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Boron presents an important role in chemistry, biology, and materials science. Diatomic transition-metal borides (MBs) are the building blocks of many complexes and materials, and they present unique electronic structures with interesting and peculiar properties and a variety of bonding schemes which are analyzed here. In the first part of this paper, we present a review on the available experimental and theoretical studies on the first-row-transition-metal borides, i.e., ScB, TiB, VB, CrB, MnB, FeB, CoB, NiB, CuB, and ZnB; the second-row-transition-metal borides, i.e., YB, ZrB, NbB, MoB, TcB, RuB, RhB, PdB, AgB, and CdB; and the third-row-transition-metal borides, i.e., LaB, HfB, TaB, WB, ReB, OsB, IrB, PtB, AuB, and HgB. Consequently, in the second part, the second- and third-row MBs are studied via DFT calculations using the B3LYP, TPSSh, and MN15 functionals and, in some cases, via multi-reference methods, MRCISD+Q, in conjunction with the aug-cc-pVQZ-PPM/aug-cc-pVQZB basis sets. Specifically, bond distances, dissociation energies, frequencies, dipole moments, and natural NPA charges are reported. Comparisons between MB molecules along the three rows are presented, and their differences and similarities are analyzed. The bonding of the diatomic borides is also described; it is found that, apart from RhB(X1Σ+), which was just recently found to form quadruple bonds, RuB(X2Δ) and TcB(X3Σ−) also form quadruple σ2σ2π2π2 bonds in their X states. Moreover, to fill the gap existing in the current literature, here, we calculate the TcB molecule.

This review details the state of the art in research on top coal drawing mechanisms in Longwall t op c oal c aving (LTCC) by examining the relevant literature over the last two decades. It starts with an introduction of the brief history and basic procedures of LTCC. The framework of research on the drawing mechanism, basic concepts, and some theoretical models of LTCC are detailed in sect. research framework of top coal drawing mechanism. The authors note that the T op c oal d rawbody (TCD), T op c oal b oundary (TCB) and T op c oal r ecovery r atio (TCRR) are key factors in the drawing mechanism. The B ody– b oundary– r atio (BBR) research system has been the classic framework for research over the last 20 years. The modified Bergmark–Roos model, which considers the effects of the supporting rear canopy, flowing velocity of top coal, and its shape factor, is optimal for characterizing the TCD. A 3D model to describe the TCB that considers the thicknesses of the coal seam and roof strata is reviewed. In sect. physical testing and numerical simulation, the physical tests and numerical simulations in the literature are classified for ease of bibliographical review, and classic conclusions regarding the drawing mechanism of top coal are presented and discussed with elaborate illustrations and descriptions. The deflection of the TCD is noted, and is caused by the shape of the rear canopy. The inclined coal seam always induces a larger TCD, and a deflection in the TCD has also been observed in it. The effects of the drawing sequence and drawing interval on the TCRR are reviewed, where a long drawing interval is found to lead to significant loss of top coal. Its flowing behavior and velocity distribution are also presented. Sect. practical applications of drawing mechanisms for LTCC mines 4 summarizes over 10 cases where the TCRR of LTCC mines improved due to the guidance of the drawing mechanism. The final section provides a summary of the work here and some open questions. Prospective investigations are highlighted to give researchers guidance on promising issues in future research on LTCC.

The tfd (tfdI and tfdII) are gene clusters originally discovered in plasmid pJP4 which are involved in the bacterial degradation of 2,4-dichlorophenoxyacetic acid (2,4-D) via the ortho-cleavage pathway of chlorinated catechols. They share this activity, with respect to substituted catechols, with clusters tcb and clc. Although great effort has been devoted over nearly forty years to exploring the structural diversity of these clusters, their evolution has been poorly resolved to date, and their classification is clearly obsolete. Employing comparative genomic and phylogenetic approaches has revealed that all tfd clusters can be classified as one of four different types. The following four-type classification and new nomenclature are proposed: tfdI, tfdII, tfdIII and tfdIV(A,B,C). Horizontal gene transfer between Burkholderiales and Sphingomonadales provides phenomenal linkage between tfdI, tfdII, tfdIII and tfdIV type clusters and their mosaic nature. It is hypothesized that the evolution of tfd gene clusters proceeded within first (tcb, clc and tfdI), second (tfdII and tfdIII) and third (tfdIV(A,B,C)) evolutionary lineages, in each of which, the genes were clustered in specific combinations. Their clustering is discussed through the prism of hot spots and driving forces of various models, theories, and hypotheses of cluster and operon formation. Two hypotheses about series of gene deletions and displacements are also proposed to explain the structural variations across members of clusters tfdII and tfdIII, respectively. Taking everything into account, these findings reconstruct the phylogeny of tfd clusters, have delineated their evolutionary trajectories, and allow the contribution of various evolutionary processes to be assessed.

Explore whether the axillary outcomes differ among HER2 positive subgroups receiving standard dual-targeted therapy, aiming to identify subgroups exhibiting enhanced sensitivity to NAT among HER2-positive/node-positive breast cancer patients. HER2 positive female patients with biopsy-proven node-positive disease from April 2020 to May 2023 were included. All patients underwent standard Neoadjuvant HER2-targeted dual therapy and axillary lymph node dissection (ALND) at Breast Surgery Center of Sichuan Cancer Hospital. Univariate and multivariate analyses were used to identify factors associate with axillary pathological complete response (ApCR). Statistical analysis and graphing were performed using SPSS 24.0 and GraphPad Prism 9.0 software. This study enrolled 215 HER2 positive patients with a total ApCR rate of 76.7%, which included 49 HER2 2+/FISH + and 166 HER2 3 + cases with approximate ApCR rates of 63.3% and 80.7% ( P  = 0.011). Univariate and multivariate analysis indicated that HER2 3 + disease (OR = 2.43, 95% CI 1.21–4.88, P  = 0.012), Ki-67 ≥ 20% disease (OR = 3.00, 95% CI 1.26–7.13, P  = 0.013) and NAC regimen of TCb (OR = 2.71, 95% CI 1.39–5.38, P  = 0.004) were more likely to achieve ApCR. Further subgroup analysis revealed that HER2 3 + patients receiving TCb regimen showed the highest ApCR rate of 88% compared to other subgroups. HER2 3 + breast cancer had a higher ApCR rate than HER2 2+/FISH + breast cancer during Neoadjuvant HER2-targeted dual therapy. HER2 positive patients could benefit from NAC regimen of TCb in axillary response.

Antibody therapy has been used to treat a variety of diseases and the success of ZMapp and other monoclonal antibody-based therapies during the 2014-2016 West African Ebola outbreak has shown this countermeasure can be a successful therapy for Ebola hemorrhagic fever. This study utilized transchromosomal bovines (TcB) vaccinated with a DNA plasmid encoding Ebola virus glycoprotein sequence to produce human polyclonal antibodies directed against Ebola virus glycoprotein. When administered 1 day postinfection, these TcB polyclonal antibodies provided partial protection and resulted in a 50% survival rate following a lethal challenge of Ebola virus Makona in rhesus macaques.

PurposeResearchers have studied supervisors’ proactive personality in its relationship with employees’ attitudes. However, little attention has been given to how employees react to instances of supervisors’ proactive behavior. Drawing from P-E fit theory, we propose that the relationship between supervisor weekly taking charge behavior (TCB, the quintessential proactive behavior) and employees’ weekly job attitudes depends on employees’ proactive personality.Design/methodology/approachUtilizing a diary study, we investigate how employees’ proactive personality moderates the within-person relationship between supervisor TCB and employees’ attitudes (measured as job satisfaction and affective commitment). We surveyed 39 employees ten times over ten weeks.FindingsMulti-level analyses partially supported our predictions on the differential effects of weekly supervisor TCB on employees’ job attitudes. Supervisors’ above-average TCB was significantly related to higher levels of employees’ job satisfaction and marginally related to affective commitment for employees with high proactive personality, but not for those with low proactive personality. Supplemental analyses revealed that our results are unique to supervisor TCB and not to supervisor helping behavior.Originality/valueOur study is among the first to utilize a dynamic approach to understand the consequences of supervisors’ proactive work behavior in the context of P-E fit research. Our findings will open several fruitful avenues for future research that continue to understand the powerful effects of supervisors’ proactivity.

Analysis of advanced physical unclonable function (PUF) applications and protocols relies on assuming that a PUF behaves like a random oracle; that is, upon receiving a challenge, a uniform random response with replacement is selected, measurement noise is added, and the resulting response is returned. In order to justify such an assumption, we need to rely on digital interface computation that to some extent remains confidential—otherwise, information about PUF challenge–response pairs leak with which the adversary can train a prediction model for the PUF. We introduce a theoretical framework that allows the adversary to have a prediction model (with a typical accuracy of 75% for predicting response bits for state-of-the-art silicon PUF designs). We do not require any confidential digital computing or digital secrets, while we can still prove rigorous statements about the bit security of a system that interfaces with the PUF. In particular, we prove the bit security of a PUF-based random oracle construction; this merges the PUF framework with fuzzy extractors.

Objectives: Developing ex vivo models that replicate immune–tumor interactions with high fidelity is essential for advancing immunotherapy research, as traditional two-dimensional in vitro systems often lack the complexity required to fully represent these interactions. Methods: In this study, we establish a comprehensive 3D redirect lysis (3D-RDL) assay using colorectal cancer spheroids and adult stem cell-derived, healthy human organoids to evaluate the efficacy and safety profile of Cibisatamab, a bispecific antibody targeting carcinoembryonic antigens (CEAs) on cancer cells and CD3 on T cells. This model allows us to assess cytotoxic activity and immune responses, capturing variations in therapeutic response not observable in simpler systems. Our model integrates live imaging and cytotoxicity analyses to enable precise, real-time tracking of antibody effects on CEA-expressing tumor cells compared to healthy cells. Additionally, by standardizing effector-to-target cell ratios in each co-culture, we establish a reproducible workflow that enhances data accuracy and comparability across assays. Flow cytometry and Granzyme B release profiling further allow us to characterize immune cell activation, revealing distinct T cell activation markers and Granzyme B release patterns tied to Cibisatamab treatment. Results: Our results show that Cibisatamab effectively induces cell death in cancer spheroids with high CEA expression while being dose-dependent on target, off-tumor binding and killing on non-cancerous cells of healthy organoids with intermediate CEA levels. This highlights our model’s potential to predict clinical immunotherapy outcomes, capturing complex responses like immune activation, therapeutic selectivity, and potential resistance mechanisms. Conclusions: These findings underscore the utility of our model as a reliable, physiologically relevant tool for screening new immunotherapies and advancing our understanding of tumor-immune dynamics.

Purpose The purpose of this paper is to present a stable and efficient numerical technique based on modified trigonometric cubic B-spline functions for solving the time-fractional diffusion equation (TFDE). The TFDE has numerous applications to model many real objects and processes. Design/methodology/approach The time-fractional derivative is used in the Caputo sense. A modification is made in trigonometric cubic B-spline (TCB) functions for handling the Dirichlet boundary conditions. The modified TCB functions have been used to discretize the space derivatives. The stability of the technique is also discussed. Findings The obtained results are compared with those reported earlier showing that the present technique gives highly accurate results. The stability analysis shows that the method is unconditionally stable. Furthermore, this technique is efficient and requires less storage. Originality/value The current work is novel for solving TFDE. This technique is unconditionally stable and gives better results than existing results (Ford et al., 2011; Sayevand et al., 2016; Ghanbari and Atangana, 2020).

Fog computing has been prioritized over cloud computing in terms of latency-sensitive Internet of Things (IoT) based services. We consider a limited resource-based fog system where real-time tasks with heterogeneous resource configurations are required to allocate within the execution deadline. Two modules are designed to handle the real-time continuous streaming tasks. The first module is task classification and buffering (TCB), which classifies the task heterogeneity using dynamic fuzzy c-means clustering and buffers into parallel virtual queues according to enhanced least laxity time. The second module is task offloading and optimal resource allocation (TOORA), which decides to offload the task either to cloud or fog and also optimally assigns the resources of fog nodes using the whale optimization algorithm, which provides high throughput. The simulation results of our proposed algorithm, called whale optimized resource allocation (WORA), is compared with results of other models, such as shortest job first (SJF), multi-objective monotone increasing sorting-based (MOMIS) algorithm, and Fuzzy Logic based Real-time Task Scheduling (FLRTS) algorithm. When 100 to 700 tasks are executed in 15 fog nodes, the results show that the WORA algorithm saves 10.3% of the average cost of MOMIS and 21.9% of the average cost of FLRTS. When comparing the energy consumption, WORA consumes 18.5% less than MOMIS and 30.8% less than FLRTS. The WORA also performed 6.4% better than MOMIS and 12.9% better than FLRTS in terms of makespan and 2.6% better than MOMIS and 4.3% better than FLRTS in terms of successful completion of tasks.