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RETRACTED ARTICLE: A multi-filter deep transfer learning framework for image-based autism spectrum disorder detection

Liquids in a glass recover a graceful shape even after being shaken

A dual-branch model combining convolution and vision transformer for crop disease classification

Computer vision holds tremendous potential in crop disease classification, but the complex texture and shape characteristics of crop diseases make disease classification challenging. To address these issues, this paper proposes a dual-branch model for crop disease classification, which combines Convolutional Neural Network (CNN) with Vision Transformer (ViT). Here, the convolutional branch is utilized to capture the local features while the Transformer branch is utilized to handle global features. A learnable parameter is used to achieve a linear weighted fusion of these two types of features. An Aggregated Local Perceptive Feed Forward Layer (ALP-FFN) is introduced to enhance the model’s representation capability by introducing locality into the Transformer encoder. Furthermore, this paper constructs a lightweight Transformer block using ALP-FFN and a linear self-attention mechanism to reduce the model’s parameters and computational cost. The proposed model achieves an exceptional classification accuracy of 99.71% on the PlantVillage dataset with only 4.9M parameters and 0.62G FLOPs, surpassing the state-of-the-art TNT-S model (accuracy: 99.11%, parameters: 23.31M, FLOPs: 4.85G) by 0.6%. On the Potato Leaf dataset, the model attains 98.78% classification accuracy, outperforming the advanced ResNet-18 model (accuracy: 98.05%, parameters: 11.18M, FLOPs: 1.82G) by 0.73%. The model proposed in this paper effectively combines the advantages of CNN and ViT while maintaining a lightweight design, providing an effective method for the precise identification of crop diseases.

Urinary iodine levels and thyroid disorder prevalence in the adult population of China: a large-scale population-based cross-sectional study

Effect on preoperative anxiety of a personalized three-dimensional kidney model prior to nephron-sparing surgery for renal tumor: study protocol for a randomized controlled trial (Rein 3D Print-Anxiety – UroCCR 113)

Background The announcement of a diagnosis can be a source of anxiety for patients. Managing this anxiety is a major challenge, in terms of quality of life but also for the use of anxiolytic and analgesic therapies. The use of 3D modeling technology in partial nephrectomy surgery has proved its worth as a surgical aid but it could also help patients to manage their own care, by reducing their anxiety and increasing their understanding of the disease and its treatment. We aim to test this hypothesis with a prospective multicenter trial. Methods R3DP-A (Rein 3D – Anxiety) is an unblinded, multicenter, randomized, prospective, superiority-controlled trial. Participants are patients with kidney tumors treated by robot-assisted partial laparoscopic nephrectomy. The 234 patients (78x3 groups) from 6 French centers will undergo a pre-operative consultation dedicated to a personalized explanation of the surgical management and its risks. They will be randomized into three (1:1:1) groups corresponding to three types of support for consultation: use of a virtual 3D model of the kidney and its tumor; a printed 3D model; or the standard information sheet from the French Association of Urology (control group). Several self-questionnaires will be sent by the UroConnect® application and completed at different times during the study. The primary endpoint will be pre-operative anxiety (STAI-state questionnaire completed the day before surgery D-1). Secondary endpoints will be changes in anxiety levels between the pre-operative and post-operative consultations (between inclusion and D15 post-op), changes in health literacy and quality of life (HLSEU-Q16 and EQ-5D-5L questionnaires at inclusion and D15), feelings of understanding of the disease and its treatment at pre-operative period (Wake questionnaire at D-1), and consultation times. Discussion We aim to highlight a benefit of using a personalized 3D model on the anxiety level of patients undergoing partial nephrectomy surgery, as well as on their level of understanding of their pathology and its surgical treatment. The use of these models could be incorporated into current practice to improve patient experience throughout care.

The reconstruction method for static exterior model of digital twin railway station based on mobile vehicle

Aspirin prevents metastasis by limiting platelet TXA2 suppression of T cell immunity

Abstract Metastasis is the spread of cancer cells from primary tumours to distant organs and is the cause of 90% of cancer deaths globally1,2. Metastasizing cancer cells are uniquely vulnerable to immune attack, as they are initially deprived of the immunosuppressive microenvironment found within established tumours3. There is interest in therapeutically exploiting this immune vulnerability to prevent recurrence in patients with early cancer at risk of metastasis. Here we show that inhibitors of cyclooxygenase 1 (COX-1), including aspirin, enhance immunity to cancer metastasis by releasing T cells from suppression by platelet-derived thromboxane A2 (TXA2). TXA2 acts on T cells to trigger an immunosuppressive pathway that is dependent on the guanine exchange factor ARHGEF1, suppressing T cell receptor-driven kinase signalling, proliferation and effector functions. T cell-specific conditional deletion of Arhgef1 in mice increases T cell activation at the metastatic site, provoking immune-mediated rejection of lung and liver metastases. Consequently, restricting the availability of TXA2 using aspirin, selective COX-1 inhibitors or platelet-specific deletion of COX-1 reduces the rate of metastasis in a manner that is dependent on T cell-intrinsic expression of ARHGEF1 and signalling by TXA2 in vivo. These findings reveal a novel immunosuppressive pathway that limits T cell immunity to cancer metastasis, providing mechanistic insights into the anti-metastatic activity of aspirin and paving the way for more effective anti-metastatic immunotherapies.

Development of a functioning metric for the ageing population using data from the survey of health, ageing and retirement in Europe (SHARE)

Background Beyond mortality and morbidity, health statistics would benefit from reporting information on functioning, the third health indicator. The objective of this article is to use data from the Swiss Survey of Health, Ageing and Retirement in Europe (SHARE) to exemplarily create a psychometrically sound and valid metric of functioning for the ageing population living in Switzerland. Methods Partial Credit Model (PCM) analysis, including analysis of targeting, item fit, local item dependencies (LID), unidimensionality, and differential item functioning (DIF), tested the psychometric properties of selected items. The DIF analysis investigated the invariance of item difficulties across sex and age groups, country, language, and the assessment Wave. Results Data from 34,092 individuals aged 50 years and older was selected across assessment Waves of SHARE. The analysis showed that a functioning metric can be constructed with a total of 33 functioning items. Items showed LID and multidimensionality initially, which was solved with a testlet approach. Aggregation into testlets resulted in good fit, unidimensionality, no LID, and no DIF for sex, country, language, and the assessment Wave. Some DIF is found for age groups. The analysis also showed that the selected items target higher levels of problems in functioning than observed in the study population. Conclusions A functioning metric can be constructed from selected functioning items of SHARE. The metric provides a sound interval-scaled score that can be used for longitudinal analyses of ageing in Switzerland and neighboring countries or as an indicator of the level of functioning in an ageing population.

Computational analysis of zoanthamine alkaloids from Zoanthus sp. as potential DKK1 and GSK-3β inhibitors for osteoporosis therapy via Wnt signaling

Mystery of medieval manuscripts revealed by ancient DNA

Patent value prediction in biomedical textiles: A method based on a fusion of machine learning models

Patent value prediction is essential for technology innovation management. This study aims to enhance technology innovation management in the field of biomedical textiles by processing complex biomedical patent information to improve the accuracy of predicting patent values. A patent value grading prediction method based on a fusion of machine learning models is proposed, utilizing 113,428 biomedical textile patents as the research sample. The method combines BERT (Bidirectional Encoder Representations from Transformers) and a stacking strategy to classify and predict the value class of biomedical textile patents using both textual information and structured patent features. We implemented this method for patent value prediction in biomedical textiles, leading to the development of BioTexVal—the first dedicated patent value prediction model for this domain. BioTexVal’s innovation lies in employing a stacking strategy that integrates multiple machine learning models to enhance predictive accuracy while leveraging unstructured data during training. Results have shown that this approach significantly outperforms previous predictive methods. Validated on 113,428 biomedical textile patents spanning from 2003 to 2023, BioTexVal achieved an accuracy of 88.38%. This study uses average annual forward citations as an indicator for distinguishing patent value grades. The method may require adjustments based on data characteristics when applied to other research fields to ensure its effectiveness.

Differential unfolded protein response regulation in KRAS silencing sensitive and innately resistant colorectal cancer cells

Bedside clinical prediction tool for mortality in critically ill children

Introduction Mortality rates among critically ill pediatric patients remain a persistent challenge. It is imperative to identify patients at higher risk to effectively allocate appropriate resources. Our study aimed to develop a prediction score based on clinical parameters and hemogram to predict pediatric intensive care unit (PICU) mortality. Methods We conducted a retrospective study to develop a clinical prediction score using data from children aged 1 month to 18 years admitted for at least 24 hours to the PICU at Chiang Mai University between January 2018 and December 2022. PICU mortality was defined as death within 28 days of admission. The score was developed using multivariable logistic regression and assessed for calibration and discrimination. Results There were 29 deaths in 330 children (8.8%). Our model for predicting 28-day ICU mortality uses four key predictors: male gender, use of vasoactive drugs, red blood cell distribution width (RDW) ≥15.9%, and platelet distribution width (PDW), categorized as follows: <10% (0 points), 10–14.9% (2 points), and ≥15% (4 points). Scores range from 0 to 8, with a cutoff value of 5 to differentiate low-risk (<5) from high-risk (≥5) groups. The tool demonstrates excellent performance with an AuROC curve of 0.86 (95% CI: 0.80–0.91, p<0.001) showing excellent discrimination and calibration, 82.8% sensitivity, and 73.1% specificity, respectively. Conclusions The score, developed from clinical data and hemogram, demonstrated potential in predicting ICU mortality among critically ill children. However, further studies are necessary to externally validate the score before it can be confidentially implemented in clinical practices.

Symbiotic interactions on middle Cambrian echinoderms reveal the oldest parasitism on deuterostomes

How Trump 2.0 is slashing NIH-backed research — in charts

Mapping zero-dose children in Kenya – A spatial analysis and examination of the socio-demographic and media exposure determinants

Despite vaccines’ proven effectiveness in preventing childhood diseases, there remains a significant population of unvaccinated children, often referred to as zero-dose children. This study examines the factors contributing to the prevalence of zero-dose children in Kenya using data from the 2022 Kenya Demographic and Health Survey (KDHS). We included all children aged 1–35 months who had not received any vaccination during the survey. In the analysis, we utilized logistic regression to explore the determinants of zero-dose status, including the mothers’ media exposure. We also employed model-based geostatistical methods to determine the fine-scale spatial distribution of zero-dose children in Kenya. Our findings reveal the disparities in the prevalence of zero-dose children, with specific regions such as Tana River, Marsabit, Turkana, and Isiolo in the north exhibiting distinct hotspots. Children aged 12–23 (aOR = 0.41; 95% CI: 0.24, 0.68) and 24–35 (aOR = 0.33; 95% CI: 0.18, 0.57) had lower odds of being zero dose than those 1–11 months of age. Compared to women who had no antenatal visits, women who attended four and above visits had 88% lower odds of having a zero-dose child (aOR=0.12;95% CI 0.05–0.27; p<0.001), while those who attended three visits had 91% lower odds of having a zero-dose child (aOR=0.09; 95% CI 0.04–0.19; p<0.001). Additional factors associated with zero-dose status included the education level, wealth index, religion, place of delivery, travel time to the nearest facility, listening to the radio, mother’s mobile phone ownership, and mother’s phone use for financial transactions. The results emphasize the unique contextual factors associated with zero-dose status, underscoring the need for tailoring public health interventions to specific socio-cultural and economic environments. While findings should be interpreted with care due to the complexity of relationships between variables, they highlight the necessity for targeted immunization initiatives that cater to the distinct needs of various regions and demographic groups. We recommend implementing enhanced education and awareness campaigns, addressing socio-economic barriers, and considering caregiver socio-behavioral factors as crucial to improving immunization coverage in Kenya.

Rational design of synthetic antimicrobial peptides based on the Escherichia coli ShoB toxin

Abstract Antibiotic resistance is an escalating global concern, necessitating the development of novel antibiotics with unique mechanisms of action, and preferably also with a lowered propensity for resistance development. Type-I Toxin-Antitoxin (TA) systems that are ubiquitous in bacterial genomes consist of a genetic toxin element encoding a hydrophobic peptide and an antitoxin element producing an sRNA that inhibits the toxin translation. Although the biological roles of these membrane-associated toxins remain incompletely understood, their inherent lethality upon overexpression suggests a potential as antimicrobial agents. In this study, we explore the ShoB toxin from the shoB-ohsC TA system in Escherichia coli (E. coli) as a basis for designing synthetic antimicrobial peptides for exogenous delivery. We demonstrate that ShoB-derived peptides can retain antimicrobial efficacy when modified into shorter, cationic analogs with enhanced solubility. Our most promising hits exhibit rapid bactericidal action and frequency of resistance within E. coli cultures indicate a limited tendency for resistance development. These findings highlight that type-I TA systems constitute a novel source of potential peptide-based antibiotics, thereby offering an alternative largely unexplored strategy to combat antibiotic-resistant bacterial infections.

Single Joint Hybrid Assistive Limb (HAL-SJ) robotic exoskeleton therapy in improving functional outcomes among workers with wrist fractures: Study protocol for a randomized controlled trial

Introduction Robotic technologies have been developed for motor rehabilitation and such robots have shown favourable results when compared with equivalent doses of usual clinical therapy. Recently, robotic interventions have been suggested to be applied in orthopaedic rehabilitation with upper extremity disorders, especially those related to hand and wrist. This study aims to determine the effectiveness of combined conventional therapy and HAL-SJ robotic therapy in restoring the wrist functionality following the fractures as compared to the standard conventional therapy solely. Methods and analysis Workers with wrist fractures will be randomized in two groups, i.e., the control group (conventional therapy) and intervention group (combination of conventional therapy and robotic HAL-SJ intervention). All participants will receive 5-day/week therapy sessions for four weeks. Primary outcomes of the Disabilities of the Arm, Shoulder and Hand (DASH) outcome measure and secondary outcomes of range of motion, grip and pinch strengths, fine and gross hand dexterity as well as pain and the Lam Assessment of Stages of Employment Readiness (LASER) will be assessed at baseline assessment and upon completion of the therapy program after 4 weeks. Data from the baseline and post intervention outcome measures will be analysed using a Repeated Measures ANOVA to compare the therapy effectiveness of both control and intervention groups. Results Participants recruitment and data collection are in progress Discussion Wrist fractures can produce some residual disability and pain that may impact the functionality of a person. The application of robotic technology in facilitating upper limb movement and functional recovery training is extensive and shows positive outcomes in the field of neurorehabilitation. However, there is a lacking of published evidence about the effectiveness of robotic intervention in orthopaedic rehabilitation, especially in the field of hand therapy. Conclusion Participants recruitment and data collection are still ongoing Clinical trial registration This trial is registered with the Australian New Zealand Clinical Trials Registry (registration number: ACTRN12622000413729).

Movement patterns of invasive red swamp crayfish vary with sex and environmental factors

Abstract Invasive species disproportionately invade freshwater ecosystems, threatening biodiversity. Defining when, where, and why aquatic invasive species move can help inform management strategies, yet the movement ecology of some of the most pervasive invasive species remains unknown. Red swamp crayfish (Procambarus clarkii; RSC) are the most widespread invasive crayfish and negatively affect ecosystems worldwide. We employed high-dimensional acoustic telemetry to investigate the movement patterns of 24 individual RSC across three months in an invaded water body. We assessed the effects of various extrinsic factors, such as time of day, temperature, precipitation, and proximity to the water’s edge, along with intrinsic factors, such as sex, reproductive form, and size, at different scales, including movement steps, range distribution, and behavioral states. We found that movement patterns across all scales were overwhelmingly driven by sex and reproductive form. Furthermore, RSC showed increased overall activity at night and near the water’s edge. By establishing baseline movement patterns and identifying key contributing factors, these findings provide a foundation for the development of adaptive management strategies for controlling invasive RSC populations.

Long-term neuropsychiatric and neuropsychological impact of the pandemic in Italian COVID-19 family clusters, including children and parents

Aim This study investigated the long-term neuropsychiatric and neuropsychological impact of COVID-19 on children and their parents in households with COVID-19 exposure. Methods A prospective cohort study was conducted on 46 families attending the COVID-19 Follow-up Clinic at the Department for Women’s and Children’s Health, Padua (Italy) from December 2021 to November 2022. Self-perceived stress-related, emotional-behavioral, and post-traumatic stress (PTSD-related) symptoms were assessed in both children and parents. Children with underlying neuropsychiatric conditions were excluded from the study. Results A total of 81 parents (median age = 38 years [IQR: 43–48], females = 44 [54.3%]), and 77 children (median age = 8 years [IQR: 5–11], females = 33 [42.9%]) participated in the study. Overall,125 (79%) and 33 (21%) participants were classified as COVID-19 cases and non-COVID-19 cases, respectively. The time interval between the COVID-19 family outbreak and the neuropsychiatric and psychological assessment was ≤4 months (median=3 months [IQR=0]) for 89 (56.3%) participants and >4 months for 69 (43.8%) (median=11.5 months [IQR=5–12]) participants. A total of 136 (86.1%) participants reported stress-related symptoms, with emotional stress being the most common. A positive correlation was observed between self-perceived stress-related symptoms in children and their parents within the same family (r=0.53, p=0.0005). Among children aged 6–18 years, 16 (37.2%) had clinical scores for internalizing symptoms at the Child Behavior Checklist (CBCL), while none children aged 1.5–5 years showed clinical score for internalizing symptoms. Similarly, total difficulty scores at the Strengths and Difficulties Questionnaire (SDQ 4–17) and assessment of PTSD-related symptoms through the Trauma Symptom Checklist for Young Children (TSCYC) questionnaire were within non-clinical cut-offs in 45 (84.9%) and 43 (75.4%) children aged 3–12 years, respectively. The Trauma Symptom Checklist for Children (TSCC) resulted in the non-clinical cut-off for 36 (92.3%) children aged 8–18 years. While a higher prevalence of self-perceived stress-related symptoms was found in COVID-19 cases compared to non-COVID-19 cases (p=.01), no differences were observed for emotional-behavioral and PTSD-related symptoms between the two groups. Conclusions This study documented the impact of the COVID-19 pandemic on Italian children and their family’s stress levels. Further research is needed to confirm our findings and explore the long-term effects of the pandemic on families.