Search

Yoram Neuman,1,3 Matthew Koslow,2,3 Alona Matveychuk,2,3 Avigdor Bar-Sef,1 Alexander Guber,2,3 David Shitrit2,3 1Division of Cardiology, 2Pulmonary Department, Meir Medical Center, Kfar Saba, Israel; 3Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Background and objective: Patients with pulmonary hypertension (PH) are considered to be at risk for complications associated with flexible bronchoscopy (FB), but data concerning the degree of PH are often lacking. We investigated whether COPD patients with PH who undergo bronchoscopy are at greater risk for complications. Methods: This prospective study included 207 consecutive COPD patients undergoing FB. All underwent an echo-Doppler to evaluate pulmonary artery pressure on the day of the bronchoscopy procedure. Pulmonologists were blinded to the echocardiogram results. Results: A total of 167 patients (80.7%) had normal pulmonary pressure. The remaining 40 patients (19.3%) had PH: 27 (13.0%) mild, eight (3.9%) moderate, and five (2.4%) severe. Noninvasive hemodynamic parameters between groups before and after FB were similar. Two patients with normal pulmonary pressure developed supraventricular tachycardia. None developed hemodynamically significant dysrhythmia. Bleeding episodes between groups in bronchoalveolar lavage (BAL) and transbronchial biopsy (TBB) did not differ. PH patients who underwent BAL and TBB had decreased O2 saturation during the procedure compared with the non-PH group (23.5% vs 6.9%, P=0.033). No deaths were attributable to FB. Conclusion: PH is common among COPD patients undergoing FB. PH patients undergoing BAL and TBB are at higher risk of decreased O2 saturation than those without PH. Further studies should assess the risk among COPD patients with moderate-to-severe PH. Keywords: bronchoscopy, hypoxemia, pulmonary hypertension

Develops and tests a model that examines the relative powers of support and work stress indicators in explaining faculty research productivity. Research publication and its determinants; Support-stress framework; Pilot study; More.

The association between aggregates of leucocytes in blood drawn from patients with various inflammatory conditions and the serum concentration of C-reactive protein (CRP) was examined: serum concentration of CRP might contribute to the development of cellular aggregations. A total of 213 patients with various inflammatory or necrotic conditions were examined (including 31 women with normal pregnancy and 59 controls). A significant correlation between the degree of leucocyte aggregation and CRP concentration was noted in patients with bacterial infections and in a group of patients with various inflammatory conditions. In contrast, there was no correlation between the extent of leucocyte aggregation and CRP concentrations in patients with viral infections, malignancies, or pregnancy. The presence or absence of aggregated leucocytes can help in differentiating between the respective bacterial or viral infections. The serum concentrations of CRP were increased in both types of infection, although when a quantitative CRP assay was used, considerably higher concentrations were detected in bacterial diseases. [ABSTRACT FROM PUBLISHER]

The present study involves the test of the power of equity measures to predict students' commitment to college. Different patterns of results were observed across three academic disciplines (physics, economics, and political science), which are characterized by different levels of environmental uncertainty. Equity was found to be a more relevant predictor of students' behavior and attitudes in fields with a high level of paradigm development. In political science, the major predictor of students' commitment was the comparison of one's group with another. The dominant independent variable in economics was the comparison within the membership group, while in physics the main predictor was the internal standard. The policy implications of these findings are discussed. Author

The computational analysis of human personality has mainly focused on the Big Five personality theory, and the psychodynamic approach is almost nonexistent despite its rich theoretical grounding and relevance to various tasks. Here, we provide a data set of 4972 synthetic utterances corresponding with five personality dimensions described by the psychodynamic approach: depressive, obsessive, paranoid, narcissistic, and anti-social psychopathic. The utterances have been generated through AI with a deep theoretical orientation that motivated the design of prompts for GPT-4. The dataset has been validated through 14 tests, and it may be relevant for the computational study of human personality and the design of authentic persona in digital domains, from gaming to the artistic generation of movie characters., (© 2024. The Author(s).)

It has been realized that situational dimensions, as represented by human beings, are crucial for understanding human behavior. The Riverside Situational Q (RSQ) is a tool that measures the psychological properties of situations. However, the RSQ-4 includes only 90 items and may have limited use for researchers interested in measuring situational dimensions using a computational approach. Here we present a corpus of 10,000 artificially generated situations corresponding mostly with the RSQ-4. The dataset was generated using GPT, the state-of-the-art large language model. The dataset validity is established through inter-judge reliability, and four experiments on large datasets support its quality. The dataset and the code used for generating 100 situational dimensions may be useful for researchers interested in measuring situational dimensions in textual data., (© 2023. The Author(s).)

Background: Direct oral anticoagulants (DOACs) are the treatment of choice for patients with non-valvular atrial fibrillation; however, bleeding risk remains significant. We reported a single-center experience with 11 patients who presented with hemorrhagic cardiac tamponade while treated with DOACs., Objectives: To evaluate the characteristics and clinical outcomes of patients under DOACs with cardiac tamponade., Methods: We retrospectively identified 11 patients treated with DOACs admitted with pericardial tamponade in our cardiology unit during 2018-2021., Results: The mean age was 84 ± 4 years; 7 males. Atrial fibrillation was the indication for anticoagulation in all cases. DOACs included apixaban (8 patients), dabigatran (2 patients), and rivaroxaban (1 patient). Urgent pericardiocentesis via a subxiphoid approach under echocardiography guidance was successfully performed in 10 patients. One patient was treated with urgent surgical drainage with a pericardial window. Reversal of anticoagulation using prothrombin complex concentrate and idarucizumab was given before the procedure to 6 patients treated with apixaban and one patient treated with dabigatran. One patient, initially treated with urgent pericardiocentesis, underwent pericardial window surgery due to re-accumulation of blood in the pericardium. The pericardial fluid analysis demonstrated hemopericardium. Cytology tests were negative for malignant cells in all cases. Discharge diagnoses regarding the cause of hemopericardium included pericarditis (3 patients) and idiopathic (8 patients). Medical therapy included non-steroidal anti-inflammatory drugs (1 patient), colchicine (3 patients), and steroids (3 patients). No patient died during hospitalization., Conclusions: Hemorrhagic cardiac tamponade is a rare complication of DOACs. We found good short-term prognosis following pericardiocentesis.

Identifying social norms and their violation is a challenge facing several projects in computational science. This paper presents a novel approach to identifying social norm violations. We used GPT-3, zero-shot classification, and automatic rule discovery to develop simple predictive models grounded in psychological knowledge. Tested on two massive datasets, the models present significant predictive performance and show that even complex social situations can be functionally analyzed through modern computational tools., (© 2023. The Author(s).)

Purpose: The mechanism by which preoperative expectations may be associated with patient satisfaction and procedural outcomes following hip preservation surgery (HPS) is far from simple or linear. The purpose of this study is to better understand patient expectations regarding HPS and their relationship with patient-reported outcomes (PROs) and satisfaction using machine learning (ML) algorithms., Methods: Patients scheduled for hip arthroscopy completed the Hip Preservation Surgery Expectations Survey (HPSES) and the pre- and a minimum 2 year postoperative International Hip Outcome Tool (iHOT-33). Patient demographics, including age, gender, occupation, and body mass index (BMI), were also collected. At the latest follow-up, patients were evaluated for subjective satisfaction and postoperative complications. ML algorithms and standard statistics were used., Results: A total of 69 patients were included in this study (mean age 33.7 ± 13.1 years, 62.3% males). The mean follow-up period was 27 months. The mean HPSES score, patient satisfaction, preoperative, and postoperative iHOT-33 were 83.8 ± 16.5, 75.9 ± 26.9, 31.6 ± 15.8, and 73 ± 25.9, respectively. Fifty-nine patients (86%) reported that they would undergo the surgery again, with no significant difference with regards to expectations. A significant difference was found with regards to expectation violation (p < 0.001). Expectation violation scores were also found to be significantly correlated with satisfaction., Conclusion: ML algorithms utilized in this study demonstrate that violation of expectations plays an important predictive role in postoperative outcomes and patient satisfaction and is associated with patients' willingness to undergo surgery again., Level of Evidence: IV., (© 2022. The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).)

Background: Bariatric patients have a high prevalence of hiatal hernia (HH). HH imposes various difficulties in performing laparoscopic bariatric surgery. Preoperative evaluation is generally inaccurate, establishing the need for better preoperative assessment., Objective: To utilize machine learning ability to improve preoperative diagnosis of HH., Methods: Machine learning (ML) prediction models were utilized to predict preoperative HH diagnosis using data from a prospectively maintained database of bariatric procedures performed in a high-volume bariatric surgical center between 2012 and 2015. We utilized three optional ML models to improve preoperative contrast swallow study (SS) prediction, automatic feature selection was performed using patients' features. The prediction efficacy of the models was compared to SS., Results: During the study period, 2482 patients underwent bariatric surgery. All underwent preoperative SS, considered the baseline diagnostic modality, which identified 236 (9.5%) patients with presumed HH. Achieving 38.5% sensitivity and 92.9% specificity. ML models increased sensitivity up to 60.2%, creating three optional models utilizing data and patient selection process for this purpose., Conclusion: Implementing machine learning derived prediction models enabled an increase of up to 1.5 times of the baseline diagnostic sensitivity. By harnessing this ability, we can improve traditional medical diagnosis, increasing the sensitivity of preoperative diagnostic workout.

The identification of extreme rare events is a challenge that appears in several real-world contexts, from screening for solo perpetrators to the prediction of failures in industrial production. In this article, we explain the challenge and present a new methodology for addressing it, a methodology that may be considered in terms of features engineering. This methodology, which is based on Jaynes inferential approach, is tested on a dataset dealing with failures in production in the pulp-and-paper industry. The results are discussed in the context of the benefits of using the approach for features engineering in practical contexts involving measurable risks.

Prediction in natural environments is a challenging task, and there is a lack of clarity around how a myopic organism can make short-term predictions given limited data availability and cognitive resources. In this context, we may ask what kind of resources are available to the organism to help it address the challenge of short-term prediction within its own cognitive limits. We point to one potentially important resource: ordinal patterns , which are extensively used in physics but not in the study of cognitive processes. We explain the potential importance of ordinal patterns for short-term prediction, and how natural constraints imposed through (i) ordinal pattern types, (ii) their transition probabilities and (iii) their irreversibility signature may support short-term prediction. Having tested these ideas on a massive dataset of Bitcoin prices representing a highly fluctuating environment, we provide preliminary empirical support showing how organisms characterized by bounded rationality may generate short-term predictions by relying on ordinal patterns., Competing Interests: No competing financial interests., (© 2021 The Authors.)

Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.

The challenge of automatically screening for potential school shooters involves several difficulties. In this paper, we present a simple and interpretable methodology for screening for potential school shooters through (1) the psychological textual signature of the shooter and (2) Jaynes approach for measuring the weight of evidence. We have tested our proposed approach on a dataset of texts written by shooters and non-shooters alike (N = 5047). Our major finding is that the methodology can successfully support the screening for potential shooters in interpretable way. The major implication for stakeholders is that there is great potential in developing screening systems for improving the safely of schools. However, developing such a system is a project that must be actualized within an integrated system of "command and control"., (© 2020 The Author(s).)

Background: Thrombolytic therapy is widely accepted for massive pulmonary embolism (PE) due to the high mortality risk associated with standard anticoagulation alone. Its role in submassive PE, however, has remained controversial. We aimed to evaluate whether the selective use of systemic thrombolytic therapy with intravenous tissue plasminogen activator (IV-tPA) improves the survival of patients with submassive PE at increased risk for clinical deterioration., Methods: A total of 184 consecutive patients diagnosed with acute PE by chest thoracic angiography (CTA) were included in a retrospective study. Pulmonary artery obstruction and right/left ventricular dysfunction were evaluated by CTA and echocardiography. Medical history and simplified PE Severity Index (sPESI) were assessed at diagnosis. Hemodynamic and respiratory status were recorded at diagnosis, admission to pulmonary unit and prior to thrombolytic therapy. Patient survival was assessed at 30 of 90 days from diagnosis by CTA., Results: All low risk patients (36%) per sPESI survived. Among the 117 remaining patients, 31% received IV-tPA. Respiratory failure was associated with decreased age-adjusted survival (P = 0.005). Among patients with respiratory failure selected for IV-tPA, age-adjusted survival was improved significantly compared to others (P = 0.043)., Conclusions: Thrombolytic therapy for hemodynamically stable PE patients with respiratory failure may improve survival., Trial Registration: MMC-0216-14., (Copyright © 2020. Published by Elsevier Inc.)

Complex social systems at various scales of analysis (e.g. dyads, families, tribes, etc.) are formed and maintained through verbal interactions. Therefore, the ability to (1) model these interactions and (2) to use models of interaction for identifying significant relations may be of interest to the social sciences. Adopting the perspective of social physics, we present a general approach for modeling interactions through relative entropy. For illustrating the benefits of the approach, we derive measures of "perspective-taking" and use them for identifying significant-romantic relations in a data set composed of the verbal interactions taken place at the famous TV series "Sex and the City". Using these measures, we show that significant-romantic relations can be identified with success. These results provide preliminary support for the benefits of using the proposed approach.

To optimize its performance, a competitive team, such as a soccer team, must maintain a delicate balance between organization and disorganization. On the one hand, the team should maintain organized patterns of behavior to maximize the cooperation between its members. On the other hand, the team's behavior should be disordered enough to mislead its opponent and to maintain enough degrees of freedom. In this paper, we have analyzed this dynamic in the context of soccer games and examined whether it is correlated with the team's performance. We measured the organization associated with the behavior of a soccer team through the Tsallis entropy of ball passes between the players. Analyzing data taken from the English Premier League (2015/2016), we show that the team's position at the end of the season is correlated with the team's entropy as measured with a super-additive entropy index. Moreover, the entropy score of a team significantly contributes to the prediction of the team's position at the end of the season beyond the prediction gained by the team's position at the end of the previous season.

Background: Elevated admission plasma glucose levels >140 mg/dl are associated with adverse clinical outcomes in both diabetic and non-diabetic patients admitted with acute coronary syndrome (ACS). We aimed to evaluate the association between admission plasma glucose levels <140 mg/dl and the outcome of non-diabetic patients admitted with acute coronary syndrome., Methods: The study population consisted of patients with acute coronary syndrome included in the Acute Coronary Syndrome Israeli Survey during 2000-2013. Diabetic patients were excluded. The primary endpoint was all-cause mortality at one year., Results: The 452 0 patients had a mean age of 61.7±13.5 years and were stratified into four quartiles according to admission plasma glucose (60-94, 95-105, 106-119, 120-140 mg/dl). Patients with higher admission plasma glucose were older and included a higher percentage of smokers. In addition, the higher the glucose so also did they have a poorer risk factor profile including a higher body mass index, total and low-density lipoprotein cholesterol and triglyceride levels, and lower high-density lipoprotein cholesterol levels. During the first year 5.2% of patients died. A comparison of one-year mortality according to admission plasma glucose quartiles demonstrated a significant and progressive increase in mortality risk as admission plasma glucose rose (3.5%, 4.1%, 6.1%, 6.4%, respectively, p=0.001). However, this association lost its clinical significance following a multivariate analysis ( p=0.08)., Conclusions: High admission plasma glucose levels within the normal to mildly impaired range are associated with increased one-year mortality in non-diabetic acute coronary syndrome patients. However, the higher glucose level is probably not the cause for the adverse outcome but rather a marker for high risk. Our findings support the definition of 140 mg/dl as the cutoff for clinically acceptable admission glucose levels in patients with acute coronary syndrome.

Background: Chronic kidney disease is a frequent comorbidity among patients with acute coronary syndrome (ACS). We aimed to evaluate treatment characteristics in ACS patients according to their renal function and to assess the effect of differences in therapy on clinical outcomes., Methods: Included were patients with ACS enrolled in the biennial Acute Coronary Syndrome Israeli Surveys (ACSIS) during 2000-2013. Excluded were patients with cardiogenic shock at presentation. The estimated glomerular filtration rate (eGFR) was calculated using the simplified Modification of Diet in Renal Disease (MDRD) formula. The distribution of the eGFRs was divided into 4 categories (<45, 45-59.9, 60-74.9, and ≥75 mL/min/1.73 m 2 ). The primary endpoint was all-cause mortality at 1 year., Results: A total of 13,194 patients with ACS were included. Patients with a reduced eGFR were less likely to be admitted to a coronary care unit and had lower rates of coronary angiograms and subsequent percutaneous coronary interventions. Furthermore, as the eGFR was lower, the patients were less frequently treated with aspirin, clopidogrel, β-blockers, and ACE inhibitors/angiotensin receptor blockers. We demonstrated an inverse association between renal function and 1-year mortality, with the highest mortality rates observed in the group with the lowest eGFR (HR = 3.8, 95% CI 2.9-4.9, p < 0.0001). Differences in mortality remained significant following a multivariate analysis for all the baseline characteristics as well as for invasive and medical treatment (HR = 2.7, 95% CI 1.9-3.7, p < 0.0001)., Conclusions: ACS patients with chronic kidney disease represent a high-risk group with an increased mortality risk. Despite this high risk, these patients are less frequently selected for an invasive treatment strategy and are less commonly treated with guideline-based medications. However, reduced renal function was associated with higher mortality regardless of the variations in therapy.

Complementing the physical examination with a point-of-care ultrasound study (POCUS) can improve patient triage. We aimed to study the impact of POCUS on the diagnosis and management of outpatients and hospitalized patients with suspected cardiac disease. In this multicenter study, a pocket-sized device was used to perform POCUS when the diagnosis or patient management was unclear based on anamnesis, physical examination, and basic diagnostic testing. Eighteen physicians (cardiac fellows 49%, cardiologists 30%, and echocardiographers 21%) performed physical examinations extended by POCUS on 207 patients (inpatients 83% and outpatients 17%). POCUS findings resulted in a change in the primary diagnosis in 14% of patients. In patients whose diagnosis remained unchanged, POCUS results reinforced the initial diagnosis in 48% of the cases. In 39% of the patients, the diagnostic plan was altered, including referral (16%) or deferral (23%) to other diagnostic techniques. Alteration in medical treatment (drug discontinuation or initiation) occurred in 11% of the patients, and in 7% POCUS results influenced the decision whether to perform a therapeutic procedure. Hospitalization or discharge was determined after POCUS in 11% of the patients. In conclusion, during patient triage, extension of the physical examination by POCUS can cause physicians to alter their initial diagnosis, resulting in an immediate change of diagnostic and therapeutic procedures. Based on POCUS results, physicians altered the diagnostic plan either by avoiding or referring patients to other diagnostic procedures in almost half of the studied population., (Copyright © 2016. Published by Elsevier Inc.)

The aim of this study was to analyze dynamic patterns for scanning femoroacetabular impingement (FAI) radiographs in orthopedics, in order to better understand the nature of expertise in radiography. Seven orthopedics residents with at least two years of expertise and seven board-certified orthopedists participated in the study. The participants were asked to diagnose 15 anteroposterior (AP) pelvis radiographs of 15 surgical patients, diagnosed with FAI syndrome. Eye tracking data were recorded using the SMI desk-mounted tracker and were analyzed using advanced measures and methodologies, mainly recurrence quantification analysis. The expert orthopedists presented a less predictable pattern of scanning the radiographs although there was no difference between experts and non-experts in the deterministic nature of their scan path. In addition, the experts presented a higher percentage of correct areas of focus and more quickly made their first comparison between symmetric regions of the pelvis. We contribute to the understanding of experts' process of diagnosis by showing that experts are qualitatively different from residents in their scanning patterns. The dynamic pattern of scanning that characterizes the experts was found to have a more complex and less predictable signature, meaning that experts' scanning is simultaneously both structured (i.e. deterministic) and unpredictable.

Background: Unrecognized renal insufficiency, defined as estimated glomerular filtration rate <60 mL/min/1.73 m(2) in the presence of normal serum creatinine, is common among patients with acute coronary syndrome. We aimed to determine the prevalence and clinical significance of unrecognized renal insufficiency in a large unselected population of patients with acute coronary syndrome., Methods: The study population consisted of patients with acute coronary syndrome included in the Acute Coronary Syndrome Israeli biennial Surveys during 2000-2013. The estimated glomerular filtration rate was calculated using the simplified Modification of Diet in Renal Disease formula. Patients were stratified into 3 groups: 1) normal renal function (estimated glomerular filtration rates ≥60 mL/min/1/73 m(2)); 2) unrecognized renal insufficiency (estimated glomerular filtration rates <60 mL/min/1/73 m(2) with serum creatinine ≤1.2 mg/dL); and 3) recognized renal insufficiency (estimated glomerular filtration rates <60 mL/min/1/73 m(2) with serum creatinine ≥1.2 mg/dL). The primary endpoint was all-cause mortality at 1 year., Results: Included in the study were 12,830 acute coronary syndrome patients. Unrecognized renal insufficiency was present in 2536 (19.8%). Patients with unrecognized renal insufficiency were older and more frequently females. All-cause mortality rates at 1 year were highest among patients with recognized renal insufficiency, followed by patients with unrecognized renal insufficiency, with the lowest mortality rates observed in patients with normal renal function (19.4%, 9.9%, and 3.3%, respectively, P <.0001). Despite their increased risk, patients with renal insufficiency were less frequently referred for coronary angiography and were less commonly treated with guideline-based cardiovascular medications., Conclusions: Acute coronary syndrome patients with unrecognized renal insufficiency should be considered as a high-risk population. The question of whether this group would benefit from a more aggressive therapeutic approach should still be evaluated., (Copyright © 2016 Elsevier Inc. All rights reserved.)

School shooters present a challenge to both forensic psychiatry and law enforcement agencies. The relatively small number of school shooters, their various characteristics, and the lack of in-depth analysis of all of the shooters prior to the shooting add complexity to our understanding of this problem. In this short paper, we introduce a new methodology for automatically profiling school shooters. The methodology involves automatic analysis of texts and the production of several measures relevant for the identification of the shooters. Comparing texts written by 6 school shooters to 6056 texts written by a comparison group of male subjects, we found that the shooters' texts scored significantly higher on the Narcissistic Personality dimension as well as on the Humilated and Revengeful dimensions. Using a ranking/prioritization procedure, similar to the one used for the automatic identification of sexual predators, we provide support for the validity and relevance of the proposed methodology.

In some investigative and interrogative contexts, the investigator is seeking to identify the location of an object (e.g., implanted bomb) which is known to a given subject (e.g., a terrorist). In this paper, we present a non-intrusive methodology for uncovering the loci of a concealed object by analyzing the subject's eye movements. Using a combination of eye tracking, psychological manipulation and a search algorithm, we have performed two experiments. In the first experiment, we have gained 58% hit rate in identifying the location of the concealed object and in the second experiment 56% hit rate. The pros and cons of the methodology for forensic investigation are discussed.

The term "personality" is used to describe a distinctive and relatively stable set of mental traits that aim to explain the organism's behavior. The concept of personality that emerged in human psychology has been also applied to the study of non-human organisms from birds to horses. In this paper, I critically review the concept of personality from an interdisciplinary perspective, and point to some ideas that may be used for developing a cognitive-biological theory of personality. Integrating theories and research findings from various fields such as cognitive ethnology, clinical psychology, and neuroscience, I argue that the common denominator of various personality theories are neural systems of threat/trust management and their emotional, cognitive, and behavioral dimensions. In this context, personality may be also conceived as a meta-heuristics both human and non-human organisms apply to model and predict the behavior of others. The paper concludes by suggesting a minimal computational model of personality that may guide future research., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Predicting a transition point in behavioral data should take into account the complexity of the signal being influenced by contextual factors. In this paper, we propose to analyze changes in the embedding dimension as contextual information indicating a proceeding transitive point, called OPtimal Embedding tRANsition Detection (OPERAND). Three texts were processed and translated to time-series of emotional polarity. It was found that changes in the embedding dimension proceeded transition points in the data. These preliminary results encourage further research into changes in the embedding dimension as generic markers of an approaching transition point.

Personality assessment and, specifically, the assessment of personality disorders have traditionally been indifferent to computational models. Computational personality is a new field that involves the automatic classification of individuals' personality traits that can be compared against gold-standard labels. In this context, we introduce a new vectorial semantics approach to personality assessment, which involves the construction of vectors representing personality dimensions and disorders, and the automatic measurements of the similarity between these vectors and texts written by human subjects. We evaluated our approach by using a corpus of 2468 essays written by students who were also assessed through the five-factor personality model. To validate our approach, we measured the similarity between the essays and the personality vectors to produce personality disorder scores. These scores and their correspondence with the subjects' classification of the five personality factors reproduce patterns well-documented in the psychological literature. In addition, we show that, based on the personality vectors, we can predict each of the five personality factors with high accuracy.