Previous research has shown that list-wide effects in the Stroop task interact with working memory capacity (WMC). The predominant explanation for this relationship is goal maintenance. However, some researchers have challenged whether list-wide effects truly reflect goal-maintenance abilities. In the current study, we examined whether goal maintenance explains higher WMC individuals' better performance within mostly congruent (MC) Stroop lists by providing periodic goal reminders to some of the participants. Two hundred and twelve participants from Montana State University first completed the Automated Operation Span and were then assigned to either a true control, goal reminder, or nongoal reminder condition. During the Stroop task, the true control condition received rest breaks every 60 trials, whereas the goal reminder and nongoal reminder conditions stopped every 12 trials to vocalize either the task goal or a rehearsed statement, respectively. We regressed Stroop errors on reminder condition and WMC, comparing each group to the true control. For the Goal Reminder × True Control comparison, there was an interaction, such that WMC negatively correlated with Stroop errors in the true control, but not in the goal reminder condition. In contrast, for the Nongoal Reminder × True Control comparison, there was only an overall effect of WMC, with greater Stroop errors for those lower in WMC. These data provide evidence that goal reminders eliminate the relationship between WMC and Stroop interference. Laryngeal and hypopharyngeal carcinoma are among the common head and neck cancers causing considerable swallowing dysfunction. The functional status of the organ (larynx) is an important point of contention while considering the patients for organ preservation protocol. The aim of this retrospective study was to assess the swallowing status in stage III/IV laryngeal and hypopharyngeal carcinoma and its influence on treatment decision. We evaluated all treatment naïve patients who were referred to the swallowing clinic in 2017 (Jan-Dec) for assessment of swallowing prior to treatment initiation. One hundred patients satisfied the eligibility criteria and were included in the study. The site and stage of laryngeal and hypopharyngeal cancer cases were almost equal in number. Their median age was 58years. Fiberoptic endoscopic evaluation of swallowing (FEES) was done in all patients. 30% of the patients only had swallowing difficulties. Only advanced T-stage (p = 0.04) had an influence on the pretreatment swallowing status. Thirty-seven patients required nasogastric tube (NGT) for feeding. By 2month post-treatment completion, most patients on NGT could resume oral feeding. Pretreatment swallowing assessment alone did not significantly seem to influence our decisions for organ preservation treatment. However, patients with aspiration could be identified and managed appropriately. Most patients on NGT could resume oral feeds post-treatment completion.Pretreatment swallowing assessment alone did not significantly seem to influence our decisions for organ preservation treatment. However, patients with aspiration could be identified and managed appropriately. Most patients on NGT could resume oral feeds post-treatment completion. This study aims to clarify the association between cervical spondylotic myelopathy (CSM) and cervical arteriosclerosis using ultrasonography that comprehensively includes spinal cord stenosis. Eighty-two consecutive patients aged over 60years who underwent spine surgery were divided into those with CSM (n = 31; CSM group) and those with lumbar spinal stenosis without cervical myelopathy (n = 51; LSS group). Maximum spinal cord compression (MSCC) was evaluated for cervical stenosis severity using magnetic resonance (MR) images. The intima-media thickness (IMT) of the common carotid artery (CCA) and pulsatility index (PI) of the bilateral internal carotid artery (ICA) and vertebral artery (VA) were evaluated for cervical arteriosclerosis using pulsed-wave Doppler ultrasonography. Symptom severity was evaluated using the Japanese Orthopaedic Association (JOA) score. Spearman's correlation coefficient was used to determine the relationship between the JOA score and MSCC or IMT and PI in each group. check details Stepwise multiple linear regression analyses were conducted with the JOA score as a dependent variable and age, sex, body mass index, cervical arteriosclerosis assessment, and MSCC as independent variables. Bilateral IMT and left-side ICA-PI were significantly negatively correlated with JOA scores in the CSM group (Right-CCA-IMT R =  - 0.412, Left-IMT R =  - 0.549, Left-ICA -PI R =  - 0.205, P < 0.05), but not in the LSS group. Multiple linear regression analyses showed that CCA-IMT was the strongest independent factor associated with the preoperative JOA score. Cervical arteriosclerosis was associated with preoperative clinical symptoms in CSM patients.Cervical arteriosclerosis was associated with preoperative clinical symptoms in CSM patients.According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O'Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python.