Primary neuroendocrine tumors (NETs) of the breast are considered a rare and undervalued subtype of breast carcinoma that occur mainly in postmenopausal women and are graded as G1 or G2 NETs or an invasive neuroendocrine carcinoma (NEC) (small cell or large cell). To establish a final diagnosis of breast carcinoma with neuroendocrine differentiation, it is essential to perform an immunohistochemical profile of the tumor, using antibodies against synaptophysin or chromogranin, as well as the MIB-1 proliferation index, one of the most controversial markers in breast pathology regarding its methodology in current clinical practice. A standardization error between institutions and pathologists regarding the evaluation of the MIB-1 proliferation index is present. Another challenge refers to the counting process of MIB-1\'s expressiveness, which is known as a time-consuming process. The involvement of AI (artificial intelligence) automated systems could be a solution for diagnosing early stages, as well. We present the case of a post-menopausal 79-year-old woman diagnosed with primary neuroendocrine carcinoma of the breast (NECB). The purpose of this paper is to expose the interpretation of MIB-1 expression in our patient\' s case of breast neuroendocrine carcinoma, assisted by artificial intelligence (AI) software (HALO-IndicaLabs), and to analyze the associations between MIB-1 and common histopathological parameters.

Introduction: Duchenne muscular dystrophy (DMD) is a genetic disorder that induces progressive muscular degeneration. Currently, the increase in DMD individuals\' life expectancy is not being matched by an increase in quality of life. The functioning of the hand and wrist is central for performing daily activities and for providing a higher degree of independence. Active exoskeletons can assist this functioning but require the accurate decoding of the users\' motor intention. These methods have, however, never been systematically analyzed in the context of DMD. Methods: This case study evaluated direct control (DC) and pattern recognition (PR), combined with an admittance model. This enabled customization of myoelectric controllers to one DMD individual and to a control population of ten healthy participants during a target-reaching task in 1- and 2- degrees of freedom (DOF). We quantified real-time myocontrol performance using target reaching times and compared the differences between the healthy individuals and the DMD individual. Results and Discussion: Our findings suggest that despite the muscle tissue degeneration, the myocontrol performance of the DMD individual was comparable to that of the healthy individuals in both DOFs and with both control approaches. It was also evident that PR control performed better for the 2-DOF tasks for both DMD and healthy participants, while DC performed better for the 1-DOF tasks. The insights gained from this study can lead to further developments for the intuitive multi-DOF myoelectric control of active hand exoskeletons for individuals with DMD.

Proficiency in clinical examination skills upon graduation from medical school is a core competency. Over the last few decades, the ability and confidence in this fundamental and crucial skill set has declined. The motivation and interest in meticulous physical examination by recently graduated residents has also eroded. In this case study, we describe a comprehensive, innovative, and immersive advanced clinical skills elective taken during the second half of the final year of medical school for students at the David Geffen School of Medicine. The course utilizes novel approaches to inspire, refresh and consolidate essential bedside observation skills and examination techniques. This approach gives senior students the confidence and fundamental understanding of how dedication to the patient exam can improve the doctor-patient relationship, core clinical reasoning and the practice of cost-effective and evidence-based care through their careers. We describe how the integration of fine art appreciation and introductory biding techniques are used to help students hone their visual diagnostic skills. We show how this is solidified through a longitudinal series of clinical image review sessions with diagnostic reasoning principles to formulate a clear differential. Point of care ultrasound, EKG analysis, advanced cardiac auscultation and diagnostic imaging skills are integrated in a comprehensive and memorable fashion. We present this case study to inspire clinical skills teachers everywhere to replicate our methods in resurrecting the importance of physical exams for their learners. Opening their trainees\' eyes to new methods of honing their visual intelligence and developing healthy habits for stress and burnout reduction will aid the rest of their professional careers.

Electrochemical fingerprinting can collect the electrochemical behavior of electrochemically active molecules in plant tissues, so it is regarded as a new plant analysis technology. Because the signal of electrochemical fingerprinting is positively correlated with the amount and type of electrochemically active molecules in plant tissues, it can also be used to reflect genetic differences between different species. Previous electrochemical fingerprinting techniques have been frequently used in phylogenetic studies of herbaceous plants. In this work, 19 Quercus species (17 evergreen or semi evergreen species and 2 deciduous species) were selected for investigation. The results indicated the electrochemical fingerprint of some species share similar features but can be distinguished after changing the recording condition (extraction solvent and electrolyte). The two sets of electrochemical fingerprint data can be used to construct different pattern recognition technology, which further speeds up the recognition efficiency. These electrochemical fingerprints were further used in phylogenetic investigations. The phylogenetic results deduced from electrochemical fingerprinting were divided mainly into three clusters. These can provide evidence for some of these arguments as well as new results.

Corona Virus Disease 2019 (COVID-19) outbreak leads to a significant downturn in the global economy and supply chain. In the maritime sector, trade volume slumped by 3.8% in 2020 compared with 2019. To explore the impacts of COVID-19 on ship visiting behaviors, a framework is proposed to analyze the impact of COVID-19 on port traffic using Automatic Identification System (AIS) data. Firstly, a ship travel behavior-based model is proposed to identify the vessel anchoring and berthing. Then, the diversity in vessel anchoring and berthing time are analyzed, reflecting the impact of COVID-19. The port congestion caused by COVID-19 is quantified by accounting for the number of visiting ships and their residence time. Finally, a case study is carried out on vessels in the Beibu Gulf, China, operating from 2019 to 2020. The results show that the average anchoring time and berthing time increase by 62% and 11% for cargo ships and by 112% and 63% for oil tankers after the outbreak of COVID-19 compared with that before COVID-19. And the density of ships increases in the port area in 2020. Accordingly, the relevant improvements and countermeasures are proposed to reduce the adverse impact of the epidemic on the port navigation system. The paper has the potential to provide a reference for port management and improving port navigation efficiency in the post-pandemic era.

Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a MyoTM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.

Source authentication of herbal medicines was essential for ensuring their safety, efficacy and quality consistency, especially those with multiple botanical origins. This study proposed a metabolomics strategy for species discrimination and source recognition. Uncariae Rammulus Cum Uncis, officially stipulating the stems with hooks of five Uncaria species as its origins, was taken as a case study. Firstly, an untargeted MSE method was developed by ultra-high performance liquid chromatography hyphenated with quadrupole time-of-flight mass spectrometry for global metabolite characterization. Subsequently, data pretreatment was conducted by using Progenesis QI software and screening rules. The obtained metabolite features were defined as variables for statistical analyses. Principal component analysis and chemical fingerprinting spectra suggested that five official species were differentiated from each other except for Uncaria hirsuta and Uncaria sinensis. Furthermore, orthogonal partial least squares discrimination analysis was performed to discriminate confused two species, and resulted in the discovery of nine contributing markers. Ultimately, a Support Vector Machine model was developed to recognize five species and predict origins of commercial materials. The study demonstrated that the developed strategy was effective in discrimination and recognition of confused species, and promising in tracking botanical origins of commercial materials.

In order to run a localization filter for parking systems in real time, the directional information must be directly available when a distance measurement of the wheel speed sensor is detected. When the vehicle is launching, the wheel speed sensors may already detect distance measurement in the form of Delta-Wheel-Pulse-Counts (DWPCs) without having defined a rolling direction. This phenomenon is particularly problematic during parking maneuvers, where many small correction strokes are made. If a localization filter is used for positioning, the restrained DWPCs cannot process in real time. Without directional information in the form of a rolling direction signal, the filter has to ignore the DWPCs or artificially stop until a rolling direction signal is present. For this reason, methods for earlier estimation of the rolling direction based on the pattern of the incoming DWPCs and based on the force equilibrium have been presented. Since the new methods still have their weaknesses and a wrong estimation of the rolling direction can occur, an extension of a so-called Dual-Localization filter approach is presented. The Dual-Localization filter uses two localization filters and an intelligent initialization logic that ensures that both filters move in opposite directions at launching. The primary localization filter uses the estimated and the secondary one the opposite direction. As soon as a valid rolling direction signal is present, an initialization logic is used to decide which localization filter has previously moved in the true direction. The localization filter that has moved in the wrong direction is initialized with the states and covariances of the other localization filter. This extension allows for a fast and real-time capability to be achieved, and the accumulated velocity error can be dramatically reduced.

Myoelectric control of multifunctional prostheses is challenging for individuals with high-level amputations due to insufficient surface electromyography (sEMG) signals. A surgical technique called targeted muscle reinnervation (TMR) has achieved impressive improvements in myoelectric control by providing more sEMG control signals. In this case, some channels of sEMG signals are coupled after TMR, which limits the performance of conventional amplitude-based control for upper-limb prostheses.
In this paper, two different ways (training and algorithms) were attempted to solve the problem in a transhumeral amputee after TMR. Firstly, effect of rehabilitation training on generating independent sEMG signals was investigated. The results indicated that some sEMG signals recorded were still coupled over the targeted muscles after rehabilitation training for about two months. Secondly, pattern recognition (PR) algorithm was then applied to classify the sEMG signals. In the second way, to further improve the real-time performance of prosthetic control, a post-processing method named as mean absolute value-based (MAV-based) threshold switches was utilized.
Using the improved algorithms, substantial improvement was shown in a subset of the modified Action Research Arm Test (ARAT). Compared with common PR control without post-processing method, the total scores increased more than 18% with majority vote and more than 58% with MAV-based threshold switches. The amputee was able to finish all the tasks within the allotted time with the standard MAV-based threshold switches. Subjectively the amputee preferred the PR control with MAV-based threshold switches and reported it to be more accurate and much smoother both in experiment and practical use.
Although the sEMG signals were still coupled after rehabilitation training on the TMR patient, the online performance of the prosthetic operation was improved through application of PR control with combination of the MAV-based threshold switches.
Retrospectively registered http://www.chictr.org.cn/showproj.aspx?proj=22058 .

Phantom motor execution (PME), facilitated by myoelectric pattern recognition (MPR) and virtual reality (VR), is positioned to be a viable option to treat phantom limb pain (PLP). A recent clinical trial using PME on upper-limb amputees with chronic intractable PLP yielded promising results. However, further work in the area of signal acquisition is needed if such technology is to be used on subjects with lower-limb amputation. We propose two alternative electrode configurations to conventional, bipolar, targeted recordings for acquiring surface electromyography. We evaluated their performance in a real-time MPR task for non-weight-bearing, lower-limb movements. We found that monopolar recordings using a circumferential electrode of conductive fabric, performed similarly to classical bipolar recordings, but were easier to use in a clinical setting. In addition, we present the first case study of a lower-limb amputee with chronic, intractable PLP treated with PME. The patient\'s Pain Rating Index dropped by 22 points (from 32 to 10, 68%) after 23 PME sessions. These results represent a methodological advancement and a positive proof-of-concept of PME in lower limbs. Further work remains to be conducted for a high-evidence level clinical validation of PME as a treatment of PLP in lower-limb amputees.