Therapeutic Cycles and Referential Activity in the Analysis of the Therapeutic Process

The present study was designed to show the usefulness of a process analysis based on a joint use of two computerized methods – Mergenthaler’s Therapeutic Cycle Model (TCM) and Bucci’s Italian Weighted Referential Activity Dictionary (IWRAD). This analysis focused on the transcripts of six sessions from the first eight months of a three-year, face-to-face psychodynamic psychotherapy. Both qualitative and quantita-tive analyses were conducted. Results showed the presence of specific indicators of a good outcome, according to the two approaches, such as the patient’s ability to link re-flective processes and felt emotions, the occurrence of referential cycles, and the pres-ence of organized and coherent narratives

Forensic flavour

Databases often receive an uninspired and uninterested response. The curriculum content of a database module generally involves the design of entity-relationship models, SQL programming, application development and advanced database applications such as data warehousing and data mining. These are often taught within the tired and relatively worn case studies of purchase order systems, retail or health care systems. However the current trend for crime scene investigation drama and the frequent stories in the news of personal tragedies involving incorrect data, missing data or data mix-up capture the attention of many. The truth is that crimes require data investigation and expert database witnesses to provide evidence and this requires database knowledge and skill. This project involved the introduction of a ‘forensic flavour’ to the teaching of databases as part of an undergraduate Computing Degree to students. The ‘forensic flavour’ involved introducing investigative and enquiry based learning techniques as well as selecting case studies based around real-life crimes and crime data. The learning objectives remained unchanged for the modules as did the curriculum content. The initial findings are that the students engaged on average 40% better and enjoyed the experience more

The Functional Psychotherapy Approach: A Process-Outcome Multiple Case Study

Objective: The present work aims to conduct the first naturalistic empirical investigation of the process and outcome assessment of functional psychotherapy (FP) treatment. The FP model of psychotherapy is rooted in psychoanalysis and integrates the verbal communication approach founded on transference and countertransference dynamics with the analysis of bodily processes. Method: The study sample included ten patients recruited on a voluntary basis and treated by clinicians in their private practices. Each patient received FP with an average duration of 40 h (min 35 and max 42). Therapies had weekly sessions, were audiorecorded with the patient’s written consent, and lasted for an average of 10 months (min 9 and max 12). Outcome and process tools included the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) and the Luborsky’s the Core Conflictual Relationship Theme (CCRT), used to assess therapeutic benefit, and the Metacognition Assessment Scale (MAS) and the Italian Discourse Attributes Analysis Program (IDAAP) system, used to evaluate therapeutic benefit and process. The MMPI-2 was used also in the followup assessment. Results: Results show that FP had a positive therapeutic outcome on the patients assessed in this study, and that the therapeutic benefits were maintained over time. Some specific features of the FP approach were found to contribute more than others to the observed therapeutic benefits. Conclusion: The current investigation constitutes a first step toward assessment of the therapeutic effectiveness of FP. Future developments should apply the methodology to a larger sample, possibly introducing different methodologies to enable detection of specific bodily oriented processes and technique

Linguistic features of the therapeutic alliance in the first session: a psychotherapy process study

Critical aspects of the therapeutic alliance appear to be established as early as the first session. Specifically, the affective bond between the therapeutic dyad appears to develop early in treatment and tends to remain stable over time, while agreements on goals and tasks tend to fluctuate over the course of treatment. Are there distinguishable early signs of a strong therapeutic alliance? In this study, we examined how some linguistic measures indicative of joint emotional elaboration correlated with a measure of the therapeutic alliance assessed within a single session. Initial intake sessions with 40 patients with varying diagnoses were videotaped, transcribed, and analyzed using linguistic measures of referential process and then scored with the Segmented Working Alliance Inventory-Observer form. Results showed that patients who were rated as more emotionally engaged in relating their experiences and then reflecting on them by mid-session also had higher scores in the therapeutic alliance by the final part of that same session. An implication of this study is that the interpersonal factors facilitating elaboration of inner experience, including elements of warmth, safety, and analytic trust, are related to the development of early therapeutic alliance. These findings did not appear to be dependent on the patient’s psychopathology. This study is one in a growing line of research exploring how patients speak rather than just the content of what they say

Compressão eficiente de sequências biológicas usando uma rede neuronal

Background: The increasing production of genomic data has led to an intensified need for models that can cope efficiently with the lossless compression of biosequences. Important applications include long-term storage and compression-based data analysis. In the literature, only a few recent articles propose the use of neural networks for biosequence compression. However, they fall short when compared with specific DNA compression tools, such as GeCo2. This limitation is due to the absence of models specifically designed for DNA sequences. In this work, we combine the power of neural networks with specific DNA and amino acids models. For this purpose, we created GeCo3 and AC2, two new biosequence compressors. Both use a neural network for mixing the opinions of multiple specific models. Findings: We benchmark GeCo3 as a reference-free DNA compressor in five datasets, including a balanced and comprehensive dataset of DNA sequences, the Y-chromosome and human mitogenome, two compilations of archaeal and virus genomes, four whole genomes, and two collections of FASTQ data of a human virome and ancient DNA. GeCo3 achieves a solid improvement in compression over the previous version (GeCo2) of 2:4%, 7:1%, 6:1%, 5:8%, and 6:0%, respectively. As a reference-based DNA compressor, we benchmark GeCo3 in four datasets constituted by the pairwise compression of the chromosomes of the genomes of several primates. GeCo3 improves the compression in 12:4%, 11:7%, 10:8% and 10:1% over the state-of-the-art. The cost of this compression improvement is some additional computational time (1:7_ to 3:0_ slower than GeCo2). The RAM is constant, and the tool scales efficiently, independently from the sequence size. Overall, these values outperform the state-of-the-art. For AC2 the improvements and costs over AC are similar, which allows the tool to also outperform the state-of-the-art. Conclusions: The GeCo3 and AC2 are biosequence compressors with a neural network mixing approach, that provides additional gains over top specific biocompressors. The proposed mixing method is portable, requiring only the probabilities of the models as inputs, providing easy adaptation to other data compressors or compression-based data analysis tools. GeCo3 and AC2 are released under GPLv3 and are available for free download at https://github.com/cobilab/geco3 and https://github.com/cobilab/ac2.Contexto: O aumento da produção de dados genómicos levou a uma maior necessidade de modelos que possam lidar de forma eficiente com a compressão sem perdas de biosequências. Aplicações importantes incluem armazenamento de longo prazo e análise de dados baseada em compressão. Na literatura, apenas alguns artigos recentes propõem o uso de uma rede neuronal para compressão de biosequências. No entanto, os resultados ficam aquém quando comparados com ferramentas de compressão de ADN específicas, como o GeCo2. Essa limitação deve-se à ausência de modelos específicos para sequências de ADN. Neste trabalho, combinamos o poder de uma rede neuronal com modelos específicos de ADN e aminoácidos. Para isso, criámos o GeCo3 e o AC2, dois novos compressores de biosequências. Ambos usam uma rede neuronal para combinar as opiniões de vários modelos específicos. Resultados: Comparamos o GeCo3 como um compressor de ADN sem referência em cinco conjuntos de dados, incluindo um conjunto de dados balanceado de sequências de ADN, o cromossoma Y e o mitogenoma humano, duas compilações de genomas de arqueas e vírus, quatro genomas inteiros e duas coleções de dados FASTQ de um viroma humano e ADN antigo. O GeCo3 atinge uma melhoria sólida na compressão em relação à versão anterior (GeCo2) de 2,4%, 7,1%, 6,1%, 5,8% e 6,0%, respectivamente. Como um compressor de ADN baseado em referência, comparamos o GeCo3 em quatro conjuntos de dados constituídos pela compressão aos pares dos cromossomas dos genomas de vários primatas. O GeCo3 melhora a compressão em 12,4%, 11,7%, 10,8% e 10,1% em relação ao estado da arte. O custo desta melhoria de compressão é algum tempo computacional adicional (1,7 _ a 3,0 _ mais lento do que GeCo2). A RAM é constante e a ferramenta escala de forma eficiente, independentemente do tamanho da sequência. De forma geral, os rácios de compressão superam o estado da arte. Para o AC2, as melhorias e custos em relação ao AC são semelhantes, o que permite que a ferramenta também supere o estado da arte. Conclusões: O GeCo3 e o AC2 são compressores de sequências biológicas com uma abordagem de mistura baseada numa rede neuronal, que fornece ganhos adicionais em relação aos biocompressores específicos de topo. O método de mistura proposto é portátil, exigindo apenas as probabilidades dos modelos como entradas, proporcionando uma fácil adaptação a outros compressores de dados ou ferramentas de análise baseadas em compressão. O GeCo3 e o AC2 são distribuídos sob GPLv3 e estão disponíveis para download gratuito em https://github.com/ cobilab/geco3 e https://github.com/cobilab/ac2.Mestrado em Engenharia de Computadores e Telemátic

The Role of Non-Verbal Interaction in a Short-Term Psychotherapy: Preliminary Analysis and Assessment of Paralinguistic Aspects

Analysis at a paralinguistic level of communication, already conceptualized within the multiple code theory, would appear to be very important in order to fully describe the quality of the patient-therapist relationship. In this study the therapeutic process and microprocess are analyzed taking into consideration a specific paraverbal aspect (speech rate) present in patient and therapist's communication. More specifically, in this paper we aim to investigate the relationship between the speech rate of both patient and therapist with the linguistic aspects of their referential process as obtained by the IDAAP dictionaries, relating to three sessions belonging to different phases of the psychotherapy. The results show that there are many significant correlations between the considered values. These findings are interpreted as an expression of the alignment between patient and therapist which can be linked to the outcome of the psychotherapy.</p