Safety of AZD1222 COVID-19 vaccine and low Incidence of SARS-CoV-2 infection in Botswana following ChAdOx1(AZD1222) vaccination: A single-arm open-label interventional study - final study results.

Objectives: We report the final analysis of the single-arm open-label study evaluating the safety and COVID-19 incidence after AZD1222 vaccination in Botswana conducted between September 2021 and August 2022. Methods: The study included three groups of adults (>18 years), homologous AZD1222 primary series and booster (AZ2), heterologous primary series with one dose AZD1222, and AZD1222 booster (HPS), and primary series other than AZD1222 and AZD1222 booster (OPS). We compared the incidence of AEs in participants with and without prior COVID-19 infection using an exact test for rate ratios. Results: Among 10,894 participants, 9192 (84.4%) were enrolled at first vaccine dose, 521 (4.8%) at second vaccine, and 1181 (10.8%) at the booster vaccine. Of 10,855 included in the full analysis set, 1700 received one dose of AZD1222; 5377 received two doses; 98 received a heterologous series including one AZD1222 and a booster; 30 in the HPS group; 1058 in the OPS group; and 2592 in the AZ2 group. No laboratory-confirmed COVID-19 hospitalizations or deaths were reported. The incidence of laboratory-confirmed symptomatic COVID infection for the AZ2 group was 6.22 (95% confidence interval: 2.51-12.78) per 1000 participant-years (1000-PY) and 3.5 (95% confidence interval: 0.42-12.57) per 1000-PY for AZ2+booster group. Most adverse events were mild, with higher incidence in participants with prior COVID-19 infection. Individuals with prior COVID-19 exposure exhibited higher binding antibody responses. No differences in outcomes were observed by HIV status. Conclusion: AZD1222 is safe, effective, and immunogenic for people living with and without HIV.

Confusion2Vec 2.0: Enriching ambiguous spoken language representations with subwords.

Word vector representations enable machines to encode human language for spoken language understanding and processing. Confusion2vec, motivated from human speech production and perception, is a word vector representation which encodes ambiguities present in human spoken language in addition to semantics and syntactic information. Confusion2vec provides a robust spoken language representation by considering inherent human language ambiguities. In this paper, we propose a novel word vector space estimation by unsupervised learning on lattices output by an automatic speech recognition (ASR) system. We encode each word in Confusion2vec vector space by its constituent subword character n-grams. We show that the subword encoding helps better represent the acoustic perceptual ambiguities in human spoken language via information modeled on lattice-structured ASR output. The usefulness of the proposed Confusion2vec representation is evaluated using analogy and word similarity tasks designed for assessing semantic, syntactic and acoustic word relations. We also show the benefits of subword modeling for acoustic ambiguity representation on the task of spoken language intent detection. The results significantly outperform existing word vector representations when evaluated on erroneous ASR outputs, providing improvements up-to 13.12% relative to previous state-of-the-art in intent detection on ATIS benchmark dataset. We demonstrate that Confusion2vec subword modeling eliminates the need for retraining/adapting the natural language understanding models on ASR transcripts.

Automated evaluation of psychotherapy skills using speech and language technologies.

With the growing prevalence of psychological interventions, it is vital to have measures which rate the effectiveness of psychological care to assist in training, supervision, and quality assurance of services. Traditionally, quality assessment is addressed by human raters who evaluate recorded sessions along specific dimensions, often codified through constructs relevant to the approach and domain. This is, however, a cost-prohibitive and time-consuming method that leads to poor feasibility and limited use in real-world settings. To facilitate this process, we have developed an automated competency rating tool able to process the raw recorded audio of a session, analyzing who spoke when, what they said, and how the health professional used language to provide therapy. Focusing on a use case of a specific type of psychotherapy called "motivational interviewing", our system gives comprehensive feedback to the therapist, including information about the dynamics of the session (e.g., therapist's vs. client's talking time), low-level psychological language descriptors (e.g., type of questions asked), as well as other high-level behavioral constructs (e.g., the extent to which the therapist understands the clients' perspective). We describe our platform and its performance using a dataset of more than 5000 recordings drawn from its deployment in a real-world clinical setting used to assist training of new therapists. Widespread use of automated psychotherapy rating tools may augment experts' capabilities by providing an avenue for more effective training and skill improvement, eventually leading to more positive clinical outcomes.

Multi-label Multi-task Deep Learning for Behavioral Coding.

We propose a methodology for estimating human behaviors in psychotherapy sessions using mutli-label and multi-task learning paradigms. We discuss the problem of behavioral coding in which data of human interactions is the annotated with labels to describe relevant human behaviors of interest. We describe two related, yet distinct, corpora consisting of therapist client interactions in psychotherapy sessions. We experimentally compare the proposed learning approaches for estimating behaviors of interest in these datasets. Specifically, we compare single and multiple label learning approaches, single and multiple task learning approaches, and evaluate the performance of these approaches when incorporating turn context. We demonstrate the prediction performance gains which can be achieved by using the proposed paradigms and discuss the insights these models provide into these complex interactions.

Machine learning and natural language processing in psychotherapy research: Alliance as example use case.

Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine learning is dramatically changing scientific research and industry and may also hold promise for addressing limitations encountered in mental health care and psychotherapy. The current paper introduces machine learning and natural language processing as related methodologies that may prove valuable for automating the assessment of meaningful aspects of treatment. Prediction of therapeutic alliance from session recordings is used as a case in point. Recordings from 1,235 sessions of 386 clients seen by 40 therapists at a university counseling center were processed using automatic speech recognition software. Machine learning algorithms learned associations between client ratings of therapeutic alliance exclusively from session linguistic content. Using a portion of the data to train the model, machine learning algorithms modestly predicted alliance ratings from session content in an independent test set (Spearman's ρ = .15, p < .001). These results highlight the potential to harness natural language processing and machine learning to predict a key psychotherapy process variable that is relatively distal from linguistic content. Six practical suggestions for conducting psychotherapy research using machine learning are presented along with several directions for future research. Questions of dissemination and implementation may be particularly important to explore as machine learning improves in its ability to automate assessment of psychotherapy process and outcome. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations.

Children speech recognition is challenging mainly due to the inherent high variability in children's physical and articulatory characteristics and expressions. This variability manifests in both acoustic constructs and linguistic usage due to the rapidly changing developmental stage in children's life. Part of the challenge is due to the lack of large amounts of available children speech data for efficient modeling. This work attempts to address the key challenges using transfer learning from adult's models to children's models in a Deep Neural Network (DNN) framework for children's Automatic Speech Recognition (ASR) task evaluating on multiple children's speech corpora with a large vocabulary. The paper presents a systematic and an extensive analysis of the proposed transfer learning technique considering the key factors affecting children's speech recognition from prior literature. Evaluations are presented on (i) comparisons of earlier GMM-HMM and the newer DNN Models, (ii) effectiveness of standard adaptation techniques versus transfer learning, (iii) various adaptation configurations in tackling the variabilities present in children speech, in terms of (a) acoustic spectral variability, and (b) pronunciation variability and linguistic constraints. Our Analysis spans over (i) number of DNN model parameters (for adaptation), (ii) amount of adaptation data, (iii) ages of children, (iv) age dependent-independent adaptation. Finally, we provide Recommendations on (i) the favorable strategies over various aforementioned - analyzed parameters, and (ii) potential future research directions and relevant challenges/problems persisting in DNN based ASR for children's speech.

Linking emotions to behaviors through deep transfer learning.

Human behavior refers to the way humans act and interact. Understanding human behavior is a cornerstone of observational practice, especially in psychotherapy. An important cue of behavior analysis is the dynamical changes of emotions during the conversation. Domain experts integrate emotional information in a highly nonlinear manner; thus, it is challenging to explicitly quantify the relationship between emotions and behaviors. In this work, we employ deep transfer learning to analyze their inferential capacity and contextual importance. We first train a network to quantify emotions from acoustic signals and then use information from the emotion recognition network as features for behavior recognition. We treat this emotion-related information as behavioral primitives and further train higher level layers towards behavior quantification. Through our analysis, we find that emotion-related information is an important cue for behavior recognition. Further, we investigate the importance of emotional-context in the expression of behavior by constraining (or not) the neural networks' contextual view of the data. This demonstrates that the sequence of emotions is critical in behavior expression. To achieve these frameworks we employ hybrid architectures of convolutional networks and recurrent networks to extract emotion-related behavior primitives and facilitate automatic behavior recognition from speech.

Design feasibility of an automated, machine-learning based feedback system for motivational interviewing.

Direct observation of psychotherapy and providing performance-based feedback is the gold-standard approach for training psychotherapists. At present, this requires experts and training human coding teams, which is slow, expensive, and labor intensive. Machine learning and speech signal processing technologies provide a way to scale up feedback in psychotherapy. We evaluated an initial proof of concept automated feedback system that generates motivational interviewing quality metrics and provides easy access to other session data (e.g., transcripts). The system automatically provides a report of session-level metrics (e.g., therapist empathy) and therapist behavior codes at the talk-turn level (e.g., reflections). We assessed usability, therapist satisfaction, perceived accuracy, and intentions to adopt. A sample of 21 novice (n = 10) or experienced (n = 11) therapists each completed a 10-min session with a standardized patient. The system received the audio from the session as input and then automatically generated feedback that therapists accessed via a web portal. All participants found the system easy to use and were satisfied with their feedback, 83% found the feedback consistent with their own perceptions of their clinical performance, and 90% reported they were likely to use the feedback in their practice. We discuss the implications of applying new technologies to evaluation of psychotherapy. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Confusion2Vec: towards enriching vector space word representations with representational ambiguities.

Word vector representations are a crucial part of natural language processing (NLP) and human computer interaction. In this paper, we propose a novel word vector representation, Confusion2Vec, motivated from the human speech production and perception that encodes representational ambiguity. Humans employ both acoustic similarity cues and contextual cues to decode information and we focus on a model that incorporates both sources of information. The representational ambiguity of acoustics, which manifests itself in word confusions, is often resolved by both humans and machines through contextual cues. A range of representational ambiguities can emerge in various domains further to acoustic perception, such as morphological transformations, word segmentation, paraphrasing for NLP tasks like machine translation, etc. In this work, we present a case study in application to automatic speech recognition (ASR) task, where the word representational ambiguities/confusions are related to acoustic similarity. We present several techniques to train an acoustic perceptual similarity representation ambiguity. We term this Confusion2Vec and learn on unsupervised-generated data from ASR confusion networks or lattice-like structures. Appropriate evaluations for the Confusion2Vec are formulated for gauging acoustic similarity in addition to semantic-syntactic and word similarity evaluations. The Confusion2Vec is able to model word confusions efficiently, without compromising on the semantic-syntactic word relations, thus effectively enriching the word vector space with extra task relevant ambiguity information. We provide an intuitive exploration of the two-dimensional Confusion2Vec space using principal component analysis of the embedding and relate to semantic relationships, syntactic relationships, and acoustic relationships. We show through this that the new space preserves the semantic/syntactic relationships while robustly encoding acoustic similarities. The potential of the new vector representation and its ability in the utilization of uncertainty information associated with the lattice is demonstrated through small examples relating to the task of ASR error correction.

Unsupervised online multitask learning of behavioral sentence embeddings.

Appropriate embedding transformation of sentences can aid in downstream tasks such as NLP and emotion and behavior analysis. Such efforts evolved from word vectors which were trained in an unsupervised manner using large-scale corpora. Recent research, however, has shown that sentence embeddings trained using in-domain data or supervised techniques, often through multitask learning, perform better than unsupervised ones. Representations have also been shown to be applicable in multiple tasks, especially when training incorporates multiple information sources. In this work we aspire to combine the simplicity of using abundant unsupervised data with transfer learning by introducing an online multitask objective. We present a multitask paradigm for unsupervised learning of sentence embeddings which simultaneously addresses domain adaption. We show that embeddings generated through this process increase performance in subsequent domain-relevant tasks. We evaluate on the affective tasks of emotion recognition and behavior analysis and compare our results with state-of-the-art general-purpose supervised sentence embeddings. Our unsupervised sentence embeddings outperform the alternative universal embeddings in both identifying behaviors within couples therapy and in emotion recognition.

IMPROVING THE PREDICTION OF THERAPIST BEHAVIORS IN ADDICTION COUNSELING BY EXPLOITING CLASS CONFUSIONS.

In this work we address the problem of joint prosodic and lexical behavioral annotation for addiction counseling. We expand on past work that employed Recurrent Neural Networks (RNNs) on multimodal features by grouping and classifying subsets of classes. We propose two implementations: One is hierarchical classification, which uses the behavior confusion matrix to cluster similar classes and makes the prediction based on a tree structure. The second is a graph-based method which uses the result of the original classification just to find a certain subset of the most probable candidate classes, where the candidate sets of different predicted classes are determined by the class confusions. We make a second prediction with simpler classifier to discriminate the candidates. The evaluation shows that the strict hierarchical approach degrades performance, likely due to error propagation, while the graph-based hierarchy provides significant gains.

Modeling Interpersonal Linguistic Coordination in Conversations using Word Mover's Distance.

Linguistic coordination is a well-established phenomenon in spoken conversations and often associated with positive social behaviors and outcomes. While there have been many attempts to measure lexical coordination or entrainment in literature, only a few have explored coordination in syntactic or semantic space. In this work, we attempt to combine these different aspects of coordination into a single measure by leveraging distances in a neural word representation space. In particular, we adopt the recently proposed Word Mover's Distance with word2vec embeddings and extend it to measure the dissimilarity in language used in multiple consecutive speaker turns. To validate our approach, we apply this measure for two case studies in the clinical psychology domain. We find that our proposed measure is correlated with the therapist's empathy towards their patient in Motivational Interviewing and with affective behaviors in Couples Therapy. In both case studies, our proposed metric exhibits higher correlation than previously proposed measures. When applied to the couples with relationship improvement, we also notice a significant decrease in the proposed measure over the course of therapy, indicating higher linguistic coordination.

ROLE SPECIFIC LATTICE RESCORING FOR SPEAKER ROLE RECOGNITION FROM SPEECH RECOGNITION OUTPUTS.

The language patterns followed by different speakers who play specific roles in conversational interactions provide valuable cues for the task of Speaker Role Recognition (SRR). Given the speech signal, existing algorithms typically try to find such patterns in the output of an Automatic Speech Recognition (ASR) system. In this work we propose an alternative way of revealing role-specific linguistic characteristics, by making use of role-specific ASR outputs, which are built by suitably rescoring the lattice produced after a first pass of ASR decoding. That way, we avoid pruning the lattice too early, eliminating the potential risk of information loss.

Cardiovascular Reactivity During Marital Conflict in Laboratory and Naturalistic Settings: Differential Associations with Relationship and Individual Functioning Across Contexts.

Cardiovascular reactivity during spousal conflict is considered to be one of the main pathways for relationship distress to impact physical, mental, and relationship health. However, the magnitude of association between cardiovascular reactivity during laboratory marital conflict and relationship functioning is small and inconsistent given the scope of its importance in theoretical models of intimate relationships. This study tests the possibility that cardiovascular data collected in laboratory settings downwardly bias the magnitude of these associations when compared to measures obtained in naturalistic settings. Ambulatory cardiovascular reactivity data were collected from 20 couples during two relationship conflicts in a research laboratory, two planned relationship conflicts at couples' homes, and two spontaneous relationship conflicts during couples' daily lives. Associations between self-report measures of relationship functioning, individual functioning, and cardiovascular reactivity across settings are tested using multilevel models. Cardiovascular reactivity was significantly larger during planned and spontaneous relationship conflicts in naturalistic settings than during planned relationship conflicts in the laboratory. Similarly, associations with relationship and individual functioning variables were statistically significantly larger for cardiovascular data collected in naturalistic settings than the same data collected in the laboratory. Our findings suggest that cardiovascular reactivity during spousal conflict in naturalistic settings is statistically significantly different from that elicited in laboratory settings both in magnitude and in the pattern of associations with a wide range of inter- and intrapersonal variables. These differences in findings across laboratory and naturalistic physiological responses highlight the value of testing physiological phenomena across interaction contexts in romantic relationships.

Experimental study of beam distortion due to fiducial markers during salvage HIFU in the prostate.

BACKGROUND: Prostate cancer is frequently treated using external beam radiation therapy (EBRT). Prior to therapy, the prostate is commonly implanted with a small number of permanent fiducial markers used to monitor the position of the prostate during therapy. In the case of local cancer recurrence, high-intensity focused ultrasound (HIFU) provides a non-invasive salvage treatment option. However, the impact of the fiducial markers on HIFU treatment has not been thoroughly studied to date. The objective of this study was to experimentally investigate the effect of a single EBRT fiducial marker on the efficacy of HIFU treatment delivery using a tissue-mimicking material (TMM). METHODS: A TMM with the acoustic properties of the prostate was developed based on a polyacrylamide hydrogel containing bovine serum albumin. Each phantom was implanted with a cylindrical fiducial marker and then sonicated using a 3.3 MHz focused bowl HIFU transducer. Two sets of experiments were performed. In the first, a single lesion was created at different positions along either the anteroposterior or left-right axes relative to the marker. In the second, a larger ablation volume was created by raster scanning. The size and position of the ablated volume were assessed using a millimetre grid overlaid on the phantom. RESULTS: The impact of the marker on the position and size of the HIFU lesion was significant when the transducer focus was positioned within 7 mm anteriorly, 18 mm posteriorly or within 3 mm laterally of the marker. Beyond this, the generated lesion was not affected. When the focus was anterior to the marker, the lesion increased in size due to reflections. When the focus was posterior, the lesion decreased in size or was not present due to shadowing. CONCLUSIONS: The presence of an EBRT fiducial marker may result in an undertreated region beyond the marker due to reduced energy arriving at the focus, and an overtreated region in front of the marker due to reflections. Depending on the position of the targeted regions and the distribution of the markers, both effects may be undesirable and reduce treatment efficacy. Further work is necessary to investigate whether these results indicate the necessity to reconsider patient selection and treatment planning for prostate salvage HIFU after failed EBRT.

Everyday couples' communication research: Overcoming methodological barriers with technology.

Relationship behaviors contribute to compromised health or resilience. Everyday communication between intimate partners represents the vast majority of their interactions. When intimate partners take on new roles as patients and caregivers, everyday communication takes on a new and important role in managing both the transition and the adaptation to the change in health status. However, everyday communication and its relation to health has been little studied, likely due to barriers in collecting and processing this kind of data. The goal of this paper is to describe deterrents to capturing naturalistic, day-in-the-life communication data and share how technological advances have helped surmount them. We provide examples from a current study and describe how we anticipate technology will further change research capabilities.

Predicting couple therapy outcomes based on speech acoustic features.

Automated assessment and prediction of marital outcome in couples therapy is a challenging task but promises to be a potentially useful tool for clinical psychologists. Computational approaches for inferring therapy outcomes using observable behavioral information obtained from conversations between spouses offer objective means for understanding relationship dynamics. In this work, we explore whether the acoustics of the spoken interactions of clinically distressed spouses provide information towards assessment of therapy outcomes. The therapy outcome prediction task in this work includes detecting whether there was a relationship improvement or not (posed as a binary classification) as well as discerning varying levels of improvement or decline in the relationship status (posed as a multiclass recognition task). We use each interlocutor's acoustic speech signal characteristics such as vocal intonation and intensity, both independently and in relation to one another, as cues for predicting the therapy outcome. We also compare prediction performance with one obtained via standardized behavioral codes characterizing the relationship dynamics provided by human experts as features for automated classification. Our experiments, using data from a longitudinal clinical study of couples in distressed relations, showed that predictions of relationship outcomes obtained directly from vocal acoustics are comparable or superior to those obtained using human-rated behavioral codes as prediction features. In addition, combining direct signal-derived features with manually coded behavioral features improved the prediction performance in most cases, indicating the complementarity of relevant information captured by humans and machine algorithms. Additionally, considering the vocal properties of the interlocutors in relation to one another, rather than in isolation, showed to be important for improving the automatic prediction. This finding supports the notion that behavioral outcome, like many other behavioral aspects, is closely related to the dynamics and mutual influence of the interlocutors during their interaction and their resulting behavioral patterns.

Multiple small intestinal perforations in a patient with Hepatitis B Virus-associated Polyarteritis Nodosa.

We present the case of a 38-year-old patient with a history of Hepatitis B Virus-associated Polyarteritis Nodosa, who presented with acute abdomen and septic shock. The patient initially had three perforations of the small intestine that were treated with segmental enterectomy and anastomosis at two sites. During his postoperative course he continued to develop new perforations and necrotic lesions along the whole length of the small intestine, that mandated repetitive laparotomies and the technique of the open abdomen was employed. Despite the aggressive surgical treatment and the medical treatment with corticosteroids, cyclophosphamide and plasma exchanges, the patient died 15 days after the first operation due to septic shock and multiple organ failure.

Clinical efficacy of omalizumab in chronic spontaneous urticaria is associated with a reduction of FcεRI-positive cells in the skin.

Background. Treatment with omalizumab, a humanized recombinant monoclonal anti-IgE antibody, results in clinical efficacy in patients with Chronic Spontaneous Urticaria (CSU). The mechanism of action of omalizumab in CSU has not been elucidated in detail. Objectives. To determine the effects of omalizumab on levels of high affinity IgE receptor-positive (FcεRI+) and IgE-positive (IgE+) dermal cells and blood basophils. Treatment efficacy and safety were also assessed. Study design. In a double-blind study, CSU patients aged 18­75 years were randomized to receive 300 mg omalizumab (n=20) or placebo (n=10) subcutaneously every 4 weeks for 12 weeks. Changes in disease activity were assessed by use of the weekly Urticaria Activity Score (UAS7). Circulating IgE levels, basophil numbers and levels of expression of FcεRI+ and IgE+ cells in the skin and in blood basophils were determined. Results. Patients receiving omalizumab showed a significantly greater decrease in UAS7 compared with patients receiving placebo. At Week 12 the mean difference in UAS7 between treatment groups was -14.82 (p=0.0027), consistent with previous studies. Total IgE levels in serum were increased after omalizumab treatment and remained elevated up to Week 12. Free IgE levels decreased after omalizumab treatment. Mean levels of FcεRI+ skin cells in patients treated with omalizumab 300 mg were decreased at Week 12 compared with baseline in the dermis of both non-lesional and lesional skin, reaching levels comparable with those seen in healthy volunteers (HVs). There were no statistically significant changes in mean FcɛRI+ cell levels in the placebo group. Similar results were seen for changes in IgE+ cells, although the changes were not statistically significant. The level of peripheral blood basophils increased immediately after treatment start and returned to Baseline values after the follow-up period. The levels of FcεRI and IgE expression on peripheral blood basophils were rapidly reduced by omalizumab treatment up to Week 12. Conclusions. Treatment with omalizumab resulted in rapid clinical benefits in patients with CSU. Treatment with omalizumab was associated with reduction in FcɛRI+ and IgE+ basophils and intradermal cells.