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Research and application of bolter miner airborne drilling rig

Kaempferia parviflora extract and its methoxyflavones as potential anti-Alzheimer assessing in vitro, integrated computational approach, and in vivo impact on behaviour in scopolamine-induced amnesic mice

Alzheimer’s disease (AD), a growing global challenge, lacks effective preventive and therapeutic strategies. This study explored the promising potential of the Kaempferia parviflora (KP) and its methoxyflavones (MFs) against the disease. We evaluated KP extract and its five MFs for antioxidant capacity, cholinesterase inhibition (AChE, and BChE), amyloid plaque (Aβ) reduction, neuroprotection, and memory improvement in a mouse model. HPLC quantified the five MFs in KP extract, with 5,7-dimethoxyflavone (F1) being the most abundant. 5,7,4′-Trimethoxyflavone (F3) and 5-hydroxy-3,7-dimethoxyflavone (F4) exhibited the strongest AChE and BChE inhibitory activities, respectively. MFs hindered Aβ1-42 aggregation and destabilized fibrils, with F3 showing the potent anti-aggregation and the strongest fibril destabilization. They also protected SH-SY5Y cells from Aβ1-42-induced damage. Notably, F3 combined anti-cholinesterase and anti-Aβ activities, suggesting its potential as a multi-target agent. KP extract ameliorated scopolamine-induced memory deficits in mice, suggesting its potential for cognitive improvement. These findings revealed that KP can be a promising candidate for herbal medicine development against AD. Its multi-target MFs offered a unique advantage by targeting multiple AD pathways. KP may have a great potential to modify the disease and overcome the challenge of drug development as cognitive enhancing herbal medicine.

Identification of biomarkers and potential drug targets for esophageal cancer: a Mendelian randomization study

The impact of digital transformation on open innovation performance: The intermediary role of digital innovation dynamic capability

In the context of digital economy, both the process and boundaries of open innovation performance are changing, and digital transformation has become an important factor for enterprises to realize open innovation performance in a complex and changing environment. This study based on the dynamic capability theory, etc. to explore how digital transformation drives the open innovation performance of enterprises and analyze its impact effects as well as the internal mechanism of action. In this paper, A-share listed companies in China’s Shanghai and Shenzhen cities from 2008–2022 are selected as research samples for empirical tests, and the research results show that digital transformation significantly stimulates enterprises’ open innovation performance, which still holds true after relying on a series of robustness tests. The study on the mechanism of action shows that digital transformation mainly enhances the dynamic capability of digital innovation, and ultimately promotes the open innovation performance of enterprises. And environmental dynamics play a positive moderating role. Heterogeneity analysis shows that the incentive effect of digital transformation on open innovation performance is more obvious in enterprises with high-tech and non-state ownership. Based on the new perspective of digital innovation dynamic capability, this study further reveals the effect and mechanism of digital transformation on open innovation performance, and provides theoretical basis and decision-making reference for enterprises to utilize the opportunity of digitalization to achieve open innovation performance results.

Identification of diagnostic and prognostic genetic alterations in uveal melanoma using RNA sequencing

Correction: Comparison between melatonin versus melatonin and photobiomodulation versus photobiomodulation in the treatment of Alopecia X in German Spitz dogs: Clinical, randomized, double-blind, parallel, non-inferiority protocol

Time series analysis of Sentinel 1 A SAR data to retrieve annual rice area maps and long-term dynamics of start of season

Abstract Rice is a vital staple crop globally, and accurate estimation of rice area was crucial for effective agricultural management and food security. Synthetic Aperture Radar (SAR) data has emerged as a valuable remote sensing tool for rice area estimation due to its ability to penetrate cloud cover and capture backscattered signals from rice fields. The backscatter signature of rice showed a minimum dB value at agronomic flooding indicating the Start of Season (SoS). The parameters viz., the minimum values of −22.03 to −17.69 dB at the start of season, maximum value of −16.10 to −14.20 dB at the peak of season coinciding with heading and corresponding mean increase of 5.07 dB during growing stages were utilized for developing rule-based classification system. Rice area was estimated over the Cauvery Delta Zone of Tamil Nadu, India for the past six years during samba (August–January) season from 2017 to 2023 using Sentinel 1 A Synthetic Aperture Radar satellite data. Rice area maps were generated for the region utilizing parameterization with a classification accuracy of 88.5 to 94.5 per cent with a kappa score of 0.77 to 0.87 during the study period. The total classified rice area during samba season in the Cauvery Delta Zone was 508,581 ha, 456,601 ha, 506,844 ha, 511,714 ha, 524,723 ha and 476,586 ha for the years 2017–18 to 2022–23, respectively. The Start of Season (SoS) maps for samba season revealed that the major planting periods for rice were between the second fortnight of September to first fortnight of November in all the years except 2018 when early planting happened during the first fortnight of September due to favorable weather conditions and assured water supply. Near real-time information on rice area, start of season, and progress of planting derived using SAR satellite data will facilitate the development of decision support systems for sustaining the productivity of rice-based ecosystems.

Correction: Novel diagnostic potential of miR-1 in patients with acute heart failure

Geometrical constraint change determines organized collective migration of follower cells

Correction: The performance of a lateral flow SARS-CoV-2 antibody assay and semi-autonomous SARS-CoV-2 antisense and sense RNA fluorescence in situ hybridization assay in a prospective cohort pilot study within a Dutch military population

Enhancing structural integrity of magnesium with hydroxyapatite and tantalum reinforcement

Deep learning-based prediction of atrial fibrillation from polar transformed time-frequency electrocardiogram

Portable and wearable electrocardiogram (ECG) devices are increasingly utilized in healthcare for monitoring heart rhythms and detecting cardiac arrhythmias or other heart conditions. The integration of ECG signal visualization with AI-based abnormality detection empowers users to independently and confidently assess their physiological signals. In this study, we investigated a novel method for visualizing ECG signals using polar transformations of short-time Fourier transform (STFT) spectrograms and evaluated the performance of deep convolutional neural networks (CNNs) in predicting atrial fibrillation from these polar transformed spectrograms. The ECG data, which are available from the PhysioNet/CinC Challenge 2017, were categorized into four classes: normal sinus rhythm, atrial fibrillation, other rhythms, and noise. Preprocessing steps included ECG signal filtering, STFT-based spectrogram generation, and reverse polar transformation to generate final polar spectrogram images. These images were used as inputs for deep CNN models, where three pre-trained deep CNNs were used for comparisons. The results demonstrated that deep learning-based predictions using polar transformed spectrograms were comparable to existing methods. Furthermore, the polar transformed images offer a compact and intuitive representation of rhythm characteristics in ECG recordings, highlighting their potential for wearable applications.

A study on the causes of viewers’ non-continuous following intention in tourism live streaming

The Influence of Political Trust on Attitude towards Public Participation in China

This study aims to examine the impact of political trust on non-institutionalized public participation in China. Using comprehensive survey data from the Chinese General Social Survey (CGSS), this study presents two key research findings. First, this study finds a non-linear relationship between political trust and attitude towards non-institutionalized public participation. Generally, political trust has a negative effect on attitude towards non-institutionalized public participation. However, political trust exerts a positive impact on attitude towards non-institutionalized public participation under notably high political trust circumstances. Second, this study identifies the moderating role of social injustice experience in the relationship between political trust and the attitude towards non-institutionalized public participation. When high expectations associated with high trust are broken by a social injustice experience, this high political trust circumstance is more likely to result in dissatisfaction and positive attitude towards non-institutionalized public participation. This study contributes to deepening the understanding of the pattern of how political trust influences non-institutionalized public participation attitude in China. Valuable implications are also presented.

Training healthcare professionals in hypnosis-derived communication to mitigate procedural pain in children

Podocyte specific knockout of the natriuretic peptide clearance receptor is podocyte protective in focal segmental glomerulosclerosis

Natriuretic peptides (NPs) bind to glomerular podocytes and attenuate glomerular injury. The beneficial effects of NPs are negatively regulated by the NP clearance receptor (NPRC), which is highly expressed in podocytes. To determine if inhibiting NPRC is podocyte protective, we examined the effects of deleting NPRC in both cultured podocytes and in vivo. We found that: 1.Both atrial NP and C-type NP inhibit podocyte apoptosis in cultured podocytes, but these podocyte protective effects are significantly attenuated in cells expressing NPRC, and 2. Atrial NP was significantly more effective than CNP at inhibiting the apoptotic response. Consistent with the protective actions of NPs, podocyte specific knockout of NPRC reduced albuminuria, glomerular sclerosis and tubulointerstitial inflammation in a mouse model of focal segmental glomerulosclerosis. These beneficial actions were associated with: 1. Decreased expression of the myofibroblast marker alpha-smooth muscle actin, 2. Reduced expression of the extracellular matrix proteins collagen 4-alpha-1 and fibronectin, and 3. Preserved expression of the podocyte proteins nephrin and podocin. Inhibiting NP clearance may be a useful therapeutic approach to treat glomerular diseases.

Appraising histone H4 lysine 5 lactylation as a novel biomarker in breast cancer

Genome-wide association analysis of Septoria tritici blotch for adult plant resistance in elite bread wheat (Triticum aestivum L) genotypes

Septoria tritici blotch (STB) is a predominant foliar disease of wheat, caused by the pathogen Zymoseptoria tritici. This disease can lead to substantial yield losses warranting control by using expensive fungicides. One effective method of STB control is the utilization of resistant wheat varieties. In this particular study, a panel comprising of 186 bread wheat genotypes was assessed for their adult plant resistance (APR) to STB. Field trials were conducted across five environments in Ethiopia during the 2022 and 2023 growing seasons under natural infestation conditions. The association panel was genotyped using 20K single nucleotide polymorphism (SNP) markers. To determine the relationship between genetic markers and STB resistance, a mixed linear model (MLM) analysis was performed using the statgen GWAS R software package. Heritability estimates for STB resistance ranged from 0.39 to 0.95, underscoring the genetic variability and the potential for selection. The study identified 52 marker-trait associations (MTAs) for STB resistance at maturity (SDSM) and 62 MTAs at heading (SDSH). Chromosome 5A contains a high concentration of MTAs that confer resistance to STB, hosting multiple significant MTAs, including four consistently associated markers (‘Kukri_c10033_724’, ‘RAC875_rep_c116420_103’, ‘TG0019’, and ‘RAC875_c30566_230’). Additionally, chromosomes 1B, 2B, 5B, and 7A were found to harbor important MTAs, contributing to resistance across various environments. Notably, two QTLs, qtSTB23 (5A) and qtSTB38 (7B), exhibited stability across multiple environments, making them robust candidates for breeding programs. Furthermore, novel resistance loci on chromosome 2A were discovered, offering new opportunities for enhancing resistance. Therefore, these findings provide an opportunity for improving STB resistance through gene stacking using marker-assisted selection (MAS).

3D volumetric tomography of clouds using machine learning for climate analysis

Abstract The prediction of climate has been a long-standing problem in contemporary science. One of the reasons stems from a gap in the ability to obtain 3D mapping of clouds, especially shallow scattered clouds. These clouds are strongly affected by mixing processes with their surroundings, rendering their internal volumetric structure highly heterogeneous. These heterogeneous clouds modulate the incoming solar energy and the outgoing long-wave radiation, thereby having a crucial role in the climate system. However, their 3D internal mapping is a major challenge. Here, we combine machine learning and space engineering to enable, for the first time, 3D mapping of scatterers in clouds. We employ ten nano-satellites in formation to simultaneously view the same clouds per scene from different angles and recover the 3D internal structure of shallow scattered clouds, from which we derive statistics, including uncertainty. We demonstrate this on real-world data. The results provide key features for predicting precipitation and renewable energy.

Global approaches to older abuse research in institutional care settings: A systematic review

Background Over the last two decades, abuse of older adults in institutional settings has been underestimated due to challenges in defining and responding to the issue. This systematic review aims to analyze empirical studies on measuring abuse of older people residing in a long-term care facility, specifically staff-to-resident abuse. Methods Following PRISMA guidelines, we searched 10 databases from January 2005 till June 2024. Inclusion criteria encompassed World Health Organization-defined abuse types (physical, psychological, financial, sexual and neglect, intentional or unintentional), reported by staff, residents, family, or public registries, with methodological critical assessment. Findings In the last 18 years, 22 studies from eight counties examined of staff-to-resident abuse, with significant heterogeneity in definitions, reporting sources, and measurement tools. Quality of studies varied, lacking consistency. Relatives and staff typically report highest abuse rates, while residents report fewer incidents, even with fewer incidents of observed abuse. Registries tend to capture extreme cases, resulting in lower reported prevalence rates, particularly of physical or sexual abuse and neglect. Physical abuse was the most reported, with 81 different descriptors identified and varying recall periods. Staff witnessing abuse ranged from 44% over four weeks to as low as 1.4% over 12 months, posing challenges for data interpretation. Conclusion These variations in study methodologies impacted the ability to synthesise the findings making it difficult to estimate a global prevalence rate of aged care abuse. From the analysis, we develop an Aged Care Abuse Research Checklist (ACARC) as a first step towards achieving a global standardized, evidence-based methodology for this field. Doing so will normalize processes within organizations and the community, allowing early interventions to change practices, reduce the risk of recurrence and improve resident quality of care and workplace cultures. Registration Number PROSPERO CRD42018055484 .