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Direct intralesional injection of specific or even generic agents, has been proposed over the years as cancer immunotherapy, in order to treat cutaneous or subcutaneous metastasis. Such treatments usually induce an effective control of disease in injected lesions, but only occasionally were able to demonstrate a systemic abscopal effect on distant metastases. The usual availability of tissue for basic and translational research is a plus in utilizing this approach, which has been used in primis for the treatment of locally advanced melanoma. Melanoma is an immunogenic tumor that could often spread superficially causing in-transit metastasis and involving draining lymph nodes, being an interesting model to study new drugs with different modality of administration from normal available routes. Talimogene laherperepvec (T-VEC) is an injectable modified oncolytic herpes virus being developed for intratumoral injection, that produces granulocyte-macrophage colony-stimulating factor (GM-CSF) and enhances local and systemic antitumor immune responses. After infection, selected viral replication happens in tumor cells leading to tumor cell lysis and activating a specific T-cell driven immune response. For this reason, a probable synergistic effect with immune checkpoints inhibition have been described. Pre-clinical studies in melanoma confirmed that T-VEC preferentially infects melanoma cells and exerts its antitumor activity through directly mediating cell death and by augmenting local and even distant immune responses. T-VEC has been assessed in monotherapy in Phase II and III clinical trials demonstrating a tolerable side-effect profile, a promising efficacy in both injected and uninjected lesions, but a mild effect at a systemic level. In fact, despite improved local disease control and a trend toward superior overall survival in respect to the comparator GM-CSF (which was injected subcutaneously daily for two weeks), responses as a single agent therapy have been uncommon in patients with visceral metastases. For this reason, T-VEC is currently being evaluated in combinations with other immune checkpoint inhibitors such as ipilimumab and pembrolizumab, with interesting confirmation of activity even systemically.

Global navigation satellite system (GNSS) differential code bias (DCB) and topside ionosphere vertical electron content (VEC) can be estimated using onboard data from low-earth-orbit (LEO) satellites. These satellites provide the potential to make up for the lack of ground-based stations in the oceanic and polar regions and establish a high-precision global ionosphere model. In order to study the influences of different LEO-topside VEC processing methods on estimates, we creatively analyzed and compared the results and accuracy of the DCBs and LEO-topside VEC estimates using two topside VEC solutions—the SH-topside VEC (spherical harmonic-topside vertical electron content) and EP-topside VEC (epoch parameter-topside vertical electron content) methods. Some conclusions are drawn as follows. (1) Using GRACE-A data (400 km in 2016), the monthly stabilities (STDs) of GPS satellite DCBs and LEO receiver DCBs using the EP-topside VEC method are better than those using the SH-topside VEC method. For JASON-2 data (1350 km), the STD results of GPS DCBs using the SH-topside VEC method are slightly superior to those using the EP-topside VEC method, and LEO DCBs using the two methods have similar STD results. However, the root mean square (RMS) results for GPS DCBs using the SH-topside VEC model relative to the Center for Orbit Determination in Europe (CODE) products are slightly superior to those using the EP-topside VEC method. (2) The peak ranges of the actual GRACE-A-topside VEC results using the SH-topside VEC and EP-topside VEC methods are within 42 and 35 TECU, respectively, while the peak ranges of the JASON-2-topside VEC results are both within 6 TECU. Additionally, only the SH-topside VEC model results are displayed due to the EP-topside VEC method not modeling VEC. Due to the difference in orbital altitude, the results and distributions of the GRACE-topside VECs differ from those of the JASON-topside VECs, with the former being more consistent with the ground-based results, indicating that there may be different height structures in the LEO-topside VECs. In addition, we applied the IRI-GIM (International Reference Ionosphere model–Global Ionosphere Map) method to compare the LEO-based topside VEC results, which indicate that the accuracy of GRACE-A-topside VEC using the EP-topside VEC method is better than that using the SH-topside VEC method, whereas for JASON-2, the two methods have similar accuracy. Meanwhile, we note that the temporal and spatial resolutions of the SH-topside VEC method are higher than those of the EP-topside VEC method, and the former has a wide range of usability and predictive characteristics. The latter seems to correspond to the single-epoch VEC mean of the former to some extent.

This paper presents a comprehensive theory for the demographic analysis of populations in which individuals are classified by both age and stage. The earliest demographic models were age classified. Ecologists adopted methods developed by human demographers and used life tables to quantify survivorship and fertility of cohorts and the growth rates and structures of populations. Later, motivated by studies of plants and insects, matrix population models structured by size or stage were developed. The theory of these models has been extended to cover all the aspects of age-classified demography and more. It is a natural development to consider populations classified by both age and stage. A steady trickle of results has appeared since the 1960s, analyzing one or another aspect of age × stage-classified populations, in both ecology and human demography. Here, we use the vec-permutation formulation of multistate matrix population models to incorporate age- and stage-specific vital rates into demographic analysis. We present cohort results for the life table functions (survivorship, mortality, and fertility), the dynamics of intra-cohort selection, the statistics of longevity, the joint distribution of age and stage at death, and the statistics of life disparity. Combining transitions and fertility yields a complete set of population dynamic results, including population growth rates and structures, net reproductive rate, the statistics of lifetime reproduction, and measures of generation time. We present a complete analysis of a hypothetical model species, inspired by poecilogonous marine invertebrates that produce two kinds of larval offspring. Given the joint effects of age and stage, many familiar demographic results become multidimensional, so calculations of marginal and mixture distributions are an important tool. From an age-classified point of view, stage structure is a form of unobserved heterogeneity. From a stage-classified point of view, age structure is unobserved heterogeneity. In an age × stage-classified model, variance in demographic outcomes can be partitioned into contributions from both sources. Because these models are formulated as matrices, they are amenable to a complete sensitivity analysis. As more detailed and longer longitudinal studies are developed, age × stage-classified demography will become more common and more important.

The use of immunotherapy in the treatment of advanced and high-risk melanoma has led to a striking improvement in outcomes. Although the incidence of melanoma has continued to rise, median survival has improved from approximately 6 months to nearly 6 years for patients with advanced inoperable stage IV disease. Recent understanding of the tumor microenvironment and its interplay with the immune system has led to the explosive development of novel immunotherapy treatments. Since the approval of the therapeutic cytokines interleukin-2 and interferon alfa-2 in the 1990s, the development of novel immune checkpoint inhibitors (ICIs), oncolytic virus therapy, and modulators of the tumor microenvironment have given way to a new era in melanoma treatment. Monoclonal antibodies directed at programmed cell death protein 1 receptor (PD-1) and its ligand (PDL-1), cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), and lymphocyte-activation gene 3 (LAG-3) have provided robust activation of the adaptive immune system, restoring immune surveillance leading to host tumor recognition and destruction. Multiple other immunomodulatory therapeutics are under investigation to overcome resistance to ICI therapy, including the toll-like receptor-9 (TLR-9) and 7/8 (TLR-7/8) agonists, stimulator of interferon genes (STING) agonists, and fecal microbiota transplantation. In this review, we focus on the recent advances in immunotherapy for the treatment of melanoma and provide an update on novel therapies currently under investigation.

Melanoma is the most aggressive and dangerous form of skin cancer that develops from transformed melanocytes. It is crucial to identify melanoma at its early stages, in situ, as it is “curable” at this stage. However, after metastasis, it is difficult to treat and the five-year survival is only 25%. In recent years, a better understanding of the etiology of melanoma and its progression has made it possible for the development of targeted therapeutics, such as vemurafenib and immunotherapies, to treat advanced melanomas. In this review, we focus on the molecular mechanisms that mediate melanoma development and progression, with a special focus on the immune evasion strategies utilized by melanomas, to evade host immune surveillances. The proposed mechanism of action and the roles of immunotherapeutic agents, ipilimumab, nivolumab, pembrolizumab, and atezolizumab, adoptive T- cell therapy plus T-VEC in the treatment of advanced melanoma are discussed. In this review, we implore that a better understanding of the steps that mediate melanoma onset and progression, immune evasion strategies exploited by these tumor cells, and the identification of biomarkers to predict treatment response are critical in the design of improved strategies to improve clinical outcomes for patients with this deadly disease.

1. Senescence (an increase in the mortality rate or force of mortality, or a decrease in fertility, with increasing age) is a widespread phenomenon. Theories about the evolution of senescence have long focused on the age trajectories of the selection gradients on mortality and fertility. In purely age-classified models, these selection gradients are non-increasing with age, implying that traits expressed early in life have a greater impact on fitness than traits expressed later in life. This pattern leads inevitably to the evolution of senescence if there are trade-offs between early and late performance. 2. It has long been suspected that the stage- or size-dependent demography typical of plants might change these conclusions. In this paper, we develop a model that includes both stage-and age-dependence and derive the age-dependent, stage-dependent and age × stage-dependent selection gradients on mortality and fertility. 3. We applied this model to stage-classified population projection matrices for 36 species of plants, from a wide variety of growth forms (from mosses to trees) and habitats. 4. We found that the age-specific selection gradients within a life cycle stage can exhibit increases with age (we call these contra-senescent selection gradients). In later stages, often large size classes in plant demography, the duration of these contra-senescent gradients can exceed the life expectancy by several fold. 5. Synthesis. The interaction of age- and stage-dependence in plants leads to selection pressures on senescence fundamentally different from those found in previous, age-classified theories. This result may explain the observation that large plants seem less subject to senescence than most kinds of animals. The methods presented here can lead to improved analysis of both age-dependent and stage-dependent demographic properties of plant populations.

In vehicular edge computing (VEC), some tasks can be processed either locally or on the mobile edge computing (MEC) server at a base station (BS) or a nearby vehicle. In fact, tasks are offloaded or not, based on the status of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. In this paper, device-to-device (D2D)-based V2V communication and multiple-input multiple-output and nonorthogonal multiple access (MIMO-NOMA)-based V2I communication are considered. In actual communication scenarios, the channel conditions for MIMO-NOMA-based V2I communication are uncertain, and the task arrival is random, leading to a highly complex environment for VEC systems. To solve this problem, we propose a power allocation scheme based on decentralized deep reinforcement learning (DRL). Since the action space is continuous, we employ the deep deterministic policy gradient (DDPG) algorithm to obtain the optimal policy. Extensive experiments demonstrate that our proposed approach with DRL and DDPG outperforms existing greedy strategies in terms of power consumption and reward.

Vehicular Edge Computing (VEC) is a crucial component of Intelligent Transportation Systems (ITS), enabling low-latency and energy-efficient services by offloading computation to the network edge. However, optimizing system performance in such environments requires careful edge server placement, especially in dynamic vehicular contexts characterized by high mobility and unpredictability. Achieving optimal performance under the constraints of latency, energy consumption, and mobility remains a significant challenge. This research proposes a comprehensive framework for optimizing deep learning architectures in VEC, utilizing advanced evolutionary algorithms. Building on real-world vehicular mobility traces, the framework employs the Synergistic Fibroblast Optimized Efficient Deep Neural Network (SFO-Eff-DNN) to identify optimal configurations and edge server placements. The dataset includes details about task offloading under different mobility levels, the data was preprocessed using Min-Max normalization to ensure smooth learning. Among the algorithms evaluated, Synergistic Fibroblast Optimization (SFO) consistently produces well-distributed Pareto-optimal solutions and effectively handles trade-offs between competing objectives. The DNN is utilized to learn complex patterns in vehicular mobility and network conditions, which helps predict the best configurations for edge server placements. The proposed system efficiently minimizes latency and energy consumption while ensuring scalability and adaptability to real-world scenarios. Results demonstrate that SFO-Eff-DNN achieves superior convergence speed and energy efficiency, making it well-suited for time-sensitive deployments. Comparative simulations validate that this approach outperforms traditional methods, providing valuable insights for deploying efficient and robust edge intelligence architectures in next-generation intelligent transportation systems.

Large carnivores are expanding in Europe, and their return is associated with con- flicts that often result in policies to regulate their population size through culling. Being wide-ranging species, their populations are often distributed across several jurisdictions, which may vary in the extent to which they use lethal control. This creates the conditions for the establishment of source-sink dynamics across bor- ders, which may frustrate the ability of countries to reach their respective man- agement objectives. 2. To explore the consequences of this issue, we constructed a vec-permutation pro- jection model, applied to the case of wolverines in south-central Scandinavia, shared between Norway (where they are culled) and Sweden (where they are pro- tected). We evaluated the effect of compensatory immigration on wolverine pop- ulation growth rates, and if the effect was influenced by the distance to the national border. We assessed to what extent compensatory immigration had an influence on the number of removals needed to keep the population at a given growth rate. 3. In Norway, the model estimated a stable trend, whereas in Sweden it produced a 10% annual increase. The effect of compensatory immigration corresponded to a 0.02 reduction in population growth rate in Sweden and to a similar increase in Norway. This effect was strong closer to the Norwegian-Swedish border, but weakwhenmovingawayfromit.Anaverageof33wolverineswasshotperyear in the Norwegian part of the study area. If no compensatory immigration from Sweden had occurred, 28 wolverines shot per year would have been sufficient to achievethesamegoal.About15.5%ofalltheindividualsharvestedinNorway between 2005 and 2012 were compensated by immigrants, causing a decrease in population growth rate in Sweden. 4. Synthesis and applications. When a population is transboundary, the consequences of management decisions are also transboundary, even though the political bodies in charge of those decisions, the stakeholders who influence them, and the tax- payers who finance them are not. It is important that managers and citizens be informed that a difference in management goals can reduce the efficiency, and increase the costs, of wildlife management.

Abstract Introduction: Intratumoral administration of pexa-vec (pexastimogene devacirepvec), an oncolytic and immunotherapeutic vaccinia virus, given to patients with hepatocellular carcinoma (HCC), is associated with both local and distant tumor responses. We hypothesized subsequent treatment with sorafenib could demonstrate superior efficacy. Methods: This random phase III open-label study evaluated the sequential treatment with pexa-vec followed by sorafenib compared to sorafenib in patients with advanced HCC and no prior systemic treatment. The primary endpoint is overall survival (OS). Key secondary endpoints included time to progression (TTP), progression-free survival, overall response rate (ORR), and disease control rate (DCR). Safety was assessed in all patients who received ≥1 dose of study treatment. Results: The study was conducted at 142 sites in 16 countries. From December 30, 2015, to the interim analysis on August 2, 2019, 459 patients were randomly assigned (pexa-vec plus sorafenib: 234, sorafenib: 225). At the interim analysis, the median OS was 12.7 months (95% CI: 9.89, 14.95) in the pexa-vec plus sorafenib arm and 14.0 months (95% CI: 11.01, 18.00) in the sorafenib arm. This led to the early termination of the study. The median TTP was 2.0 months (95% CI: 1.77, 2.96) and 4.2 months (95% CI: 2.92, 4.63); ORR was 19.2% (45 patients) and 20.9% (47 patients); and DCR was 50.0% (117 patients) and 57.3% (129 patients) in the pexa-vec plus sorafenib and sorafenib arms, respectively. Serious adverse events were reported in 117 (53.7%) patients in the pexa-vec plus sorafenib and 77 (35.5%) patients in the sorafenib arm. Liver failure was the most frequently reported in both groups. Conclusion: Sequential pexa-vec plus sorafenib treatment did not demonstrate increased clinical benefit in advanced HCC and fared worse compared to sorafenib alone. The advent of the added value of checkpoint inhibitors should direct any further development of oncolytic virus therapy strategies.