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Long-term pituitary function following transsphenoidal surgery for non-functional pituitary neuroendocrine tumor with apoplexy: a single-center retrospective analysis
Exploiting FcRn engagement of an albumin-CpG oligonucleotide covalent conjugate for potent TLR9 immune induction
A precise spatiotemporal fusion crop classification framework based on parcels
Abstract The precise extraction of crop type information on agricultural land supports applications such as agricultural information statistics and planning. It is also a crucial foundation for improving agricultural production efficiency and promoting agricultural informatization. In smallholder agricultural regions, such as the southern agricultural areas of China, a significant number of small parcels exist. These small parcels often exhibit deficiencies and discrepancies in feature representation for time series classification of crop types, leading to considerable classification challenges. To achieve more precise crop type differentiation in smallholder agricultural systems, this study designs a parcel-based classification framework, PITT (Parcel-level Integration of Time series and Texture). The PITT framework categorizes small parcels in smallholder systems by area into small parcels and micro parcels, which are then separately used as inputs for time series classification methods and high-resolution texture classification methods. During the process, the time series classification results guide the high-resolution texture classification method. Finally, the results from the texture classification are fused with the time series classification results, achieving more accurate crop classification outcomes. The study focuses on the Jiang area of Zongyang County, Tongling city, Anhui Province. Experimental validations using Pearson correlation coefficients and TWDTW similarity comparisons reveal that larger parcels have time series features that more strongly represent the features of typical samples. Additionally, when the PITT framework was compared with other time series classification models using real labels, the F1 scores for small parcels of approximately 0.1–0.5 hectares increased for rapeseed and wheat, reaching 0.93 and 0.94, respectively. For micro parcels (less than 0.1 ha), the F1 scores improved by at least 4.11% and 17.05%, respectively. This demonstrates the ability to achieve high crop classification performance with minimal labelling in smallholder systems, advancing the informatization of smallholder agriculture.
Ex vivo study of Porcine esophagus magnetic resonance imaging with a compact endoluminal coil
Association between spinopelvic parameters and postoperative urinary retention in thoracolumbar spine surgery: a propensity-matched analysis
Spatiotemporal evolution characteristics and deformation mechanism of Suijiang landslide in Xiangjiaba reservoir, China
Stable and switchable $$\textrm{VO}_2$$-based metasurfaces in the near-infrared region
Association between cesarean delivery and childhood allergic diseases in a longitudinal population-based birth cohort from Japan
Spatiotemporal evolution of agricultural carbon emissions intensity in China and analysis of influencing factors
The impact of environmental factors on public engagement on WeChat in China’s national parks- a socio-cognitive analysis
Association between health literacy and body mass index among Iranian high school students
Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app
Spatiotemporal dynamics and policy impact on farmer suicides in Maharashtra, India
Abstract Maharashtra has long faced a severe agricultural crisis, with a high number of farmer suicides, making it crucial to evaluate how government agricultural policies affect farmer suicides. This study investigated the influence of the Pradhan Mantri Fasal Bima Yojana (PMFBY), a crop insurance scheme, on patterns of farmer suicides across the state. Data for this study is acquired from the “Crime in Maharashtra” and the “Pradhan Mantri Fasal Bima Yojana (PMFBY) scheme” report spanning 2017–2021. This study focuses on analyzing the spatiotemporal trends to identify suicide-prone regions and assess whether the PMFBY has mitigated this distress. Further, this study employs statistical techniques to highlight key factors influencing suicides in these clustered regions. The findings suggest a notable reduction in suicide occurrences following the implementation of PMFBY, especially in high-risk districts. The study highlights that the districts of Aurangabad, Nagpur, and Amravati divisions emerged as significant clusters during the period from 2017 to 2019, and predicted rainfall, insurance units, gender-specific distribution, and agricultural land as positively influencing in mitigating the occurrence of farmer suicides within these clustered regions. Overall, this research underscores a notable reduction in the occurrence of both concentrated and dispersed regions before and after the introduction of the PMFBY. This study offers insights to policymakers for designing more targeted interventions aimed at reducing agrarian distress, improving farmer well-being, and offering valuable insights to improve suicide prevention initiatives in Maharashtra’s districts and more broadly across India.
Effects of ultrasonic modification on physicochemical and structural properties of pomelo peel soluble dietary fiber extracted by alkali
The global, regional, and national brain and CNS cancers burden and trends from 1990 to 2021
Design and synthesis of a novel quinoline thiazolidinedione hybrid as a potential antidiabetic PPARγ modulator
Abstract Peroxisome proliferator-activated receptor γ (PPARγ) remains a critical target for antidiabetic drug development due to its role in glucose and lipid metabolism. However, the adverse effects associated with full agonists of the thiazolidinedione (TZD) class, such as weight gain and hepatotoxicity, limit their clinical utility. Herein, we report the design and synthesis of (Z)-5-benzylidene-3-((2-chloroquinolin-3-yl)methyl)thiazolidine-2,4-dione (compound 7), a novel TZD derivative that functions as a potential PPARγ modulator. Compound 7 reduced blood glucose level (BGL) by 22.33% after 15 days of treatment with a daily single oral dose, demonstrating an antidiabetic effect comparable to TZDs. Additionally, it elevated PPARγ expression to 75% of the activation level induced by Pioglitazone (PIO). Further characterization of its safety profile reveals that compound 7 is safer on the liver compared to PIO, as alanine transaminase (ALT) and aspartate transaminase (AST) levels remained significantly lower (147.4 ± 4.2 IU/L and 229.9 ± 2.7 IU/L, respectively). Moreover, compound 7 exerts a protective effect on hepatic and pancreatic tissues. Computational metabolic studies predict that compound 7 does not produce toxic metabolites or undergo hydrolysis of the TZD ring, contributing to its improved safety. The docking of Compound 7 into the PPARγ ligand-binding domain (LBD) demonstrates a unique binding mode, positioning it centrally within the LBD and interacting with key amino acids critical for selective modulation. These findings emphasize the potential of compound 7 as a selective PPARγ modulator to dissociate insulin-sensitizing effects from adverse side effects, offering a safer alternative to current TZD-based therapies.
Decision based algorithm for circular pythagorean fuzzy framework and advanced petroleum exploration methods
Epidemiological and demographic analysis of liver cancer attributable to modifiable risk factors from 1990 to 2021
Environmental risk assessment of the Nile Delta, Egypt, based on radar interferometry, altimetry, and geodetic measurements
Abstract Egypt is confronted with a number of hazardous environmental incidents, mainly sea level rise (SLR) and land subsidence. The Nile Delta is a low-relief surface that is particularly vulnerable to flooding and SLR, making it important to study inundation scenarios for the region. Potential social and economic consequences of this anticipated sea encroachment were projected utilizing (1) crustal deformation calculations derived from the time series analysis using the Persistent Scatterer Interferometry (PSI) technique based on Least Squares Estimation. (a stack of 191 Sentinel-1 ascending scenes), and eight permanent stations of Global Navigation Satellite System (GNSS); both spanning the period 2014–2019, (2) SLR values using Satellite Altimetry, and (3) a high-resolution digital elevation model (TerraSAR-X/TanDEM-X). The The key findings of this study are summarized as follows; (1) large cities and urban regions adjacent to the two main active branches of the Nile Delta (Rosetta and Damietta) experienced the majority of subsidence rates, (2) the cities of Damietta, Mansoura and Port said (eastern side of the Nile Delta) experienced the maximum rates of subsidence (− 11 ± 0.6, − 8.9 ± 0.7, and − 6.3 ± 0.7 mm/year, respectively), (3) the cities of Shebin El Kom, Damanhour, Tanta, New-Damietta, Kafr El-Sheikh had moderate subsidence rates (− 3.2 ± 0.6, − 2.4 ± 0.7, − 4.2 ± 0.6, − 3.8 ± 0.7, − 3.2 ± 0.7 mm/year, respectively), (4) the Nile Delta subsidence seems to be dominated by anthropogenic reasons such as urbanization, ground water and hydrocarbon extraction, (5) the linear trend of sea level anomaly (SLA) from satellite altimetry data over the period from 1993 to 2019 along the Delta shoreline, the SLR is ~ 3.42 ± 0.5 mm/year, and (6) based on GIS tools and IDW interpolation, wide swaths of the northern Nile Delta would be flooded in the worst-case scenario, which would result in approximately 482 km2 being flooded in fifty years, 2433 km2 in one hundred years, and 3320 km2 in one hundred and fifty years due to the ongoing land subsidence and SLR of 3.4 mm/year.