The Chinese pangolin Manis pentadactyla is categorized as Critically Endangered on the IUCN Red List, but the development of effective conservation strategies is hindered by a lack of data on its distribution range and population dynamics. In addition, standardized survey and analysis methods are required to facilitate the sharing of results and maximize conservation effectiveness. To fill these knowledge and methodological gaps, we investigated the occurrence of pangolin burrows in the subtropical forest ecosystem of Fujian, China. We surveyed a total of 70 transects across five land-cover types within the Fujian Junzifeng National Nature Reserve and detected 87 burrows. The majority of burrows (87%) were located in mixed conifer and broadleaf forests. We used six environmental variables in a generalized linear model to examine the relationship between the occurrence of burrows and environmental factors. The average model results from the best model set showed that the distribution of burrows was significantly influenced by forest type. For effective pangolin conservation, we recommend that local conservation authorities prioritize the protection of mixed conifer and broadleaf forests. Our findings support the local conservation of the Chinese pangolin and the standardization of surveys and conservation efforts across the species’ range.

Persistent malnutrition is associated with poor clinical outcomes in cancer. However, assessing its reversibility can be challenging. The present study aimed to utilise machine learning (ML) to predict reversible malnutrition (RM) in patients with cancer. A multicentre cohort study including hospitalised oncology patients. Malnutrition was diagnosed using an international consensus. RM was defined as a positive diagnosis of malnutrition upon patient admission which turned negative one month later. Time-series data on body weight and skeletal muscle were modelled using a long short-term memory architecture to predict RM. The model was named as WAL-net, and its performance, explainability, clinical relevance and generalisability were evaluated. We investigated 4254 patients with cancer-associated malnutrition (discovery set = 2977, test set = 1277). There were 2783 men and 1471 women (median age = 61 years). RM was identified in 754 (17·7 %) patients. RM/non-RM groups showed distinct patterns of weight and muscle dynamics, and RM was negatively correlated to the progressive stages of cancer cachexia (r = –0·340, P < 0·001). WAL-net was the state-of-the-art model among all ML algorithms evaluated, demonstrating favourable performance to predict RM in the test set (AUC = 0·924, 95 % CI = 0·904, 0·944) and an external validation set (n 798, AUC = 0·909, 95 % CI = 0·876, 0·943). Model-predicted RM using baseline information was associated with lower future risks of underweight, sarcopenia, performance status decline and progression of malnutrition (all P < 0·05). This study presents an explainable deep learning model, the WAL-net, for early identification of RM in patients with cancer. These findings might help the management of cancer-associated malnutrition to optimise patient outcomes in multidisciplinary cancer care.

Emission line galaxies (ELGs) are crucial for cosmological studies, particularly in understanding the large-scale structure of the Universe and the role of dark energy. ELGs form an essential component of the target catalogue for the Dark Energy Spectroscopic Instrument (DESI), a major astronomical survey. However, the accurate selection of ELGs for such surveys is challenging due to the inherent uncertainties in determining their redshifts with photometric data. In order to improve the accuracy of photometric redshift estimation for ELGs, we propose a novel approach CNN–MLP that combines convolutional neural networks (CNNs) with multilayer perceptrons (MLPs). This approach integrates both images and photometric data derived from the DESI Legacy Imaging Surveys Data Release 10. By leveraging the complementary strengths of CNNs (for image data processing) and MLPs (for photometric feature integration), the CNN–MLP model achieves a $\sigma_{\mathrm{NMAD}}$ (normalised median absolute deviation) of 0.0140 and an outlier fraction of 2.57%. Compared to other models, CNN–MLP demonstrates a significant improvement in the accuracy of ELG photometric redshift estimation, which directly benefits the target selection process for DESI. In addition, we explore the photometric redshifts of different galaxy types (Starforming, Starburst, AGN, and Broadline). Furthermore, this approach will contribute to more reliable photometric redshift estimation in ongoing and future large-scale sky surveys (e.g. LSST, CSST, and Euclid), enhancing the overall efficiency of cosmological research and galaxy surveys.

Hand, foot, and mouth disease (HFMD) shows spatiotemporal heterogeneity in China. A spatiotemporal filtering model was constructed and applied to HFMD data to explore the underlying spatiotemporal structure of the disease and determine the impact of different spatiotemporal weight matrices on the results. HFMD cases and covariate data in East China were collected between 2009 and 2015. The different spatiotemporal weight matrices formed by Rook, K-nearest neighbour (KNN; K = 1), distance, and second-order spatial weight matrices (SO-SWM) with first-order temporal weight matrices in contemporaneous and lagged forms were decomposed, and spatiotemporal filtering model was constructed by selecting eigenvectors according to MC and the AIC. We used MI, standard deviation of the regression coefficients, and five indices (AIC, BIC, DIC, R2, and MSE) to compare the spatiotemporal filtering model with a Bayesian spatiotemporal model. The eigenvectors effectively removed spatial correlation in the model residuals (Moran’s I < 0.2, p > 0.05). The Bayesian spatiotemporal model’s Rook weight matrix outperformed others. The spatiotemporal filtering model with SO-SWM was superior, as shown by lower AIC (92,029.60), BIC (92,681.20), and MSE (418,022.7) values, and higher R2 (0.56) value. All spatiotemporal contemporaneous structures outperformed the lagged structures. Additionally, eigenvector maps from the Rook and SO-SWM closely resembled incidence patterns of HFMD.

Major depressive disorder (MDD) and coronary heart disease (CHD) can both cause significant morbidity and mortality. The association of MDD and CHD has long been identified, but the mechanisms still require further investigation. Seven mRNA microarray datasets containing samples from patients with MDD and CHD were downloaded from Gene Expression Omnibus. Combined matrixes of MDD and CAD were constructed for subsequent analysis. Differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on shared DEGs were conducted to identify pivotal pathways. A protein-protein network was also applied to further investigate the functional interaction. Results showed that 24 overlapping genes were identified. Enrichment analysis indicated that the shared genes are mainly associated with immune function and ribosome biogenesis. The functional interactions of shared genes were also demonstrated by PPI network analysis. In addition, three hub genes including MMP9, S100A8, and RETN were identified. Our results indicate that MDD and CHD have a genetic association. Genes relevant to immune function, especially IL-17 signalling pathway may be involved in the pathogenesis of MDD and CHD.

Loneliness and social isolation are prevalent concerns among older adults and can lead to negative health consequences and a reduced lifespan. New technologies are increasingly being developed to help address loneliness and social isolation in older adults, including monitoring systems, social networks, robots, companions, smart televisions, augmented reality (AR) and virtual reality (VR) applications. This systematic review maps human-centered design (HCD) and user-centered design (UCD) approaches, human needs, and contextual factors considered in current technological interventions designed to address the problems of loneliness and social isolation in older adults. We conducted a scoping review and in-depth examination of 98 papers through a qualitative content analysis. We found 12 studies applying either an HCD or UCD approach and observed strengths in continuous user involvement and implementation in field studies but limitations in participant inclusion criteria and methodological reporting. We also observed the consideration of important human needs and contextual factors. However, more research is needed on stakeholder perspectives, the functioning of applications in different housing environments, as well as studies that include diverse socio-economic groups.

Purple nutsedge (Cyperus rotundus L.) is one of the world’s resilient upland weeds, primarily spreading through its tubers. Its emergence in rice (Oryza sativa L.) fields has been increasing, likely due to changing paddy-farming practices. This study aimed to investigate how C. rotundus, an upland weed, can withstand soil flooding and become a problematic weed in rice fields. The first comparative analysis focused on the survival and recovery characteristics of growing and mature tubers of C. rotundus exposed to soil-flooding conditions. Notably, mature tubers exhibited significant survival and recovery abilities in these environments. Based on this observation, further investigation was carried out to explore the morphological structure, nonstructural carbohydrates, and respiratory mechanisms of mature tubers in response to prolonged soil flooding. Over time, the mature tubers did not form aerenchyma but instead gradually accumulated lignified sclerenchymal fibers, with lignin content also increasing. After 90 d, the lignified sclerenchymal fibers and lignin contents were 4.0 and 1.1 times higher than those in the no soil-flooding treatment. Concurrently, soluble sugar content decreased while starch content increased, providing energy storage, and alcohol dehydrogenase activity rose to support anaerobic respiration via alcohol fermentation. These results indicated that mature tubers survived in soil-flooding conditions by adopting a low-oxygen quiescence strategy, which involves morphological adaptations through the development of lignified sclerenchymal fibers, increased starch reserves for energy storage, and enhanced anaerobic respiration. This mechanism likely underpins the flooding tolerance of mature C. rotundus tubers, allowing them to endure unfavorable conditions and subsequently germinate and grow once flooding subsides. This study provides a preliminary explanation of the mechanism by which mature tubers of C. rotundus from the upland areas confer flooding tolerance, shedding light on the reasons behind this weed’s increasing presence in rice fields.

Dietary n-3 PUFA may have potential benefits in preventing peptic ulcer disease (PUD). However, data from observational epidemiological studies are limited. Thus, we conducted a Mendelian randomisation analysis to reveal the causal impact of n-3 PUFA on PUD. Genetic variants strongly associated with plasma levels of total or individual n-3 PUFA including plant-derived α-linolenic acid and marine-derived EPA, DPA and DHA were enrolled as instrumental variables. Effect size estimates of the n-3 PUFA-associated genetic variants with PUD were evaluated using data from the UK biobank. Per one sd increase in the level of total n-3 PUFA in plasma was significantly associated with a lower risk of PUD (OR = 0·91; 95 % CI 0·85, 0·99; P = 0·020). The OR were 0·81 (95 % CI 0·67, 0·97) for EPA, 0·72 (95 % CI 0·58, 0·91) for DPA and 0·87 (95 % CI 0·80, 0·94) for DHA. Genetically predicted α-linolenic acid levels in plasma had no significant association with the risk of PUD (OR = 5·41; 95 % CI 0·70, 41·7). Genetically predicted plasma levels of n-3 PUFA were inversely associated with the risk of PUD, especially marine-based n-3 PUFA. Such findings may have offered an effective and feasible strategy for the primary prevention of PUD.

MicroRNAs (miRNAs) are endogenous, non-coding RNAs, which are functional in a variety of biological processes through post-transcriptional regulation of gene expression. However, the role of miRNAs in the interaction between Bacillus thuringiensis and insects remains unclear. In this study, small RNA libraries were constructed for B. thuringiensis-infected (Bt) and uninfected (CK) Spodoptera exigua larvae (treated with double-distilled water) using Illumina sequencing. Utilising the miRDeep2 and Randfold, a total of 233 known and 726 novel miRNAs were identified, among which 16 up-regulated and 34 down-regulated differentially expressed (DE) miRNAs were identified compared to the CK. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that potential target genes of DE miRNAs were associated with ABC transporters, fatty acid metabolism and MAPK signalling pathway which are related to the development, reproduction and immunity. Moreover, two miRNA core genes, SeDicer1 and SeAgo1 were identified. The phylogenetic tree showed that lepidopteran Dicer1 clustered into one branch, with SeDicer1 in the position closest to Spodoptera litura Dicer1. A similar phylogenetic relationship was observed in the Ago1 protein. Expression of SeDicer1 increased at 72 h post infection (hpi) with B. thuringiensis; however, expression of SeDicer1 and SeAgo1 decreased at 96 hpi. The RNAi results showed that the knockdown of SeDicer1 directly caused the down-regulation of miRNAs and promoted the mortality of S. exigua infected by B. thuringiensis GS57. In conclusion, our study is crucial to understand the relationship between miRNAs and various biological processes caused by B. thuringiensis infection, and develop an integrated pest management strategy for S. exigua via miRNAs.

In particle-laden turbulent wall flows, lift forces can influence the near-wall turbulence. This has been observed recently in particle-resolved simulations, which, however, are too expensive to be used in upscaled models. Instead, point-particle simulations have been the method of choice to simulate the dynamics of these flows during the last decades. While this approach is simpler, cheaper and physically sound for small inertial particles in turbulence, some issues remain. In the present work, we address challenges associated with lift force modelling in turbulent wall flows and the impact of lift forces in the near-wall flow. We performed direct numerical simulations of small inertial point particles in turbulent channel flow for fixed Stokes number and mass loading while varying the particle size. Our results show that the particle dynamics in the buffer region, causing the apparent particle-to-fluid slip velocity to vanish, raises major challenges for modelling lift forces accurately. While our results confirm that lift forces have little influence on particle dynamics for sufficiently small particle sizes, for inner-scaled diameters of order one and beyond, lift forces become quite important near the wall. The different particle dynamics under lift forces results in the modulation of streamwise momentum transport in the near-wall region. We analyse this lift-induced turbulence modulation for different lift force models, and the results indicate that realistic models are critical for particle-modelled simulations to correctly predict turbulence modulation by particles in the near-wall region.

We report an experimental study of the formation and evolution of laminar thermal structures generated by a small heat source, with a focus on their correlation to the thermal boundary layer and effects of heating time $t_{heat}$. The experiments are performed over the flux Rayleigh number ($Ra_f$) range $2.1\times 10^6 \leq Ra_f \leq 3.6\times 10^{7}$ and the Prandtl number ($Pr$) range $28.6 \leq Pr \leq 904.7$. The corresponding Rayleigh number ($Ra= t_{heat}\,Ra_{f}/\tau _0\,Pr$) range is $900 \leq Ra \leq 4\times 10^{4}$, where $\tau _0$ is a diffusion time scale. For thermal structures generated by continuous heating (i.e. starting plumes), their formation process exists three characteristic times that are well reflected by changes in the thermal boundary layer thickness. These characteristic times, denoted as $t_{emit}$, $t_{recover}$ and $t_{static}$, correspond to the moments when the plume emission begins and completes, and when the thermal boundary layer becomes quasi-static, respectively. Their $Ra_f$–$Pr$ dependencies are found to be $t_{emit}/\tau _0\sim Ra_f^{-0.41}\,Pr^{0.41}$, $t_{recover}/\tau _0\sim Ra_f^{-0.48}\,Pr^{0.48}$ and $t_{static}/\tau _0\sim Ra_f^{-0.49}\,Pr^{0.33}$, respectively. Thermal structures generated by finite $t_{heat}$ exhibit similar evolution dynamics once $t_{heat} \ge t_{emit}$, with the accelerating stage behaving like starting plumes and the decay stage like thermals (i.e. a finite amount of buoyant fluids). It is further found that their maximum rising velocity experiences a transition in the $Ra$-dependence from $Ra$ to $(Ra\ln Ra)^{0.5}$ at $Ra \simeq 6000$; and their maximum acceleration reaches the value of starting plumes at $t_{heat}\simeq t_{recover}$, and remains unchanged for larger $t_{heat}$. In particular, the maximum rising velocity for the cases with $t_{heat} = t_{recover}$ follows a scaling relation $Ra_f^{0.37}\,Pr^{-0.37}$, in contrast to the relation $Ra_f^{0.48}\,Pr^{-0.48}$ for starting plumes. This study provides a more comprehensive understanding of laminar thermal structures, which are relevant to a range of processes in nature and laboratory systems such as Rayleigh–Bénard convection.

Natural clays are often employed as substrates for heterogeneous catalysts. However, the direct use of raw clays as catalysts has received less research attention. The objective of the present study was to help fill this gap by investigating catalytic properties of raw pelagic clays (PC) collected from the Indian Ocean. The raw PC were discovered to be efficient catalysts in the reduction of 4-nitrophenol (4-NP) in the presence of NaBH4. The effects of parameters including pH values, dosages of PC, and initial concentration of 4-NP and NaBH4 on the conversion or degradation rate of 4-NP have been investigated. The 4-NP was observed to be completely degraded within 480 s under conditions of 0.10 mM 4-NP, 25.0 mM NaBH4, and 0.20 g/L PC at an initial pH value of 7.0. The apparent rate constant was evaluated to be 27.53 × 10–3 s–1. Unlike previous pseudo-first order kinetics experiments, the induction period and degradation stages were observed to occur simultaneously during the PC catalysis. The S-shaped kinetics for 4-NP conversion was found to be perfectly matched by Fermi's function, and the enzyme-like catalysis by PC was appointed to describe the kinetics. Species of Fe(III), Mn(IV), and Mn(III) in PC were found to be essential, and were partly reduced to Fe(0) and Mn(II) by NaBH4 in our reaction, contributing to rapid conversion of 4-NP to 4-aminophenol (4-AP). The raw PC was converted to magnetic PC (m-PC) particles, which made PC particles separate easily for cycling use. This discovery would also have applications in continuous flow-fluid catalysis.

We demonstrate a continuous-wave (CW) Nd:YVO4-potassium gadolinium tungstate (KGW) intracavity Raman laser with a diode-to-Stokes optical efficiency of 34.2%. By optimizing the cavity arrangement and reducing the cavity losses, 8.47 W Stokes output at 1177 nm was obtained under an incident 878.6 nm diode pump power of 24.8 W. The influence of cavity losses on the power and efficiency of the CW Raman laser, as well as the potential for further optimization, was investigated based on the numerical model. The observation of thermally-induced output rollover was well explained by the calculation of the thermal lensing and cavity stability, indicating that the end-face curvature played an important role when the end-face of the crystal was highly reflective coated to make the cavity. A 10.9 W Stokes output under 40.9 W incident pump was also demonstrated with a cavity arrangement less sensitive to the end-face curvature, which is the highest output power of CW intracavity Raman lasers reported.

Accurately predicting neurosyphilis prior to a lumbar puncture (LP) is critical for the prompt management of neurosyphilis. However, a valid and reliable model for this purpose is still lacking. This study aimed to develop a nomogram for the accurate identification of neurosyphilis in patients with syphilis. The training cohort included 9,504 syphilis patients who underwent initial neurosyphilis evaluation between 2009 and 2020, while the validation cohort comprised 526 patients whose data were prospectively collected from January 2021 to September 2021. Neurosyphilis was observed in 35.8% (3,400/9,504) of the training cohort and 37.6% (198/526) of the validation cohort. The nomogram incorporated factors such as age, male gender, neurological and psychiatric symptoms, serum RPR, a mucous plaque of the larynx and nose, a history of other STD infections, and co-diabetes. The model exhibited good performance with concordance indexes of 0.84 (95% CI, 0.83–0.85) and 0.82 (95% CI, 0.78–0.86) in the training and validation cohorts, respectively, along with well-fitted calibration curves. This study developed a precise nomogram to predict neurosyphilis risk in syphilis patients, with potential implications for early detection prior to an LP.

In preparation for an experiment with a laser-generated intense proton beam at the Laser Fusion Research Center at Mianyang to investigate the 11B(p,α)2α reaction, we performed a measurement at very low proton energy between 140 keV and 172 keV using the high-voltage platform at the Institute of Modern Physics, Lanzhou. The aim of the experiment was to test the ability to use CR-39 track detectors for cross-section measurements and to remeasure the cross-section of this reaction close to the first resonance using the thick target approach. We obtained the cross-section σ = 45.6 ± 12.5 mb near 156 keV. Our result confirms the feasibility of CR-39 type track detector for nuclear reaction measurement also in low-energy regions.

A wideband tunable balanced phase shifter is achieved by utilizing varactor-loaded coupled lines (VLCLs)-embedded multistage branch-line structure. The tunable phase shift with low in-band phase deviation is attributed to the regulation in phase shift of the VLCLs and the horizontal microstrip lines in series. The wideband differential-mode (DM) impedance matching and common-mode (CM) suppression are due to multiple DM transmission poles and CM transmission zeros, which are brought about by the cascade of VLCLs and a microstrip line with short-circuited stubs in the DM-equivalent circuit and open-circuited stubs in the CM-equivalent circuit, respectively. Compared with the state-of-the-art tunable balanced phase shifters, the proposed design not only has the advantages of wide operating bandwidth (BW) with low in-band phase deviation but also has low insertion loss and easily fabricated structure. Theoretical analysis and design procedure were conducted, resulting in a prototype covering the frequency of 1.8 GHz. This prototype offers a tunable phase shift capability ranging from 0° to 90°. The prototype exhibits an operating BW of 45%, with a maximum phase deviation of ±6°. It also achieves a 10 dB DM return loss and CM suppression, while maintaining a maximum insertion loss of 2.5 dB.