ARRDC5 deficiency impairs spermatogenesis by affecting SUN5 and NDC1.

Sperm with normal morphology and motility are essential for successful fertilization, and the strong attachment of the sperm head-tail coupling apparatus to the nuclear envelope during spermatogenesis is required to ensure the integrity of sperm for capacitation and fertilization. Here, we report that Arrdc5 is associated with spermatogenesis. The Arrdc5 knockout mouse model showed male infertility characterized by a high bent-head rate and reduced motility in sperm, which led to capacitation defects and subsequent fertilization failure. Through mass spectrometry, we found that ARRDC5 affects spermatogenesis by affecting NDC1 and SUN5. We further found that ARRDC5 might affect the vesicle-trafficking protein SEC22A-mediated transport and localization of NDC1, SUN5 and other head-tail coupling apparatus-related proteins that are responsible for initiating the attachment of the sperm head and tail. We finally performed intracytoplasmic sperm injection as a way to explore therapeutic strategies. Our findings demonstrate the essential role and the underlying molecular mechanism of ARRDC5 in anchoring the sperm head to the tail during spermatogenesis.

Probing a General Strategy to Break the C-C Bond of Benzene by a Cyclic (Alkyl)(Amino)Aluminyl Anion.

The oxidative addition of C-C bonds in aromatic hydrocarbons by low valent main group species has attracted considerable attention from both theoretical and experimental chemists due to the big challenge in breaking their aromaticity. Herein, a general strategy to break the C-C bonds in benzene by cyclic (alkyl)(amino)aluminyl anion is demonstrated via density functional theory (DFT) calculations. The results suggest that the activation of the C-C bond of benzene by this anion is both kinetically and thermodynamically unfavorable whereas introducing electron-withdrawing groups makes such C-C bond activation becomes favorable both kinetically and thermodynamically. Such a sharp change on the kinetics and thermodynamics could be rationalized by the frontier molecular orbital theory by decreasing the lowest unoccupied molecular orbitals of the mono- and disubstituted benzenes. Aromaticity is found to stabilize the transition state for the ring open step. All these findings can help develop the chemistry of small-molecule activation.

Recent advances in utilization of pectins in biomedical applications: a review focusing on molecular structure-directing health-promoting properties.

The numerous health benefits of pectins justify their inclusion in human diets and biomedical products. This review provides an overview of pectin extraction and modification methods, their physico-chemical characteristics, health-promoting properties, and pharmaceutical/biomedical applications. Pectins, as readily available and versatile biomolecules, can be tailored to possess specific functionalities for food, pharmaceutical and biomedical applications, through judicious selection of appropriate extraction and modification technologies/processes based on green chemistry principles. Pectin's structural and physicochemical characteristics dictate their effects on digestion and bioavailability of nutrients, as well as health-promoting properties including anticancer, immunomodulatory, anti-inflammatory, intestinal microflora-regulating, immune barrier-strengthening, hypercholesterolemia-/arteriosclerosis-preventing, anti-diabetic, anti-obesity, antitussive, analgesic, anticoagulant, and wound healing effects. HG, RG-I, RG-II, molecular weight, side chain pattern, and degrees of methylation, acetylation, amidation and branching are critical structural elements responsible for optimizing these health benefits. The physicochemical characteristics, health functionalities, biocompatibility and biodegradability of pectins enable the construction of pectin-based composites with distinct properties for targeted applications in bioactive/drug delivery, edible films/coatings, nano-/micro-encapsulation, wound dressings and biological tissue engineering. Achieving beneficial synergies among the green extraction and modification processes during pectin production, and between pectin and other composite components in biomedical products, should be key foci for future research.

A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge-Cloud Computing.

With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge-LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge-LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network's total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%.

[Advances in brain-computer interface based on high-frequency steady-state visual evoked potential].

Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.

Probing hyperconjugative aromaticity in 2H-pyrrolium and cyclopentadiene containing group 9 transition metal substituents: bridged carbonyl ligands can enhance aromaticity.

Aromaticity and hyperconjugation are two fundamental concepts in organic chemistry. By combination of the two concepts together, the resulting hyperconjugative aromaticity has attracted considerable attention from both theoretical and computational chemists. However, previous studies are mainly focused on the main group chemistry. For the hyperconjugative aromaticity in the transition metal chemistry, the studies are limited to groups 10 and 11. Here, we demonstrate that hyperconjugative aromaticity can be achieved in 2H-pyrrolium and cyclopentadiene containing group 9 transition metal substituents via density functional theory calculations. More importantly, further studies reveal that the metal-metal bonding interaction between two substituents could reduce hyperconjugative aromaticity, whereas the bridged carbonyl ligands will enhance aromaticity due to the significantly elevated HOMO orbital of the CR2 fragment. All these findings not only extend the scope of the concept of hyperconjugative aromaticity but also are helpful to develop the chemistry of metalla-aromatics.

Effects of reduced platelet count on the prognosis for patients with non-small cell lung cancer treated with EGFR-TKI: a retrospective study.

BACKGROUND: Progressive lung cancer is associated with abnormal coagulation. Platelets play a vital part in evading immune surveillance and angiogenesis in the case of tumor metastasis. The study aimed to analyze the predictive and prognostic effects of platelet count on non-small cell lung cancer (NSCLC) patients treated with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). METHODS: This study retrospectively analyzed the prognostic effects of platelets on 52 NSCLC patients with epidermal growth factor receptor (EGFR) mutant following EGFR-TKI treatment. Related data, together with the progression-free survival (PFS) and overall survival (OS) were collected before and after 2 cycles of treatments (60 days). RESULTS: The anti-EGFR treatment markedly reduced the platelet count in 33 (63.5%) patients after 2 cycles of treatment. Multivariate Cox analysis revealed that, the decreased platelet count was closely correlated with the longer OS (HR = 0.293; 95%CI: 0.107-0.799; p = 0.017). Besides, the median OS was 326 days in the decreased platelet count group and 241 days in the increased platelet count group (HR = 0.311; 95%CI: 0.118-0.818; P = 0.018), as obtained from the independent baseline platelet levels and other clinical features. CONCLUSIONS: The platelet count may predict the prognosis for EGFR-TKI treatment without additional costs. Besides, changes in platelet count may serve as a meaningful parameter to establish the prognostic model for NSCLC patients receiving anti-EGFR targeted therapy.

Novel anthranilic amide derivatives bearing the chiral thioether and trifluoromethylpyridine: Synthesis and bioactivity.

Ten anthranilic amides bearing skeletons of chiral thioether and trifluoromethylpyridine (5a-5j) were designed and synthesized. Bioassays indicated that some of compounds had excellent insecticidal activity. For example, compounds 5a, 5e and 5g had the median lethal concentrations (LC50) against Plutella xylostella of 7.3, 8.7 and 8.1 µg/mL respectively. The LC50 of 5a against Ostrinia nubilalis and Pseudaletia separata were 21.7 and 44.2 µg/mL respectively. Anti-TMV tests indicated that some compounds also showed good antiviral activity. For instance, the curative activities of compounds 5b and 5e were 57.2% and 63.6%, and with half maximal effective concentration (EC50) of 304.5 and 203.0 µg/mL, respectively, which were much higher than these of ribavirin (39.4%, EC50 = 819.8 µg/mL) and ningnanmycin (56.2%, EC50 = 361.4 µg/mL). The molecular docking between the most active compounds and ryanodine receptor of the Plutella xylostella were also discussed. Those results indicated that the novel anthranilic amide derivatives in present work were worthy of further research and development as novel pesticides.

Reaction Mechanisms on [3 + 2] Cycloaddition of Azides with Metal Carbyne Complexes: Significant Effects of Aromaticity, Substituent, and Metal Center.

Density functional theory calculations were used to investigate the reaction mechanisms on [3 + 2] cycloaddition reactions of azides with metal carbyne complexes. Our results reveal that the formation of a 1,4-metallatriazole regioisomer is a kinetically favorable process in comparison with the formation of 1,5-metallatriazole. Aromaticity plays an important role in stabilizing the products in these reactions. Further analyses show that the electron-donating ligand on metal centers or the electron-withdrawing group on the azide could accelerate the [3 + 2] cycloaddition reaction. All of these findings could be useful for experimental chemists to develop "click reactions" in organometallic chemistry.

Diverse Oxidative C(sp2)-N Bond Cleavages of Aromatic Fused Imidazoles for Synthesis of α-Ketoamides and N-(pyridin-2-yl)arylamides.

An efficient and chemoselective C(sp2)-N bond cleavage of aromatic imidazo[1,2- a]pyridine molecules is developed. A broad scope of amide compounds such as α-ketoamides and N-(pyridin-2-yl)arylamides are afforded as the final products in up to quantitative yields. Diverse C-N bond cleavages are controlled by the oxidative species used in this transformation, with various amide products afforded in a chemoselective fashion. A preliminary study indicated that some α-ketoamides exhibit anti-Tobacco Mosaic Virus activity for potential use in plant protection.

Pyrazolo[3,4-d]pyrimidine derivatives containing a Schiff base moiety as potential antiviral agents.

A series of pyrazolo[3,4-d]pyrimidine derivatives containing a Schiff base moiety were synthesized, characterised, and evaluated for their activity against tobacco mosaic virus (TMV). Biological assays indicated that several of the derivatives exhibited significant activity against TMV. In particularly, compounds 5y and 5aa displayed excellent inactivating activity against TMV, with half maximal effective concentration (EC50) values of 70.3 and 53.65 µg/mL, respectively, which were much better than that of ribavirin (150.45 µg/mL), and 5aa was superior to ningnanmycin (EC50 = 55.35 µg/mL). Interactions of compounds 5y and 5aa with TMV coat protein (TMV-CP) were investigated using microscale thermophoresis and molecular docking. Compounds 5y and 5aa displayed strong binding capability to TMV-CP with dissociation constant (Kd) values of 22.6 and 9.8 µM, respectively. These findings indicate that pyrazolo[3,4-d]pyrimidine derivatives containing a Schiff base may be potential antiviral agents.

Detection of dynamic changes of electrodermal activity to predict the classroom performance of college students.

It is emphasized in the Self-regulated learning (SRL) framework that self-monitoring of learning state is vital for students to keep effective in studying. However, it's still challenging to get an accurate and timely understanding of their learning states during classes. In this study, we propose to use electrodermal activity (EDA) signals which are deemed to be associated with physiological arousal state to predict the college student's classroom performance. Twenty college students were recruited to attend eight lectures in the classroom, during which their EDA signals were recorded simultaneously. For each lecture, the students should complete pre- and after-class tests, and a self-reported scale (SRS) on their learning experience. EDA indices were extracted from both time and frequency domains, and they were furtherly mapped to the student's learning efficiency. As a result, the indices relevant to the dynamic changes of EDA had significant positive correlations with the learning efficiency. Furthermore, compared with only using SRS, a combination with EDA indices had significantly higher accuracy in predicting the learning efficiency. In conclusion, our findings demonstrate that the EDA dynamics are sensitive to the changes in learning efficiency, suggesting a promising approach to predicting the classroom performance of college students.

Establishment of a predictive model of CT value in the diagnosis and differential diagnosis of pulmonary cryptococcosis.

Objective: To investigate the application value of computed tomography (CT) value (HU) in the diagnosis and differential diagnosis of pulmonary cryptococcosis (PC) and to construct a prediction model. Methods: Retrospective analysis of the clinical data of 73 patients who presented with nodular/mass-type occupations on lung CT and confirmed by histopathology in our hospital from January 2019 to May 2022 were divided into PC group (23 patients) and non-PC group (50 patients) according to the pathological findings, and the CT values of each patient's lung lesions were measured. The differences in age, gender, symptoms, lesion involvement in one/both lungs, lung lobe distribution, number of lesions, maximum lesion diameter (cm), lesion margin condition, and CT value results were compared between the two groups. Independent risk factors for PC were analyzed for indicators with statistically significant differences, clinical prediction models were constructed and column line plots were drawn, C (correction) indices were calculated, subject characteristics (ROC) curves were drawn, calibration curves and clinical decision curve analysis (DCA) were performed to further evaluate the predictive efficacy of the models. Results: Comparative analysis of patient data between the two groups showed statistically significant differences in central, peripheral and global CT values (P < 0.05), and multiple regression analysis indicated that central CT value, peripheral CT value and global CT value could be used as independent risk factors for the diagnosis and differential diagnosis of PC. The area under the ROC curve of the model predicting PC was 0.814 (95 % CI: 0.7011-0.9267), and the corrected C-index (Bootstrap = 1000) was 0.781; the actual curve overlapped well with the calibration curve; the DCA results indicated that the column line graph model has high clinical application value. Conclusions: CT value measurements of lesions can be used as an independent risk factor for PC, and clinical prediction models based on the above factors are predictive for the diagnosis and differential diagnosis of PC.

Variation in brain connectivity during motor imagery and motor execution in stroke patients based on electroencephalography.

Objective: The objective of this study was to analyze the changes in connectivity between motor imagery (MI) and motor execution (ME) in the premotor area (PMA) and primary motor cortex (MA) of the brain, aiming to explore suitable forms of treatment and potential therapeutic targets. Methods: Twenty-three inpatients with stroke were selected, and 21 right-handed healthy individuals were recruited. EEG signal during hand MI and ME (synergy and isolated movements) was recorded. Correlations between functional brain areas during MI and ME were compared. Results: PMA and MA were significantly and positively correlated during hand MI in all participants. The power spectral density (PSD) values of PMA EEG signals were greater than those of MA during MI and ME in both groups. The functional connectivity correlation was higher in the stroke group than in healthy people during MI, especially during left-handed MI. During ME, functional connectivity correlation in the brain was more enhanced during synergy movements than during isolated movements. The regions with abnormal functional connectivity were in the 18th lead of the left PMA area. Conclusion: Left-handed MI may be crucial in MI therapy, and the 18th lead may serve as a target for non-invasive neuromodulation to promote further recovery of limb function in patients with stroke. This may provide support for the EEG theory of neuromodulation therapy for hemiplegic patients.

Combining EEG Features and Convolutional Autoencoder for Neonatal Seizure Detection.

Neonatal epilepsy is a common emergency phenomenon in neonatal intensive care units (NICUs), which requires timely attention, early identification, and treatment. Traditional detection methods mostly use supervised learning with enormous labeled data. Hence, this study offers a semi-supervised hybrid architecture for detecting seizures, which combines the extracted electroencephalogram (EEG) feature dataset and convolutional autoencoder, called Fd-CAE. First, various features in the time domain and entropy domain are extracted to characterize the EEG signal, which helps distinguish epileptic seizures subsequently. Then, the unlabeled EEG features are fed into the convolutional autoencoder (CAE) for training, which effectively represents EEG features by optimizing the loss between the input and output features. This unsupervised feature learning process can better combine and optimize EEG features from unlabeled data. After that, the pre-trained encoder part of the model is used for further feature learning of labeled data to obtain its low-dimensional feature representation and achieve classification. This model is performed on the neonatal EEG dataset collected at the University of Helsinki Hospital, which has a high discriminative ability to detect seizures, with an accuracy of 92.34%, precision of 93.61%, recall rate of 98.74%, and F1-score of 95.77%, respectively. The results show that unsupervised learning by CAE is beneficial to the characterization of EEG signals, and the proposed Fd-CAE method significantly improves classification performance.

Time-Frequency-Space EEG Decoding Model Based on Dense Graph Convolutional Network for Stroke.

Stroke, a sudden cerebrovascular ailment resulting from brain tissue damage, has prompted the use of motor imagery (MI)-based Brain-Computer Interface (BCI) systems in stroke rehabilitation. However, analyzing EEG signals from stroke patients is challenging because of their low signal-to-noise ratio and high variability. Therefore, we propose a novel approach that combines the modified S-transform (MST) and a dense graph convolutional network (DenseGCN) algorithm to enhance the MI-BCI performance across time, frequency, and space domains. MST is a time-frequency analysis method that efficiently concentrates energy in EEG signals, while DenseGCN is a deep learning model that uses EEG feature maps from each layer as inputs for subsequent layers, facilitating feature reuse and hyper-parameters optimization. Our approach outperforms conventional networks, achieving a peak classification accuracy of 90.22% and an average information transfer rate (ITR) of 68.52 bits per minute. Moreover, we conduct an in-depth analysis of the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon in the deep-level EEG features of stroke patients. Our experimental results confirm the feasibility and efficacy of the proposed approach for MI-BCI rehabilitation systems.

A biodegradable multifunctional pectin-montmorillonite fertilizer coating: Controlled-release, water-retention and soil-cementation.

Coated fertilizers have been widely used to improve fertility in barren land. However, improving soil structure and water-retention capacity is also essential for arid and semi-arid areas with sandy soils to promote crop growth. Most currently available coated fertilizers rarely meet these requirements, limiting their application scope. Therefore, this study "tailored" pectin-montmorillonite (PM) multifunctional coatings for arid areas, featuring intercalation reactions and nanoscale entanglement between pectin and montmorillonite via hydrogen bonding and electrostatic and van der Waals forces. Notably, PM coatings have demonstrated an effective "relay" model of action. First, the PM-50 coating could act as a "shield" to protect urea pills, increasing the mechanical strength (82.12 %). Second, this coating prolonged the release longevity of urea (<0.5 h to 15 days). Further, the remaining coating performed a water-retention function. Subsequently, the degraded coating improved the soil properties. Thus, this coating facilitated the growth of wheat seedlings in a simulated arid environment. Moreover, the cytotoxicity test, life cycle assessment, and soil biodegradation experiment showed that the PM coating exhibited minimal environmental impact. Overall, the "relay" model of PM coating overcomes the application limitations of traditional coated fertilizers and provides a sustainable strategy for developing coating materials in soil degradation areas.

Predictive value of serum magnesium levels for prognosis in patients with non-small cell lung cancer undergoing EGFR-TKI therapy.

The aim of this study is to assess the impact of serum magnesium (Mg) levels on prognostic outcomes in patients with non-small cell lung cancer (NSCLC) undergoing treatment with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). A cohort comprising 91 patients with NSCLC with epidermal growth factor receptor mutations received EGFR-TKI therapy. Assessments of liver and kidney function and electrolyte levels were conducted before treatment initiation and after completing two cycles of EGFR-TKI therapy. Data on variables such as age, gender, presence of distant metastasis, smoking history, other therapeutic interventions, and the specific TKI used were collected for analysis. Cox regression analysis revealed that patients with higher Mg levels prior to EGFR-TKI therapy had significantly longer progression-free survival (PFS) and overall survival (OS). Elevated Mg levels remained predictive of PFS and OS after two cycles of EGFR-TKI therapy. Multiple regression analysis confirmed these findings. Additionally, it was observed that smokers might represent a unique population, demonstrating a correlation between OS and Mg levels. Our findings indicate that serum Mg level is a prognostic factor in patients with NSCLC undergoing EGFR-TKI therapy. This may provide new insights into the underlying mechanisms of EGFR-TKI therapy related to electrolyte balance.

EMPT: a sparsity Transformer for EEG-based motor imagery recognition.

Introduction: Transformer network is widely emphasized and studied relying on its excellent performance. The self-attention mechanism finds a good solution for feature coding among multiple channels of electroencephalography (EEG) signals. However, using the self-attention mechanism to construct models on EEG data suffers from the problem of the large amount of data required and the complexity of the algorithm. Methods: We propose a Transformer neural network combined with the addition of Mixture of Experts (MoE) layer and ProbSparse Self-attention mechanism for decoding the time-frequency-spatial domain features from motor imagery (MI) EEG of spinal cord injury patients. The model is named as EEG MoE-Prob-Transformer (EMPT). The common spatial pattern and the modified s-transform method are employed for achieving the time-frequency-spatial features, which are used as feature embeddings to input the improved transformer neural network for feature reconstruction, and then rely on the expert model in the MoE layer for sparsity mapping, and finally output the results through the fully connected layer. Results: EMPT achieves an accuracy of 95.24% on the MI EEG dataset for patients with spinal cord injury. EMPT has also achieved excellent results in comparative experiments with other state-of-the-art methods. Discussion: The MoE layer and ProbSparse Self-attention inside the EMPT are subjected to visualisation experiments. The experiments prove that sparsity can be introduced to the Transformer neural network by introducing MoE and kullback-leibler divergence attention pooling mechanism, thereby enhancing its applicability on EEG datasets. A novel deep learning approach is presented for decoding EEG data based on MI.

Electroencephalography-based biological and functional characteristics of spinal cord injury patients with neuropathic pain and numbness.

Objectives: To identify potential treatment targets for spinal cord injury (SCI)-related neuropathic pain (NP) by analysing the differences in electroencephalogram (EEG) and brain network connections among SCI patients with NP or numbness. Participants and methods: The EEG signals during rest, as well as left- and right-hand and feet motor imagination (MI), were recorded. The power spectral density (PSD) of the θ (4-8 Hz), α (8-12 Hz), and ß (13-30 Hz) bands was calculated by applying Continuous Wavelet Transform (CWT) and Modified S-transform (MST) to the data. We used 21 electrodes as network nodes and performed statistical measurements of the phase synchronisation between two brain regions using a phase-locking value, which captures nonlinear phase synchronisation. Results: The specificity of the MST algorithm was higher than that of the CWT. Widespread non-lateralised event-related synchronization was observed in both groups during the left- and right-hand MI. The PWP (patients with pain) group had lower θ and α bands PSD values in multiple channels of regions including the frontal, premotor, motor, and temporal regions compared with the PWN (patients with numbness) group (all p < 0.05), but higher ß band PSD values in multiple channels of regions including the frontal, premotor, motor, and parietal region compared with the PWN group (all p < 0.05). During left-hand and feet MI, in the lower frequency bands (θ and α bands), the brain network connections of the PWP group were significantly weaker than the PWN group except for the frontal region. Conversely, in the higher frequency bands (ß band), the brain network connections of the PWP group were significantly stronger in all regions than the PWN group. Conclusion: The differences in the power of EEG and network connectivity in the frontal, premotor, motor, and temporal regions are potential biological and functional characteristics that can be used to distinguish NP from numbness. The differences in brain network connections between the two groups suggest that the distinct mechanisms for pain and numbness.