a temperate to subtropical gradient in the Himalayas-Hengduan mountains of China to examine the relationship between species abundance and (1) elevational range size, (2) body size, (3) elevational range center and, (4) endemicity. We tested a priori predictions for abundance-elevational range size relationship, abundance-body size relationship, and abundance-elevational range center relationship and explored how these relationships change along this temperate to subtropical mountain ecosystem. We found that species abundance was significantly positively correlated with elevational range size across the study sites, demonstrating the key importance of elevational range size towards species abundance. Body size and elevational range center are weakly correlated with abundance. A novel finding of our study is that the abundance-elevational range size relationship gradually weakened from temperate to subtropical ecosystems, adding to a growing body of evidence suggesting that abundance-elevational range size tracks a temperate to tropical ecotone. Our study demonstrates that abundance range-size relationship can transition across ecotones where faunas of different evolutionary origins converge. Furthermore, measuring abundance relationships across different environmental variables at the same spatial scale with comparable biogeography is a key strategy that can reveal the underlying mechanisms behind abundance patterns.
Modeling the Biomechanics of the Lamina Cribrosa Microstructure in the Human Eye

Glaucoma is among the leading causes of blindness worldwide that is characterized by irreversible damage to the retinal ganglion cell axons in the lamina cribrosa (LC) region of the optic nerve head (ONH), most often associated with elevated intraocular pressure (IOP). The LC is a porous, connective tissue structure that provides mechanical support to the axons as they exit the eye and the biomechanics of the LC microstructure likely play a crucial role in protecting the axons passing through it. There is a limited knowledge of the IOP-driven biomechanics of the LC microstructure, primarily due to its small size and the difficulty with imaging the LC both in vitro and in vivo. We present finite element (FE) models of three human eye posterior poles that include the LC microstructure and interspersed neural tissues (NT) composed of retinal axons that are constructed directly from segmented, binary images of the LC. These models were used to estimate the stresses and strains in the LC and NT for an acute IOP elevation from 0 to 45 mmHg and compared with identical models except that the LC was represented as a homogenized continuum material with either homogeneous isotropic neo-Hookean properties or heterogeneous properties derived from local connective tissue volume fraction (CTVF) and predominant LC beam orientation. Stresses and strains in the LC and NT microstructure were investigated, and results were compared against those from the models wherein the LC was represented as a homogenized continuum. The regionalized volumetric average stresses and strains showed that the microstructural model yielded similar patterns to our prior approach using an LC continuum representation with mapped LC CTVF/anisotropy, but the microstructural modeling approach allows analysis of the stresses and strains in the LC and NT separately. As expected, the LC beams carried most of the IOP load in the microstructural models but exhibited less strain, while the encapsulated NT exhibited lower stresses and much higher strains. Results also revealed that the continuum models underestimate the maximum strains in the LC beams and NT by a factor of 2-3. Microstructural modeling should provide greater insight into the biomechanical factors driving damage to the axons (NT) and LC connective tissue remodeling that occur in glaucoma. The methods presented are ideal for modeling any structure with a complex microstructure composed of different materials, such as trabecular bone, lung, and tissue engineering scaffolds such as decellularized LC. Matlab code for mesh generation from a segmented image stack of the microstructure is included as Supplemental Material.
Pathogenic Variants in the Genes Affected in Alport Syndrome (COL4A3-COL4A5) and Their Association With Other Kidney Conditions: A Review.

Massively Parallel Sequencing identifies pathogenic variants in the genes affected in Alport syndrome (COL4A3 - COL4A5) in up to 30 % of individuals with focal and segmental glomerulosclerosis (FSGS), 10 % of those with kidney failure of unknown cause and 20 % with familial IgA glomerulonephritis. FSGS associated with COL4A3 - COL4A5 variants is usually present by kidney failure onset and may develop because the abnormal glomerular membranes result in podocyte loss and secondary hyperfiltration. The association of COL4A3 - COL4A5 variants with kidney failure or IgA glomerulonephritis may be coincidental and not pathogenic. However, since some of these variants occur more often than they should by chance, some may be pathogenic. COL4A3 - COL4A5 variants are sometimes also found in cystic kidney diseases after autosomal dominant polycystic kidney disease (ADPKD) has been excluded. COL4A3 - COL4A5 variants should be suspected in individuals with FSGS, kidney failure of unknown cause, or familial IgA glomerulonephritis, especially where there is persistent haematuria, and a family history of haematuria or kidney failure.
Chemical Waves and Patterns

Introducing a new hobby for other people may inspire them to join with you. Reading, as one of mutual hobby, is considered as the very easy hobby to do. But, many people are not interested in this hobby. Why? Boring is the reason of why. However, this feel actually can deal with the book and time of you reading. Yeah, one that we will refer to break the boredom in reading is choosing chemical waves and patterns as the reading material.
Studies on diverse genotypes of amaranth (Amaranthus species) for their antioxidant and nutritional activities

An investigation was carried out with eight diverse genotypes of amaranth (Amaranthus species) to estimate the biochemical composition (including anthocyanin, total carotenoids, ascorbic acid, antioxidant, i e Ferric Reducing Antioxidant Power (FRAP) and Cupric ion Reducing Antioxidant Capacity (CUPRAC) and total phenolic content) at IARI, New Delhi, during 2011. Among genotypes, anthocyanin ranged from 5.33 mg/100 g of fresh sample (Renu Sree) to 40.81 mg/100 g (Pusa Lal Chaulai). Maximum ascorbic acid was recorded in Co 1 (81.2 mg/100 g), followed by Pusa Kirti (78.4 mg/100 g). Total carotenoids were maximum in Pusa Lal Chaulai (69.41 mg per 100 g). Total antioxidant activity measured by CUPRAC and FRAP showed that three genotypes, Pusa Lal Chaulai, Krishna Sree and Co 1, had higher value of antioxidant in CUPRAC method, however in FRAP method highest antioxidant activity was recorded in Pusa Lal Chaulai (13.157 µmol ascorbic acid equivalent, AAE/g), followed by Pusa Kirti (13.033 µmol AAE/g). Total phenolic content was recorded maximum in Co 1 (1 411.62 μg gallic acid equivalent (GAE) per g), followed by Pusa Lal Chaulai (1 138.5 μg GAE per g). By considering all, it was observed that Co 1, Pusa Lal Chaulai, Pusa Kirti and Renu Sree were potential genotypes and hence may be utilized for improvement of amaranth for high nutritional activity.
Glucocorticoid induced adrenal insufficiency

ABSTRACT Synthetic glucocorticoids are widely used for their anti-inflammatory and immunosuppressive actions. A possible unwanted effect of glucocorticoid treatment is suppression of the hypothalamic-pituitary-adrenal axis, which can lead to adrenal insufficiency. Factors affecting the risk of glucocorticoid induced adrenal insufficiency (GI-AI) include the duration of glucocorticoid therapy, mode of administration, glucocorticoid dose and potency, concomitant drugs that interfere with glucocorticoid metabolism, and individual susceptibility. Patients with exogenous glucocorticoid use may develop features of Cushing’s syndrome and, subsequently, glucocorticoid withdrawal syndrome when the treatment is tapered down. Symptoms of glucocorticoid withdrawal can overlap with those of the underlying disorder, as well as of GI-AI. A careful approach to the glucocorticoid taper and appropriate patient counseling are needed to assure a successful taper. Glucocorticoid therapy should not be completely stopped until recovery of adrenal function is achieved. In this review, we discuss the factors affecting the risk of GI-AI, propose a regimen for the glucocorticoid taper, and make suggestions for assessment of adrenal function recovery. We also describe current gaps in the management of patients with GI-AI and make suggestions for an approach to the glucocorticoid withdrawal syndrome, chronic management of glucocorticoid therapy, and education on GI-AI for patients and providers.
Applying a science‐based systems perspective to dispel misconceptions about climate effects of forest bioenergy

The scientific literature contains contrasting findings about the climate effects of forest bioenergy, partly due to the wide diversity of bioenergy systems and associated contexts, but also due to differences in assessment methods. The climate effects of bioenergy must be accurately assessed to inform policy‐making, but the complexity of bioenergy systems and associated land, industry and energy systems raises challenges for assessment. We examine misconceptions about climate effects of forest bioenergy and discuss important considerations in assessing these effects and devising measures to incentivize sustainable bioenergy as a component of climate policy. The temporal and spatial system boundary and the reference (counterfactual) scenarios are key methodology choices that strongly influence results. Focussing on carbon balances of individual forest stands and comparing emissions at the point of combustion neglect system‐level interactions that influence the climate effects of forest bioenergy. We highlight the need for a systems approach, in assessing options and developing policy for forest bioenergy that: (1) considers the whole life cycle of bioenergy systems, including effects of the associated forest management and harvesting on landscape carbon balances; (2) identifies how forest bioenergy can best be deployed to support energy system transformation required to achieve climate goals; and (3) incentivizes those forest bioenergy systems that augment the mitigation value of the forest sector as a whole. Emphasis on short‐term emissions reduction targets can lead to decisions that make medium‐ to long‐term climate goals more difficult to achieve. The most important climate change mitigation measure is the transformation of energy, industry and transport systems so that fossil carbon remains underground. Narrow perspectives obscure the significant role that bioenergy can play by displacing fossil fuels now, and supporting energy system transition. Greater transparency and consistency is needed in greenhouse gas reporting and accounting related to bioenergy.
CircadianVisor: Image Presentation with an Optical See-Through Display in Consideration of Circadian Illuminance

In modern society, the impact of nighttime artificial lighting on the human sleep/wake cycle (the circadian rhythm), has long been an important issue. In augmented reality, such health hazards should be prevented if we are to become a society that wears optical see-through head-mounted displays (OST-HMDs) on a daily basis. We present CircadianVisor, an OST display system that controls circadian performance. Our system combines an OST-HMD with a liquid crystal (LC) shutter and a spectrometer to control the circadian illuminance (CIL, biolux) of light incident on the user’s eyes. To prevent the CIL at the eyes from exceeding the threshold, correct for the displayed image based on RGB values, and adjust the transmittance of the LC visor to pass through the environment light based on spectral measurements, we build a proof-of-concept system to evaluate the feasibility of the system’s CIL control and test it with a spectrometer installed at the user’s viewpoint. The evaluation shows that the CIL at the user’s viewpoint can be kept below the threshold.
From Strangers to Friends: Augmenting Face-to-face Interactions with Faceted Digital Self-Presentations

Sharing a digital presentation of self amongst collocated people can be used to enhance social interactions by supporting conversations. However, as there are different levels of disclosure within social relationships, it is currently unknown how to facet people’s digital content towards others. This research investigates faceting digital self-presentations according to the audience by looking at the differences in the creation and usage of private profiles (shared with a friend) and public profiles (shared amongst strangers) in face-to-face interactions. Digital profiles were accessed through head-mounted displays in social gatherings. Over three gatherings with twenty participants, we identified the importance of having different profiles. We found that, for strangers, public profile supported starting and maintaining conversations. For friends, the private profile was designed to support deeper social penetration, and for close friends, the private profile was designed from the friendship maintenance perspective. Additionally, participants wished to disclose content from their private profile to strangers as the conversations developed. These results suggest that there is a need for a tailored way of faceting digital self-presentation towards multiple audiences. We propose using augmentations that consist of a base profile that is shared with all collocated others, and a dynamically tailorable part, which can be targeted to specific individuals.
DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization

We propose a novel domain-specific generative pre-training (DSGPT) method for text generation and apply it to the product title and review summarization problems on E-commerce mobile display. First, we adopt a decoder-only transformer architecture, which fits well for fine-tuning tasks by combining input and output all together. Second, we demonstrate utilizing only small amount of pre-training data in related domains is powerful. Pre-training a language model from a general corpus such as Wikipedia or the Common Crawl requires tremendous time and resource commitment, and can be wasteful if the downstream tasks are limited in variety. Our DSGPT is pre-trained on a limited dataset, the Chinese short text summarization dataset (LCSTS). Third, our model does not require product-related human-labeled data. For title summarization task, the state of art explicitly uses additional background knowledge in training and predicting stages. In contrast, our model implicitly captures this knowledge and achieves significant improvement over other methods, after fine-tuning on the public Taobao.com dataset. For review summarization task, we utilize JD.com in-house dataset, and observe similar improvement over standard machine translation methods which lack the flexibility of fine-tuning. Our proposed work can be simply extended to other domains for a wide range of text generation tasks.
Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback

The user feedbacks could be delayed in many streaming recommendation scenarios. As an example, the user feedbacks to a recommended coupon consist of the immediate feedback on the click event and the delayed feedback on the resultant conversion. The delayed feedbacks pose a challenge of training recommendation models using instances with incomplete labels. When being applied to real products, the challenge becomes more severe as the streaming recommendation models need to be retrained very frequently and the training instances need to be collected over very short time scales. Existing approaches either simply ignore the unobserved feedbacks or heuristically adjust the feedbacks on a static instance set, resulting in biases in the training data and hurting the accuracy of the learned recommenders. In this paper, we propose a novel and theoretic sound counterfactual approach to adjusting the user feedbacks and learning the recommendation models, called CBDF (Counterfactual Bandit with Delayed Feedback). CBDF formulates the streaming recommendation with delayed feedback as a problem of sequential decision making and models it with a batched bandit. To deal with the issue of delayed feedback, at each iteration (episode), a counterfactual importance sampling model is employed to re-weight the original feedbacks and generate the modified rewards. Based on the modified rewards, a batched bandit is learned for conducting online recommendation at the next iteration. Theoretical analysis showed that the modified rewards are statistically unbiased, and the learned bandit policy enjoys a sub-linear regret bound. Experimental results demonstrated that CBDF can outperform the state-of-the-art baselines on a synthetic dataset, the Criteo dataset, and a dataset from Tencent's WeChat app.
Boiling Mind - A Dataset of Physiological Signals during an Exploratory Dance Performance

The relationship between audience and performers is crucial to what makes live events so special. The aim of this work is to develop a new approach amplifying the link between audiences and performers. Specifically, we explore the use of wearable sensors in gathering real-time audience data to augment the visuals of a live dance performance. We used the J! NS MEME, smart glasses with integrated electrodes enabling eye movement analysis (e.g. blink detection) and inertial motion sensing of the head (e.g. nodding recognition). This data is streamed from the audience and visualised live on stage during a performance, alongside we also collected heart rate and eye gaze of selected audience. In this paper we present the recorded dataset, including accelerometer, electrooculography(EOG), and gyroscope data from 23 audience members.
Improving Neural Text Style Transfer by Introducing Loss Function Sequentiality

Text style transfer is an important issue for conversational agents as it may adapt utterance production to specific dialogue situations. It consists in introducing a given style within a sentence while preserving its semantics. Within this scope, different strategies have been proposed that either rely on parallel data or take advantage of non-supervised techniques. In this paper, we follow the latter approach and show that the sequential introduction of different loss functions into the learning process can boost the performance of a standard model. We also evidence that combining different style classifiers that either focus on global or local textual information improves sentence generation. Experiments on the Yelp dataset show that our methodology strongly competes with the current state-of-the-art models across style accuracy, grammatical correctness, and content preservation.
Cough Activated Dynamic Face Visor

In this demo, we present a cough activated face visor, where the transparent visor screen moves to cover the wearer’s face when coughing sounds are detected. The cough detection is performed by a TinyML machine learning model, running on a microcontroller integrated to the visor’s headband. We make no claims regarding the direct impact of our prototype in preventing the spread of Covid-19, but hope it inspires discussion on future smart solutions in the area of personal protective equipment (PPE). Additionally, we note that visibility of the visor’s operation potentially encourages observers to adopt preventative measures such as hygiene practices and social distancing.
The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation

Next basket recommendation aims to infer a set of items that a user will purchase at the next visit by considering a sequence of baskets he/she has purchased previously. This task has drawn increasing attention from both the academic and industrial communities. The existing solutions mainly focus on sequential modeling over their historical interactions. However, due to the diversity and randomness of users' behaviors, not all these baskets are relevant to help identify the user's next move. It is necessary to denoise the baskets and extract credibly relevant items to enhance recommendation performance. Unfortunately, this dimension is usually overlooked in the current literature. To this end, in this paper, we propose a Contrastive Learning Model~(named CLEA) to automatically extract items relevant to the target item for next basket recommendation. Specifically, empowered by Gumbel Softmax, we devise a denoising generator to adaptively identify whether each item in a historical basket is relevant to the target item or not. With this process, we can obtain a positive sub-basket and a negative sub-basket for each basket over each user. Then, we derive the representation of each sub-basket based on its constituent items through a GRU-based context encoder, which expresses either relevant preference or irrelevant noises regarding the target item. After that, a novel two-stage anchor-guided contrastive learning process is then designed to simultaneously guide this relevance learning without requiring any item-level relevance supervision. To the best of our knowledge, this is the first work of performing item-level denoising for a basket in an end-to-end fashion for next basket recommendation. Extensive experiments are conducted over four real-world datasets with diverse characteristics. The results demonstrate that our proposed CLEA achieves significantly better recommendation performance than the existing state-of-the-art alternatives. Moreover, further analysis also shows that CLEA can successfully discover the real relevant items towards the recommendation decision.