Functionalization of screw implants with superelastic structured Nitinol anchoring elements

Background Demographic change is leading to an increase in the number of osteoporotic patients, so a rethink is required in implantology in order to be able to guarantee adequate anchoring stability in the bone. The functional modification of conventional standard screw implants using superelastic, structured Ti6Al4V anchoring elements promises great potential for increasing anchoring stability. Methods For this purpose, conventional screw implants were mechanically machined and extended so that structured-superelastic-positionable-Ti6Al4V anchoring elements could be used. The novel implants were investigated with three tests. The setup of the anchoring elements was investigated in CT studies in an artificial bone. In a subsequent simplified handling test, the handling of the functional samples was evaluated under surgical conditions. The anchorage stability compared to standard screw implants was investigated in a final pullout test according to ASTM F543—the international for the standard specification and test methods for metallic medical bone screws. Results The functionalization of conventional screw implants with structured superelastic Ti6Al4V anchoring elements is technically realizable. It was demonstrated that the anchoring elements can be set up in the artificial bone without any problems. The anchorage mechanism is easy to handle under operating conditions. The first simplified handling test showed that at the current point of the investigations, the anchoring elements have no negative influence on the surgical procedure (especially under the focus of screw implantation). Compared to conventional standard screws, more mechanical work is required to remove the functional patterns completely from the bone. Conclusion In summary, it was shown that conventional standard screw implants can be functionalized with Ti6Al4V-structured NiTi anchoring elements and the new type of screws are suitable for orthopedic and neurosurgical use. A first biomechanical test showed that the anchoring stability could be increased by the anchoring elements.

Omicron: The pandemic propagator and lockdown instigator – what can be learnt from South Africa and such discoveries in future

The SARS-CoV-2 virus which causes the disease termed COVID-19 ripped through the globe in the latter part of 2019 and has left a state of fear, death and destruction in its wake. The Omicron variant was officially announced by the South African authorities on the 24th of November 2021, with the first confirmed sample of the infection being collected on the 9th of November 2021. The initial cases were flagged as a possible new variant due to the stark differences in the presentation and clinical features of the patients. At the time of Omicron’s discovery, the predominant variant circulating within South Africa was the Delta variant B.1.617.2 which typically presented with more severe and stark symptoms. Omicron spread rapidly within the Southern African content and abroad, principally South Africa, Botswana, Hongkong and Israel were among the first countries to record cases of the new variant. The first European case of the Omicron variant was confirmed on the 26th of November 2021 in Belgium. Towards the end of November 2021 cases of the new variant had been confirmed and recorded in France, the United Kingdom, Germany, Portugal and Scotland. Additional cases of the Omicron variant have been confirmed in Canada and Australia. At this current point in the development of the Omicron upsurge in cases the international community should aim for further vaccinations among their fellow countrymen, but more so vaccine equality should be ensured. Such equality should be ensured in the developing nations as the virus does not respect any boundaries or territories and thus a higher level of vaccination worldwide will confer greater protection to the global community as a whole.

Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in online sequential filtering does not match the model posterior filtered based on data up to a current point in time. The examples considered encompass parameter regime shifts and stochastic volatility. The filter adapts to regime shifts extremely rapidly and delivers a clear heuristic for distinguishing between regime shifts and stochastic volatility, even though the model dynamics assumed by the filter exhibit neither of those features.

Contextual movement models based on normalizing flows

Movement models predict positions of players (or objects in general) over time and are thus key to analyzing spatiotemporal data as it is often used in sports analytics. Existing movement models are either designed from physical principles or are entirely data-driven. However, the former suffers from oversimplifications to achieve feasible and interpretable models, while the latter relies on computationally costly, from a current point of view, nonparametric density estimations and require maintaining multiple estimators, each responsible for different types of movements (e.g., such as different velocities). In this paper, we propose a unified contextual probabilistic movement model based on normalizing flows. Our approach learns the desired densities by directly optimizing the likelihood and maintains only a single contextual model that can be conditioned on auxiliary variables. Training is simultaneously performed on all observed types of movements, resulting in an effective and efficient movement model. We empirically evaluate our approach on spatiotemporal data from professional soccer. Our findings show that our approach outperforms the state of the art while being orders of magnitude more efficient with respect to computation time and memory requirements.

Minimally instrumented SHERLOCK (miSHERLOCK) for CRISPR-based point-of-care diagnosis of SARS-CoV-2 and emerging variants

The COVID-19 pandemic highlights the need for diagnostics that can be rapidly adapted and deployed in a variety of settings. Several SARS-CoV-2 variants have shown worrisome effects on vaccine and treatment efficacy, but no current point-of-care (POC) testing modality allows their specific identification. We have developed miSHERLOCK, a low-cost, CRISPR-based POC diagnostic platform that takes unprocessed patient saliva; extracts, purifies, and concentrates viral RNA; performs amplification and detection reactions; and provides fluorescent visual output with only three user actions and 1 hour from sample input to answer out. miSHERLOCK achieves highly sensitive multiplexed detection of SARS-CoV-2 and mutations associated with variants B.1.1.7, B.1.351, and P.1. Our modular system enables easy exchange of assays to address diverse user needs and can be rapidly reconfigured to detect different viruses and variants of concern. An adjunctive smartphone application enables output quantification, automated interpretation, and the possibility of remote, distributed result reporting.

An algorithm for trajectory planning of complex surface parts for laser cladding remanufacturing

This paper aims to solve the problem of laser cladding for complex parts, a trajectory planning method for complex parts is proposed. First, the part model is transformed into stereolithography (STL) files, the plane groups are established where the spacing of plane groups is determined by the overlap ratio, and the direction is perpendicular to the laser scanning. Further, to obtain the excellent cladding performance, a search algorithm based on the height error of adjacent cladding is proposed to select effective points. After this, adjacent points are clustered to a dataset according to the relative position that decided by the angle. Meanwhile, the position and the attitude of all points are calculated by the geometrical characteristics and then stored. The cooperate work of positioner and robot is adopted, the attitude of the current point is adjusted using the positioner, that is, the molten pool of the current point is in a horizontal state, which can ensure the forming accuracy of curved parts after cladding. Finally, the cladding experiment of curved surface parts is carried out. The results of the analysis showed that the coating had a smooth surface and a compact structure without defects. This method can be applied in other curved parts.

GEOMETRIC MODELING OF MULTI-FACTOR PROCESSES BASED ON VARIABLE POINT ALGORITHMS

In this paper, the geometric theory of multidimensional interpolation was further developed. It has been established that the geometric models of multivariate processes obtained using multidimensional interpolation are characterized by variability, which is a consequence of the multiplicity of choice of reference lines in the process of developing a geometric modeling scheme. At the same time, all possible variations of geometric interpolants fully satisfy the initial experimental and statistical data, but have different curvature between the node points of the interpolation. As the dimension of the space increases, the number of variations increases significantly. The variable approach to geometric modeling of multifactorial processes generates a number of scientific problems that require further research, such as: comparison of geometric objects of multidimensional space, development of criteria for choosing the best solutions, construction of averaged geometric objects as one of the tools for optimizing the results of modeling, etc. The article also presents the results of a computational experiment on geometric modeling of the dependence of the physical and mechanical properties of fine-grained concrete on the composition of the combined aggregate based on variable point algorithms with the subsequent construction of an averaged response surface, the current point of which is the center of gravity of a multidimensional tetrahedron, for which the dimension of space depends on the amount possible interpolation options.

Direct Diagnosis of Echovirus 12 Meningitis Using Metagenomic Next Generation Sequencing

The current point-of-care diagnosis of enterovirus meningitis does not identify the viral genotype, which is prognostic. In this case report, more than 81% of an Echovirus 12 genome were detected and identified by metagenomic next-generation sequencing, directly from the cerebrospinal fluid collected in a 6-month-old child with meningeal syndrome and meningitis: introducing Echovirus 12 as an etiological agent of acute meningitis in the pediatric population.