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Immune checkpoint inhibitors have been successful across several tumor types; however, their efficacy has been uncommon and unpredictable in glioblastomas (GBM), where <10% of patients show long-term responses. To understand the molecular determinants of immunotherapeutic response in GBM, we longitudinally profiled 66 patients, including 17 long-term responders, during standard therapy and after treatment with PD-1 inhibitors (nivolumab or pembrolizumab). Genomic and transcriptomic analysis revealed a significant enrichment of PTEN mutations associated with immunosuppressive expression signatures in non-responders, and an enrichment of MAPK pathway alterations (PTPN11, BRAF) in responders. Responsive tumors were also associated with branched patterns of evolution from the elimination of neoepitopes as well as with differences in T cell clonal diversity and tumor microenvironment profiles. Our study shows that clinical response to anti-PD-1 immunotherapy in GBM is associated with specific molecular alterations, immune expression signatures, and immune infiltration that reflect the tumor’s clonal evolution during treatment.Genomic, transcriptomic, and microenvironmental analyses of samples from patients with glioblastoma treated with nivolumab or pembrolizumab identifies features associated with treatment response that may help in refining patient stratification.

Viral recombination is a major evolutionary mechanism driving adaptation processes, such as the ability of host-switching. Understanding global patterns of recombination could help to identify underlying mechanisms and to evaluate the potential risks of rapid adaptation. Conventional approaches (e.g., those based on linkage disequilibrium) are computationally demanding or even intractable when sequence alignments include hundreds of sequences, common in viral data sets. We present a comprehensive analysis of recombination across 30 genomic alignments from viruses infecting humans. In order to scale the analysis and avoid the computational limitations of conventional approaches, we apply newly developed topological data analysis methods able to infer recombination rates for large data sets. We show that viruses, such as ZEBOV and MARV, consistently displayed low levels of recombination, whereas high levels of recombination were observed in Sarbecoviruses, HBV, HEV, Rhinovirus A, and HIV. We observe that recombination is more common in positive single-stranded RNA viruses than in negatively single-stranded RNA ones. Interestingly, the comparison across multiple viruses suggests an inverse correlation between genome length and recombination rate. Positional analyses of recombination breakpoints along viral genomes, combined with our approach, detected at least 39 nonuniform patterns of recombination (i.e., cold or hotspots) in 18 viral groups. Among these, noteworthy hotspots are found in MERS-CoV and Sarbecoviruses (at spike, Nucleocapsid and ORF8). In summary, we have developed a fast pipeline to measure recombination that, combined with other approaches, has allowed us to find both common and lineage-specific patterns of recombination among viruses with potential relevance in viral adaptation.

Automatic license plate recognition (ALPR) plays an important role in applications such as intelligent traffic systems, vehicle access control in specific areas, and law enforcement. The main novelty brought by the present research consists in the development of an automatic vehicle license plate recognition system adapted to the Romanian context, which integrates the YOLOv12 detection architecture with the PaddleOCR library while also providing functionalities for recognizing the type of vehicle on which the license plate is mounted and identifying the county of registration. The integration of these functionalities allows for an extension of the applicability range of the proposed solution, including for addressing issues related to restricting access for certain types of vehicles in specific areas, as well as monitoring vehicle traffic based on the county of registration. The dataset used in the study was manually collected and labeled using the makesense.ai platform and was made publicly available for future research. It includes 744 images of vehicles registered in Romania, captured in real traffic conditions (the training dataset being expanded by augmentation). The YOLOv12 model was trained to automatically detect license plates in images with vehicles, and then it was evaluated and validated using standard metrics such as precision, recall, F1 score, mAP@0.5, mAP@0.5:0.95, etc., proving very good performance. Experimental results demonstrate that YOLOv12 achieved superior performance compared to YOLOv11 for the analyzed issue. YOLOv12 outperforms YOLOv11 with a 2.3% increase in precision (from 97.4% to 99.6%) and a 1.1% improvement in F1 score (from 96.7% to 97.8%).

Modernizing irrigation systems (ISs) from traditional gravity methods to sprinkler and drip technologies has significantly improved water use efficiency. However, it has simultaneously increased electricity demand and operational costs. Integrating photovoltaic generators into ISs represents a promising solution, as solar energy availability typically aligns with peak irrigation periods. Despite this potential, photovoltaic pumping systems (PVPSs) often face reliability issues due to fluctuations in solar irradiance, resulting in frequent start/stop cycles and premature equipment wear. The IEC 62253 standard establishes procedures for evaluating PVPS performance but primarily addresses steady-state conditions, neglecting transient regimes. As the main contribution, the current paper proposes a non-intrusive, high-resolution monitoring system and a methodology to assess the performance of an isolated, high-power PVPS, considering also transient regimes. The system records critical electrical, hydraulic and environmental parameters every second, enabling in-depth analysis under various weather conditions. Two performance indicators, pumped volume efficiency and equivalent operating time, were used to evaluate the system’s performance. The results indicate that near-optimal performance is only achievable under clear sky conditions. Under the appearance of clouds, control strategies designed to protect the system reduce overall efficiency. The proposed methodology enables detailed performance diagnostics and supports the development of more robust PVPSs.

Fifty percent of diffuse large B cell lymphoma (DLBCL) cases lack cell-surface expression of the class I major histocompatibility complex (MHC-I), thus escaping recognition by cytotoxic T cells. Here we show that, across B cell lymphomas, loss of MHC-I, but not MHC-II, is preferentially restricted to DLBCL. To identify the involved mechanisms, we performed whole exome and targeted HLA deep-sequencing in 74 DLBCL samples, and found somatic inactivation of B2M and the HLA-I loci in 80% (34 of 42) of MHC-INEG tumors. Furthermore, 70% (22 of 32) of MHC-IPOS DLBCLs harbored monoallelic HLA-I genetic alterations (MHC-IPOS/mono), indicating allele-specific inactivation. MHC-INEG and MHC-IPOS/mono cases harbored significantly higher mutational burden and inferred neoantigen load, suggesting potential coselection of HLA-I loss and sustained neoantigen production. Notably, the analysis of >500,000 individuals across different cancer types revealed common germline HLA-I homozygosity, preferentially in DLBCL. In mice, germinal-center B cells lacking HLA-I expression did not progress to lymphoma and were counterselected in the context of oncogene-driven lymphomagenesis, suggesting that additional events are needed to license immune evasion. These results suggest a multistep process of HLA-I loss in DLBCL development including both germline and somatic events, and have direct implications for the pathogenesis and immunotherapeutic targeting of this disease.

In recent years, the growth of environmental awareness has increased the interest in the development of biocomposites which are sustainable materials with an excellent price–performance ratio and low weight. The current study aimed to obtain and characterize the biocomposites prepared by thermoforming using coir fibres as reinforcing material and polypropylene as matrix. The biocomposites were produced with different coir fibres/polypropylene ratios and were characterized by physical–mechanical indices, thermal analysis, crystallinity, attenuated total reflection-Fourier transform infrared spectroscopy analysis (ATR-FTIR), scanning electron microscopy (SEM), and chromatic measurements. Both tensile and bending strength of biocomposites decreased when the coir fibre content increased. The melting temperature of biocomposite materials has decreased with the increase of the coir fibre loading. Regarding the thermal stability, the weight loss and degradation temperature increased with decreasing coir fibre content. The ATR-FTIR and SEM analyses underlined the modifications that took place in the structure of the biocomposites by modifying the coir fibres/matrix ratio.

Currently, the most implemented solution for driving a pumping system (PS) at variable speed is using a variable frequency drive (VFD). Because most of the VFDs have integrated only one proportional-integral (PI) frequency controller whose parameters (proportional gain/the integration time) can be off-line tuned but cannot be changed during real-time operation, and many PS must operate in different regimes (at variable speed, variable flow rate, at variable pumping head or even at variable power, e.g., those powered by renewable energy sources), the adaptive control strategy proposed in this paper overcomes these problems with very good performances. The proposed adaptive control strategy uses only simple PI controllers for managing several operating regimes. The adaptive character is not ensured by re-tuning the PI controller parameters as in self-tuning controllers but by readjusting the control law through a change in the control loop depending on the controlled output of the process (pump speed, pump discharge, pumping head or absorbed power). The deviations of the mentioned controlled outputs from their referenced values are converted into electrical frequency error (the deviation of the current value from its reference), which is the regular input to the already tuned controller; therefore, no controller re-tuning is required.

Viral respiratory infections are an important public health concern due to their prevalence, transmissibility, and potential to cause serious disease. Disease severity is the product of several factors beyond the presence of the infectious agent, including specific host immune responses, host genetic makeup, and bacterial coinfections. To understand these interactions within natural infections, we designed a longitudinal cohort study actively surveilling respiratory viruses over the course of 19 months (2016 to 2018) in a diverse cohort in New York City. We integrated the molecular characterization of 800+ nasopharyngeal samples with clinical data from 104 participants. Transcriptomic data enabled the identification of respiratory pathogens in nasopharyngeal samples, the characterization of markers of immune response, the identification of signatures associated with symptom severity, individual viruses, and bacterial coinfections. Specific results include a rapid restoration of baseline conditions after infection, significant transcriptomic differences between symptomatic and asymptomatic infections, and qualitatively similar responses across different viruses. We created an interactive computational resource (Virome Data Explorer) to facilitate access to the data and visualization of analytical results.

Respiratory viruses are common in human populations, causing significant levels of morbidity. Understanding the distribution of these viruses is critical for designing control methods. However, most data available are from medical records and thus predominantly represent symptomatic infections. Estimates for asymptomatic prevalence are sparse and span a broad range. In this study, we aimed to measure more precisely the proportion of infections that are asymptomatic in a general, ambulatory adult population. We recruited participants from a New York City tourist attraction and administered nasal swabs, testing them for adenovirus, coronavirus, human metapneumovirus, rhinovirus, influenza virus, respiratory syncytial virus, and parainfluenza virus. At recruitment, participants completed surveys on demographics and symptomology. Analysis of these data indicated that over 6% of participants tested positive for shedding of respiratory virus. While participants who tested positive were more likely to report symptoms than those who did not, over half of participants who tested positive were asymptomatic. Most observation of human respiratory virus carriage is derived from medical surveillance; however, the infections documented by this surveillance represent only a symptomatic fraction of the total infected population. As the role of asymptomatic infection in respiratory virus transmission is still largely unknown and rates of asymptomatic shedding are not well constrained, it is important to obtain more-precise estimates through alternative sampling methods. We actively recruited participants from among visitors to a New York City tourist attraction. Nasopharyngeal swabs, demographics, and survey information on symptoms, medical history, and recent travel were obtained from 2,685 adults over two seasonal arms. We used multiplex PCR to test swab specimens for a selection of common respiratory viruses. A total of 6.2% of samples (168 individuals) tested positive for at least one virus, with 5.6% testing positive in the summer arm and 7.0% testing positive in the winter arm. Of these, 85 (50.6%) were positive for human rhinovirus (HRV), 65 (38.7%) for coronavirus (CoV), and 18 (10.2%) for other viruses (including adenovirus, human metapneumovirus, influenza virus, and parainfluenza virus). Depending on the definition of symptomatic infection, 65% to 97% of infections were classified as asymptomatic. The best-fit model for prediction of positivity across all viruses included a symptom severity score, Hispanic ethnicity data, and age category, though there were slight differences across the seasonal arms. Though having symptoms is predictive of virus positivity, there are high levels of asymptomatic respiratory virus shedding among the members of an ambulatory population in New York City. IMPORTANCE Respiratory viruses are common in human populations, causing significant levels of morbidity. Understanding the distribution of these viruses is critical for designing control methods. However, most data available are from medical records and thus predominantly represent symptomatic infections. Estimates for asymptomatic prevalence are sparse and span a broad range. In this study, we aimed to measure more precisely the proportion of infections that are asymptomatic in a general, ambulatory adult population. We recruited participants from a New York City tourist attraction and administered nasal swabs, testing them for adenovirus, coronavirus, human metapneumovirus, rhinovirus, influenza virus, respiratory syncytial virus, and parainfluenza virus. At recruitment, participants completed surveys on demographics and symptomology. Analysis of these data indicated that over 6% of participants tested positive for shedding of respiratory virus. While participants who tested positive were more likely to report symptoms than those who did not, over half of participants who tested positive were asymptomatic.

Background Respiratory viral infections are a major cause of morbidity and mortality worldwide. However, their characterization is incomplete because prevalence estimates are based on syndromic surveillance data. Here, we address this shortcoming through the analysis of infection rates among individuals tested regularly for respiratory viral infections, irrespective of their symptoms. Methods We carried out longitudinal sampling and analysis among 214 individuals enrolled at multiple New York City locations from fall 2016 to spring 2018. We combined personal information with weekly nasal swab collection to investigate the prevalence of 18 respiratory viruses among different age groups and to assess risk factors associated with infection susceptibility. Results 17.5% of samples were positive for respiratory viruses. Some viruses circulated predominantly during winter, whereas others were found year round. Rhinovirus and coronavirus were most frequently detected. Children registered the highest positivity rates, and adults with daily contacts with children experienced significantly more infections than their counterparts without children. Conclusion Respiratory viral infections are widespread among the general population with the majority of individuals presenting multiple infections per year. The observations identify children as the principal source of respiratory infections. These findings motivate further active surveillance and analysis of differences in pathogenicity among respiratory viruses.