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Adverse effects and multidrug resistance remain significant obstacles in conventional cancer therapy. Nanomedicines, with their intrinsic properties such as nano-sized dimensions and tunable surface characteristics, have the potential to mitigate the side effects of traditional cancer treatments. While nanomaterials have been widely applied in cancer treatment, challenges such as low targeting efficiency and poor tumor penetration persist. Recent research has shown that anaerobic bacteria exhibit high selectivity for primary tumors and metastatic cancers, offering good safety and superior tumor penetration capabilities. This suggests that combining nanomaterials with bacteria could complement their respective limitations, opening vast potential applications in cancer therapy. The use of bacteria in combination with nanomaterials for anticancer treatments, including chemotherapy, radiotherapy, and photothermal/photodynamic therapy, has contributed to the rapid development of the field of bacterial oncology treatments. This review explores the mechanisms of bacterial tumor targeting and summarizes strategies for synthesizing bacterial-nanomaterial and their application in cancer therapy. The combination of bacterial-nanomaterial hybrids with modern therapeutic approaches represents a promising avenue for future cancer treatment research, with the potential to improve treatment outcomes for cancer patients. Graphical Abstract

The Projection onto Convex Sets (POCS) interpolation algorithm is widely adopted in seismic data processing, benefiting from its low computational complexity and strong data adaptability. However, the conventional POCS method fails to fully explore the inter-trace correlation information of seismic data, which leads to interpolation results with poor lateral continuity and high interpolation noise. To address this issue, this paper takes the traditional alternating projection framework for biconvex sets as the foundation, introduces a laterally constrained convex set, and thus effectively improves the interpolation quality of seismic data in terms of lateral continuity, signal-to-noise ratio (SNR) and interpolation accuracy. The specific research work is outlined as follows: First, the triple convex sets are defined in detail, the projection formula of the lateral constrained set is derived, and the triple convex set interpolation workflow is established. Second, the convergence of the new algorithm is theoretically proven, and its computational efficiency is compared and analyzed, which provides a reliable theoretical foundation for the stability of the algorithm. Finally, to verify the effectiveness of the proposed method, experiments are conducted on both synthetic seismic data and field seismic data, with a quantitative comparison of the interpolation accuracy between the two algorithms. The results demonstrate that the proposed algorithm significantly enhances interpolation accuracy while ensuring reconstruction efficiency, and therefore possesses excellent practical value.

Forest hydrology, the study of water dynamics within forested catchments, is crucial for understanding the intricate relationship between forest cover and water balances across different scales, from ecosystems to landscapes, or from catchment watersheds. The intensified global changes in climate, land use and cover, and pollution that occurred over the past century have brought about adverse impacts on forests and their services in water regulation, signifying the importance of forest hydrological research as a re-emerging topic of scientific interest. This article reviews the literature on recent advances in forest hydrological research, intending to identify leading countries, institutions, and researchers actively engaged in this field, as well as highlighting research hotspots for future exploration. Through a systematic analysis using VOSviewer, drawing from 17,006 articles retrieved from the Web of Science Core Collection spanning 2000–2022, we employed scientometric methods to assess research productivity, identify emerging topics, and analyze academic development. The findings reveal a consistent growth in forest hydrological research over the past two decades, with the United States, Charles T. Driscoll, and the Chinese Academy of Sciences emerging as the most productive country, author, and institution, respectively. The Journal of Hydrology emerges as the most co-cited journal. Analysis of keyword co-occurrence and co-cited references highlights key research areas, including climate change, management strategies, runoff-erosion dynamics, vegetation cover changes, paired catchment experiments, water quality, aquatic biodiversity, forest fire dynamics and hydrological modeling. Based on these findings, our study advocates for an integrated approach to future research, emphasizing the collection of data from diverse sources, utilization of varied methodologies, and collaboration across disciplines and institutions. This holistic strategy is essential for developing sustainable approaches to forested watershed planning and management. Ultimately, our study provides valuable insights for researchers, practitioners, and policymakers, guiding future research directions towards forest hydrological research and applications.

The Sichuan Basin is a significant region for exploration and development of shale gas in China, and it is essential to clarify the impact of deep shale gas reservoir parameters on cost-effective development at scale to ensure national energy security. Rock physics modeling is a significant means of communicating the physical and elastic parameters of rocks. A rock physics modeling method applicable to fractured shale gas reservoirs is proposed for the current situation of complex fluid relationships in shale gas reservoirs and unclear characteristics of gas identification seismic response. In this paper, based on the Self Consistent Approximation (SCA) model and the differential effective medium (DEM) model, the anisotropic source of shale is used as a starting point to add bound water, kerogen, clay, and brittle minerals, the Schoenberg linear slip theory is used to add fracture disturbance effects, and then the Brown–Korringa model is used to perform fluid replacement under anisotropic conditions. Finally a rock physics model applicable to fractured shale gas reservoirs is obtained, and the established rock physics model is used for analysis of elastic parameters, Thomsen parameters, and fracture weakness parameters. Rock physics tests were performed on shale in southern Sichuan as an example. The experimental results show that the model established by the process can accurately invert the longitudinal and transverse wave velocities of the shale, which can provide a conceptual basis for the study of fractured shale gas reservoirs.

Brittleness is an important factor that indicates shale properties, as well as fracturability, and it can be well-represented using the elasticity parameter Eρ. Seismic inversion allows direct access to the Eρ ofbrittleness parameters. Seismic inversion is a typical ill-posed problem that has an enormous multiplicity of solutions. In order to invert reservoir brittleness parameters more stably and reliably, a direct inversion method for determining brittleness parameters based on a reweighted Lp-norm is proposed, and the reweighted Lp method is introduced to brittleness parameter inversion for the first time. The alternating direction method of multipliers (ADMM) is used to establish the inversion structure and to optimize the objective function in blocks, which effectively improves the convergence speed. We first introduce a reweighted Lp method and establish a pre-stack inversion objective function based on the reweighted Lp method. Then, theoretical simulation data are applied to compare the inversion outcomes of the new method with those of the traditional method, and the effect of the method in this manuscript is verified. Finally, the feasibility of this method is further verified using actual data for experimental analysis. Through an analysis of the experimental results, we find that this method can be well-applied to seismic pre-stack inversion calculation and provides a new direct inversion method for the determination of brittleness parameters for exploration geophysics.

Background Brain abscesses are uncommon but life-threatening in extremely preterm (EP, Gestational Age < 28 weeks) infants. The information of long-time follow-up is rare, but very few cases presented almost intact neural function after injury. Case presentation We report the clinical course and the outcome of a 27-week preterm infant with multiple brain abscesses. The brain abscesses were detected by cranial magnetic resonance imaging (MRI) and were treated with surgical aspiration twice and a 7-week course of intravenous antibiotics. The patient had two episodes of seizure like activities at 8 and 11 years old respectively, whereas she had normal results of electroencephalogram (EEG). MRI showed encephalomalacia and periventricular leukomalacia. Otherwise, she had no obvious neurological deficits based on multiple physical examination and her intellectual quotient (IQ) was in normal range in the long-time follow-up. Conclusions Early diagnosis of brain abscesses and appropriate therapy can improve the prognosis. Furthermore, this case report provides an example of the possible neuroplasticity of brain in EP infants.

Conventional sparse spike deconvolution algorithms that are based on the iterative shrinkage-thresholding algorithm (ISTA) are widely used. The aim of this type of algorithm is to obtain accurate seismic wavelets. When this is not fulfilled, the processing stops being optimum. Using a recurrent neural network (RNN) as deep learning method and applying backpropagation to ISTA, we have developed an RNN-like ISTA as an alternative sparse spike deconvolution algorithm. The algorithm is tested with both synthetic and real seismic data. The algorithm first builds a training dataset from existing well-logs seismic data and then extracts wavelets from those seismic data for further processing. Based on the extracted wavelets, the new method uses ISTA to calculate the reflection coefficients. Next, inspired by the backpropagation through time (BPTT) algorithm, backward error correction is performed on the wavelets while using the errors between the calculated reflection coefficients and the reflection coefficients corresponding to the training dataset. Finally, after performing backward correction over multiple iterations, a set of acceptable seismic wavelets is obtained, which is then used to deduce the sequence of reflection coefficients of the real data. The new algorithm improves the accuracy of the deconvolution results by reducing the effect of wrong seismic wavelets that are given by conventional ISTA. In this study, we account for the mechanism and the derivation of the proposed algorithm, and verify its effectiveness through experimentation using theoretical and real data.

In shale gas development, fracture density is an important lithologic parameter to properly characterize reservoir reconstruction, establish a fracturing scheme, and calculate porosity and permeability. The traditional methods usually assume that the fracture reservoir is one set of aligned vertical fractures, embedded in an isotropic background, and estimate some alternative parameters associated with fracture density. Thus, the low accuracy caused by this simplified model, and the intrinsic errors caused by the indirect substitution, affect the estimation of fracture density. In this paper, the fractured rock of monoclinic symmetry assumes two non-orthogonal vertical fracture sets, embedded in a transversely isotropic background. Firstly, assuming that the fracture radius, width, and orientation are known, a new form of P-wave reflection coefficient, in terms of weak anisotropy (WA) parameters and fracture density, was obtained by substituting the stiffness coefficients of vertical transverse isotropic (VTI) background, normal, and tangential fracture compliances. Then, a linear amplitude versus offset and azimuth (AVOA) inversion method, of WA parameters and fracture density, was constructed by using Bayesian theory. Tests on synthetic data showed that WA parameters, and fracture density, are stably estimated in the case of seismic data containing a moderate noise, which can provide a reliable tool in fracture prediction.

Due to complex geological conditions and instrument inaccuracy, raw seismic data are often characterized by low resolution. Deep learning is an emerging technique for seismic resolution improvement; however, its performance is often limited by the small amount of labeled data available. In this paper, we design a deep feature mining network (DFMN) to deal with this issue. DFMN has three components: shallow feature extraction block (SFB), deep feature mining block (DFB), and enhanced reconstruction block (ERB). First, the SFB component uses multi-scale kernels to learn rich information from low-resolution data. The convolutions incorporate the benefits of different kernel sizes, which are effective for shallow feature extraction. Second, the DFB component employs a dual-branch network architecture for deep feature mining. The dual-branch network learns more complementary features than a single-branch network, thus alleviating the requirement for large amounts of training data. Third, the ERB component combines the shuffled image and the interpolated image during reconstruction. Interpolated images, incorporating prior knowledge, can provide more contextual information in our model. The results show that DFMN is superior to a traditional upscaling algorithm and other deep learning methods in terms of (1) perceptual effects: more complete structural information, such as texture details; (2) quantitative evaluation indices: higher PSNR and SSIM; and (3) generalization ability: better performance on other data.

Based on the energy ratio method, an automatic picking method with strong noise resistance is proposed. It considers the influence of the current point's position on the first-arrival characteristic value. Specifically, an outlier detection technique is proposed to eliminate abnormal first arrivals for low signal-to-noise ratio (SNR) seismic data. First, the first arrivals of adjacent shots obtained by the new method are arranged according to the offsets. Then, combined with the distribution characteristics of the first arrivals, a symmetric window centered on the current point is established as the calculation range, and the distance-based outlier detection method is adopted for the abnormal first arrivals. The size of the calculation time window is determined by scanning the given value range. In order to optimize the processing results, we further propose an outlier detection method based on grid density. After this step, the abnormal first arrivals will be further eliminated. Following these steps, the abnormal first arrivals of all shots can be removed effectively. The actual data processing results show that the proposed program can accurately pick up the first arrivals and has a good performance in detecting the abnormal first arrivals.