This study was intended to determine the effect of adding flax seeds to a concentrate for lamb fattening on the fatty acid composition of the omental fat depot in Pramenka lambs reared indoors. A total of 63 lambs (10±3 kg of live body weight, 30±7 days of age, 30 males and 33 females) were used. They were divided into two groups: a control (CON) fed on hay, ewe's milk, and a 300-g daily ration of a commercial concentrate, and an experimental group (FS) fed on hay, ewe's milk and 300 g/day of the concentrate enriched with 5% of flax seeds. After a 60-day fattening period for each group, 10 lambs (5 males and 5 females) were selected and omental fat samples were analysed for fatty acid composition. Highly significant differences (p⟨0.001) were found between CON and FS in α-linolenic acid, the sum of n-3 fatty acids, and the ratio n-6/n-3 fatty acids. The effect of sex on the fatty acid content in the fat depot was only significant for C20:0 fatty acid (p⟨0.05).

Due to the implementation and performance limitations of centralized learning automatic modulation classification (CentAMC) method, this paper proposes a decentralized learning AMC (DecentAMC) method using model consolidation and lightweight design. Specifically, the model consolidation is realized by a central device (CD) for edge device (ED) model averaging (MA) and multiple EDs for ED model training. The lightweight is designed by separable convolutional neural network (S-CNN), in which the separable convolutional layer is utilized to replace the standard convolution layer and most of fully connected layers are cut off. Simulation results show that the proposed method substantially reduces the storage and computational capacity requirements of the EDs and communication overhead. The training efficiency also shows remarkable improvement. Compared with convolutional neural network (CNN), the space complexity (i.e., model parameters and output feature map) is decreased by about 94% and the time complexity (i.e., floating point operations) of S-CNN is decreased by about 96% while degrading the average correct classification probability by less than 1%. Compared with S-CNN-based CentAMC, without considering model weights uploading and downloading, the training efficiency of our proposed method is about <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> times of it, where <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> is the number of EDs. Considering the model weights uploading and downloading, the training efficiency of our proposed method can still be maintained at a high level (e.g., when the number of EDs is 12, the training efficency of the proposed AMC method is about 4 times that of S-CNN-based CentAMC in dataset <inline-formula> <tex-math notation="LaTeX">$D_{1} = \{2{\mathrm {FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, 16QAM}}\}$ </tex-math></inline-formula> and about 5 times that of S-CNN-based CentAMC in dataset <inline-formula> <tex-math notation="LaTeX">$D_{2} = \{2 {\mathrm {FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, PAM2, PAM4, PAM8, 16QAM}}\}$ </tex-math></inline-formula>), while the communication overhead is reduced more than 35%.

Radio frequency fingerprint (RFF) identification is a popular topic in the field of physical layer security. However, machine learning based RFF identification methods require complicated feature extraction manually while deep learning based methods are hard to achieve robust identification performance. To solve these problems, we propose a novel RFF identification method based on heat constellation trace figure (HCTF) and slice integration cooperation (SIC). HCTF is utilized to avoid the manual feature extraction and SIC is adopted to extract more features automatically in RF signals. Experimental results show that our proposed HCTF-SIC identification method can achieve higher accuracy than the existing RFF methods. The identification accuracy achieves 91.07% when SNR <inline-formula> <tex-math notation="LaTeX">$\pmb {=}$ </tex-math></inline-formula> 0 dB and it is even higher than 99.64% when the SNR <inline-formula> <tex-math notation="LaTeX">$\pmb {\ge }$ </tex-math></inline-formula> 5 dB.

Automatic modulation classification (AMC) is a promising technology for identifying modulation types, and deep learning (DL)-based AMC is one of its main research directions. Conventional DL-based AMC methods are centralized solutions (i.e., CentAMC), which are trained on abundant data collected from local clients and stored in the server and generally have advanced performance, but their major problem is the risk of data leakage. Besides, if DL-based AMC is only trained with the data from their corresponding clients, it may exhibit weak performance. Thus, a federated learning (FL)-based AMC (FedeAMC) is proposed under the condition of class imbalance and noise varying. Its advantage is low risk of data leakage without severe performance loss, because data and training are in each local client, while only knowledge (i.e., gradient or model weight), rather than data, is shared with the server. In addition, there is generally class imbalance problem in each local client, and balanced cross entropy is introduced as loss function for solving this problem. Simulation results demonstrated that average accuracy gap between FedeAMC and CentAMC is less than 2%.

Background Most trials comparing endovascular treatment (EVT) alone versus intravenous thrombolysis with alteplase (IVT) + EVT in directly admitted patients with a stroke are non-inferiority trials. However, the margin based on the level of uncertainty regarding non-inferiority of the experimental treatment that clinicians are willing to accept to incorporate EVT alone into clinical practice remains unknown. Objective To characterize what experienced stroke clinicians would consider an acceptable level of uncertainty for hypothetical decisions on whether to administer IVT or not before EVT in patients admitted directly to EVT-capable centers. Methods A web-based, structured survey was distributed to a cross-section of 600 academic neurologists/neurointerventionalists. For this purpose, a response framework for a hypothetical trial comparing IVT+EVT (standard of care) with EVT alone (experimental arm) was designed. In this trial, a similar proportion of patients in each arm achieved functional independence at 90 days. Invited physicians were asked at what level of certainty they would feel comfortable skipping IVT in clinical practice, considering these hypothetical trial results. Results There were 180 respondents (response rate: 30%) and 165 with complete answers. The median chosen acceptable uncertainty suggesting reasonable comparability between both treatments was an absolute difference in the rate of day 90 functional independence of 3% (mode 5%, IQR 1–5%), with higher chosen margins observed in interventionalists (aOR 2.20, 95% CI 1.06 to 4.67). Conclusion Physicians would generally feel comfortable skipping IVT before EVT at different certainty thresholds. Most physicians would treat with EVT alone if randomized trial data suggested that the number of patients achieving functional independence at 90 days was similar between the two groups, and one could be sufficiently sure that no more than 3 out of 100 patients would not achieve functional independence at 90 days due to skipping IVT.

Simple Summary Head and neck cancer is the sixth most common cancer type worldwide, comprising tumors of the upper aero/digestive tract. Approximately 50% of these cancers originate in the oral cavity. Depending on disease stage, oral cancer patients are treated with single-modality surgery, or in combination with radiotherapy with or without chemotherapy. Despite advances in these modalities, the 5-year survival rate is merely 50%. Therefore, implementation of targeted therapies, directed against signaling molecules, has gained attention. One potential target is the MET protein, which can be present on the surface of cancer cells, orchestrating aggressive behavior. As cancer cells can shed the extracellular part of MET from their surface, it is important to identify for MET positive patients whether they possess the entire and/or only the intracellular part of the receptor to assess whether targeted therapies directed against the extracellular, intracellular, or both parts of MET need to be implemented. Abstract The receptor tyrosine kinase MET has gained attention as a therapeutic target. Although MET immunoreactivity is associated with progressive disease, use of targeted therapies has not yet led to major survival benefits. A possible explanation is the lack of companion diagnostics (CDx) that account for proteolytic processing. During presenilin-regulated intramembrane proteolysis, MET’s ectodomain is shed into the extracellular space, which is followed by γ-secretase-mediated cleavage of the residual membranous C-terminal fragment. The resulting intracellular fragment is degraded by the proteasome, leading to downregulation of MET signaling. Conversely, a membrane-bound MET fragment lacking the ectodomain (MET-EC-) can confer malignant potential. Use of C- and N-terminal MET monoclonal antibodies (moAbs) has illustrated that MET-EC- occurs in transmembranous C-terminal MET-positive oral squamous cell carcinoma (OSCC). Here, we propose that ectodomain shedding, resulting from G-protein-coupled receptor transactivation of epidermal growth factor receptor signaling, and/or overexpression of ADAM10/17 and/or MET, stabilizes and possibly activates MET-EC- in OSCC. As MET-EC- is associated with poor prognosis in OSCC, it potentially has impact on the use of targeted therapies. Therefore, MET-EC- should be incorporated in the design of CDx to improve patient stratification and ultimately prolong survival. Hence, MET-EC- requires further investigation seen its oncogenic and predictive properties.

The aim of the study is to investigate consumer attitudes towards specific Customer Social Responsibility Activities in Bosnia and Herzegovina. In order to support this research, a case study method, combined with qualitative and quantitative research methods (referenced survey) was used. To get an insight into the companies’ perspective, when it comes to an engagement in the employment practices, as an integral part of Corporate Social Responsibility, data regarding “The Most Desirable Employer” project- “Najpoželjniji poslodavac”, organized by Kolektiv d.o.o.-MojPosao.ba, for years 2020, 2019 and 2018 will be analyzed. The study should illustrate the exact scenario of customer loyalty in Bosnia and Herzegovina to the companies included in CSR activities and contribute to an advancement of overall knowledge in the field of CSR in Bosnia and Herzegovina. The directing outcome uncovers those corporate capacities have a critical part in fortifying the relationship between corporate social responsibility drives and customer trust in Bosnia and Herzegovina, in light of the fact that high corporate capacities with professional corporate social responsibility actions lead to high faithfulness of customers. The examination features the meaning of the corporate social responsibility activities, which are obligatory for authoritative achievement and guides the policymakers of companies, supervisors, and researchers.

The European Synchrotron Radiation Facility (ESRF) has recently commissioned the new Extremely Brilliant Source (EBS). The gain in brightness as well as the continuous development of beamline instruments boosts the beamline performances, in particular in terms of accelerated data acquisition. This has motivated the development of new access modes as an alternative to standard proposals for access to beamtime, in particular via the “block allocation group” (BAG) mode. Here, we present the recently implemented “historical materials BAG”: a community proposal giving to 10 European institutes the opportunity for guaranteed beamtime at two X-ray powder diffraction (XRPD) beamlines—ID13, for 2D high lateral resolution XRPD mapping, and ID22 for high angular resolution XRPD bulk analyses—with a particular focus on applications to cultural heritage. The capabilities offered by these instruments, the specific hardware and software developments to facilitate and speed-up data acquisition and data processing are detailed, and the first results from this new access are illustrated with recent applications to pigments, paintings, ceramics and wood.