Sleep disturbances and poor sleep are a common complaint in the population with multiple sclerosis (MS) disease. The most commonly reported scale is the Pittsburgh Sleep Quality Index (PSQI), measuring seven components of sleep quality. Yet, till today, the PSQI instrument has not been validated in people with multiple sclerosis (pwMS). The objective of our study was to add precision in sleep quality assessment by investigating the psychometric properties of PSQI (factor structure, reliability, validity based on relations with other variables, cut-off scores) in pwMS. The cross-sectional study included data on a total of 87 patients with MS and 216 control subjects. Demographic information, education level, and MS-related variables were ascertained. Psychometric properties were examined by estimating the validity, including factor structure, metric invariance, and relations with other MS- and non-MS-related variables, reliability, and discrimination ability of the PSQI. The Croatian version of the PSQI had a two-factor structure which demonstrated loading and partial intercept invariance between pwMS and the control group. The global score and both subscales had high internal consistencies (McDonald’s omega and Cronbach’s alpha coefficients) in pwMS and showed expected relations with demographic and MS-related variables. PwMS differed significantly in the PSQI global score from the control groups, although receiver operating characteristics (ROC) curve analysis did not indicate a clear cut-off point. The PSQI is a reliable and valid scale and can be applied in clinical settings for assessing sleep quality in pwMS.

Transcranial magnetic stimulation (TMS) is a noninvasive technique mainly used for the assessment of corticospinal tract integrity and excitability of the primary motor cortices. Motor evoked potentials (MEPs) play a pivotal role in TMS studies. TMS clinical guidelines, concerning the use and interpretation of MEPs in diagnosing and monitoring corticospinal tract integrity in people with multiple sclerosis (pwMS), were established almost ten years ago and refer mainly to the use of TMS implementation; this comprises the magnetic stimulator connected to a standard EMG unit, with the positioning of the coil performed by using the external landmarks on the head. The aim of the present work was to conduct a narrative literature review on the MEP assessment and outcome measures in clinical and research settings, assessed by TMS Methodological characteristics of different TMS system implementations (TMS without navigation, line-navigated TMS and e-field-navigated TMS); these were discussed in the context of mapping the corticospinal tract integrity in MS. An MEP assessment of two case reports, by using an e-field-navigated TMS, was presented; the results of the correspondence between the e-field-navigated TMS with MRI, and the EDSS classifications were presented. Practical and technical guiding principles for the improvement of TMS studies in MEP assessment for MS are discussed, suggesting the use of e-field TMS assessment in the sense that it can improve the accuracy of corticospinal tract integrity testing by providing a more objective correspondence of the neurophysiological (e-field-navigated TMS) and clinical (Expanded Disability Status Scale—EDSS) classifications.

Automated surveillance systems based on machine learning and computer vision constantly evolve to improve shipping and assist port authorities. The data obtained can be used for port and port property surveillance, traffic density analysis, maritime safety, pollution assessment, etc. However, due to the lack of datasets for video surveillance and ship classification in real maritime zones, there is a need for a reference dataset to compare the obtained results. This paper presents a new dataset for estimating detection and classification performance which provides versatile ship annotations and classifications for passenger ports with a large number of small- to medium-sized ships that were not monitored by the automatic identification system (AIS) and/or the vessel traffic system (VTS). The dataset is considered general for the Mediterranean region since many ports have a similar maritime traffic configuration as the Port of Split, Croatia. The dataset consists of 19,337 high-resolution images with 27,849 manually labeled ship instances classified into 12 categories. The vast majority of the images contain the port and starboard sides of the ships. In addition, the images were acquired in a real maritime zone at different times of the year, day, weather conditions, and sea state conditions.

Evaluation of motor evoked potential (MEP) signals elicited by transcranial magnetic stimulation (TMS) over the motor cortex provide a measure of cortico-motor excitability at the time of stimulation. In the research and clinical medical practice, the MEP latency is a relevant neurophysiological parameter to determine conduction time for neural impulses from the cortex to peripheral muscles. State changes at different levels of signal propagation through the neural tissue can significantly influence MEP latency, based on which different medical diagnoses can be issued. This study aims to present the Squared Hard Threshold Estimator (SHTE), which is a novel and improved algorithm for MEP latency estimation. Analyses presented in the paper were based on the SHTE algorithm, which was efficiently applied to a large number of MEP signals recorded from hand muscles. The SHTE algorithm was compared with other prominent methods such as the absolute hard threshold estimation (AHTE) algorithm, the statistical measures (SM) algorithm, and manual assessment. Results obtained in terms of robustness test and statistical analysis show that the proposed SHTE algorithm is reliable in estimating MEP latency, especially for the MEP signals having peak-to-peak (PTP) amplitudes lower than one hundred microvolts. Compared with the AHTE and SM algorithms, the SHTE shows a lower percentage deviation index in MEP latency estimation of the MEP signals with the PTP amplitudes lower than one hundred microvolts. Hence, the proposed SHTE algorithm represents an improved armamentarium in automatic MEP latency estimation.