IDEA challenge 2022 dataset: Prototypes from a design Hackathon.

The IDEA Challenge 2022 prototyping dataset comprises a total of 240 prototype entries with 1049 edges (connections) and can provide valuable insights into prototyping practices, offering practical knowledge essential for developing prototyping strategies and generating hypotheses for future studies. Data were collected using Pro2booth - an online platform which captured comprehensive information about prototypes and participating teams' development process, including details about the creators, purpose, timing, and methods of creation. It is particularly relevant to design researchers, engineering and design students, educators, and industry professionals seeking to enhance their prototyping skills and strategies. It serves as a robust foundation for subsequent studies, allowing for comparative analyses, hypothesis verification, and trend exploration. It also has the potential to inform meta-analyses across similar design scenarios, providing a comprehensive understanding of prototyping processes.

The TrollLabs open hackathon dataset: Generative AI and large language models for prototyping in engineering design.

The TrollLabs Open dataset includes comprehensive information that offers a comparison of design practices and outcomes between human participants and Generative AI during a hackathon event. The dataset was curated through the running of a prototyping hackathon designed to assess the abilities and performance of generative AI, specifically ChatGPT, in the early stages of engineering design. This assessment involved comparing ChatGPT's performance to that of experienced engineering students in a hackathon setting, where participants competed by making a prototype that fires a NERF dart as far as possible. In this setup, all ideas, concepts, strategies, and actions undertaken by the AI-controlled team were autonomously generated by the ChatGPT, without human intervention or guidance, but implemented by two participants. Five self-directed baseline teams competed against the AI team. The dataset comprises 116 prototype entries and 433 edges (connection) that enable comparative analysis of design practices and performance between the team instructed solely by generative AI and baseline teams of experienced engineering design students. Prototypes and their attribute data were captured using Pro2booth, an online prototype capture platform running on participants' phones and computers. The dataset includes a transcript of the conversation between ChatGPT and the team responsible for implementing its recommendations, featuring 97 exchanges of prompts and responses. It contains the initial prompt used to instruct the AI, the objective and rules of the hackathon and the objective performance of teams, showing the ChatGPT team finishing 2nd among six teams. To the authors' knowledge, the TrollLabs Open dataset is the first and only open resource that directly compares the performance of generative AI with human teams in an engineering design context. Thus, it is intended to be a valuable resource to design researchers, engineering and design students, educators, and industry professionals seeking to find strategies for implementing generative AI tools in their design processes. By offering a comprehensive data collection, the dataset enables external researchers to conduct in-depth analyses that could highlight the practical implications of integrating generative AI in design practices, possibly providing an overview of its limitations and presenting recommendations for improved integration in the design process.

Wireless sensor system for real-time performance monitoring in sports.

In Paralympic sports, investigating seating ergonomics and optimizing for performance is crucial due to individual impairments. Usually, experiments are conducted in laboratory environments and for skiing, usually on a treadmill. In this paper, we are moving experiments out of the laboratory setting to in-slope performance monitoring of kinetics and kinematics. A wireless sensor system is developed and validated in terms of delay. The results show a median delay of 52 ms for the wired main system and 53 ms for the wireless sub-system. The sensor system was implemented on a highly adjustable Paralympic sit-ski, and an experiment was conducted to pinpoint optimal equipment settings for an individual athlete. In addition, the system provided force data from both knees, seat, belt, and both poles. The data collected can also be used to analyze the technique, in addition to assisting in the classification process in the LW10-12 class. The proposed system design also allows for adding a vast amount of different sensor types, and by testing for delay, synchronized with well-known GNSS and IMU sensors already used in many sports to analyze athlete performance.

Playing the pipes: acoustic sensing and machine learning for performance feedback during endotracheal intubation simulation.

Objective: In emergency medicine, airway management is a core skill that includes endotracheal intubation (ETI), a common technique that can result in ineffective ventilation and laryngotracheal injury if executed incorrectly. We present a method for automatically generating performance feedback during ETI simulator training, potentially augmenting training outcomes on robotic simulators. Method: Electret microphones recorded ultrasonic echoes pulsed through the complex geometry of a simulated airway during ETI performed on a full-size patient simulator. As the endotracheal tube is inserted deeper and the cuff is inflated, the resulting changes in geometry are reflected in the recorded signal. We trained machine learning models to classify 240 intubations distributed equally between six conditions: three insertion depths and two cuff inflation states. The best performing models were cross validated in a leave-one-subject-out scheme. Results: Best performance was achieved by transfer learning with a convolutional neural network pre-trained for sound classification, reaching global accuracy above 98% on 1-second-long audio test samples. A support vector machine trained on different features achieved a median accuracy of 85% on the full label set and 97% on a reduced label set of tube depth only. Significance: This proof-of-concept study demonstrates a method of measuring qualitative performance criteria during simulated ETI in a relatively simple way that does not damage ecological validity of the simulated anatomy. As traditional sonar is hampered by geometrical complexity compounded by the introduced equipment in ETI, the accuracy of machine learning methods in this confined design space enables application in other invasive procedures. By enabling better interaction between the human user and the robotic simulator, this approach could improve training experiences and outcomes in medical simulation for ETI as well as many other invasive clinical procedures.

The Effect of Cellulose Nanofibres on Dewatering during Wet-Forming and the Mechanical Properties of Thermoformed Specimens Made of Thermomechanical and Kraft Pulps.

Due to environmental concerns regarding single-use plastic materials, major efforts are being made to develop new material concepts based on biodegradable and renewable resources, e.g., wood pulp. In this study, we assessed two types of wood pulp fibres, i.e., thermomechanical pulp (TMP) and Kraft pulp fibres, and tested the performance of the fibres in wet-moulding and thermopressing trials. Kraft pulp fibres appeared to retain more water than TMP, increasing the dewatering time during wet-moulding and apparently increasing the compression resistance of the pulp during thermoforming. Additionally, cellulose nanofibres (CNF) were added to the pulps, which improved the mechanical properties of the final thermopressed specimens. However, the addition of CNF to the pulps (from 2 to 6%) had a further decrease in the dewatering efficiency in the wet-moulding process, and this effect was more pronounced in the Kraft pulp specimens. The mechanical performance of the thermoformed specimens was in the same range as the plastic materials that are conventionally used in food packaging, i.e., modulus 0.6-1.2 GPa, strength 49 MPa and elongation 6-9%. Finally, this study demonstrates the potential of wood pulps to form three-dimensional thermoformed products.

Wrist ballistocardiography and invasively recorded blood pressure in healthy volunteers during reclining bike exercise.

Objective: Ballistocardiogram (BCG) features are of interest in wearable cardiovascular monitoring of cardiac performance. We assess feasibility of wrist acceleration BCG during exercise for estimating pulse transit time (PTT), enabling broader cardiovascular response studies during acute exercise and improved monitoring in individuals at risk for cardiovascular disease (CVD). We also examine the relationship between PTT, blood pressure (BP), and stroke volume (SV) during exercise and posture interventions. Methods: 25 participants underwent a bike exercise protocol with four incremental workloads (0 W, 50 W, 100 W, and 150 W) in supine and semirecumbent postures. BCG, invasive radial artery BP, tonometry, photoplethysmography (PPG) and echocardiography were recorded. Ensemble averages of BCG signals determined aortic valve opening (AVO) timings, combined with peripheral pulse wave arrival times to calculate PTT. We tested for significance using Wilcoxon signed-rank test. Results: BCG was successfully recorded at the wrist during exercise. PTT exhibited a moderate negative correlation with systolic BP (ρSup = -0.65, ρSR = -0.57, ρAll = -0.54). PTT differences between supine and semirecumbent conditions were significant at 0 W and 50 W (p < 0.001), less at 100 W (p = 0.0135) and 150 W (p = 0.031). SBP and DBP were lower in semirecumbent posture (p < 0.01), while HR was slightly higher. Echocardiography confirmed association of BCG features with AVO and indicated a positive relationship between BCG amplitude and SV (ρ = 0.74). Significance: Wrist BCG may allow convenient PTT and possibly SV tracking during exercise, enabling studies of cardiovascular response to acute exercise and convenient monitoring of cardiovascular performance.

Collaborative Work with Highly Automated Marine Navigation Systems.

In navigation applications, Artificial Intelligence (AI) can improve efficiency and decision making. It is not clear, however, how designers should account for human cooperation when integrating AI systems in navigation work. In a novel empirical study, we examine the transition in the maritime domain towards higher levels of machine autonomy. Our method involved interviewing technology designers (n = 9) and navigators aboard two partially automated ferries (n = 5), as well as collecting field observations aboard one of the ferries. The results indicated a discrepancy between how designers construed human-AI collaboration compared to navigators' own accounts in the field. Navigators reflected upon their role as one of 'backup,' defined by ad-hoc control takeovers from the automation. Designers positioned navigators 'in the loop' of a larger control system but discounted the role of in-situ skills and heuristic decision making in all but the most controlled takeover actions. The discrepancy shed light on how integration of AI systems may be better aligned to human cooperation in navigation. This included designing AI systems that render computational activities more visible and that incorporate social cues that articulate human work in its natural setting. Positioned within the field of AI alignment research, the main contribution is a formulation of human-AI interaction design insights for future navigation and control room work.

Facing the FACS-Using AI to Evaluate and Control Facial Action Units in Humanoid Robot Face Development.

This paper presents a new approach for evaluating and controlling expressive humanoid robotic faces using open-source computer vision and machine learning methods. Existing research in Human-Robot Interaction lacks flexible and simple tools that are scalable for evaluating and controlling various robotic faces; thus, our goal is to demonstrate the use of readily available AI-based solutions to support the process. We use a newly developed humanoid robot prototype intended for medical training applications as a case example. The approach automatically captures the robot's facial action units through a webcam during random motion, which are components traditionally used to describe facial muscle movements in humans. Instead of manipulating the actuators individually or training the robot to express specific emotions, we propose using action units as a means for controlling the robotic face, which enables a multitude of ways to generate dynamic motion, expressions, and behavior. The range of action units achieved by the robot is thus analyzed to discover its expressive capabilities and limitations and to develop a control model by correlating action units to actuation parameters. Because the approach is not dependent on specific facial attributes or actuation capabilities, it can be used for different designs and continuously inform the development process. In healthcare training applications, our goal is to establish a prerequisite of expressive capabilities of humanoid robots bounded by industrial and medical design constraints. Furthermore, to mediate human interpretation and thus enable decision-making based on observed cognitive, emotional, and expressive cues, our approach aims to find the minimum viable expressive capabilities of the robot without having to optimize for realism. The results from our case example demonstrate the flexibility and efficiency of the presented AI-based solutions to support the development of humanoid facial robots.

Automated Quantification of Human Osteoclasts Using Object Detection.

A balanced skeletal remodeling process is paramount to staying healthy. The remodeling process can be studied by analyzing osteoclasts differentiated in vitro from mononuclear cells isolated from peripheral blood or from buffy coats. Osteoclasts are highly specialized, multinucleated cells that break down bone tissue. Identifying and correctly quantifying osteoclasts in culture are usually done by trained personnel using light microscopy, which is time-consuming and susceptible to operator biases. Using machine learning with 307 different well images from seven human PBMC donors containing a total of 94,974 marked osteoclasts, we present an efficient and reliable method to quantify human osteoclasts from microscopic images. An open-source, deep learning-based object detection framework called Darknet (YOLOv4) was used to train and test several models to analyze the applicability and generalizability of the proposed method. The trained model achieved a mean average precision of 85.26% with a correlation coefficient of 0.99 with human annotators on an independent test set and counted on average 2.1% more osteoclasts per culture than the humans. Additionally, the trained models agreed more than two independent human annotators, supporting a more reliable and less biased approach to quantifying osteoclasts while saving time and resources. We invite interested researchers to test their datasets on our models to further strengthen and validate the results.

A low-cost vibration isolation chamber - Making high precision experiments accessible.

Mechanical vibrations greatly influence sensitive instruments and experiments, yet they are unavoidable. Commercial solutions that mitigate the transfer of mechanical vibrations into experiments and instruments are often associated with high prices and big footprints and are not readily available for low investment explorative testing, experimenting, and prototyping. In this paper, an open-source design for a vibration isolation chamber is presented that is constructed from readily available components and hardware such as off-the-shelf furniture and honey. An extensive guide on how to construct the simple spring-damper-based passive vibration isolation chamber is presented, and its performance is validated using a high-precision seismic accelerometer. The vibration isolation system consists of steel springs and dashpots made of steel spheres suspended in high viscosity honey. The system resonates at 1.2 Hz and successfully mitigates the transfer of vibrations of frequencies determined to be of critical interest in the 5-20 Hz range. The well-performing system has proven to be an invaluable asset in the laboratory toolbox when sensitive experiments are carried out and has already been used in a multitude of projects. The design is shared so that others may also benefit from this tool.

High-performance polymer 3D printing - Open-source liquid cooled scalable printer design.

To print high-performance polymers, a stable running printer that can reach high temperatures is needed. There is currently a lack of low-cost solutions that allow manipulation of process parameters and expansion of sensors to monitor the printer as well as the process. This paper presents an open-source hardware upgrade for low-cost 3D printers to enable research on new high-temperature polymers as well as manufacturing from all currently available polymers. The hardware cost less than $1700, including the printer. Open-source firmware by Klipper and Fluidd is used for control. The printer is able to reach 500 °C nozzle, 200 °C heated bed, and 135 °C heated chamber with all electronics inside operating within the recommended temperature range. The presented design produced a CF-PEEK 3DBenchy and a spiral vase with excellent surface quality and no signs of delamination. Test specimens according to ISO527 using PA-CF performed similarly to the datasheet provided by the manufacturer for samples produced in the XY-orientation and outperformed the datasheet by 15 % in the ZX direction. Compared to specimens made on an Original Prusa i3 MK3S, the modified printer produced specimens with 22% higher strength in the YX-direction and 25% in ZX. By continuously monitoring and carefully calibrating both hardware and firmware, the presented design can perform as a research tool in material science and produce large-scale components of high-performance polymers.

Perception by Palpation: Development and Testing of a Haptic Ferrogranular Jamming Surface.

Tactile hands-only training is particularly important for medical palpation. Generally, equipment for palpation training is expensive, static, or provides too few study cases to practice on. We have therefore developed a novel haptic surface concept for palpation training, using ferrogranular jamming. The concept's design consists of a tactile field spanning 260 x 160 mm, and uses ferromagnetic granules to alter shape, position, and hardness of palpable irregularities. Granules are enclosed in a compliant vacuum-sealed chamber connected to a pneumatic system. A variety of geometric shapes (output) can be obtained by manipulating and arranging granules with permanent magnets. The tactile hardness of the palpable output can be controlled by adjusting the chamber's vacuum level. A psychophysical experiment (N = 28) investigated how people interact with the palpable surface and evaluated the proposed concept. Untrained participants characterized irregularities with different position, form, and hardness through palpation, and their performance was evaluated. A baseline (no irregularity) was compared to three irregularity conditions: two circular shapes with different hardness (Hard Lump and Soft Lump), and an Annulus shape. 100% of participants correctly identified an irregularity in the three irregularity conditions, whereas 78.6% correctly identified baseline. Overall agreement between participants was high (κ= 0.723). The Intersection over Union (IoU) for participants sketched outline over the actual shape was IoU Mdn = 79.3% for Soft Lump, IoU Mdn = 68.8% for Annulus, and IoU Mdn = 76.7% for Hard Lump. The distance from actual to drawn center was Mdn = 6.4 mm (Soft Lump), Mdn = 5.3 mm (Annulus), and Mdn = 7.4 mm (Hard Lump), which are small distances compared to the size of the field. The participants subjectively evaluated Soft Lump to be significantly softer than Hard Lump and Annulus. Moreover, 71% of participants thought they improved their palpation skills throughout the experiment. Together, these results show that the concept can render irregularities with different position, form, and hardness, and that users are able to locate and characterize these through palpation. Participants experienced an improvement in palpation skills throughout the experiment, which indicates the concepts feasibility as a palpation training device.

Real-World fNIRS Brain Activity Measurements during Ashtanga Vinyasa Yoga.

Functional near-infrared spectroscopy (fNIRS) is often praised for its portability and robustness towards motion artifacts. While an increasing body of fNIRS research in real-world environments is emerging, most fNIRS studies are still conducted in laboratories, and do not incorporate larger movements performed by participants. This study extends fNIRS applications in real-world environments by conducting a single-subject observational study of a yoga practice with considerable movement (Ashtanga Vinyasa Yoga) in a participant's natural environment (their apartment). The results show differences in cognitive load (prefrontal cortex activation) when comparing technically complex postures to relatively simple ones, but also some contrasts with surprisingly little difference. This study explores the boundaries of real-world cognitive load measurements, and contributes to the empirical knowledge base of using fNIRS in realistic settings. To the best of our knowledge, this is the first demonstration of fNIRS brain imaging recorded during any moving yoga practice. Future work with fNIRS should take advantage of this by accomplishing studies with considerable real-world movement.

Case Report: Adjusting Seat and Backrest Angle Improves Performance in an Elite Paralympic Rower.

Paralympic rowers with functional impairments of the legs and trunk rely on appropriate seat configurations for performance. We compared performance, physiology, and biomechanics of an elite Paralympic rower competing in the PR1 class during ergometer rowing in a seat with three different seat and backrest inclination configurations. Unlike able-bodied rowers, PR1 rowers are required to use a seat with a backrest. For this study, we examined the following seat/backrest configurations: conA: 7.5°/25°, conB: 0°/25°, and conC: 0°/5° (usually used by the participant). All data was collected on a single day, i.e., in each configuration, one 4-min submaximal (100 W) and one maximal (all-out) stage was performed. The rowing ergometer provided the average power and (virtual) distance of each stage, while motion capture provided kinematic data, a load cell measured the force exerted on the ergometer chain, and an ergospirometer measured oxygen uptake ( V ˙ O 2 ). Where appropriate, a Friedman's test with post-hoc comparisons performed with Wilcoxon signed-ranked tests identified differences between the configurations. Despite similar distances covered during the submaximal intensity (conA: 793, conB: 793, conC: 787 m), the peak force was lower in conC (conA: 509, conB: 458, conC: 312 N) while the stroke rate (conA: 27 conB: 31, conC: 49 strokes·min-1) and V ˙ O 2 (conA: 34.4, conB: 35.4, conC: 39.6 mL·kg-1·min-1) were higher. During the maximal stage, the virtual distances were 7-9% longer in conA and conB, with higher peak forces (conA: 934 m, 408 N, conB: 918 m, 418 N, conC: 856 m, 331 N), and lower stroke rates (conA: 51, conB: 54, conC: 56 strokes·min-1), though there was no difference in V ˙ O 2 peak (~47 ml-1·kg-1·min-1). At both intensities, trunk range of motion was significantly larger in configurations conA and conB. Although fatigue may have accumulated during the test day, this study showed that a more inclined seat and backrest during ergometer rowing improved the performance of a successful Paralympic PR1 rower. The considerable increase in ergometer rowing performance in one of the top Paralympic rowers in the world is astonishing and highlights the importance of designing equipment that can be adjusted to match the individual needs of Paralympic athletes.

Detecting glass and metal in consumer trash bags during waste collection using convolutional neural networks.

We present a proof-of-concept method to classify the presence of glass and metal in consumer trash bags. With the prevalent utilization of waste collection trucks in municipal solid waste management, the aim of this method is to help pinpoint the locations where waste sorting quality is below accepted standards, making it possible and more efficient to develop tailored procedures that can improve the waste sorting quality in areas with the most urgent needs. Using trash bags containing various amounts of glass and metal, in addition to common waste found in households, we use a combination of sound recording and a beat-frequency oscillation metal detector as inputs to a machine learning algorithm to identify the occurrence of glass and metal in trash bags. A custom-built test rig was developed to mimic a real waste collection truck, which was used to test different sensors and build the datasets. Convolutional neural networks were trained for the classification task, achieving accuracies of up to 98%. These promising results support this method's potential implementation in real waste collection trucks, enabling location-specific and long-term monitoring of consumer waste sorting quality, which can provide decision support for waste management systems, and research on consumer behavior.

Integrated Flow Chamber System for Live Cell Microscopy.

In vitro quantification of the effect of mechanical loads on cells by live microscopy requires precise control of load and culture environment. Corresponding systems are often bulky, their setup and maintenance are time consuming, or the cell yield is low. Here, we show the design and initial testing of a new cell culture system that fits on standard light microscope stages. Based on the parallel plate principle, the system allows for live microscopy of cells exposed to flow-induced shear stress, features short setup time and requires little user interaction. An integrated feedback-controlled heater and a bubble trap enable long observation times. The key design feature is the possibility for quick exchange of the cultured cells. We present first test results that focus on verifying the robustness, biocompatibility, and ease of use of the device.

Resuscitative endovascular balloon occlusion of the aorta (REBOA) in non-traumatic out-of-hospital cardiac arrest: evaluation of an educational programme.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is a critical incident with a high mortality rate. Augmentation of the circulation during cardiopulmonary resuscitation (CPR) might be beneficial. Use of resuscitative endovascular balloon occlusion of the aorta (REBOA) redistribute cardiac output to the organs proximal to the occlusion. Preclinical data support that patients in non-traumatic cardiac arrest might benefit from REBOA in the thoracic level during CPR. This study describes a training programme to implement the REBOA procedure to a prehospital working team, in preparation to a planned clinical study. METHODS: We developed a team-based REBOA training programme involving the physicians and paramedics working on the National Air Ambulance helicopter base in Trondheim, Norway. The programme consists of a four-step approach to educate, train and implement the REBOA procedure in a simulated prehospital setting. An objective structured assessment of prehospital REBOA application scoring chart and a special designed simulation mannequin was made for this study. RESULTS: Seven physicians and 3 paramedics participated. The time needed to perform the REBOA procedure was 8.5 (6.3-12.7) min. The corresponding time from arrival at scene to balloon inflation was 12.0 (8.8-15) min. The total objective assessment scores of the candidates' competency was 41.8 (39-43.5) points out of 48. The advanced cardiovascular life support (ACLS) remained at standard quality, regardless of the simultaneous REBOA procedure. CONCLUSION: This four-step approach to educate, train and implement the REBOA procedure to a prehospital working team ensures adequate competence in a simulated OHCA setting. The use of a structured training programme and objective assessment of skills is recommended before utilising the procedure in a clinical setting. In a simulated setting, the procedure does not add significant time to the prehospital resuscitation time nor does the procedure interfere with the quality of the ACLS. TRIAL REGISTRATION NUMBER: NCT03534011.

Using Low-Cost Sensors to Develop a High Precision Lifting Controller Device for an Overhead Crane-Insights and Hypotheses from Prototyping a Heavy Industrial Internet Project.

The subject of this study was the product development project creating a new innovative proof-of-concept (POC) prototype device that could control a connected industrial overhead crane in order to perform automatic or semi-automatic high precision lifts within a limited time frame. The development work focused on innovating a new measuring concept, which was parallel to finding suitable sensors for the application. Furthermore, the project resulted in a closed loop control system with Industrial Internet connected sensors and a user interface for factory workers. The prototyping journey is depicted to illustrate the decisions made during the product development project to contribute to both the pragmatic and the process discussion in the field of Industrial Internet. The purpose of this research was to explore and generate hypotheses for how new applications should be developed for heavy industry connected devices. The research question is: what are the implications of applying agile product development methods, such as Wayfaring, to heavy industrial machinery and Industrial Internet -based problems? The methodologies used in this paper, in addition to developing the device, are case study research and hypotheses generated from case studies. The hypotheses generated include that it is also possible to prototype large size connected machinery with low-cost and in a short time, and investment decisions for heavy Industrial Internet products become easier with concrete data from proof-of-concept prototypes by creating knowledge about the investment risk and the value proposition.

ALG6-CDG: a recognizable phenotype with epilepsy, proximal muscle weakness, ataxia and behavioral and limb anomalies.

INTRODUCTION: Alpha-1,3-glucosyltransferase congenital disorder of glycosylation (ALG6-CDG) is a congenital disorder of glycosylation. The original patients were described with hypotonia, developmental disability, epilepsy, and increased bleeding tendency. METHODS: Based on Euroglycan database registration, we approached referring clinicians and collected comprehensive data on 41 patients. RESULTS: We found hypotonia and developmental delay in all ALG6-CDG patients and epilepsy, ataxia, proximal muscle weakness, and, in the majority of cases, failure to thrive. Nine patients developed intractable seizures. Coagulation anomalies were present in <50 % of cases, without spontaneous bleedings. Facial dysmorphism was rare, but seven patients showed missing phalanges and brachydactyly. Cyclic behavioral change, with autistic features and depressive episodes, was one of the most significant complaints. Eleven children died before the age of 4 years due to protein losing enteropathy (PLE), sepsis, or seizures. The oldest patient was a 40 year-old Dutch woman. The most common pathogenic protein alterations were p.A333V and p.I299Del, without any clear genotype-phenotype correlation. DISCUSSION: ALG6-CDG has been now described in 89 patients, making it the second most common type of CDG. It has a recognizable phenotype and a primary neurologic presentation.