Temperature dependence of pollen germination and tube growth in conifers relates to their distribution along an elevational gradient in Washington State, USA.

BACKGROUND AND AIMS: Pollen germination and tube growth are essential processes for successful fertilization. They are among the most temperature-vulnerable stages and subsequently affect seed production and determine population persistence and species distribution under climate change. Our study aims to investigate intra- and inter-specific variations in the temperature dependence of pollen germination and tube length growth and to explore how these variations differ for pollen from elevational gradients. METHODS: We focused on three conifer species, Pinus contorta, Picea engelmannii, and Pinus ponderosa, with pollen collected from 350 to 2200m elevation in Washington State, USA. We conducted pollen viability tests at temperatures from 5 to 40°C in 5°C intervals. After testing for four days, we took images of these samples under a microscope to monitor pollen germination percentage (GP) and tube length (TL). We applied the Gamma function to describe the temperature dependence of GP and TL and estimated key parameters, including the optimal temperature for GP (Topt_GP) and TL (Topt_TL). KEY RESULTS: Results showed that pollen from three species and different elevations within a species have different GP, TL, Topt_GP, and Topt_TL. The population with a higher Topt_GP would also have a higher Topt_TL, while Topt_TL was generally higher than Topt_GP, i.e., a positive but not one-to-one relationship. However, only Pinus contorta showed that populations from higher elevations have lower Topt_GP and Topt_TL and vice versa. The variability in GP increased at extreme temperatures, whereas the variability in TL was greatest near Topt_TL. CONCLUSIONS: Our study demonstrates the temperature dependences of three conifers across a wide range of temperatures. Pollen germination and tube growth are highly sensitive to temperature conditions and vary among species and elevations, affecting their reproduction success during warming. Our findings can provide valuable insights to advance our understanding of how conifer pollen responds to rising temperatures.

Robust Human Face Emotion Classification Using Triplet-Loss-Based Deep CNN Features and SVM.

Human facial emotion detection is one of the challenging tasks in computer vision. Owing to high inter-class variance, it is hard for machine learning models to predict facial emotions accurately. Moreover, a person with several facial emotions increases the diversity and complexity of classification problems. In this paper, we have proposed a novel and intelligent approach for the classification of human facial emotions. The proposed approach comprises customized ResNet18 by employing transfer learning with the integration of triplet loss function (TLF), followed by SVM classification model. Using deep features from a customized ResNet18 trained with triplet loss, the proposed pipeline consists of a face detector used to locate and refine the face bounding box and a classifier to identify the facial expression class of discovered faces. RetinaFace is used to extract the identified face areas from the source image, and a ResNet18 model is trained on cropped face images with triplet loss to retrieve those features. An SVM classifier is used to categorize the facial expression based on the acquired deep characteristics. In this paper, we have proposed a method that can achieve better performance than state-of-the-art (SoTA) methods on JAFFE and MMI datasets. The technique is based on the triplet loss function to generate deep input image features. The proposed method performed well on the JAFFE and MMI datasets with an accuracy of 98.44% and 99.02%, respectively, on seven emotions; meanwhile, the performance of the method needs to be fine-tuned for the FER2013 and AFFECTNET datasets.

Self-Relation Attention and Temporal Awareness for Emotion Recognition via Vocal Burst.

Speech emotion recognition (SER) is one of the most exciting topics many researchers have recently been involved in. Although much research has been conducted recently on this topic, emotion recognition via non-verbal speech (known as the vocal burst) is still sparse. The vocal burst is concise and has meaningless content, which is harder to deal with than verbal speech. Therefore, in this paper, we proposed a self-relation attention and temporal awareness (SRA-TA) module to tackle this problem with vocal bursts, which could capture the dependency in a long-term period and focus on the salient parts of the audio signal as well. Our proposed method contains three main stages. Firstly, the latent features are extracted using a self-supervised learning model from the raw audio signal and its Mel-spectrogram. After the SRA-TA module is utilized to capture the valuable information from latent features, all features are concatenated and fed into ten individual fully-connected layers to predict the scores of 10 emotions. Our proposed method achieves a mean concordance correlation coefficient (CCC) of 0.7295 on the test set, which achieves the first ranking of the high-dimensional emotion task in the 2022 ACII Affective Vocal Burst Workshop & Challenge.

Survival time prediction by integrating cox proportional hazards network and distribution function network.

BACKGROUND: The Cox proportional hazards model is commonly used to predict hazard ratio, which is the risk or probability of occurrence of an event of interest. However, the Cox proportional hazard model cannot directly generate an individual survival time. To do this, the survival analysis in the Cox model converts the hazard ratio to survival times through distributions such as the exponential, Weibull, Gompertz or log-normal distributions. In other words, to generate the survival time, the Cox model has to select a specific distribution over time. RESULTS: This study presents a method to predict the survival time by integrating hazard network and a distribution function network. The Cox proportional hazards network is adapted in DeepSurv for the prediction of the hazard ratio and a distribution function network applied to generate the survival time. To evaluate the performance of the proposed method, a new evaluation metric that calculates the intersection over union between the predicted curve and ground truth was proposed. To further understand significant prognostic factors, we use the 1D gradient-weighted class activation mapping method to highlight the network activations as a heat map visualization over an input data. The performance of the proposed method was experimentally verified and the results compared to other existing methods. CONCLUSIONS: Our results confirmed that the combination of the two networks, Cox proportional hazards network and distribution function network, can effectively generate accurate survival time.

Context-Aware Emotion Recognition in the Wild Using Spatio-Temporal and Temporal-Pyramid Models.

Emotion recognition plays an important role in human-computer interactions. Recent studies have focused on video emotion recognition in the wild and have run into difficulties related to occlusion, illumination, complex behavior over time, and auditory cues. State-of-the-art methods use multiple modalities, such as frame-level, spatiotemporal, and audio approaches. However, such methods have difficulties in exploiting long-term dependencies in temporal information, capturing contextual information, and integrating multi-modal information. In this paper, we introduce a multi-modal flexible system for video-based emotion recognition in the wild. Our system tracks and votes on significant faces corresponding to persons of interest in a video to classify seven basic emotions. The key contribution of this study is that it proposes the use of face feature extraction with context-aware and statistical information for emotion recognition. We also build two model architectures to effectively exploit long-term dependencies in temporal information with a temporal-pyramid model and a spatiotemporal model with "Conv2D+LSTM+3DCNN+Classify" architecture. Finally, we propose the best selection ensemble to improve the accuracy of multi-modal fusion. The best selection ensemble selects the best combination from spatiotemporal and temporal-pyramid models to achieve the best accuracy for classifying the seven basic emotions. In our experiment, we take benchmark measurement on the AFEW dataset with high accuracy.

EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels.

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in countering the effect of deceptive external expressions of humans, like faces or speeches. Emotion recognition based on EEG signals heavily relies on the features and their delineation, which requires the selection of feature categories converted from the raw signals and types of expressions that could display the intrinsic properties of an individual signal or a group of them. Moreover, the correlation or interaction among channels and frequency bands also contain crucial information for emotional state prediction, and it is commonly disregarded in conventional approaches. Therefore, in our method, the correlation between 32 channels and frequency bands were put into use to enhance the emotion prediction performance. The extracted features chosen from the time domain were arranged into feature-homogeneous matrices, with their positions following the corresponding electrodes placed on the scalp. Based on this 3D representation of EEG signals, the model must have the ability to learn the local and global patterns that describe the short and long-range relations of EEG channels, along with the embedded features. To deal with this problem, we proposed the 2D CNN with different kernel-size of convolutional layers assembled into a convolution block, combining features that were distributed in small and large regions. Ten-fold cross validation was conducted on the DEAP dataset to prove the effectiveness of our approach. We achieved the average accuracies of 98.27% and 98.36% for arousal and valence binary classification, respectively.

Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm.

One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate. However, among the above organs, the esophagus is one of the most difficult organs to segment because of its small size, ambiguous boundary, and very low contrast in CT images. To address these challenges, we propose a fully automated framework for the esophagus segmentation from CT images. The proposed method is based on the processing of slice images from the original three-dimensional (3D) image so that our method does not require large computational resources. We employ the spatial attention mechanism with the atrous spatial pyramid pooling module to locate the esophagus effectively, which enhances the segmentation performance. To optimize our model, we use group normalization because the computation is independent of batch sizes, and its performance is stable. We also used the simultaneous truth and performance level estimation (STAPLE) algorithm to reach robust results for segmentation. Firstly, our model was trained by k-fold cross-validation. And then, the candidate labels generated by each fold were combined by using the STAPLE algorithm. And as a result, Dice and Hausdorff Distance scores have an improvement when applying this algorithm to our segmentation results. Our method was evaluated on SegTHOR and StructSeg 2019 datasets, and the experiment shows that our method outperforms the state-of-the-art methods in esophagus segmentation. Our approach shows a promising result in esophagus segmentation, which is still challenging in medical analyses.

Endophytes alleviate the elevated CO2-dependent decrease in photosynthesis in rice, particularly under nitrogen limitation.

The positive effects of high atmospheric CO2 concentrations [CO2] decrease over time in most C3 plants because of down-regulation of photosynthesis. A notable exception to this trend is plants hosting N-fixing bacteria. The decrease in photosynthetic capacity associated with an extended exposure to high [CO2] was therefore studied in non-nodulating rice that can establish endophytic interactions. Rice plants were inoculated with diazotrophic endophytes isolated from the Salicaceae and CO2 response curves of photosynthesis were determined in the absence or presence of endophytes at the panicle initiation stage. Non-inoculated plants grown under elevated [CO2] showed a down-regulation of photosynthesis compared to those grown under ambient [CO2]. In contrast, the endophyte-inoculated plants did not show a decrease in photosynthesis associated with high [CO2], and they exhibited higher photosynthetic electron transport and mesophyll conductance rates than non-inoculated plants under high [CO2]. The endophyte-dependent alleviation of decreases in photosynthesis under high [CO2] led to an increase in water-use efficiency. These effects were most pronounced when the N supply was limited. The results suggest that inoculation with N-fixing endophytes could be an effective means of improving plant growth under high [CO2] by alleviating N limitations.

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.

Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2 ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2 ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2 ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

A process-based model for leaf development and growth in hardneck garlic (Allium sativum).

BACKGROUND AND AIMS: Phenology and morphology are two major aspects of crop growth models. A new process-based model built for hardneck garlic (Allium sativum) is presented, focusing on phenology and morphology processes and how they translate to whole-plant growth. The tight coupling between the two processes and their dynamic changes throughout the growing season were captured while incorporating storage effects and reproductive aspects unique to bulbous crops. METHODS: Non-linear temperature dependences of leaf development were integrated into the model and dynamically coupled with changes in leaf growth throughout the growing season. Bulb storage effects on leaf development and photoperiod effects on the vegetative-to-reproductive transition were also incorporated. The model was parameterized with data from a set of experiments and the literature, while its performance was tested with additional observations that had not been used for parameterization under a range of environmental conditions, management practices and cultivar choices. KEY RESULTS: The model successfully captured the dynamic nature of leaf development and growth in garlic plants throughout the growing season. It simulated with reasonable accuracy the timing of leaf initiation, maturation and senescence, as well as changes in green leaf area over time. Most parameters were relatively stable across cultivars, and parameter sensitivity tests revealed the importance of bulb storage effects. CONCLUSIONS: The model embodies a novel approach to capture the phenology and morphology of garlic under a range of environments, genotypes and management practices. The process-oriented nature of the model and inclusion of storage effects set the foundation for bulbous crop growth simulations, allowing the understanding and discovery of key processes that coordinate and integrate the dynamics of growth and development from organ to whole plant, with implications for crop improvement programmes while opening opportunities for modelling other bulbous crops.

Excimer laser/light treatment of alopecia areata: A systematic review and meta-analyses.

BACKGROUND: The excimer laser/light (EL) has been reported to be effective for alopecia areata (AA), but its treatment response has not been systematically reviewed. OBJECTIVE: To determine the treatment response and safety of EL treatment of AA. METHODS: A comprehensive search of the Medline, EMBASE, Cochrane library, and Web of Science (from inception to December 31, 2018) was conducted to identify prospective clinical studies assessing the treatment response of EL for AA. The primary outcome was cosmetically acceptable hair regrowth (hair regrowth ≥75%); random-effects meta-analyses using generic inverse variance weighting were performed to estimate treatment responses. The study was registered with PROSPERO (CRD42019121092). RESULTS: Of 52 records initially identified, 13 full-text articles were finally assessed in terms of eligibility. A total of 9 prospective clinical studies (129 AA patients) including 5 controlled clinical trials were identified. Cosmetically acceptable hair regrowth was achieved in 50.2% (95% confidence interval 31.5%-68.9%; 8 studies). EL treatment significantly improved hair regrowth compared with untreated controls (relative risk 7.83; 95% confidence interval 2.11-29.11; 5 controlled clinical trials). No serious adverse effect was noted. CONCLUSIONS: EL treatment appeared to produce a favorable therapeutic response in AA patients. The use of EL should be encouraged for AA patients with the advantages of the non-invasiveness and no systemic effect.

Distance Error Correction in Time-of-Flight Cameras Using Asynchronous Integration Time.

A distance map captured using a time-of-flight (ToF) depth sensor has fundamental problems, such as ambiguous depth information in shiny or dark surfaces, optical noise, and mismatched boundaries. Severe depth errors exist in shiny and dark surfaces owing to excess reflection and excess absorption of light, respectively. Dealing with this problem has been a challenge due to the inherent hardware limitations of ToF, which measures the distance using the number of reflected photons. This study proposes a distance error correction method using three ToF sensors, set to different integration times to address the ambiguity in depth information. First, the three ToF depth sensors are installed horizontally at different integration times to capture distance maps at different integration times. Given the amplitude maps and error regions are estimated based on the amount of light, the estimated error regions are refined by exploiting the accurate depth information from the neighboring depth sensors that use different integration times. Moreover, we propose a new optical noise reduction filter that considers the distribution of the depth information biased toward one side. Experimental results verified that the proposed method overcomes the drawbacks of ToF cameras and provides enhanced distance maps.

Fabrication of Silver-Doped (Bi,Sb)2Te3 Thermoelectric Film Prepared from Ag Nanoparticles/Bi-Sb-Te Pastes.

Film-type thermoelectric generator (TEG) utilizing Bi-Te based paste has been highly considered as advanced power sources for the wearable electronic devices due to its light, thin and flexible characteristics when producing electricity from certain thermal resources such as human body heat. However, the application of the film-typed TEG has been often limited due to its low TE conversion efficiency caused by low electrical conductivity resulting from severe porosity. Thus, it is crucial to increase electrical properties via densification of the TE film. Here, we synthesized silver nanoparticle (AgNP)-dispersed (Bi,Sb)2Te3 (BSbT) powders to fabricate AgNP-BSbT pastes by adding organic binder. The synthesized AgNP-BSbT pastes were printed through a hand-painting process and were consolidated into Ag-doped BSbT (Ag-BSbT) thick film with a few hundreds µm with controlled 2-step heat treatment. The microstructures of Ag-BSbT films show abnormally elongated grains but also the amount of porosities in the film significantly decreased by addition of AgNP. As a result, it is confirmed that the 0.072 at% Ag-BSbT thick film exhibits power factor of 2.93 × 10-3 W/mK² at room temperature, which is comparable to that of practically utilized bulk materials. It is elucidated that the increase in power factor originates from the modulation between electrical conductivity and Seebeck coefficients due to increased hole carrier density at room temperature.

Phytochrome B regulates resource allocation in Brassica rapa.

Crop biomass and yield are tightly linked to how the light signaling network translates information about the environment into allocation of resources, including photosynthates. Once activated, the phytochrome (phy) class of photoreceptors signal and re-deploy carbon resources to alter growth, plant architecture, and reproductive timing. Most of the previous characterization of the light-modulated growth program has been performed in the reference plant Arabidopsis thaliana. Here, we use Brassica rapa as a crop model to test for conservation of the phytochrome-carbon network. In response to elevated levels of CO2, B. rapa seedlings showed increases in hypocotyl length, shoot and root fresh weight, and the number of lateral roots. All of these responses were dependent on nitrogen and polar auxin transport. In addition, we identified putative B. rapa orthologs of PhyB and isolated two nonsense alleles. BrphyB mutants had significantly decreased or absent CO2-stimulated growth responses. Mutant seedlings also showed misregulation of auxin-dependent genes and genes involved in chloroplast development. Adult mutant plants had reduced chlorophyll levels, photosynthetic rate, stomatal index, and seed yield. These findings support a recently proposed holistic role for phytochromes in regulating resource allocation, biomass production, and metabolic state in the developing plant.

Do Endophytes Promote Growth of Host Plants Under Stress? A Meta-Analysis on Plant Stress Mitigation by Endophytes.

Endophytes are microbial symbionts living inside plants and have been extensively researched in recent decades for their functions associated with plant responses to environmental stress. We conducted a meta-analysis of endophyte effects on host plants' growth and fitness in response to three abiotic stress factors: drought, nitrogen deficiency, and excessive salinity. Ninety-four endophyte strains and 42 host plant species from the literature were evaluated in the analysis. Endophytes increased biomass accumulation of host plants under all three stress conditions. The stress mitigation effects by endophytes were similar among different plant taxa or functional groups with few exceptions; eudicots and C4 species gained more biomass than monocots and C3 species with endophytes, respectively, under drought conditions. Our analysis supports the effectiveness of endophytes in mitigating drought, nitrogen deficiency, and salinity stress in a wide range of host species with little evidence of plant-endophyte specificity.

Cool night-time temperatures induce the expression of CONSTANS and FLOWERING LOCUS T to regulate flowering in Arabidopsis.

Day length and ambient temperature are major stimuli controlling flowering time. To understand flowering mechanisms in more natural conditions, we explored the effect of daily light and temperature changes on Arabidopsis thaliana. Seedlings were exposed to different day/night temperature and day-length treatments to assess expression changes in flowering genes. Cooler temperature treatments increased CONSTANS (CO) transcript levels at night. Night-time CO induction was diminished in flowering bhlh (fbh)-quadruple mutants. FLOWERING LOCUS T (FT) transcript levels were reduced at dusk, but increased at the end of cooler nights. The dusk suppression, which was alleviated in short vegetative phase (svp) mutants, occurred particularly in younger seedlings, whereas the increase during the night continued over 2 wk. Cooler temperature treatments altered the levels of FLOWERING LOCUS M-ß (FLM-ß) and FLM-δ splice variants. FT levels correlated strongly with flowering time across treatments. Day/night temperature changes modulate photoperiodic flowering by changing FT accumulation patterns. Cooler night-time temperatures enhance FLOWERING BHLH (FBH)-dependent induction of CO and consequently increase CO protein. When plants are young, cooler temperatures suppress FT at dusk through SHORT VEGETATIVE PHASE (SVP) function, perhaps to suppress precocious flowering. Our results suggest day length and diurnal temperature changes combine to modulate FT and flowering time.

Biological nitrogen fixation and biomass accumulation within poplar clones as a result of inoculations with diazotrophic endophyte consortia.

Sustainable production of biomass for bioenergy relies on low-input crop production. Inoculation of bioenergy crops with plant growth-promoting endophytes has the potential to reduce fertilizer inputs through the enhancement of biological nitrogen fixation (BNF). Endophytes isolated from native poplar growing in nutrient-poor conditions were selected for a series of glasshouse and field trials designed to test the overall hypothesis that naturally occurring diazotrophic endophytes impart growth promotion of the host plants. Endophyte inoculations contributed to increased biomass over uninoculated control plants. This growth promotion was more pronounced with multi-strain consortia than with single-strain inocula. Biological nitrogen fixation was estimated through (15)N isotope dilution to be 65% nitrogen derived from air (Ndfa). Phenotypic plasticity in biomass allocation and branch production observed as a result of endophyte inoculations may be useful in bioenergy crop breeding and engineering programs.

How do various maize crop models vary in their responses to climate change factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 µmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Separation of specular and diffuse components using tensor voting in color images.

Most methods for the detection and removal of specular reflections suffer from nonuniform highlight regions and/or nonconverged artifacts induced by discontinuities in the surface colors, especially when dealing with highly textured, multicolored images. In this paper, a novel noniterative and predefined constraint-free method based on tensor voting is proposed to detect and remove the highlight components of a single color image. The distribution of diffuse and specular pixels in the original image is determined using tensors' saliency analysis, instead of comparing color information among neighbor pixels. The achieved diffuse reflectance distribution is used to remove specularity components. The proposed method is evaluated quantitatively and qualitatively over a dataset of highly textured, multicolor images. The experimental results show that our result outperforms other state-of-the-art techniques.