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Chimeric antigen receptor (CAR) T-cell therapy is highly effective for treating blood cancers such as B-cell malignancies, however, its effectiveness as an approach to treat solid tumors remains to be further explored. Here, we focused on the development of CAR-T cell therapies targeting tropomyosin-related kinase receptor B (TRKB), a highly expressed protein that is significantly associated with tumor progression, malignancy, and drug resistance in multiple forms of aggressive solid tumors. To achieve this, we screened brain-derived neurotrophic factor (BDNF) and neurotrophin 4 (NTF4) ligand-based CAR-T cells for their efficiency in targeting the TRKB receptor in the context of solid tumors, particularly hepatocellular carcinoma and pancreatic cancer. We demonstrated that TRKB is overexpressed not only in hepatocellular carcinoma and pancreatic carcinoma cell lines but also in cancer stem-like cells (CSCs). Notably, BDNF-CAR T and NTF4-CAR T cells could not only effectively target and kill TRKB-expressing pan-cancer cell lines in a dose-dependent manner but also effectively kill CSCs. We also performed in vivo studies to show that NTF4-CAR T cells have a better potential to inhibit the tumor growth of hepatocellular carcinoma xenografts in mice, compared with BDNF-CAR T cells. Taken together, our findings suggest that CAR-T targeting TRKB may be a promising approach for developing novel therapies to treat solid cancers. [ABSTRACT FROM AUTHOR]

The most common type of non-small cell lung cancer is lung adenocarcinoma (LUAD), which is characterized by high morbidity and poor survival. Up-regulation of SASS6 expression can lead to the progression of various malignant tumors. However, there are no relevant studies on the role of SASS6 in LUAD. SASS6 was highly expressed in most tumors, reflecting a good diagnostic value, and its overexpression in LUAD indicated discouraging overall prognosis. Functional enrichment analysis suggested that SASS6 was associated with cell cycle in LUAD. In addition, patients with high SASS6 expression had worse immune infiltration, but higher TMB and immune checkpoint, and higher sensitivity to multiple targeted drugs such as osimertinib. Cell experiments confirmed that knockdown of SASS6 could inhibit the viability of tumor cells.SASS6 has important value in the diagnosis of cancer. In particular, SASS6 is a crucial factor in the progression of LUAD, and has important clinical value, especially in the diagnosis, prognosis and treatment [ABSTRACT FROM AUTHOR]

Current medical therapies for treating acute myeloid leukemia (AML) remain unmet, and AML patients may benefit from targeted immunotherapy approaches that focus on specific tumor antigens. GRP78, which is upregulated in various malignant tumors such as AML, is partially expressed as cell surface GRP78 (csGRP78) on the cell membrane, making it an ideal target for redirecting T cells, including T-cell engagers. However, considering the conventional approach of using two scFv segments to construct a bispecific T-cell engager (BiTE), we have undertaken the development of a novel BiTE that utilizes a cyclic peptide ligand to specifically target csGRP78, which we refer to as GRP78-CD3/BiTE. We studied the effects of GRP78-CD3/BiTE on treatments for AML in vitro and in vivo and assessed the pharmacokinetics of this engager. Our findings demonstrated that GRP78-CD3/BiTE could not only effectively mediate the cytotoxicity of T cells against csGRP78-expressing AML cells but also specifically eliminate primary AML tumor cells in vitro. Furthermore, GRP78-CD3/BiTE exhibited a longer half-life despite having a lower molecular weight than CD19-CD3/BiTE. In a xenograft mouse model of AML, treatment with GRP78-CD3/BiTE prolonged the survival time of the mice. Our findings demonstrate that GRP78-CD3/BiTE is effective and selective for eliminating csGRP78-expressing AML cells and suggest that this approach to targeted immunotherapy could lead to effective new treatments for AML. [ABSTRACT FROM AUTHOR]

Whether in natural scenes or laboratory environments, leaf instance segmentation is still a challenging task in high-throughput plant phenotypic research. Because compared with normal instance objects, leaves have more complex boundaries and severe inter-leaf occlusions. In this paper, we present an effective two-stage method called Bilayer Convolution Mask (BCMask) for high-quality leaf instance segmentation. BCMask consists of three main modules: (1) Bottom-up Path Augmentation (BPA) module is added after Feature Pyramid Network (FPN) in Faster R-CNN. BPA shortens the information path between lower layers and high-level layers, and helps accurate semantical features in lower layers to enhance the entire feature hierarchy; (2) Bilayer Occlusion Module. This module consists of two convolutional layers with a residual structure, which decouples the occluding leaves and the partially occluded target leaf during the mask regression; (3) Mask Refining Module. This module uses an iterative refinement method with adaptive selection to classify pixels, which effectively alleviates the problem of inaccurate leaf boundary segmentation. To validate BCMask, this paper takes the chrysanthemum seedling leaf dataset for experiment, which is collected in the natural environment with complex boundaries and severe occlusions. Two remarkable public datasets CVPPA and Komatsuna under laboratory environments are also added as supplements to validate the robustness of BCMask. The proposed method achieves the 60.42% average precision (AP) score outperforming state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Emotional reactions to movies are typically similar between people. However, depressive symptoms decrease synchrony in brain responses. Less is known about the effect of depressive symptoms on intersubject synchrony in conscious stimulus-related processing. In this study, we presented amusing, sad and fearful movie clips to dysphoric individuals (those with elevated depressive symptoms) and control participants to dynamically rate the clips' valences (positive vs. negative). We analysed both the valence ratings' mean values and intersubject correlation (ISC). We used electrodermal activity (EDA) to complement the measurement in a separate session. There were no group differences in either the EDA or mean valence rating values for each movie type. As expected, the valence ratings' ISC was lower in the dysphoric than the control group, specifically for the sad movie clips. In addition, there was a negative relationship between the valence ratings' ISC and depressive symptoms for sad movie clips in the full sample. The results are discussed in the context of the negative attentional bias in depression. The findings extend previous brain activity results of ISC by showing that depressive symptoms also increase variance in conscious ratings of valence of stimuli in a mood-congruent manner. [ABSTRACT FROM AUTHOR]

This article aims to realize the combined application of satellite remote sensing data and Lidar data fusion of atmospheric ozone in the ozone distribution prediction in western China. Firstly, the ozone remote sensing data and Lidar vertical monitoring data detected by ozone monitoring instruments are collected from various provinces in western China; the recurrent neural network (RNN) model based on the gated recurrent unit and the convolutional neural network (CNN) model based on time series are constructed and integrated to obtain the RNN–CNN model for ozone concentration prediction, and the prediction performance is compared with that of the RNN and CNN model. Satellite remote sensing data show that the total ozone concentration in western China has obvious spatial distribution characteristics, and can be elevated with the increase in latitude; the ozone concentration in the north is higher and the ozone concentration in the south is lower in different seasons in western China, and it in autumn and summer is lower than that in spring and winter generally. The hour-by-hour change of ozone concentration shows that the ozone concentration in different seasons reaches a trough around 9–10 o'clock, and reaches a peak around 16–17 o'clock. When the RNN–CNN model is applied to predict the hourly change of ozone concentration within a day, it can achieve the best root-mean-squared error (18.13), mean absolute error (11.64), and index of agreement (0.973). The monitoring results of Lidar data at a certain site are compared and analyzed, and the results show that the fitness is the best between the predicted value of ozone concentration obtained by the RNN–CNN model and the actually measured value. This research provides the possibility for the intelligent prediction of atmospheric ozone concentration distribution and has reliable theoretical value and practical significance. [ABSTRACT FROM AUTHOR]

Previous studies have associated visual working memory (VWM) capacity with the use of internal attention. Retrocues, which direct internal attention to a particular object or feature dimension, can improve VWM performance (i.e., retrocue benefit, RCB). However, so far, no study has investigated the relationship between VWM capacity and the magnitudes of RCBs obtained from object-based and dimension-based retrocues. The present study explored individual differences in the magnitudes of object- and dimension-based RCBs and their relationships with VWM capacity. Participants completed a VWM capacity measurement, an object-based cue task, and a dimension-based cue task. We confirmed that both object- and dimension-based retrocues could improve VWM performance. We also found a significant positive correlation between the magnitudes of object- and dimension-based RCB indexes, suggesting a partly overlapping mechanism between the use of object- and dimension-based retrocues. However, our results provided no evidence for a correlation between VWM capacity and the magnitudes of the object- or dimension-based RCBs. Although inadequate attention control is usually assumed to be associated with VWM capacity, the results suggest that the internal attention mechanism for using retrocues in VWM retention is independent of VWM capacity. [ABSTRACT FROM AUTHOR]

Sustained attention encompasses a cascade of fundamental functions. The human ability to implement a sustained attention task is supported by brain networks that dynamically formed and dissolved through oscillatory synchronization. The decrement of vigilance induced by prolonged task engagement affects sustained attention. However, little is known about which stage or combinations are affected by vigilance decrement. Here, we applied an analysis framework composed of weighted phase lag index (wPLI) and tensor component analysis (TCA) to an EEG dataset collected during 80 min sustained attention task to examine the electrophysiological basis of such effect. We aimed to characterize the phase-coupling networks to untangle different phases involved in sustained attention and study how they are modulated by vigilance decrement. We computed the time–frequency domain wPLI from each block and subject and constructed a fourth-order tensor, containing the time, frequency, functional connectivity (FC), and blocks × subjects. This tensor was subjected to the TCA to identify the interacted and low-dimensional components representing the frequency-specific dynamic FC (fdFC). We extracted four types of neuromakers during a sustained attention task, namely the pre-stimulus alpha right-lateralized parieto-occipital FC, the post-stimulus theta fronto-parieto-occipital FC, delta fronto-parieto-occipital FC, and beta right/left sensorimotor FCs. All these fdFCs were impaired by vigilance decrement. These fdFCs, except for the beta left sensorimotor network, were restored by rewards, although the restoration by reward in the beta right sensorimotor network was transient. These findings provide implications for dissociable effects of vigilance decrement on sustained attention by utilizing the tensor-based framework. [ABSTRACT FROM AUTHOR]

Two hypotheses have been proposed to explain the formation manner for visual working memory (VWM) representations during the consolidation process: an all-or-none process hypothesis and a coarse-to-fine process hypothesis. However, neither the all-or-none process hypothesis nor the coarse-to-fine process hypothesis can stipulate clearly how VWM representations are formed during the consolidation process. In the current study, we propose a two-stage process hypothesis to reconcile these hypotheses. The two-stage process hypothesis suggests that the consolidation of coarse information is an all-or-none process in the early consolidation stage, while the consolidation of detailed information is a coarse-to-fine process in the late consolidation stage. By systematically manipulating the encoding time of memory stimuli, we asked participants to memorize one (Experiment 1) or two (Experiment 2) orientations in different encoding time intervals. We found that the memory rate increased linearly as the encoding time increased. More importantly, VWM precision remained constant when the encoding time was short, while the precision increased linearly as the encoding time increased when the encoding time was sufficient. These results supported the two-stage process hypothesis, which reconciles previous conflicting findings in the literature. [ABSTRACT FROM AUTHOR]

Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during freely listening to music. We used a data-driven method that combined music information retrieval with spatial Fourier Independent Components Analysis (spatial Fourier–ICA) to probe the interplay between the spatial profiles and the spectral patterns of the brain network emerging from music listening. Correlation analysis was performed between time courses of brain networks extracted from EEG data and musical feature time series extracted from music stimuli to derive the musical feature related oscillatory patterns in the listening brain. We found brain networks of musical feature processing were frequency-dependent. Musical feature time series, especially fluctuation centroid and key feature, were associated with an increased beta activation in the bilateral superior temporal gyrus. An increased alpha oscillation in the bilateral occipital cortex emerged during music listening, which was consistent with alpha functional suppression hypothesis in task-irrelevant regions. We also observed an increased delta–beta oscillatory activity in the prefrontal cortex associated with musical feature processing. In addition to these findings, the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing. [ABSTRACT FROM AUTHOR]