Medulloblastomas comprise a molecularly diverse set of malignant pediatric brain tumors in which patients are stratified according to different prognostic risk groups that span from very good to very poor. Metastasis at diagnosis is most often a marker of poor prognosis and the relapse incidence is higher in these children. Medulloblastoma relapse is almost always fatal and recurring cells have, apart from resistance to standard of care, acquired genetic and epigenetic changes that correlate with an increased dormancy state, cell state reprogramming and immune escape. Here, we review means to carefully study metastasis and relapse in preclinical models, in light of recently described molecular subgroups. We will exemplify how therapy resistance develops at the cellular level, in a specific niche or from therapy-induced secondary mutations. We further describe underlying molecular mechanisms on how tumors acquire the ability to promote leptomeningeal dissemination and discuss how they can establish therapy-resistant cell clones. Finally, we describe some of the ongoing clinical trials of high-risk medulloblastoma and suggest or discuss more individualized treatments that could be of benefit to specific subgroups. Full article

Peer-to-peer (P2P) energy trading has attracted a lot of attention and the number of electric vehicles (EVs) has increased in the past couple of years. Toward sustainable mobility, EVs meet the standard development goals (SDGs) for attaining a sustainable future in the transport sector. This development and increasing number of EVs creates an opportunity for prosumers to trade electricity. Considering this opportunity, this review article aims to provide an in-depth analysis of P2P energy trading of EVs using blockchain in centralized and decentralized networks, which enables prosumers to exchange energy directly with one another. The paper is aimed to provide the reader with a state-of-the-art review on the P2P energy trading for EVs, considering different blockchain algorithms that are practically implemented or still in the research phase. Moreover, the paper presents blockchain applications, current trends, and future challenges of EVs’ energy trading. P2P energy trading for EVs using blockchain algorithms can be successfully implemented considering real-time scenarios and economically benefits smart sustainable societies. Full article

(1) Background: Walnut consumption has been associated with having a positive effect on controlling and/or reducing the co-morbidities associated with cardiovascular disease (CVD). The effects of consuming walnuts of Portuguese origin on risk factors related to CVD were evaluated by measuring glucose, urea, TC, HDL-C, LDL-C, TG, AST, and ALT levels, anthropometric profiles, and blood pressure. (2) Methods: This trial study involved 24 volunteers, both female (n = 15) and male (n = 9), from Fernando Pessoa University, Porto. It consisted of a daily intake of 25 g of walnut kernels over a period of 45 days. Before and after intake, biochemical parameters, BMI and BP were measured. (3) Results: Despite the intake of nuts revealing a reduction in mean values of most of the parameters assessed, a significant drop was only observed in AST (p = 0.04).There was also a significant reduction in the mean values for Glu (p = 0.01), UR (p = 0.01) and HDL-C (p = 0.02) for women but not for men. (4) Conclusions: The dose and the period of intake were not effective in lowering the lipid profile but may have had a protective effect on liver function. The benefits were greater in women than in men. Full article

Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based method for optimizing abnormal conditions during hydraulic fracturing of unconventional natural gas reservoirs. Firstly, the main controlling factors of abnormal conditions are selected through a hybrid controlling analysis, upon which a surrogate model is established for predicting the occurrence probability of abnormal conditions, rather than whether abnormal conditions happen or not. Subsequently, a machine learning-based optimization algorithm is developed to minimize the occurrence probability of abnormal conditions, acknowledging their inevitability during the fracturing process. The optimal results demonstrate the proposed method outperforms traditional methods, on average. The proposed methodology is more in line with the needs of practical operation in an environment full of uncertainty. Full article

We propose an efficient scheme to enhance the generation of optical second-order sidebands (OSSs) in an atom-assisted optomechanical system. The cavity field is coupled with a strong driving field and a weak probe field, and a control field is applied to the atom. We use the steady-state method to analyze the nonlinear interaction in the system, which is different from the traditional linear analysis method. The existence of an auxiliary three-level atom driven by the control field significantly enhances the generation of an OSS. It is found that the efficiency of the OSS can be effectively modulated by adjusting the Rabi frequency of the control field, optomechanical cooperativity and atomic coupling strength. Our scheme provides a promising solution for controlling light propagation and has potential application in quantum optical devices and quantum information networks. Full article

Background: The natural history of spinal muscular atrophy (SMA) is well understood, with progressive muscle weakness resulting in declines in function. The development of contractures is common and negatively impacts function. Clinically, joint hypermobility (JH) is observed but is poorly described, and its relationship with function is unknown. Methods: Lower-limb ROM (range of motion) assessments of extension and flexion at the hip, knee, and ankle were performed. ROMs exceeding the published norms were included in the analysis. The functional assessments performed included the six-minute walk test (6 MWT) and the Hammersmith Functional Motor Scale—Expanded (HFMSE). Results: Of the 143 participants, 86% (n = 123) had at least one ROM measure that was hypermobile, and 22% (n = 32) had three or more. The HFMSE scores were inversely correlated with hip extension JH (r = −0.60, p = 0.21; n = 6) and positively correlated with knee flexion JH (r = 0.24, p = 0.02, n = 89). There was a moderate, inverse relationship between the 6 MWT distance and ankle plantar flexion JH (r = −0.73, p = 0.002; n = 15). Conclusions: JH was identified in nearly all participants in at least one joint in this study. Hip extension, knee flexion and ankle plantar flexion JH was associated with function. A further understanding of the trajectory of lower-limb joint ROM is needed to improve future rehabilitation strategies. Full article

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP. Full article

Haptic feedback holds the potential to enhance the engagement and expressivity of future digital and electric musical instruments. This study investigates the impact of artificial vibration on the perceived quality of a silent electric cello. We developed a haptic cello prototype capable of rendering vibration signals of varying degree of congruence with the produced sound. Experienced cellists participated in an experiment comparing setups with and without vibrotactile feedback, rating them on preference, perceived power, liveliness, and feel. Results show nuanced effects, with added vibrations moderately enhancing feel and liveliness, and significantly increasing perceived power when using vibrations obtained from the pickup at the cello’s bridge. High uncertainty in our statistical model parameters underscores substantial individual differences in the participants responses, as commonly found in qualitative assessments, and highlights the importance of consistent feedback in the vibrotactile and auditory channels. Our findings contribute valuable insights to the intersection of haptics and music technology, paving the way for creating richer and more engaging experiences with future musical instruments. Full article

Despite a continuous effort devoted by the scientific community, a large-scale employment of Pulsating Heat Pipes for thermal management applications is still nowadays undermined by the low reliability of such heat transfer systems. The main reason underlying this critical issue is linked to the strongly chaotic thermofluidic behavior of these devices, which prevents a robust prediction of their working behavior for different geometries and operating conditions, consequently hampering proper industrial design. The present work proposes to thoroughly compare data referring to previous infrared investigations on different Pulsating Heat Pipe layouts, which have focused on the estimation of heat fluxes locally exchanged at the wall–fluid interfaces. The aim is to understand the beneficial contribution of local heat transfer quantities in the prediction of the complex physics underlying such heat transfer systems. The results have highlighted that, regardless of the considered geometry and working conditions, wall-to-fluid heat fluxes are able to provide useful quantities to be employed, to some extent, to generalize Pulsating Heat Pipe operation and to improve their existing numerical models. Full article

Outcomes for glioblastoma (GBM) remain poor despite standard-of-care treatments including surgical resection, radiation, and chemotherapy. Intratumoral heterogeneity contributes to treatment resistance and poor prognosis, thus demanding novel therapeutic approaches. Drug repositioning studies on antiretroviral therapy (ART) have shown promising potent antineoplastic effects in multiple cancers; however, its efficacy in GBM remains unclear. To better understand the pleiotropic anticancer effects of ART on GBM, we conducted a comprehensive drug repurposing analysis of ART in GBM to highlight its utility in translational neuro-oncology. To uncover the anticancer role of ART in GBM, we conducted a comprehensive bioinformatic and in vitro screen of antiretrovirals against glioblastoma. Using the DepMap repository and reversal of gene expression score, we conducted an unbiased screen of 16 antiretrovirals in 40 glioma cell lines to identify promising candidates for GBM drug repositioning. We utilized patient-derived neurospheres and glioma cell lines to assess neurosphere viability, proliferation, and stemness. Our in silico screen revealed that several ART drugs including reverse transcriptase inhibitors (RTIs) and protease inhibitors (PIs) demonstrated marked anti-glioma activity with the capability of reversing the GBM disease signature. RTIs effectively decreased cell viability, GBM stem cell markers, and proliferation. Our study provides mechanistic and functional insight into the utility of ART repurposing for malignant gliomas, which supports the current literature. Given their safety profile, preclinical efficacy, and neuropenetrance, ARTs may be a promising adjuvant treatment for GBM. Full article

Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of drug mechanism. The cysteine trapping assay is a method for detecting reactive metabolites that bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silico classification model for predicting a positive/negative outcome in the cysteine trapping assay. We collected 475 compounds (436 in-house compounds and 39 publicly available drugs) based on experimental data performed in this study, and the composition of the results showed 248 positives and 227 negatives. Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. In the time-split dataset, AUC-ROC of MPNN and RF were 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that the in silico MPNN classification model for the cysteine trapping assay has a better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous results. Full article

The treatment landscape of chronic lymphocytic leukemia (CLL), the most frequent leukemia in adults, is constantly changing. CLL patients can be divided into three risk categories, based on their IGHV mutational status and the occurrence of TP53 disruption and/or complex karyotype. For the first-line treatment of low- and intermediate-risk CLL, both the BCL2 inhibitor venetoclax plus obinutuzumab and the second generation BTK inhibitors (BTKi), namely acalabrutinib and zanubrutinib, are valuable and effective options. Conversely, venetoclax-based fixed duration therapies have not shown remarkable results in high-risk CLL patients, while continuous treatment with acalabrutinib and zanubrutinib displayed favorable outcomes, similar to those obtained in TP53 wild-type patients. The development of acquired resistance to pathway inhibitors is still a clinical challenge, and the optimal treatment sequencing of relapsed/refractory CLL is not completely established. Covalent BTKi-refractory patients should be treated with venetoclax plus rituximab, whereas venetoclax-refractory CLL may be treated with second generation BTKi in the case of early relapse, while venetoclax plus rituximab might be used if late relapse has occurred. On these grounds, here we provide an overview of the current state-of-the-art therapeutic algorithms for treatment-naïve patients, as well as for relapsed/refractory disease. Full article

Probe-based confocal laser endoscopy (pCLE) has emerged as a powerful tool for disease diagnosis, yet it faces challenges such as the formation of hexagonal patterns in images due to the inherent characteristics of fiber bundles. Recent advancements in deep learning offer promise in image denoising, but the acquisition of clean-noisy image pairs for training networks across all potential scenarios can be prohibitively costly. Few studies have explored training denoising networks on such pairs. Here, we propose an innovative self-supervised denoising method. Our approach integrates noise prediction networks, image quality assessment networks, and denoising networks in a collaborative, jointly trained manner. Compared to prior self-supervised denoising methods, our approach yields superior results on pCLE images and fluorescence microscopy images. In summary, our novel self-supervised denoising technique enhances image quality in pCLE diagnosis by leveraging the synergy of noise prediction, image quality assessment, and denoising networks, surpassing previous methods on both pCLE and fluorescence microscopy images. Full article

Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned for its computational efficacy in forecasting streamflow events with a forecast horizon of 7 days. The novel integration of the groundwater level, precipitation, and river discharge as predictive variables offers a holistic view of the hydrological cycle, enhancing the model’s accuracy. Our findings reveal that for a 7-day forecasting period, the STA-GRU model demonstrates superior performance, with a notable improvement in mean absolute percentage error (MAPE) values and R-square ($R^2$) alongside reductions in the root mean squared error (RMSE) and mean absolute error (MAE) metrics, underscoring the model’s generalizability and reliability. Comparative analysis with seven conventional deep learning models, including the Long Short-Term Memory (LSTM), the Convolutional Neural Network LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the Spatio-Temporal Attention LSTM (STA-LSTM), the Gated Recurrent Unit (GRU), the Convolutional Neural Network GRU (CNNGRU), and the STA-GRU, confirms the superior predictive power of the STA-LSTM and STA-GRU models when faced with long-term prediction. This research marks a significant shift towards an integrated network of real-time monitoring stations with advanced deep-learning algorithms for streamflow forecasting, emphasizing the importance of spatially and temporally encompassing streamflow variability within an urban watershed’s stream network. Full article

HLA-matched allogeneic hematopoietic cell transplantation (HCT) is a curative therapy for many patients. Unrelated HLA-matched donors are the most frequently used donor for HCT. When more than one donor transplant option is available, transplant centers can select donors based on non-HLA factors. With improved ability to prevent and treat immune complications, such as graft-versus-host disease and infections, it may be possible to proceed more often using HLA-mismatched donors, allowing greater consideration of non-HLA factors, such as donor age, CMV serostatus, and ABO blood group matching, which have demonstrated important impacts on transplant outcomes. Additional factors to consider are donor availability rates and the usage of domestic donors to optimize outcomes. A review of non-HLA factors and considerations on the selection of optimal unrelated donors for HCT are provided within this updated current context. Full article

The inherent power fluctuations of wind, photovoltaic (PV) and bioenergy with carbon capture and storage (BECCS) create a temporal mismatch between energy supply and demand. This mismatch could lead to a potential resurgence of fossil fuels, offsetting the effects of decarbonization and affecting the realization of the Paris target by limiting global warming to below 2 °C in the 21st century. While application of energy storage is widely recommended to address this limitation, there is a research gap to quantify the impacts of energy storage limitation on global warming. Here, we analyzed the hourly variation of global wind and PV power during the period 1981–2020 and the monthly capacity of biomass production in 2019, and thus quantified the impact of decreasing the capacity of energy storage on global warming using a state-of-the-art Earth system model. We found that global warming by 2100 in the SSP1-2.6 scenario would increase by about 20% and exceed 2 °C without deploying energy storage facilities. Achieving the 2 °C target requires reducing power losses of wind and PV by at least 30% through energy storage. This requirement delivers to a cumulative storage capacity of 16.46 TWh using batteries during the period 2021–2100, leading to the international trade of cobalt and manganese across countries due to deficits of minerals at a country level. In the context of energy security, we highlight the importance of considering the limitations of energy storage and mineral shortage in the forthcoming policies of decarbonization. Full article

Beneficial health effects of omega-3 polyunsaturated fatty acids (n-3 PUFA) are partly attributed to specialized pro-resolving mediators (SPMs), which promote inflammation resolution. Strategies to improve n-3 PUFA conversion to SPMs may, therefore, be useful to treat or prevent chronic inflammatory disorders. Here, we explored a synbiotic strategy to increase circulating SPM precursor levels. Healthy participants (n = 72) received either SynΩ3 (250 mg eicosapentaenoic acid (EPA) plus docosahexaenoic acid (DHA) lysine salts; two billion CFU Bacillus megaterium; n = 23), placebo (n = 24), or fish oil (300 mg EPA plus DHA; N = 25) capsules daily for 28 days in a randomized, double-blind placebo-controlled parallel 3-group design. Biomarkers were assessed at baseline and after 2 and 28 days of intervention. The primary analysis involved the comparison between SynΩ3 and placebo. In addition, SynΩ3 was compared to fish oil. The synbiotic SynΩ3 comprising Bacillus megaterium DSM 32963 and n-3 PUFA salts significantly increased circulating SPM precursor levels, including 18-hydroxy-eicosapentaenoic acid (18-HEPE) plus 5-HEPE, which was not achieved to this extent by fish oil with a similar n-3 PUFA content. Omega-3 indices were increased slightly by both SynΩ3 and fish oil. These findings suggest reconsidering conventional n-3 PUFA supplementation and testing the effectiveness of SynΩ3 particularly in conditions related to inflammation. Full article

Peptide toxins from marine invertebrates have found use as drugs and in biotechnological applications. Many marine habitats, however, remain underexplored for natural products, and the Southern Ocean is among them. Here, we report toxins from one of the top predators in Antarctic waters: the nemertean worm Parborlasia corrugatus (McIntosh, 1876). Transcriptome mining revealed a total of ten putative toxins with a cysteine pattern similar to that of alpha nemertides, four nemertide-beta-type sequences, and two novel full-length parborlysins. Nemertean worms express toxins in the epidermal mucus. Here, the expression was determined by liquid chromatography combined with mass spectrometry. The findings include a new type of nemertide, 8750 Da, containing eight cysteines. In addition, we report the presence of six cysteine-containing peptides. The toxicity of tissue extracts and mucus fractions was tested in an Artemia assay. Notably, significant activity was observed both in tissue and the high-molecular-weight mucus fraction, as well as in a parborlysin fraction. Membrane permeabilization experiments display the membranolytic activity of some peptides, most prominently the parborlysin fraction, with an estimated EC₅₀ of 70 nM. Full article

In September 2021, 14 smallmouth bass (SMB; Micropterus dolomieu) with skin lesions were collected from Green Bay waters of Lake Michigan and submitted for diagnostic evaluation. All the skin samples tested positive for largemouth bass virus (LMBV) by conventional PCR. The complete genome of the LMBV (99,328 bp) isolated from a homogenized skin sample was determined using an Illumina MiSeq sequencer. A maximum likelihood (ML) phylogenetic analysis based on the 21 core iridovirus genes supported the LMBV isolated from SMB (LMBV-WVL21117) as a member of the species Santee-Cooper ranavirus. Pairwise nucleotide comparison of the major capsid protein (MCP) gene showed that LMBV-WVL21117 is identical to other LMBV reported from the United States and nearly identical to doctor fish virus and guppy virus 6 (99.2%) from Southeast Asia, as well as LMBV isolates from China and Thailand (99.1%). In addition, ML phylogenetic analysis based on the MCP gene suggests three genotypes of LMBV separated by region: genotype one from the United States, genotype two from Southeast Asia, and genotype three from China and Thailand. Additional research is needed to understand the prevalence and genetic diversity of LMBV strains circulating in wild and managed fish populations from different regions. Full article

Accurate and fast traffic prediction is the data-based foundation for achieving traffic control and management, and the accuracy of prediction results will directly affect the effectiveness of traffic control and management. This paper proposes a new spatiotemporal periodic polynomial model for road traffic, which integrates the temporal, spatial, and periodic features of speed time series and can effectively handle the nonlinear mapping relationship from input to output. In terms of the model, we establish a road traffic speed prediction model based on polynomial regression. In terms of spatial feature extraction methods, we introduce a maximum mutual information coefficient spatial feature extraction method. In terms of periodic feature extraction methods, we introduce a periodic trend modeling method into the prediction of speed time series, and effective fusion is carried out. Four strategies are evaluated based on the Guangzhou road speed dataset: a univariate polynomial model, a spatiotemporal polynomial model, a periodic polynomial model, and a spatiotemporal periodic polynomial model. The test results show that the three methods proposed in this article can effectively improve prediction accuracy. Comparing the spatiotemporal periodic polynomial model with multiple machine learning models and deep learning models, the prediction accuracy is improved by 5.94% compared to the best feedforward neural network. The research in this article can effectively deal with the temporal, spatial, periodic, and nonlinear characteristics of speed prediction, and to a certain extent, improve the accuracy of speed prediction. Full article

Objectives: This study aimed to explore the impact of irritating sounds on the postural control of healthy adults, considering both linear and nonlinear parameters, subjective assessments, and gender differences. Methods: Thirty-four young participants (17 females, 17 males) completed three 30 s bipedal standing stability tests on a balance platform: one with visual control (EO), another without visual control (EC), and a third without visual control but accompanied by irritating sounds (ECS). Additionally, participants filled out a questionnaire evaluating their sound sensitivity. Linear and nonlinear parameters from each balance test were considered for statistical analysis. Results: The findings reveal significant gender-based variations in sensitivity to sound, with women exhibiting higher sensitivity. No statistically significant differences in postural control were observed between males and females, except for a notable increase in irregularity (SampEn values) in the anterior–posterior direction for females in the ECS trial. Correlation analyses revealed a moderate and statistically significant correlation between SampEn values in the AP direction and SE scores. Conclusions: This study highlights the intricate relationship between sensory stimuli, attention, and the body’s ability to maintain balance. The presence of irritating sounds led to increased irregularity in postural control, particularly in the absence of visual control. Full article