A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes

Commerce as a Service Solution Accelerates Transition to E-commerce for Traditional Manufacturing Enterprises and Retailers

Using internet to operate business has become very important for traditional manufacturing enterprises and retailers. These enterprises are facing great risk when entering e-commerce due to lack of experiences and large volume of initial IT investment. This study analyzed the Commerce as a Service (CaaS) solution which can perfectly solve these problems. The solution is based on leading e-commerce platform and cloud computing technology, which provides small to medium sized clients with a low cost alternative e-commerce. Clients will be relieved of the responsibility of managing an IT shop while still maintaining full control over their site through business user tools

Deep learning in crowd counting: A survey

Counting high-density objects quickly and accurately is a popular area of research. Crowd counting has significant social and economic value and is a major focus in artificial intelligence. Despite many advancements in this field, many of them are not widely known, especially in terms of research data. The authors proposed a three-tier standardised dataset taxonomy (TSDT). The Taxonomy divides datasets into small-scale, large-scale and hyper-scale, according to different application scenarios. This theory can help researchers make more efficient use of datasets and improve the performance of AI algorithms in specific fields. Additionally, the authors proposed a new evaluation index for the clarity of the dataset: average pixel occupied by each object (APO). This new evaluation index is more suitable for evaluating the clarity of the dataset in the object counting task than the image resolution. Moreover, the authors classified the crowd counting methods from a data-driven perspective: multi-scale networks, single-column networks, multi-column networks, multi-task networks, attention networks and weak-supervised networks and introduced the classic crowd counting methods of each class. The authors classified the existing 36 datasets according to the theory of three-tier standardised dataset taxonomy and discussed and evaluated these datasets. The authors evaluated the performance of more than 100 methods in the past five years on different levels of popular datasets. Recently, progress in research on small-scale datasets has slowed down. There are few new datasets and algorithms on small-scale datasets. The studies focused on large or hyper-scale datasets appear to be reaching a saturation point. The combined use of multiple approaches began to be a major research direction. The authors discussed the theoretical and practical challenges of crowd counting from the perspective of data, algorithms and computing resources. The field of crowd counting is moving towards combining multiple methods and requires fresh, targeted datasets. Despite advancements, the field still faces challenges such as handling real-world scenarios and processing large crowds in real-time. Researchers are exploring transfer learning to overcome the limitations of small datasets. The development of effective algorithms for crowd counting remains a challenging and important task in computer vision and AI, with many opportunities for future research.BHF, AA/18/3/34220Hope Foundation for Cancer Research, RM60G0680GCRF, P202PF11;Sino‐UK Industrial Fund, RP202G0289LIAS, P202ED10, P202RE969Data Science Enhancement Fund, P202RE237Sino‐UK Education Fund, OP202006Fight for Sight, 24NN201Royal Society International Exchanges Cost Share Award, RP202G0230MRC, MC_PC_17171BBSRC, RM32G0178B

Identification of high-quality cancer prognostic markers and metastasis network modules

There has been great interest in attempting to identify gene expression signatures that predict cancer survival. In this study a new algorithm is developed to analyse gene expression datasets that accurately classify both ER+ and ER− breast cancers into low- and high-risk groups

Parents\u27 Attitudes as Mediators Between Knowledge and Behaviours in Unintentional Injuries at Home of Children Aged 0-3 in Shanghai, Eastern China: A Cross-Sectional Study

Objective: Parental behaviours are important in preventing unintentional injury at home among young children. Previous research showed an inconsistent relationship between knowledge and behaviours, indicating that the mechanisms may vary for different behaviours. This study aimed to examine the mediating roles of different attitudes in the mechanism of knowledge acting on different behaviours. Design: Cross-sectional study. Setting: Eastern China Participants: Participants were recruited using stratified community-based sampling. A total of 488 parents of children aged 0–3 years participated in the study and 476 (97.5%) valid questionnaires were recovered. Primary outcome measures: Parents’ knowledge, attitudes (including injury attribution, preventability and responsibility) and behaviours (including supervision behaviours, risky behaviours and providing a safe home environment). Results: The results of mediation analysis showed that the mediator variables were different for different behaviours and that all associations were positive. Parents’ knowledge (β 0.19, 95% CI 0.13 to 0.24) and attitude of injury attribution (β 0.37, 95% CI 0.21 to 0.46) were directly associated with risky behaviours. Attitude of preventability was directly associated with parents’ supervision behaviour (β 0.27, 95% CI 0.14 to 0.40). Parents’ attitude of preventability mediated the positive association between knowledge, attitudes of injury attribution and responsibility, and supervision behaviours, as well as providing a safe home environment. In addition, the occurrence of child injuries at home was directly associated with home environment (β −0.41, 95% CI −0.82 to −0.01). Conclusions: The current findings confirm that attitudes play varying mediating roles between knowledge and different behaviours. An important recommendation is that parents’ attitudes, especially towards preventability and responsibility, need to be considered when health providers develop health education programmes targeted at improving parental supervision behaviours and providing a safe home environment

Bound states at disclinations: an additive rule of real and reciprocal space topology

Focusing on the two-dimensional (2D) Su-Schrieffer-Heeger (SSH) model, we propose an additive rule between the real-space topological invariant s of disclinations (related to the Burgers vector B) and the reciprocal-space topological invariant p of bulk wave functions (the vectored Zak phase). The disclination-induced bound states in the 2D SSH model appear only if (s + p/2π) is nonzero modulo the lattice constant. These disclination-bound states are robust against perturbations respecting C4 point group symmetry and other perturbations within an amplitude determined by p. Besides the disclination-bound states, the proposed additive rule also suggests that a half-bound state extends over only half of a sample and a hybrid-bound state, which always have a nonvanishing component of s + p/2π

Comprehensive analysis of LRR-RLKs and key gene identification in Pinus massoniana resistant to pine wood nematode

Pinus massoniana is a pioneer tree widely planted for afforestation on barren hills in southern China where the total planted area is 8.04 million ha. The invasive pine wood nematode (Bursaphelenchus xylophilus) poses a serious threat to the survival of P. massoniana. Plant resistance genes encoded by leucine-rich repeat-containing transmembrane-receptor proteins play important roles in plant defense. Leucine-rich repeat receptor-like kinases (LRR-RLKs), the largest subfamily of the RLK protein family, play an important role in sensing stress signals in plants. However, the LRR-RLKs of P. massoniana have not been characterized previously, and their role in resistance to B. xylophilus is unknown. In this study, 185 members of the LRR-RLK subfamily were identified in P. massoniana and were categorized into 14 subgroups. Transcriptomic and quantitative real-time RT-PCR analyses showed that PmRLKs32 was highly expressed in the stem tissue after inoculation with B. xylophilus. The gene exhibited high homology with AtFLS2 of Arabidopsis thaliana. PmRLKs32 was localized to the plasma membrane and was significantly upregulated in nematode-resistant and nematode-susceptible individuals. The transient expression of PmRLKs32 resulted in a burst of reactive oxygen species production in P. massoniana and Nicotiana benthamiana seedlings. These results lay a foundation for further exploration of the regulatory mechanism of LRR-RLKs in response to biotic stress in P. massoniana

The RNA landscape of Dunaliella salina in response to short-term salt stress

Using the halotolerant green microalgae Dunaliella salina as a model organism has special merits, such as a wide range of salt tolerance, unicellular organism, and simple life cycle and growth conditions. These unique characteristics make it suitable for salt stress study. In order to provide an overview of the response of Dunaliella salina to salt stress and hopefully to reveal evolutionarily conserved mechanisms of photosynthetic organisms in response to salt stress, the transcriptomes and the genome of the algae were sequenced by the second and the third-generation sequencing technologies, then the transcriptomes under salt stress were compared to the transcriptomes under non-salt stress with the newly sequenced genome as the reference genome. The major cellular biological processes that being regulated in response to salt stress, include transcription, protein synthesis, protein degradation, protein folding, protein modification, protein transport, cellular component organization, cell redox homeostasis, DNA repair, glycerol synthesis, energy metabolism, lipid metabolism, and ion homeostasis. This study gives a comprehensive overview of how Dunaliella salina responses to salt stress at transcriptomic level, especially characterized by the nearly ubiquitous up-regulation of the genes involving in protein folding, DNA repair, and cell redox homeostasis, which may confer the algae important mechanisms to survive under salt stress. The three fundamental biological processes, which face huge challenges under salt stress, are ignored by most scientists and are worth further deep study to provide useful information for breeding economic important plants competent in tolerating salt stress, other than only depending on the commonly acknowledged osmotic balance and ion homeostasis

Phase Modulation of (1T-2H)-MoSe2/TiC-C Shell/Core Arrays via Nitrogen Doping for Highly Efficient Hydrogen Evolution Reaction

Tailoring molybdenum selenide electrocatalysts with tunable phase and morphology is of great importance for advancement of hydrogen evolution reaction (HER). In this work, phase‐ and morphology‐modulated N‐doped MoSe2/TiC‐C shell/core arrays through a facile hydrothermal and postannealing treatment strategy are reported. Highly conductive TiC‐C nanorod arrays serve as the backbone for MoSe2 nanosheets to form high‐quality MoSe2/TiC‐C shell/core arrays. Impressively, continuous phase modulation of MoSe2 is realized on the MoSe2/TiC‐C arrays. Except for the pure 1T‐MoSe2 and 2H‐MoSe2, mixed (1T‐2H)‐MoSe2 nanosheets are achieved in the N‐MoSe2 by N doping and demonstrated by spherical aberration electron microscope. Plausible mechanism of phase transformation and different doping sites of N atom are proposed via theoretical calculation. The much smaller energy barrier, longer HSe bond length, and diminished bandgap endow N‐MoSe2/TiC‐C arrays with substantially superior HER performance compared to 1T and 2H phase counterparts. Impressively, the designed N‐MoSe2/TiC‐C arrays exhibit a low overpotential of 137 mV at a large current density of 100 mA cm−2, and a small Tafel slope of 32 mV dec−1. Our results pave the way to unravel the enhancement mechanism of HER on 2D transition metal dichalcogenides by N doping

Tightly-bound and room-temperature-stable excitons in van der Waals degenerate-semiconductor Bi4O4SeCl2 with high charge-carrier density

Excitons, which represent a type of quasi-particles consisting of electron-hole pairs bound by the mutual Coulomb interaction, were often observed in lowly-doped semiconductors or insulators. However, realizing excitons in the semiconductors or insulators with high charge carrier densities is a challenging task. Here, we perform infrared spectroscopy, electrical transport, ab initio calculation, and angle-resolved-photoemission spectroscopy studies of a van der Waals degenerate-semiconductor Bi4O4SeCl2. A peak-like feature (i.e., alpha peak) is present around ~ 125 meV in the optical conductivity spectra at low temperature T = 8 K and room temperature. After being excluded from the optical excitations of free carriers, interband transitions, localized states and polarons, the alpha peak is assigned as the exciton absorption. Moreover, assuming the existence of weakly-bound excitons--Wannier-type excitons in this material violates the Lyddane-Sachs-Teller relation. Besides, the exciton binding energy of ~ 375 meV, which is about an order of magnitude larger than those of conventional semiconductors, and the charge-carrier concentration of ~ 1.25 * 10^19 cm^-3, which is higher than the Mott density, further indicate that the excitons in this highly-doped system should be tightly bound. Our results pave the way for developing the optoelectronic devices based on the tightly-bound and room-temperature-stable excitons in highly-doped van der Waals degenerate semiconductors.Comment: Accepted by Communications Material