Enhancing Aboveground Biomass Prediction through Integration of the SCDR Paradigm into the U-Like Hierarchical Residual Fusion Model.

Deep learning methodologies employed for biomass prediction often neglect the intricate relationships between labels and samples, resulting in suboptimal predictive performance. This paper introduces an advanced supervised contrastive learning technique, termed Improved Supervised Contrastive Deep Regression (SCDR), which is adept at effectively capturing the nuanced relationships between samples and labels in the feature space, thereby mitigating this limitation. Simultaneously, we propose the U-like Hierarchical Residual Fusion Network (BioUMixer), a bespoke biomass prediction network tailored for image data. BioUMixer enhances feature extraction from biomass image data, facilitating information exchange and fusion while considering both global and local features within the images. The efficacy of the proposed method is validated on the Pepper_Biomass dataset, which encompasses over 600 original images paired with corresponding biomass labels. The results demonstrate a noteworthy enhancement in deep regression tasks, as evidenced by performance metrics on the Pepper_Biomass dataset, including RMSE = 252.18, MAE = 201.98, and MAPE = 0.107. Additionally, assessment on the publicly accessible GrassClover dataset yields metrics of RMSE = 47.92, MAE = 31.74, and MAPE = 0.192. This study not only introduces a novel approach but also provides compelling empirical evidence supporting the digitization and precision improvement of agricultural technology. The research outcomes align closely with the identified problem and research statement, underscoring the significance of the proposed methodologies in advancing the field of biomass prediction through state-of-the-art deep learning techniques.

Selective oxidation of ß-keto ester modulated by the d-band centers in D-A conjugated microporous metallaphotoredox catalysts containing M-salen (MZn, Cu and Co) and triazine monomers.

Photocatalytic selective oxidation plays an important role in developing green chemistry. However, it is challenging to design an efficient photocatalyst for controlling the selectivity of photocatalytic oxidation reaction and exploring its detailed mechanism. Here, we synthesized three conjugated microporous polymers (CMPs) with D-A structures, named M-SATE-CMPs (MZn, Cu and Co), with different d-band centers based on different metal centers, resulting in the discrepancy in adsorption and activation capacities for the reactants, which produces the selectivity of ß-keto esters being catalyzed into α-hydroperoxide ß-keto esters (ROOH) or to α-hydroxyl ß-keto esters (ROH). Density functional theory (DFT) calculations also demonstrate that the adsorption and activation capacities of the metal active centers in M-SATE-CMPs (MZn, Cu and Co) for ROOH are the key factors to influence the photocatalytic selective oxidation of ß-keto ester. This study provides a promising strategy for designing a metallaphotoredox catalyst whose photocatalytic selectivity depends on the d-band center of metal site in the catalyst.

DUSP1 regulates the JAK2/STAT3 signaling pathway through targeting miR-21 in cervical cancer cells.

This study was to investigate the effect of DUSP1 on cervical cancer (CC) cells by targeting the miR-21 regulatory JAK2/STAT3 signaling pathway. For this purpose, fifteen CC patients treated at our hospital from January 2021 to February 2023 were selected. CC tissues and para-cancerous (PC) tissues were collected from the patients, and DUSP1 protein and mRNA expression levels were detected by Western blot and qPCR. The C33a control group (COG) and DUSP1 overexpression group (OVG) were set up: human cervical squamous carcinoma cells (CSCC) in the C33a COG were cultured without any treatment, while the DUSP1 OVG was cultured using DUSP1 gene overexpression lentivirus infection progeny. The proliferation ability of the three groups of cells was measured by CCK8, protein and mRNA expression by Western blot and qPCR, and cell migration and invasion ability by Transwell. It was found that DUSP1 protein and mRNA in CC tissues were reduced compared with those in PC tissues (P<0.05). The miR-21 in the DUSP1 OVG was reduced than those in the C33a COG (P<0.05). The expression of JAK2, STAT3 mRNA and protein in the DUSP1 OVG were reduced compared with those in the C33a COG (P<0.05). In conclusion, overexpression of DUSP1 can target and reduce the expression of miR-21, block the JAK2/STAT3 signaling pathway, reduce the viability of CC cells, inhibit the proliferation and migration and invasion ability of CC cells, and induce apoptosis of CC cells, thus providing a theoretical basis for the targeted treatment of clinical CC.

An α-hemoglobin-derived peptide (m)VD-hemopressin (α) promotes NREM sleep via the CB1 cannabinoid receptor.

Hemopressin and related peptides have shown to function as the endogenous ligands or the regulator of cannabinoid receptors. The previous studies demonstrated that the endocannabinoid system played important roles in modulating several physiological functions such as sleep, olfaction, emotion, learning and memory, and reward behaviors. Mouse VD-hemopressin (α) [(m)VD-HPα], an 11-residue peptide derived from the α1 chain of hemoglobin, was recently presumed as a selective agonist of the CB1 receptor. The present study was undertaken to investigate the effects of (m)VD-HPα on the sleep-wake cycle and power spectrum of cortical EEG in freely moving rats and the potential neurons in the brain activated by (m)VD-HPα. The results showed that 20.1 nmol of (m)VD-HPα i.c.v. administration increased non-rapid eye movement (NREM) sleep in the first 2 h section accompanied by an increase in EEG delta (0.5-4 Hz) activity. The (m)VD-HPα-induced NREM sleep enhancement was due to extended episode duration instead of the episode number. In addition, the effect of (m)VD-HPα (20.1 nmol) on sleep-wake states was significantly attenuated by an antagonist of the CB1 receptor, AM251 (20 nmol, i.c.v.) but not by the CB2 receptor antagonist, AM630 (20 nmol, i.c.v.). In comparison with vehicle, (m)VD-HPα increased Fos-immunoreactive (-ir) neurons in the ventrolateral preoptic nucleus (VLPO), but reduced Fos-ir neurons in the lateral hypothalamus (LH), tuberomammillary nucleus (TMN), and locus coeruleus (LC). These findings suggest that (m)VD-HPα promotes NREM sleep via the CB1 cannabinoid receptor to probably activate VLPO GABAergic neurons, but inactivates the LH orexinergic, LC noradrenergic, and TMN histaminergic neurons.

MicroRNA-150-5p inhibits the proliferation and invasion of human larynx epidermiod cancer cells though regulating peptidyl-prolyl cis/trans isomerase

Abstract Objective This study aimed to investigate the molecular mechanism of miR-150-5p regulating the malignant biological behavior of Human Epidermoid cancer cell (HEp-2) by targeting peptidyl-prolyl cis/trans isomerase NIMA-Interacting-1 (PIN1). Methods Firstly, qRT-PCR and Western blot were adopted to detect the expression levels of miR-150-5p and PIN1 in cancer tissue and paracancerous tissues of patients with LSCC, and those in human bronchial epithelial cells (16 HBE) and HEp-2. Next, the targeted relationship between miR-150-5p and PIN1 was assessed by bioinformatics website and dual-luciferase reporter assay, followed by their correlation analysis. Besides, after interfering with miR-150-5p or PIN1 expression in HEp-2 cells, CCK-8, cell colony formation assay, and transwell assay were utilized to detect the proliferation, viability, and invasion of cells, respectively. Subsequently, the protein levels of MMP-2, MMP-9, and EMT-related proteins in HEp-2 cells were checked by Western blot. Results Expression of miR-150-5p was down-regulated in LSCC tissues and HEp-2 cells. Moreover, miR-150-5p suppressed proliferation and invasion of HEp-2 cells, affected protein expression related to MMP and EMT, thereby inhibiting development of cancer. The expression of PIN1 was significantly increased in cancer tissues and HEp-2 cells, and there was a targeted relationship and negative correlation between miR-150-5p and PIN1 in cancer tissue. However, overexpression of PIN1 could reverse the effect of miR-150-5p on the proliferation and invasion of HEp-2 cells. Conclusion In a nutshell, there is a targeted relationship between PIN1 and miR-150-5p. Besides, miR-150-5p can inhibit the proliferation and invasion of HEp-2 cells by regulating the expression of PIN1. Level of evidence 3.

MicroRNA-150-5p inhibits the proliferation and invasion of human larynx epidermiod cancer cells though regulating peptidyl-prolyl cis/trans isomerase.

OBJECTIVE: This study aimed to investigate the molecular mechanism of miR-150-5p regulating the malignant biological behavior of Human Epidermoid cancer cell (HEp-2) by targeting peptidyl-prolyl cis/trans isomerase NIMA-Interacting-1 (PIN1). METHODS: Firstly, qRT-PCR and Western blot were adopted to detect the expression levels of miR-150-5p and PIN1 in cancer tissue and paracancerous tissues of patients with LSCC, and those in human bronchial epithelial cells (16 HBE) and HEp-2. Next, the targeted relationship between miR-150-5p and PIN1 was assessed by bioinformatics website and dual-luciferase reporter assay, followed by their correlation analysis. Besides, after interfering with miR-150-5p or PIN1 expression in HEp-2 cells, CCK-8, cell colony formation assay, and transwell assay were utilized to detect the proliferation, viability, and invasion of cells, respectively. Subsequently, the protein levels of MMP-2, MMP-9, and EMT-related proteins in HEp-2 cells were checked by Western blot. RESULTS: Expression of miR-150-5p was down-regulated in LSCC tissues and HEp-2 cells. Moreover, miR-150-5p suppressed proliferation and invasion of HEp-2 cells, affected protein expression related to MMP and EMT, thereby inhibiting development of cancer. The expression of PIN1 was significantly increased in cancer tissues and HEp-2 cells, and there was a targeted relationship and negative correlation between miR-150-5p and PIN1 in cancer tissue. However, overexpression of PIN1 could reverse the effect of miR-150-5p on the proliferation and invasion of HEp-2 cells. CONCLUSION: In a nutshell, there is a targeted relationship between PIN1 and miR-150-5p. Besides, miR-150-5p can inhibit the proliferation and invasion of HEp-2 cells by regulating the expression of PIN1.

Does environmental regulation improve residents' health? Evidence from China.

Environmental pollution is an important factor that harms public health, and environmental regulation is the policy instrument to govern pollution, so what impact does environmental regulation have on the public health? What are the mechanisms? To answer these questions, this paper constructs ologit model and uses China General Social Survey data for empirical analysis. The study found first that environmental regulation has a significant effect on improving the health level of residents, and this effect has been increasing with the passage of time. Second, the impact of environmental regulation on residents' health is different among residents with different characteristics. Specifically, the positive impact of environmental regulation on residents' health is stronger among residents with at least a university degree, residents with urban-registered residences, and residents living in economically developed areas. Third, the mechanism analysis found that environmental regulation can improve residents' health by reducing pollutant emissions and improving environmental quality. Finally, by introducing a cost benefit model, it was found that environmental regulation has a significant effect on improving the welfare level of individual residents and society as a whole. Hence, Environmental regulation is an effective means to improve residents' health, but when implementing environmental regulation, we should also pay attention to its negative impact on residents' employment and income.

Extrusion Effect or Promotion Effect? The Effect of Environmental Regulation on Enterprise Green Innovation.

This paper took the policy of China' Air Pollution Prevention and Control Action Plan as an exogenous shock to reflect the change in environmental regulation intensity. By matching environmental policies with micro data of listed companies in China, this paper explored the effect and mechanisms of environmental regulation on enterprise green innovation. Through constructing difference-in-difference (DID) and difference-in-difference-in-difference (DDD) models, we found the following to be the case: (1) Environmental regulation had a significant positive effect with the green innovation level of Chinese listed companies. (2) Compared with non-regulated industries, this policy has led to a significant increase (5.4%) in the amount of firms' green patent applications in regulated industries, and the promoting effect was more obvious in key areas that are strictly controlled by this policy. (3) Compared with non-state-owned enterprises, it had a stronger impact on the green innovation of state-owned enterprises. (4) Mechanistic analysis showed that China's environmental regulation can play a resource compensation effect by increasing environmental protection subsidies for enterprises' green innovation behaviors. Additionally, it can force firms to increase investment in environmental pollution governance by raising pollution penalties, thus exerting the forcing effect. This paper provides new evidence for Porter's hypothesis and can provide a reference for developing countries promoting green innovation through environmental policies and regulations.

High sensitivity temperature sensor based on enhanced Vernier effect through two parallel Fabry-Perot cavities.

In this paper, an enhanced Vernier effect temperature sensor based on two parallel Fabry-Perot interferometers (FPIs) is proposed and demonstrated experimentally. Among them, F P I 1 is composed of a single-mode fiber (SMF), a quartz capillary, and AB glue filled in the capillary. F P I 2 is formed by filling a capillary with polyimide (PI) solution and inserting two-segment SMF from both sides of the capillary. Since AB glue and PI have good thermal sensitivity, F P I 1 and F P I 2 are highly sensitive to temperature. Due to their different structures, the temperature sensitivity of F P I 1 is negative, and that of F P I 2 is positive. When F P I 1 and F P I 2 with similar free spectral range are connected in parallel, they will act as reference cavities for each other, resulting in an enhanced Vernier effect, which enlarges the sensitivity of the sensor more. In the temperature range of 40°C-58°C, the temperature sensitivity of the sensor is as high as -13.09n m/∘ C, and the fitting coefficient is 0.9974. The experimental results show that in the enhanced Vernier effect sensor structure, only two FPIs with opposite temperature sensitivity are required, which does not increase the difficulty and cost of sensor manufacturing. In addition, the sensor has good stability and repeatability.

Predicting circRNA-drug sensitivity associations by learning multimodal networks using graph auto-encoders and attention mechanism.

Recent studies have shown that the expression of circRNAs would affect drug sensitivity of cells and thus significantly influence the efficacy of drugs. Traditional biomedical experiments to validate such relationships are time-consuming and costly. Therefore, developing effective computational methods to predict potential associations between circRNAs and drug sensitivity is an important and urgent task. In this study, we propose a novel method, called MNGACDA, to predict possible circRNA-drug sensitivity associations for further biomedical screening. First, MNGACDA uses multiple sources of information from circRNAs and drugs to construct multimodal networks. It then employs node-level attention graph auto-encoders to obtain low-dimensional embeddings for circRNAs and drugs from the multimodal networks. Finally, an inner product decoder is applied to predict the association scores between circRNAs and drug sensitivity based on the embedding representations of circRNAs and drugs. Extensive experimental results based on cross-validations show that MNGACDA outperforms six other state-of-the-art methods. Furthermore, excellent performance in case studies demonstrates that MNGACDA is an effective tool for predicting circRNA-drug sensitivity associations in real situations. These results confirm the reliable prediction ability of MNGACDA in revealing circRNA-drug sensitivity associations.

LINC02489 with m6a modification increase paclitaxel sensitivity by inhibiting migration and invasion of ovarian cancer cells.

The long non-coding RNA LINC02489 has been shown to be significantly downregulated in advanced ovarian cancer (OC). However, the function of LINC02489 remains unknown. This study aims to explain the role and mechanism of LINC02489 in OC. The expression of LINC02489 was examined by qRT-PCR in primary OC tissues. Additionally, MTT, wound healing, transwell, and flow cytometry assays were used to analyze the function of LINC02489. The mechanism of LINC02489 in OC was investigated by high-throughput RNA-sequencing, qRT-PCR, western blot, and N6-methyladenosine (m6A) meRIP. A total of 1101 and 827 genes are significantly down-regulated and up-regulated in metastatic and chemoresistant OC tissues. The expression of LINC02489 is decreased in metastatic and chemoresistant OC tissues compared with the primary OC tissues (p < 0.05). Overexpression of LINC02489 inhibits proliferation, invasion, and migration of drug-resistant OC cells. In the LINC02489 overexpressed chemoresistant SKOV3 cells, the m6A modified LINC02489 is significantly up-regulated. Furthermore, the expression of PKNOX2 is increased during overexpression of LINC02489, while the expression of PTEN and mTOR plummets. This study demonstrates that LINC02489 can inhibit the invasion and migration of chemoresistant OC cells by increasing its m6A modification and up-regulating PKNOX2 expression. In addition, LINC02489 regulates the invasion ability of OC cells through the PTEN/mTOR signaling pathway, thereby regulating the sensitivity of SKOV3 cells to paclitaxel. This result provides a potential therapeutic target for chemoresistant OC.

Predicting miRNA-disease associations based on lncRNA-miRNA interactions and graph convolution networks.

Increasing studies have proved that microRNAs (miRNAs) are critical biomarkers in the development of human complex diseases. Identifying disease-related miRNAs is beneficial to disease prevention, diagnosis and remedy. Based on the assumption that similar miRNAs tend to associate with similar diseases, various computational methods have been developed to predict novel miRNA-disease associations (MDAs). However, selecting proper features for similarity calculation is a challenging task because of data deficiencies in biomedical science. In this study, we propose a deep learning-based computational method named MAGCN to predict potential MDAs without using any similarity measurements. Our method predicts novel MDAs based on known lncRNA-miRNA interactions via graph convolution networks with multichannel attention mechanism and convolutional neural network combiner. Extensive experiments show that the average area under the receiver operating characteristic values obtained by our method under 2-fold, 5-fold and 10-fold cross-validations are 0.8994, 0.9032 and 0.9044, respectively. When compared with five state-of-the-art methods, MAGCN shows improvement in terms of prediction accuracy. In addition, we conduct case studies on three diseases to discover their related miRNAs, and find that all the top 50 predictions for all the three diseases have been supported by established databases. The comprehensive results demonstrate that our method is a reliable tool in detecting new disease-related miRNAs.

Causal optimal and optically transparent ultra-wideband microwave metamaterials absorber with high angular stability.

Wideband microwave absorbers, especially those with high optical transparency, are significantly used in civil and military fields. This paper proposes an ultra-wideband optically transparent metamaterial absorber (MMA) with causal optimal thickness and high angular stability. Based on the equivalent circuits model of the MMA, a genetic algorithm is adopted to identify the best circuit parameters that can realize broadband microwave absorption. High transparent indium tin oxide and poly-methyl methacrylate are utilized to realize the absorber. Optimization and simulation results show that the designed MMA presents a high microwave absorption above 90%, covering a wide frequency of 2.05-15.5 GHz with an impressive FBW of 153.3%. The proposed MMA exhibits extraordinary angular stability. For TM polarization, it can still maintain a fractional bandwidth (FBW) over 114.5% at an incidence angle of 70° and over 142% at an incidence angle of 60°, while the FBW of both TE polarization and TM polarization exceeds 150% when the incidence angle is below 45°. Furthermore, the proposed absorber has the advantages of high transparency and polarization insensitiveness. A prototype of the proposed MMA is fabricated and experimentally tested. The measured results are in excellent agreement with the optimized design and the full-wave simulation results, demonstrating its excellent performance. Most significantly, the overall thickness of the absorber is 0.102 λ at the lowest working frequency and only 1.08 times the causality-dictated minimum sample thickness. The MMA proposed herein provides methods to achieve high compatibility with wideband microwave absorption, optical transparency, and wide-angle incidence, thus enabling a wide range of applications in stealth, electromagnetic pollution reduction, and electromagnetic compatible facilities.

Highly sensitive gas pressure sensor based on the enhanced Vernier effect through a cascaded Fabry-Perot and Mach-Zehnder interferometer.

A high sensitivity optical fiber gas pressure sensor based on the enhanced Vernier effect is proposed. The sensor is composed of a fiber Fabry-Perot interferometer (FPI) and Mach-Zehnder interferometer (MZI). Since the interference fringes of FPI and MZI drift in the opposite direction with the change of gas pressure, when their free spectral ranges are similar, the enhanced Vernier effect is formed after their cascading. Compared with the traditional Vernier effect gas pressure sensor, the enhanced Vernier effect gas pressure sensor realizes much higher sensitivity gas pressure measurement without complex manufacturing process or desensitized reference interferometer. The experimental results show that the sensitivity of the enhanced Vernier effect sensor is 241.87 nm/MPa. In the two traditional Vernier effect gas pressure sensors formed by cascading FPI and MZI, the sensitivity of sensor is 63.02 nm/MPa and 171.26 nm/MPa, respectively. Compared with the two traditional Vernier effect sensors, the sensitivity of the enhanced Vernier effect sensor is increased by 3.8 times and 1.4 times, respectively. The proposed sensor also has the advantages of good repeatability and stability, fast response, low cost and easy manufacture. Our structure also provides a new design scheme for a high sensitivity optical fiber gas pressure sensor.

Predicting potential miRNA-disease associations based on more reliable negative sample selection.

BACKGROUND: Increasing biomedical studies have shown that the dysfunction of miRNAs is closely related with many human diseases. Identifying disease-associated miRNAs would contribute to the understanding of pathological mechanisms of diseases. Supervised learning-based computational methods have continuously been developed for miRNA-disease association predictions. Negative samples of experimentally-validated uncorrelated miRNA-disease pairs are required for these approaches, while they are not available due to lack of biomedical research interest. Existing methods mainly choose negative samples from the unlabelled ones randomly. Therefore, the selection of more reliable negative samples is of great importance for these methods to achieve satisfactory prediction results. RESULTS: In this study, we propose a computational method termed as KR-NSSM which integrates two semi-supervised algorithms to select more reliable negative samples for miRNA-disease association predictions. Our method uses a refined K-means algorithm for preliminary screening of likely negative and positive miRNA-disease samples. A Rocchio classification-based method is applied for further screening to receive more reliable negative and positive samples. We implement ablation tests in KR-NSSM and find that the combination of the two selection procedures would obtain more reliable negative samples for miRNA-disease association predictions. Comprehensive experiments based on fivefold cross-validations demonstrate improvements in prediction accuracy on six classic classifiers and five known miRNA-disease association prediction models when using negative samples chose by our method than by previous negative sample selection strategies. Moreover, 469 out of 1123 selected positive miRNA-disease associations by our method are confirmed by existing databases. CONCLUSIONS: Our experiments show that KR-NSSM can screen out more reliable negative samples from the unlabelled ones, which greatly improves the performance of supervised machine learning methods in miRNA-disease association predictions. We expect that KR-NSSM would be a useful tool in negative sample selection in biomedical research.

Rapid Eye Movement Sleep during Early Life: A Comprehensive Narrative Review.

The ontogenetic sleep hypothesis suggested that rapid eye movement (REM) sleep is ontogenetically primitive. Namely, REM sleep plays an imperative role in the maturation of the central nervous system. In coincidence with a rapidly developing brain during the early period of life, a remarkably large amount of REM sleep has been identified in numerous behavioral and polysomnographic studies across species. The abundant REM sleep appears to serve to optimize a cerebral state suitable for homeostasis and inherent neuronal activities favorable to brain maturation, ranging from neuronal differentiation, migration, and myelination to synaptic formation and elimination. Progressively more studies in Mammalia have provided the underlying mechanisms involved in some REM sleep-related disorders (e.g., narcolepsy, autism, attention deficit hyperactivity disorder (ADHD)). We summarize the remarkable alterations of polysomnographic, behavioral, and physiological characteristics in humans and Mammalia. Through a comprehensive review, we offer a hybrid of animal and human findings, demonstrating that early-life REM sleep disturbances constitute a common feature of many neurodevelopmental disorders. Our review may assist and promote investigations of the underlying mechanisms, functions, and neurodevelopmental diseases involved in REM sleep during early life.

High sensitivity gas pressure sensor based on different inner diameter quartz capillary cascading and Vernier effect.

In this paper, a highly sensitive optical fiber gas pressure sensor is proposed and experimentally verified. The sensor is composed of two Fabry-Pérot (F-P) cavities, and two F-P cavities are fabricated by a single-mode fiber and two quartz capillaries with different inner diameters splicing. Among them, the small inner diameter capillary is used as a gas channel connecting the large inner diameter capillary and the external environment. The manufacturing process of the sensor only involves capillary cleaver and splicing and does not involve other complex manufacturing technologies. By correctly adjusting the length of the two quartz capillaries, when the free spectral range of the two F-P cavities is very close, the optical Vernier effect will be observed and used as a sensitive probe for detecting gas pressure. The experimental results show that, in the pressure range of 0-0.8 MPa, the gas pressure sensitivity of the sensor reaches -81.73 nm/MPa with a linearity of 99.7%, and the temperature cross-sensitivity is only 1.82 kPa/°C. Due to its easy manufacture, high sensitivity, compact structure, and small volume, the sensor has become one of the preferred structures for large-scale use in the field of gas sensing.

γ-secretase inhibitors augment efficacy of BCMA-targeting bispecific antibodies against multiple myeloma cells without impairing T-cell activation and differentiation.

We here defined the impacts of γ-secretase inhibitors (GSIs) on T-cell-dependent BCMA-specific multiple myeloma (MM) cell lysis and immunomodulatory effects induced by bispecific antibodies (BisAbs). GSIs-induced membrane BCMA (mBCMA) accumulation reached near maximum within 4 h and sustained over 42h-study period on MM cell lines and patient MM cells. GSIs, i.e., 2 nM LY-411575 or 1 µM DAPT, robustly increased mBCMA densities on CD138+ but not CD3+ patient cells, concomitantly with minimum soluble/shed BCMA (sBCMA) in 1 day-culture supernatants. In ex vivo MM-T-cell co-cultures, GSIs overcame sBCMA-inhibited MM cell lysis and further enhanced autologous patient MM cell lysis induced by BCMAxCD3 BisAbs, accompanied by significantly enhanced cytolytic markers (CD107a, IFNγ, IL2, and TNFα) in patient T cells. In longer 7 day-co-cultures, LY-411575 minimally affected BCMAxCD3 BisAb (PL33)-induced transient expression of checkpoint (PD1, TIGIT, TIM3, LAG3) and co-stimulatory (41BB, CD28) proteins, as well as time-dependent increases in % effector memory/central memory subsets and CD8/CD4 ratios in patient T cells. Importantly, LY41157 rapidly cleared sBCMA from circulation of MM-bearing NSG mice reconstituted with human T cells and significantly enhanced anti-MM efficacy of PL33 with prolonged host survival. Taken together, these results further support ongoing combination BCMA-targeting immunotherapies with GSI clinical studies to improve patient outcome.

Stereotaxic atlas of the infant rat brain at postnatal days 7-13.

Recently, researchers have paid progressively more attention to the study of neural development in infant rats. However, due to the lack of complete intracerebral localization information, such as clear nuclear cluster boundaries, identified main brain structures, and reliable stereotaxic coordinates, it is difficult and restricted to apply technical neuroscience to infant rat's brain. The present study was undertaken to refine the atlas of infant rats. As such, we established a stereotaxic atlas of the infant rat's brain at postnatal days 7-13. Furthermore, dye calibration surgery was performed in P7-P13 infant rats by injecting Methylene blue, and sections were incubated in Nissl solutions. From the panoramic images of the brain sections, atlases were made. Our article has provided the appearance and measurements of P7-P13 Sprague-Dawley rat pups. Whereas the atlas contains a series of about 530 coronal brain section images from olfactory bulbs to the brainstem, a list of abbreviations of the main brain structures, and reliable stereotaxic coordinates, which were demonstrated by vertical and oblique injections with fluorescent dye DiI. The present findings demonstrated that our study of P7-P13 atlases has reasonable nucleus boundaries and accurate and good repeatability of stereotaxic coordinates, which can make up for the shortage of postnatal rat brain atlas currently in the field.

Predicting miRNA-disease associations based on graph attention networks and dual Laplacian regularized least squares.

Increasing biomedical evidence has proved that the dysregulation of miRNAs is associated with human complex diseases. Identification of disease-related miRNAs is of great importance for disease prevention, diagnosis and remedy. To reduce the time and cost of biomedical experiments, there is a strong incentive to develop efficient computational methods to infer potential miRNA-disease associations. Although many computational approaches have been proposed to address this issue, the prediction accuracy needs to be further improved. In this study, we present a computational framework MKGAT to predict possible associations between miRNAs and diseases through graph attention networks (GATs) using dual Laplacian regularized least squares. We use GATs to learn embeddings of miRNAs and diseases on each layer from initial input features of known miRNA-disease associations, intra-miRNA similarities and intra-disease similarities. We then calculate kernel matrices of miRNAs and diseases based on Gaussian interaction profile (GIP) with the learned embeddings. We further fuse the kernel matrices of each layer and initial similarities with attention mechanism. Dual Laplacian regularized least squares are finally applied for new miRNA-disease association predictions with the fused miRNA and disease kernels. Compared with six state-of-the-art methods by 5-fold cross-validations, our method MKGAT receives the highest AUROC value of 0.9627 and AUPR value of 0.7372. We use MKGAT to predict related miRNAs for three cancers and discover that all the top 50 predicted results in the three diseases are confirmed by existing databases. The excellent performance indicates that MKGAT would be a useful computational tool for revealing disease-related miRNAs.