5G Standalone (SA) networks introduce a range of new applications, including enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communications (mMTC). Each of these applications has distinct network requirements, which current commercial network architectures, such as 4G and 5G Non-Standalone (NSA), struggle to meet simultaneously due to their one-size-fits-all design. The 5G SA architecture addresses this challenge through Network Slicing, creating multiple isolated virtual networks on top a single physical infrastructure. Isolation between slices is crucial for performance, security, and reliability. Each slice owns virtual resources, based on the physical resources (e.g., CPU, memory, antennas, and network interfaces) shared by the overall infrastructure. To deploy Network Slicing, it is essential to understand the concept of isolation. The Third Generation Partnership Project (3GPP) is standardizing security for Network Slicing, focusing on authentication, authorization, and slice management. However, the standards do not clearly define the meaning of isolation and its implementation in the infrastructure layer.In this paper, we define and showcase a real-life Proof of Concept (PoC), which guarantees isolation between slices in 5G SA networks, for each network domain i.e., Radio Access Network (RAN), Transport Network (TN), and 5G Core (5GC) network. Furthermore, we describe the 5G SA architecture of the PoC, explaining the isolation concepts within the Network Slicing framework, how to implement isolation in each network domain, and how to evaluate it.

5G Standalone (SA) networks introduce a range of new applications, including enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communications (mMTC). Each of these applications has distinct network requirements, which current commercial network architectures, such as 4G and 5G Non-Standalone (NSA), struggle to meet simultaneously due to their one-size-fits-all design. The 5G SA architecture addresses this challenge through Network Slicing, creating multiple isolated virtual networks on top of a single physical infrastructure. Isolation between slices is crucial for performance, security, and reliability. Each slice owns virtual resources, based on the physical resources (e.g., CPU, memory, antennas, and network interfaces) shared by the overall infrastructure.In this demo, we define and showcase a real-life Proof of Concept (PoC), which enables Network Slicing guaranteeing isolation between slices in 5G SA networks, for each network domain i.e., Radio Access Network (RAN), Transport Network (TN), and 5G Core (5GC) network.

Introduction Techniques for assessing the blood plasma proteome with high precision and at great depth are rapidly developing and have demonstrated utility in carrying diagnostic and prognostic information for patients with cancer, including hematological malignancies. However, it is not known whether the plasma proteome can be useful in distinguishing the more closely related cancer entities, such as different B-cell lymphomas (BCLs). Performing affinity-based plasma proteomics analyses in a population-based cohort of BCLs, we aimed at discovering plasma proteome differences between BCL subtypes and identifying potential biomarkers that can aid differential diagnosis. Material and Methods We analyzed 592 BCLs (221 diffuse large BCL (DLBCL), 94 follicular lymphoma (FL), 123 Hodgkin lymphoma (HL), 91 mantle cell lymphoma (MCL), and 63 primary CNS lymphoma (PCNSL)) from the U-CAN biobank (www.u-can.uu.se). Plasma samples collected at diagnosis were analyzed using the Olink Explore 1536 platform, which provided relative quantification of 1463 unique proteins. The plasma proteomes between a given group and all the remaining groups were compared with a two-sided t test and further adjusted for age and sex in multivariable linear limma models. To identify panels of plasma proteins that can differentiate between the different subtypes of BCLs, we trained two types of machine learning (ML) models based on the random forest (RF) algorithm and logistic regression with regularization (LRR). The entire dataset was proportionally partitioned into a training (70%) and testing (30%) dataset. Both model types were trained in one thousand iterations, with cross-validation, on a non-filtered dataset and implementing different filtering approaches based on varying cut-offs of mean log2-difference (log2-diff) of differentially altered proteins (DAPs) and 0.1% false discovery rate (FDR). Finally, the best-performing model from the iterations of the two ML methods on the training data was selected and tested on the testing dataset for performance. Both balanced accuracy and area under the curve (AUC) were considered as main outcomes of performance. Results Comparing the plasma proteomes between BCL subtypes showed many DAPs in each subtype compared to the rest of the cohort at 5% FDR. PCNSL patients had the largest number of DAPs, followed by HL, MCL, DLBCL, and FL. However, most of these alterations were of smaller log2-diff between the subgroups. Less than ten proteins per group had a log2-diff > 1 in a subgroup compared to other subtypes, apart from MCL patients, who had 64 DAPs with log2-diff > 1. The findings remained consistent in the multivariable analyses, where the log2-diff between subgroups was adjusted for age and sex. Yet, each subgroup had more DAPs that were uniquely altered in that subgroup and in no other group, regardless of the log2-FC, with most DAPs observed again in the MCL, followed by DLBCL, HL, FL, and PCNSL. This was reflected in the ML models, where combining smaller differences in protein levels into multivariate models showed reliable performance in differentiating the BCLs. Filtering improved the model's accuracy, and the derived best-performing LRR model showed moderate to high accuracy in differentiating the BCLs on testing data. The LRR model had the highest accuracy in classifying MCL, with AUC of 91%, followed by HL (90%), PCNSL (89%), DLBCL (85%), and FL (80%), the latter being repeatedly misclassified in the ML iterations. Although the model's sensitivity was variable, being highest for HL and lowest for FL, the specificity was very high (>93%) for excluding FL (94%), HL (96%), MCL (98%), and particularly PCNSL (99%), with the negative predictive value of the model for CNS involvement being 98%. Conclusions Plasma proteomics can differentiate between distinct types of BCLs with a moderate to high accuracy, between 80% and 91%. The models showed the highest accuracy in classifying MCL, likely due to the highest number of unique DAPs and proteins with large log2-diff observed in this subtype On average, the models showed better specificity, which is highly relevant for DLBCL, where a blood biomarker can serve as a quick diagnostic tool for initial exclusion of CNS involvement in a patient, with very high predictive value. This suggests that plasma proteomics could assist in the differential diagnosis of B-cell lymphomas and potentially for CNS-involvement.

Background: Graft vs. host disease (GVHD) remains a major complication of allogeneic hematopoietic stem cell transplantation (alloHSCT). To create space for donor stem cells and prevent their rejection, alloHSCT protocols rely on conditioning regimens involving chemotherapy and radiation. Conditioning causes tissue damage, which increases the tissue injury signal or “alarmin” interleukin (IL)-33 in fibroblastic reticular cells (FRC) of the secondary lymphoid organs (SLO). Mechanisms releasing IL-33 from its sequestration in the nucleus remain elusive, but free IL-33 directly stimulates donor CD4 T cells to prime IL-12-independent Type 1 T helper cell (Th1) differentiation and expansion. Targeting IL-33 early after alloHSCT limits GVHD in pre-clinical models. The gastrointestinal tract (GIT) also upregulates IL-33 in response to TBI and GVHD, but a direct role for local IL-33 in sustaining pathogenic donor responses is unclear. Our goal was to manipulate the IL-33 pathway in the SLO or GIT to better understand how stromal communications with donor T cells initiate and shape GVHD and graft vs. lymphoma (GVL) responses. Methods: We compared donor T cells (plus or minus inducible deletion of the IL-33 receptor, ST2) for their ability to mediate GVHD vs. GVL (A20 lymphoma) in BALB/c recipients receiving total body irradiation (TBI) and CD45.1+ B6 T cell depleted bone marrow (TCD BM). To define the role for IL-33-derived from the SLO vs. the GIT, we assessed survival of B6 recipients deficient in IL-33 in FRCs (CCL19-CrexIl33fl/fl) vs. those deficient in IL-33 in the epithelium of the GI tract (Vil-CrexIl33fl/fl) receiving TBI and BALB/c T cells. To investigate if donor T cells mediate IL-33 release, we completed an ex vivo model using B6 St2+/+, St2-/-, and GzmB-/- CD3 T cells co-cultured for 5 days with BALB/c TCD splenocytes and LN-derived FRCs that had been irradiated at 3500 cGy alone or with the IL-33 antagonist, sST2. Similar in vivo studies were conducted where the above donor B6 St2+/+, St2-/-, and GzmB-/- CD3 T cells were transplanted into BALB/c recipients and assessed for GzmB and donor T cell expansion on day 5 post-alloHSCT. Results: Ablating ST2 at days 10-14 post-transplant (after initial GVHD development) improved clinical scores and limited mortality. Further, sustained IL-33 signaling was not required for GVL activity. Mechanistically, late ST2 deletion was associated with increased Foxp3 expression and reciprocal Tbet decrease in donor CD4+ T cells from both SLO and GVHD target tissues. Sustained IL-33 signaling also maintained donor T cell TCF1 expression in SLO. Surprisingly, isolated deletion of FRC-derived IL-33 increased GVHD mortality in the CCL19-CrexIl33fl/flrecipients. Mechanistic studies showing FRC-derived IL-33 stimulated CD4+ PD-1 expression and blunted the total number of CD4 and CD8 T effectors in the GIT at day 21 post-alloHSCT. Whereas, deletion of IL-33 in the gut epithelium in the Vil-CrexIl33fl/fl recipients was protective and prolonged survival. RNAseq analysis suggested that IL-33 stimulates T cell granzyme B (GzmB) expression. GzmB deficient (Gzmb-/-) donor T cells displaying reduced activation and expansion in vitro and in vivo, in a phenotype similar to ST2 deficient CD4 T cells. Consistent with the importance of GzmB in mediating IL-33 signals, antagonizing IL-33 had no impact on GzmB-/- T cell responses similar to ST2 deficient CD4 T cells when compared to Gzmb+/+, which failed to expand when IL-33 was sequestered. Conclusions: Our data reveals that GzmB-mediated crosstalk between donor T cells and IL-33+ stroma orchestrates donor T cell identities and tunes local alloimmune responses after alloHSCT. Delayed deletion of ST2 signaling on donor T cells promotes survival through an upregulation of regulatory mechanisms in GVHD target tissues. Similarly, targeted deletion of IL-33 in the GIT provides protection from donor driven pathology. Whereas, targeted deletion of IL-33 from SLO FRC promotes GVHD mortality by down regulating intrinsic T cell exhaustion mechanisms in the SLO, which impacts later CD4+ T cell alloimmune responses to available IL-33 in target tissues, driving GVHD pathology. These data suggest distinct temporal and tissue specific roles for IL-33-driven programing of donor CD4+ T cells. In total, these data indicate that continual feedback between donor T cells and recipient stroma is central to the development and maintenance of GVHD.

Lavandula species are one of the most popular aromatic plants in the world and have a high content of high-quality essential oil (EO). Although there are many species in this genus, only lavandin (Lavandula intermedia Emeric ex Loisel.) and lavender (L. angustifolia Mill.)  are highly valued worldwide. The quality and yield of lavandin and lavender depend on genetic factors, environmental conditions and cultivation methods. Therefore, the aim of this paper is to research the effects of the application of biostimulant on the inflorescence yield and the quality of lavandin and lavender. The treatments used in this research consisted of a combination of different species (lavandin and lavender) and biostimulant (applied and unapplied). The research results show that all the research traits significantly depended on the used species and the applied biostimulant. The inflorescence yield, the content of total flavonoids, and the content of EO were higher in the lavandin species (477.3 g plant-1, 17.21 mg CAE g-1, 8.57 mL 100 g-1, respectively) than in the lavender species (180.5 g plant-1, 13.41 mg CAE g-1, and 3.69 mL 100 g-1, respectively). EOs of lavandin and lavender were rich in linalool and linalyl acetate. The use of biostimulators had a positive effect on the inflorescence yield and the content of essential oil. Furthermore, the applied biostimulant increased the linalool content in the essential oil of both researched species, i.e. it positively affected its quality.

Atrial cardiomyopathy is closely associated with atrial fibrillation (AF), and some patients exhibit no dysfunction at rest but demonstrate evident changes in left atrial (LA) function and LA volume during exercise. This study aimed to identify distinguishing signs during exercise stress echocardiography (ESE) among patients in sinus rhythm (SR), with and without history of paroxysmal/persistent AF (PAF). A prospective cohort of 1055 patients in SR was enrolled across 12 centers. The main study cohort was divided into two groups: the modeling group (n = 513) and the verification group (n = 542). All patients underwent ESE, which included B-lines, LA volume index (LAVi), and LA strain of the reservoir phase (LASr). Age, resting and stress LAVi and LASr, and B-lines were identified as a combination of detectors for PAF in both groups. In the entire cohort, aside from resting and stress LAVi and LASr, additional parameters differentiating PAF and non-PAF patients were the presence of systemic hypertension, exercise E/e’ > 7, worse right ventricle (RV) contraction during exercise (∆ tricuspid annular plane systolic excursion < 5 mm), a lower left ventricular contractile reserve (< 1.6), and a reduced chronotropic reserve (heart rate reserve < 1.64). The composite score, summing all 9 items, yielded a score of > 4 as the best sensitivity (79%) and specificity (65%). ESE can complement rest echocardiography in the identification of previous PAF in patients with SR through the evaluation of LA functional reservoir and volume reserve, LV chronotropic, diastolic, and systolic reserve, and RV contractile reserve. A scoring system predicting the probability of PAF. The score was computed using the cutoff values as in the illustration. The score >4 demonstrated a sensitivity of 79% and a specificity of 65% of PAF.

Despite the popularity of large language model (LLM) quantization for inference acceleration, significant uncertainty remains regarding the accuracy-performance trade-offs associated with various quantization formats. We present a comprehensive empirical study of quantized accuracy, evaluating popular quantization formats (FP8, INT8, INT4) across academic benchmarks and real-world tasks, on the entire Llama-3.1 model family. Additionally, our study examines the difference in text generated by quantized models versus their uncompressed counterparts. Beyond benchmarks, we also present a couple of quantization improvements which allowed us to obtain state-of-the-art accuracy recovery results. Our investigation, encompassing over 500,000 individual evaluations, yields several key findings: (1) FP8 weight and activation quantization (W8A8-FP) is lossless across all model scales, (2) INT8 weight and activation quantization (W8A8-INT), when properly tuned, incurs surprisingly low 1-3% accuracy degradation, and (3) INT4 weight-only quantization (W4A16-INT) is competitive with 8-bit integer weight and activation quantization. To address the question of the"best"format for a given deployment environment, we conduct inference performance analysis using the popular open-source vLLM framework on various GPU architectures. We find that W4A16 offers the best cost-efficiency for synchronous deployments, and for asynchronous deployment on mid-tier GPUs. At the same time, W8A8 formats excel in asynchronous"continuous batching"deployment of mid- and large-size models on high-end GPUs. Our results provide a set of practical guidelines for deploying quantized LLMs across scales and performance requirements.

Spherically symmetric Einstein-{\ae}ther (E{\AE}) theory with a Maxwell-like kinetic term is revisited. We consider a general choice of the metric and the \ae{}ther field, finding that:~(i) there is a gauge freedom allowing one always to use a diagonal metric; and~(ii) the nature of the Maxwell equation forces the \ae{}ther field to be time-like in the coordinate basis. We derive the vacuum solution and confirm that the innermost stable circular orbit (ISCO) and photon ring are enlarged relative to general relativity (GR). Buchdahl's theorem in E\AE{} theory is derived. For a uniform physical density, we find that the upper bound on compactness is always lower than in GR. Additionally, we observe that the Newtonian and E\AE{} radial acceleration relations run parallel in the low pressure limit. Our analysis of E\AE{} theory may offer novel insights into its interesting phenomenological generalization: \AE{}ther--scalar--tensor theory ({\AE}ST).

Quantization is a powerful tool for accelerating large language model (LLM) inference, but the accuracy-performance trade-offs across different formats remain unclear. In this paper, we conduct the most comprehensive empirical study to date, evaluating FP8, INT8, and INT4 quantization across academic benchmarks and real-world tasks on the entire Llama-3.1 model family. Through over 500,000 evaluations, our investigation yields several key findings: (1) FP8 (W8A8-FP) is effectively lossless across all model scales, (2) well-tuned INT8 (W8A8-INT) achieves surprisingly low (1-3\%) accuracy degradation, and (3) INT4 weight-only (W4A16-INT) is more competitive than expected, rivaling 8-bit quantization. Further, we investigate the optimal quantization format for different deployments by analyzing inference performance through the popular vLLM framework. Our analysis provides clear deployment recommendations: W4A16 is the most cost-efficient for synchronous setups, while W8A8 dominates in asynchronous continuous batching. For mixed workloads, the optimal choice depends on the specific use case. Our findings offer practical, data-driven guidelines for deploying quantized LLMs at scale -- ensuring the best balance between speed, efficiency, and accuracy.

Atherosclerotic cardiovascular disease (ASCVD) and consequent acute coronary syndromes (ACS) are substantial contributors to morbidity and mortality across Europe. Fortunately, as much as two thirds of this disease’s burden is modifiable, in particular by lipid-lowering therapy (LLT). Current guidelines are based on the sound premise that, with respect to low-density lipoprotein cholesterol (LDL-C), “lower is better for longer”, and recent data have strongly emphasised the need for also “the earlier the better”. In addition to statins, which have been available for several decades, ezetimibe, bempedoic acid (also as fixed dose combinations), and modulators of proprotein convertase subtilisin/kexin type 9 (PCSK9 inhibitors and inclisiran) are additionally very effective approaches to LLT, especially for those at very high and extremely high cardiovascular risk. In real life, however, clinical practice goals are still not met in a substantial proportion of patients (even in 70%). However, with the options we have available, we should render lipid disorders a rare disease. In April 2021, the International Lipid Expert Panel (ILEP) published its first position paper on the optimal use of LLT in post-ACS patients, which complemented the existing guidelines on the management of lipids in patients following ACS, which defined a group of “extremely high-risk” individuals and outlined scenarios where upfront combination therapy should be considered to improve access and adherence to LLT and, consequently, the therapy’s effectiveness. These updated recommendations build on the previous work, considering developments in the evidential underpinning of combination LLT, ongoing education on the role of lipid disorder therapy, and changes in the availability of lipid-lowering drugs. Our aim is to provide a guide to address this unmet clinical need, to provide clear practical advice, whilst acknowledging the need for patient-centred care, and accounting for often large differences in the availability of LLTs between countries.

Biomarkers associated with the progression from gastric intestinal metaplasia (GIM) to gastric adenocarcinoma (GA), i.e., GA-related GIM, could provide valuable insights into identifying patients with increased risk for GA. The aim of this study was to utilize multi-bioinformatics to reveal potential biomarkers for the GA-related GIM and predict potential drug repurposing for GA prevention in patients. The multi-bioinformatics included gene expression matrix (GEM) by microarray gene expression (MGE), ScType (a fully automated and ultra-fast cell-type identification based solely on a given scRNA-seq data), Ingenuity Pathway Analysis, PageRank centrality, GO and MSigDB enrichments, Cytoscape, Human Protein Atlas and molecular docking analysis in combination with immunohistochemistry. To identify GA-related GIM, paired surgical biopsies were collected from 16 GIM-GA patients who underwent gastrectomy, yielding 64 samples (4 biopsies per stomach x 16 patients) for MGE. Co-analysis was performed by including scRNAseq and immunohistochemistry datasets of endoscopic biopsies of 37 patients. The results of the present study showed potential biomarkers for GA-related GIM, including GEM of individual patients, individual genes (such as RBP2 and CD44), signaling pathways, network of molecules, and network of signaling pathways with key topological nodes. Accordingly, potential treatment targets with repurposed drugs were identified including epidermal growth factor receptor, proto-oncogene tyrosine-protein kinase Src, paxillin, transcription factor Jun, breast cancer type 1 susceptibility protein, cellular tumor antigen p53, mouse double minute 2, and CD44.

Numerous studies suggest that common genetic and epigenetic factors such as p53, histone deacetylase (HDAC), brain-derived neurotrophic factor (BDNF), the (Ataxia Telangiectasia mutated) ATM gene, cyclin-dependent kinase 5 (CDK5), glycogen synthase kinase 3 (GSK3) and altered expression of microRNA (miRNA) play a crucial role in cancer and neurodegeneration. As there is growing evidence that epigenetic aberrations in cancer and neurological diseases lead to complex pathophysiological changes, the simultaneous targeting of epigenetic and other related pathways by dual-target inhibitors may contribute to the discovery of more effective and personalized therapeutic options. Computer-Aided Drug Design (CADD) provides comprehensive bioinformatic, chemoinformatic, and chemometric approaches for the design of novel chemotypes of epigenetic dual-target inhibitors, enabling efficient discovery of new drug candidates for innovative treatments of these multifactorial diseases. The detailed anticancer mechanisms by which the epigenetic dual-target inhibitors alter metastatic and tumorigenic properties, influence the tumor microenvironment, or regulate the immune response are also presented and discussed in the review. To improve our understanding of the pathogenesis of cancer and neurodegeneration, this review discusses novel therapeutic agents targeting different molecular mechanisms involved in these multifactorial diseases.

Leveraging recent advancements in machine learning-based flavor tagging, we develop an optimal analysis for measuring the hadronic cross-section ratios R_bRb, R_cRc, and R_sRs at the FCC-ee during its WWWW, ZhZh, and t\bar{t}tt‾ runs. Our results indicate up to a two-order-of-magnitude improvement in precision, providing an unprecedented test of the SM. Using these observables, along with R_\ellRℓ and R_tRt, we project sensitivity to flavor non-universal four-fermion (4F) interactions within the SMEFT, contributing both at the tree-level and through the renormalization group (RG). We highlight a subtle complementarity with RG-induced effects at the FCC-ee’s ZZ-pole. Our analysis demonstrates significant improvements over the current LEP-II and LHC bounds in probing flavor-conserving 4F operators involving heavy quark flavors and all lepton flavors. As an application, we explore simplified models addressing current BB-meson anomalies, demonstrating that FCC-ee can effectively probe the relevant parameter space. Finally, we design optimized search strategies for quark flavor-violating 4F interactions.

(1) Background: This case study analyzed the successful performances of female gymnasts in the finals of the 39th and 40th World Cup in Maribor (SLO). The aim was to identify variations in their execution of the Clear Hip Circle to Handstand (CHCH) on uneven bars based on kinematic parameters. (2) Methods: This study involved elite female gymnasts from the 39th (n = 5, age: 17 ± 6 months) and 40th (n = 8, age: 17.5 ± 6 months) World Cups, totaling 13 gymnasts. Kinematic analysis was performed on 15 successful routines using the Ariel Performance Analysis System (Ariel Dynamics Inc., San Diego, CA). The analysis focused on 16 anthropometric reference points and 8 body segments, including the body mass center of gravity (CG). The main reference points analyzed were the hip joint, the shoulder joint, and the CG along the xy-axes. Trajectory, velocity, angle, and angular velocity of the hips and shoulders were calculated. Pearson correlation analysis was employed to assess the relationships between the kinematic variables. (3) Results: High intercorrelations between the reference points along the xy-axes (0.81–0.99) and optimal movement velocity were found. Dispersed results were observed for kinematic parameters of angle (0.10–0.16) and angular velocity of the hip joints (0.60–1.00), with similar dispersions for shoulder joints (0.51–1.00). Three distinct techniques were identified: (1) stretched body with minimal hip joint flexion throughout; (2) extended body with a short, quick hip joint extension during shoulder movement; and (3) hyperextension in the hip joint. (4) Conclusions: The kinematic analysis revealed three different performance styles of the CHCH among finalists. These variations in technique do not affect the success of the performance. This research contributes to a better understanding of the technique but does not prefer one style over another.

This paper introduces a control system for Doubly Fed Induction Generator (DFIG) based on a Disturbance Observer (DOB) for island mode operation. The proposed control system is validated through experiments, confirming its effectiveness in maintaining stable operation during island mode. The system responded efficiently to variations in wind speed and load conditions, demonstrating the efficacy of the implemented control scheme. The proposed control system unifies the design approach for both the inner and outer loops of the cascaded control system structure, simplifying implementation and parameter tuning.