Search Results (49,157)

Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men worldwide. Although traditionally considered a disease of older men, the incidence of early-onset PCa (diagnosis < 55 years) is steadily rising. Advances in screening and therapy have significantly improved survival, creating a growing cohort of younger survivors for whom post-treatment quality of life—notably reproductive function—is paramount. Curative treatments such as radical prostatectomy, pelvic radiotherapy, androgen-deprivation therapy (ADT), and chemotherapy often cause irreversible infertility via multiple mechanisms, including surgical disruption of the ejaculatory tract, endocrine suppression of spermatogenesis, direct gonadotoxic injury to the testes, and oxidative sperm DNA damage. Despite these risks, fertility preservation is frequently overlooked in pre-treatment counseling, leaving many patients unaware of their options. This narrative review synthesizes current evidence on how PCa therapies impact male fertility, elucidates the molecular and physiological mechanisms of iatrogenic infertility, and evaluates both established and emerging strategies for fertility preservation and restoration. Key interventions covered include sperm cryopreservation, microsurgical testicular sperm extraction (TESE), and assisted reproductive technologies (ART). Psychosocial factors influencing decision-making, novel biomarkers predictive of post-treatment spermatogenic recovery, and long-term offspring outcomes are also examined. The review underscores the urgent need for timely, multidisciplinary fertility consultation as a routine component of PCa care. As PCa increasingly affects men in their reproductive years, proactively integrating preservation into standard oncologic practice should become a standard survivorship priority. Full article

Hyperspectral image classification (HSIC) is a key task in the field of remote sensing, but the complex nature of hyperspectral data poses a serious challenge to traditional methods. Although deep learning significantly improves classification performance through automatic feature extraction, manually designed network architectures suffer from issues such as dependence on expert experience and lack of flexibility. Neural architecture search (NAS) provides new ideas for HSIC through automated network structure optimization. This article systematically reviews the application progress of NAS in HSIC: firstly, the core components of NAS are analyzed, and the characteristics of various methods are compared from three aspects: search space, search strategy, and performance evaluation. Furthermore, the focus is on exploring NAS technology based on convolutional neural networks, covering 1D, 2D, and 3D convolutional architectures and their innovative integration with various technologies, revealing the advantages of NAS in HSIC. However, NAS still faces challenges such as high computing resource requirements and insufficient interpretability. This article systematically reviews the application of NAS in the field of HSIC for the first time, facilitating readers to quickly understand the development process of NAS in HSIC and the advantages and disadvantages of various technologies, proposing possible future research directions. Full article

This paper provides geospatial and correlation analysis of heavy metal distribution in the soil cover of the city of Temirtau and its industrial zones. Based on 25 soil samples taken in 2024, concentrations of nine heavy metals (As, Pb, Zn, Cu, Ni, Co, Mn, Cr, Ba) were determined using X-ray fluorescence analysis. Spatial data interpolation was performed using the Kriging method in the ArcGIS Pro environment. The results showed the presence of localized extreme pollution zones, primarily near the Qarmet JSC metallurgical plant. The most significant exceedances of maximum permissible concentrations (MPC), up to 348× MPC for Cr, 160× MPC for Zn, and 72× MPC for As, were recorded at individual locations. Correlation analysis revealed a moderate positive relationship between several elements, particularly Mn and Cu (r = 0.64). Comparison of the spatial distribution of pollution with population data allowed for the assessment of potential environmental risks. This research emphasizes the need to implement systematic monitoring, sustainable land management practices, ecological maps, and preventive measures to reduce the long-term impact of heavy metals on ecosystems and public health, and to promote environmental sustainability in industrial regions. Full article

White-tailed deer (Odocoileus virginianus) are a cervid species native to the Americas with ecological, social, and economic significance. Managers must consider several factors when working to maintain the health and sustainability of these wild herds, including reproduction, particularly pregnancy and recruitment rates. White-tailed deer have a variable reproductive capacity, with age, health, and habitat influencing this variability. However, it is unknown whether chronic wasting disease (CWD) impacts reproduction and, more specifically, if CWD infection alters a female deer’s probability of pregnancy. Our study addressed this question using data from 9783 female deer culled in northern Illinois between 2003 and 2023 as part of the Illinois Department of Natural Resources’ ongoing CWD management program. Multilevel Bayesian logistic regression was employed to quantify the relationship between pregnancy probability and covariates like maternal age, deer population density, and date of culling. Maternal infection with CWD was found to have no significant effect on pregnancy probability, raising concerns that the equal ability of infected and non-infected females to reproduce could make breeding, which inherently involves close physical contact, an important source of disease transmission between males and females and females and their fawns. The results also identified that female fawns (<1 year old) are sensitive to county-level deer land cover utility (LCU) and deer population density, and that there was no significant difference in how yearlings (1–2 years old) and adult (2+ years old) responded to these variables. Full article

Water-related ecosystem services provide a broad range of benefits, including the mitigation of extreme hydrometeorological events, the provision of water for various uses, the support of tourism, and the provision of cultural services. This study assesses the perceptions and accessibility of these services among communities located near the Alafiarou and Okpara dams in Benin and the Bagré dam in Burkina Faso. The methodology involved designing and implementing a questionnaire in KoboCollect, with trained agents deployed to conduct data collection at each of the three sites. Data analysis indicates that respondents identified biodiversity conservation and the provision of drinking water as the most crucial ecosystem services. Over two-thirds of participants reported observing both positive and negative changes in the services provided by rivers and in socio-economic activities since the construction of the dams. While the majority noted improvements in agriculture, irrigation, water quality, fisheries, and flow rates, other changes included biodiversity loss, a decrease in vegetation cover (notably trees and shrubs), an increase in the population of mosquitoes and other insects, and a decline in fishery resources downstream. Despite these challenges, local communities were strongly willing to participate in initiatives aimed at protecting and restoring river ecosystems and their related services. Full article

This study aims to demonstrate how mathematics, especially calculus concepts, can be expanded to include semi-entities and how these can be applied to sampling activities. Here, the multivalued logic uses pseudo-complemented lattices, instead of Boolean algebras. Truth values can express the intensity of a property: for example, the property of being heavy intensifies as weight increases. They can also express the state-of-the-art knowledge of an individual about a certain thing. To express that a number $x$ approaches $a$ is to say that the statement “$x=b$” is not fully true but approaches the full-true value as $b-a$ approaches zero. This approach generalizes the concept of a limit and the concepts derived from it, such as differentiation and integration. A Monte Carlo algorithm replaces one function with another with finite domain, preferably its finite part, by sampling the domain and calculating its map. The discussion extends to integration over an unbounded interval, integral transforms, and differential equations. This study then covers strategies for producing Monte Carlo estimates of respective problems and determining their crucial truth values. In the discussion, a topic related to axiomatizing set theory is also suggested. Full article

As direct detection methods of Borrelia burgdorferi are limited, serology plays an important role in diagnosing Lyme disease (LD). There are various types of Lyme serological tests with varying diagnostic accuracy, so it is necessary to compare and rank them. The aim of this study is to compare the accuracy of various serological diagnostic methods for LD using network meta-analysis (NMA). We searched the Cochrane Library and PubMed databases for all serological diagnostic accuracy studies published from the discovery of LD until June 2024. After screening, we assessed the quality of the included studies with QUADAS-C and extracted relevant data. We calculated the Q* index of the receiver operating characteristic curve for each diagnostic test. Meta-disc 2.0 and Stata 15.0 were used to perform traditional meta-analysis and NMA with the gold standard (the comprehensive evaluation) as a reference. We then compared the Q* index values between different methods using two-by-two comparisons and ranked them accordingly. A total of 52 studies with 181,032 participants, including 5318 patients with LD, were included. These studies covered 14 diagnostic methods. The results of the NMA suggest that modified two-tiered testing (MTTT), C6 enzyme immunoassay (EIA), and standard two-tiered testing (STTT) rank in the top three among the 14 methods in terms of Q* index, with MTTT being the highest, followed by C6 EIA and STTT. MTTT and C6 EIA have higher overall diagnostic performance, and their accuracy is not inferior to that of the widely used STTT (PROSPERO CRD42022378326). Full article

This study presents the validation of the Player Personality and Dynamics Scale (PPDS), designed to identify player profiles in educational gamification contexts with narrative elements. Through a sample of 635 participants, a questionnaire was developed and applied, covering sociodemographic data, lifestyle habits, gaming practices, and a classification system of 40 items on a six-point Likert scale. The results of the factorial analysis confirm a structure of five factors: Toxic Profile, Joker Profile, Tryhard Profile, Aesthetic Profile, and Coacher Profile, with high fit and reliability indices (RMSEA = 0.06; CFI = 0.95; TLI = 0.91). The resulting classification enables the design of personalized gamified experiences that enhance learning and interaction in the classroom, highlighting the importance of understanding players’ motivations to better adapt educational dynamics. Applying this scale fosters meaningful learning through the creation of narratives tailored to students’ individual preferences. Full article

This paper describes creating a new course for engineering technology students at Southeast Missouri State University that focuses on application-based mathematics instruction through laboratory experiments. The course structure comprises beginning the semester with pre-calculus and trigonometry and progressing to calculus and differential equations. The topics are covered from the engineering and engineering technology applications standpoint, with examples from electrical and mechanical systems. A hands-on, problem-based learning approach is selected for teaching the application of mathematics to engineering technology students. This entails students being presented with real-world problems, solving them using mathematical concepts, and then verifying results by using software tools. Specifically, twelve laboratory exercises using Scilab have been incorporated into the course. For engineering students, using engineering examples improves both their ability to understand the material’s relevance and their understanding of mathematical techniques that may be applied to real-world problems that they will likely encounter in their future jobs. Full article

The Tibetan Plateau (TP) is experiencing higher warming rates than elsewhere, which may affect regional vegetation growth. Particularly on the Eastern Tibetan Plateau (ETP), where the topography is diverse and rich in biodiversity, it is necessary to clarify the drivers of climate and topography on vegetation cover. In this research, we selected the Shaluli Mountains (SLLM) in the ETP as the study area, monitored the spatial and temporal dynamics of the regional vegetation cover using remote sensing methods, and quantified the drivers of vegetation change using Geodetector (GD). The results showed a decreasing trend in annual precipitation (PRE) (−2.4054 mm/year) and the Palmer Drought Severity Index (PDSI) (−0.1813/year) in the SLLM. Annual maximum temperature (TMX) on the spatial and temporal scales showed an overall increasing trend, and the regional climate tended to become warmer and drier. Since 2000, fractional vegetation cover (FVC) has shown a fluctuating upward trend, with an average value of 0.6710, and FVC has spatially shown a pattern of “low in the middle and high in the surroundings”. The areas with non-significant increases (p > 0.05) and significant increases (p < 0.05) in FVC accounted for 46.03% and 5.76% of the SLLM. Altitude (q = 0.3517) and TMX (q = 0.3158) were the main drivers of FVC changes. As altitude and TMX increased, FVC showed a trend of increasing and then decreasing. The results of this study help us to clarify the influence of climate and topography on the vegetation ecosystem of the ETP and provide a scientific basis for regional biodiversity conservation and sustainable development. Full article

As key components of the climate system, clouds exert a significant influence on the Earth’s radiation budget and hydrological cycle. However, studies focusing on cloud properties over Central Asia are still limited, and the impacts of cloud variability on regional temperature and precipitation remain poorly understood. This study uses reanalysis and multi-source remote sensing datasets to investigate the spatiotemporal characteristics of clouds and their influence on regional climate. The cloud cover increases from the southwest to the northeast, with mid and low-level clouds predominating in high-altitude regions. All clouds have shown a declining trend during 1981–2020. According to satellite data, the sharpest decline in total cloud cover occurs in summer, while reanalysis data show a more significant reduction in spring. In addition, cloud cover changes influence the local climate through radiative forcing mechanisms. Specifically, the weakening of shortwave reflective cooling and the enhancement of longwave heating of clouds collectively exacerbate surface warming. Meanwhile, precipitation is positively correlated with cloud cover, and its spatial distribution aligns with the cloud water path. The cloud phase composition in Central Asia is dominated by liquid water, accounting for over 40%, a microphysical characteristic that further impacts the regional hydrological cycle. Full article

The intensification of anthropogenic pressures, particularly those related to agriculture driven by increasing demands for food and cash crops, generates negative environmental externalities. Assessing these externalities is essential to better identify and implement measures that promote the environmental sustainability of rural landscapes. This study aims to develop a multi-criteria assessment method of the negative environmental externalities of rural landscapes in the northern Benin agricultural basin, based on satellite-derived data. Starting from a 12-class land cover map produced through satellite image classification, the evaluation was conducted in three steps. First, the 12 land cover classes were reclassified into Human Disturbance Coefficients (HDCs) via a weighted sum model multi-criteria analysis based on nine criteria related to the negative environmental externalities of anthropogenic activities. Second, the HDC classes were spatially aggregated using a regular grid of 1 km² landscape cells to produce the Landscape Environmental Sustainability Index (LESI). Finally, various discretization methods were applied to the LESI for cartographic representation, enhancing spatial interpretation. Results indicate that most areas exhibit moderate environmental externalities (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low environmental externalities (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include, among others, refining the evaluation criteria and introducing variable criteria weighting schemes depending on land cover or region. Full article

Chickpea is a highly weed-prone crop with limited herbicide options and high labor demands, raising the following question: Can fall-planted legume–cereal cover crops (CCs) improve soil properties while reducing herbicide use and manual weeding pressure? To explore this, we evaluated the effect of fall-planted winter rye (WR) alone in 2021 and mixed with hairy vetch (HV) in 2022 and 2023 at Randolph farm in Petersburg, Virginia. The objectives were two-fold: (a) to examine the effect of CCs on soil properties using monthly growth dynamics and biomass harvested from fifteen 0.25 m²-quadrants and (b) to evaluate the efficiency of five termination methods: (1) green manure (GM); (2) GM plus pre-emergence herbicide (GMH); (3) burn (BOH); (4) crimp mulch (CRM); and (5) mow-mulch (MW) in suppressing weeds in chickpea fields. Weed distribution, particularly nutsedge, was patchy and dominant on the eastern side. Growth dynamics followed an exponential growth rate in fall 2022 (R² ≥ 0.994, p < 0.0002) and a three-parameter sigmoidal curve in 2023 (R² ≥ 0.972, p < 0.0047). Biomass averaged 55.8 and 96.9 t/ha for 2022 and 2023, respectively. GMH consistently outperformed GM in weed suppression, though GM was not significantly different from no-till systems by the season’s end. Kabuli-type chickpeas under GMH had significantly higher yields than desi types. Pooled data fitted well to a three-parametric logistic curve, predicting half-time to 50% weed coverage at 35 (MM), 38 (CRM), 40 (BOH), 46 (GM), and 53 (GMH) days. Relapses of CCs were consistent in no-till systems, especially BOH and MW. Although soil properties improved, CCs alone did not significantly suppress weed. Full article

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article

The application of satellite-derived sea surface temperature in coastal regions is critical for resolving the dynamics of frontal features and coastal upwelling. Here, we examine and compare sea surface temperature (SST) gradients derived from two satellite products, the Multi-Scale Ultra-High Resolution SST Product (MUR, 0.01° grid scale) and the Operational SST and Ice Analysis (OSTIA, 0.05° grid scale), available through the Group for High Resolution SST (GHRSST). Both products show similar seasonal variability, with maxima occurring in the summer time frame. Additionally, both products show an increasing trend of SST gradients near the coast. However, differences exist between the two products (maximum gradient intensities were around 0.11 and 0.06 °C/km for OSTIA and MUR, respectively). The potential contributions of both cloud cover and the collocation of the MUR SST onto the OSTIA SST grid product to these differences were examined. Spectra and coherences were examined at two specific latitudes along the coast where upwelling can occur. A major conclusion is that future work needs to focus on cloud cover and its impact on the derivation of SST in coastal regions. Future comparisons also need to apply collocation methodologies that maintain, as much as possible, the spatial variability of the high-resolution product. Full article