Daily Online Adaptive Radiation Therapy of Postoperative Endometrial and Cervical Cancer With PTV Margin Reduction to 5 mm: Dosimetric Outcomes, Acute Toxicity, and First Clinical Experience.

Purpose: This study evaluated the first clinical implementation of daily iterative cone beam computed tomography (iCBCT)-guided online adaptive radiation therapy (oART) in the postoperative treatment of endometrial and cervical cancer. Methods and Materials: Seventeen consecutive patients treated with daily iCBCT-guided oART were enrolled in this prospective study, with a reduced uniform 3-dimensional PTV margin of 5 mm. Treatment plans were designed to deliver 45 or 50.4 Gy in 1.8 Gy daily fractions to PTV. Pre- and posttreatment ultrasound and iCBCT scans were performed to record intrafractional bladder and rectal volume changes. The accuracy of contouring, oART procedure time, dosimetric outcomes, and acute toxicity were evaluated. Results: The average time from first iCBCT acquisition to completion of treatment was 22 minutes and 26 seconds. During this period, bladder volume increased by 44 cm3 using iCBCT contouring, whereas rectal volume remained stable (62.9 cm3 pretreatment vs 61.9 cm3 posttreatment). A total of 91.6% of influencers and 88.1% of CTVs required no or minor edits. The adapted plan was selected in all (434) fractions and significantly improved the dosimetry coverage for CTV and PTV, especially the vaginal PTV coverage by nearly 7% (P < .05). The adapted bladder Dmean was 104.61 cGy, and the rectum Dmean was 123.67 cGy, significantly lower than the scheduled plan of 108.24 and 128.19 cGy, respectively. The bone marrow and femur head left and right dosimetry were also improved with adaptation. Grade 2 acute gastrointestinal and genitourinary toxicities were 24% and 0, respectively. There was a grade 3 acute toxicity of decreased white blood cell count in 1 patient. Conclusions: Daily oART was associated with favorable dosimetry improvement and low acute toxicity, supporting its safety and efficacy for postoperative treatment of endometrial and cervical cancer. These results need to be validated in a larger prospective randomized controlled cohort.

Targeted improvement of narrow micropores in porous carbon for enhancing trace benzene vapor removal: Revealing the adsorption mechanism via experimental and molecular simulation.

Porous carbon materials are highly desirable for removing benzene due to their low energy for capture and regeneration. Research has demonstrated that narrow microporous volume is crucial for effective adsorption of benzene at ultra-low concentration. Unfortunately, achieving directional increase in the narrow microporous volume in porous carbon remains a challenge. Here, nitrogen-doped hydrothermal carbon was prepared using urea-assisted hydrothermal method, and then porous carbon (PUC800) was prepared by KOH activation. The resulting material had 180 % higher pore volume and 179 % higher surface area compared to non-nitrogen activation methods. Then, using mechanochemical (mechanical compaction and KOH activation) approach to produce PUC800-3T, which had a 30 % increase in pore volume and a 33 % increase in surface area compared to PUC800. PUC800-3T showed benzene adsorption capacity of 4.2 mmol g-1 at 1 Pa and 5.8 mmol g-1 at 5 Pa. Experimental and molecular simulation indicate that the benzene adsorption at 1 and 5 Pa is determined by pore volume of less than 0.8 and 0.9 nm, respectively. Density functional theory calculations provided insight into the CH⋯X (X = N/O) interactions drive benzene adsorption on the carbon framework. This work provides valuable theoretical and experimental support for designing, preparing, and applying adsorbents for trace removal of benzene vapor.

Prognostic model incorporating immune checkpoint genes to predict the immunotherapy efficacy for lung adenocarcinoma: a cohort study integrating machine learning algorithms.

This study aimed to develop and validate a nomogram based on immune checkpoint genes (ICGs) for predicting prognosis and immune checkpoint blockade (ICB) efficacy in lung adenocarcinoma (LUAD) patients. A total of 385 LUAD patients from the TCGA database and 269 LUAD patients in the combined dataset (GSE41272 + GSE50081) were divided into training and validation cohorts, respectively. Three different machine learning algorithms including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and support vector machine (SVM) were employed to select the predictive markers from 82 ICGs to construct the prognostic nomogram. The X-tile software was used to stratify patients into high- and low-risk subgroups based on the nomogram-derived risk scores. Differences in functional enrichment and immune infiltration between the two subgroups were assessed using gene set variation analysis (GSVA) and various algorithms. Additionally, three lung cancer cohorts receiving ICB therapy were utilized to evaluate the ability of the model to predict ICB efficacy in the real world. Five ICGs were identified as predictive markers across all three machine learning algorithms, leading to the construction of a nomogram with strong potential for prognosis prediction in both the training and validation cohorts (all AUC values close to 0.800). The patients were divided into high- (risk score ≥ 185.0) and low-risk subgroups (risk score < 185.0). Compared to the high-risk subgroup, the low-risk subgroup exhibited enrichment in immune activation pathways and increased infiltration of activated immune cells, such as CD8 + T cells and M1 macrophages (P < 0.05). Furthermore, the low-risk subgroup had a greater likelihood of benefiting from ICB therapy and longer progression-free survival (PFS) than did the high-risk subgroup (P < 0.05) in the two cohorts receiving ICB therapy. A nomogram based on ICGs was constructed and validated to aid in predicting prognosis and ICB treatment efficacy in LUAD patients.

Role of Probiotics in Gut Microbiome and Metabolome in Non-Alcoholic Fatty Liver Disease Mouse Model: A Comparative Study.

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver condition worldwide. Numerous studies conducted recently have demonstrated a connection between the dysbiosis of the development of NAFLD and gut microbiota. Rebuilding a healthy gut ecology has been proposed as a strategy involving the use of probiotics. The purpose of this work is to investigate and compare the function of probiotics Akkermansia muciniphila (A. muciniphila) and VSL#3 in NAFLD mice. Rodent NAFLD was modeled using a methionine choline-deficient diet (MCD) with/without oral probiotic delivery. Subsequently, qPCR, histological staining, and liver function tests were conducted. Mass spectrometry-based analysis and 16S rDNA gene sequencing were used to investigate the liver metabolome and gut microbiota. We found that while both A. muciniphila and VSL#3 reduced hepatic fat content, A. muciniphila outperformed VSL#3. Furthermore, probiotic treatment restored the ß diversity of the gut flora and A. muciniphila decreased the abundance of pathogenic bacteria such as Ileibacterium valens. These probiotics altered the metabolism in MCD mice, especially the glycerophospholipid metabolism. In conclusion, our findings distinguished the role of A. muciniphila and VSL#3 in NAFLD and indicated that oral-gavage probiotics remodel gut microbiota and improve metabolism, raising the possibility of using probiotics in the cure of NAFLD.

Underwater sound speed profile estimation from vessel traffic recordings and multi-view neural networks.

Sound speed is a critical parameter in ocean acoustic studies, as it determines the propagation and interpretation of recorded sounds. The potential for exploiting oceanic vessel noise as a sound source of opportunity to estimate ocean sound speed profile is investigated. A deep learning-based inversion scheme, relying upon the underwater radiated noise of moving vessels measured by a single hydrophone, is proposed. The dataset used for this study consists of Automatic Identification System data and acoustic recordings of maritime vessels transiting through the Santa Barbara Channel between January 2015 and December 2017. The acoustic recordings and vessel descriptors are used as predictors for regressing sound speed for each meter in the top 200 m of the water column, where sound speeds are most variable. Multiple (typically ranging between 4 and 10) transits were recorded each day; therefore, this dataset provides an opportunity to investigate whether multiple acoustic observations can be leveraged together to improve inversion estimates. The proposed single-transit and multi-transit models resulted in depth-averaged root-mean-square errors of 1.79 and 1.55 m/s, respectively, compared to the seasonal average predictions of 2.80 m/s.

Cannabidiol Inhibits Epithelial Ovarian Cancer: Role of Gut Microbiome.

Epithelial ovarian cancer is among the deadliest gynecological tumors worldwide. Clinical treatment usually consists of surgery and adjuvant chemo- and radiotherapies. Due to the high rate of recurrence and rapid development of drug resistance, the current focus of research is on finding effective natural products with minimal toxic side effects for treating epithelial ovarian tumors. Cannabidiol is among the most abundant cannabinoids and has a non-psychoactive effect compared to tetrahydrocannabinol, which is a key advantage for clinical application. Studies have shown that cannabidiol has antiproliferative, pro-apoptotic, cytotoxic, antiangiogenic, anti-inflammatory, and immunomodulatory properties. However, its therapeutic value for epithelial ovarian tumors remains unclear. This study aims to investigate the effects of cannabidiol on epithelial ovarian tumors and to elucidate the underlying mechanisms. The results showed that cannabidiol has a significant inhibitory effect on epithelial ovarian tumors. In vivo experiments demonstrated that cannabidiol could inhibit tumor growth by modulating the intestinal microbiome and increasing the abundance of beneficial bacteria. Western blot assays showed that cannabidiol bound to EGFR/AKT/MMPs proteins and suppressed EGFR/AKT/MMPs expression in a dose-dependent manner. Network pharmacology and molecular docking results suggested that cannabidiol could affect the EGFR/AKT/MMPs signaling pathway.

Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms.

We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCGA database were assigned to training and validation cohort, respectively. The differences in expression of immune checkpoint genes (ICGs) and immune infiltration landscape were compared between LTS and short time survivor (STS) (OS<1.5 years). The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify the genes differentially expressed between LTS and STS. Three different machine learning algorithms were employed to select the predictive genes from the overlapping region of DEGs and WGCNA to construct the nomogram. The comparison between LTS and STS revealed that STS exhibited an immune-resistant status, with higher expression of ICGs (P<0.05) and greater infiltration of immune suppression cells compared to LTS (P<0.05). Four genes, namely, OSMR, FMOD, CXCL14, and TIMP1, were identified and incorporated into the nomogram, which possessed good potential in predicting LTS probability among GB patients both in the training (C-index, 0.791; 0.772-0.817) and validation cohort (C-index, 0.770; 0.751-0.806). STS was found to be more likely to exhibit an immune-cold phenotype. The identified predictive genes were used to construct the nomogram with potential to identify LTS among GB patients.

Pelvic target volume inter-fractional motion during radiotherapy for cervical cancer with daily iterative cone beam computed tomography.

BACKGROUND: Tumor regression and organ movements indicate that a large margin is used to ensure target volume coverage during radiotherapy. This study aimed to quantify inter-fractional movements of the uterus and cervix in patients with cervical cancer undergoing radiotherapy and to evaluate the clinical target volume (CTV) coverage. METHODS: This study analyzed 303 iterative cone beam computed tomography (iCBCT) scans from 15 cervical cancer patients undergoing external beam radiotherapy. CTVs of the uterus (CTV-U) and cervix (CTV-C) contours were delineated based on each iCBCT image. CTV-U encompassed the uterus, while CTV-C included the cervix, vagina, and adjacent parametrial regions. Compared with the planning CTV, the movement of CTV-U and CTV-C in the anterior-posterior, superior-inferior, and lateral directions between iCBCT scans was measured. Uniform expansions were applied to the planning CTV to assess target coverage. RESULTS: The motion (mean ± standard deviation) in the CTV-U position was 8.3 ± 4.1 mm in the left, 9.8 ± 4.4 mm in the right, 12.6 ± 4.0 mm in the anterior, 8.8 ± 5.1 mm in the posterior, 5.7 ± 5.4 mm in the superior, and 3.0 ± 3.2 mm in the inferior direction. The mean CTV-C displacement was 7.3 ± 3.2 mm in the left, 8.6 ± 3.8 mm in the right, 9.0 ± 6.1 mm in the anterior, 8.4 ± 3.6 mm in the posterior, 5.0 ± 5.0 mm in the superior, and 3.0 ± 2.5 mm in the inferior direction. Compared with the other tumor (T) stages, CTV-U and CTV-C motion in stage T1 was larger. A uniform CTV planning treatment volume margin of 15 mm failed to encompass the CTV-U and CTV-C in 11.1% and 2.2% of all fractions, respectively. The mean volume change of CTV-U and CTV-C were 150% and 51%, respectively, compared with the planning CTV. CONCLUSIONS: Movements of the uterine corpus are larger than those of the cervix. The likelihood of missing the CTV is significantly increased due to inter-fractional motion when utilizing traditional planning margins. Early T stage may require larger margins. Personal radiotherapy margining is needed to improve treatment accuracy.

Protamine-grafted carboxymethyl chitosan based hydrogel with adhesive and long-term antibacterial properties for hemostasis and skin wound healing.

In this study, we developed a tissue-adhesive and long-term antibacterial hydrogel consisting of protamine (PRTM) grafted carboxymethyl chitosan (CMC) (PCMC), catechol groups modified CMC (DCMC), and oxidized hyaluronic acid (OHA), named DCMC-OHA-PCMC. According to the antibacterial experiments, the PCMC-treated groups showed obvious and long-lasting inhibition zones against E. coli (and S. aureus), and the corresponding diameters varied from 10.1 mm (and 15.3 mm) on day 1 to 9.8 mm (and 15.3 mm) on day 7. The DCMC-OHA-PCMC hydrogel treated groups also exhibited durable antibacterial ability against E. coli (and S. aureus), and the antibacterial rates changed from 99.3 ± 0.21 % (and 99.6 ± 0.36 %) on day 1 to 76.2 ± 1.74 % (and 84.2 ± 1.11 %) on day 5. Apart from good mechanical and tissue adhesion properties, the hydrogel had excellent hemostatic ability mainly because of the grafted positive-charged PRTM. As the animal assay results showed, the hydrogel was conducive to promoting the deposition of new collagen (0.84 ± 0.03), the regeneration of epidermis (98.91 ± 6.99 µm) and wound closure in the process of wound repairing. In conclusion, the presented outcomes underline the prospective potential of the multifunctional CMC-based hydrogel for applications in wound dressings.

SEU2-Net: multi-scale U2-Net with SE attention mechanism for liver occupying lesion CT image segmentation.

Liver occupying lesions can profoundly impact an individual's health and well-being. To assist physicians in the diagnosis and treatment of abnormal areas in the liver, we propose a novel network named SEU2-Net by introducing the channel attention mechanism into U2-Net for accurate and automatic liver occupying lesion segmentation. We design the Residual U-block with Squeeze-and-Excitation (SE-RSU), which is to add the Squeeze-and-Excitation (SE) attention mechanism at the residual connections of the Residual U-blocks (RSU, the component unit of U2-Net). SEU2-Net not only retains the advantages of U2-Net in capturing contextual information at multiple scales, but can also adaptively recalibrate channel feature responses to emphasize useful feature information according to the channel attention mechanism. In addition, we present a new abdominal CT dataset for liver occupying lesion segmentation from Peking University First Hospital's clinical data (PUFH dataset). We evaluate the proposed method and compare it with eight deep learning networks on the PUFH and the Liver Tumor Segmentation Challenge (LiTS) datasets. The experimental results show that SEU2-Net has state-of-the-art performance and good robustness in liver occupying lesions segmentation.

Construction of a Functional Nucleic Acid-Based Artificial Vesicle-Encapsulated Composite Nanoparticle and Its Application in Retinoblastoma-Targeted Theranostics.

Retinoblastoma (RB) is an aggressive tumor of the infant retina. However, the ineffective targeting of its theranostic agents results in poor imaging and therapeutic efficacy, which makes it difficult to identify and treat RB at an early stage. In order to improve the imaging and therapeutic efficacy, we constructed an RB-targeted artificial vesicle composite nanoparticle. In this study, the MnO2 nanosponge (hMNs) was used as the core to absorb two fluorophore-modified DNAzymes to form the Dual/hMNs nanoparticle; after loaded with the artificial vesicle derived from human red blood cells, the RB-targeted DNA aptamers were modified on the surface, thus forming the Apt-EG@Dual/hMNs complex nanoparticle. The DNA aptamer endows this nanoparticle to target the nucleolin-overexpressed RB cell membrane specifically and enters cells via endocytosis. The nanoparticle could release fluorophore-modified DNAzymes and supplies Mn2+ as a DNAzyme cofactor and a magnetic resonance imaging (MRI) agent. Subsequently, the DNAzymes can target two different mRNAs, thereby realizing fluorescence/MR bimodal imaging and dual-gene therapy. This study is expected to provide a reliable and valuable basis for ocular tumor theranostics.

LdCyPA attenuates MAPK pathway to assist Leishmania donovani immune escape in host cells.

BACKGROUND: Visceral leishmaniasis is a neglected tropical disease affecting millions of people worldwide. Macrophages serve as the primary host cells for L. donovani, the immune response capability of these host cells is crucial for parasites' intracellular survival. L. donovani peptidyl-prolyl cis/trans isomerase Cyclophilin A (LdCypA) is a key protein for L. donovani intracellular proliferation, while the molecular mechanism conducive to intracellular survival of parasites remains elusive. METHODS: In this study, we generated a macrophage cell line overexpressing LdCyPA to investigate its role in controlling host immunity and promoting intracellular immune escape of L. donovani. RESULTS: It was discovered that the overexpression of the LdCyPA cell line regulated the host immune response following infection by downregulating the proportion of M1-type macrophages, promoting the secretion of the anti-inflammatory factor IL-4, and inhibiting the secretion of pro-inflammatory factors like IL-12, IFN-γ, TNF-α, and INOS. Transcriptome sequencing and mechanistic validation, meanwhile, demonstrated that cells overexpressing LdCyPA controlled the immune responses that followed infection by blocking the phosphorylation of P38 and JNK1/2 proteins in the MAPK signaling pathway and simultaneously increasing the phosphorylation of ERK proteins, which helped the L. donovani escape immune recognition. CONCLUSION: Our findings thus pave the way for the development of host-directed antiparasitic drugs by illuminating the pro-Leishmania survival mechanism of L. donovani cyclophilin A and exposing a novel immune escape strategy for L. donovani that targets host cellular immune regulation.

Prospects for daily online adaptive radiotherapy for cervical cancer: Auto-contouring evaluation and dosimetric outcomes.

BACKGROUND: Training senior radiation therapists as "adapters" to manage influencers and target editing is critical in daily online adaptive radiotherapy (oART) for cervical cancer. The purpose of this study was to evaluate the accuracy and dosimetric outcomes of automatic contouring and identify the key areas for modification. METHODS: A total of 125 oART fractions from five postoperative cervical cancer patients and 140 oART fractions from five uterine cervical cancer patients treated with daily iCBCT-guided oART were enrolled in this prospective study. The same adaptive treatments were replanned using the Ethos automatic contours workflow without manual contouring edits. The clinical target volume (CTV) was subdivided into several separate regions, and the average surface distance dice (ASD), centroid deviation, dice similarity coefficient (DSC), and 95% Hausdorff distance (95% HD) were used to evaluate contouring for the above portions. Dosimetric results from automatic oART plans were compared to supervised oART plans to evaluate target volumes and organs at risk (OARs) dose changes. RESULTS: Overall, the paired CTV had high overlap rates, with an average DSC value greater than 0.75. The uterus had the largest consistency differences, with ASD, centroid deviation, and 95% HD being 2.67 ± 1.79 mm, 17.17 ± 12 mm, and 10.45 ± 5.68 mm, respectively. The consistency differences of the lower nodal CTVleft and nodal CTVright were relatively large, with ASD, centroid deviation, and 95% HD being 0.59 ± 0.53 mm, 3.6 ± 2.67 mm, and 5.41 ± 4.08 mm, and 0.59 ± 0.51 mm, 3.6 ± 2.54 mm, and 4.7 ± 1.57 mm, respectively. The automatic online-adapted plan met the clinical requirements of dosimetric coverage for the target volume and improved the OAR dosimetry. CONCLUSIONS: The accuracy of automatic contouring from the Ethos adaptive platform is considered clinically acceptable for cervical cancer, and the uterus, upper vaginal cuff, and lower nodal CTV are the areas that need to be focused on in training.

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements.

Gaze estimation, as a technique that reflects individual attention, can be used for disability assistance and assisting physicians in diagnosing diseases such as autism spectrum disorder (ASD), Parkinson's disease, and attention deficit hyperactivity disorder (ADHD). Various techniques have been proposed for gaze estimation and achieved high resolution. Among these approaches, electrooculography (EOG)-based gaze estimation, as an economical and effective method, offers a promising solution for practical applications. OBJECTIVE: In this paper, we systematically investigated the possible EOG electrode locations which are spatially distributed around the orbital cavity. Afterward, quantities of informative features to characterize physiological information of eye movement from the temporal-spectral domain are extracted from the seven differential channels. METHODS AND PROCEDURES: To select the optimum channels and relevant features, and eliminate irrelevant information, a heuristical search algorithm (i.e., forward stepwise strategy) is applied. Subsequently, a comparative analysis of the impacts of electrode placement and feature contributions on gaze estimation is evaluated via 6 classic models with 18 subjects. RESULTS: Experimental results showed that the promising performance was achieved both in the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) within a wide gaze that ranges from -50° to +50°. The MAE and RMSE can be improved to 2.80° and 3.74° ultimately, while only using 10 features extracted from 2 channels. Compared with the prevailing EOG-based techniques, the performance improvement of MAE and RMSE range from 0.70° to 5.48° and 0.66° to 5.42°, respectively. CONCLUSION: We proposed a robust EOG-based gaze estimation approach by systematically investigating the optimal channel/feature combination. The experimental results indicated not only the superiority of the proposed approach but also its potential for clinical application. Clinical and translational impact statement: Accurate gaze estimation is a key step for assisting disabilities and accurate diagnosis of various diseases including ASD, Parkinson's disease, and ADHD. The proposed approach can accurately estimate the points of gaze via EOG signals, and thus has the potential for various related medical applications.

Enhanced ability of toluene oxidation by controlling inversion degree of spinel composed of only Co, Mn.

Exploring the single relationship between the inversion degree of spinel and its catalytic performance is a great challenge, but has important significance for further structural design and application. A series of CoMn inverse spinels were prepared and the general formula [Formula: see text] was deduced through X-ray diffraction refinement to find a decreased inversion degree x as calcination temperature rose. Catalytic oxidation of toluene showed that higher inversion degree (S-300 with x ≈ 0.95) can reach larger conversion rate (90 % at about 250 °C for 400 ppm toluene) with greater reaction stability (140 h). Density Functional Theory (DFT) calculations on density of states indicated its metallic nature, and found that the strength of O-p and Transition metal-d orbitals at Fermi energy is positively correlated to the inversion degree, meaning stronger electron migration ability. Along with the adsorption calculation analysis that lattice oxygen species are proved to work dominantly (S-300 with lowest adsorption energy but highest performance), this work uncovered a theoretical insight into inverse spinel oxide, to provide the possibility of elevated oxidation ability through structural control.

Genome resequencing reveals the evolutionary history of garlic reproduction traits.

The propagation of cultivated garlic relies on vegetative cloves, thus flowers become non-essential for reproduction in this species, driving the evolution of reproductive feature-derived traits. To obtain insights into the evolutionary alteration of reproductive traits in the clonally propagated garlic, the evolutionary histories of two main reproduction-related traits, bolting and flower differentiation, were explored by genome analyses using 134 accessions displaying wide diversity in these two traits. Resequencing identified 272.8 million variations in the garlic genome, 198.0 million of which represent novel variants. Population analysis identified five garlic groups that have evolved into two clades. Gene expression, single-cell transcriptome sequencing, and genome-wide trait association analyses have identified numerous candidates that correlate with reproductive transition and flower development, some of which display distinct selection signatures. Selective forces acting on the B-box zinc finger protein-encoding Asa2G00291.1, the global transcription factor group E protein-encoding Asa5G01527.1, and VERNALIZATION INSENSITIVE 3-like Asa3G03399.1 appear to be representative of the evolution of garlic bolting. Plenty of novel genomic variations and trait-related candidates represent valuable resources for biological studies of garlic. Numerous selective signatures from genes associated with the two chosen reproductive traits provide important insights into the evolutionary history of reproduction in this clonally propagated crop.

KL Divergence-based transfer learning for cross-subject eye movement recognition with EOG signals.

Human-machine interfaces (HMIs) based on Electro-oculogram (EOG) signals have been widely explored. However, due to the individual variability, it is still challenging for an EOG-based eye movement recognition model to achieve favorable results among cross-subjects. The classical transfer learning methods such as CORrelation Alignment (CORAL), Transfer Component Analysis (TCA), and Joint Distribution Adaptation (JDA) are mainly based on feature transformation and distribution alignment, which do not consider similarities/dissimilarities between target subject and source subjects. In this paper, the Kullback-Leibler (KL) divergence of the log-Power Spectral Density (log-PSD) features of horizontal EOG (HEOG) between the target subject and each source subject is calculated for adaptively selecting partial subjects that suppose to have similar distribution with target subject for further training. It not only consider the similarity but also reduce computational consumption. The results show that the proposed approach is superior to the baseline and classical transfer learning methods, and significantly improves the performance of target subjects who have poor performance with the primary classifiers. The best improvement of Support Vector Machines (SVM) classifier has improved by 13.1% for subject 31 compared with baseline result. The preliminary results of this study demonstrate the effectiveness of the proposed transfer framework and provide a promising tool for implementing cross-subject eye movement recognition models in real-life scenarios.

Deprivation of Sexual Reproduction during Garlic Domestication and Crop Evolution.

Garlic, originating in the mountains of Central Asia, has undergone domestication and subsequent widespread introduction to diverse regions. Human selection for adaptation to various climates has resulted in the development of numerous garlic varieties, each characterized by specific morphological and physiological traits. However, this process has led to a loss of fertility and seed production in garlic crops. In this study, we conducted morpho-physiological and transcriptome analyses, along with whole-genome resequencing of 41 garlic accessions from different regions, in order to assess the variations in reproductive traits among garlic populations. Our findings indicate that the evolution of garlic crops was associated with mutations in genes related to vernalization and the circadian clock. The decline in sexual reproduction is not solely attributed to a few mutations in specific genes, but is correlated with extensive alterations in the genetic regulation of the annual cycle, stress adaptations, and environmental requirements. The regulation of flowering ability, stress response, and metabolism occurs at both the genetic and transcriptional levels. We conclude that the migration and evolution of garlic crops involve substantial and diverse changes across the entire genome landscape. The construction of a garlic pan-genome, encompassing genetic diversity from various garlic populations, will provide further insights for research into and the improvement of garlic crops.

Prognostic Analysis of KCNQ2 Patients via Combining EEG Deep Features and Machine Learning Classifiers.

Pathogenic variants of the KCNQ2 gene often induces neonatal epilepsy in clinical. For better treatment, infants with confirmed KCNQ2 pathogenic variant and epilepsy symptoms need to adjust their treatment plans according to the outcome after taking antiseizure medicines (ASMs). This process is often time-consuming and requires long-term follow-up, which undoubtedly causes unnecessary psychological and economic burdens. In this study, we investigate the feasibility to predict the outcome of KCNQ2 patients via Electroencephalogram (EEG). By using the combination of deep networks and classical classifiers, the abnormal brain pathological activities recorded in EEGs can be encoded into deep features and decoded into specific KCNQ2 outcomes, thus taking the advantage of both powerful feature extraction capability from deep networks and stronger classification ability from classical classifiers. Specifically, we acquire 10-channel EEG signals from 33 infants with KCNQ2 pathogenic variants after taking ASMs. Two well-trained models (Resnet-50 and Resnet-18) are employed to extract deep features from the EEG spectrums. We achieve an accuracy of 78.7% to predict the KCNQ2 outcome of each infant. To our best knowledge, this is the first study to employ potential EEG pathological differences to predict the outcomes of KCNQ2 patients. The investigation of automatic KCNQ2 outcome prediction may contribute to a more convenient diagnosis mechanism for KCNQ2 patients.

Active matrix driven electrophoretic display with in-plane switching layout and driving waveform for reflective and transparent modes.

In-plane switching electrophoretic display (IPS-EPD) is an emerging field of display technology which achieves particles moving horizontally through a lateral electric field. Compared to vertically driven electrophoretic display (V-EPD), IPS-EPD exhibits the feasibility of transparent display function. However, most of the previous research was hindered by long response time, low optical transmittance, or complex structures. In this paper, we have proposed a newly developed electrode layout and driving waveform for IPS-EPD, achieving a device with fast response time of 0.32 s, high transmittance of 58.07%, good transmittance-contrast ratio of 11.25, and simple structure, which show a significant improvement over other related research. Additionally, we elucidated the physical mechanism for the device through developing a particles motion simulation. Finally, we presented a prototype of an IPS-EPD with TFT panel, which exhibits excellent performance in various application scenarios, making it a possible application prospect in mobile phone cases, glasses, windows, and so on.