The circadian clock drives daily changes of physiology, including sleep-wake cycles, through regulation of transcription, protein abundance, and function. Circadian phosphorylation controls cellular ...processes in peripheral organs, but little is known about its role in brain function and synaptic activity. We applied advanced quantitative phosphoproteomics to mouse forebrain synaptoneurosomes isolated across 24 hours, accurately quantifying almost 8000 phosphopeptides. Half of the synaptic phosphoproteins, including numerous kinases, had large-amplitude rhythms peaking at rest-activity and activity-rest transitions. Bioinformatic analyses revealed global temporal control of synaptic function through phosphorylation, including synaptic transmission, cytoskeleton reorganization, and excitatory/inhibitory balance. Sleep deprivation abolished 98% of all phosphorylation cycles in synaptoneurosomes, indicating that sleep-wake cycles rather than circadian signals are main drivers of synaptic phosphorylation, responding to both sleep and wake pressures.
Functional dependencies are important metadata used for schema normalization, data cleansing and many other tasks. The efficient discovery of functional dependencies in tables is a well-known ...challenge in database research and has seen several approaches. Because no comprehensive comparison between these algorithms exist at the time, it is hard to choose the best algorithm for a given dataset. In this experimental paper, we describe, evaluate, and compare the seven most cited and most important algorithms, all solving this same problem.
First, we classify the algorithms into three different categories, explaining their commonalities. We then describe all algorithms with their main ideas. The descriptions provide additional details where the original papers were ambiguous or incomplete. Our evaluation of careful re-implementations of all algorithms spans a broad test space including synthetic and real-world data. We show that all functional dependency algorithms optimize for certain data characteristics and provide hints on when to choose which algorithm. In summary, however, all current approaches scale surprisingly poorly, showing potential for future research.
Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account. We introduce PerseusNet, the new Perseus network ...module for the biological analysis of proteomics data. Proteomics is commonly used to generate networks, e.g., with affinity purification experiments, but networks are also used to explore proteomics data. PerseusNet supports the biomedical researcher for both modes of data analysis with a multitude of activities. For affinity purification, a volcano-plot-based statistical analysis method for network generation is featured which is scalable to large numbers of baits. For posttranslational modifications of proteins, such as phosphorylation, a collection of dedicated network analysis tools helps in elucidating cellular signaling events. Co-expression network analysis of proteomics data adopts established tools from transcriptome co-expression analysis. PerseusNet is extensible through a plugin architecture in a multi-lingual way, integrating analyses in C#, Python, and R, and is freely available at http://www.perseus-framework.org.
Additive Manufacturing (AM) is increasingly used in the industrial part production. More than almost any other manufacturing technology, AM embodies the fourth industrial revolution (Industry 4.0). ...Even though AM allows a nearly direct manufacturing of parts out of their CAD data, the order processing still requires a lot of manual work. This paper addresses this issue by presenting a cloud-based platform, which has the intension to integrate and automate the order processing for additively manufactured parts. In addition to facilitating the order processing of the manufacturing service provider, the platform also serves as an interface to the customer. The focus of the platform is on an automation of the order acceptance, the offer calculation, and the part screening for the identification of appropriate AM parts. The paper builds an exemplary Industry 4.0 showcase by illustrating concepts and methods for an automation of the order processing and introducing the architecture of the implemented system. This includes web-based services for the management of parts and orders as well as an integrated analysis of geometry data for checking of manufacturability and quotation costing. The evaluation examines the efficiency and effectiveness of the platform.
Quantum sensors based on coherent matter-waves are precise measurement devices whose ultimate accuracy is achieved with Bose-Einstein condensates (BECs) in extended free fall. This is ideally ...realized in microgravity environments such as drop towers, ballistic rockets and space platforms. However, the transition from lab-based BEC machines to robust and mobile sources with comparable performance is a challenging endeavor. Here we report on the realization of a miniaturized setup, generating a flux of quantum degenerate 87Rb atoms every 1.6 s. Ensembles of atoms can be produced at a 1 Hz rate. This is achieved by loading a cold atomic beam directly into a multi-layer atom chip that is designed for efficient transfer from laser-cooled to magnetically trapped clouds. The attained flux of degenerate atoms is on par with current lab-based BEC experiments while offering significantly higher repetition rates. Additionally, the flux is approaching those of current interferometers employing Raman-type velocity selection of laser-cooled atoms. The compact and robust design allows for mobile operation in a variety of demanding environments and paves the way for transportable high-precision quantum sensors.
The genomic and transcriptomic landscapes of breast cancer have been extensively studied, but the proteomes of breast tumors are far less characterized. Here, we use high-resolution, high-accuracy ...mass spectrometry to perform a deep analysis of luminal-type breast cancer progression using clinical breast samples from primary tumors, matched lymph node metastases, and healthy breast epithelia. We used a super-SILAC mix to quantify over 10,000 proteins with high accuracy, enabling us to identify key proteins and pathways associated with tumorigenesis and metastatic spread. We found high expression levels of proteins associated with protein synthesis and degradation in cancer tissues, accompanied by metabolic alterations that may facilitate energy production in cancer cells within their natural environment. In addition, we found proteomic differences between breast cancer stages and minor differences between primary tumors and their matched lymph node metastases. These results highlight the potential of proteomic technology in the elucidation of clinically relevant cancer signatures.
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•Deep proteomic profiling of luminal breast cancer progression•Super-SILAC quantified over 10,000 proteins across a cohort of 88 clinical samples•Tumor cells exhibit higher protein turnover and metabolic remodeling•Proteomic profiles of primary tumors and lymph node metastases are highly similar
Deep proteomic analysis of ER-positive clinical breast tumors points to key proteins and processes that are associated with tumorigenesis and metastatic spread.
Additive manufacturing (AM) is increasingly used in industrial production. Compared to conventional manufacturing technologies, such as milling and casting, AM offers a high degree of design freedom. ...Nevertheless, still some manufacturing restrictions and design guidelines have to be considered to ensure a flawless production. Therefore, a checking of design guidelines is a necessary step in order acceptance. Addressing this need, this paper presents an integrated analysis of design guidelines for an automated order acceptance.
In recent times, guideline catalogs for the design of additively manufactured parts have been developed. However, the analysis of a part's geometry with regard to these guidelines still requires a lot of manual work and expert knowledge. This paper introduces different algorithmic approaches, which automate the analysis and assessment of a part's geometry. Based on a preselection of guidelines from existing design catalogs for selective laser melting and sintering, this paper presents algorithms to automatically check the manufacturability of a part. The algorithms use the triangulated surface geometry (STL) of a part. They are implemented within a web-based platform for the automated order acceptance of additive manufactured parts. The evaluation compares the different algorithms regarding their efficiency and effectiveness.
Abstract
Artificial intelligence (AI) algorithms evaluating supine chest radiographs (SCXRs) have remarkably increased in number recently. Since training and validation are often performed on subsets ...of the same overall dataset, external validation is mandatory to reproduce results and reveal potential training errors. We applied a multicohort benchmarking to the publicly accessible (S)CXR analyzing AI algorithm CheXNet, comprising three clinically relevant study cohorts which differ in patient positioning (SCXRs), the applied reference standards (CT-/SCXR-based) and the possibility to also compare algorithm classification with different medical experts’ reading performance. The study cohorts include 1 a cohort, characterized by 563 CXRs acquired in the emergency unit that were evaluated by 9 readers (radiologists and non-radiologists) in terms of 4 common pathologies, 2 a collection of 6,248 SCXRs annotated by radiologists in terms of pneumothorax presence, its size and presence of inserted thoracic tube material which allowed for subgroup and confounding bias analysis and 3 a cohort consisting of 166 patients with SCXRs that were evaluated by radiologists for underlying causes of basal lung opacities, all of those cases having been correlated to a timely acquired computed tomography scan (SCXR and CT within < 90 min). CheXNet non-significantly exceeded the radiology resident (RR) consensus in the detection of suspicious lung nodules (cohort 1, AUC AI/RR: 0.851/0.839,
p
= 0.793) and the radiological readers in the detection of basal pneumonia (cohort 3, AUC AI/reader consensus: 0.825/0.782,
p
= 0.390) and basal pleural effusion (cohort 3, AUC AI/reader consensus: 0.762/0.710,
p
= 0.336) in SCXR, partly with AUC values higher than originally published (“Nodule”: 0.780, “Infiltration”: 0.735, “Effusion”: 0.864). The classifier “Infiltration” turned out to be very dependent on patient positioning (best in CXR, worst in SCXR). The pneumothorax SCXR cohort 2 revealed poor algorithm performance in CXRs without inserted thoracic material and in the detection of small pneumothoraces, which can be explained by a known systematic confounding error in the algorithm training process. The benefit of clinically relevant external validation is demonstrated by the differences in algorithm performance as compared to the original publication. Our multi-cohort benchmarking finally enables the consideration of confounders, different reference standards and patient positioning as well as the AI performance comparison with differentially qualified medical readers.
Characteristics of COVID-19 patients have mainly been reported within confirmed COVID-19 cohorts. By analyzing patients with respiratory infections in the emergency department during the first ...pandemic wave, we aim to assess differences in the characteristics of COVID-19 vs. Non-COVID-19 patients. This is particularly important regarding the second COVID-19 wave and the approaching influenza season.
We prospectively included 219 patients with suspected COVID-19 who received radiological imaging and RT-PCR for SARS-CoV-2. Demographic, clinical and laboratory parameters as well as RT-PCR results were used for subgroup analysis. Imaging data were reassessed using the following scoring system: 0 - not typical, 1 - possible, 2 - highly suspicious for COVID-19.
COVID-19 was diagnosed in 72 (32,9%) patients. In three of them (4,2%) the initial RT-PCR was negative while initial CT scan revealed pneumonic findings. 111 (50,7%) patients, 61 of them (55,0%) COVID-19 positive, had evidence of pneumonia. Patients with COVID-19 pneumonia showed higher body temperature (37,7 ± 0,1 vs. 37,1 ± 0,1 °C; p = 0.0001) and LDH values (386,3 ± 27,1 vs. 310,4 ± 17,5 U/l; p = 0.012) as well as lower leukocytes (7,6 ± 0,5 vs. 10,1 ± 0,6G/l; p = 0.0003) than patients with other pneumonia. Among abnormal CT findings in COVID-19 patients, 57 (93,4%) were evaluated as highly suspicious or possible for COVID-19. In patients with negative RT-PCR and pneumonia, another third was evaluated as highly suspicious or possible for COVID-19 (14 out of 50; 28,0%). The sensitivity in the detection of patients requiring isolation was higher with initial chest CT than with initial RT-PCR (90,4% vs. 79,5%).
COVID-19 patients show typical clinical, laboratory and imaging parameters which enable a sensitive detection of patients who demand isolation measures due to COVID-19.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Selective laser melting (SLM) is increasingly used in the industrial production of metallic parts. This creates the need for an efficient and accurate quotation costing. The manufacturing costs of a ...part mainly result from the machine running time for coating and exposure. At the time of the offer calculation the final orientation of the part in the build chamber and the composition of the build job are typically not known. Addressing this need, this paper presents and evaluates different statistical based methods for an automated and self-learning calculation for SLM given a part's CAD data.
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