The Cocktail Party Effect refers to the ability of the human sense of hearing to extract a specific target sound source from a mixture of background noises in complex acoustic scenarios. The ease with which normal hearing people perform this challenging task is in stark contrast to the difficulties that hearing-impaired subjects face in these situations. To help patients with hearing aids and implants, scientists are trying to imitate this ability of human hearing, with modest success so far. To support the scientific community in its efforts, we provide the Bern Cocktail Party (BCP) dataset consisting of 55938 Cocktail Party scenarios recorded from 20 people and a head and torso simulator wearing cochlear implant audio processors. The data were collected in an acoustic chamber with 16 synchronized microphones placed at purposeful positions on the participants’ heads. In addition to the multi-channel audio source and image recordings, the spatial coordinates of the microphone positions were digitized for each participant. Python scripts were provided to facilitate data processing.A metadata schema, named Plasma-MDS, is introduced to support research data management in plasma science. Plasma-MDS is suitable to facilitate the publication of research data following the FAIR principles in domain-specific repositories and with this the reuse of research data for data driven plasma science. In accordance with common features in plasma science and technology, the metadata schema bases on the concept to separately describe the source generating the plasma, the medium in which the plasma is operated in, the target the plasma is acting on, and the diagnostics used for investigation of the process under consideration. These four basic schema elements are supplemented by a schema element with various attributes for description of the resources, i.e. the digital data obtained by the applied diagnostic procedures. The metadata schema is first applied for the annotation of datasets published in INPTDAT-the interdisciplinary data platform for plasma technology.Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults and the second most common form of acute leukemia in children. Despite this, very little improvement in survival rates has been achieved over the past few decades. This is partially due to the heterogeneity of AML and the need for more targeted therapeutics than the traditional cytotoxic chemotherapies that have been a mainstay in therapy for the past 50 years. In the past 20 years, research has been diversifying the approach to treating AML by investigating molecular pathways uniquely relevant to AML cell proliferation and survival. Here we review the development of novel therapeutics in targeting apoptosis, receptor tyrosine kinase (RTK) signaling, hedgehog (HH) pathway, mitochondrial function, DNA repair, and c-Myc signaling. There has been an impressive effort into better understanding the diversity of AML cell characteristics and here we highlight important preclinical studies that have supported therapeutic development and continue to promote new ways to target AML cells. In addition, we describe clinical investigations that have led to FDA approval of new targeted AML therapies and ongoing clinical trials of novel therapies targeting AML survival pathways. We also describe the complexity of targeting leukemia stem cells (LSCs) as an approach to addressing relapse and remission in AML and targetable pathways that are unique to LSC survival. This comprehensive review details what we currently understand about the signaling pathways that support AML cell survival and the exceptional ways in which we disrupt them.Endocannabinoids retrogradely regulate synaptic transmission and their abundance is controlled by the fine balance between endocannabinoid synthesis and degradation. While the common assumption is that “on-demand” release determines endocannabinoid signaling, their rapid degradation is expected to control the temporal profile of endocannabinoid action and may impact neuronal signaling. Here we show that memory formation through fear conditioning selectively accelerates the degradation of endocannabinoids in the cerebellum. Learning induced a lasting increase in GABA release and this was responsible for driving the change in endocannabinoid degradation. Conversely, Gq-DREADD activation of cerebellar Purkinje cells enhanced endocannabinoid signaling and impaired memory consolidation. Our findings identify a previously unappreciated reciprocal interaction between GABA and the endocannabinoid system in which GABA signaling accelerates endocannabinoid degradation, and triggers a form of learning-induced metaplasticity.Monitoring the internal conditions of a machine is essential to increase its production efficiency and to reduce energy waste. Non-intrusive condition monitoring techniques, such as analysing electrical signals, provide insights by disaggregating a composite signal of a machine as a whole into the individual components to determine their states. Developing and evaluating new algorithms for condition monitoring and maintenance-related analysis tasks require a fully-labelled dataset for a machine, which comprises standard industrial components that are triggered following a typical manufacturing process to produce goods. For this purpose, we introduce CREAM, a component level electrical measurement dataset for two industrial-grade coffeemakers, simulating industrial processes. The dataset contains continuous voltage and current measurements provided at 6400 samples per second, as well as the product and maintenance-related event labels, such as 370600 expert-labelled component-level electrical events, 1734 product ones and 3646 maintenance ones. CREAM provides fully-labelled ground-truth to establish a benchmark and comparative studies of manufacturing-related analysis in a controlled and transparent environment.It has been proven challenging to conduct traditional efficacy trials for Ebola virus (EBOV) vaccines. In the absence of efficacy data, immunobridging is an approach to infer the likelihood of a vaccine protective effect, by translating vaccine immunogenicity in humans to a protective effect, using the relationship between vaccine immunogenicity and the desired outcome in a suitable animal model. We here propose to infer the protective effect of the Ad26.ZEBOV, MVA-BN-Filo vaccine regimen with an 8-week interval in humans by immunobridging. Immunogenicity and protective efficacy data were obtained for Ad26.ZEBOV and MVA-BN-Filo vaccine regimens using a fully lethal EBOV Kikwit challenge model in cynomolgus monkeys (nonhuman primates [NHP]). The association between EBOV neutralizing antibodies, glycoprotein (GP)-binding antibodies, and GP-reactive T cells and survival in NHP was assessed by logistic regression analysis. Necrostatin-1 datasheet Binding antibodies against the EBOV surface GP were identified as the immune parameter with the strongest correlation to survival post EBOV challenge, and used to infer the predicted protective effect of the vaccine in humans using published data from phase I studies.