Search Results (5,845)

The accuracy of classification and localization of plants on images obtained from the board of an unmanned aerial vehicle (UAV) is of great importance when implementing precision farming technologies. It allows for the effective application of variable rate technologies, which not only saves chemicals but also reduces the environmental load on cultivated fields. Machine learning algorithms are widely used for plant classification. Research on the application of the YOLO algorithm is conducted for simultaneous identification, localization, and classification of plants. However, the quality of the algorithm significantly depends on the training set. The aim of this study is not only the detection of a cultivated plant (soybean) but also weeds growing in the field. The dataset developed in the course of the research allows for solving this issue by detecting not only soybean but also seven weed species common in the fields of Kazakhstan. The article describes an approach to the preparation of a training set of images for soybean fields using preliminary thresholding and bound box (Bbox) segmentation of marked images, which allows for improving the quality of plant classification and localization. The conducted research and computational experiments determined that Bbox segmentation shows the best results. The quality of classification and localization with the application of Bbox segmentation significantly increased (f1 score increased from 0.64 to 0.959, mAP50 from 0.72 to 0.979); for a cultivated plant (soybean), the best classification results known to date were achieved with the application of YOLOv8x on images obtained from the UAV, with an f1 score = 0.984. At the same time, the plant detection rate increased by 13 times compared to the model proposed earlier in the literature. Full article

It is believed that caspases may play a significant role in the development of cancer, and the expression levels of genes encoding these proteins may influence the prognosis and clinical course of cancer. Taking into account the information presented, we examined the expression profiles of 11 genes from the caspase family in patients diagnosed with triple-negative breast cancer (TNBC). We qualified 29 patients with TNBC. A fragment of the tumor and a fragment of normal tissue surrounding the tumor were collected from each patient. Then, RNA was isolated, and the reverse transcription process was performed. The expression levels of caspase family genes were determined using the real-time PCR method. The obtained data were correlated with clinical data and compared with data from the Cancer Genome Atlas database using the Breast Cancer Gene Expression Miner v4.8 and Ualcan. Based on the results of the conducted research, it can be assumed that the levels of expression of caspase family genes may be correlated with the clinical course of cancer in patients with TNBC, and further research may indicate that profiling the expression levels of these genes may be used in selecting personalized treatment methods. Full article

Functional foods like flax (Linum usitatissimum L.) are rich sources of specialized metabolites that contribute to their nutritional and health-promoting properties. Understanding the biosynthesis of these compounds is essential for improving their quality and potential applications. However, dissecting complex metabolic networks in plants remains challenging due to the dynamic nature and interconnectedness of biosynthetic pathways. In this study, we present a synergistic approach combining stable isotopic labeling (SIL), Candidate Substrate–Product Pair (CSPP) networks, and a time-course study with high temporal resolution to reveal the biosynthetic fluxes shaping phenylpropanoid metabolism in young flax seedlings. By feeding the seedlings with ¹³C₃-p-coumaric acid and isolating isotopically labeled metabolization products prior to the construction of CSPP networks, the biochemical validity of the connections in the network was supported by SIL, independent of spectral similarity or abundance correlation. This method, in combination with multistage mass spectrometry (MSⁿ), allowed confident structural proposals of lignans, neolignans, and hydroxycinnamic acid conjugates, including the presence of newly identified chicoric acid and related tartaric acid esters in flax. High-resolution time-course analyses revealed successive waves of metabolite formation, providing insights into distinct biosynthetic fluxes toward lignans and early lignification intermediates. No evidence was found here for the involvement of chlorogenic or caftaric acid intermediates in chicoric acid biosynthesis in flax, as has been described in other species. Instead, our findings suggest that in flax seedlings, chicoric acid is synthesized through successive hydroxylation steps of p-coumaroyl tartaric acid esters. This work demonstrates the power of combining SIL and CSPP strategies to uncover novel metabolic routes and highlights the nutritional potential of flax sprouts rich in chicoric acid. Full article

Background and Purpose: Stratification based on specific image biomarkers applicable in clinical settings could help optimize treatment outcomes for recurrent non-small cell lung cancer patients. For this purpose, we aimed to determine the clinical impact of positive delta changes (any difference above zero > 0) between baseline [¹⁸F]FDG PET/CT metrics before the first treatment course and reirradiation. Material/Methods: Forty-seven patients who underwent thoracic reirradiation with curative intent at our institute between 2013 and 2021 met the inclusion criteria. All patients had histologically verified NSCLC, ECOG (Eastern Cooperative Oncology Group) ≤ 2, and underwent [¹⁸F]FDG PET/CT for initial staging and re-staging before primary radiotherapy and reirradiation, respectively. The time interval between radiation treatments was at least nine months. Quantitative metabolic volume and intensity parameters were measured before first irradiation and before reirradiation, and the difference above zero (>0; delta change) between them was statistically correlated to locoregional control (LRC), progression-free survival (PFS), and overall survival (OS). Results: Patients were followed for a median time of 33 months after reirradiation. The median OS was 21.8 months (95%-CI: 16.3–27.3), the median PFS was 12 months (95%-CI: 6.7–17.3), and the median LRC was 13 months (95%-CI: 9.0–17.0). Multivariate analysis revealed that the delta changes in SULpeak, SUVmax, and SULmax of the lymph nodes significantly impacted OS (SULpeak p = 0.017; SUVmax p = 0.006; SULmax p = 0.006), PFS (SULpeak p = 0.010; SUVmax p = 0.009; SULmax p = 0.009), and LRC (SULpeak p < 0.001; SUVmax p = 0.003; SULmax p = 0.003). Conclusions: Delta changes in SULpeak, SUVmax, and SULmax of the metastatic lymph nodes significantly impacted all clinical endpoints (OS, PFS and LRC) in recurrent NSCLC patients treated with reirradiation. Hence, these imaging biomarkers could be helpful with regard to patient selection in this challenging clinical situation. Full article

Background: Intracerebral hemorrhage (ICH) is a severe form of stroke associated with high morbidity and mortality. While neurosurgical evacuation may offer theoretical benefits, its impact on survival and hospital course remains debated. We aimed to compare the outcomes of surgical versus conservative management in patients with lobar, capsulo-lenticular, and thalamic ICH and to identify factors influencing mortality and the surgical decision. Methods: This single-center, retrospective cohort study included adult patients admitted to the County Clinical Emergency Hospital of Sibiu (2017–2023) with spontaneous supratentorial ICH confirmed via CT (deepest affected structure determining lobar, capsulo-lenticular, or thalamic location). We collected data on demographics, clinical presentation (Glasgow Coma Scale [GCS], anticoagulant use), hematoma characteristics (volume, extension), treatment modality (surgical vs. conservative), and in-hospital outcomes (mortality, length of stay). Statistical analyses included t-tests, χ², correlation tests, and logistic regression to identify independent predictors of mortality and surgery. Results: A total of 445 patients were analyzed: 144 lobar, 150 capsulo-lenticular, and 151 thalamic. Surgical intervention was more common in patients with larger volumes and lower GCS. Overall, in-hospital mortality varied by location, reaching 13% in the lobar group, 20.7% in the capsulo-lenticular group, and 35.1% in the thalamic group. Within each location, surgical intervention did not significantly reduce overall in-hospital mortality despite the more severe baseline presentation in surgical patients. In lobar ICH specifically, no clear survival advantage emerged, although surgery may still benefit those most severely compromised. For capsulo-lenticular hematomas > 30 mL, surgery was associated with lower mortality (39.4% vs. 61.5%). In patients with large lobar ICH, surgical intervention was associated with mortality rates similar to those seen in less severe, conservatively managed cohorts. Multivariable adjustment confirmed GCS and hematoma volume as independent mortality predictors; age and volume predicted the likelihood of surgical intervention. Conclusions: Despite targeting more severe cases, neurosurgical evacuation did not uniformly lower in-hospital mortality. In lobar ICH, surgical patients with larger hematomas (~48 mL) and lower GCS (~11.6) had mortality rates (~13%) comparable to less severe, conservative cohorts, indicating that surgical intervention was associated with similar mortality rates despite higher baseline risk. However, these findings do not establish a causal survival benefit and should be interpreted in the context of non-randomized patient selection. For capsulo-lenticular hematomas > 30 mL, surgery was associated with lower observed mortality (39.4% vs. 61.5%). Thalamic ICH remained most lethal, highlighting the difficulty of deep-brain bleeds and frequent ventricular extension. Across locations, hematoma volume and GCS were the primary outcome predictors, indicating the need for timely intervention, better patient selection, and possibly minimally invasive approaches. Future prospective multicenter research is necessary to refine surgical indications and validate these findings. To our knowledge, this investigation represents the largest and most contemporary single-center cohort study of supratentorial intracerebral hemorrhage conducted in Romania. Full article

While prior work has explored learner behavior using learning management systems (LMS) data, few studies provide week-level clustering validated against external engagement labels. To understand and assist students in online learning platforms and environments, this study presents a week-level engagement profiling framework for e-learning environments, utilizing K-means clustering and label-based validation. Leveraging log data from 127 students over a 13-week course, 44 activity-based features were engineered to classify student engagement into high, moderate, and low levels. The optimal number of clusters (k = 3) was identified using the elbow method and assessed through internal metrics, including a silhouette score of 0.493 and R² of 0.80. External validation confirmed strong alignment with pre-labeled engagement levels based on activity frequency and weighting. The clustering approach successfully revealed distinct behavioral patterns across engagement tiers, enabling a nuanced understanding of student interaction dynamics over time. Regression analysis further demonstrated a significant association between engagement levels and academic performance, underscoring the model’s potential as an early warning system for identifying at-risk learners. These findings suggest that clustering based on LMS behavior offers a scalable, data-driven strategy for improving learner support, personalizing instruction, and enhancing retention and academic outcomes in digital education settings such as MOOCs. Full article

Improving curriculum structure is critical for enhancing student success and on-time graduation, yet few methods exist to evaluate how prerequisite paths shape student progression and graduation outcomes. This study proposes a data-driven, graph-based framework that integrates course prerequisite networks with student performance data to systematically analyze curricular structure and student outcomes. We identify high-risk courses by jointly modeling their structural importance and pass rates, and quantify the time and survivability of different prerequisite paths using probabilistic models. Additionally, we introduced grade transition patterns to capture more nuanced transitions in student performance and pinpoint bottlenecks along prerequisite paths. Applying the model on four science and engineering majors from a public institution, the results not only identify high-risk courses often missed in conventional analyses, but also reveal path-level disparities and structural bottlenecks that affect student progression and time to graduation. For example, in the Computer Science major, we identified that the architecture and operating systems pathway is more challenging than the software engineering pathway. A closer examination of the course pairs along this trajectory revealed that the difficulty stems from a significant drop in student performance between a prerequisite–successor course pairs.This type of analysis fills a gap in conventional curriculum studies, which often overlook path-level dynamics, and offers actionable insights for educators a to identify high risk curricular components. Full article

Background/Objectives: The number needed to vaccinate (NNV) is a metric commonly used to evaluate the public health impact of a vaccine as it represents the number of individuals that must be vaccinated to prevent one case of disease. Traditional calculations may underestimate vaccine benefits by neglecting indirect effects and duration of protection (DOP), resulting in NNV overestimation. This study evaluated the NNV for the pediatric 20-valent pneumococcal conjugate (PCV20) US immunization program, as compared to PCV13, with a unique approach to NNV. Methods: A multi-cohort, population-based Markov model accounting for indirect effects was employed to calculate the NNV of PCV20 to avert a case of pneumococcal disease, invasive pneumococcal disease (IPD), hospitalized non-bacteremic pneumonia (NBP), ambulatory NBP, and otitis media (OM), as well as to prevent antibiotic-resistant cases and antibiotic prescriptions. Results: The mean NNV over a 25-year time horizon to prevent one case of pneumococcal disease was 6, with NNVs of 854 for IPD, 106 for hospitalized NBP, 25 for outpatient NBP, and 9 for OM, 11 for a course of antibiotic, and 4 for resistant disease. The mean NNV per year decreased over time, reflecting the DOP and increasing indirect effects over time. Conclusions: This study presents a novel approach to NNVs and shows that relatively few vaccinations are required to prevent disease. The decrease in NNV over time highlights the necessity of including DOP and indirect effects in NNV calculations, ensuring a more realistic assessment of a vaccine’s impact. Full article

Unmanned surface vehicles (USVs) course control research constitutes a vital branch of ship motion control studies and serves as a key technology for the development of marine critical equipment. Aiming at the problems of model uncertainties, external marine disturbances, performance optimization, and actuator constraints encountered by the autopilot system, this paper proposes a composite disturbance cancellation optimized control method based on fuzzy reinforcement learning. Firstly, a coupling design of the finite-time disturbance observer and fuzzy logic system is conducted to estimate and reject the composite disturbance composed of internal model uncertainty and ocean disturbances. Secondly, a modified backstepping control technique is employed to design the autopilot controller and construct the error system. Based on the designed performance index function, the fuzzy reinforcement learning is utilized to propose an optimized compensation term for the error system. Meanwhile, to address the actuator saturation issue, an auxiliary system is introduced to modify the error surface, reducing the impact of saturation on the system. Finally, the stability of the autopilot system is proved using the Lyapunov stability theory. Simulation studies conducted on the ocean-going training ship “Yulong” demonstrate the effectiveness of the proposed algorithm. Under the strong and weak ocean conditions designed, this algorithm can ensure that the tracking error converges within 7 s. Full article

Background: Adhesive capsulitis (AC) causes a global limitation of both active and passive range of motion (ROM) in the shoulder, with or without pain, and no specific radiographic findings. Its course is self-limiting and progresses through three or four stages. The diagnosis is primarily clinical, since imaging tests are nonspecific. Treatment options include physical therapy (PT), intra-articular corticosteroid injections, suprascapular nerve block (SSNB), and hydrodilatation (HD). The latter is useful for expanding and reducing inflammation of the joint capsule through the insufflation of saline solution, anesthetics, and corticosteroids. Objectives: To compare whether patients with AC, stratified by phase 1 and 2, who receive high-volume HD as treatment achieve better outcomes in terms of shoulder pain and function compared to patients who receive low-volume HD. To compare whether there are differences in PT times and to determine mean axillary recess (AR) values. Methods: A randomized, parallel-block, triple-blind clinical trial will be conducted in 64 patients with AC in phases 1 and 2, aged 30 to 70 years, with limited active and passive ROM in two planes, and shoulder pain lasting more than 3 months. HD will be administered with volumes of 20 mL or 40 mL, followed by a conventional rehabilitation program. Outcomes will be reviewed at the 1st, 3rd, and 6th months of HD. Variables collected will include Shoulder Pain and Disability Index (SPADI), Visual Analog Scale (VAS), Range of motion (ROM), Lattinen index (LI), AR size, and time to completion of PT. Results: HD has been gaining clinical relevance in interventional rehabilitation as a treatment for AC, although its medium- and long-term efficacy remains a matter of debate. The variability in the volumes used for capsular expansion, with studies ranging from 18 mL to 47 mL, is compounded by the fact that most of these studies do not differentiate between AC stages. This could influence treatment effectiveness. Furthermore, diagnosis remains a challenge since valid and specific diagnostic parameters are lacking. Conclusions: Understanding the differences between HD techniques, considering the influence of certain factors such as the volume used or the stages of AC, as well as improving diagnosis and the coordination of scientific work. This could facilitate the development of protocols for the use of HD in AC. Full article

Objective: This study aimed to evaluate the relationship between the Prognostic Nutritional Index (PNI) and demographic characteristics, presenting complaints, clinical diagnoses, and patient outcomes in patients admitted to the emergency department for gastrointestinal (GI) emergencies. The predictive value of PNI for the clinical course of patients with GI emergencies was investigated. Method: This retrospective cross-sectional study included 583 patients with a diagnosis of GI emergencies in the emergency department of a tertiary university hospital between January 2021 and December 2024. Data such as age, sex, presenting complaints, final diagnosis, and emergency department outcomes (discharge, ward admission, and transfer to intensive care unit) were collected. The PNI value was calculated using serum albumin (g/dL) and total lymphocyte count (/mm³) with the formula PNI = 10 × albumin + 0.005 × lymphocyte. The PNI was calculated based on serum albumin levels and peripheral lymphocyte counts. Results: The mean age of the study group was 63.4 ± 17.4 years, and 52.1% of the patients were female. The number of patients with a PNI value < 38 was significantly higher in the intensive care unit (p < 0.001). PNI values were considerably lower, especially in patients diagnosed with malignancy, cirrhosis, and GI hemorrhage (X² = 71.387; p < 0.001). The PNI was an independent predictor of outcomes in patients with GI emergencies. The mean PNI was significantly higher in discharged patients but significantly lower in patients admitted to the intensive care unit (p < 0.002). The cut-off score for PNI was calculated using the median value, and the cut-off score for PNI was <38. Conclusion: PNI is a powerful biomarker for predicting the clinical severity and prognosis of patients with GI emergencies. Since it can be easily calculated from routine biochemical tests, it can be used as a practical and effective risk stratification tool. The evaluation of PNI, especially for the early detection of critically ill patients at high risk of malnutrition, may contribute to the reduction of morbidity and mortality through the timely initiation of appropriate supportive therapies. Full article

Background: As a disease with a still largely unknown course, post-COVID requires a comprehensive multidimensional perspective and structured monitoring, as offered by the C19-YRSm. There has not yet been a German version of the scale. Methods: After the translation of the COVID-19 Yorkshire Rehabilitation Scale (modified version, C19-YRSm) into German, we conducted an online survey with it between 23 May 2023 and 10 May 2024 for patients with post-COVID condition. Participation took place twice; people received either only the German version or both the German and the original English versions of the scale at one-week intervals. Based on the results, reliability and validity of the German version of the C19-YRSm were extensively tested. Results: Data of 414 participants were analysed, 156 of whom took part twice at least seven days apart. Cronbach’s alpha coefficients of the subscales of the German version ranged from 0.75 to 0.93. In the English version, it ranged from 0.82 to 0.93. All the subscales correlate with each other with p < 0.001. For the overall scale and for three of the four subscales, the intraclass coefficients, as a measure of the agreement between measurement results at two points in time, showed good consistency. Conclusions: This study confirmed the reliability, applicability, and clinical utility of the C19-YRSm, aligning with previous studies. With its multidimensional structure and excellent quality criteria, the C19-YRS is a valuable asset for clinical practice and research. The validated German C19-YRSm holds significant potential to facilitate tailored interventions, destigmatise post-COVID conditions, and enhance patient care in medical contexts. Full article

Network security courses rely on hands-on labs where students configure virtual Linux networks to practice attack and defense. Automated feedback is scarce because no standard exists for exchanging detailed configurations—interfaces, bridging, routing tables, iptables policies—between exercise software and large language models (LLMs) that could serve as tutors. We address this interoperability gap with an exercise-oriented YANG profile that augments the Internet Engineering Task Force (IETF) ietf-network module with a new network-devices module. The profile expresses Linux interface settings, routing, and firewall rules, and tags each node with roles such as linux-server or linux-firewall. Integrated into our LiNeS Cloud platform, it enables LLMs to both parse and generate machine-readable network states. We evaluated the profile on four topologies—from a simple client–server pair to multi-subnet scenarios with dedicated security devices—using ChatGPT-4o, Claude 3.7 Sonnet, and Gemini 2.0 Flash. Across 1050 evaluation tasks covering profile understanding (n = 180), instance analysis (n = 750), and instance generation (n = 120), the three LLMs answered correctly in 1028 cases, yielding an overall accuracy of 97.9%. Even with only minimal follow-up cues (≦3 turns) —rather than handcrafted prompt chains— analysis tasks reached 98.1% accuracy and generation tasks 93.3%. To our knowledge, this is the first exercise-focused YANG profile that simultaneously captures Linux/iptables semantics and is empirically validated across three proprietary LLMs, attaining 97.9% overall task accuracy. These results lay a practical foundation for artificial intelligence (AI)-assisted security labs where real-time feedback and scenario generation must scale beyond human instructor capacity. Full article

Memory is a fundamental neurological function, essential for animal survival. Over the course of vertebrate evolution, elaborations in the forebrain telencephalon create new memory mechanisms, meaning basal vertebrates such as amphibians must have a less sophisticated system of memory acquisition, storage, and retrieval than the well-known hippocampal-based circuitry of mammals. Personality also appears to be a fundamental vertebrate trait and is generally defined as consistent individual behavior over time and across life history stages. In anuran amphibians (frogs), personality studies generally ask whether adult frogs retain the personality of their tadpole stage or whether personality shifts with metamorphosis, an idea behavioral ecologists term adaptive decoupling. Using a multidisciplinary perspective and recognizing there are ~7843 species of frogs, each with some molecular, morphological, physiological, or behavioral feature that makes it unique, we review, clarify, and provide perspective on what we collectively know about memory and personality and their mechanisms in anuran amphibians. We propose four working hypotheses: (1) as tadpoles grow, new telencephalic neurons become integrated into functional networks, producing behaviors that become more sophisticated with age; (2) since carnivores tend to be more bold/aggressive than herbivores, carnivorous anuran adults will be more aggressive than herbivorous tadpoles; (3) each amphibian species, and perhaps life history stage, will have a set point on the Shy–Bold Continuum; and (4) around this set point there will be a range of individual responses. We also suggest that several factors are slowing our understanding of the variety and depth of memory and personality possibilities in anurans. These include the scala natura approach to comparative studies (i.e., the idea that one frog represents all frogs); the assumption that amphibians are no more than simple reflex machines; that study species tend to be chosen more for convenience than taxonomic representation; and that studies are designed to prove or disprove a construct. This latter factor is a particular hindrance because what we are really seeking as scientists is not the confirmation or refutation of ideas, but rather what those ideas are intended to produce, which is understanding. Full article

Cannabis vaping, particularly involving cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC), rapidly delivers highly concentrated cannabinoids to the brain, potentially affecting the hippocampus. This study examined differential expression of long noncoding RNAs (lncRNAs) and mRNAs in the hippocampus after acute exposure to vaporized CBD or THC. Male ICR mice were exposed to vaporized CBD or THC (50 mg, n = 5/group), and hippocampal tissues were collected at 1, 3, and 14 days post-exposure. Total RNA sequencing was conducted on day 1 samples, and selected transcripts were validated using qRT-PCR across multiple time points. CBD led to significant up- or downregulation of L3mbtl1, Wnt7a, and Camk2b at day 1. However, Wnt7a showed gradual recovery at days 3 and 14. In the THC group, Grin2a, Gria3, and Golga2 were significantly upregulated, while Drd1, Drd2, Gnal, and Adcy5 were significantly downregulated at day 1. Time-course analysis showed that Drd2 expression returned to baseline by day 14, whereas Adcy5 remained persistently downregulated through days 3 and 14. In the CBD group, NONMMUT069014.2 was upregulated, while NONMMUT033147.2 and NONMMUT072606.2 were downregulated at day 1; notably, NONMMUT072606.2 showed a transient increase at day 3 before returning to baseline. In the THC group, NONMMUT085523.1 and NONMMUT123548.1 were upregulated, whereas NONMMUT019734.2, NONMMUT057101.2, and NONMMUT004928.2 were downregulated, with most showing gradual recovery by day 14. Correlation analysis revealed that THC-responsive lncRNAs—including NONMMUT004928.2, NONMMUT057101.2, and NONMMUT019734.2—were strongly associated with downregulated mRNAs such as Drd2 and Adcy5. These findings highlight cannabinoid-specific hippocampal transcriptomic responses and suggest potential regulatory roles for lncRNA–mRNA interactions in cannabinoid-induced neural changes. Full article