Search Results (143)

Malicious actors often exploit persistence mechanisms, such as unauthorized modifications to Windows startup directories or registry keys, to achieve privilege escalation and maintain access on compromised systems. While information technology (IT) teams legitimately use these AutoStart Extension Points (ASEPs), adversaries frequently deploy malicious binaries with non-standard naming conventions or execute files from transient directories (e.g., Temp or Public folders). This study proposes a threat-hunting framework using a custom Elasticsearch Security Information and Event Management (SIEM) system to detect such persistence tactics. Two hypothesis-driven investigations were conducted: the first focused on identifying unauthorized ASEP registry key modifications during user logon events, while the second targeted malicious Dynamic Link Library (DLL) injections within temporary directories. By correlating Sysmon event logs (e.g., registry key creation/modification and process creation events), the researchers identified attack chains involving sequential registry edits and malicious file executions. Analysis confirmed that Sysmon Event ID 12 (registry object creation) and Event ID 7 (DLL loading) provided critical forensic evidence for detecting these tactics. The findings underscore the efficacy of real-time event correlation in SIEM systems in disrupting adversarial workflows, enabling rapid mitigation through the removal of malicious entries. This approach advances proactive defense strategies against privilege escalation and persistence, emphasizing the need for granular monitoring of registry and filesystem activities in enterprise environments. Full article

Gastrointestinal (GI) cancers represent a significant global health burden, with high morbidity and mortality often linked to late-stage detection and metastatic disease. The progression of these malignancies is critically driven by angiogenesis, the formation of new blood vessels, and the surrounding dynamic tumor microenvironment (TME), a complex ecosystem comprising various cell types and non-cellular components. This comprehensive review, based on a systematic search of the PubMed database, synthesizes the existing literature to define the intertwined roles of angiogenesis and the TME in GI tumorigenesis. The TME’s influence creates conditions favorable for tumor growth, invasion, and metastasis, but sometimes induces resistance to current therapies. Available therapeutic strategies for inhibiting angiogenesis involve antibodies and oral tyrosine kinase inhibitors, while immune modulation within the tumor microenvironment is mainly achieved through checkpoint inhibitor antibodies and chemotherapy. Creative emerging strategies encompassing cellular therapies, bispecific antibodies, and new targets such as CD40, DLL4, and Ang2, amongst others, are focused on inhibiting proangiogenic pathways more profoundly, reversing resistance to prior drugs, and modulating the TME to enhance therapeutic efficacy. A deeper understanding of the complex interactions between components of the TME is crucial for addressing the unmet need for novel and effective therapeutic interventions against aggressive GI cancers. Full article

Morphological problems for distinguishing between glacier ice, glacier ice with a debris cover (debris-covered glaciers), and rock glaciers are outlined with respect to recognising and mapping these features. Decimal latitude–longitude [dLL] values are used for geolocation. One model for rock glacier formation and flow discusses the idea that they consist of ‘mountain permafrost’. However, signs of permafrost-derived ice, such as flow features, have not been identified in these landsystems; talus slopes in the neighbourhoods of glaciers and rock glaciers. An alternative view, whereby rock glaciers are derived from glacier ice rather than permafrost, is demonstrated with examples from various locations in the mountain domain, 𝔻𝕞. A Google Earth and field examination of many rock glaciers shows glacier ice exposed below a rock debris mantle. Ice exposure sites provide ground truth for observations and interpretations stating that rock glaciers are indeed formed from glacier ice. Exposure sites include bare ice at the headwalls of cirques and above debris-covered glaciers; additionally, ice cliffs on the sides of meltwater pools are visible at various locations along the lengths of rock glaciers. Inspection using Google Earth shows that these pools can be traced downslope and their sizes can be monitored between images. Meltwater pools occur in rock glaciers that have been previously identified in inventories as being indictive of permafrost in the mountain domain. Glaciers with a thick rock debris cover exhibit ‘hidden ice’ and are shown to be geomorphological units mapped as rock glaciers. Full article

High-frequency (HF) communication is critical for applications such as over-the-horizon positioning and ionospheric detection. However, precise time-delay estimation in complex HF channels faces significant challenges from multipath fading, Doppler shifts, and noise. This paper proposes a Modulation Signal-based Adaptive Time-Delay Estimation (MATE) algorithm, which effectively decouples carrier and modulation signals and integrates phase-locked loop (PLL) and delay-locked loop (DLL) techniques. By leveraging the autocorrelation properties of 8PSK (Eight-Phase Shift Keying) signals, MATE compensates for carrier frequency deviations and mitigates multipath interference. Simulation results based on the Watterson channel model demonstrate that MATE achieves an average time-delay estimation error of approximately 0.01 ms with a standard deviation of approximately 0.01 ms, representing a 94.12% reduction in mean error and a 96.43% reduction in standard deviation compared to the traditional Generalized Cross-Correlation (GCC) method. Validation with actual measurement data further confirms the robustness of MATE against channel variations. MATE offers a high-precision, low-complexity solution for HF time-delay estimation, significantly benefiting applications in HF communication systems. This advancement is particularly valuable for enhancing the accuracy and reliability of time-of-arrival (TOA) detection in HF-based sensor networks and remote sensing systems. Full article

Background: Intercellular communication, facilitated by exosomes (Exos) derived from endothelial cells (ECs), significantly influences the regulation of angiogenesis. Leech extract significantly reduces ischemia–reperfusion injury, promotes angiogenesis, and improves neurological function in mice with stroke. However, further investigation is required to determine whether leech promotes angiogenesis through EC-Exo. Objective: This study aims to further explore whether leech regulates Exos to promote the establishment of collateral circulation in mice with ischemic stroke (IS) and the specific mechanisms involved. Methods: Here, we utilized an in vitro co-culture system comprising ECs and pericytes to investigate the impact of Leech-EC-Exo on enhancing the proliferation and migration of mouse brain microvascular pericytes (MBVPs). We further established an in vivo mouse model of middle cerebral artery occlusion/reperfusion (MCAO/R) to investigate the effects and underlying mechanisms of leech on collateral circulation establishment. Results: The findings demonstrated that leech significantly enhanced the in vitro cell migration number and migration number of pericytes. Therefore, it can also enhance the effect of EC-Exo on improving the infarct area and gait of mice, as well as modulating the HIFα-VEGF-DLL4-Notch1 signaling pathway to promote cerebral angiogenesis and facilitating the stable maturation of neovascularization in vivo. Conclusions: These results suggest that leech has the potential to enhance collateral circulation establishment, and its mechanism may involve the modulation of miRNA content in Exos and the promotion of signaling pathways associated with angiogenesis and vascular maturation. Full article

Currently, the Global Navigation Satellite System (GNSS) plays a critical role in providing position information for UAVs. Traditional GNSS receivers typically employ the scalar-tracking (ST) method to track signals and extract observations. An advanced alternative to ST is the Vector Delay Lock Loop (VDLL), which enables more reliable navigation solution estimation for GNSS receivers. To enhance GNSS positioning performance for UAVs, this paper proposes a cooperative VDLL (CoVDLL) that incorporates ranging information. While single VDLL (S-VDLL) explores the inherent relationship between signal tracking parameters and navigation solutions, the CoVDLL leverages signal tracking parameters from multiple UAVs along with their ranging data. To optimize navigation solution estimation in the CoVDLL, a Factor Graph Optimization (FGO) algorithm is employed to realize the navigation solution’s optimal estimation. Experiments conducted under various settings demonstrate that the CoVDLL method reduces positioning errors by an average of approximately 30% compared to the single-VDLL approach. Full article

Spondylocostal dysostosis (SCDO) is a group of rare genetic disorders characterized by segmental vertebral defects and rib deformities due to congenital misalignment, fusion, or reduction in the number of ribs. The causes of the disease have been found in seven genes, including DLL3 (SCDO1, OMIM 602768), MESP2 (SCDO2, OMIM 608681), LFNG (SCDO3, OMIM 609813), HES7 (SCDO4, OMIM 608059), TBX6 (SCDO5, OMIM 602427), RIPPLY2 (SCDO6, OMIM 616566), and DLL1 (SCDO7). Among these, SCDO4, characterized by a short trunk, short neck, and mild nonprogressive scoliosis, is a rare form of reported cases. SCDO4 is identified as caused by homozygous or compound heterozygous variants in the HES7 gene (NM_001165967.2; NP_001159439.1). This study reports a novel homozygous HES7 splice variant (c.43-9T>A) detected in an SCDO4 patient by whole-exome sequencing and confirmed by Sanger sequencing. This variant was evaluated as an acceptor loss variant in intron 1 in the HES7 transcript by in silico analysis and was inherited from the patient’s parent. This study also reviews previous reports to provide a comprehensive overview of SCDO and help us to understand the pathogenesis to develop future treatment strategies. Full article

An information content approach is taken to producing a ‘digital description’ of a landscape utilising georeferencing within Digital Earth. A general view of the geomorphology of ‘northern England’ is used as a discussion area. Data points are geolocated using decimal latitude-longitude (dLL) that can be used as recording and search items in the literature, information landscapes, or ‘information fields’. Investigations, whether about landforms, events, sampling points, material properties, or dates, provide an ‘information set’ about geo-referenced points. Using the dLL format, such points also provide the basis for starts of transects and data points on topographic surfaces. The data sites provide an ‘information field’ about the area of interest and examples are given in the information landscape. The work of the late Cuchlaine King, physical geographer and geomorphologist, is used as examples of this information field approach by setting landforms and investigations into digitized physical landscapes. The paper also suggests ways of extending the information field idea to cover previous investigations and the possible implementation of Large Language Geographical Models in the employment of ‘big data’. The FAIR data principles of findability, accessibility, interoperability, and reusability are germane to the development of such models and their use. Full article

In order to address the Distributed Displacement Flow Shop Scheduling Problem (DPFSP) with uncertain processing times in real production environments, Plant Simulation is employed to construct a simulation model for the MSRDPFSP. The model conducts quantitative analyses of workshop layout, assembly line design, worker status, operating status of robotic arms and AGV vehicles, and production system failure rates. A hybrid NEH-DDQN algorithm is integrated into the simulation model via a COM interface and DLL, where the NEH algorithm ensures the model maintains optimal performance during the early training phase. Four scheduling strategies are designed for workpiece allocation across different workshops. A deep neural network replaces the traditional Q-table for greedy selection among these four scheduling strategies, using each workshop’s completion time as a simplified state variable. This approach reduces algorithm training complexity by abstracting away intricate workpiece allocation details. Experimental comparisons show that for the data of 500 workpieces, the NEH algorithm in 3 s demonstrates equivalent quality to that produced by the GA algorithm in 300 s. After 2000 iterations, the DDQN algorithm achieves a 15% reduction in makespan with only a 2.5% increase in computational time compared to random search, this joint simulation system offers an efficient and stable solution for the modeling and optimization of the MSRDPFSP issue. Full article

This paper presents KlyH, a new 1D (one-dimensional) large-signal simulation software for klystrons, designed to deliver efficient and accurate simulation and optimization tools. KlyH integrates a Fortran-based dynamic link library (DLL) as its computational core, which employs high-performance numerical algorithms to rapidly compute critical parameters such as efficiency, gain, and bandwidth. Compared with traditional 1D simulation tools, which often lack open interfaces and extensibility, KlyH is built with a modular and open architecture that supports seamless integration with advanced optimization and intelligent design algorithms. KlyH incorporates multi-objective optimization frameworks, notably the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Optimized Multi-Objective Particle Swarm Optimization (OMOPSO), enabling automated parameter tuning for efficiency maximization and interaction length optimization. Its bandwidth-of-klystron-analysis module predicts gain and output power across operational bandwidths, with optimization algorithms further enhancing bandwidth performance. A Java-based graphical user interface (GUI) provides an intuitive workflow for parameter configuration and real-time visualization of simulation results. The open architecture also lays the foundation for future integration of artificial intelligence algorithms, promoting intelligent and automated klystron design workflows. The accuracy of KlyH and its potential for parameter optimization are confirmed by a case study on an X-band relativistic klystron amplifier. Discrepancies observed between 1D simulations and 3D PIC (three-dimensional particle-in-cell) simulation results are analyzed to identify model limitations, providing critical insights for advancing high-performance klystron designs. Full article

Advanced prostate cancer (PCa) remains lethal despite standard therapies, and immune checkpoint inhibitors offer limited benefit in its “immune-cold” microenvironment. T-cell engagers (TCEs)—bispecific antibodies linking CD3 on T-cells to tumor-associated antigens (TAAs)—provide potent, MHC-independent cytotoxicity, overcoming a key resistance mechanism. While early PSMA-targeted TCEs established proof-of-concept, recent data, notably for six transmembrane epithelial antigen of the prostate 1 (STEAP1)-targeting agents like Xaluritamig, demonstrate more substantial objective responses, highlighting progress through improved target selection and molecular design. This review synthesizes the evolving landscape of TCEs targeting PSMA, STEAP1, and DLL3 in PCa. We critically evaluate emerging clinical evidence, arguing that realizing the significant therapeutic potential of TCEs requires overcoming key challenges, including cytokine release syndrome (CRS), limited response durability, and antigen escape. We contend that future success hinges on sophisticated engineering strategies (e.g., affinity tuning, masking, multispecific constructs) and rationally designed combination therapies tailored to disease-specific hurdles. Strategies for toxicity mitigation, the crucial role of biomarker-driven patient selection, and potential integration with existing treatments are also discussed. Accumulating evidence supports TCEs becoming a new therapeutic pillar for advanced PCa, but achieving this demands sustained innovation focused on optimizing efficacy and safety. This review critically connects molecular engineering advancements with clinical realities and future imperatives. Full article

Pituitary adenylate cyclase-activating polypeptide (PACAP) is a neuropeptide that plays significant regulatory roles in the differentiation of the central nervous system and peripheral organs. A lack of the neuropeptide can lead to abnormalities in long bone development. In callus formation, a possible signaling balance shift in PACAP KO mice has been demonstrated, but Notch signalization, with its potential connection with PACAP 1-38, has not been investigated in ossification. Our main goal was to show connections between PACAP and Notch signaling in osteogenesis. Notch signalization showed an elevation in the long bones of PACAP-gene-deficient mice, and it was also elevated during the PACAP 1-38 treatment of UMR-106 and MC3T3-E1 osteogenic cells. Moreover, the inhibition of Notch signaling was compensated by the addition of PACAP 1-38 in vitro. The inorganic and organic matrix production of UMR-106 cells was increased during PACAP 1-38 treatment under the inhibition of Notch signaling. As a possible common target, the expression and nuclear translocation of NFATc1 transcription factor was increased during the disturbance of PACAP and Notch signaling. Our results indicate a possible synergistic regulation during bone formation by PACAP and Notch signalization. The crosstalk between Notch and PACAP signaling pathways highlights the complexity of bone development and homeostasis. Full article

As malware has become increasingly complex, advanced techniques have emerged to improve traditional detection systems. The increasing complexity of malware poses significant challenges in cybersecurity due to the inability of existing methods to understand detailed and contextual relationships in modern software behavior. Therefore, developing innovative detection frameworks that can effectively analyze and interpret these complex patterns has become critical. This work presents a novel framework integrating API call sequences and DLL information into a unified, graph-based representation to analyze malware behavior comprehensively. The proposed model generates initial embeddings using Node2Vec, which uses a random walk approach to understand structural relationships between nodes. Graph Attention Network (GAT) then enhances these initial embeddings, which utilizes attention mechanisms to incorporate contextual dependencies and enhance semantic representations. Finally, the enhanced embeddings are classified using Convolutional Neural Network (CNN) and Gated Recurrent Units (GRU)s, a custom hybrid CNN-GRU-3 deep learning-based model capable of effectively modeling sequential patterns. The dual role of GAT as a classifier and feature extractor is also analyzed to evaluate its impact on embedding quality and classification accuracy. Experimental results show that the proposed model achieves superior results with an accuracy rate of 0.9961 compared to state-of-the-art approaches such as ensemble learning and standalone GAT. This achievement highlights the framework’s ability to utilize contextual information for malware detection. The real-world dataset used provides a benchmark for future work, and this research lays a comprehensive foundation for advancing graph-based malware analysis. Full article

Delta-like 3 (DLL3) is an oncogenic protein aberrantly expressed in several tumors, particularly in small-cell lung cancer. DLL3-targeted therapies have recently made significant progress, demonstrating promising preclinical and clinical efficacy. This review aims to explore the mechanisms, challenges, and future opportunities associated with therapies targeting DLL3 for cancer treatment. The biological characteristics of DLL3 and its role in the Notch signaling pathway are introduced first, delving into the role of DLL3 in tumorigenesis and cancer progression. Next, current therapeutic approaches targeting DLL3 are described, including antibody–drug conjugates, T cell engagers, chimeric antigen receptor T cells, and radiopharmaceutical therapy, highlighting their effectiveness and safety in clinical trials. Despite the promising prospects, difficulties remain in the use of DLL3 as a therapeutic target due to tumor heterogeneity, the development of resistance, potential adverse effects, and barriers to patient stratification. Therefore, the potential of combination therapies, the use of innovative drug delivery systems, and ongoing clinical trial advancements are also discussed. Finally, the potential of DLL3-targeted therapies is summarized, highlighting the importance of multidisciplinary research to guide the clinical application and optimization of this emerging treatment strategy. These approaches might provide new therapeutic options, potentially starting a new era in cancer treatment. Full article

Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid progression, early metastasis, and high recurrence rates. Historically considered a homogeneous disease, recent multi-omic studies have revealed distinct molecular subtypes driven by lineage-defining transcription factors, including ASCL1, NEUROD1, POU2F3, and YAP1, as well as an inflamed subtype (SCLC-I). These subtypes exhibit unique therapeutic vulnerabilities, thereby paving the way for precision medicine and targeted therapies. Despite recent advances in molecular classification, tumor heterogeneity, plasticity, and therapy resistance continue to hinder clinical success in treating SCLC patients. To this end, novel therapeutic strategies are being explored, including BCL2 inhibitors, DLL3-targeting agents, Aurora kinase inhibitors, PARP inhibitors, and epigenetic modulators. Additionally, immune checkpoint inhibitors (ICIs) show promise, particularly in immune-enriched subtypes of SCLC patients. Hence, a deeper understanding of SCLC subtype characteristics, evolution, and the regulatory mechanisms of subtype-specific transcription factors is crucial for rationally optimizing precision therapy. This knowledge not only facilitates the identification of subtype-specific therapeutic targets, but also provides a foundation for overcoming resistance and developing personalized combination treatment strategies. In the future, the integration of multi-omic data, dynamic molecular monitoring, and precision medicine approaches are expected to further advance the clinical translation of SCLC subtype-specific therapies, ultimately improving patient survival and outcomes. Full article