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• Speech emotion recognition in real static and dynamic HRI scenarios was addressed. • Two classifiers were evaluated: ladder network and wav2vec. • Training conditions considered the acoustic model of the target room and beamforming responses. • Simulating the acoustic environment in the training dataset provided the highest average CCC. The use of speech-based solutions is an appealing alternative to communicate in human-robot interaction (HRI). An important challenge in this area is processing distant speech which is often noisy, and affected by reverberation and time-varying acoustic channels. It is important to investigate effective speech solutions, especially in dynamic environments where the robots and the users move, changing the distance and orientation between a speaker and the microphone. This paper addresses this problem in the context of speech emotion recognition (SER), which is an important task to understand the intention of the message and the underlying mental state of the user. We propose a novel setup with a PR2 robot that moves as target speech and ambient noise are simultaneously recorded. Our study not only analyzes the detrimental effect of distance speech in this dynamic robot-user setting for speech emotion recognition but also provides solutions to attenuate its effect. We evaluate the use of two beamforming schemes to spatially filter the speech signal using either delay-and-sum (D&S) or minimum variance distortionless response (MVDR). We consider the original training speech recorded in controlled situations, and simulated conditions where the training utterances are processed to simulate the target acoustic environment. We consider the case where the robot is moving (dynamic case) and not moving (static case). For speech emotion recognition, we explore two state-of-the-art classifiers using hand-crafted features implemented with the ladder network strategy and learned features implemented with the wav2vec 2.0 feature representation. MVDR led to a signal-to-noise ratio higher than the basic D&S method. However, both approaches provided very similar average concordance correlation coefficient (CCC) improvements equal to 116 % with the HRI subsets using the ladder network trained with the original MSP-Podcast training utterances. For the wav2vec 2.0-based model, only D&S led to improvements. Surprisingly, the static and dynamic HRI testing subsets resulted in a similar average concordance correlation coefficient. Finally, simulating the acoustic environment in the training dataset provided the highest average concordance correlation coefficient scores with the HRI subsets that are just 29 % and 22 % lower than those obtained with the original training/testing utterances, with ladder network and wav2vec 2.0, respectively. [ABSTRACT FROM AUTHOR]

Background: Sialolithiasis or salivary gland stones are associated with high clinical morbidity. The advances in the treatment of sialolithiasis has been limited, however, by our understanding of their composition. More specifically, there is little information regarding the formation and composition of the protein matrix, the role of mineralogical deposition, or the contributions of cell epithelium and secretions from the salivary glands. A better understanding of these stone characteristics could pave the way for future non-invasive treatment strategies. Methods: Twenty-nine high-quality ductal stone samples were analyzed. The preparation included successive washings to avoid contamination from saliva and blood. The sialoliths were macerated in liquid nitrogen and the maceration was subjected to a sequential, four-step, protein extraction. The four fractions were pooled together, and a standardized aliquot was subjected to tandem liquid chromatography mass spectrometry (LCMS). The data output was subjected to a basic descriptive statistical analysis for parametric confirmation and a subsequent G.O.-KEGG data base functional analysis and classification for biological interpretation. Results: The LC–MS output detected 6934 proteins, 824 of which were unique for individual stones. An example of our sialolith protein data is available via ProteomeXchange with the identifier PXD012422. More important, the sialoliths averaged 53% homology with bone-forming proteins that served as a standard comparison, which favorably compared with 62% homology identified among all sialolith sample proteins. The non-homologous protein fraction had a highly variable protein identity. The G.O.-KEGG functional analysis indicated that extracellular exosomes are a primary cellular component in sialolithiasis. Light and electron microscopy also confirmed the presence of exosomal-like features and the presence of intracellular microcrystals. Conclusion: Sialolith formation presents similarities with the hyperoxaluria that forms kidney stones, which suggests the possibility of a common origin. Further verification of a common origin could fundamentally change the way in which lithiasis is studied and treated. [ABSTRACT FROM AUTHOR]

Deep clustering is a popular unsupervised technique for feature representation learning. We recently proposed the chunk-based DeepEmoCluster framework for speech emotion recognition (SER) to adopt the concept of deep clustering as a novel semi-supervised learning (SSL) framework, which achieved improved recognition performances over conventional reconstruction-based approaches. However, the vanilla DeepEmoCluster lacks critical sentence-level temporal information that is useful for SER tasks. This study builds upon the DeepEmoCluster framework, creating a powerful SSL approach that leverages temporal information within a sentence. We propose two sentence-level temporal modeling alternatives using either the temporal-net or the triplet loss function, resulting in a novel temporal-enhanced DeepEmoCluster framework to capture essential temporal information. The key contribution to achieving this goal is the proposed sentence-level uniform sampling strategy, which preserves the original temporal order of the data for the clustering process. An extra network module (e.g., gated recurrent unit) is utilized for the temporal-net option to encode temporal information across the data chunks. Alternatively, we can impose additional temporal constraints by using the triplet loss function while training the DeepEmoCluster framework, which does not increase model complexity. Our experimental results based on the MSP-Podcast corpus demonstrate that the proposed temporal-enhanced framework significantly outperforms the vanilla DeepEmoCluster framework and other existing SSL approaches in regression tasks for the emotional attributes arousal, dominance, and valence. The improvements are observed in fully-supervised learning or SSL implementations. Further analyses validate the effectiveness of the proposed temporal modeling, showing (1) high temporal consistency in the cluster assignment, and (2) well-separated emotional patterns in the generated clusters. [Display omitted] • We propose the novel semi-supervised temporal-enhanced DeepEmoCluster framework. • The model achieves better performance compared to other existing SSL frameworks in SER. • The DeepEmoCluster framework leads to high temporal consistency in the clusters. • The DeepEmoCluster framework leads to well-separable emotional clusters. [ABSTRACT FROM AUTHOR]

Controlled burning is often used to improve plant structure on rangelands adapted to fire. However, possible damage resulting from post-fire grazing to reappearing desirable plant species may be a serious issue hindering usage of this method. Axillary buds are essential for the re-establishment of the photosynthetic leaf area. We studied if post-burn defoliation affects the number of axillary buds and/or their metabolic activity in Poa ligularis, Nassella tenuis (both desirable plant species) and Amelichloa ambigua (undesirable plant species). We found that the treatments limited neither bud production nor their metabolic activity. The greatest number of total and viable buds in the studied plant species was recorded at the vegetative stage. Poa ligularis had the lowest bud number per tiller but the greatest per plant. Our results thus indicate that moderate defoliation after controlled burning does not affect the survival and productivity of the desirable species. [ABSTRACT FROM AUTHOR]

• This study proposes an end-to-end framework based on the bimodal recurrent neural network (BRNN) for AV-SAD that explicitly captures the temporal dynamic between acoustic and visual features. • The acoustic and visual features are directly learned from the data during training, creating a powerful end-to-end system. • The experimental evaluations on the CRSS- 4English-14 corpus (over 60h) demonstrate the benefits of using the proposed approach, which leads to statistically significant performance improvements over state-of-the-art methods. • This is the first end-to-end system for audiovisual speech activity detection. Speech activity detection (SAD) plays an important role in current speech processing systems, including automatic speech recognition (ASR). SAD is particularly difficult in environments with acoustic noise. A practical solution is to incorporate visual information, increasing the robustness of the SAD approach. An audiovisual system has the advantage of being robust to different speech modes (e.g., whisper speech) or background noise. Recent advances in audiovisual speech processing using deep learning have opened opportunities to capture in a principled way the temporal relationships between acoustic and visual features. This study explores this idea, proposing the bimodal recurrent neural network (BRNN) framework for SAD. The approach models the temporal dynamic of the sequential audiovisual data, improving the accuracy and robustness of the proposed SAD system. Instead of estimating hand-crafted features, the study investigates an end-to-end training approach, where acoustic and visual features are directly learned from the raw data during training. The experimental evaluation considers a large audiovisual corpus with over 60.8 h of recordings, collected from 105 speakers. The results demonstrate that the proposed framework leads to absolute improvements up to 1.2% under practical scenarios over a VAD baseline using only audio implemented with deep neural network (DNN). The proposed approach achieves 92.7% F1-score when it is evaluated using the sensors from a portable tablet under noisy acoustic environment, which is only 1.0% lower than the performance obtained under ideal conditions (e.g., clean speech obtained with a high definition camera and a close-talking microphone). [ABSTRACT FROM AUTHOR]

Conversational agents (CAs) play an important role in human computer interaction (HCI). Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, and (2) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained baseline model. [ABSTRACT FROM AUTHOR]

The objectives of this study were to determine the percentage of root colonization by arbuscular mycorrhizal (AM) fungi at various levels of plant species richness and developmental morphology stages in various perennial grass, and herbaceous and woody dicots species using experimental plots during 2013 and 2014. An auger was used to obtain six replicate root + soil samples at each sampling time on each of the study parameters. Roots were washed free of soil, and percentage AM was determined. The shrub Larrea divaricata was the species which showed the lowest percentage of colonization by AM at the vegetative developmental morphology stage at the monocultures and six-species-mixtures on the experimental plots. Dicots, but not grass, species showed a greater percentage colonization by AM fungi at the greatest (i.e., six-species-mixtures) than lowest (i.e., monocultures) species richness. Although at different degrees of species richness and developmental morphology stages, the perennial grasses Nassella longiglumis and N. tenuis, the herbaceous dicot Atriplex semibaccata, and the shrubs L. divaricata and Schinus fasciculatus showed a greater (p < 0.050) percentage colonization by AM fungi during the second than the first study year. Even though it was species- and sampling time-dependent, percentage colonization by AM fungi increased as species richness also increased most of the times. Our results demonstrated that the plant species differences in percentage colonization by AM fungi in the experimental plots were species richness-, developmental morphology stage-, and sampling-time dependents. [ABSTRACT FROM AUTHOR]

Human gaze directly signals visual attention, therefore, estimation of gaze has been an important research topic in fields such as human attention modeling and human-computer interaction. Accurate gaze estimation requires user, system and even session dependent parameters, which can be obtained by calibration process. However, this process has to be repeated whenever the parameter changes (head movement, camera movement, monitor movement). This study aims to eliminate the calibration process of gaze estimation by building a user-independent, appearance-based gaze estimation model. The system is ideal for multimodal interfaces, where the gaze is tracked without the cooperation from the users. The main goal is to capture the essential representation of the gaze appearance of the target user. We investigate the tensor analysis framework that decomposes the high dimension gaze data into different factors including individual differences, gaze differences, user-screen distances and session differences. The axis that is representative for a particular subject is automatically chosen in the tensor analysis framework using LASSO regression. The proposed approaches show promising results on capturing the test subject gaze changes. To address the estimation shift caused by the variations in individual heights, or relative position to the monitor, we apply domain adaptation to adjust the gaze estimation, observing further improvements. These promising results suggest that the proposed gaze estimation approach is a feasible and flexible scheme to facilitate gaze-based multimodal interfaces. [ABSTRACT FROM AUTHOR]

The synthesis of lip movements is an important problem for a socially interactive agent (SIA). It is important to generate lip movements that are synchronized with speech and have realistic co-articulation. We hypothesize that combining lexical information (i.e., sequence of phonemes) and acoustic features can lead not only to models that generate the correct lip movements matching the articulatory movements, but also to trajectories that are well synchronized with the speech emphasis and emotional content. This work presents attention-based frameworks that use acoustic and lexical information to enhance the synthesis of lip movements. The lexical information is obtained from automatic speech recognition (ASR) transcriptions, broadening the range of applications of the proposed solution. We propose models based on conditional generative adversarial networks (CGAN) with self-modality attention and cross-modalities attention mechanisms. These models allow us to understand which frames are considered more in the generation of lip movements. We animate the synthesized lip movements using blendshapes. These animations are used to compare our proposed multimodal models with alternative methods, including unimodal models implemented with either text or acoustic features. We rely on subjective metrics using perceptual evaluations and an objective metric based on the LipSync model. The results show that our proposed models with attention mechanisms are preferred over the baselines on the perception of naturalness. The addition of cross-modality attentions and self-modality attentions has a significant positive impact on the performance of the generated sequences. We observe that lexical information provides valuable information even when the transcriptions are not perfect. The improved performance observed by the multimodal system confirms the complementary information provided by the speech and text modalities. [Display omitted] • Conditional generative adversarial networks and attention methods for lip synthesis. • We proposed a method that fuses speech and text modalities for 3D lip synthesis. • Cross/self-modality attention for fusion and context contribution for lip synthesis. • Lip synthesis animation by using blendshapes and the generated 2D facial landmarks. • The addition of attention mechanisms improves the performance of lip synthesis. [ABSTRACT FROM AUTHOR]

As a native tree species, Ulmus pumila var. sabulosa (sandy elm) is widely distributed in the Horqin Sandy Land, China. However, seedlings of this species have to withstand various depths of sand burial after emergence because of increasing soil degradation, which is mainly caused by overgrazing, climate change, and wind erosion. An experiment was conducted to evaluate the changes in its survivorship, morphological traits, and biomass allocation when seedlings were buried at different burial depths: unburied controls and seedlings buried vertically up to 33, 67, 100, or 133% of their initial mean seedling height. The results showed that partial sand burial treatments (i.e., less than 67% burial) did not reduce seedling survivorship, which still reached 100 %. However, seedling mortality increased when sand burial was equal to or greater than 100 %. In comparison with the control treatment, seedling height and stem diameter increased at least by 6 and 14% with partial burial, respectively. In the meantime, seedling taproot length, total biomass, and relative mass growth rates were at least enhanced by 10, 15.6, and 27.6 %, respectively, with the partial sand burial treatment. Furthermore, sand burial decreased total leaf area and changed biomass allocation in seedlings, partitioning more biomass to aboveground organs (e.g., leaves) and less to belowground parts (roots). Complete sand burial after seedling emergence inhibited its reemergence and growth, even leading to death. Our findings indicated that seedlings of sandy elm showed some resistance to partial sand burial and were adapted to sandy environments from an evolutionary perspective. The negative effect of excessive sand burial after seedling emergence might help in understanding failures in recruitments of sparse elm in the study region. [ABSTRACT FROM AUTHOR]

As a native tree species, Ulmus pumila var. sabulosa (Sandy elm) is widely distributed in Horqin Sandy Land. However, seedlings of this species have to withstand various depths of sand burial after emergence because of increasing soil degradation. So an experiment was conducted to evaluate the changes in the survivorship, morphological traits and biomass allocation buried with different burial depths (unburied, and seedlings buried vertically up to 33, 67, 100 or 133% of the initial mean seedling height). The results showed that partial sand burial treatments (i.e., less than 67% burial) did not influence seedling survivorship, which still reached 100%. However, seedling mortality increased as sand burial was equal to or greater than 100%. Seedling height and stem diameter increased at least by 6 to 14% with partial burial in comparison with control treatment. Whilst seeding taproot length, total biomass, and relative growth rates at least enhanced by 10%, 15.6%, and 27.6%, respectively, with the partial sand burial treatment. Furthermore, sand burial decreased total leaf area and changed biomass allocation on seedlings, transferring more biomass to aboveground rather than belowground parts. Complete sand burial after seedling emergence inhibited its growth, and even lead to death. Our findings indicated that seedling of sandy elm had a certain resistance to partial sand burial and acclimated to sandy environments. The negative effects of common excessive sand burial after seedling emergence help to understand failures in recruitment of sparse elm woodland in the Horqin sandy land. [ABSTRACT FROM AUTHOR]

Highlights: [•] An exhaustive analysis of various acoustic features to identify their dependencies on lexical, speaker and emotional aspects. [•] A novel trajectory-based normalization scheme to compensate for speaker or lexical variabilities. [•] Classification experiments that validate the proposed normalization approach on the IEMOCAP database. [Copyright &y& Elsevier]

Highlights: [•] We propose neutral reference models to detect emotions in the fundamental frequency. [•] A novel scheme based on functional data analysis is presented. [•] The proposed approach achieves higher accuracies than the state-of-the-art method. [•] The scheme is also employed to the to detect the most emotionally salient segments. [•] The approach is validated at the sub-sentence level by employing a natural database. [Copyright &y& Elsevier]

Abstract: Pappophorum vaginatum is the most abundant C4, warm-season perennial grass in rangelands of northeastern, arid Patagonia, which is often exposed to continuous grazing. Eragrostis curvula is the only naturalized, C4 warm-season genotype that has successfully established from seed during moist years in the study region, that is characterized by a scarce forage availability to livestock during the spring and summer. Successful introduction of perennial forage genotypes is then critical in that region. No studies have yet been conducted to determine the greater abundance of P. vaginatum compared to that of other native C4 grass genotypes (Aristida subulata, A. spegazzinii, Sporobolus cryptandrus) in the study area. The effect of defoliation on arbuscular mycorrhizal colonization was investigated on the four native, one naturalized and five introduced, C3 perennial grass genotypes (Leymus cinereus cvs. “Magnar” and “Trailhead”, and Achnatherum hymenoides cvs. “Paloma”, “Rimrock” and “Nezpar”) growing at the field. We hypothesized that the magnitude of mycorrhizal colonization (1) is similar on defoliated than on undefoliated plants of the native and naturalized genotypes, (2) is greater in the native and naturalized than in the introduced genotypes, and (3) is greatest in P. vaginatum than in all remaining genotypes. Plants of all ten genotypes were either exposed or not to a severe defoliation twice in the growing seasons of 2007/2008 and 2008/2009. The results showed that all studied genotypes maintained arbuscular mycorrhizal colonization levels following defoliation similar to undefoliated controls. The greatest arbuscular mycorrhizal colonization was often found on both cultivars of L. cinereus among all genotypes. The greatest arbuscular mycorrhizal colonization in P. vaginatum than in the other native genotypes at the end of the study, parameter that contributes to defoliation tolerance, could help to explain its greater abundance in rangelands of central Argentina. The higher arbuscular mycorrhizal colonization in both cultivars of L. cinereus might contribute to their successful establishment and survival in rangelands of northeastern, arid Patagonia. Genotype introduction in these rangelands is likely to affect community dynamics because of variable effects of the mycorrhizal symbiosis on the various plant genotypes. [Copyright &y& Elsevier]

Quantitative trait loci (QTLs) for frost and drought tolerance, and winter survival in the field, were mapped in meadow fescue ( Festuca pratensis Huds.) and compared with corresponding traits in Triticeae and rice to study co-location with putatively orthologous QTLs and known abiotic stress tolerance genes. The genomes of grass species are highly macrosyntenic; however, the Festuca/Lolium and Triticeae homoeologous chromosomes 4 and 5 show major structural differences that is especially interesting in comparative genomics of frost tolerance. The locations of two frost tolerance/winter survival QTLs on Festuca chromosome 5F correspond most likely to the Fr- A1 and Fr- A2 loci on wheat homoeologous group 5A chromosomes. A QTL for long-term drought tolerance on chromosome 3F (syntenic with rice 1) support evidence from introgression of Festuca genome segments onto homoeologous Lolium chromosomes (3L) that this genome region is an excellent source of tolerance towards drought stress. The coincident location of several stress tolerance QTL in Festuca with QTL and genes in Triticeae species, notably dehydrins, CBF transcription factors and vernalisation response genes indicate the action of structural or regulatory genes conserved across evolutionarily distant species. [ABSTRACT FROM AUTHOR]

A key issue in studying mammalian DNA base excision repair is how its component proteins respond to a plethora of cell-signaling mediators invoked by DNA damage and stress-inducing agents such as reactive oxygen species, and how the actions of individual BER proteins are attributed to cell survival or apoptotic/necrotic death. This article reviews the past and recent progress on posttranslational modification (PTM) of mammalian apurinic/apyrimidinic (AP) endonuclease 1 (APE1). [ABSTRACT FROM AUTHOR]

Abstract: This study was conducted at two sites, 1.5km from 1 to another in arid Argentina (39°S, 69°W). Vegetation is distributed in patches. Four microenvironments can be identified in the soil surface of these patches, and the bare interspaces among them. Hypotheses were that (1) at any time during the sampling periods, buried (viable+damaged) seeds of the most common vegetation in the patches, Larrea divaricata Cav., Atriplex lampa Gill ex Moquin, Stipa neaei Nees ex Steudel and Leymus erianthus (Phil.) Dubcovsky, are present in the soil seed bank at all four microenvironments within any vegetation patch and its associated interspace, and (2) natural plant recruitment from the soil seed bank occurs for L. divaricata, A. lampa, S. neaei, L. erianthus, Bromus tectorum L. and Poa lanuginosa Poiret ap. Lamarck in all study microenvironments. In undisturbed field areas, 32 soil samples were periodically taken using an auger in 1999. Viable and damaged seeds contained in the soil organic matter were counted for L. divaricata, A. lampa, S. neaei, and L. erianthus. In a further study, emergence and subsequent growth of L. divaricata, A. lampa, S. neaei, L. erianthus, B. tectorum and P. lanuginosa from the soil seed bank were evaluated in the various microenvironments; permanent plots (0.04m2) were placed on each of the two study sites using four vegetation patches and their associated interspaces per site. Results supported the first hypotheses only for L. divaricata and A. lampa. However, natural recruitment from the soil only included S. neaei and P. lanuginosa through asexual, and B. tectorum through sexual reproduction. Despite the presence of buried seeds of L. divaricata and A. lampa in microenvironment 4 during most of the sampling period, bare interspaces among vegetation patches can be naturally vegetated through sexual reproduction by B. tectorum, during years of abundant, higher than long-term, annual precipitation. [Copyright &y& Elsevier]

Since emotions are expressed through a combination of verbal and non-verbal channels, a joint analysis of speech and gestures is required to understand expressive human communication. To facilitate such investigations, this paper describes a new corpus named the “interactive emotional dyadic motion capture database” (IEMOCAP), collected by the Speech Analysis and Interpretation Laboratory (SAIL) at the University of Southern California (USC). This database was recorded from ten actors in dyadic sessions with markers on the face, head, and hands, which provide detailed information about their facial expressions and hand movements during scripted and spontaneous spoken communication scenarios. The actors performed selected emotional scripts and also improvised hypothetical scenarios designed to elicit specific types of emotions (happiness, anger, sadness, frustration and neutral state). The corpus contains approximately 12 h of data. The detailed motion capture information, the interactive setting to elicit authentic emotions, and the size of the database make this corpus a valuable addition to the existing databases in the community for the study and modeling of multimodal and expressive human communication. [ABSTRACT FROM AUTHOR]

In this field study, the effects of water-stress, rainfed, or irrigated conditions on soil phosphorus (P) concentration under the canopy of the native grasses of semi-arid Argentina (late seral Stipa clarazii Ball., early serals S. tenuis Phil., and S. gynerioides Wild. Ex Steud) at different developmental stages and sampling dates during 1996 and 1997 were determined. Plants grew in a uniform matrix such that each plant of S. clarazii and S. tenuis was surrounded by four plants of S. gynerioides. In September 1996, Bray and Kurtz extracted soil P concentrations were greater under plants of S. clarazii if water stress was imposed during the vegetative stage than under rainfed conditions. Bray and Kurtz extracted soil P concentrations were either similar or between 8.7 to 31% greater under plants of S. clarazii than under those of the other two species. The higher nutrient concentration under S. clarazii than under S. gynerioides and S. tenuis may explain previously measured increases in density, frequency, and cover throughout a long-term span (years) in the late over the earlier seral perennial grasses under field exclosures to domestic livestock or appropriate grazing management conditions. [ABSTRACT FROM AUTHOR]

Background Aims Discussion Post‐traumatic stress disorder (PTSD) poses a global public health challenge. Evidence‐based psychotherapies (EBPs) for PTSD reduce symptoms and improve functioning (Forbes et al., Guilford Press, 2020, 3). However, a number of barriers to access and engagement with these interventions prevail. As a result, the use of EBPs in community settings remains disappointingly low (Charney et al., Psychological Trauma: Theory, Research, Practice, and Policy, 11, 2019, 793; Richards et al., Community Mental Health Journal, 53, 2017, 215), and not all patients who receive an EBP for PTSD benefit optimally (Asmundson et al., Cognitive Behaviour Therapy, 48, 2019, 1). Advancements in artificial intelligence (AI) have introduced new possibilities for increasinfg access to and quality of mental health interventions.The present paper reviews key barriers to accessing and engaging in EBPs for PTSD, discusses current applications of AI in PTSD treatment and provides recommendations for future AI integrations aimed at reducing barriers to access and engagement.We propose that AI may be utilized to (1) assess treatment fidelity; (2) elucidate novel predictors of treatment dropout and outcomes; and (3) facilitate patient engagement with the tasks of therapy, including therapy practice. Potential avenues for technological advancements are also considered. [ABSTRACT FROM AUTHOR]

Vegetation management practices, such as defoliation may alter the composition of plant communities and/or the fungi-forming arbuscular mycorrhiza (AMF). We determined the species identity, density, frequency and diversity of AMF spores from soil under the canopies of three native perennial grass species in rangelands of Argentina: 1) Poa ligularis and Nassella tenuis (preferred by livestock) and 2) Amelichloa ambigua (not preferred). For each species, plants either remained undefoliated or were defoliated twice to a 5 cm stubble height during the growing season. Most active meristems remained on the plants after defoliations. AMF communities were sampled prior to (i.e., 48 soil samples) and following (i.e., 72 soil samples) each defoliation event. Spores were grouped in 15 morphospecies. Density, richness and diversity of AMF spores were not influenced by defoliation, and species richness and diversity of AMF were similar among species. Total spore density was greatest for P. ligularis at the sampling prior to defoliation, but this difference did not persist following the defoliation events. The most abundant AMF families were: Acaulosporaceae, Diversisporaceae and Glomeraceae. These results demonstrate that responses of the studied variables were insensitive to the defoliation treatments, and were largely unaffected by the studied grass species. [ABSTRACT FROM AUTHOR]

Speech-driven head movement methods are motivated by the strong coupling that exists between head movements and speech, providing an appealing solution to create behaviors that are timely synchronized with speech. This paper offers solutions for two of the problems associated with these methods. First, speech-driven methods require all the potential utterances of the conversational agent (CA) to be recorded, which limits their applications. Using existing text to speech (TTS) systems scales the applications of these methods by providing the flexibility of using text instead of pre-recorded speech. However, simply training speech-driven models with natural speech, and testing them with synthetic speech creates a mismatch affecting the performance of the system. This paper proposes a novel strategy to solve this mismatch. The proposed approach starts by creating a parallel corpus either with neutral or emotional synthetic speech timely aligned with the original speech for which we have the motion capture recordings. This parallel corpus is used to retrain the models from scratch, or adapt the models originally built with natural speech. Both subjective and objective evaluations show the effectiveness of this solution in reducing the mismatch. Second, creating head movement with speech-driven methods can disregard the meaning of the message, even when the movements are perfectly synchronized with speech. The trajectory of head movements in conversations also has a role in conveying meaning (e.g. head nods for acknowledgment). In fact, our analysis reveals that head movements under different discourse functions have distinguishable patterns. Building on the best models driven by synthetic speech, we propose to extract dialog acts directly from the text and use this information to directly constrain our models. Compared to the unconstrained model, the model generates head motion sequences that not only are closer to the statistical patterns of the original head movements, but also are perceived as more natural and appropriate. [ABSTRACT FROM AUTHOR]

It was evaluated variability and phenotypic correlation in traits related to seedling vigor in two natural populations of P.vaginatum. They were weighted and sowed individually 100 caryopses of each population ("A"; "B"). After 43 days it was determined: lenghts aerial (LA), root (LR), total (LT) and longest adventitious root (Ladv), number of adventitious root (n°adv), of leaves (n°hoj), of senescent leaves (n°sen) and of tillers (n°mac) and dry weight aerial (PSA) and root (PSR). It was calculated total dry weight (PST), PSA/PSR and LA/LR. Variability was analyzed by means of descriptive statistic and t test. It was analyzed correlation among traits and multiple regression of traits on PST. Populations did not differ in PST (p≥0.05). "A" had greater (p≤0.01) PSA/PSR, LA/LR and LA. "B" had higher (p≤0.05) LR, Ladv and PSR. Both populations showed significant and positive correlation of PST with LA, LT, Ladv, n°adv, n°hoj y n°mac. The regression was significant (p≤0.01). The variability found within and between populations implies expectations of response to selection. PST varied only within populations suggesting the convenience of intrapopulation selection. The association found between the traits LA and n°mac and the seedling vigor, should be considered for the genetic improvement of the implantation. [ABSTRACT FROM AUTHOR]

Abstract: Automated emotion state tracking is a crucial element in the computational study of human communication behaviors. It is important to design robust and reliable emotion recognition systems that are suitable for real-world applications both to enhance analytical abilities to support human decision making and to design human–machine interfaces that facilitate efficient communication. We introduce a hierarchical computational structure to recognize emotions. The proposed structure maps an input speech utterance into one of the multiple emotion classes through subsequent layers of binary classifications. The key idea is that the levels in the tree are designed to solve the easiest classification tasks first, allowing us to mitigate error propagation. We evaluated the classification framework on two different emotional databases using acoustic features, the AIBO database and the USC IEMOCAP database. In the case of the AIBO database, we obtain a balanced recall on each of the individual emotion classes using this hierarchical structure. The performance measure of the average unweighted recall on the evaluation data set improves by 3.37% absolute (8.82% relative) over a Support Vector Machine baseline model. In the USC IEMOCAP database, we obtain an absolute improvement of 7.44% (14.58%) over a baseline Support Vector Machine modeling. The results demonstrate that the presented hierarchical approach is effective for classifying emotional utterances in multiple database contexts. [Copyright &y& Elsevier]

Social impairment is a cardinal feature of schizophrenia spectrum disorders (SZ). Smaller social network size, diminished social skills, and loneliness are highly prevalent. Existing, gold-standard assessments of social impairment in SZ often rely on self-reported information that depends on retrospective recall and detailed accounts of complex social behaviors. This is particularly problematic in people with SZ given characteristic cognitive impairments and reduced insight. Ecological Momentary Assessment (EMA; repeated self-reports completed in the context of daily life) allows for the measurement of social behavior as it occurs in vivo , yet still relies on participant input. Momentary characterization of behavior using smartphone sensors (e.g., GPS, microphone) may also provide ecologically valid indicators of social functioning. In the current study we tested associations between both active (e.g., EMA-reported number of interactions) and passive (GPS-based mobility, conversations captured by microphone) smartphone-based measures of social activity and measures of social functioning and loneliness to examine the promise of such measures for understanding social impairment in SZ. Our results indicate that passive markers of mobility were more consistently associated with EMA measures of social behavior in controls than in people with SZ. Furthermore, dispositional loneliness showed associations with mobility metrics in both groups, while general social functioning was less related to these metrics. Finally, interactions detected in the ambient audio were more tied to social functioning in SZ than in controls. Findings speak to the promise of smartphone-based digital phenotyping as an approach to understanding objective markers of social activity in people with and without schizophrenia. • Smartphone sensors allow for continuous, unobtrusive assessment. • Mobility from GPS and speech from microphone can be used to quantify social activity. • Each sensor provides unique information for social activity in people with and without schizophrenia. [ABSTRACT FROM AUTHOR]

Knowledge of pre-germination seed treatments is critical for ecological restoration practices such as outplantings or direct seeding. However, in drylands, there is a lack of this kind of information about some species with high potential value for ecological restoration. This is the case of Larrea divaricata Cav., a native species that is dominant and can grow in degraded environments to the Monte region and arid ecosystems in Argentina. We evaluated the effects of the following treatments to break seed dormancy in this species: 1) alternation of soaking and drying periods; (2) periods of soaking and rinsing with running water; (3) mechanical scarification, and (4) control. Seed scarification was done using a novel mechanical method using two pieces of sandpaper: one fixed and the other spun using a 710 W hand drill. The variables evaluated were: (a) germination percentage (over time since imbibition and the final percentage), (b) mean time to germination, and (c) number of days before the start of germination. The mechanical scarification treatment improved all germination variables compared to the other treatments. In the mechanical scarification treatment, germination percentage was 60.7%; mean time to germination was 6.32 days, and the number of days before the start of germination was 2 days. Our results indicate that mechanical scarification is an appropriate technique for propagation of L. divaricata Cav. for large scale restoration, since many seeds can be treated quickly and this technique is easily replicable. • Seed pre-germination treatments may be critical for ecological restoration practices. • Mechanical scarification was the best treatment to improve all germination variables. • Seed scarification increased spreading of Larrea divaricata on restoration projects. [ABSTRACT FROM AUTHOR]

The stability of artificial, sand-binding communities has not yet fully studied. A similarity index was developed to evaluate the stability of artificial communities in shifting and semi-fixed sand dunes. This similarity index consisted of 8 indicators (i.e., vegetation cover, Shannon-Wiener Index, biomass, organic matter, Total N, available P and K, and sand particle ratio). The relative weight of these indicators was obtained using an analytic hierarchy process (AHP) method. Stability was compared on Artemisia halodendron Turczaninow ex Besser, Bull communities in shifting and semifixed sand dunes, and of Caragana microphylla Lam. communities with different planting ages. The similarity indexes of the A. halodendron communities were 0.24 and 0.54 in shifting and semi-fixed sand dunes, respectively. The peak stability of C. microphylla communities was 0.55, and it was reached when these communities were 20-year-old. It is suggested that A. halodendron communities should be planted preferentially in semi-fixed to moving sand dunes. Furthermore, the planting age of artificial communities should be included in planting programs. This study improved the understanding of some mechanisms contributing to maintain community stability, and is critical for guiding the artificial planting in sand dunes. [ABSTRACT FROM AUTHOR]

Improvised acting is a viable technique to study expressive human communication and to shed light into actors' creativity. The USC CreativeIT database provides a novel, freely-available multimodal resource for the study of theatrical improvisation and rich expressive human behavior (speech and body language) in dyadic interactions. The theoretical design of the database is based on the well-established improvisation technique of Active Analysis in order to provide naturally induced affective and expressive, goal-driven interactions. This database contains dyadic theatrical improvisations performed by 16 actors, providing detailed full body motion capture data and audio data of each participant in an interaction. The carefully engineered data collection, the improvisation design to elicit natural emotions and expressive speech and body language, as well as the well-developed annotation processes provide a gateway to study and model various aspects of theatrical performance, expressive behaviors and human communication and interaction. [ABSTRACT FROM AUTHOR]

Baccharis ulicina is a weed widely distributed in pastures of the semiarid zone of Argentina. With the aim of evaluate potential allelopathic effects, aqueous extracts of B. ulicina (leaf, stem, root and whole plant) at two concentrations (50 and 150 g tissue/L water) were used on germination and initial growth of Avena sativa, Lolium perenne and Raphanus sativus. For each species, 100 seeds were watered and maintained at 25°C at darkness. Seed germination and length of coleoptile / hypocotyl (C/H) and radicle (R) was measured. Then, germination percentage, germination rate coefficient, germination vigor, inhibition of germination, inhibition of radicle length and coleoptile / hypocotyl length, radicle vigor index and the coleoptile / hypocotyl vigor index were calculated. High concentration extracts reduced germination an average of 65%, in addition, they produced a length inhibition of 58.8% (C/H) and 93.3% (R) to A. sativa; 71.5% (C/H) and 87.9% (R) to L. perenne, and 93.8% (C/H) and 94.5% (R) to R. sativus. There was an evident phytotoxic effect of B. ulicina on evaluated species, indicating that allelopathy would be an ecological strategy of the species. [ABSTRACT FROM AUTHOR]

Changes in forest openings affect light quality and quantity, and the magnitude of rainfall that reaches the soil surface. Osmorhiza depauperata, a geophyte, acclimates to changes imposed because of forest openings. We studied which changes in biomass allocation allow acclimation of O. depauperata to the various environments that this species inhabits, and where it develops better. Three light intensities (I4 = 4 %, I26 = 26 %, I64 = 64 % of ambient sunlight) and two moisture levels (M40 = 40-60 %, M80 = 80-100 % field capacity) were evaluated on O. depauperata under greenhouse conditions. Plant biomasses per pot were 0.81, 0.56 and 0.48 g at I26, I4 and I64 light intensities, respectively, after one growing season. The biomass allocation to aboveground tissues and leaf area decreased as light intensity increased. Soil moisture modified only belowground biomass and weight of fine roots. The interaction between soil moisture content and light intensity was consistent. This was because of a significant reduction in total plant biomass under high both soil moisture content and high light intensity. Osmorhiza depauperata growth was favored most at medium light intensities. Changes in biomass allocation among various organs allow this species to inhabit forest habitats with different light intensities. [ABSTRACT FROM AUTHOR]

Orthologous regions in barley, rice, sorghum, and wheat were studied by bacterial artificial chromosome sequence analysis. General microcolinearity was observed for the four shared genes in this region. However, three genic rearrangements were observed. First, the rice region contains a cluster of 48 predicted small nucleolar RNA genes, but the comparable region from sorghum contains no homologous loci. Second, gene 2 was inverted in the barley lineage by an apparent unequal recombination after the ancestors of barley and wheat diverged, 11-1.5 million years ago (mya). Third, gene 4 underwent direct tandem duplication in a common ancestor of barley and wheat 29-41 mya. All four of the shared genes show the same synonymous substitution rate, but nonsynonymous substitution rates show significant variations between genes 4a and 4b, suggesting that gene 4b was largely released from the strong purifying selection that acts on gent 4a in both barley and wheat. Intergenic retrotransposon blocks, many of thegn organized as nested insertions, mostly account for the lower gene density of the barley and wheat regions. All but two of the retrotransposons were found in the regions between genes, while all but 2 of the 51 inverted repeat transposable elements were found as insertions in genic regions and outside the retrotransposon blocks. [ABSTRACT FROM AUTHOR]

Simple Summary: Complete resection of dysplastic and malignant tissue improves overall survival and delays cancer recurrence in oral cancer patients; however, intraoperative surgical margin assessment is limited to visual inspection and palpation, making it difficult to achieve total resection. There is currently no tool capable of providing real-time, accurate, and continuous margin-assessment guidance during oral cancer resection surgery. Multispectral autofluorescence lifetime imaging (maFLIM) is a label-free imaging modality that enables quantifying a plurality of metabolic and compositional autofluorescence biomarkers of oral dysplasia and cancer. We have developed and validated a machine-learning assisted computer aided detection (CAD) system for automated discrimination of dysplastic and cancerous from healthy oral tissue based on in vivo widefield maFLIM endoscopy data. This CAD system can be potentially embedded into maFLIM endoscopes to enable continuous in situ detection of positive margins during oral cancer resection surgery, thus facilitating maximal tumor resection and improving surgical outcomes for oral cancer patients. Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate dysplastic and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy imaging of cancerous and dysplastic oral lesions was performed at two clinical centers. Endoscopic maFLIM images from 34 patients acquired at one of the clinical centers were used to optimize ML models for automated discrimination of dysplastic and cancerous from healthy oral tissue. A computer-aided detection system was developed and applied to a set of endoscopic maFLIM images from 23 patients acquired at the other clinical center, and its performance was quantified in terms of the area under the receiver operating characteristic curve (ROC-AUC). Discrimination of dysplastic and cancerous from healthy oral tissue was achieved with an ROC-AUC of 0.81. This study demonstrates the capabilities of widefield maFLIM endoscopy to clinically image autofluorescence biomarkers that can be used in ML models to discriminate dysplastic and cancerous from healthy oral tissue. Widefield maFLIM endoscopy thus holds potential for automated in situ detection of oral dysplasia and cancer. [ABSTRACT FROM AUTHOR]