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قضية باول كاميرير، وهي حياة مثيرة لعالم الأحياء الأكثر إثارة للجدل في عصره، ومن ترجمة الدكتورة هند أسعد. شهد يوم الثالث والعشرين من شهر سبتمبر لعام 1926 حادثة تسلق \"باول كاميرير\" لتل في منطقة \"شنيبيرج\" الجبلية، ثم وضع مسدسا بجانب صدغه ليسطر مشهد النهاية لحياته، قبل ذلك بفترة قليلة تعرض \"كاميرير\"، الذي ينظر إليه بوصفه أكبر عالم أحياء منذ \"داروين\"، للاتهام بتزوير تجاربه حول توريث الصفات المكتسبة. أحدثت القضية جدلا واسع النطاق حول العالم، وحتى الآن لم تتكشف ملابسات الحادث، بعد مرور تسعين عاما على رحيله يعد\" كاميرير\" بالنسبة للبعض\"أبا\" لعلم التخلق، لكن من عساه إذن، إن لم يكن هو، قد قام بعملية التزوير تلك؟ لماذا انتحر \"كاميرير\" الذي ذاع صيته أيضا بوصفه مؤلفا موسيقيا ومن أتباع الماسونية وعاشقا \"لألما مالر\"؟ يقلب \"كلاوس تاشفر\" صفحات حياة \"مقبل العلجوم\" ووفاته من جديد، ليقدم لنا أول أثر حديث ساخن في \"قضية \"كاميرير\" الباردة\"، حيث يقودنا هذا الأثر إلى مؤامرة معادية للسامية، إنها رواية بوليسية حقيقية تبعث الحياة في البيئة الإبداعية لمدينة فيينا في بداية القرن العشرين وتعيد إحياء أزمات الفترة الزمنية الواقعة بين الحربين العالميتين.

For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.

IntroductionDementia is one of the most relevant widespread diseases, with a prevalence of currently 55 million people with dementia worldwide. However, about 60–75% of people with dementia have not yet received a formal diagnosis. Asymptomatic screening of cognitive impairments using neuropsychiatric tests has been proven to efficiently enhance diagnosis rates. Digital screening tools, in particular, provide the advantage of being accessible without spatial or time restrictions. The study aims to validate a digital cognitive screening test (digiDEM-SCREEN) as an app in the German language.Methods and analysisThis is a multicentre study in Bavaria. Participants are people with mild cognitive impairment, people with dementia in an early stage and cognitively healthy people. Recruitment will take place in specialised diagnostic facilities (memory outpatient clinics). 135 participants are aimed based on a power analysis. Sociodemographic data, diagnosis and results of neuropsychiatric tests (Consortium to Establish a Registry for Alzheimer’s Disease, Montreal Cognitive Assessment, digiDEM-SCREEN) will be collected at one point per person via electronic data capturing. The sensitivity, specificity and corresponding cut-off values will be determined based on receiver-operating-characteristic curves. The correlation of the digiDEM-SCREEN test with existing cognitive screening/testing procedures will be analysed.Ethics and disseminationThe study obtained ethical approval from the Ethics Committee of the Julius-Maximilians-Universität of Würzburg (JMU) (application number: 177/23-sc). The test will give feedback about the current cognitive status and possible cognitive impairments that should lead to the users seeking further diagnostic measures by medical professionals. It will be accessible free of charge in established app stores. The results of the validation study will be published in peer-reviewed journals.

Visual analytics are becoming increasingly important in the light of big data and related scenarios. Along this trend, the field of immersive analytics has been variously furthered as it is able to provide sophisticated visual data analytics on one hand, while preserving user-friendliness on the other. Furthermore, recent hardware developments such as smart glasses, as well as achievements in virtual-reality applications, have fanned immersive analytic solutions. Notably, such solutions can be very effective when they are applied to high-dimensional datasets. Taking this advantage into account, the work at hand applies immersive analytics to a high-dimensional production dataset to improve the digital support of daily work tasks. More specifically, a mixed-reality implementation is presented that will support manufacturers as well as data scientists to comprehensively analyze machine data. As a particular goal, the prototype will simplify the analysis of manufacturing data through the usage of dimensionality reduction effects. Therefore, five aspects are mainly reported in this paper. First, it is shown how dimensionality reduction effects can be represented by clusters. Second, it is presented how the resulting information loss of the reduction is addressed. Third, the graphical interface of the developed prototype is illustrated as it provides (1) a correlation coefficient graph, (2) a plot for the information loss, and (3) a 3D particle system. In addition, an implemented voice recognition feature of the prototype is shown, which was considered to be being promising to select or deselect data variables users are interested in when analyzing the data. Fourth, based on a machine learning library, it is shown how the prototype reduces computational resources using smart glasses. The main idea is based on a recommendation approach as well as the use of subspace clustering. Fifth, results from a practical setting are presented, in which the prototype was shown to domain experts. The latter reported that such a tool is actually helpful to analyze machine data daily. Moreover, it was reported that such a system can be used to educate machine operators more properly. As a general outcome of this work, the presented approach may constitute a helpful solution for the industry as well as other domains such as medicine.

A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can acquire quantitatively valuable knowledge about a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities (e.g., authors) and group them into clusters representing subareas or communities. Calculating and visualizing bibliometric networks is a nontrivial and time-consuming data science task that requires highly skilled individuals. In addition to domain knowledge, researchers must often provide statistical knowledge and programming skills or use software tools having limited functionality and usability. In this paper, we present the ambalytics bibliometric platform, which reduces the complexity of bibliometric network analysis and the visualization of results. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistical knowledge, while preserving advanced functionality, such as algorithm parameterization, for experts. As a proof-of-concept, and as an example of bibliometric analyses outcomes, the calculation of research fronts networks based on a hybrid similarity approach is shown. Being designed to scale, ambalytics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. This paper presents the initial building block of a comprehensive bibliometric analysis platform called ambalytics, which aims at a high usability for users as well as scalability.

Dementia is a widespread syndrome that currently affects more than 55 million people worldwide. Digital screening instruments are one way to increase diagnosis rates. Developing an app for older adults presents several challenges, both technical and social. In order to make the app user-friendly, feedback from potential future end users is crucial during this development process. This study aimed to establish a user-centered design process for the development of digiDEM-SCREEN, a user-friendly app to support early identification of persons with slight symptoms of dementia. This research used qualitative and quantitative methods and involved 3 key stakeholder groups: the digiDEM research team, the software development team, and the target user group (older adults ≥65 years with and without cognitive impairments). The development of the screening app was based on an already existing and scientifically analyzed screening test (Self-Administered Tasks Uncovering Risk of Neurodegeneration; SATURN). An initial prototype was developed based on the recommendations for mobile health apps and the teams' experiences. The prototype was tested in several iterations by various end users and continuously improved. The app's usability was evaluated using the System Usability Scale (SUS), and verbal feedback by the end users was obtained using the think-aloud method. The translation process during test development took linguistic and cultural aspects into account. The texts were also adapted to the German-speaking context. Additional instructions were developed and supplemented. The test was administered using different randomization options to minimize learning effects. digiDEM-SCREEN was developed as a tablet and smartphone app. In the first focus group discussion, the developers identified and corrected the most significant criticism in the next version. Based on the iterative improvement process, only minor issues needed to be addressed after the final focus group discussion. The SUS score increased with each version (score of 72.5 for V1 vs 82.4 for V2), while the verbal feedback from end users also improved. The development of digiDEM-SCREEN serves as an excellent example of the importance of involving experts and potential end users in the design and development process of health apps. Close collaboration with end users leads to products that not only meet current standards but also address the actual needs and expectations of users. This is also a crucial step toward promoting broader adoption of such digital tools. This research highlights the significance of a user-centered design approach, allowing content, text, and design to be optimally tailored to the needs of the target audience. From these findings, it can be concluded that future projects in the field of health apps would also benefit from a similar approach.

Background and objectivesSchizophrenia and Alzheimer’s disease are characterised by glutamatergic pathway abnormalities related to N-methyl-d-aspartate (NMDA) receptor hypofunction and cognitive impairment. Glycine is an NMDA receptor co-agonist; inhibition of glycine transporter 1 (GlyT1) should improve NMDA receptor hypofunction. This study evaluated safety and pharmacokinetic properties of BI 425809—a potent and selective GlyT1 inhibitor.MethodsIn the single-rising dose (SRD) component of this study, subjects were randomised to a single dose of BI 425809 [doses (mg): 0.5, 1, 2, 5, 10, 25, 50, 100 and 150], or placebo. The bioavailability/food effect (BA/FE) component investigated BI 425809 pharmacokinetics following single dosing (25-mg tablet) after overnight fasting or with a high-calorie meal or as solution (25 mg) after overnight fasting.ResultsOverall, 33/83 (39.8%) subjects had ≥ 1 treatment-related adverse event (AE); there were no deaths or serious AEs. Reported SRD part AEs trended towards dose dependency, occurring at the higher doses (mostly central nervous system related). BI 425809 plasma concentration–time profiles were similarly shaped across all doses and plasma exposure increased proportional to dose. In the BA/FE component, geometric mean ratios for the area under the concentration–time curve from time zero to the last measurable concentration and the maximum plasma concentration for tablet fasted versus solution fasted were 80.5 and 50.0%, respectively, and for tablet fed versus fasted were 125.9 and 142.1%, respectively.ConclusionBI 425809 was generally well-tolerated at doses expected to be clinically relevant. The AE profile suggested possible GlyT1-inhibiting effects.Clinical trial identifierNCT02068690.

Resection of the lower patellar pole provides good results in the treatment of jumper’s knee. Therefore we hypothesized that the length of the lower patellar pole is increased in patients with chronic patellar tendinopathy. Cohort study, level of evidence 2. Between 2000 and 2005, 25 patients with chronic patellar tendinopathy underwent conservative and surgical treatment in our clinic. All of them had preoperative MRI were three independent examiners measured the Caton Index, the length and the ratio of the articular and non-articular patellar surface, tendon length and thickness and the thickness and length of the hypodens lesions in the patellar tendon. The measurements were compared with 50 MRI of a control group with no clinical patellofemoral disorders or patellar tendinopathy. Significant changes in tendon thickness (9.42 ± 2.87 vs. 4.88 ± 1.13; P  < 0.0001), a longer non-articular surface of the patella (10.62 ± 2.86 vs. 7.098 ± 2.53; P  < 0.0001) and significant higher ratio between the articular and the non-articular patellar surface (0.32 vs. 0.24; P  < 0.0001) were found in the jumper’s knee group. No significant changes were seen in the length of the articular surface or the Caton Index. The development of chronic patellar tendinopathy in athletes might be associated with a longer lower patellar pole as patients with jumper’s knee showed a longer non-articular patellar surface compared with the control group.

Challenging behaviour (CB) is a common issue among children with autism spectrum disorder or intellectual and developmental disability. Mental health applications are low-threshold cost-effective tools to address the lack of resources for caregivers. This pre-post study evaluated the feasibility and preliminary effectiveness of the smartphone app using algorithm-based behaviour analysis to identify causes of CB and provide individualized practical guidance to manage and prevent CB. A total of 18 caregivers (  = 38.9 ± 5.0) of children with a diagnosis of autism spectrum disorder (44%), intellectual and developmental disabilities (33%) or both (22%) aged 4-11 years (  = 7.6 ± 1.8) were included. Assessments were performed before and after an 8-week intervention period. The primary outcome was the change in parental stress. Caregiver stress experience due to CB was also rated daily via ecological momentary assessments within the app. Secondary outcomes included the intensity of the child's CB, dysfunctional parenting, feelings of parental competency as well as caregivers' mood (rated daily in the app) and feedback on the app collected via the Mobile Application Rating Scale. We observed increases in parental stress in terms of conscious feelings of incompetence. However, we also saw improvements in parental stress experience due to CB and overreactive parenting, and descriptive improvements in CB intensity and caregiver mood. pioneers behaviour analysis in a digital and automated format, with participants reporting high acceptance. Pilot results highlight the potential of the app to positively influence child behaviour and caregiver mental health over a longer intervention period. The study was registered at https://www.drks.de (ID = DRKS00029039) on May 31, 2022.

The increasing interconnection of machines in industrial production on one hand, and the improved capabilities to store, retrieve, and analyze large amounts of data on the other, offer promising perspectives for maintaining production machines. Recently, predictive maintenance has gained increasing attention in the context of equipment maintenance systems. As opposed to other approaches, predictive maintenance relies on machine behavior models, which offer several advantages. In this highly interdisciplinary field, there is a lack of a literature review of relevant research fields and realization techniques. To obtain a comprehensive overview on the state of the art, large data sets of relevant literature need to be considered and, best case, be automatically partitioned into relevant research fields. A proper methodology to obtain such an overview is the bibliometric analysis method. In the presented work, we apply a bibliometric analysis to the field of equipment maintenance systems. To be more precise, we analyzed clusters of identified literature with the goal to obtain deeper insight into the related research fields. Moreover, cluster metrics reveal the importance of a single paper and an investigation of the temporal cluster development indicates the evolution of research topics. In this context, we introduce a new measure to compare results from different time periods in an appropriate way. In turn, among others, this simplifies the analysis of topics, with a vast amount of subtopics. Altogether, the obtained results particularly provide a comprehensive overview of established techniques and emerging trends for equipment maintenance systems.