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Abstract The photoinduced non-thermalized hot electrons at an interface play a pivotal role in determining plasmonic driven chemical events. However, understanding non-thermalized electron dynamics, which precedes electron thermalization (~125 fs), remains a grand challenge. Herein, we simultaneously captured the dynamics of both molecules and non-thermalized electrons in the MXene/molecule complexes by femtosecond time-resolved spectroscopy. The real-time observation allows for distinguishing non-thermalized and thermalized electron responses. Differing from the thermalized electron/heat transfer, our results reveal two non-thermalized electron dynamical pathways: (i) the non-thermalized electrons directly transfer to attached molecules at an interface within 50 fs; (ii) the non-thermalized electrons scatter at the interface within 125 fs, inducing adsorbed molecules heating. These two distinctive pathways are dependent on the irradiating wavelength and the energy difference between MXene and adsorbed molecules. This research sheds light on the fundamental mechanism and opens opportunities in photocatalysis and interfacial heat transfer theory.

High-order harmonic generation (HHG) using femtosecond lasers is an efficient and cost-effective method for producing attosecond and femtosecond light in the extreme ultraviolet and soft x-ray regions. This technique generates high-brightness harmonic beams with stable spectra, intensity, and wavefronts, making it a valuable tool for applications such as nanoimaging, material metrology, and studies of ultrafast dynamics. Accurate wavefront measurement of HHG beams is essential for analyzing complex incident fields, understanding atomic responses, and enhancing applications such as attosecond-driven ultrafast processes. However, quantifying the wavefronts of each harmonic component remains a challenge. Here, we present a multicolor wavefront sensing for HHG beams using the Talbot effect, validated theoretically and experimentally. Our method reconstructs individual high-order harmonic wavefronts with a spectral resolution of 4.8 eV, capturing multiple harmonic wavelengths in a single scan along the propagation direction, without the need for additional spectrometers or complex calibrations. This approach surpasses conventional Talbot effect wavefront sensing, which typically relies on quasimonochromatic waves or wavelength-averaging. Additionally, we introduce a high-accuracy centroid detection strategy that reduces tracking errors to the thousandths of a pixel while maintaining high computational efficiency, paving the way for broader applications in imaging and metrology.

Current diagnostic systems for Alzheimer's disease (AD) rely upon clinical signs and symptoms, despite the fact that the multiplicity of clinical symptoms renders various neuropsychological assessments inadequate to reflect the underlying pathophysiological mechanisms. Since putative neuroimaging biomarkers play a crucial role in understanding the etiology of AD, we sought to stratify the diverse relationships between AD biomarkers and cognitive decline in the aging population and uncover risk factors contributing to the diversities in AD. To do so, we capitalized on a large amount of neuroimaging data from the ADNI study to examine the inflection points along the dynamic relationship between cognitive decline trajectories and whole-brain neuroimaging biomarkers, using a state-of-the-art statistical model of change point detection. Our findings indicated that the temporal relationship between AD biomarkers and cognitive decline may differ depending on the synergistic effect of genetic risk and biological sex. Specifically, tauopathy-PET biomarkers exhibit a more dynamic and age-dependent association with Mini-Mental State Examination scores (p

Ulcerative colitis (UC) is a non-specific inflammatory bowel disease that has a high rate of recurrence, development of novel therapeutic approaches with high efficacy and few adverse effects are still needed. Dioscoreae persimilis is an edible plant that has been widely consumed as a remedy for gastrointestinal diseases in traditional Chinese medicine. Polysaccharides have been proven to have protective effects on UC. However, the role of polysaccharides from D. persimilis in UC has not been studied yet. The refined D. persimilis Polysaccharide (DP), which consists of glucose and galactose, was extracted and purified using three-phase partitioning (TPP) method. The primary chemical and structural characteristics of DP were investigated by UV, FT-IR, molecular weight, and monosaccharide composition. Based on dextran sulfate sodium (DSS) induced UC in mice, the alleviatory effect of DP on UC was explored. DP was found to alleviate histopathological changes of colon, improve colonic antioxidant capacity and ameliorate inflammation response in colitis mice. Moreover, 16S rDNA sequencing of fecal revealed that DP could restore the diversity and composition of gut microbiota, especially up-regulates the abundance of Acetatifactor, Lachnospiraceae, and Lactobacillus, and increase the ratio of Firmicutes/Bacteroidetes. According to this study, DP has the potential to serve as an effective nutritional supplement for improving colitis.

How electron energy damp to lattice vibrations (phonons) in MXenes has not yet been unraveled. Here, the authors demonstrate an energy damping channel in which the Ti3C2Tx plasmonic electron energy transfers to coherent phonons by nonthermal electron mediation after Landau damping, without involving electron-electron scattering.

The photoinjector laser system of Shenzhen Superconducting Soft X-Ray Free Electron Laser (S3FEL) is reported in this paper. This laser system operates at up to 1 MHz and produces more than 50 μJ infrared (IR) laser pulses. With a customized fourth harmonic generation (FHG) module, more than 2 μJ ultraviolet (UV) laser pulses were obtained. The power standard deviations of the IR laser and the UV laser are 0.093% and 0.395% respectively. While the pulse energy standard deviations are 1.087% and 1.746% correspondingly. We implemented the pulse stacking scheme to generate flat-top pulses. With four birefringent uniaxial crystals, the Gaussian pulses were converted to flat-top shape, featuring 10 ps pulse width and 0.5 ps rising and falling edges. A cut-Gaussian transverse profile with very sharp rising and falling edges can be produced after the spatial pulse shaper.

An efficient palladium-catalyzed sulfonylative coupling for the synthesis of benzyl(allyl) sulfones from sulfonyl hydrazides and carbonates was developed. The novel methodology employs easily accessible chemical feedstocks including stable and readily available arylsulfonyl hydrazides and benzyl(allyl) carbonates. A variety of benzyl(allyl) sulfones could be obtained in good to excellent yields in the presence of Pd(dppf)Cl2 without additional base under mild conditions.

Understanding the source of vibrationally excited molecular hydrogen is an essential prerequisite for understanding the chemical evolution in the universe. Here the authors report a photodissociation pathway to produce vibrationally excited H2 via the water photochemistry.

The photodissociation dynamics of small molecules in the vacuum ultraviolet range can have key implications for astrochemical modelling, but revealing such dynamical details is a challenging task. Here the authors, combining high resolution experimental techniques, provide a detailed description of the fragmentation dynamics of selected rotational levels of a predissociated Rydberg state of H2S.

Abstract Background Recent advances in tissue clearing techniques, combined with high-speed image acquisition through light sheet microscopy, enable rapid three-dimensional (3D) imaging of biological specimens, such as whole mouse brains, in a matter of hours. Quantitative analysis of such 3D images can help us understand how changes in brain structure lead to differences in behavior or cognition, but distinguishing densely packed features of interest, such as nuclei, from background can be challenging. Recent deep learning-based nuclear segmentation algorithms show great promise for automated segmentation, but require large numbers of accurate manually labeled nuclei as training data. Results We present Segmentor, an open-source tool for reliable, efficient, and user-friendly manual annotation and refinement of objects (e.g., nuclei) within 3D light sheet microscopy images. Segmentor employs a hybrid 2D-3D approach for visualizing and segmenting objects and contains features for automatic region splitting, designed specifically for streamlining the process of 3D segmentation of nuclei. We show that editing simultaneously in 2D and 3D using Segmentor significantly decreases time spent on manual annotations without affecting accuracy as compared to editing the same set of images with only 2D capabilities. Conclusions Segmentor is a tool for increased efficiency of manual annotation and refinement of 3D objects that can be used to train deep learning segmentation algorithms, and is available at https://www.nucleininja.org/ and https://github.com/RENCI/Segmentor .

Three-body dissociation of water, producing one oxygen and two hydrogen atoms, has been difficult to investigate due to the lack of intense vacuum ultraviolet sources. Here, using a tunable free-electron laser, the authors obtain quantum yields for this channel showing that it is a possible route to prebiotic oxygen formation in interstellar environments.

With the continuous development of manufacturing technology of micro/nano devices based on GaN, focused gallium ion beam has become a momentous method for machining complex GaN structures. Therefore, this study focuses on the characterization and simulation of sputtering etched profile by Ga FIB on GaN substrate. First, the profile characterization experiments are carried out to analyze the influence of ion dose, dwelling time, and scan area, and the results reflect that the sputtering etched regularity on GaN substrate differs from that on Si substrate, especially in redeposition effect, milling rate, and droplet phenomenon. In addition, the forming mechanism of droplet phenomenon is discussed, and the milling experiments are conducted to research the droplets avoidance method, which shows that the appearance of droplets is impacted by ion dose and dwelling time. Finally, the sputtering yield of GaN under focused gallium ion beam is measured and sorted, and the simulation program is developed based on continuous cellular automaton (CCA) model. This work can provide a guidance for the further application of GaN-based materials.

Spectroscopic studies of water clusters provide insight into the hydrogen bond structure of water and ice. The authors measure infrared spectra of neutral water octamers using a threshold photoionization technique based on a tunable vacuum-UV free electron laser, identifying two cubic isomers in addition to those previously observed.

Background: Accelerated intermittent Theta Burst Stimulation (aiTBS) has been shown to be an effective antidepressant treatment. Although neurobiological changes shortly after this intervention have been reported, whether aiTBS results in structural brain changes must still be determined. Furthermore, it possible that rapid volumetric changes are driven by factors other than neurotrophic processes. Objectives: We examined whether possible grey matter volumetric (GMV) increases after aiTBS treatment could be driven by increased brain perfusion, measured by Arterial Spin Labeling (ASL). Methods: 46 treatment-resistant depressed patients were randomized to receive 20 sessions of active or sham iTBS applied to the left dorsolateral prefrontal cortex. All sessions were delivered over 4 days at 5 sessions per day (trial registration: http://clinicaltrials.gov/show/NCT01832805). Patients were scanned the day before starting stimulation and three days after aiTBS. Results: There was a significant cluster of increased left hippocampal GMV in the dentate gyrus related to HRSD changes after active aiTBS, but not after sham stimulation. These GMV increases became more pronounced when accounting for changes in cerebral perfusion. Conclusions: Active, but not sham, aiTBS, resulted in acute volumetric changes in parts of the left dentate gyrus, suggesting a connection with adult neurogenesis. Furthermore, taking cerebral perfusion measurements into account impacts on detection of the GMV changes. Whether these hippocampal volumetric changes produced by active aiTBS are necessary for long-term clinical improvement remains to be determined.

Sulfur is abundant in the Universe, but the observed abundance ratio of SH to H2S doesn’t agree with astrochemical models. The authors measure product state-resolved translational energy spectra of photoproducts in a jet-cooled H2S beam as a function of wavelength, showing that SH yield is lower than assumed in the models.

Summary: As a key species in understanding the hydrogen-bonding network transitions between liquid water and ice, the neutral water heptamer is a challenging experimental target, owing to the richness of low-lying isomers. Here, we report the size-specific infrared spectra of confinement-free, neutral water heptamer (H2O)7 based on threshold photoionization using a tunable vacuum ultraviolet free electron laser. The complexity of the observed spectra indicates that many nearly isoenergetic isomers are present at finite temperatures. Two classes of prism- and cage-like structures are identified in a high-pressure pulsed supersonic expansion of water heptamer, in which the former is favored energetically at low temperatures and serves as a major contributor to the experimental spectrum. These findings provide key information for filling the gap between well-studied water hexamer and octamer.

In chemical production processes, the most suitable operation regime for A-Type powders such as typical FCC particles is high-speed fast fluidization owing to their uniquely advantageous properties. Discrete element method (DEM) for modelling fast fluidization with A-Type powders has rarely been reported. How to appropriately select the DEM time step and the stiffness coefficient is one of the most critical problems for stable and accurate calculation. This article mainly discusses the effect of the stiffness coefficient and DEM time step on simulations of A-type FCC particles. In order to describe the effect of both parameters and their complex interaction, a dimensionless relative DEM time step is introduced. In total, nine cases with different numbers of relative time steps are adopted for modelling a microfluidized bed of A-Type FCC particles, the regime of which is proved to be fast fluidization. Results show that three bifurcations occur in all the simulations. Only the moderate relative time step possesses the capability of modelling the process of particle collision and thus predicts the right flow regime with asymmetric and heterogeneous typical fast fluidized structures. When the relative time step increases to large rank, simulations predict untrue fluidization regimes with symmetric and homogeneous structures. Moreover, both over-large and over-low relative time steps cause excessive particle overlap and thus a divergence of simulation. The further optimization of moderate relative DEM time step in relation to real particle property is unidentifiable and is thus an outstanding issue. That the range of the moderate relative time step is limited indicates that the common soft-sphere model is poor at modelling fast fluidization of A-Type particles. It is suggested that possible future work should be focused on improving the simulation frame and employing the molecular dynamics model to more appropriately deal with particle contact.

Free electron lasers provide a state-of-the-art tool to investigate the photochemistry of water. Here, the authors show that highly rotationally excited hydroxyl radicals, so-called “super rotors” existing above the bond dissociation energy, are observed from the photodissociation of water, which may have implications for understanding the interstellar medium.

Mounting evidence shows that brain functions and cognitive states are dynamically changing even in the resting state rather than remaining at a single constant state. Due to the relatively small changes in BOLD (blood-oxygen-level-dependent) signals across tasks, it is difficult to detect the change of cognitive status without requiring prior knowledge of the experimental design. To address this challenge, we present a dynamic graph learning approach to generate an ensemble of subject-specific dynamic graph embeddings, which allows us to use brain networks to disentangle cognitive events more accurately than using raw BOLD signals. The backbone of our method is essentially a representation learning process for projecting BOLD signals into a latent vertex-temporal domain with the greater biological underpinning of brain activities. Specifically, the learned representation domain is jointly formed by (1) a set of harmonic waves that govern the topology of whole-brain functional connectivities and (2) a set of Fourier bases that characterize the temporal dynamics of functional changes. In this regard, our dynamic graph embeddings provide a new methodology to investigate how these self-organized functional fluctuation patterns oscillate along with the evolving cognitive status. We have evaluated our proposed method on both simulated data and working memory task-based fMRI datasets, where our dynamic graph embeddings achieve higher accuracy in detecting multiple cognitive states than other state-of-the-art methods.

This paper presents a new method to recognize human activities based on weighted classification for the features extracted by human body. Towards this end, new features depend on weight taken from image or video used in proposed descriptor. Human pose plays an important role in extracted features; then these features are used as the weight input with classifier. We use machine learning during two steps of training and testing images of standard dataset that can be used during benchmarking the system. Unlike previous methods that need size or length of shapes mainly to represent the cues when machine learning is used to recognize human activities, accurate experimental results coming from appropriate segments of the human body proved the worthiness of proposed method. Twelve activities are used in challenging of availability comparison with dataset to demonstrate our method. The results show that we achieved 87.3% in training set, while in testing set, we achieved 94% in terms of precision.

Melatonin exerts neuroprotective effects on isoflurane-induced cognitive impairment. However, the underlying mechanism has yet to be elucidated. The present study sought to determine if melatonin confers its beneficial effects by acting on mammalian target of rapamycin (mTOR) and attenuates the neuroinflammation in the hippocampus of aged mice. A total of 72 male C57BL/6 mice, 16-month-old, were randomly and equally divided into six groups: (1) the control group (CON); (2) the rapamycin group (RAP); (3) the melatonin group (MEL); (4) the isoflurane group (ISO); (5) the rapamycin + isoflurane group (RAP + ISO); and (6) the melatonin + isoflurane group (MEL + ISO). RAP, RAP + ISO, MEL, MEL + ISO groups received 1 mg/kg/day mTOR inhibitor rapamycin solution or 10 mg/kg/day melatonin solution, respectively, intraperitoneally at 5:00 p.m. for 14 days consecutively. Mice in the CON and ISO groups were administered an equivalent volume of saline. Subsequently, ISO, RAP + ISO, and MEL + ISO groups were exposed to inhale 2% isoflurane for 4 h; the CON, RAP, and MEL mice received only the vehicle gas. Then, the memory function and spatial learning of the mice were examined via the Morris water maze (MWM) test. mTOR expression was detected via Western blot, whereas the concentration of inflammatory cytokines tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6 and that of melatonin was quantified with enzyme-linked immunosorbent assay (ELISA). Melatonin and rapamycin significantly ameliorated the isoflurane-induced cognitive impairment and also led to a decrease in the melatonin levels as well as the expression levels of TNF-α, IL-1β, IL-6, and p-mTOR in the hippocampus. In conclusion, these results showed that melatonin and rapamycin attenuates mTOR expression while affecting the downstream proinflammatory cytokines. Thus, these molecular findings could be associated with an improved cognitive function in mice exposed to isoflurane.

The M55–M51 sub-members of the Middle Ordovician Majiagou Formation in the gas fields of the central Ordos Basin were taken as a case to analyze the vertical zonation characteristics of weathered crust ancient karst and to evaluate and predict reservoirs through plenty of core and thin section observation, with geological background and petrological characteristics as main clue. According to detailed underground geological surveys, description of the typical karst section, systematic sampling, and laboratory test, there mainly developed karst formation rock and transformation rock in the ancient weathered crust in the top of the Majiagou Formation, which were divided into surface karst zone, vertical vadose zone, horizontal underflow zone and deep slow flow zone from surface to subsurface. Karst intensity in each zone has great effect on the storage and flow capacity of the reservoir. In the strong karstification subzone of the horizontal underflow zone with over strong karst, the parent rock was extensively damaged and formed karst caves filled with karst formation rock rarely with pores. In the deep slow flow zone with over weak karstification, there are sedimentary and filling activities. The medium karstification subzone of the horizontal underflow zone and the vertical seepage zone had a moderate karstification, which not only created storage and flow space but also prevented collapse, and were most beneficial to reservoir development. Key words: weathered crust ancient karst, vertical zonation, reservoir evaluation and prediction, Ordos Basin, Majiagou Formation

Effective and efficient spatial normalization of a large population of brain images is critical for many clinical and research studies, but it is technically very challenging. A commonly used approach is to choose a certain image as the template and then align all other images in the population to this template by applying pairwise registration. To avoid the potential bias induced by the inappropriate template selection, groupwise registration methods have been proposed to simultaneously register all images to a latent common space. However, current groupwise registration methods do not make full use of image distribution information for more accurate registration. In this paper, we present a novel groupwise registration method that harnesses the image distribution information by capturing the image distribution manifold using a hierarchical graph with its nodes representing the individual images. More specifically, a low-level graph describes the image distribution in each subgroup, and a high-level graph encodes the relationship between representative images of subgroups. Given the graph representation, we can register all images to the common space by dynamically shrinking the graph on the image manifold. The topology of the entire image distribution is always maintained during graph shrinkage. Evaluations on two datasets, one for 80 elderly individuals and one for 285 infants, indicate that our method can yield promising results.

We implement the Ising model on a structural connectivity matrix describing the brain at two different resolutions. Tuning the model temperature to its critical value, i.e. at the susceptibility peak, we find a maximal amount of total information transfer between the spin variables. At this point the amount of information that can be redistributed by some nodes reaches a limit and the net dynamics exhibits signature of the law of diminishing marginal returns, a fundamental principle connected to saturated levels of production. Our results extend the recent analysis of dynamical oscillators models on the connectome structure, taking into account lagged and directional influences, focusing only on the nodes that are more prone to became bottlenecks of information. The ratio between the outgoing and the incoming information at each node is related to the the sum of the weights to that node and to the average time between consecutive time flips of spins. The results for the connectome of 66 nodes and for that of 998 nodes are similar, thus suggesting that these properties are scale-independent. Finally, we also find that the brain dynamics at criticality is organized maximally to a rich-club w.r.t. the network of information flows.

Internet addiction disorder (IAD) is increasingly recognized as a mental health disorder, particularly among adolescents. The pathogenesis associated with IAD, however, remains unclear. In this study, we aim to explore the encephalic functional characteristics of IAD adolescents at rest using functional magnetic resonance imaging data. We adopted a graph-theoretic approach to investigate possible disruptions of functional connectivity in terms of network properties including small-worldness, efficiency, and nodal centrality on 17 adolescents with IAD and 16 socio-demographically matched healthy controls. False discovery rate-corrected parametric tests were performed to evaluate the statistical significance of group-level network topological differences. In addition, a correlation analysis was performed to assess the relationships between functional connectivity and clinical measures in the IAD group. Our results demonstrate that there is significant disruption in the functional connectome of IAD patients, particularly between regions located in the frontal, occipital, and parietal lobes. The affected connections are long-range and inter-hemispheric connections. Although significant alterations are observed for regional nodal metrics, there is no difference in global network topology between IAD and healthy groups. In addition, correlation analysis demonstrates that the observed regional abnormalities are correlated with the IAD severity and behavioral clinical assessments. Our findings, which are relatively consistent between anatomically and functionally defined atlases, suggest that IAD causes disruptions of functional connectivity and, importantly, that such disruptions might link to behavioral impairments.

Longitudinal brain image analysis is critical for revealing subtle but complex structural and functional changes of brain during aging or in neurodevelopmental disease. However, even with the rapid increase of clinical research and trials, a software toolbox dedicated for longitudinal image analysis is still lacking publicly. To cater for this increasing need, we have developed a dedicated 4D Adult Brain Extraction and Analysis Toolbox (aBEAT) to provide robust and accurate analysis of the longitudinal adult brain MR images. Specially, a group of image processing tools were integrated into aBEAT, including 4D brain extraction, 4D tissue segmentation, and 4D brain labeling. First, a 4D deformable-surface-based brain extraction algorithm, which can deform serial brain surfaces simultaneously under temporal smoothness constraint, was developed for consistent brain extraction. Second, a level-sets-based 4D tissue segmentation algorithm that incorporates local intensity distribution, spatial cortical-thickness constraint, and temporal cortical-thickness consistency was also included in aBEAT for consistent brain tissue segmentation. Third, a longitudinal groupwise image registration framework was further integrated into aBEAT for consistent ROI labeling by simultaneously warping a pre-labeled brain atlas to the longitudinal brain images. The performance of aBEAT has been extensively evaluated on a large number of longitudinal MR T1 images which include normal and dementia subjects, achieving very promising results. A Linux-based standalone package of aBEAT is now freely available at http://www.nitrc.org/projects/abeat.

We analyze simple dynamical network models which describe the limited capacity of nodes to process the input information. For a proper range of their parameters, the information flow pattern in these models is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. We apply this analysis to effective connectivity networks from human EEG signals, obtained by Granger Causality, which has recently been given an interpretation in the framework of information theory. From the distributions of the incoming versus the outgoing values of the information flow it is evident that the incoming information is exponentially distributed whilst the outgoing information shows a fat tail. This suggests that overall brain effective connectivity networks may also be considered in the light of the law of diminishing marginal returns. Interestingly, this pattern is reproduced locally but with a clear modulation: a topographic analysis has also been made considering the distribution of incoming and outgoing values at each electrode, suggesting a functional role for this phenomenon.

Studies for infants are usually hindered by the insufficient image contrast, especially for neonates. Prior knowledge, in the form of atlas, can provide additional guidance for the data processing such as spatial normalization, label propagation, and tissue segmentation. Although it is highly desired, there is currently no such infant atlas which caters for all these applications. The reason may be largely due to the dramatic early brain development, image processing difficulties, and the need of a large sample size.To this end, after several years of subject recruitment and data acquisition, we have collected a unique longitudinal dataset, involving 95 normal infants (56 males and 39 females) with MRI scanned at 3 ages, i.e., neonate, 1-year-old, and 2-year-old. State-of-the-art MR image segmentation and registration techniques were employed, to construct which include the templates (grayscale average images), tissue probability maps (TPMs), and brain parcellation maps (i.e., meaningful anatomical regions of interest) for each age group. In addition, the longitudinal correspondences between age-specific atlases were also obtained. Experiments of typical infant applications validated that the proposed atlas outperformed other atlases and is hence very useful for infant-related studies.We expect that the proposed infant 0-1-2 brain atlases would be significantly conducive to structural and functional studies of the infant brains. These atlases are publicly available in our website, http://bric.unc.edu/ideagroup/free-softwares/.

The beam energy spread at the entrance of an undulator system is of paramount importance for efficient density modulation in high-gain seeded free-electron lasers (FELs). In this paper, the dependences of high harmonic bunching efficiency in high-gain harmonic generation (HGHG), echo-enabled harmonic generation (EEHG) and phase-merging enhanced harmonic generation (PEHG) schemes on the electron beam energy spread distribution are studied. Theoretical investigations and multidimensional numerical simulations are applied to the cases of uniform and saddle beam energy distributions and compared to a traditional Gaussian distribution. It shows that the uniform and saddle electron energy distributions significantly enhance the bunching performance of HGHG FELs, while they almost have no influence on EEHG and PEHG schemes. A further start-to-end simulation example demonstrated that, with the saddle distribution of sliced beam energy spread controlled by a laser heater, the 30th harmonic can be directly generated by a single-stage HGHG scheme for a soft x-ray FEL facility.