Monoclonal antibodies are among the most clinically effective drugs used to treat cancer. However, their target repertoire is limited as there are relatively few tumor-specific or tumor-associated ...cell surface or soluble antigens. Intracellular molecules represent nearly half of the human proteome and provide an untapped reservoir of potential therapeutic targets. Antibodies have been developed to target externalized antigens, have also been engineered to enter into cells or may be expressed intracellularly with the aim of binding intracellular antigens. Furthermore, intracellular proteins can be degraded by the proteasome into short, commonly 8-10 amino acid long, peptides that are presented on the cell surface in the context of major histocompatibility complex class I (MHC-I) molecules. These tumor-associated peptide-MHC-I complexes can then be targeted by antibodies known as T-cell receptor mimic (TCRm) or T-cell receptor (TCR)-like antibodies, which recognize epitopes comprising both the peptide and the MHC-I molecule, similar to the recognition of such complexes by the TCR on T cells. Advances in the production of TCRm antibodies have enabled the generation of multiple TCRm antibodies, which have been tested
and
, expanding our understanding of their mechanisms of action and the importance of target epitope selection and expression. This review will summarize multiple approaches to targeting intracellular antigens with therapeutic antibodies, in particular describing the production and characterization of TCRm antibodies, the factors influencing their target identification, their advantages and disadvantages in the context of TCR therapies, and the potential to advance TCRm-based therapies into the clinic.
A
bstract
The general Next-to-Minimal Supersymmetric Standard Model (NMSSM) describes the singlino-dominated dark-matter (DM) property by four independent parameters: singlet-doublet Higgs coupling ...coefficient
λ
, Higgsino mass
μ
tot
, DM mass
m
χ
˜
1
0
, and singlet Higgs self-coupling coefficient
κ
. The first three parameters strongly influence the DM-nucleon scattering rate, while
κ
usually affects the scattering only slightly. This characteristic implies that singlet-dominated particles may form a secluded DM sector. Under such a theoretical structure, the DM achieves the correct abundance by annihilating into a pair of singlet-dominated Higgs bosons by adjusting
κ
’s value. Its scattering with nucleons is suppressed when
λv/μ
tot
is small. This speculation is verified by sophisticated scanning of the theory’s parameter space with various experiment constraints considered. In addition, the Bayesian evidence of the general NMSSM and that of
Z
3
-NMSSM is computed. It is found that, at the cost of introducing one additional parameter, the former is approximately 3
.
3 × 10
3
times the latter. This result corresponds to Jeffrey’s scale of 8.05 and implies that the considered experiments strongly prefer the general NMSSM to the
Z
3
-NMSSM.
In order to avoid overuse of a certain battery energy storage system (BESS) and prolong the cycle life of battery in AC microgrid, an improved SoC-based droop control based on multi-agent system ...(MAS) is proposed for achieving State of Charge (SoC) balance of multiple BESS units. A proportional-integral (PI) adjustment item using the average SoC is added to the P-f droop to regulate the charging and discharging power of BESS, then SoC changes towards in the direction of consistency and finally reaches equalization. The dynamic average consensus algorithm is utilized to obtain the average SoC. The proposed SoC-based droop method needs not to be changed in different operating mode of BESS and can achieve SoC balance regardless of whether the capacities of different batteries are the same, which improve the applicability of this method. A complete small-signal state space model including all BESS units is built and analyzed to select the appropriate control parameters of the improved droop. The impact of communication time delay on the improved droop method for balancing SoC is investigated and the effectiveness of the proposed strategy is verified through the simulation results of different case studies.
Correlation filter-based methods have recently performed remarkably well in terms of accuracy and speed in the visual object tracking research field. However, most existing correlation filter-based ...methods are not robust to significant appearance changes in the target, especially when the target undergoes deformation, illumination variation, and rotation. In this paper, a novel parallel correlation filters (PCF) framework is proposed for real-time visual object tracking. Firstly, the proposed method constructs two parallel correlation filters, one for tracking the appearance changes in the target, and the other for tracking the translation of the target. Secondly, through weighted merging the response maps of these two parallel correlation filters, the proposed method accurately locates the center position of the target. Finally, in the training stage, a new reasonable distribution of the correlation output is proposed to replace the original Gaussian distribution to train more accurate correlation filters, which can prevent the model from drifting to achieve excellent tracking performance. The extensive qualitative and quantitative experiments on the common object tracking benchmarks OTB-2013 and OTB-2015 have demonstrated that the proposed PCF tracker outperforms most of the state-of-the-art trackers and achieves a high real-time tracking performance.
Extracorporeal membrane oxygenation (ECMO) has evolved considerably over the past two decades and has been gradually utilized in severe trauma. However, the indications for the use of ECMO in trauma ...remain uncertain and the clinical outcomes are different. We performed a systematic review to provide an overall estimate of the current performance of ECMO in the treatment of trauma patients.
We searched PubMed and MEDLINE databases up to the end of December 2019 for studies on ECMO in trauma. The PRISMA statement was followed. Data on demographics of the patient, mechanism of injury, injury severity scores (ISS), details of ECMO strategies, and clinical outcome were extracted.
A total of 58 articles (19 retrospective reports and 39 case reports) were deemed eligible and included. In total, 548 patients received ECMO treatment for severe trauma (adult 517; children 31; mean age of adults 34.9 ± 12.3 years). Blunt trauma (85.4%) was the primary injury mechanism, and 128 patients had traumatic brain injury (TBI). The mean ISS was 38.1 ± 15.0. A total of 71.3% of patients were initially treated with VV ECMO, and 24.5% were placed on VA ECMO. The median time on ECMO was 9.6 days, and the median time to ECMO was 5.7 days. A total of 60% of patients received initially heparin anticoagulation. Bleeding (22.9%) and thrombosis (19%) were the most common complications. Ischemia of the lower extremities occurred in 9 patients. The overall hospital mortality was 30.3%.
ECMO has been gradually utilized in a lifesaving capacity in severe trauma patients, and the feasibility and advantages of this technique are becoming widely accepted. The safety and effectiveness of ECMO in trauma require further study. Several problems with ECMO in trauma, including the role of VA-ECMO, the time to institute ECMO, and the anticoagulation strategy remain controversial and must be solved in future studies.
With the increasing use of electric vehicles (EVs) and the development of emerging transportation network services, autonomous EVs (AEVs) may play an important role in the future of transportation. ...AEVs can automatically plan their route, park in the charging station, and support the vehicle-to-grid (V2G) services. However, V2G services may influence user dissatisfaction due to the task delays. There is a tradeoff between the optimization of electricity cost and user dissatisfaction. In this article, we formulate the problem to minimize the electricity cost of AEVs and the degradation cost of the charging station batteries with the constraint of V2G services and user dissatisfaction, which is a nonconvex problem and is difficult to solve. To solve the nonconvex optimization problem, we design a suboptimal charging algorithm with some constraints (SCAC) based on the Lyapunov optimization technique to find a tradeoff between the total cost and user dissatisfaction. This algorithm cannot find the optimal solution but can give a selection criterion. Furthermore, in order to get a global charging schedule, we use the criterion from the SCAC algorithm as a priori knowledge to design the charging scheduling reinforcement-learning-based (CSRL) algorithm, which is more efficient than the reinforcement learning (RL) method without any particular criterion. We do simulations by using day-ahead price and practical profiles of AEVs to evaluate the proposed algorithms. The numerical results show that the CSRL algorithm has a better performance 5.12% than the SCAC algorithm and both algorithms are 12.66% and 17.14% better than the benchmark algorithm which is the shortest path (SP)-based algorithm. The CSRL algorithm has more efficiency <inline-formula> <tex-math notation="LaTeX">\varepsilon (1-{\mathrm{ Pr}}(\Lambda (t)=0)) </tex-math></inline-formula> than the SCAC algorithm, where <inline-formula> <tex-math notation="LaTeX">{\mathrm{ Pr}}(\Lambda (t)=0) </tex-math></inline-formula> is a selection criterion calculated from the SCAC algorithm.
Limited clinical benefits derived from anti-VEGF therapy have driven the identification of new targets involved in tumor angiogenesis. Here, we report an integrative meta-analysis to define the ...transcriptional program underlying angiogenesis in human cancer. This approach identified ELTD1, an orphan G-protein-coupled receptor whose expression is induced by VEGF/bFGF and repressed by DLL4 signaling. Extensive analysis of multiple cancer types demonstrates significant upregulation of ELTD1 in tumor-associated endothelial cells, with a higher expression correlating with favorable prognosis. Importantly, ELTD1 silencing impairs endothelial sprouting and vessel formation in vitro and in vivo, drastically reducing tumor growth and greatly improving survival. Collectively, these results provide insight into the regulation of tumor angiogenesis and highlight ELTD1 as key player in blood vessel formation.
•A common core angiogenesis metasignature exists for different types of cancer•ELTD1 is upregulated in tumor endothelial cells and is a good prognostic marker•VEGF/DLL4 regulate ELTD1, which is important for tip cell function and angiogenesis•ELTD1 inhibition blocks tumor angiogenesis and substantially inhibits tumor growth
The discriminative correlation filters-based methods struggle deal with the problem of fast motion and heavy occlusion, the problem can severely degrade the performance of trackers, ultimately ...leading to tracking failures. In this paper, a novel Motion-Aware Correlation Filters (MACF) framework is proposed for online visual object tracking, where a motion-aware strategy based on joint instantaneous motion estimation Kalman filters is integrated into the Discriminative Correlation Filters (DCFs). The proposed motion-aware strategy is used to predict the possible region and scale of the target in the current frame by utilizing the previous estimated 3D motion information. Obviously, this strategy can prevent model drift caused by fast motion. On the base of the predicted region and scale, the MACF detects the position and scale of the target by using the DCFs-based method in the current frame. Furthermore, an adaptive model updating strategy is proposed to address the problem of corrupted models caused by occlusions, where the learning rate is determined by the confidence of the response map. The extensive experiments on popular Object Tracking Benchmark OTB-100, OTB-50 and unmanned aerial vehicles (UAV) video have demonstrated that the proposed MACF tracker performs better than most of the state-of-the-art trackers and achieves a high real-time performance. In addition, the proposed approach can be integrated easily and flexibly into other visual tracking algorithms.
Non‐small cell lung cancer (NSCLC) accounts for most lung cancer. To develop new therapy required the elucidation of NSCLC pathogenesis. The deubiquitinating enzymes USP 28 has been identified and ...studied in colon and breast carcinomas. However, the role of USP28 in NSCLC is unknown. The level mRNA or protein level of USP28 were measured by qRT‐PCR or immunohistochemistry (IHC). The role of USP28 in patient survival was revealed by Kaplan–Meier plot of overall survival in NSCLC patients. USP28 was up or down regulated by overexpression plasmid or siRNA transfection. Cell proliferation and apoptosis was assayed by MTT and FACS separately. Potential microRNAs, which targeted USP28, were predicated by bioinformatic algorithm and confirmed by Dual Luciferase reporter assay system. High mRNA and protein level of USP28 in NSCLC were both correlated with low patient survival rate. Overexpression of USP28 promoted NSCLC cells growth and vice versa. Down‐regulation of USP28 induced cell apoptosis. USP28 was targeted by miR‐4295. Overexpression of USP28 promoted NSCLC cells proliferation, and was associated with poor prognosis in NSCLC patients. The expression of USP28 may be regulated by miR‐4295. Our data suggested that USP28 was a tumour‐promoting factor and a promising therapeutic target for NSCLC.
The 3D reconstruction of ancient buildings through inclined photogrammetry finds a wide range of applications in surveying, visualization and heritage conservation. Unlike indoor objects, ...reconstructing ancient buildings presents unique challenges, including the slow speed of 3D reconstruction using traditional methods, the complex textures of ancient structures and geometric issues caused by repeated textures. Additionally, there is a hash conflict problem when rendering outdoor scenes using neural radiation fields. To address these challenges, this paper proposes a 3D reconstruction method based on depth-supervised neural radiation fields. To enhance the representation of the geometric neural network, the addition of a truncated signed distance function (TSDF) supplements the existing signed distance function (SDF). Furthermore, the neural network’s training is supervised using depth information, leading to improved geometric accuracy in the reconstruction model through depth data obtained from sparse point clouds. This study also introduces a progressive training strategy to mitigate hash conflicts, allowing the hash table to express important details more effectively while reducing feature overlap. The experimental results demonstrate that our method, under the same number of iterations, produces images with clearer structural details, resulting in an average 15% increase in the Peak Signal-to-Noise Ratio (PSNR) value and a 10% increase in the Structural Similarity Index Measure (SSIM) value. Moreover, our reconstruction model produces higher-quality surface models, enabling the fast and highly geometrically accurate 3D reconstruction of ancient buildings.
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