Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.

BACKGROUND: Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data. METHODS: Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features. RESULTS: For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer. CONCLUSION: This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

Immunological profiling for short-term predictive analysis in PD-1/PD-L1 therapy for lung cancer.

BACKGROUND: Immune checkpoint inhibitors, such as anti-programmed cell death-1 (PD-1) and PD-1 ligand-1 (PD-L1) antibodies, have achieved breakthrough results in improving long-term survival rates in lung cancer. Although high levels of PD-L1 expression and tumor mutational burden have emerged as pivotal biomarkers, not all patients derive lasting benefits, and resistance to immune checkpoint blockade remains a prevalent issue. Comprehending the immunological intricacies of lung cancer is crucial for uncovering the mechanisms that govern responses and resistance to immunomodulatory treatments. This study aimed to explore the potential of peripheral immune markers in predicting treatment efficiency among lung cancer patients undergoing PD-1/PD-L1 checkpoint inhibitors. METHODS: This study enrolled 71 lung cancer patients undergoing PD-1/PD-L1 inhibitor therapy and 20 healthy controls. Immune cell subsets (CD4 + T cells, CD8 + T cells, B cells, NK cells, and NKT cells), phenotypic analysis of T cells and B cells, and PMA/Ionomycin-stimulated lymphocyte function assay were conducted. RESULTS: Lung cancer patients exhibited significant alterations in immune cell subsets, notably an increased percentage of Treg cells. Post-treatment, there were substantial increases in absolute numbers of CD3 + T cells, CD8 + T cells, and NKT cells, along with heightened HLA-DR expression on CD3 + T and CD8 + T cells. Comparison between complete remission and non-complete remission (NCR) groups showed higher Treg cell percentages and HLA-DR + CD4 + T cells in the NCR group. CONCLUSION: The study findings suggest potential predictive roles for immune cell subsets and phenotypes, particularly Treg cells, HLA-DR + CD4 + T cells, and naïve CD4 + T cells, in evaluating short-term PD-1/PD-L1 therapy efficacy for lung cancer patients. These insights offer valuable prospects for personalized treatment strategies and underscore the importance of immune profiling in lung cancer immunotherapy.

A Lu3Sc2Ga3O12: Eu3+ red-emitting phosphor with excellent optical properties and thermal stability was obtained by partially replacing Lu3+ with Gd3+ / Y3.

A novel red phosphor Lu3(1-x)Sc2Ga3O12: xEu3+(0 ≤ x ≤ 0.3) was successfully prepared by high temperature solid state method. The Lu2.4Sc2Ga3O12: 0.2Eu3+ phosphor shows strong high internal quantum efficiency and thermal stability with values of 64.79 % and 87.0 %, respectively. Based on Lu2.4Sc2Ga3O12: 0.2Eu3+ phosphor, the partial replacement of Lu3+ ions in the host by Gd3+ / Y3+ ions changes the local crystal field environment of Eu3+ ions, resulting in wonderful changes in the luminous center, and the luminous intensity at 593 nm is increased by 3.66 and 3.54 times, respectively. The decay time of Eu3+ ions is analyzed from the perspective of dynamics, and the reasons for the enhancement of luminescence after partial replacement of Lu3+ ions are discussed in detail from two aspects of phosphor structure and crystal field effect around Eu3+ ions. In addition, with the substitution of Gd3+ / Y3+ ions, the thermal stability of the sample is 90.3 %/89.4 % with excellent low thermal quenching. The thermal quenching mechanism is described by combining Debye temperature and activation energy. The sample also has a high internal quantum efficiency IQE=79.03 % / 78.24 %. Finally, under the excitation of 365 nm chip, the phosphors of Lu2.34Sc2Ga3O12: 0.2Eu3+, 0.02Gd3+ and Lu2.34Sc2Ga3O12: 0.2Eu3+, 0.02Y3+ synthesized R-LED device has extremely high color rendering index, Ra is 78.23/77.15 and color temperature is 1640.38 K/1642.97 K. The experimental results show that the Lu2.34Sc2Ga3O12: 0.2Eu3+, 0.02Gd3+ / Y3+ phosphors prepared has a wide application prospect in w-LED devices.

Evaluating the diagnostic and therapeutic significance of KL-6 in patients with interstitial lung diseases.

Background: This study aimed to assess the diagnostic value of Krebs von den Lungen-6 (KL-6), Surfactant protein-A (SP-A), SP-D and molecular matrixmetalloproteinase-7 (MMP-7) in discriminating patients with interstitial lung diseases (ILDs) from disease control subjects. Methods: Serum levels of KL-6, SP-A, SP-D and MMP-7 were measured in both the ILD and non-ILD (NILD) groups. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic potential of these markers and laboratory indices. High-resolution computed tomography (HRCT) fibrosis scores were determined, and their correlation with the serum markers was analyzed. Results: Serum levels of KL-6 and MMP-7 were significantly elevated in the ILD group compared to the control group, while no significant differences were observed for SP-A and SP-D. ROC analysis of KL-6 demonstrated superior diagnostic accuracy, with a sensitivity of 76.36%, specificity of 91.07%, and an area under curve (AUC) of 0.902 (95%CI 0.866-0.945). These findings were consistent across an additional cohort. Correlation analysis revealed a link between KL-6 levels at initial diagnosis and HRCT fibrosis scores, indicating disease severity. Moreover, a negative correlation was found between KL-6 and pulmonary function indices, reflecting disease progression. Patients with increased 12-month HRCT fibrosis score showed higher lactate dehydrogenase (LDH) levels, with LDH exhibiting an AUC of 0.767 (95% CI: 0.520-0.927) as a predictor of progression. Conclusions: Serum KL-6 detection proves to be a valuable tool for accurately distinguishing ILDs from control subjects. While KL-6 shows a correlation with HRCT fibrosis scores and a negative association with pulmonary function indices, its predictive value for ILDs prognosis is limited. Trial registration: This study received retrospective approval from the Ethical Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (institutional review board ID: TJ-IRB20210331, date: 2021.03.30).

Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases.

OBJECTIVE: Systemic autoimmune rheumatic diseases (SARDs) encompass a diverse group of complex conditions with overlapping clinical features, making accurate diagnosis challenging. This study aims to develop a multiclass machine learning (ML) model for early-stage SARDs classification using accessible laboratory indicators. METHODS: A total of 925 SARDs patients were included, categorised into SLE, Sjögren's syndrome (SS) and inflammatory myositis (IM). Clinical characteristics and laboratory markers were collected and nine key indicators, including anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (indirect immunofluorescence assay), haemoglobin (Hb), platelet, neutrophil percentage and cytoplasmic patterns (AC-19, AC-20), were selected for model building. Various ML algorithms were used to construct a tripartite classification ML model. RESULTS: Patients were divided into two cohorts, cohort 1 was used to construct a tripartite classification model. Among models assessed, the random forest (RF) model demonstrated superior performance in distinguishing SLE, IM and SS (with area under curve=0.953, 0.903 and 0.836; accuracy= 0.892, 0.869 and 0.857; sensitivity= 0.890, 0.868 and 0.795; specificity= 0.910, 0.836 and 0.748; positive predictive value=0.922, 0.727 and 0.663; and negative predictive value= 0.854, 0.915 and 0.879). The RF model excelled in classifying SLE (precision=0.930, recall=0.985, F1 score=0.957). For IM and SS, RF model outcomes were (precision=0.793, 0.950; recall=0.920, 0.679; F1 score=0.852, 0.792). Cohort 2 served as an external validation set, achieving an overall accuracy of 87.3%. Individual classification performances for SLE, SS and IM were excellent, with precision, recall and F1 scores specified. SHAP analysis highlighted significant contributions from antibody profiles. CONCLUSION: This pioneering multiclass ML model, using basic laboratory indicators, enhances clinical feasibility and demonstrates promising potential for SARDs classification. The collaboration of clinical expertise and ML offers a nuanced approach to SARDs classification, with potential for enhanced patient care.

A novel single-phase color tunable LiYGeO4:Dy3+, Eu3+ phosphor exhibiting warm white light and excellent thermal stability.

LiYGeO4 phosphors doped with Dy3+ and Eu3+ ions were synthesized using the solid phase method, and their color characteristics were adjustable. The bandgap value of LiYGeO4 calculated by diffuse reflection data is very close to the theoretical value of 3.669 eV, indicating that LiYGeO4 is an ideal candidate for doped rare earth activated ions. The analysis of the emission spectra and fluorescence attenuation curves of Dy3+ and Eu3+ co-doped LiYGeO4 phosphors revealed a clear energy transfer process: energy transfer from Dy3+ to Eu3+. Analysis of emission spectra and fluorescence attenuation curves revealed a transfer of energy from Dy3+ to Eu3+. This transfer mechanism is attributed to the dipole-dipole interactions. In addition, by constantly adjusting the doping levels of Dy3+ and Eu3+, a warm white phosphor with a color temperature of 3881 K was obtained. Finally, the emission intensity of the LiYGeO4:0.015Dy3+,0.02Eu3+ phosphor at 423 K was 86%, indicating that the phosphor has excellent thermal stability and 40% internal quantum efficiency, which proves the potential application of the LiYGeO4 phosphor in white light-emitting diodes (w-LEDs).

Garnet Structure-Activated Warm White Light Phosphors Ca3Al2Ge3O12: Dy3+, Eu3+ with Ultrahigh Thermal Stability and Tunable Luminescence.

A series of Ca3Al2Ge3O12: xDy3+, yEu3+ phosphors were successfully prepared by the high-temperature solid-phase method. The phase and morphology of the phosphors were studied by means of Rietveld refinement and scanning electron microscopy. The results show that the phase is pure, and the crystal structure is the Ia3̅d space group. In the Ca3Al2Ge3O12: xDy3+ phosphors, using 380 nm excitation, phosphors showed blue (4F9/2 → 6H15/2) and yellow (4F9/2 → 6H13/2) emission peaks at 481 and 581 nm, respectively. In Ca3Al2Ge3O12: xDy3+, yEu3+ phosphors, the energy transfer was inferred by the spectrum overlap of Dy3+ and Eu3+, and the lifetime attenuation was analyzed from the perspective of dynamics; finally, the band gap structure of the phosphors was analyzed by combining diffuse reflection spectra with the first principle, and the energy transfer mechanism and luminescence mechanism were elaborated by combining theory and practice. The transition from blue white light to red light can be achieved by tuning the range of y in Ca3Al2Ge3O12: 0.015Dy3+, yEu3+. Wherein, when y = 0.07, phosphors, the chromaticity coordinate of warm white CIE is (0.3932, 0.3203), the color temperature is 3093 K, and the warm white light is synthesized. The thermal stability of the synthesized warm white phosphors is 90.1% (423 K), the thermal sensing factors are Samax = 5.51 × 10-4 K-1 (303 K) and Srmax = 0.0359% K-1 (303 K), and the actual quantum efficiency is IQE = 52.48%. These results prove that Ca3Al2Ge3O12: Dy3+, Eu3+ have good application prospects as single-component warm w-LED devices.

A de novo evolved gene contributes to rice grain shape difference between indica and japonica.

The role of de novo evolved genes from non-coding sequences in regulating morphological differentiation between species/subspecies remains largely unknown. Here, we show that a rice de novo gene GSE9 contributes to grain shape difference between indica/xian and japonica/geng varieties. GSE9 evolves from a previous non-coding region of wild rice Oryza rufipogon through the acquisition of start codon. This gene is inherited by most japonica varieties, while the original sequence (absence of start codon, gse9) is present in majority of indica varieties. Knockout of GSE9 in japonica varieties leads to slender grains, whereas introgression to indica background results in round grains. Population evolutionary analyses reveal that gse9 and GSE9 are derived from wild rice Or-I and Or-III groups, respectively. Our findings uncover that the de novo GSE9 gene contributes to the genetic and morphological divergence between indica and japonica subspecies, and provide a target for precise manipulation of rice grain shape.

Detection of serum human neutrophil lipocalin is an effective biomarker for the diagnosis and monitoring of children with bacterial infection.

BACKGROUND: The study aimed to investigate the diagnostic efficiency of human neutrophil lipocalin (HNL) in bacterial infections in children. METHODS: This study included 49 pediatric patients with bacterial infections, 37 patients with viral infections, 30 patients with autoimmune diseases (AID) and 41 healthy controls (HCs). HNL, procalcitonin (PCT), C-reactive protein (CRP), white blood cell (WBC) and neutrophil counts were detected in the initial diagnosis and the following days. RESULTS: In the patients with bacterial infections, the levels of HNL, PCT, CRP, WBC and neutrophils were significantly increased than that of disease controls and HCs. The dynamic of these markers was monitored during antibiotic treatment. The level of HNL was decreased rapidly in patients with effective treatment, but maintained at high levels in deteriorated patients according to the clinical progression. CONCLUSIONS: HNL detection is an effective biomarker to identify bacterial infections from viral infections and other AIDs, and has potential value to evaluate the effect of antibiotic treatment in pediatric patients.

Assessing freight structure and its effect on transport CO2 emissions: heterogeneous and mediating effect analysis.

Understanding the effect of freight structure (FS) on carbon dioxide (CO2) emissions is significant for policymakers in the transport sector. However, few studies have investigated fully how the freight structure influences transport CO2 emissions. In the paper, a comprehensive freight structure index is proposed to assess the freight structure in 16 provinces of China during 2005-2019. Then, the heterogeneous effect of the freight structure and other factors on CO2 emissions are quantified by the quantile regression model developed. The mediating effect between freight structure and CO2 emissions is further examined. And the panel causality test is used to explore the directions of causality among variables. The main results indicate that (1) the level of freight structure shows a distribution of increase from western and central regions to eastern regions in 16 provinces. (2) Freight structure has a positive effect on transport CO2 emissions, and its effect is significantly heterogeneous in all quantiles. (3) Freight structure has an indirect impact on CO2 emissions through the scale effect. (4) The bidirectional causality is confirmed between economic growth, industrial structure, and information and communication technology with carbon emissions. A unidirectional causality from CO2 emissions to freight structure is also found. Finally, some policy recommendations are proposed for sustainable transport development in China. The results facilitate the formulation of the regionally tailored carbon emission reduction strategy in the transport sector.

Genetic and molecular factors in determining grain number per panicle of rice.

It was suggested that the most effective way to improve rice grain yield is to increase the grain number per panicle (GN) through the breeding practice in recent decades. GN is a representative quantitative trait affected by multiple genetic and environmental factors. Understanding the mechanisms controlling GN has become an important research field in rice biotechnology and breeding. The regulation of rice GN is coordinately controlled by panicle architecture and branch differentiation, and many GN-associated genes showed pleiotropic effect in regulating tillering, grain size, flowering time, and other domestication-related traits. It is also revealed that GN determination is closely related to vascular development and the metabolism of some phytohormones. In this review, we summarize the recent findings in rice GN determination and discuss the genetic and molecular mechanisms of GN regulators.

The nonlinear effect of land freight structure on carbon emission intensity: new evidence from road and rail freight in China.

The extensive literature has debated the varying effects of factors on carbon dioxide (CO2) emissions. However, it has paid little attention to land freight structure (FS), including road and rail freight share, which may have different effects on CO2 emissions. Based on the data from 6 eastern provinces in China during 2005-2019, the panel threshold model is used to explore the dynamic influence mechanism of road and rail freight share on transport carbon emission intensity (CE), respectively. The results show different nonlinear relationships between the share of road and rail freight and transport carbon emission intensity. First, the effect of road freight share on carbon emission intensity is all positive across different stages of trade openness, while such effect goes through a process of increasing and then decreasing with the level of trade openness improving. Second, the driving effect of rail freight share on carbon emission intensity exhibits a "negative-positive-negative" feature as the level of trade openness increases. Third, trade openness generates a double-threshold effect on carbon emission intensity. The differentiated nonlinear effects provide significant evidence of the modal shift from road to rail freight, which would be effective to alleviate transport CO2 emissions.

The plant streptolysin S (SLS)-associated gene B confers nitroaromatic tolerance and detoxification.

Nitroaromatic compounds, as the important chemical feedstock, have caused widespread environmental contaminations, and exhibited high toxicity and mutagenic activity to nearly all living organisms. The clean-up of nitroaromatic-contaminated soil and water has long been a major international concern. Here, we uncovered the role of a novel nitroreductase family gene, streptolysin S (SLS)-associated gene B (SagB), in enhancing nitroaromatic tolerance and detoxification of plants, and its potential application in phytoremediation of nitroaromatic contaminations. The expression of both the Arabidopsis and rice SagB genes is significantly induced by multiple hazardous nitroaromatic substances, including explosive pollutant 2,4,6-trinitrotoluene (TNT), natural compound 1-nitropyrene (1-NP) and herbicide pendimethalin (Pen). In vitro and in vivo evidences revealed that plant SagBs possess activities in degradation of these nitroaromatic substances. Arabidopsis and rice transgenic assays suggested that plant SagB genes increase tolerance and detoxification of nitroaromatic through facilitating its transformation to the amino derivative. More importantly, overexpression of plant SagBs increase their ability in TNT uptake, and remove more TNT from the growth culture. Our findings shed novel insights into a plant endogenous nitroreductase-mediated nitroaromatic tolerance and detoxification, and provide a new gene target for phytoremediation of nitroaromatic-contaminated environments.

Genistein mitigates oxidative stress and inflammation by regulating Nrf2/HO-1 and NF-κB signaling pathways in hypoxic-ischemic brain damage in neonatal mice.

Background: Oxidative stress and neuroinflammation play crucial roles in the progression of neonatal hypoxic-ischemic brain damage (HIBD). Genistein, a natural phytoestrogen, has been found to protect against ischemic brain injury. However, its effects and potential mechanisms in HIBD have not yet been explored. Methods: A neonatal mouse model of hypoxia-ischemia (HI) and a cell model of oxygen-glucose deprivation/reperfusion (OGD/R) were employed. In the in vivo study, genistein (10 mg/kg; ip) was administered in mice once daily for 3 consecutive days before the operation and once immediately after HI. The effects of genistein treatment on acute brain damage and long-term responses were evaluated. Neuronal injury and apoptosis were estimated using hematoxylin and eosin (H&E) and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining, respectively. The expression of apoptosis-related proteins were also measured by Western blot analysis. Dihydroethidium (DHE) staining and glutathione (GSH) and malondialdehyde (MDA) production were determined to assess the extent of oxidative stress. The messenger RNA (mRNA) levels of proinflammatory cytokines were detected using real-time quantitative polymerase chain reaction (RT-qPCR) to evaluate the extent of neuroinflammation. In the in vitro study, cell counting kit-8 (CCK-8) and lactate dehydrogenase (LDH) assays, as well as propidium iodide (PI) staining, were performed to analyse the neuroprotective effects of genistein on primary cortical neurons. Western blot assays were used to detect the levels of nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1), phosphorylated inhibitor kappa B-α (p-IκB-α) and phosphorylated nuclear factor-kappa B (p-NF-κB) both in vivo and in vitro. Results: Our results showed that genistein treatment effectively reduced cerebral infarction, attenuated neuronal injury and apoptosis, and contributed to the long-term recovery of neurological outcomes and brain atrophy in neonatal HIBD mice. Moreover, genistein ameliorated HIBD-induced oxidative stress and neuroinflammation. Meanwhile, genistein significantly increased cell viability, reversed neuronal injury and decreased cell apoptosis after OGD/R injury. Finally, the activation of the Nrf2/HO-1 pathway and inhibition of the NF-κB pathway by genistein were verified in the brain tissues of neonatal mice subjected to HIBD and in primary cortical neurons exposed to OGD/R. Conclusions: Genistein exerted neuroprotective effects on HIBD by attenuating oxidative stress and neuroinflammation through the Nrf2/HO-1 and NF-κB signalling pathways.

OsCOMT, encoding a caffeic acid O-methyltransferase in melatonin biosynthesis, increases rice grain yield through dual regulation of leaf senescence and vascular development.

Melatonin, a natural phytohormone in plants, plays multiple critical roles in plant growth and stress responses. Although melatonin biosynthesis-related genes have been suggested to possess diverse biological functions, their roles and functional mechanisms in regulating rice grain yield remain largely unexplored. Here, we uncovered the roles of a caffeic acid O-methyltransferase (OsCOMT) gene in mediating rice grain yield through dual regulation of leaf senescence and vascular development. In vitro and in vivo evidence revealed that OsCOMT is involved in melatonin biosynthesis. Transgenic assays suggested that OsCOMT significantly delays leaf senescence at the grain filling stage by inhibiting degradation of chlorophyll and chloroplast, which, in turn, improves photosynthesis efficiency. In addition, the number and size of vascular bundles in the culms and leaves were significantly increased in the OsCOMT-overexpressing plants, while decreased in the knockout plants, suggesting that OsCOMT plays a positive role in vascular development of rice. Further evidence indicated that OsCOMT-mediated vascular development might owe to the crosstalk between melatonin and cytokinin. More importantly, we found that OsCOMT is a positive regulator of grain yield, and overexpression of OsCOMT increase grain yield per plant even in a high-yield variety background, suggesting that OsCOMT can be used as an important target for enhancing rice yield. Our findings shed novel insights into melatonin-mediated leaf senescence and vascular development and provide a possible strategy for genetic improvement of rice grain yield.

Complete Genome Sequence of Aeromonas hydrophila Bacteriophage BUCT552.

We report the complete genome sequence of Aeromonas hydrophila bacteriophage BUCT552 whose full length of the linear dsDNA genome is 59,685 bp and G+C content is 60.0%. It contains 74 open reading frames but no tRNA. The results of TEM showed BUCT552 is a member of the family Siphoviridae.

Nucleotide Diversity of the Maize ZmCNR13 Gene and Association With Ear Traits.

The maize (Zea mays L.) ZmCNR13 gene, encoding a protein of fw2.2-like (FWL) family, has been demonstrated to be involved in cell division, expansion, and differentiation. In the present study, the genomic sequences of the ZmCNR13 locus were re-sequenced in 224 inbred lines, 56 landraces and 30 teosintes, and the nucleotide polymorphism and selection signature were estimated. A total of 501 variants, including 415 SNPs and 86 Indels, were detected. Among them, 51 SNPs and 4 Indels were located in the coding regions. Although neutrality tests revealed that this locus had escaped from artificial selection during the process of maize domestication, the population of inbred lines possesses lower nucleotide diversity and decay of linkage disequilibrium. To estimate the association between sequence variants of ZmCNR13 and maize ear characteristics, a total of ten ear-related traits were obtained from the selected inbred lines. Four variants were found to be significantly associated with six ear-related traits. Among them, SNP2305, a non-synonymous mutation in exon 2, was found to be associated with ear weight, ear grain weight, ear diameter and ear row number, and explained 4.59, 4.61, 4.31, and 8.42% of the phenotypic variations, respectively. These results revealed that natural variations of ZmCNR13 might be involved in ear development and can be used in genetic improvement of maize ear-related traits.

Adaptive innovation of green plants by horizontal gene transfer.

Horizontal gene transfer (HGT) refers to the movement of genetic material between distinct species by means other than sexual reproduction. HGT has contributed tremendously to the genome plasticity and adaptive evolution of prokaryotes and certain unicellular eukaryotes. The evolution of green plants from chlorophyte algae to angiosperms and from water to land represents a process of adaptation to diverse environments, which has been facilitated by acquisition of genetic material from other organisms. In this article, we review the occurrence of HGT in major lineages of green plants, including chlorophyte and charophyte green algae, bryophytes, lycophytes, ferns, and seed plants. In addition, we discuss the significance of horizontally acquired genes in the adaptive innovations of green plants and their potential applications to crop breeding and improvement.

Origin, evolution and functional characterization of the land plant glycoside hydrolase subfamily GH5_11.

Colonization of the land by plants was a critical event in the establishment of modern terrestrial ecosystems, and many characteristics of land plants originated during this process, including the emergence of rosette terminal cellulose-synthesizing complexes. Cellulases are non-homologous isofunctional enzymes, encoded by glycosyl hydrolase (GH) gene families. Although the plant GH5_11 gene subfamily is presumed to encode a cell-wall degrading enzyme, its evolutionary and functional characteristics remain unclear. In the present study, we report the evolution of the land plant GH5_11 subfamily, and the functions of its members in terms of cellulase activity, through comprehensive phylogenetic analyses and observation of Arabidopsis mutants. Phylogenetic and sequence similarity analyses reveal that the ancestor of land plants acquired the GH5_11 gene from fungi through a horizontal gene transfer (HGT) event. Subsequently, positive selection with massive gene duplication and loss events contributed to the evolution of this subfamily in land plants. In Arabidopsis and rice, expression of GH5_11 genes are regulated by multiple abiotic stresses, the duplicated genes showing different patterns of expression. The Arabidopsis mutants atgh5_11a and atgh5_11c display low levels of cellulase and endoglucanase activities, with correspondingly high levels of cellulose, implying that the encoded proteins may function as endoglucanases. However, atgh5_11a and atgh5_11c also display an enlarged rosette leaf phenotype, and atgh5_11c is late-flowering under short photoperiods. These observations suggest that plant GH5_11s possess more functions beyond being endonucleases. To summarize, we demonstrate that the ancestor of land plants has acquired GH5_11 gene through HGT, which extends the cellulose degradation complexity. Our investigations illuminate features of part of the molecular framework underlying the origin of land plants and provide a focus on the cellulose degradation pathway.

Synergistic Processing of Visual Contours across Cortical Layers in V1 and V2.

Visual cortical areas are interconnected via layer-specific feedforward and feedback projections. Such intricate connections are thought to be essential for parsing complex visual images, but the synergy among different layers in different cortical areas remains unclear. By simultaneously mapping neuronal activities across cortical depths in V1 and V2 of behaving monkeys, we identified spatiotemporally dissociable processes for grouping contour fragments and segregating background components. These processes generated and amplified contour signals within specific layers in V1 and V2. Contour-related inter-areal interactions, measured as Granger causality, were also dominant between these cortical layers within a time window when the contour signals were rapidly augmented. The grouping process became much faster for isolated contour elements compared with visual contours embedded in a complex background. Our results delineate the mode whereby image components are grouped and segmented through synergistic inter-laminar and inter-areal processes that are dynamically adjusted during interpretation of sensory inputs.