Miracle-dz/UniFAR-FTUs huggingface dataset:
Out-of-Plane Corrections for Autonomous Robotic Breast Ultrasound Acquisitions

Breast cancer affects one out of eight women. Ultrasound (US) plays an important role in the diagnostic workflow, especially during the biopsy phase, in which tissue is extracted from the lesion for further analysis. The extension from 2D to 3D US acquisitions has multiple benefits including enhanced lesion localization and improved registration with MRI data. Current commercial 3D US systems lack the ability to preserve the breast’s original shape. Robotic US scanners follow tailored trajectories and produce high quality volumes by accurate localization of 2D slices captured with a conventional linear probe. Current methods require a patient specific model to plan the scanning trajectory.In this study we investigate how to change the direction of the scanning trajectory based on US feedback, such that no patient specific model is required to perform a scan. In our method, the scanning trajectory is kept tangent to the breast based on confidence maps of the US images and an estimation of current radius of curvature of the surface. We evaluated our approach on a realistic breast phantom. The robot revolves around the breast without prior knowledge of its shape. In ten scans, the RMS error between the probe’s scanning plane and the breast’s surface normal is 12.6° out-of-plane, and 4.3° in-plane. A 3D US reconstruction shows the acquired data. This is a step forward to fully autonomous, high quality robotic US volume acquisitions.
PLOP: Probabilistic Polynomial Objects trajectory Prediction for autonomous driving

: To navigate safely in urban environments, an autonomous vehicle ( ego vehicle ) must understand and anticipate its surroundings, in particular the behavior and intents of other road users ( neighbors ). Most of the times, multiple decision choices are acceptable for all road users (e.g., turn right or left, or different ways of avoiding an obstacle), leading to a highly uncertain and multi-modal decision space. We focus here on predicting multiple feasible future trajectories for both ego vehicle and neighbors through a probabilistic framework. We rely on a conditional imitation learning algorithm, conditioned by a navigation command for the ego vehicle (e.g., “turn right”). Our model processes ego vehicle front-facing camera images and bird-eye view grid, computed from Lidar point clouds, with detections of past and present objects, in order to generate multiple trajectories for both ego vehicle and its neighbors. Our approach is computationally efﬁcient and relies only on on-board sensors. We evaluate our method ofﬂine on the publicly available dataset nuScenes, achieving state-of-the-art performance, investigate the impact of our architecture choices on online simulated experiments and show preliminary insights for real vehicle control.
Affect Recognition Using Dynamic Characteristics of Motion

Recently, there has been a lot of work on affect recognition which is a relatively new research field. Here the detection and recognition of multiple emotions such as anger, joy, fear, etc. are being carried out. Furthermore, the research on affective computing is being conducted using various modalities, such as; facial expressions, bodily expressions, and speech, with facial expressions being the most researched modality. However, many researchers have lately highlighted the significance of bodily expressions in affect detection. In this paper, we have considered bodily expressions for recognizing affect and proposed a modified approach using dynamic characteristics of motion. The experiments were performed in MATLAB using the MPIIEmo dataset and results were compared with the existing research.
Distributed Predictive Drone Swarms in Cluttered Environments

Recent works in aerial robotics show that the self-organized and cohesive flight of swarms can emerge from the exchange of purely local information between neighboring agents. However, most of the current swarm models are not capable of flight in densely cluttered environments. Predictive models have the potential to incorporate safe collision avoidance capabilities and give the agents the ability to anticipate and synchronize their trajectories in real-time. Here, we propose a distributed predictive swarm model that generates self-organized, safe, and cohesive trajectories by solving an optimization problem in real-time. In simulation, we show that our method is scalable to large numbers of agents and suitable for deployment in different environments, specifically a forest and a funnel-like environment. Furthermore, our results show that the agents are capable of collision-free flight with noisy sensor measurements for a noise level of up to 70% of the magnitude of the agent safety distance. Real-world experiments with a swarm of up to 16 quadrotors in an indoor artificial environment validate our method. Supplementary Materials can be found at https://doi.org/10.5281/zenodo.5245214.
Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review

At the present time, water and sewer pipe networks are predominantly inspected manually. In the near future, smart cities will perform intelligent autonomous monitoring of buried pipe networks, using teams of small robots. These robots, equipped with all necessary computational facilities and sensors (optical, acoustic, inertial, thermal, pressure and others) will be able to inspect pipes whilst navigating, self-localising and communicating information about the pipe condition and faults such as leaks or blockages to human operators for monitoring and decision support. The predominantly manual inspection of pipe networks will be replaced with teams of autonomous inspection robots that can operate for long periods of time over a large spatial scale. Reliable autonomous navigation and reporting of faults at this scale requires effective localization and mapping, which is the estimation of the robot’s position and its surrounding environment. This survey presents an overview of state-of-the-art works on robot simultaneous localization and mapping (SLAM) with a focus on water and sewer pipe networks. It considers various aspects of the SLAM problem in pipes, from the motivation, to the water industry requirements, modern SLAM methods, map-types and sensors suited to pipes. Future challenges such as robustness for long term robot operation in pipes are discussed, including how making use of prior knowledge, e.g. geographic information systems (GIS) can be used to build map estimates, and improve multi-robot SLAM in the pipe environment.
Saliency Features for 3D CAD-Data in the Context of Sampling-Based Motion Planning

In this paper, we consider disassembly scenarios for real-world 3D CAD-data, where each component is defined by a triangle mesh. For a fast construction of collision-free disassembly paths, common approaches use sampling-based rigid body motion planning which is well studied in the literature. One fact that has so far received little attention is that in industrial disassembly scenarios components are often attached to each other with flexible fastening elements like clips. In the planning process, the fastening elements show the following characteristics: 1) They can cause complex non-linear disassembly paths. 2) They are often deformable. 3) They are usually modeled in a relaxed state and as an unknown part of the rigid mesh. That leads to the problem that unavoidable collisions occur during the planning process. Hence, the localization of the fastening elements and the integration of this information into the motion planning process is crucial for an automatic disassembly.We present a new geometric solution to extract salient features of 3D meshes which is specialized to find the fastening elements within the otherwise rigid mesh. Our approach measures a vertex-based surface feature using a local Gauss map in combination with a local thickness computation of the mesh. We compare our surface feature to state-of-the-art mesh saliency methods on various examples. Further, we integrate this measure of per-vertex saliency into a motion planning process and demonstrate the effectiveness of our result on real-world planning scenarios from the automotive industry.
Test–Retest Reliability and Reliable Change Estimates for Four Mobile Cognitive Tests Administered Virtually in Community-Dwelling Adults

Objective: Remote mobile cognitive testing (MCT) is an expanding area of research, but psychometric data supporting these measures are limited. We provide preliminary data on test–retest reliability and reliable change estimates in four MCTs from SWAY Medical, Inc. Methods: Fifty-five adults from the U.S. Midwest completed the MCTs remotely on their personal mobile devices once per week for 3 consecutive weeks, while being supervised with a video-based virtual connection. The cognitive assessment measured simple reaction time (“Reaction Time”), go/no-go response inhibition (“Impulse Control”), timed visual processing (“Inspection Time”), and working memory (“Working Memory”). For each cognitive test except Working Memory, we analyzed both millisecond (ms) responses and an overall SWAY composite score. Results: The mean age of the sample was 26.69years (SD=9.89; range=18–58). Of the 55 adults, 38 (69.1%) were women and 49 (89.1%) used an iPhone. Friedman’s ANOVAs examining differences across testing sessions were nonsignificant (ps>0.31). Intraclass correlations for Weeks 1–3 were: Reaction Time (ms): 0.83, Reaction Time (SWAY): 0.83, Impulse Control (ms): 0.68, Impulse Control (SWAY): 0.80, Inspection Time (ms): 0.75, Inspection Time (SWAY): 0.75, and Working Memory (SWAY): 0.88. Intraclass correlations for Weeks 1–2 were: Reaction Time (ms): 0.75, Reaction Time (SWAY): 0.74, Impulse Control (ms): 0.60, Impulse Control (SWAY): 0.76, Inspection Time (ms): 0.79, Inspection Time (SWAY): 0.79, and Working Memory (SWAY): 0.83. Natural distributions of difference scores were calculated and reliable change estimates are presented for 70, 80, and 90% CIs. Conclusion: Test–retest reliability was adequate or better for the MCTs in this virtual remote testing study. Reliable change estimates allow for the determination of whether a particular level of improvement or decline in performance is within the range of probable measurement error. Additional reliability and validity data are needed in other age groups.
Expression of TSP50, SERCA2 and IL-8 in Colorectal Adenoma and Carcinoma: Correlation to Clinicopathological Factors

Background: Colorectal cancer (CRC) is the third most common type of cancer, it is considered a genetically heterogeneous disease with different molecular pathways being involved in its initiation and progression. Testes-specific protease 50 (TSP50) gene is a member of cancer/testis antigens that encodes for threonine protease enzyme. Overexpression of TSP50 was found to enhance the progression and invasion of breast cancer and other malignant tumors. SERCA2 is widely expressed in several body tissues; its aberrant expression has been involved in many cancers. IL-8 is an inflammatory cytokine. Alongside its role in inflammation, its expression was reported to induce the migration of tumor cells. Aim: Study the expression of TSP50, SERCA2 and IL-8 in colorectal adenoma (CRA), CRC and normal colonic tissues to compare the expression of these biomarkers in relation to clinicopathological parameters and prognostic factors. Results: TSP50, SERCA2 and IL-8 expression varied between normal colonic tissues, CRA and CRC. Significant statistical association was detected between the three biomarkers’ overexpression and degree of dysplasia in CRA. Also, significant statistical relation was found between the three biomarkers’ overexpression and presence of lympho-vascular invasion, advanced TNM staging and high intra-tumoral inflammatory infiltrate. Multivariable analysis showed that the overexpression of the three biomarkers is significantly associated with worse prognosis. Conclusion: The expression of TSP50, SERCA2 and IL-8 was different between the normal tissue and neoplastic colorectal tissue on one hand and between CRA and CRC on the other. Increased expression of these biomarkers in neoplastic epithelial cells of colorectal carcinoma is associated with adverse prognostic factors and could be considered as independent prognostic factors.
ReForm: A Robot Learning Sandbox for Deformable Linear Object Manipulation

Recent advances in machine learning have triggered an enormous interest in using learning-based approaches for robot control and object manipulation. While the majority of existing algorithms are evaluated under the assumption that the involved bodies are rigid, a large number of practical applications contain deformable objects. In this work we focus on Deformable Linear Objects (DLOs) which can be used to model cables, tubes or wires. They are present in many applications such as manufacturing, agriculture and medicine. New methods in robotic manipulation research are often demonstrated in custom environments impeding reproducibility and comparisons of algorithms. We introduce ReForm, a simulation sandbox and a tool for benchmarking manipulation of DLOs. We offer six distinct environments representing important characteristics of deformable objects such as elasticity, plasticity or self-collisions and occlusions. A modular framework is used, enabling design parameters such as the end-effector degrees of freedom, reward function and type of observation. ReForm is a novel robot learning sandbox with which we intend to facilitate testing and reproducibility in manipulation research for DLOs.
Risk-based EMC System Analysis Platform of Automotive Environments

Constantly on-going changes in new technologies applied in modern vehicles introduce many challenges in the automotive electromagnetic compatibility engineering. So far, the currently implemented EMC requirements and methods present sufficient performance. However, they do not illustrate thoroughly an actual automotive environment. They tend to focus on the EMC validation of each system individually without always considering other possible influential factors and coexisting systems. Aim of this paper is to introduce an EMC system investigation platform using a simplified model that demonstrates an automotive environment in order to point out the importance and scale of significance of various parameters. In this paper, the structure of the introduced three-point model is first described and explained. Then, experiments are presented in order to point out the influence of selected macro-parameters. Finally, suggestions for further extension of the model through Monte Carlo simulations are proposed with a brief presentation of a modelling procedure.
A Nomogram to Predict Disease-Free Survival Following Neoadjuvant Chemotherapy for Triple Negative Breast Cancer

Background Neoadjuvant chemotherapy (NACT) is considered a standard treatment strategy for locally advanced triple negative breast cancer (TNBC). TNBC patients who achieve a pathologic complete response (pCR) are predicted to have a better prognosis while unfavorable chemo-sensitivity is still associated with a higher risk of disease relapse. The objective of this study was to construct a nomogram to predict disease-free survival (DFS) for TNBC patients following NACT. Methods A total of 165 TNBC patients who underwent standard NACT and surgery were retrospectively reviewed, and data on their clinicopathological factors before and after NACT were collected. Independent prognostic factors for DFS were identified by Cox regression based on lower Akaike information criteria (AIC) and Bayesian information criterion (BIC). A nomogram to predict the 2-year and 5-year DFS following NACT for TNBC was constructed based on training cohort (n = 132) and validated by a validation cohort (n = 33). Results Either limited or full pCR (breast-only pCR, node-only pCR, or both-pCR) indicated significantly improved DFS and overall survival (OS) (p < 0.001). Lager residual tumor size (hazard ratio [HR] 1.175, p = 0.011) and the presence of lymphatic vessel invasion (LVI) (HR 3.168, p = 0.001) were identified as independent predictors of disease relapse in the training cohort. Five variables, including age, primary tumor size, histological grade, residual tumor size, and LVI were used to establish the nomogram. The C-index of the nomogram was 0.815, and calibration curves showed an acceptable consistency between the actual and nomogram-predicted 2-year and 5-year DFS. The proposed nomogram demonstrated superior predictive performance compared with Residual Cancer Burden (RCB) classification and the 8th American Joint Committee on Cancer Post Neoadjuvant Therapy Classification (AJCC ypTNM) staging system (area under the curve [AUC] for 2-year DFS: 0.870 vs. 0.758 vs. 0.711, respectively; AUC for 5-year DFS: 0.794 vs. 0.731 vs. 0.702, respectively) in the validation cohort. Conclusions The nomogram proposed in our study enabled to quantify the risk of disease relapse and demonstrated superior predictive performance than a survival predict instrument. It was an easy-to-use tool for clinicians to guide individualized surveillance of TNBC patients following standard NACT.
The Acoustic Change Complex in Response to Frequency Changes and Its Correlation to Cochlear Implant Speech Outcomes

One of the biggest challenges that face cochlear implant (CI) users is the highly variable hearing outcomes of implantation across patients. Since speech perception requires the detection of various dynamic changes in acoustic features (e.g., frequency, intensity, timing) in speech sounds, it is critical to examine the ability to detect the within-stimulus acoustic changes in CI users. The primary objective of this study was to examine the auditory event-related potential (ERP) evoked by the within-stimulus frequency changes (F-changes), one type of the acoustic change complex (ACC), in adult CI users, and its correlation to speech outcomes. Twenty-one adult CI users (29 individual CI ears) were tested with psychoacoustic frequency change detection tasks, speech tests including the Consonant-Nucleus-Consonant (CNC) word recognition, Arizona Biomedical Sentence Recognition in quiet and noise (AzBio-Q and AzBio-N), and the Digit-in-Noise (DIN) tests, and electroencephalographic (EEG) recordings. The stimuli for the psychoacoustic tests and EEG recordings were pure tones at three different base frequencies (0.25, 1, and 4 kHz) that contained a F-change at the midpoint of the tone. Results showed that the frequency change detection threshold (FCDT), ACC N1′ latency, and P2′ latency did not differ across frequencies (p > 0.05). ACC N1′-P2 amplitude was significantly larger for 0.25 kHz than for other base frequencies (p < 0.05). The mean N1′ latency across three base frequencies was negatively correlated with CNC word recognition (r = −0.40, p < 0.05) and CNC phoneme (r = −0.40, p < 0.05), and positively correlated with mean FCDT (r = 0.46, p < 0.05). The P2′ latency was positively correlated with DIN (r = 0.47, p < 0.05) and mean FCDT (r = 0.47, p < 0.05). There was no statistically significant correlation between N1′-P2′ amplitude and speech outcomes (all ps > 0.05). Results of this study indicated that variability in CI speech outcomes assessed with the CNC, AzBio-Q, and DIN tests can be partially explained (approximately 16–21%) by the variability of cortical sensory encoding of F-changes reflected by the ACC.
One Health Approaches to Trace Mycobacterium leprae’s Zoonotic Potential Through Time

Hansen’s disease (leprosy), mainly caused by infection with Mycobacterium leprae, has accompanied humanity for thousands of years. Although currently rare in Europe, there are over 200,000 new infections annually in South East Asia, Africa, and South America. Over the years many disciplines – palaeopathology, ancient DNA and other ancient biomolecules, and history – have contributed to a better understanding of leprosy’s past, in particular its history in medieval Europe. We discuss their contributions and potential, especially in relation to the role of inter-species transmission, an unexplored phenomenon in the disease’s history. Here, we explore the potential of interdisciplinary approaches that understand disease as a biosocial phenomenon, which is a product of both infection with M. leprae and social behaviours that facilitate transmission and spread. Genetic evidence of M. leprae isolated from archaeological remains combined with systematic zooarchaeological and historical analysis would not only identify when and in what direction transmission occurred, but also key social behaviours and motivations that brought species together. In our opinion, this combination is crucial to understand the disease’s zoonotic past and current potential.
Efficacy and Safety of Rotigotine Transdermal Patch on Neuropsychiatric Symptoms of Parkinson's Disease: An Updated Meta-Analysis and Systematic Review

Objective: The effects of rotigotine transdermal patch (RTG) on the neuropsychiatric symptoms of Parkinson's disease (PD) outcomes remain controversial. The aim of this review was to determine the efficacy and safety of RTG on the neuropsychiatric symptoms of PD. Methods: In this systematic review and meta-analysis, PubMed, Cochrane Library, EMBASE, and Web of Science were searched for randomized controlled trials comparing RTG and placebo in PD up to May 10, 2021. We analyzed the data using Review Manager 5.2 software. The quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation Approach. In order to avoid false-positive results caused by random error, we use TSA software for trial sequential analysis (TSA). Results: We included 10 studies (1,844 patients). The meta-analysis showed that, compared with placebo, RTG can significantly improve the scores for Apathy Scale (MD = −1.68, 95% confidence interval, CI: −2.74 to −0.62, P = 0.002; moderate certainty), Beck Depression Inventory-II (MD = −1.19, 95% CI: −2.26 to −0.11, P = 0.03; moderate certainty), the Non-Motor Symptoms Scale (MD = −3. 66, 95% CI: −4. 30 to −3.01, P < 0.00001; moderate certainty), the sleep/fatigue domains of the Parkinson's Disease Non-motor Symptom Assessment Scale (MD = −2.03, 95% CI: −3.08 to −0.98, P = 0.0001; moderate certainty), the mood/apathy domains of the Non-motor Symptom Scale (MD = −2.48, 95% CI: −4.07 to −0.89, P = 0.002; high certainty), the eight-item Parkinson's Disease Questionnaire (MD = −4. 93, 95% CI: −6.79 to −3.07, P < 0.00001; moderate certainty), and the 39-item Parkinson's Disease Questionnaire (MD = −3.52, 95% CI: −5.25 to −1.79, P < 0.0001; high certainty). However, there was no statistically significant difference on the Snaith–Hamilton Pleasure Scale (MD = −0.12, 95% CI: −0.58 to 0.34, P = 0.61). Our results showed that RTG exerts a positive effect on sleep. According to the TSA, the results implied that, except for the Beck Depression Inventory-II, conclusive evidence have been obtained in the RTG group. It has been proven in our meta-analysis that rotigotine has good safety and tolerability. Conclusions: RTG can effectively improve the neuropsychiatric symptoms, sleep quality, and quality of life in patients with PD.