Explore the vast range of titles available.

Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain.

There is some evidence supporting that surveys conducted 12–18 months after a disaster can provide accurate assessments of people’s disaster responses during disasters. Studies suggest that people have good memories of events that are personally relevant to them and that there appear to be reasonable justifications for taking post-event survey data at face value. Nonetheless, according to the Protective Action Decision Model, people’s disaster response activities include behavioral and emotional responses. Since these two types of responses are different in nature, it is unclear whether people have good memory recollection of both types of responses. Thus, it is important to obtain additional evidence to test survey respondents’ memory recollection over time. To do this, this study collected the 2013 Colorado flood household response data 7 months and 14 months after the event. Box’s homogeneity test is used to test the equivalence of covariance matrices. The results indicate that survey respondents’ behavioral responses follow similar patterns between two survey samples, but the emotional responses do not. This finding suggests that survey studies are able to acquire accurate disaster behavioral response data 14 months after a disaster; however, emotional response is considered ephemeral data.

The great success of the Tropical Rainfall Measuring Mission (TRMM) and its successor Global Precipitation Measurement (GPM) has accelerated the development of global high-resolution satellite-based precipitation products (SPP). However, the quantitative accuracy of SPPs has to be evaluated before using these datasets in water resource applications. This study evaluates the following GPM-era and TRMM-era SPPs based on two years (2014–2015) of reference daily precipitation data from rain gauge networks in ten mountainous regions: Integrated Multi-SatellitE Retrievals for GPM (IMERG, version 05B and version 06B), National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center Morphing Method (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Multi-Source Weighted-Ensemble Precipitation (MSWEP), which represents a global precipitation data-blending product. The evaluation is performed at daily and annual temporal scales, and at 0.1 deg grid resolution. It is shown that GSMaPV07 surpass the performance of IMERGV06B Final for almost all regions in terms of systematic and random error metrics. The new orographic rainfall classification in the GSMaPV07 algorithm is able to improve the detection of orographic rainfall, the rainfall amounts, and error metrics. Moreover, IMERGV05B showed significantly better performance, capturing the lighter and heavier precipitation values compared to IMERGV06B for almost all regions due to changes conducted to the morphing, where motion vectors are derived using total column water vapor for IMERGV06B.

Mutual gaze enables people to share attention and increase engagement during social interactions through intentional and implicit messages. Although previous studies have explored gaze behaviors and neural mechanisms underlying in-person eye contact, the growing prevalence of remote communication has raised questions about how to establish mutual gaze remotely and how the brains of interacting individuals synchronize. To address these questions, we conducted a study using eye trackers to create a pseudo-mutual gaze channel that mirrors the gazes of each interacting dyad on their respective remote screens. To demonstrate fluctuations in coupling across brains, we incorporated electroencephalographic hyperscanning techniques to simultaneously record the brain activity of interacting dyads engaged in a joint attention task in player-observer, collaborative, and competitive modes. Our results indicated that mutual gaze could improve the efficiency of joint attention activities among remote partners. Moreover, by employing the phase locking value, we could estimate interbrain synchrony (IBS) and observe low-frequency couplings in the frontal and temporal regions that varied based on the interaction mode. While dyadic gender composition significantly affected gaze patterns, it did not impact the IBS. These results provide insight into the neurological mechanisms underlying remote interaction through the pseudo-mutual gaze channel and have significant implications for developing effective online communication environments.

An extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using aminimum of 6 years (within the period of 2000–13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cévennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.25° spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP). SBR products are categorized into those that include gauge adjustment versus unadjusted. Results show that performance of SBR is highly dependent on the rainfall variability. Many SBR products usually underestimate wet season and overestimate dry season precipitation. The performance of gauge adjustment to the SBR products varies by region and depends greatly on the representativeness of the rain gauge network.

Dairy farming is the most important economic activity in animal husbandry. Mastitis is the most common disease in dairy cattle and has a significant impact on milk quality and yield. The natural extract allicin, which is the main active ingredient of the sulfur-containing organic compounds in garlic, has anti-inflammatory, anticancer, antioxidant, and antibacterial properties; however, the specific mechanism underlying its effect on mastitis in dairy cows needs to be determined. Therefore, in this study, whether allicin can reduce lipopolysaccharide (LPS)-induced inflammation in the mammary epithelium of dairy cows was investigated. A cellular model of mammary inflammation was established by pretreating bovine mammary epithelial cells (MAC-T) with 10 µg/mL LPS, and the cultures were then treated with varying concentrations of allicin (0, 1, 2.5, 5, and 7.5 µM) added to the culture medium. MAC-T cells were examined using RT–qPCR and Western blotting to determine the effect of allicin. Subsequently, the level of phosphorylated nuclear factor kappa-B (NF-κB) was measured to further explore the mechanism underlying the effect of allicin on bovine mammary epithelial cell inflammation. Treatment with 2.5 µM allicin considerably decreased the LPS-induced increase in the levels of the inflammatory cytokines interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor-α (TNF-α) and inhibited activation of the NOD-like receptor protein 3 (NLRP3) inflammasome in cow mammary epithelial cells. Further research revealed that allicin also inhibited the phosphorylation of inhibitors of nuclear factor kappa-B-α (IκB-α) and NF-κB p65. In mice, LPS-induced mastitis was also ameliorated by allicin. Therefore, we hypothesize that allicin alleviated LPS-induced inflammation in the mammary epithelial cells of cows probably by affecting the TLR4/NF-κB signaling pathway. Allicin will likely become an alternative to antibiotics for the treatment of mastitis in cows.

Seven new phenol derivatives, subversins A–E (1–5), subversic acid A (6) and epi-wortmannine G (7); one new natural product, 4-hydroxy-7-methoxyphthalide (8); and five known compounds (9–13) were isolated from the fungus Aspergillus subversicolor CYH-17 collected from the Haima cold seep. The structures and absolute configurations of these compounds were determined via NMR, MS, optical rotation, electronic circular dichroism (ECD) calculation, X-ray diffraction analysis and comparison with the literature. Compounds 2 and 5 were two pairs of enantiomers. All compounds were tested for their α-glucosidase and acetylcholinesterase (AChE) inhibitory activity, antioxidant activity and antibacterial activity, but no obvious activity was observed among these studied compounds.

•The present study investigated the brain-to-brain connectivity during a peer collaboration task in young child dyads.•A reduction in connectivity was observed in the dyad group with ASD in the beta oscillation over the right parietal to left temporal coupling between subjects.•Increased full-band connectivity over the right-to-right temporal region following the task was observed in the TD-TD group, but not in the TD-ASD group.•Significant correlations were identified between ASD symptom rating scores and the selected significant interbrain connectivity index. Young children with autism spectrum disorder (ASD) traits frequently encounter difficulties in peer interaction. Assessing peer interaction performance is crucial but challenging within the clinical diagnostic paradigm of ASD. Hyperscanning, which simultaneously monitors brain activity in multiple individuals, has become a popular tool for assessing social interaction's neural features. The present study aims to investigate the brain-to-brain connectivity between child-dyads engaged in a game-like collaborative peer interaction task via the hyperscanning electroencephalogram (EEG) approach. The final sample comprised 66 young children: 18 child dyads with typical development (TD), TD-TD, and 15 with ASD traits matched to TD, TD-ASD. The study indicated a depressed level of connectivity in the dyad group with ASD as the responder, with a notable decrease observed in the beta oscillation over the right parietal to left temporal coupling between subjects. A pattern that differed from that observed in the TD-TD group was identified with regard to full-band connectivity over the right-to-right temporal region. It was observed that the TD-TD group exhibited enhanced connectivity following the completion of the task, which was not the case for the TD-ASD group. Significant correlations were observed between scores on the ASD symptom rating scale and the selected significant interbrain connectivity index. The application of a hyperscanning EEG paradigm demonstrated that the participating children with autistic traits exhibited an attenuated and apparently distinct alteration pattern of interbrain connectivity in comparison to a control group. These findings highlight the value of physiologically based measures in informing etiological and interventional studies in neuropsychiatry.

Background The timing to start passive or active range of motion (ROM) after arthroscopic rotator cuff repair remains unclear. This systematic review and meta-analysis evaluated early versus delayed passive and active ROM protocols following arthroscopic rotator cuff repair. The aim of this study is to systematically review the literature on the outcomes of early active/passive versus delayed active/passive postoperative arthroscopic rotator cuff repair rehabilitation protocols. Methods A systematic review and meta-analysis of randomized controlled trials (RCTs) published up to April 2022 comparing early motion (EM) versus delayed motion (DM) rehabilitation protocols after arthroscopic rotator cuff repair for partial and full-thickness tear was conducted. The primary outcome was range of motion (anterior flexion, external rotation, internal rotation, abduction) and the secondary outcomes were Constant-Murley score (CMS), Simple Shoulder Test Score (SST score) and Visual Analogue Scale (VAS). Results Thirteen RCTs with 1,082 patients were included in this study (7 RCTs for early passive motion (EPM) vs. delayed passive motion (DPM) and 7 RCTs for early active motion (EAM) vs. delayed active motion (DAM). Anterior flexion (1.40, 95% confidence interval (CI), 0.55–2.25) and abduction (2.73, 95%CI, 0.74–4.71) were higher in the EPM group compared to DPM. Similarly, EAM showed superiority in anterior flexion (1.57, 95%CI, 0.62–2.52) and external rotation (1.59, 95%CI, 0.36–2.82), compared to DAM. There was no difference between EPM and DPM for external rotation, retear rate, CMS and SST scores. There was no difference between EAM and DAM for retear rate, abduction, CMS and VAS. Conclusion EAM and EPM were both associated with superior ROM compared to the DAM and DPM protocols. EAM and EPM were both safe and beneficial to improve ROM after arthroscopic surgery for the patients with small to large sized tears.

Purpose The purpose of this study is to propose the use of a pre-deposition heating system for fused filament fabrication (FFF) as a means to enhance interlayer bonding by elevating the substrate temperature. The effects of the heating on thermal profile at the bonding interface and the mechanical properties of three-dimensional printed parts are investigated. Design/methodology/approach A 12-W laser head is integrated to a commercial printer as the pre-deposition heating system. The laser beam heats up substate before the deposition of a fresh filament. Effects of laser powers are investigated and the thermal profile is measured with thermocouple, infrared camera and finite element model. The correlation between the temperature at the bonding interface and the bonding quality is investigated by conducting tensile testing and neck width measurement with microscope. Findings The pre-deposition heating system is proven to be effective in enhancing the inter-layer strength in FFF parts. Tensile testing of specimens along build direction (Z) shows an increase of around 50% in ultimate strength. A linear relationship is observed between the pre-deposition temperature at bond interface and bonding strength. It is evident that elevating the pre-deposition temperature promotes interlayer polymer diffusion as shown by the increased neck width between layers. Originality/value Thermocouples that are sandwiched between layers are used to achieve accurate measurement of the interfacial temperature. The temperature profiles under pre-deposition heating are analyzed and correlated to the interlayer bonding strengths.