机器人

Cerebrovascular disease remains a leading cause of global mortality. While endovascular therapies like mechanical thrombectomy and aneurysm coiling have revolutionized management, manual techniques are limited by operator fatigue, tremor, and occupational radiation exposure. This review examines the current state of robotics in neuroendovascular surgery. FDA-approved systems demonstrate safety and technical feasibility, achieving high success rates (94-100%) in aneurysm coiling and carotid stenting while significantly reducing operator radiation exposure (60-80%). In ischemic stroke, preclinical and recent clinical milestones, including the first fully remote, in-human diagnostic angiogram and transcontinental thrombectomy simulations, validate the feasibility of long-distance tele-interventions. Robotics in cerebrovascular management is rapidly evolving from a novel adjunct to clinical reality. Although challenges regarding cost, haptic feedback, and training persist, next-generation platforms integrating artificial intelligence and magnetic navigation hold the potential to overcome these barriers, establishing remote telerobotic networks to expand access to time-critical stroke care.

This article examines the growing role of robotics in the cardiac catheterization laboratory as interventional cardiology moves toward more complex coronary and structural heart procedures. Robotic systems improve precision through motion scaling and tremor reduction while reducing operator fatigue and cumulative radiation exposure. The article argues that successful adoption depends not only on mechanical accuracy, but also on workflow integration, human-machine collaboration, safety design, regulatory readiness, and financial viability. It highlights the challenges of fitting robotic platforms into fast-paced cath lab environments where setup time, team coordination, and rapid conversion to manual control are critical. The discussion also explores remote robotic intervention to distribute specialist expertise across sites with limited staffing depth. Finally, the article considers how artificial intelligence, computer vision, and predictive analytics may extend robotic platforms from assistive tools into more intelligent systems that support safer, more scalable, and more accessible cardiovascular care.

Tomato leaf diseases substantially reduce tomato yields and quality and remain a persistent challenge for efficient crop management. Although deep learning-based detectors have achieved strong accuracy in controlled benchmarks, many existing solutions are still difficult to transfer to resource-constrained agricultural systems because they rely on high-end GPUs, consume considerable power, and often lose performance after deployment on embedded devices. To address this practical gap, this study proposes HGS-YOLO, a system-oriented deployable lightweight adaptation of YOLOv11 for leaf-level tomato disease detection, together with an end-to-end edge sensing pipeline for low-power agricultural deployment. The main contribution lies in the coordinated system-level co-design of model structure, optimization, and deployment rather than in a novel detector architecture. Specifically, YOLOv11 is adapted through three coordinated modifications: an HGNetV2 backbone for efficient feature extraction, an HS-FPN neck with channel attention for lightweight multi-scale fusion, and an MPDIoU loss function for more stable localization optimization. Beyond the model architecture, the study establishes a complete engineering pipeline that includes training, optimization, post-training quantization, and hardware deployment with BPU acceleration on a D-Robotics RDK X5 handheld platform. Comprehensive benchmark experiments indicate that HGS-YOLO achieves 93.6% mAP50 and 72.1% mAP@[0.5:0.95] with 86.5% recall, only 1.3 M parameters, and a 3.1 MB model size, substantially reducing the model complexity and storage cost relative to the YOLOv11 baseline. A three-seed retraining comparison shows that HGS-YOLO trades roughly 0.5 mAP50 points for this compactness (a statistically significant but small concession) and recovers the cost on the deployment side: on the RDK X5 chip, HGS-YOLO is the fastest, most memory-efficient, and lowest-power model among all compared detectors. Indoor deployment tests using separately collected tomato leaf samples further achieve 90.3% mAP50, 82.3% recall, 89.0% precision, 25.0 ± 0.4 ms end-to-end latency, 40.0 ± 0.6 FPS, and 9.8 ± 0.4 W average system power. After PTQ, the mAP50 drops from 93.6% to 93.0% on the same benchmark; because this figure was measured under controlled imaging conditions, it is presented as an in-distribution reference point rather than as evidence of robustness in the open field. We also took the handheld system into a working tomato greenhouse for a small outdoor field round, where it ran end-to-end and produced on-device disease detections under natural sunlight, specular highlights, partial occlusion, background clutter, and handheld motion blur. These results show that HGS-YOLO reaches a good balance of accuracy, efficiency, and deployability and that it works in the field on an independent small-scale test; validating it more widely across sites, seasons, and weather is left to future work.

The convergence of embodied intelligence and world models has catalyzed growing interest in integrating physical laws into AI systems. While prior surveys have examined world models and embodied intelligence separately, we focus on the progression that connects these capabilities as a unified developmental pathway from passive observation to active physical comprehension. This survey provides a systematic framework revealing how physical AI advances through four interconnected stages: perception transforms sensory data into structured physical representations, reasoning derives explanations from observed phenomena, modeling enables predictive simulation grounded in physical principles, and embodied interaction closes the loop through physical manipulation and environmental feedback. Each stage enables and enhances the next: perceptual grounding supports causal reasoning, reasoning unlocks predictive capabilities, and robust models drive genuine physical interaction. Through analysis of developments spanning architectural innovations, training methodologies, causal inference, and embodied systems, we synthesize how physical understanding emerges through cumulative integration across this progression. Our framework reveals the evolution from isolated, task-specific solutions toward integrated architectures that advance from pattern recognition toward causal reasoning and counterfactual prediction. This perspective provides foundations for next-generation physical AI systems with direct implications for safe, generalizable, and interpretable deployment across robotics, scientific discovery, and autonomous systems. We maintain a continuously updated taxonomy repository at https://github.com/AI4Phys/Awesome-AI-for-Physics.

As robotics technology advances, quadruped robots have become capable of operating in complex environments with varying elevation, including ramps and level changes that are challenging for conventional wheeled platforms. While this terrain adaptability opens new opportunities for inspection, rescue, and exploration tasks, the repetitive impacts, frequent ground-contact transitions, and abrupt postural changes inherent to legged locomotion pose significant challenges for LiDAR odometry. High-frequency gait vibrations and abrupt attitude changes introduce intra-scan motion distortion that conventional single-twist deskewing cannot adequately suppress. In addition, sparse vertical geometric constraints in elevation-varying environments weaken Z-axis observability, allowing vertical drift to corrupt the horizontal pose estimate through Hessian coupling. To address these failure modes within a LiDAR-only framework, we propose a Piecewise-Constant Velocity deskewing scheme that partitions each scan into multiple temporal segments with safety clamping on vertical and attitude components, together with a two-stage ICP that decouples SE(3) optimization into horizontal (x, y, yaw) and vertical (z, roll, pitch) stages and applies observability-aware weighting in the vertical update. The proposed odometry front-end is evaluated on four real-world sequences collected with a Unitree Go2 quadruped robot equipped with a Velodyne VLP-16 LiDAR. Experimental results show consistently lower Absolute Pose Error (APE) than ICP, KISS-ICP, and F-LOAM across all sequences. Vertical drift suppression is most pronounced in the ramp-containing sequences, where baseline methods exhibit substantial Z-axis divergence.

Multimodal sensors can collect multiple signals and have great potential in robotics and other technical fields. However, such sensors often encounter challenges of signal crosstalk and insufficient real-time performance, particularly in the detection of pressure and temperature, which significantly affect measurement accuracy. To address this issue, a multimodal PCSC sensor was developed. This sensor reduces signal crosstalk by separating force and temperature signals. It uses the pressure-resistance variation of carbon quantum dots (CQDs) to detect force and the thermochromic properties of spiropyran (SP) to detect temperature. When pressure and temperature act on the sensor simultaneously, the resistance increases with pressure and stabilizes when the pressure becomes constant. The response time is 0.4 s. As the temperature rises, the resistance decreases, and the color becomes deeper. Both resistance and color stabilize within 7.5 s. To improve temperature sensing accuracy, a lightweight ResNet-Transformer network (LRTNet) was proposed. This algorithm combines ResNet's ability to extract features and Transformer's ability to model sequences. It efficiently fuses color and resistance signals for temperature detection. Tests on a robotic manipulator for dual recognition of temperature and force showed that LRTNet achieved a runtime of 152.08 ms and a temperature sensing accuracy of 95%. LRTNet improved overall performance by at least 11% compared to traditional algorithms. The sensor and algorithm improved the performance and reliability of multimodal sensors.

On-orbit micro-vibration has emerged as a critical constraint impairing the imaging performance and ultra-high pointing accuracy of space optical payloads. Most existing investigations separately concentrate on disturbance modeling, vibration isolation design, or line-of-sight (LOS) stabilization, leaving the full-link integrated dynamic modeling and analysis severely insufficient. To address this gap, this paper proposes an integrated dynamic modeling methodology for spacecraft equipped with optical payloads, which synergizes disturbance identification, finite element modeling, model order reduction, hybrid active-passive vibration isolation mechanism control, and fast steering mirror (FSM) regulation. The experimental and simulation results demonstrate that the root mean square (RMS) acceleration induced by flywheels and pumps at the mounting interface of the vibration isolation mechanism approximates 4.50 mg. Specifically, the passive vibration isolation scheme attains an attenuation of -16 dB, while the hybrid active-passive strategy achieves a remarkable -30 dB attenuation. Moreover, flywheels generate lower acceleration amplitude but more severe LOS jitter, owing to their time-varying disturbance characteristics and dispersed frequency energy distribution. Additionally, a full-spacecraft micro-vibration ground test incorporating horizontal gravity unloading via suspension is implemented to validate the model. The model-calculated acceleration and pointing angle exhibit excellent consistency with the experimental data, with the relative acceleration error below 7% and the angular error less than 9%. The proposed integrated dynamic model enables accurate prediction of micro-vibration transmission and suppression performance, laying a dependable theoretical foundation for design optimization of high-precision spacecraft systems.

Bile leaks are serious complications after cholecystectomy. Their management is usually individualised and requires interdisciplinary surgical and endoscopic expertise. In cases of leaks caused by aberrant bile ducts, closure of the leak may be impossible-depending on the size of the drained liver area; therefore, drainage-e.g., by means of a biliodigestive anastomosis-is required. We report the case of a 66-year-old female patient referred from an external hospital with a bile duct leak caused by an aberrant bile duct of segments V and VIII and concomitant cystic duct stump insufficiency following laparoscopic cholecystectomy. Due to the extent of the drained liver area, surgical management with drainage of the bile duct was pursued. Preservation of the option for ERCP in the event of potential secondary complications, such as bile duct strictures, was considered important. Given the prior laparoscopic surgery, a minimally invasive approach was favoured. A robot-assisted exploration of the hepatic hilum and bile ducts was performed. Primary anastomosis was not feasible, due to the distance between the structures. For drainage of the bile duct, a combined anastomosis was created: the cystic duct stump was anastomosed to the aberrant bile duct on the posterior wall, and a biliodigestive anastomosis using a Roux-en-Y reconstruction was performed on the anterior wall. Intraoperatively, a biliary stent was placed via the cystic duct stump into the aberrant bile duct. The postoperative course was uneventful, with no recurrence of the bile leak. The patient was discharged after 11 days. The biliary stents were removed after 7 weeks, demonstrating good contrast drainage from the aberrant bile duct into the biliodigestive anastomosis. Robot-assisted management of bile duct injuries should be considered in patients with prior laparoscopic surgery. Roux-en-Y reconstruction incorporating the cystic duct stump to preserve the option of ERCP is a viable treatment strategy for leaks originating from larger aberrant bile ducts. Galleleckagen sind schwere Komplikationen nach Cholezystektomien. Die Therapie ist meist eine Einzelfallentscheidung und erfordert interdisziplinäre, chirurgische und endoskopische Expertise. Bei Leckagen durch aberrante Gallengänge ist ein Verschluss des Lecks je nach Größe des drainierten Leberareals unmöglich, eine Drainage, z. B. mittels biliodigestiver Anastomose, ist erforderlich.Wir berichten über eine extern zugewiesene 66-jährige Patientin mit einer Gallengangleckage durch einen aberranten Gallengang der Segmente V + VIII und einer Zystikusstumpfinsuffizienz nach laparoskopischer Cholezystektomie. Aufgrund des drainierten Leberareals wurde eine operative Versorgung mit Drainage des Gallenganges angestrebt. Die Möglichkeit einer ERCP sollte bei potenziellen sekundären Komplikationen wie Gallengangstenosen erhalten bleiben. Bei laparoskopischer Voroperation wurde zudem eine minimalinvasive Technik favorisiert.Es erfolgte eine robotisch assistierte Exploration des Leberhilus und der Gallengänge. Dabei zeigte sich, dass eine primäre Anastomosierung aufgrund des Abstandes nicht möglich war. Zur Drainage des Ganges erfolgte eine kombinierte Anastomosierung des Zystikusstumpfs mit dem aberranten Gang an der Hinterwand sowie eine biliodigestive Anastomose mittels Y-Roux-Rekonstruktion an der Vorderwand. Intraoperativ erfolgte außerdem die Einlage eines Gallengangstents über den Zystikusstumpf in den aberranten Gang. Der postoperative Verlauf war unauffällig, es kam zu keinem Rezidiv des Gallelecks. Die Entlassung erfolgte nach 11 Tagen, die einliegenden Gallengangstents konnten nach 7 Wochen entfernt werden, es zeigte sich ein guter Kontrastmittelabfluss des aberranten Ganges in die biliodigestive Anastomose.Eine robotisch assistierte Versorgung von Gallengangverletzungen ist bei laparoskopischer Voroperation erwägenswert. Eine Y-Roux-Rekonstruktion unter Nutzung des Zystikusstumpfs zur Erhaltung der Option einer ERCP ist eine mögliche Therapie einer Leckage aus einem größeren aberranten Gallengang.

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Craniotomies represent one of the oldest surgical procedures in human history and have evolved significantly through centuries of medical innovation and wartime necessity. From prehistoric trepanation practices to modern neurosurgical interventions, military conflicts have repeatedly accelerated advances in cranial surgery. This historical review examines the evolution of craniotomies across major historical periods, including prehistoric civilizations, the Renaissance, and modern warfare. Emphasis was placed on the influence of battlefield medicine, technological innovation, and ethical considerations in shaping contemporary neurosurgical practice. Early civilizations such as the Egyptians, Greeks, and Incas performed trepanation for therapeutic, traumatic, and ritualistic purposes, demonstrating surprising procedural sophistication and postoperative survival. During the Renaissance and subsequent military conflicts, including World Wars I and II, the Korean War, Vietnam War, and recent Middle Eastern conflicts, craniotomy techniques rapidly advanced due to the urgent demands of combat-related neurotrauma. Innovations including standardized debridement techniques, mobile neurosurgical units, rapid evacuation systems, neuroimaging, minimally invasive procedures, and robotic-assisted surgery significantly improved survival and neurological outcomes. Modern military neurosurgery additionally recognizes the importance of integrating psychological and rehabilitative care alongside surgical intervention. The evolution of craniotomies reflects the continuous interaction between warfare, technological progress, and medical innovation. Although modern neurosurgery has achieved remarkable precision and improved outcomes, ongoing ethical and logistical challenges remain, particularly in military settings. Understanding the historical development of craniotomies highlights both the resilience of surgical innovation and the enduring pursuit of improved care for patients with traumatic brain injury.

The objectives of this study were to determine the accuracy of component positioning, patient satisfaction, functional outcomes, component survivorship, and complications of robotic total hip arthroplasty (THA) in patients who have atypical hip anatomy. This study included 192 robotic THAs performed in 182 patients for developmental dysplasia of the hip (n = 122), Leg-Calve-Perthes disease (n = 27), slipped capital femoral epiphysis (n = 20), previous acetabular fracture (n = 12), and skeletal dysplasia (n = 11). Predefined radiological outcomes, patient satisfaction, University of California at Los Angeles (UCLA) activity score, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Oxford Hip Score (OHS), Forgotten Joint Score (FJS), component survivorship, and any complications were recorded. The mean follow-up time was 3.8 &#xb1; 1.7 years (range, 2.1 to 5.4). Robotic THA was associated with high levels of accuracy in executing the planned horizontal (root mean square error (RMSE) 1.5 &#xb1; 1.4 mm) and vertical centers of rotation (RMSE: 1.8 &#xb1; 1.7 mm), combined offset (RMSE: 2.9 &#xb1; 3.8 mm), and leg-length correction (RMSE: 1.5 &#xb1; 1.4 mm). Acetabular component positioning within Lewinnek's safe zones was 94.7%, and Callanan's safe zones was 93.8%. The median patient satisfaction score was 90 (interquartile range (IQR), 85 to 95), the median WOMAC score was 18 (IQR, 12 to 20), and the mean FJS score was 77.8 &#xb1; 10.8 at final follow-up. Robotic THA was associated with improvements in the mean UCLA (P < 0.001) and OHS (P < 0.001) at final follow-up. The five-year survivorship was 98.8% (95% CI [confidence interval]: 95.8 to 100) with implant revision for any reason as the end point. Robotic THA in patients who have atypical hip anatomy was associated with high levels of accuracy in executing the planned component positioning. In this challenging patient population, robotic THA was associated with encouraging early component survivorship, satisfactory functional outcomes, and low risk of complications at short-term follow-up.

Background Diabetic retinopathy is one of the leading causes of preventable blindness worldwide, yet it can be stopped through early detection. AI models are increasingly being used as a key enabler to automate this screening, and the results in research settings look very promising. The real challenge is not building more sophisticated models but deploying one that works safely in real clinics. Clinical safety standards require the system to catch nearly every true case of disease, even if that means sending many healthy patients for unnecessary follow-up. This trade-off between keeping patients safe and avoiding a flood of false alarms is the core problem this paper addresses. Given that clinics typically fix a minimum sensitivity target in advance, this study compares decision-making strategies held to the same sensitivity requirement to determine which produces the fewest unnecessary referrals.&#xa0; Methods We evaluate five decision strategies under identical conditions on the public EyePACS dataset of 5,270 retinal fundus images, with 1,366 labeled as having diabetic retinopathy. The strategies range from a single AI model making every referral decision independently, to ensemble methods that combine the probability scores of multiple models into one unified output, to a two-tiered method in which all images are first screened by a primary model, and a group of secondary models can overturn a referral when their disagreement is high enough. Each strategy is evaluated under two clinically grounded sensitivity targets, a strict 95% requirement and a more moderate 90% requirement, so the results reflect realistic deployment conditions rather than unconstrained optimal performance. Results When the system is required to achieve 95% sensitivity, all strategies produce high false-positive rates, with the best single model reaching only 17.5% specificity. Ensembles offer only marginal improvement at this threshold, while majority voting consistently performs worst across both sensitivity levels. Reducing the sensitivity target from 95% to 90% alone decreases false positives by about 17%. When this lower threshold is combined with an ensemble strategy, unnecessary referrals drop by nearly 25% compared with a single model at 95%. Neither adjustment alone produces this level of improvement; the benefit appears only when both are applied together. Conclusions This study shows that two decisions matter most in AI-based diabetic retinopathy screening: the sensitivity target a clinic sets and the decision strategy it pairs with that target. Although the sensitivity target had a greater influence on referral burden, the best outcomes occurred only when both the target and the strategy were carefully chosen. Pairing a 90% sensitivity target with a weighted ensemble reduced unnecessary referrals by nearly 25% compared with a single model at the stricter 95% target, while majority voting produced the highest false-positive burden at both thresholds. These findings suggest that clinically grounded threshold selection is just as important as the decision strategy itself and that seemingly intuitive approaches such as majority voting may underperform when evaluated under the same safety constraints.

In large-scale road environments, constructing and maintaining high-precision maps is challenging, while GNSS-denied conditions exacerbate accumulated drift due to the lack of global references. Additionally, existing methods largely rely on LiDAR data but inadequately preprocess the data, which often leads to degraded accuracy and instability. To address these issues, this study proposes large-scale drift-resilient localization via multi-sensor fusion and topological map matching. The method leverages digital maps to extract topological road networks, eliminating the need for high-precision map construction. Accumulated drift is corrected by matching the odometry trajectory with the topological map, while localization accuracy and stability are further improved through precise ground point filtering and the integration of wheel odometry into a LiDAR-inertial odometry. Experiments on two campus datasets and KITTI 05 demonstrate the high accuracy and generalization of the proposed method in large-scale localization. Notably, on the longer School Dataset (3645 m), the mean error drops by 48.1% relative to LIO-SAM and 44.2% relative to FAST-LIO2. Repeated ablation trials further confirm the stability of the proposed method. These results demonstrate accurate and stable large-scale localization without high-precision prior maps.

&#x27a2; Romanian orthopaedic practice has demonstrated a growing uptake of digital infrastructure, including the implementation of robotic-assisted surgical workflows and emerging applications of artificial intelligence.&#x27a2; Romania ranks among the European countries with the highest hospital bed capacity per capita, supporting the provision of sustained care for elderly patients, including those with fragility fractures and oncologic orthopaedic conditions and those undergoing complex revision surgeries.&#x27a2; The Romanian Arthroplasty Register was modernized and relaunched in 2025 as an initiative to align with other long-standing national European counterparts.

Continuum robots offer unique advantages for applications such as minimally invasive surgery, navigation through confined environments, and safe human-robot interaction. However, while most continuum robot segments are designed to exhibit constant curvature over their length, they passively deform into a non-constant curvature s-shape when holding payloads at the tip, and their dynamic movement is often subject to unwanted vibration of the passive non-constant curvature modes. In this paper, we propose a simple solution to dramatically improve these issues: a continuum robot segment design that utilizes a diagonal backbone and flexible push-pull actuation rods. This simple modification to common continuum-robot construction enables us to eliminate the passive s-shaped mode, creating a bending segment that can handle large loads without significant deformation or vibration while requiring no more actuation force than conventional designs. We show that a modified version of 1-DOF constant-curvature kinematics accurately describes the structure when actuator translations are equal and opposite. We also develop and validate a 2-DOF model that predicts tip position and orientation resulting from more general actuation inputs. The models and increased output stiffness were verified experimentally and the concept was demonstrated on a multi-segment robot following a 3D trajectory with minimal disturbance from added loads.

Hemophagocytic lymphohistiocytosis (HLH) is a syndrome involving extreme hyperinflammation driven by a cytokine storm, which leads to the pathological autophagocytosis of blood cells. Although secondary HLH is frequently associated with underlying malignancies, the majority of which are hematological, cases triggered by colorectal cancer are quite rare. We report herein a case of HLH induced by ascending colon cancer. An 83-year-old female patient presented to her primary care physician with a 1-month history of anorexia, persistent fever (>38&#xb0;C), and cognitive decline. Colonoscopy revealed a tumor at the hepatic flexure of the ascending colon, which was identified as a well-differentiated adenocarcinoma on the basis of a biopsy specimen analysis. Ascending colon cancer (cT3N2bM0, cStage IIIc) was finally diagnosed. As the fever was presumed to be a paraneoplastic manifestation, the patient underwent robotic-assisted right hemicolectomy with lymphadenectomy. Recurrences of the fever, persistent anorexia, and generalized edema were observed postoperatively. On POD 25, peripheral blood smears revealed macrophage-mediated hemophagocytosis, confirming the diagnosis of HLH based on the HLH-2004 diagnostic criteria. Despite an escalation in corticosteroid therapy, the patient remained refractory to treatment and died on POD 46. Clinicians should include cancer-induced HLH in the differential diagnosis of fever and bicytopenia in the presence of an advanced solid tumor.

This paper addresses the scaled containment control problem (SCCP) for first/second-order stochastic multi-agent systems (SMASs) in a noisy environment. The agents receive neighbor's state information subjected to nonzero scaling factors, which causes the followers to finally converge to a scaled deterministic constant formed by the leaders. A stochastic approximation protocol with time-varying gains is designed to attenuate the noise effects. Using a state decomposition method, some sufficient and necessary conditions for the SCCP of first/second-order SMASs are given under the topological requirement of containing a directed spanning forest. For first-order systems, the followers converge to the scaled deterministic constant formed by the leaders. For second-order systems, two interaction modes are analyzed: under constant velocities mode, followers' positions converge to the unbounded scaled deterministic constant formed by the leaders' positions and followers' velocities converge to the scaled deterministic constant formed by the leaders' velocities; under zero velocity mode, the convergence behavior of the second-order systems is analogous to that of first-order systems, where followers' positions converge to the scaled deterministic constant formed by the leaders' positions and followers' velocities converge to zero. The numerical examples demonstrate the validity of the theoretical findings.

Active assembly of matter is a defining trait of living systems, enabling the creation of far-from-equilibrium materials essential for the functionality of life. This is achieved through energy-dissipative, multi-step processes facilitated by biomolecular nanomachines performing bottom-up chemical and mechanical assembly of matter. Mimicking such active assembly synthetically remains a challenge. Here, a bio-inspired bottom-up strategy for energy-dissipative material assembly, driven by biomolecular nanomachines and overcoming thermodynamic and diffusive constraints, is demonstrated. Specifically, two chemically-fueled biomolecular nanomachines-DNA polymerase and kinesin-are used to demonstrate a multi-step chemical synthesis and mechanical manipulation process. This results in a DNA biopolymer network with complex hierarchical morphologies unattainable by self-assembly alone. DNA polymerase generates DNA, which forms a fibrous 2D-network when actively connected and pulled between kinesin-powered motile microtubules. Experimental data and simulations show that both DNA-DNA interactions and active mechanical forces from molecular motors are essential to this process. Furthermore, key factors for network formation are investigated by systematically investigating DNA polymerase incubation time and microtubule density. The present work provides a key step toward bottom-up fabrication of complex and dynamic materials by mimicking the sophisticated assembly strategies of living systems, potentially providing a framework for future materials assembled by nanomachines.

Micrographia is a symptom characterized by abnormally small handwriting and is a common motor symptom in Parkinson's disease. Two subtypes-consistent micrographia (CM) and progressive micrographia (PM)-are thought to reflect distinct underlying mechanisms. CM has been linked to bradykinesia and often improves with dopaminergic treatment, whereas PM typically persists and may involve altered sensorimotor processing. However, the relative contributions of motor execution, feedback integration, and motor awareness to these subtypes remain unclear. This study investigated whether awareness of motor control differs between people with Parkinson's disease (PwP) and healthy controls (HC), and whether such differences are associated with micrographia subtypes. Forty-five PwP and twenty age-matched HC completed a handwriting task and a control detection task (CDT), which assesses the ability to distinguish self-generated from externally generated movements. Based on handwriting metrics, PwP were classified as having no micrographia, CM, or PM. CDT accuracy was significantly lower in PwP than in HC, with particularly reduced performance in individuals with PM. These findings suggest that PM is associated with reduced motor awareness, potentially involving altered prediction-feedback integration, and may inform the development of therapeutic approaches that complement dopaminergic treatment.

Cannabis sativa L. plant has acquired significant attention in recent years considering that numerous countries around the world are legalizing it for medical uses or recreational purposes. Due to its increasing popularity, farmers are particularly prone to use pesticides such as insecticides, acaricides, and fungicides in order to eliminate, repel, or minimize aphids, spider mites, and thrips, respectively. Accurate determination of pesticide residues in cannabis plants is mandatory to safeguard consumer health. This research study is focused on the employment of a robotic platform online coupled to UHPLC-MS/MS instrument for the rapid screening of pesticides in Cannabis sativa L. flowering tops. The developed automated procedure required only 30&#x202f;mg of dried sample and 200&#x202f;&#x3bc;L of acetonitrile as unique extraction solvent. The total analysis time was 25&#x202f;min per sample, including extraction cycle (10&#x202f;min). The method was proved by defining the following figures of merit: intra- and inter-day repeatability, linearity range, limits of quantification (LoQs), recovery, and accuracy. The LoQs for all the analytes were of 0.005&#x202f;&#x3bc;g&#x202f;g-1, except for aldicarb and boscalid compounds (0.01&#x202f;&#x3bc;g&#x202f;g-1). Matrix-matched calibration curves showed good linearity over the range with coefficients of determination &#x2265; 0.9991. Recoveries ranged from 72.3&#x202f;% to 116.2&#x202f;% in accordance with 70-120&#x202f;% allowed range by SANTE/11312/2021v2026 guidelines. The sample preparation greenness was assessed using sample preparation method of sustainability (SPMS) and analytical greenness metric (AGREEprep) tools, providing scores of 6.42 and 0.55 respectively, higher than those obtained for conventional QuEChERS workflow (3.79 and 0.16).