Browse Articles

Distinct terpene metabolite blends serve as core drivers of consumer aroma preferences in fir Christmas tree species

Late emergence of pathological oscillatory activity in the retina of the Retinitis pigmentosa model RCS (Royal College of Surgeons) rat

Retinitis pigmentosa (RP) is a leading cause of blindness. The best studied models of human RP are the rd1 and rd10 mouse and the RCS rat (Royal College of Surgeons). In many models after degeneration of the photoreceptors, a pathological rhythmic activity of the retina as well as lowered efficiency of electrical stimulation were observed. In rd10 retina, both events were shown to be intimately linked. Surprisingly, to our knowledge no retinal oscillations have been reported in RCS retina. As oscillations might interfere with the performance of therapeutic approaches to restore vision, e.g., retinal prostheses, it is important to know, whether they are a common feature of retinal degeneration. Electrical activity was recorded in retinae of 3–19 months (M3-19) old RCS rats in vitro using planar and penetrating multi-electrode-arrays. Short deflections in the local field potential resembling those observed in oscillations in rd1 and rd10 retinae were only sporadically found in M3 RCS retinae. Oscillations at appr. 2 Hz occurred more often and were more pronounced the older the animals were. Yet, even at M18-19 oscillatory periods were short and separated by long periods of non-oscillatory activity. In summary, in advanced stages of degeneration, RCS retinae display oscillations similar to rd1 and rd10 retinae. However, in RCS retina oscillatory periods are shorter than in mouse models and may, therefore, have escaped detection in earlier studies. These results together with results observed in non-rodent models suggest that pathological rhythmic activity is a common feature in RP models.

MIMO radar DOA element position error correction method based on overlapping reference element matrix reconstruction

Abstract Aiming at the problem of the degradation of angle estimation performance of the MUSIC algorithm due to the position error of the array element, this paper proposes a MIMO radar DOA array element position error correction method based on overlapping reference array element matrix reconstruction. This method uses overlapping virtual array elements as references, corrects the array element position error by phase difference, and constructs an error compensation matrix to eliminate the virtual array position error. At the same time, the nuclear norm optimization is introduced to reconstruct the Toeplitz structure to reduce the influence of system noise and array element disturbance on angle estimation, and the MUSIC algorithm is used to achieve accurate angle estimation. Simulation results show that the angle estimation error of this method is 0.2 $$^{\circ }$$ under a signal-to-noise ratio of 20 dB. The actual traffic scene verification shows that this method effectively improves the angle resolution capability of the MUSIC algorithm and meets the requirements of radar accuracy for traffic applications.

Development and validation of a dynamic light scattering-based method for viral quantification: A straightforward protocol for a demanding task

This study aimed to develop a reliable, rapid method for viral particle quantification using Dynamic Light Scattering (DLS) as an alternative to traditional virological techniques. Conventional methods, such as plaque assays, though widely recognized, are time-intensive and depend on the observation of cytopathic effects, which can be subjective. In this study, Influenza A virus (IAV) supernatants were quantified using DLS and subsequently compared with titers determined by plaque assays and TCID50. DLS measures the fluctuations in light scattering caused by particles in Brownian motion, allowing direct, non-destructive measurement of viral concentration within minutes. Results indicated a strong correlation between the DLS-derived viral titers and those obtained from plaque assays (R2 = 0.9967) and TCID50 (R2 = 0.9984), demonstrating DLS potential as a complementary or alternative method for rapid quantification. The technique non-reliance on cell viability and ability to measure intact viral particles enhance its applicability for assays where infectivity is not the primary concern. Additionally, DLS facilitated the detection of complete viral particles, which is advantageous for vaccine development and antiviral testing. The protocol’s reproducibility across various dilutions, coupled with minimal sample preparation requirements, underscores DLS as a feasible quantification method adaptable for different viral strains. Limitations, including the inability to distinguish between infectious and non-infectious particles, suggest that while DLS serves as a valuable initial quantification tool, further infectivity assessments may be required for comprehensive viral characterization.

Author Correction: Single-cell transcriptomics analysis reveals dynamic changes and prognostic signature in tumor microenvironment of PDAC

Certificateless aggregate signcryption scheme with multi-ciphertext equality test for the internet of vehicles

The Internet of Vehicles (IoV) facilitates connectivity among vehicles, roadside units, and smart terminals, enabling the evolution of traditional traffic networks into intelligent transport systems. The IoV has an open communication character, which enables various applications and services. However, this also exposes it to the risk of message tampering or the leaking of private data in the communication process. Such vulnerabilities may lead to security issues. At present, there are many solutions to solve the above problems, but most of them have great computational and communication overhead. Multi-ciphertext equality test can compare the equality between two ciphertexts without decryption, which avoids the user ’s repeated decryption of the same ciphertext to a certain extent. However, it still has a large computational overhead. For the above problems, we propose a certificateless aggregate signcryption scheme for the IoV that supports multi-ciphertext equality testing. The proposed scheme addresses the key escrow and certificate management issues inherent in identity-based systems by employing a certificateless signcryption mechanism. To prevent redundant retrieval of ciphertexts that correspond to identical plaintexts, a multi-ciphertext equivalence test feature has been incorporated. Furthermore, the aggregation capability of this scheme significantly enhances the efficiency of signing multiple vehicle data entries. By leveraging the computational complexities associated with the Diffie-Hellman problem and the discrete logarithm problem, it is demonstrated that the scheme maintains confidentiality and unforgeability within the random oracle model. When compared to similar schemes, this approach exhibits reduced computational overhead while providing superior security features.

Exploring disparities in self-reported knowledge about neurotechnology

Abstract With advances in neurotechnology and its use for medical treatment and beyond, it is important to understand the public’s awareness of such technologies and potential disparities in self-reported knowledge, because knowledge is known to influence the acceptance and use of new technologies. This study utilizes a large sample (N = 10,339) to depict the existence and extent of self-reported knowledge of these neurotechnologies and to examine knowledge disparities between respondents. Results show that most respondents self-reported at least some knowledge of ultrasound and electroencephalography (EEG), but limited knowledge of BCIs. Prior use, being a healthcare professional, and health literacy increased the odds of self-reporting some knowledge. Also gender and age disparities exist. These findings may help identify uninformed groups in society and enhance information campaigns.

Research on equipment fault diagnosis model based on gan and inverse PINN: Solutions for data imbalance and rare faults

In the field of medical imaging equipment, fault diagnosis plays a vital role in guaranteeing stable operation and prolonging service life. Traditional diagnostic approaches, though, are confronted with issues like intricate fault modes, as well as scarce and imbalanced data. This paper puts forward a fault diagnosis model integrating digital twin technology and Inverse Physics - Informed Neural Networks (Inverse PINN).The practical significance of this research lies in its potential to revolutionize the engineering aspects of medical imaging equipment management. By constructing a physical model of equipment operation and leveraging inverse PINN to deal with imbalanced datasets, the model can accurately identify and predict potential faults. This not only optimizes the full lifecycle management of the equipment but also has the potential to reduce maintenance costs, improve equipment availability, and enhance the overall efficiency of medical imaging services.Experimental results show that the proposed model outperforms in fault detection and prediction for medical imaging equipment, especially making breakthroughs in data generation and fault detection accuracy. Finally, the paper discusses the model’s limitations and future development directions.

Mysterious seismoacoustic signals of eastern Helwan quarry blasts 2022

Abstract Mysterious seismoacoustic events were reported at the beginning of 2022 near Helwan Cairo, Egypt. The majority of these events were recorded by the Egyptian National Seismic Network. The source characteristics of the events were unknown. In May 2022, a temporary infrasound array station was established with a small aperture of 450 m in Helwan. Throughout the 6-month monitoring period, we employed a recursive short-term average/long-term average trigger method across all sensors, leading to the detection of the impulsive seismoacoustic events. Infrasound propagation models, coupled with F-K analysis, further confirmed the locations and directions of the recorded events, providing robust data that could be correlated with planet satellite images of the azimuth directions detected via the infrasound array analyses. The mysterious signals were identified as originating from a major construction project in Egypt: the high-speed railway train corridor. Our study demonstrates the effectiveness of integrating seismic sensors with infrasound arrays for enhanced source characterisation. The combination of these tools enabled precise discrimination of quarry blasts in eastern Helwan. Additionally, our findings suggest that inexpensive sensors can be a cost-effective solution for monitoring higher-frequency events.

Development and preliminary validation of the GebStart-tool for advising nulliparous women in early labour

Objectives Nulliparous women in early labour are unsure when to go to hospital. The aim of this study was to develop and preliminary validate a tool for advising for or against hospital admission. Methods We developed the preliminary long version of the GebStart-tool with 32 items based on focus group discussions and a scoping review. It was applied in a multicentre study with n = 394 women during their contact with the hospital. Because of the formative and complex character of the GebStart-tool, factor analysis was not appropriate. Instead, items were subdivided deductively into the domains ‘Physical symptoms’, ‘Emotional state’, Self-management’ and ‘Resources’. Distribution of response options, adjusted Cox regressions with time intervals describing care needs as outcomes and adjusted multinomial regression with the outcome ‘Care decision’ were used to reduce items and for preliminary validation. Results The reduced GebStart-tool contained 15 items and cutoff points at 22 and 33 points. The total score of the instrument was significantly associated with all time intervals describing care needs (duration between completion of the tool and hospital admission (HR = 1.08, 95% CI [1.05–1.10], p < 0.001), onset of active labour (HR = 1.06, 95% CI [1.04–1.08], p < 0.001), first use of medical pain management (HR = 1.08, 95% CI [1.06–1.11], p < 0.001), first use of alternative pain management (HR = 1.08, 95% CI [1.05–1.10], p < 0.001)). However, a higher total score of the reduced GebStart-tool was not significantly associated with a reduced risk for the decision ‘Stay at home’ (RR = 0.98, 95% CI [0.94–1.02], p = 0.421), but with a significantly higher risk for the decision ‘Hospital admission’ (RR = 1.13, 95% CI [1.05–1.22], p = 0.001) compared to ‘Keep in contact’. Conclusion We developed a practical instrument with 15 items based on scientific evidence. Further research of the GebStart-tool in larger samples is necessary. Moreover, the use in clinical practice accompanied by implementation research and translation into other languages should be envisaged.

Influencing factors affecting health-promoting lifestyles in patients with primary liver cancer: a latent profile analysis

Abstract To explore the potential profile categories of health promoting lifestyle in patients with primary liver cancer and analyze the influencing factors of different categories. Primary liver cancer patients who visited a tertiary hospital in Nanjing from October 2021 to May 2023 were selected as the survey subjects. A general information questionnaire and a health-promotion lifestyle scalewere used for data collection. Potential profile analysis was conducted to classify the health-promoting lifestyles of patients with primary liver cancer, and the influencing factors of different categories were explored through univariate analysis and multiple logistic regression analysis. Clinical registration trial number: ChiCTR2400079823 (12/01/2024). A total of 328 questionnaires were collected, of which 324 were valid, resulting in a validity rate of 98.78%.The latent profile analysis indicates that the health promotion lifestyle of primary liver cancer patients can be categorized into three potential profiles: “low health promotion neglect” (n = 128, 39.5%), “moderate health promotion balance” (n = 87, 26.9%), and “high health promotion lone hero” (n = 109, 33.6%). The influencing factors include educational level, per capita monthly income, disease-related economic burden, postoperative duration, work status, sleep disorders, and whether neoadjuvant therapy was administered preoperatively (P < 0.05). Conclusion: There is significant heterogeneity in the health promotion lifestyles of patients with primary liver cancer. Nursing staff should develop targeted health promotion strategies, focusing on improving patients’ health behaviors to enhance their quality of life.

Deep learning-enhanced signal detection for communication systems

Traditional communication signal detection heavily relies on manually designed features, making it difficult to fully characterize the essential characteristics of the signal, resulting in limited detection accuracy. Based on this, the study innovatively combines Multiple Input Multiple Output (MIMO) with orthogonal frequency division multiplexing technology to construct a data-driven detection system. The system adopts a Multi-DNN method with a dual-DNN cascade structure and mixed activation function design to optimize the channel estimation and signal detection coordination process of the MIMO part. At the same time, a DCNet decoder based on a convolutional neural network batch normalization mechanism is designed to suppress inter-subcarrier interference in OFDM systems effectively. The results showed that on the simulation training set, the accuracy of the research model was 93.8%, the symbol error rate was 17.6%, the throughput was 81.3%, and the modulation error rate was 0.004%. On the simulation test set, its accuracy, symbol error rate, throughput, and modulation error rate were 90.7%, 18.1%, 81.2%, and 0.006%. In both 2.4 GHz and 5 GHz WiFi signals, the signal detection accuracy of the research model reached 91.5% and 91.6%, with false detection rates of 1.9% and 1.5%, and missed detection rates of 1.6% and 4.2%. In resource consumption assessment, the detection speed of this model reached 120 signals/s, with an average latency of 50 ms. The model loading time was only 2.4 s, and the CPU usage was as low as 25%, with moderate memory usage. Overall, the research model has achieved significant results in improving detection accuracy, optimizing real-time performance, and reducing resource consumption. It has broad application prospects in the field of communication signal detection.

Research on digital matching methods integrating user intent and patent technology characteristics

Neuro-symbolic procedural semantics for explainable visual dialogue

This paper introduces a novel approach to visual dialogue that is based on neuro-symbolic procedural semantics. The approach builds further on earlier work on procedural semantics for visual question answering and expands it with neuro-symbolic mechanisms that handle the challenges that are inherent to dialogue, in particular the incremental nature of the information that is conveyed. Concretely, we introduce (i) the use of a conversation memory as a data structure that explicitly and incrementally represents the information that is expressed during the subsequent turns of a dialogue, and (ii) the design of a neuro-symbolic procedural semantic representation that is grounded in both visual input and the conversation memory. We validate the methodology using the MNIST Dialog and CLEVR-Dialog benchmark challenges and achieve a question-level accuracy of 99.8% and 99.2% respectively. The methodology presented in this paper contributes to the growing body of research in artificial intelligence that tackles tasks that involve both low-level perception and high-level reasoning using a combination of neural and symbolic techniques. It thereby leads the way towards the development of conversational agents that will be able to hold more explainable, natural and coherent conversations with their human interlocutors.

Intergenerational effects of cafeteria diet-induced obesity on metabolic and reproductive outcome in rats

Women’s experiences of and satisfaction with childbirth: Development and validation of a measurement scale for low- and middle-income countries

Background Measuring person-centered maternity care outcomes typically consists of two types of measures: experiences of care and satisfaction with care. There are limited validated measurement tools for these measures, particularly in low- and middle-income countries (LMICs). The QUALI-DEC study aims to improve decision-making around caesarean section. We describe development of the QUALI-DEC Study Birth Experience and Satisfaction (QD-BES) scale, and scale validation in Argentina, Burkina Faso, Thailand, and Viet Nam. Methods We used a three-phase scale development and validation approach: 1) item development, 2) scale development, and 3) scale evaluation. We systematically identified existing tools, and assessed them using the QUALI-DEC theory of change, study context, and psychometric qualities. We proposed the 10-item QD-BES scale to balance feasibility, theoretical coverage, and comprehensiveness. We conducted a baseline exit survey with post-partum women in 32 hospitals in 4 countries. We conducted exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). Results 3127 women participated, most were multiparous (61.0%), without previous caesarean section (77.2%), and preferred vaginal birth (72.8%) despite high rates of caesarean section (39.4%). EFA identified three dimensions: emotional satisfaction (3-items), support and respect by providers (4-items), and communication with providers (3-items), with high loading coefficients (0.5–0.97). CFA confirmed the three-dimension scale, with good model fit (CFI and IFI: 0.95, Cronbach’s alpha: 0.70–0.90). Criterion validity was assessed by exploring characteristics of women, obstetric histories, and birth experiences. Conclusions We present psychometric validation of a scale measuring women’s satisfaction with care and experiences of childbirth care, using a systematic approach to development and validation in four LMICs. The 10-item QD-BES-scale is short, easily-administered, valid, and reliable. The QD-BES-scale is useful to contribute to the generation of new knowledge about quality of maternity care in LMICs, as well as help to meet the major challenge of implementing and measuring respectful care at scale.

A qualitative analysis of perceived barriers and motivators to physical activity in children from ethnic minority groups and white British children

Physical inactivity is a problem worldwide despite the well-known health benefits of physical activity participation. Children from ethnic minority groups report some of the lowest levels of physical activity. In trying to address this, the voices and perspectives of children, especially children from ethnic minority groups, are often overlooked. The primary purpose of this study was to identify and compare the perceived barriers and motivators to physical activity of children from ethnic minority groups and white British children in a UK city. A semi-structured interview on physical activity participation was conducted in English with 20 children aged 8–11 years old (9 from a range of different ethnic minority groups and 11 white British children). The data were coded and analysed via inductive thematic analysis using Nvivo-12. A total of three barriers and eight motivators were identified for both groups. The barriers to physical activity participation were structure of sports; inadequate resources; and children’s social circle. The motivators were self-autonomy; self-confidence; enjoyment of physical activity; positive mental health and wellbeing; social circle; structure of sports; institutional motivators; and the introduction of more gender-balanced organised sports. Differences between groups were however noted amongst the subthemes, particularly in relation to barriers. For children from ethnic minority groups, barrier subthemes comprised parental barriers and obligations to siblings; lack of gender-balanced team sports; lack of variety of sports and lack of sporting equipment; and negative feedback from teammates. For white British children, barrier subthemes were lack of adequate play time in school and lack of financial resources. Such barriers may underpin differences in physical activity participation and should be considered in intervention design.

Image key information processing using convolutional neural network and rotational invariant-hierarchical max pooling algorithm

In the information age, the effectiveness of image processing determines the quality of a large number of image analysis tasks. A fusion algorithm-based processing technique was proposed to process key image information. A feature dictionary was introduced as the matching template model and the standard model. The convolutional layer sampling feature block optimization was carried out using image segmentation ideas. The optimal threshold of the image to be segmented was obtained using the least squares method. The feature extraction layer was structurally supplemented and expressed at multiple scales in a two-dimensional linear graph. In the method training loss test, the research method achieved a loss value that dropped to near 0 after 32 iterations when training in low-contrast images. When testing the processing time of image key information, the research method achieved a processing time of 183ms when the image contained 6 features. When conducting scale ratio change testing, the research method achieved the highest image processing accuracy at a scale ratio of 1.0, which was 95.7%. This indicated that the research method had higher accuracy in processing key image information and higher efficiency. This research method can provide certain technical support for image recognition and feature extraction.

How people with type 2 diabetes in Kuwait manage the condition: A thematic analysis and conceptual framework

Background Type 2 diabetes is a growing non-communicable disease burden across the Eastern Mediterranean region, particularly in Kuwait. The methods that patients adopt to manage the condition following diagnosis is poorly understood. This study aimed to explore these methods using a qualitative approach, and develop a conceptual framework characterising the phases that patients transition through. Methods This was a qualitative, thematic evaluation of a grounded theory methodology investigating the methods that patients with type 2 diabetes employ to manage their condition. Qualitative coding of semi-structured interview transcripts with 22 patients, over three phases: initial, focused and theoretical, enabled categorical themes and phases to be identified. The findings were synthesised into a conceptual framework that represented the transitional journey. Results The development of conceptual framework revealed five transitional categorical phases that characterised the journey: (1) experiencing unusual symptoms; (2) accepting the diagnosis; (3) adopting management strategies; (4) adherence and relapse; and (5) adaptation. Conclusion In Kuwait, patients with type 2 diabetes appear to transition through relatively predictable stages in learning to manage their condition. Clinical consideration of this transition could improve the quality of diabetes care provision for these patients.

Non-guided, mobile, CBT-I-based sleep intervention in War-torn Ukraine: A feasibility study

Background War conditions can severely impact sleep and mental health at the population level, especially in the conflicts of such tremendous scale as in Ukraine. The aim of this research was to study whether a mobile, unguided Cognitive Behavioral Therapy-based Intervention for sleep problems, Sleep2, is feasible, acceptable, and potentially able to reduce mental health/sleep problems symptoms. Methods A single-arm, open-label, uncontrolled pre-post evaluation study was conducted with 487 registered participants: 283 started, 160 (56.55%) finished, out of which 95 completed without an ambulatory heart rate (HR) sensor and 65 with. Assessments were conducted through online questionnaires and objective measurements via HR sensors. Besides feasibility and acceptability, outcome measures included symptoms in several mental health domains alongside self-reported and objectively reported sleep parameter. Results Engagement with the Sleep2 app was high, achieving an 80.72% compliance rate, alongside high levels of feasibility and acceptance. Participants reported significant pre-post reductions in the severity of symptoms, with sleep problems decreasing by 22.60% (Cohen’s d = 0.53), insomnia by 35.08% (d = 0.69), fear of sleep by 32.43% (d = 0.25), anxiety by 27.72% (d = 0.48), depression by 28.67% (d = 0.52), PTSD by 32.41% (d = 0.51), somatic symptoms by 24.52% (d = 0.51), and perceived stress by 17.90% (d = 0.39). Objective sleep measurements showed a significant reduction in sleep onset latency only. Conclusion The ‘Sleep2Ukraine’ program demonstrated high feasibility and acceptance, with significant improvements in subjective sleep and mental health measures among participants. However, given the study’s uncontrolled design and reliance on self-selected participants, these findings should be considered preliminary. Randomized controlled trials are needed to establish efficacy. Nonetheless, the results highlight the potential of culturally adapted, scalable, mobile-based CBT-I interventions to address sleep and mental health needs in war-affected populations.