Metacarpal bone reconstruction renders a surgical challenge. We describe a case using 3D printing assisted medial femoral condyle flap for extensive metacarpal reconstruction after wide resection of a large giant cell tumor recurrence. Thus, the length and stability of the entire third ray could be restored without any tumor recurrence.
Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.
The use of trigonometric polynomials as Lagrange multipliers in the harmonic mortar method enables an efficient and elegant treatment of relative motion in the stator-rotor coupling of electric machine simulation. Explicit formulas for the torque computation are derived by energetic considerations, and their realization by harmonic mortar finite element and isogeometric analysis discretizations is discussed. Numerical tests are presented to illustrate the theoretical results and demonstrate the potential of harmonic mortar methods for the evaluation of torque ripples.
Abstract Study design Monocentric, prospective, observational study. Objective The clinical relevance of bacterial colonization of intervertebral discs is controversial. This study aimed to determine a possible relationship between bacterial and viral colonization and low-grade infection of the discs. Methods We investigated 447 disc samples from 392 patients. Microbiological culture was used to examine the samples for bacterial growth, polymerase chain reaction (PCR) was used for detection of herpes simplex virus types 1 and 2 (HSV-1, HSV-2) and Cytomegalovirus (CMV), and histopathological analysis was used to detect signs of inflammation. The results were compared between subgroups organized according to gender, age, location of the samples, surgical approach, preoperative C-reactive protein (CRP), preoperative and 6 months postoperative Oswestry Disability Index (ODI) and Neck Disability Index (NDI), and Modic changes (MC) of the corresponding endplates. Also, we assessed the occurrence of postoperative infections within 6 months. Results Microbiological culture was positive in 38.78% of the analyzed intervertebral discs. Altogether, 180 bacteria were isolated. Coagulase-negative staphylococci (CONS) (23.41%) and Cutibacterium acnes (18.05%) were the most frequently detected microorganisms. None of HSV-1, HSV-2, or CMV were detected. Male patients (p = 0.00036) and cervical segments (p = 0.00001) showed higher rates of positive culture results. Ventral surgical approaches ( p
The success of Density Functional Theory (DFT) is partly due to that of simple approximations, such as the Local Density Approximation (LDA), which uses results of a model, the homogeneous electron gas, to simulate exchange-correlation effects in real materials. We turn this intuitive approximation into a general and in principle exact theory by introducing the concept of a connector: a prescription how to use results of a model system in order to simulate a given quantity in a real system. In this framework, the LDA can be understood as one particular approximation for a connector that is designed to link the exchange-correlation potentials in the real material to that of the model. Formulating the in principle exact connector equations allows us to go beyond the LDA in a systematic way. Moreover, connector theory is not bound to DFT, and it suggests approximations also for other functionals and other observables. We explain why this very general approach is indeed a convenient starting point for approximations. We illustrate our purposes with simple but pertinent examples.
Background The clinical signs and symptoms of hypophosphatasia (HPP) can manifest during any stage of life. The age at which a patient’s symptoms are reported can impact access to targeted treatment with enzyme replacement therapy (asfotase alfa), as this treatment is indicated for patients with pediatric-onset HPP in most countries. As such, many patients reported to have adult-onset HPP typically do not receive treatment. Comparison of the disease in treated and untreated adult patients is confounded by the approved indication. To avoid this confounding factor, a comparison between baseline disease manifestations prominent among treated versus untreated adult patients was limited to those with pediatric-onset HPP using data collected from the Global HPP Registry. The hypothesis was that treated adults will have a greater disease burden at baseline than untreated adults. The analysis of disease manifestations in adults with adult-onset HPP was conducted separately. Results A total of 398 adults with HPP were included; 213 with pediatric-onset (114 treated, 99 untreated) and 141 with adult-onset HPP (2 treated and 139 untreated). The treated, pediatric-onset patients were more likely to have a history of pain (prevalence ratio [PR]: 1.3, 95% confidence interval [CI] 1.1, 1.4), skeletal (PR: 1.3, 95% CI 1.1, 1.6), constitutional/metabolic (PR: 1.7, 95% CI 1.3, 2.0), muscular (PR: 1.8, 95% CI 1.4, 2.1) and neurological (PR: 1.7, 95% CI 1.1, 2.3) manifestations of HPP, and also had poorer measures for health-related quality of life, pain, and disability compared with untreated pediatric-onset patients. In patients with adult-onset HPP, the most frequent signs and symptoms were chronic bone pain (52.5%), dental manifestations (42.6%), fatigue (23.4%), recurrent fractures or pseudofractures (22.0%), and generalized body pain (22.0%). Conclusions Along with the more classical skeletal signs and symptoms, pain, muscular, and constitutional/metabolic manifestations are common in adults with HPP, regardless of age of disease onset, highlighting a full spectrum of HPP manifestations.
Sustained exposure of the lung to various environmental or occupational toxins may eventually lead to pulmonary fibrosis, a devastating disease with no cure. Pulmonary fibrosis is characterized by excessive deposition of extracellular matrix (ECM) proteins such as fibronectin and collagens. The peptidase plasmin degrades the ECM, but protein levels of the plasmin activator inhibitor-1 (PAI-1) are increased in fibrotic lung tissue, thereby dampening plasmin activity. Transforming growth factor-β1 (TGF-β1)-induced activation of SMAD transcription factors promotes ECM deposition by enhancing collagen, fibronectin and PAI-1 levels in pulmonary fibroblasts. Hence, counteracting TGF-β1-induced signaling is a promising approach for the therapy of pulmonary fibrosis. Transient receptor potential cation channel subfamily M Member 7 (TRPM7) supports TGF-β1-promoted SMAD signaling in T-lymphocytes and the progression of fibrosis in kidney and heart. Thus, we investigated possible effects of TRPM7 on plasmin activity, ECM levels and TGF-β1 signaling in primary human pulmonary fibroblasts (pHPF). We found that two structurally unrelated TRPM7 blockers enhanced plasmin activity and reduced fibronectin or PAI-1 protein levels in pHPF under basal conditions. Further, TRPM7 blockade strongly inhibited fibronectin and collagen deposition induced by sustained TGF-β1 stimulation. In line with these data, inhibition of TRPM7 activity diminished TGF-β1-triggered phosphorylation of SMAD-2, SMAD-3/4-dependent reporter activation and PAI-1 mRNA levels. Overall, we uncover TRPM7 as a novel supporter of TGF-β1 signaling in pHPF and propose TRPM7 blockers as new candidates to control excessive ECM levels under pathophysiological conditions conducive to pulmonary fibrosis.
Switched reluctance machines (SRMs) provide a potential candidate and a feasible solution with increased interest for industrial applications due to their simple and rigid structure without permanent magnets, low manufacturing cost, excellent power-speed characteristics, and high reliability. However, the nonlinear inductance/flux linkage characteristics caused by the double-salient structure of SRM have created the challenges like high torque ripple and vibration. To solve this problem, a significant number of research works focus on the design and optimization of SRMs. Accordingly, this paper presents an in-depth literature review on the status and potential trends of design optimization techniques for SRMs, including design theory, electromagnetic and thermal modeling methods, novel topologies, optimization classifications, and techniques for optimization efficiency and effects. Existing approaches regarding the above aspects of SRMs are extensively discussed and comprehensively summarized. In addition, some essential trends in design optimization development are presented and highlighted as future perspectives. All the highlighted insights and recommendations of this review will hopefully lead to increasing efforts toward the performance and reliability enhancements of SRMs for future applications.
We examine the effects of three basic but effective control strategies, namely uniform blowing, uniform suction, and body-force damping, on the intense Reynolds-stress events in the turbulent boundary layer (TBL) developing on the suction side of a NACA4412 airfoil. This flow is subjected to a non-uniform adverse pressure gradient (APG), which substantially modifies its turbulence statistics with respect to a zero-pressure-gradient (ZPG) boundary layer, and it also changes how control strategies affect the flow. The strong APG results in intense events that are shorter and more often detached from the wall than in ZPG TBLs. In a quadrant analysis, ejections remain the most relevant structures, but sweeps become more important than in ZPG TBLs, a fact that results in a lower contribution to the wall-normal velocity from intense Reynolds-stress events. Control effects are relatively less important on intense events than on the turbulent statistics. Uniform blowing has an impact similar to that of an even more intense APG, while uniform suction has more complex effects, most likely due to the particular behavior of the wall-normal velocity component near the wall. Body-force damping also reduces the probability of occurrence of very-large attached structures and that of intense events in the proximity of the actuation region. Our results show that intense Reynolds-stress events are robust features of the flow. If control strategies do not target directly these structures, their effects on the strong events is less pronounced than the effects on the mean flow.
Both children and adults have been shown to benefit from the integration of multisensory and sensorimotor enrichment into pedagogy. For example, integrating pictures or gestures into foreign language (L2) vocabulary learning can improve learning outcomes relative to unisensory learning. However, whereas adults seem to benefit to a greater extent from sensorimotor enrichment such as the performance of gestures in contrast to multisensory enrichment with pictures, this is not the case in elementary school children. Here, we compared multisensory- and sensorimotor-enriched learning in an intermediate age group that falls between the age groups tested in previous studies (elementary school children and young adults), in an attempt to determine the developmental time point at which children’s responses to enrichment mature from a child-like pattern into an adult-like pattern. Twelve-year-old and fourteen-year-old German children were trained over 5 consecutive days on auditorily presented, concrete and abstract, Spanish vocabulary. The vocabulary was learned under picture-enriched, gesture-enriched, and non-enriched (auditory-only) conditions. The children performed vocabulary recall and translation tests at 3 days, 2 months, and 6 months post-learning. Both picture and gesture enrichment interventions were found to benefit children’s L2 learning relative to non-enriched learning up to 6 months post-training. Interestingly, gesture-enriched learning was even more beneficial than picture-enriched learning for the 14-year-olds, while the 12-year-olds benefitted equivalently from learning enriched with pictures and gestures. These findings provide evidence for opting to integrate gestures rather than pictures into L2 pedagogy starting at 14 years of age.
This paper presents the kinematics about the center of mass (CoM) for robotic mechanisms based on Lie Group theory because the movements of CoM is very important for mobile manipulating robots. Different from general kinematics, the CoM kinematics relates the position of the CoM to the joint angles and the pose of the robot. The concept of the homogeneous coordinates of mass points is define as the product of the mass and the general homogeneous coordinates. Then, the mass translation matrix is introduced to derive the formula of the product of exponentials and Jacobian matrix for CoM (COM-POE). The COM-POE has the same form as the standard POE formula used to model the kinematics of serial manipulators. Hence the traditional methods to deal with kinematic problems can be adopted directly. Two application instances based on the CoM-POE have been presented. The first one is a mobile platform with a redundant serial manipulator and the second one is a quadruped robot. The simulation results show that the CoM kinematics is very useful in motion planning to guarantee the stability of mobile manipulating robots.
In this work, a new basic framework of robotic mechanism topology is proposed, which includes: ① two important concepts (i.e., topological structure and kinematic characteristics), ② three basic formulas (i.e., POC equation of serial mechanisms, POC equation of parallel mechanisms (PMs), and DOF formula) and ③ topological structure synthesis methods of PMs. Based upon this framework, literature review and comparative study of four original theories for topological structure synthesis of PMs (i.e., screw theory, velocity space, subgroup and POC set) are carried out. Moreover, it is found that these four theories and their computable methods (linear operation, nonlinear symbolic operation and linear symbolic operation) originate from different definitions and their mathematical representations of the two concepts. These basic formulas accounting for mechanism topology are derived based on the two concepts, which reveal the mapping relationship among topological structures, POCs and DOF of mechanisms. Further, these four theories are grouped into two categories: ① the algebraic methods related to the fixed coordinate system for screw theory, velocity space and subgroup; ② the intrinsic geometry method independent of the fixed coordinate system for POC set.
Software companies commonly develop and maintain variants of systems, with different feature combinations for different customers. Thus, they must cope with variability in space. Software companies further must cope with variability in time, when updating system variants by revising existing software features. Inevitably, variants evolve orthogonally along these two dimensions, resulting in challenges for software maintenance. Our work addresses this challenge with ECSEST (Extraction and Composition for Systems Evolving in Space and Time), an approach for locating feature revisions and composing variants with different feature revisions. We evaluated ECSEST using feature revisions and variants from six highly configurable open source systems. To assess the correctness of our approach, we compared the artifacts of input variants with the artifacts from the corresponding composed variants based on the implementation of the extracted features. The extracted traces allowed composing variants with 99-100% precision, as well as with 97-99% average recall. Regarding the composition of variants with new configurations, our approach can combine different feature revisions with 99% precision and recall on average. Additionally, our approach retrieves hints when composing new configurations, which are useful to find artifacts that may have to be added or removed for completing a product. The hints help to understand possible feature interactions or dependencies. The average time to locate feature revisions ranged from 25 to 250 seconds, whereas the average time for composing a variant was 18 seconds. Therefore, our experiments demonstrate that ECSEST is feasible and effective.
A recent article by Herzog provides a much-needed integration of ethical and epistemological arguments in favor of explicable AI (XAI) in medicine. In this short piece, I suggest a way in which its epistemological intuition of XAI as “explanatory interface” can be further developed to delineate the relation between AI tools and scientific research.
The importance of detecting whether a per- son wears a face mask while speaking has tremendously increased since the outbreak of SARS-CoV-2 (COVID-19), as wearing a mask can help to reduce the spread of the virus and mitigate the public health crisis. Besides affecting human speech characteristics related to frequency, face masks cause temporal interferences in speech, altering the pace, rhythm, and pronunciation speed. In this regard, this paper presents two effective neural network models to detect surgical masks from audio. The proposed architectures are both based on Convolutional Neural Net- works (CNNs), chosen as an optimal approach for the spatial processing of the audio signals. One architecture applies a Long Short-Term Memory (LSTM) network to model the time-dependencies. Through an additional attention mechanism, the LSTM-based architecture enables the extraction of more salient temporal information. The other architecture (named ConvTx) retrieves the relative position of a sequence through the positional encoder of a transformer module. In order to assess to which extent both architectures can complement each other when modelling temporal dynamics, we also explore the combination of LSTM and Transformers in three hybrid models. Finally, we also investigate whether data augmentation techniques, such as, using transitions between audio frames and considering gender-dependent frameworks might impact the performance of the proposed architectures. Our experimental results show that one of the hybrid models achieves the best performance, surpassing existing state-of-the-art results for the task at hand.
Realizing a sustainable, technologically advanced future will necessitate solving the electronic waste problem. Biodegradable forms of electronics offer a viable path through their environmental benignity. With both the sheer number of devices produced every day as well as their areas of application ever increasing, new concepts of degradable batteries able to sustain the high power demands of modern electronics must be developed. Simultaneously, integration of electronics in close interaction with its user or powering soft robotic devices necessitates high degrees of compliance, rendering stretchable batteries indispensable. Here, a concept for merging intrinsically stretchable materials with engineered stretchability by kirigami‐patterning on a component level is shown to yield high‐power biodegradable batteries with reversible elasticity up to 35% when stretched uniaxially and 20% for biaxial extension. Using a combination of molybdenum metal foils, a molybdenum trioxide paste and magnesium metal foils as electrode materials, a peak power output of 196 μW cm–2 and an energy density of 1.72 mWh cm–2 is achieved. The biodegradable batteries are used to power an on‐skin biomedical sensor patch, enabling monitoring of sodium concentration in sweat. This concept provides a versatile route for high‐power biodegradable batteries, enabling untethered soft electronic devices in a sustainable future. This article is protected by copyright. All rights reserved
Myelofibrosis (MF) is a clonal myeloproliferative neoplasm, typically associated with disease-related symptoms, splenomegaly, cytopenias and bone marrow fibrosis. Patients experience a significant symptom burden and a reduced life expectancy. Patients with MF receive ruxolitinib as the current standard of care, but the depth and durability of responses and the percentage of patients achieving clinical outcome measures are limited; thus, a significant unmet medical need exists. Pelabresib is an investigational small-molecule bromodomain and extraterminal domain inhibitor currently in clinical development for MF. The aim of this article is to describe the design of the ongoing, global, phase III, double-blind, placebo-controlled MANIFEST-2 study evaluating the efficacy and safety of pelabresib and ruxolitinib versus placebo and ruxolitinib in patients with JAKi treatment-naive MF. Clinical Trial Registration: NCT04603495 ( ClinicalTrials.gov )
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