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A dual-band dual-polarized wearable antenna that applies to two different operating modes of wireless body area networks is proposed in this letter. The antenna radiates simultaneously in the ISM band at 2.45 and 5.8 GHz. It consists of a rigid button-like radiator and a flexible fabric radiator. At 2.45 GHz, an omnidirectional circularly polarized pattern is radiated by the flexible radiator, which is suitable for the on-body communication. At the same time, a linearly polarized broadside pattern for off-body communication is generated by button radiator at 5.8 GHz. The antenna has been validated in free space and human body environments. The impedance bandwidth at 2.45 and 5.8 GHz are 5% and 35%, and the gain is measured to be 0.15 and 5.95 dBi, respectively. Furthermore, the specific absorption rates are simulated. At 2.45 and 5.8 GHz, the results averaged over 1 g of body tissue are 0.128 and 0.055 W/kg. The maximum value at both bands is below the IEEE C95.3 standard of 1.6 W/kg.
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.
Clinical high risk for psychosis (CHR) is often managed with antipsychotic medications, but their effects on neurocognitive performance and clinical outcomes remain insufficiently explored. This study investigates the association between aripiprazole and olanzapine use and cognitive and clinical outcomes in CHR individuals, compared to those receiving no antipsychotic treatment.
Methods
A retrospective analysis was conducted on 127 participants from the Shanghai At Risk for Psychosis (SHARP) cohort, categorized into three groups: aripiprazole, olanzapine, and no antipsychotic treatment. Neurocognitive performance was evaluated using the MATRICS Consensus Cognitive Battery (MCCB), while clinical symptoms were assessed through the Structured Interview for Prodromal Syndromes (SIPS) at baseline, 8 weeks, and one year.
Results
The non-medicated group demonstrated greater improvements in cognitive performance, clinical symptoms, and functional outcomes compared to the medicated groups. Among the antipsychotic groups, aripiprazole was associated with better visual learning outcomes than olanzapine. Improvements in neurocognition correlated significantly with clinical symptom relief and overall functional gains at follow-up assessments.
Conclusions
These findings suggest potential associations between antipsychotic use and cognitive outcomes in CHR populations while recognizing that observed differences may reflect baseline illness severity rather than medication effects alone. Aripiprazole may offer specific advantages over olanzapine, underscoring the importance of individualized risk-benefit evaluations in treatment planning. Randomized controlled trials are needed to establish causality.
Patients with chronic insomnia are characterized by alterations in default mode network and alpha oscillations, for which the medial parietal cortex (MPC) is a key node and thus a potential target for interventions.
Methods
Fifty-six adults with chronic insomnia were randomly assigned to 2 mA, alpha-frequency (10 Hz), 30 min active or sham transcranial alternating current stimulation (tACS) applied over the MPC for 10 sessions completed within two weeks, followed by 4- and 6-week visits. The connectivity of the dorsal and ventral posterior cingulate cortex (vPCC) was calculated based on resting functional MRI.
Results
For the primary outcome, the active group showed a higher response rate (≥ 50% reduction in Pittsburgh Sleep Quality Index (PSQI)) at week 6 than that of the sham group (71.4% versus 3.6%) (risk ratio 20.0, 95% confidence interval 2.9 to 139.0, p = 0.0025). For the secondary outcomes, the active therapy induced greater and sustained improvements (versus sham) in the PSQI, depression (17-item Hamilton Depression Rating Scale), anxiety (Hamilton Anxiety Rating Scale), and cognitive deficits (Perceived Deficits Questionnaire-Depression) scores. The response rates in the active group decreased at weeks 8–14 (42.9%–57.1%). Improvement in sleep was associated with connectivity between the vPCC and the superior frontal gyrus and the inferior parietal lobe, whereas vPCC-to-middle frontal gyrus connectivity was associated with cognitive benefits and vPCC-to-ventromedial prefrontal cortex connectivity was associated with alleviation in rumination.
Conclusions
Targeting the MPC with alpha-tACS appears to be an effective treatment for chronic insomnia, and vPCC connectivity represents a prognostic marker of treatment outcome.
Broadband frequency-tripling pulses with high energy are attractive for scientific research, such as inertial confinement fusion, but are difficult to scale up. Third-harmonic generation via nonlinear frequency conversion, however, remains a trade-off between bandwidth and conversion efficiency. Based on gradient deuterium deuterated potassium dihydrogen phosphate (KDxH2-xPO4, DKDP) crystal, here we report the generation of frequency-tripling pulses by rapid adiabatic passage with a low-coherence laser driver facility. The efficiency dependence on the phase-matching angle in a Type-II configuration is studied. We attained an output at 352 nm with a bandwidth of 4.4 THz and an efficiency of 36%. These results, to the best of our knowledge, represent the first experimental demonstration of gradient deuterium DKDP crystal in obtaining frequency-tripling pulses. Our research paves a new way for developing high-efficiency, large-bandwidth frequency-tripling technology.
A high-energy pulsed vacuum ultraviolet (VUV) solid-state laser at 177 nm with high peak power by the sixth harmonic of a neodymium-doped yttrium aluminum garnet (Nd:YAG) amplifier in a KBe2BO3F2 prism-coupled device was demonstrated. The ultraviolet (UV) pump laser is a 352 ps pulsed, spatial top-hat super-Gaussian beam at 355 nm. A high energy of a 7.12 mJ VUV laser at 177 nm is obtained with a pulse width of 255 ps, indicating a peak power of 28 MW, and the conversion efficiency is 9.42% from 355 to 177 nm. The measured results fitted well with the theoretical prediction. It is the highest pulse energy and highest peak power ever reported in the VUV range for any solid-state lasers. The high-energy, high-peak-power, and high-spatial-uniformity VUV laser is of great interest for ultra-fine machining and particle-size measurements using UV in-line Fraunhofer holography diagnostics.
The geometrical properties of streamlines, such as the curvatures, directions and positions, are studied in steady inviscid compressible flow fields via differential geometry theories and conservation laws. The influences of the streamline geometries on the flow speeds and pressures are also identified and discussed. By transforming the streamlines to fill the domain and satisfy the boundary conditions, a unified geometry-based solver, the streamline transformation method, is proposed for both subsonic and supersonic regions. The governing equations and boundary conditions along streamlines and shock waves are also derived. This method is verified by numerical results of three typical flow fields, including the subsonic channel flow, the supersonic downstream of attached shock waves and especially the subsonic/supersonic downstream of detached bow shock waves. Both two-dimensional planar and axisymmetric flow fields are considered. Compared with the results from computational fluid dynamics, good agreements are achieved by this method, while fewer computational resources, by an order of magnitude, are consumed. Features of these flow fields are also analysed from a geometrical perspective, such as flow speeds and pressures deviated by the wall curvatures, and three-dimensional effects in the after-shock flow fields. For a hyperbolic-shaped bow shock wave, the stand-off distances and the transitions from subsonic to supersonic regions are also discussed. As indicated by the accuracy, efficiency and applicability in a wide range of flow speeds, the streamline transformation method would be a potential candidate for the theoretical analysis and inverse design of high-speed flow fields, especially where the subsonic regions exist downstream of strong shock waves.
Double-cone ignition [Zhang et al., Phil. Trans. R. Soc. A 378, 20200015 (2020)] was proposed recently as a novel path for direct-drive inertial confinement fusion using high-power lasers. In this scheme, plasma jets with both high density and high velocity are required for collisions. Here we report preliminary experimental results obtained at the Shenguang-II upgrade laser facility, employing a CHCl shell in a gold cone irradiated with a two-ramp laser pulse. The CHCl shell was pre-compressed by the first laser ramp to a density of 3.75 g/cm3 along the isentropic path. Subsequently, the target was further compressed and accelerated by the second laser ramp in the cone. According to the simulations, the plasma jet reached a density of up to 15 g/cm3, while measurements indicated a velocity of 126.8 ± 17.1 km/s. The good agreements between experimental data and simulations are documented.
Mythimna separata (Lepidoptera: Noctuidae) is an omnivorous pest that poses a great threat to food security. Insect antimicrobial peptides (AMPs) are small peptides that are important effector molecules of innate immunity. Here, we investigated the role of the AMP cecropin B in the growth, development, and immunity of M. separata. The gene encoding M. separata cecropin B (MscecropinB) was cloned. The expression of MscecropinB was determined in different developmental stages and tissues of M. separata. It was highest in the prepupal stage, followed by the pupal stage. Among larval stages, the highest expression was observed in the fourth instar. Tissue expression analysis of fourth instar larvae showed that MscecropinB was highly expressed in the fat body and haemolymph. An increase in population density led to upregulation of MscecropinB expression. MscecropinB expression was also upregulated by the infection of third and fourth instar M. separata with Beauveria bassiana or Bacillus thuringiensis (Bt). RNA interference (RNAi) targeting MscecropinB inhibited the emergence rate and fecundity of M. separata, and resulted in an increased sensitivity to B. bassiana and Bt. The mortality of M. separata larvae was significantly higher in pathogen plus RNAi-treated M. separata than in controls treated with pathogens only. Our findings indicate that MscecropinB functions in the eclosion and fecundity of M. separata and plays an important role in resistance to infection by B. bassiana and Bt.
Mild cognitive deficits (MCD) emerge before the first episode of psychosis (FEP) and persist in the clinical high-risk (CHR) stage. This study aims to refine risk prediction by developing MCD models optimized for specific early psychosis stages and target populations.
Methods
A comprehensive neuropsychological battery assessed 1059 individuals with FEP, 794 CHR, and 774 matched healthy controls (HCs). CHR subjects, followed up for 2 years, were categorized into converters (CHR-C) and non-converters (CHR-NC). The MATRICS Consensus Cognitive Battery standardized neurocognitive tests were employed.
Results
Both the CHR and FEP groups exhibited significantly poorer performance compared to the HC group across all neurocognitive tests (all p < 0.001). The CHR-C group demonstrated poorer performance compared to the CHR-NC group on three sub-tests: visuospatial memory (p < 0.001), mazes (p = 0.005), and symbol coding (p = 0.023) tests. Upon adjusting for sex and age, the performance of the MCD model was excellent in differentiating FEP from HC, as evidenced by an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.895 (p < 0.001). However, when applied in the CHR group for predicting CHR-C (AUC = 0.581, p = 0.008), the performance was not satisfactory. To optimize the efficiency of psychotic risk assessment, three distinct MCD models were developed to distinguish FEP from HC, predict CHR-C from CHR-NC, and identify CHR from HC, achieving accuracies of 89.3%, 65.6%, and 80.2%, respectively.
Conclusions
The MCD exhibits variations in domains, patterns, and weights across different stages of early psychosis and diverse target populations. Emphasizing precise risk assessment, our findings highlight the importance of tailored MCD models for different stages and risk levels.
The laboratory generation and diagnosis of uniform near-critical-density (NCD) plasmas play critical roles in various studies and applications, such as fusion science, high energy density physics, astrophysics as well as relativistic electron beam generation. Here we successfully generated the quasistatic NCD plasma sample by heating a low-density tri-cellulose acetate (TCA) foam with the high-power-laser-driven hohlraum radiation. The temperature of the hohlraum is determined to be 20 eV by analyzing the spectra obtained with the transmission grating spectrometer. The single-order diffraction grating was employed to eliminate the high-order disturbance. The temperature of the heated foam is determined to be T = 16.8 ± 1.1 eV by analyzing the high-resolution spectra obtained with a flat-field grating spectrometer. The electron density of the heated foam is about under the reasonable assumption of constant mass density.
The relationship between vacant Mn structural sites in birnessites and heavy-metal adsorption is a current and important research topic. In this study, two series of birnessites with different average oxidation states (AOS) of Mn were synthesized. One birnessite series was prepared in acidic media (49.6–53.6 wt.% Mn) and the other in alkaline media (50.0–56.2 wt.% Mn). Correlations between the Pb2+ adsorption capacity and the d110 interlayer spacing, the AOS by titration, and the release of Mn2+, H+, and K+ during adsorption of Pb2+ were investigated. The maximum Pb2+ adsorption by the birnessites synthesized in acidic media ranged from 1320 to 2457 mmol/kg with AOS values that ranged from 3.67 to 3.92. For birnessites synthesized in alkaline media, the maximum Pb2+ adsorption ranged from 524 to 1814 mmol/kg, with AOS values between 3.49 and 3.89. Birnessite AOS values and Pb2+ adsorption increased as the Mn content decreased. The maximum Pb2+ adsorption to the synthetic birnessites calculated from a Langmuir fit of the Pb adsorption data was linearly related to AOS. Birnessite AOS was positively correlated to Pb2+ adsorption, but negatively correlated to the d110 spacing. Vacant Mn structural sites in birnessite increased with AOS and resulted in greater Pb2+ adsorption. Birnessite AOS values apparently reflect the quantity of vacant sites which largely account for Pb2+ adsorption. Therefore, the Pb2+ adsorption capacity of birnessite is mostly determined by the Mn site vacancies, from which Mn2+, H+, and K+ released during adsorption were derived.
There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression.
Methods
In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis.
Results
In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset.
Conclusions
These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.
Customer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based approach has the new capability of modelling the effects of interactions and interdependencies (e.g., social relationships among customers) on customers’ decisions via network structures (e.g., using triangles to model peer influence), existing research can only model customers’ consideration decisions, and it cannot predict individual customer’s choices, as what the traditional utility-based discrete choice models (DCMs) do. However, the ability to make choice predictions is essential to predicting market demand, which forms the basis of decision-based design (DBD). This paper fills this gap by developing a novel ERGM-based approach for choice prediction. This is the first time that a network-based model can explicitly compute the probability of an alternative being chosen from a choice set. Using a large-scale customer-revealed choice database, this research studies the customer preferences estimated from the ERGM-based choice models with and without network structures and evaluates their predictive performance of market demand, benchmarking the multinomial logit (MNL) model, a traditional DCM. The results show that the proposed ERGM-based choice modelling achieves higher accuracy in predicting both individual choice behaviours and market share ranking than the MNL model, which is mathematically equivalent to ERGM when no network structures are included. The insights obtained from this study further extend the DBD framework by allowing explicit modelling of interactions among entities (i.e., customers and products) using network representations.
We present a high-energy, hundred-picosecond (ps) pulsed mid-ultraviolet solid-state laser at 266 nm by a direct second harmonic generation (SHG) in a barium borate (BaB2O4, BBO) nonlinear crystal. The green pump source is a 710 mJ, 330 ps pulsed laser at a wavelength of 532 nm with a repetition rate of 1 Hz. Under a green pump energy of 710 mJ, a maximum output energy of 253.3 mJ at 266 nm is achieved with 250 ps pulse duration resulting in a peak power of more than 1 GW, corresponding to an SHG conversion efficiency of 35.7% from 532 to 266 nm. The experimental data were well consistent with the theoretical prediction. To the best of our knowledge, this laser exhibits both the highest output energy and highest peak power ever achieved in a hundred-ps/ps regime at 266 nm for BBO-SHG.
We report here the first hundred-watt continuouswave fiber gas laser in H2-filled hollow-core photonic crystal fiber (PCF) by stimulated Raman scattering. The pump source is a homemade narrow-linewidth fiber oscillator with a 3 dB linewidth of 0.15 nm at the maximum output power of 380 W. To efficiently and stably couple several-hundred-watt pump power into the hollow core and seal the gas, a hollow-core fiber end-cap is fabricated and used at the input end. A maximum power of 110 W at 1153 nm is obtained in a 5 m long hollow-core PCF filled with 36 bar H2, and the conversion efficiency of the first Stokes power is around 48.9%. This work paves the way for high-power fiber gas Raman lasers.
The study presents an adaptive robust control method for the Pendubot subjects to matched and mismatched uncertainty. First, the control task is formatted as a reduced-dimension equality constraint of the system states. To handle the matched and mismatched uncertainties, an orthogonal decomposition method is employed to make the mismatched part disappear after decomposition. Based on the above, an adaptive robust control law based on constraint-following is devised. By the Lyapunov approach, it is rigorously proven that the proposed approach ensures the uniform boundedness and uniform ultimate boundedness of the closed-loop control system and thus renders approximate constraint-following, regardless of uncertainty. Simulation and experimental results are provided and discussed, demonstrating the good performance of the proposed approach.
The role of dietary factors in osteoporotic fractures (OFs) in women is not fully elucidated. We investigated the associations between incidence of OF and dietary calcium, magnesium and soy isoflavone intake in a longitudinal study of 48 584 postmenopausal women. Multivariable Cox regression was applied to derive hazard ratios (HRs) and 95 % confidence intervals (CIs) to evaluate associations between dietary intake, based on the averages of two assessments that took place with a median interval of 2⋅4 years, and fracture risk. The average age of study participants is 61⋅4 years (range 43⋅3–76⋅7 years) at study entry. During a median follow-up of 10⋅1 years, 4⋅3 % participants experienced OF. Compared with daily calcium intake ≤400 mg/d, higher calcium intake (>400 mg/d) was significantly associated with about a 40–50 % reduction of OF risk among women with a calcium/magnesium (Ca/Mg) intake ratio ≥1⋅7. Among women with prior fracture history, high soy isoflavone intake was associated with reduced OF risk; the HR was 0⋅72 (95 % CI 0⋅55, 0⋅93) for the highest (>42⋅0 mg/d) v. lowest (<18⋅7 mg/d) quartile intake. This inverse association was more evident among recently menopausal women (<10 years). No significant association between magnesium intake and OF risk was observed. Our findings provide novel information suggesting that the association of OF risk with dietary calcium intake was modified by Ca/Mg ratio, and soy isoflavone intake was modified by history of fractures and time since menopause. Our findings, if confirmed, can help to guide further dietary intervention strategies for OF prevention.
Ammannia multiflora Roxb. is a dominant broadleaf weed that is a serious problem in southern China rice fields, and acetolactate synthase (ALS)-inhibiting herbicides have been used for its control for more than 20 years. Excessive reliance on ALS-inhibiting herbicides has led to herbicide resistance in A. multiflora. In this study, 10 A. multiflora populations from the Jiangsu Province of China were collected, and the resistance levels and target site–resistance mechanisms to ALS-inhibiting herbicides bensulfuron-methyl and penoxsulam were investigated. The dose–response assays showed that eight populations evolved resistance to bensulfuron-methyl (9.1- to 90.9-fold) and penoxsulam (5.0- to 103.1-fold). Amplification of ALS genes indicated that there were three ALS genes (AmALS1, AmALS2, and AmALS3) in A. multiflora. Sequence analysis revealed amino acid mutations at Pro-197 in either AmALS1 (Pro-197-Ala, Pro-197-Ser, and Pro-197-His) or AmALS2 (Pro-197-Ser and Pro-197-Arg) in resistant populations, and no mutations were found in AmALS3. Moreover, two independent mutations (Pro-197-Ala in AmALS1 and Pro-197-Ser in AmALS2 or Pro-197-Ala in AmALS1 and Pro-197-Arg in AmALS2) coexisted in two resistant populations, respectively. In addition, the auxin mimic herbicides MCPA and florpyrauxifen-benzyl, the photosystem II inhibitor bentazon, and the protoporphyrinogen oxidase inhibitor carfentrazone-ethyl can effectively control the resistant A. multiflora populations. Our study demonstrates the wide prevalence of ALS inhibitor–resistant A. multiflora populations in Jiangsu Province and the diversity of Pro-197 mutations in ALS genes and provides alternative herbicide options for controlling resistant A. multiflora populations.