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By the method of artificial water contamination, and using transmission electron microscope (TEM) and flow cytometer (FCM), this paper studied the effects of cadmium on the germ cell apoptosis in testis and the quality of mature spermatozoa in epididymis of mice. Compared with that in control group, the amount of the germ cells under apoptosis in experimental groups was remarkably higher, and the ultrastructure of the germ cells showed the phenomena such as mitochondrion vacuole, karyotheca invagination, chromatin margination, and karyopyknosis, indicating that cadmium could easily cause testis spermatocyte apoptosis of mice. The proportion of the germ cells with early apoptosis was significantly higher in experimental groups than in control group, while that of active germ cells was in reverse (P<0.05). In high dosage group (0.10 mmol CdCl2·L-1), the survival rate of germ cells (75.1%) was significantly lower than that in control group and other experimental groups, and the early apoptosis rate of the germ cells (22.6%) was significantly higher than that of the control. The DNA breakage rates of the germ cells in testis and epididymis were significantly higher in high dosage group (18.2% and 26.5%) than in control group (3.3% and 5.6%) (P <0.05), and the amounts of testis germ cells with DNA breakage in experimental groups were lower than those of the epididymis germ cells with DNA breakage. The apoptosis rates of the germ cells in testis and epididymis increased gradually with the increase of cadmium dosage. It was concluded that the dosage of cadmium had definite effects on the apoptosis and DNA damage of mice germ cells. [ABSTRACT FROM AUTHOR]

ylonitrile/ N-[4-(aminosulfonyl)phenyl]acrylamide (AN/ASPAA) copolymers were synthesized and used as a host of lithium ion conducting electrolytes. The composition, molecular weight and molecular weight distribution of AN/ASPAA copolymers were determined, and the influence of copolymer composition on the glass temperature of AN/ASPAA copolymers and the ion conductivity of electrolytes were investigated. The molecular weights of AN/ASPAA copolymers were lower than those of AN and ASPAA homopolymers due to the cross-termination reaction. The glass temperatures of AN/ASPAA copolymers increased as the molar fraction of ASPAA units in copolymers increased. The lithium ion conductivities of the polymer electrolytes increased initially as the molar fraction of ASPAA units in copolymers increased, and a maximum conductivity was achieved when the molar fraction of ASPAA in the copolymer was 16.8%. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed cooperative learning (DCL) from adaptive neural network (NN) control for a group of strict-feedback systems, where the structure of all strict-feedback systems is identical. In order to achieve DCL easily, the system transformation method is employed for the strict-feedback systems. For an agent, only one radial basis function NN is used to approximate the lumped uncertainty in control design. Then the output tracking performance of all strict-feedback systems is guaranteed. What's more, we prove that weights of all NNs in a multi-agent system converge to a small neighborhood around their common optimal value if the topology of the multi-agent system is connected and undirected. Thus, the approximation domain of all NNs is enlarged. Further, the previous learned NNs are used to improve the control performance. Finally, we provide two examples to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The emergence of DNA nanotechnology has enabled the systematic design of diverse bionic dissipative behaviors under the precise control of nucleic acid nanodevices. Nevertheless, when compared to the dissipation observed in robust living systems, it is highly desirable to enhance the anti-interference for artificial DNA dissipation to withstand perturbations and facilitate repairs within the complex biological environments. In this study, we introduce strategically designed "trash cans" to facilitate kinetic control over interferences, transforming the stochastic binding of individual components within a homogeneous solution into a competitive binding process. This approach effectively eliminates incorrect binding and the accumulation of systemic interferences while ensuring a consistent pattern of energy fluctuation from response to silence. Remarkably, even in the presence of numerous interferences differing by only one base, we successfully achieve complete system reset through multiple cycles, effectively restoring the energy level to a minimum. The system was able to operate stably without any adverse effect under conditions of irregular interference, high-abundance interference, and even multiplex interferences including DNA and RNA crosstalk. This work not only provides an effective paradigm for constructing robust DNA dissipation systems but also greatly broadens the potential of DNA dissipation for applications in high-precision molecular recognition and complex biological reaction networks. [Display omitted] • By eliminating cumulative errors we built an anti-interference DNA dissipation system first time. • Affinity trash bring the dissipation network closer to living organisms to multiple complex stimuli. • Be able to stably work in high abundance, irregular, multiple interference, and RNA and DNA crosstalk. [ABSTRACT FROM AUTHOR]

Predicting water quality data is an important measure for ecological environment protection in watersheds. Aiming at the problem that existing prediction algorithms rarely analyze the characteristics of future changes in water quality indicators, this paper proposes an out-of-sample prediction model for water quality parameters based on the dual-attention mechanism. The model adopts the Encoder-Decoder architecture to realize the prediction of data series, and combines the dual attention of dimension and time step to improve the prediction performance of out-of-sample data. The model is used to predict the water quality parameters of a multi-parameter river, analyze the trend of the out-of-sample data, and compare the prediction results with the traditional LSTM network and Encoder-Decoder LSTM network, the prediction accuracies of the water quality indicators are improved, and the prediction accuracy of the out-of-sample data of the water quality indicators reaches 80%. This will be of great significance to the comprehensive management of river waters and the high-quality development of ecological environment. • The model achieved out-of-sample prediction of water quality parameters and analyzed the future trend of the data. • Seq2Seq codec structure has been adopted to improve out-of-sample prediction performance. • A dual-attention mechanism has been created to assign weights in both time step and dimension simultaneously. [ABSTRACT FROM AUTHOR]

• Autonomous data partitioning algorithm are filtered by Hammersley sequence. • General trapezoidal T2FSs can be approximated by symmetrical trapezoidal T2FSs. • Consequent part is a tensor, and it is unfolded for consequent part parameters learning. In the paper, trapezoidal type-2 fuzzy inference system (TT2FIS) is introduced. To construct the type-2 fuzzy inference system, trapezoidal type-2 fuzzy sets are adopted to construct the antecedent part of the fuzzy rules, the centers of the fuzzy sets are roughly clustered by an evolving autonomous data partitioning algorithm. To further eliminate the effect of the centers that are generated by dataset itself, and it can be easily impacted by the unbalance data, the data clouds that are generated by autonomous data partitioning algorithm are filtered by Hammersley sequence, and symmetrical trapezoidal type-2 fuzzy sets are generated by the generated cluster centers and problem-free standard deviation generating method. To the consequent part, generalized type-2 TSK consequent is formulated by tensor, which is obtained via three constituent parts of the trapezoidal type-2 fuzzy sets (lower membership function, upper membership function and type-reduction set of data samples), the parameters of consequent part are the iterative results of a matrix equation that is unfolded from the tensor. Finally, simulation results are carried out to verify the effectiveness of the proposed trapezoidal type-2 fuzzy inference system. Simulation results show that the generalization of the TT2FIS is better than the coincide type-2 fuzzy inference system or adaptive type-1 fuzzy inference system. [ABSTRACT FROM AUTHOR]

Detecting low‐frequency DNA mutations hotspots cluster is critical for cancer diagnosis but remains challenging. Quantitative PCR (qPCR) is constrained by sensitivity, and allele‐specific PCR is restricted by throughput. Here we develop a long blocker displacement amplification (LBDA) coupled with qPCR for ultrasensitive and multiplexed variants detection. By designing long blocker oligos to perfectly match wildtype sequences while mispairing with mutants, long blockers enable 14–44 nt enrichment regions which is 2‐fold longer than normal BDA in the experiments. For wild template with a specific nucleotide, LBDA can detect different mutation types down to 0.5 % variant allele frequency (VAF) in one reaction, with median enrichment fold of 1,000 on 21 mutant DNA templates compared to the wild type. We applied LBDA‐qPCR to detect KRAS and NRAS mutation hotspots, utilizing a single plex assay capable of covering 81 mutations and tested in synthetic templates and colorectal cancer tissue samples. Moreover, the mutation types were verified through Sanger sequencing, demonstrating concordance with results obtained from next generation sequencing. Overall, LBDA‐qPCR provides a simple yet ultrasensitive approach for multiplexed detection of low VAF mutations hotspots, presenting a powerful tool for cancer diagnosis and monitoring. [ABSTRACT FROM AUTHOR]

Resonance absorption mechanism-based metasurface absorbers can realize perfect optical absorption. Further, all-dielectric metasurface absorbers have more extensive applicability than metasurface absorbers that contain metal components. However, the absorption peaks of the all-dielectric metasurface absorbers reported to date are very sharp. In this work, we propose a broadband optical absorption all-dielectric metasurface, where a unit cell of this metasurface is composed of two coupled subwavelength semiconductor resonators arrayed in the direction of the wave vector and embedded in a low-index material. The results indicate that the peak absorption for more than 99% is achieved across a 60 nm bandwidth in the short-wavelength infrared region. This absorption bandwidth is three times that of a metasurface based on the conventional design scheme that consists of only a single layer of semiconductor resonators. Additionally, the coupled semiconductor resonator-based all-dielectric metasurface shows robust perfect absorption properties when the geometrical and material parameters—including the diameter, height, permittivity, and loss tangent of the resonator and the vertical and horizontal distances between the two centers of the coupled resonators—are varied over a wide range. With the convenience of use of existing semiconductor technologies in micro/nano-processing of the surface, this proposed broadband absorption all-dielectric metasurface offers a path toward realizing potential applications in numerous optical devices. [ABSTRACT FROM AUTHOR]

Knee osteoarthritis (KOA) stands as a degenerative ailment with a substantial and escalating prevalence. The practice of traditional Chinese non-pharmacological therapy has become a prevalent complementary and adjunctive approach. A mounting body of evidence suggests its efficacy in addressing KOA. Recent investigations have delved into its underlying mechanism, yielding some headway. Consequently, this comprehensive analysis seeks to encapsulate the clinical application and molecular mechanism of traditional Chinese non-pharmacological therapy in KOA treatment. The review reveals that various therapies, such as acupuncture, electroacupuncture, warm needle acupuncture, tuina, and acupotomy, primarily target localized knee components like cartilage, subchondral bone, and synovium. Moreover, their impact extends to the central nervous system and intestinal flora. More perfect experimental design and more comprehensive research remain a promising avenue in the future. [ABSTRACT FROM AUTHOR]

Background: Although triptorelin is increasingly used in China for biochemical castration, its effects on primary prostate cancer symptoms remain unclear. This study aimed to assess the prevalence of lower urinary tract symptoms (LUTS) in Chinese prostate cancer patients and the effectiveness of triptorelin on LUTS.Methods: In this 48-week multicenter, non-interventional, prospective study, we enrolled patients with locally advanced or metastatic prostate cancer. Patients received triptorelin (15 mg) intramuscularly at baseline and at weeks 12, 24, and 36 with symptom assessment using the International Prostate Symptoms Score (IPSS). The primary endpoints were the prevalence of LUTS at baseline per IPSS categories and the percentage of patients with moderate to severe LUTS (IPSS > 7) at baseline, having at least a 3-point reduction of IPSS score at week 48.Results: A total of 398 patients were included; 211 (53.0%) and 160 (40.2%) among them had severe and moderate LUTS, respectively. Of the patients with IPSS scores available at baseline and at week 48 (n = 213), 81.2% achieved a reduction in IPSS of at least 3 points. Of the patients with moderate to severe LUTS at baseline and IPSS scores available at baseline and at week 48 (n = 194), 86.6% achieved a total IPSS reduction of at least 3 points.Conclusions: The vast majority of Chinese patients with locally advanced or metastatic prostate cancer scheduled to receive triptorelin as part of their standard treatment have severe or moderate LUTS. Triptorelin therapy resulted in sustained improvement of LUTS in these patients. [ABSTRACT FROM AUTHOR]

The incidence of thyroid nodules is increasing year by year. Accurate determination of benign and malignant nodules is an important basis for formulating treatment plans. Ultrasonography is the most widely used methodology in the diagnosis of benign and malignant nodules, but diagnosis by doctors is highly subjective, and the rates of missed diagnosis and misdiagnosis are high. To improve the accuracy of clinical diagnosis, this paper proposes a new diagnostic model based on deep learning. The diagnostic model adopts the diagnostic strategy of localization-classification. First, the distribution laws of the nodule size and nodule aspect ratio are obtained through data statistics, a multiscale localization network structure is a priori designed, and the nodule aspect ratio is obtained from the positioning results. Then, uncropped ultrasound images and nodule area image are correspondingly input into a two-way classification network, and an improved attention mechanism is used to enhance the feature extraction performance. Finally, the deep features, the shallow features, and the nodule aspect ratio are fused, and a fully connected layer is used to complete the classification of benign and malignant nodules. The experimental dataset consists of 4021 ultrasound images, where each image has been labeled under the guidance of doctors, and the ratio of the training set, validation set, and test set sizes is close to 3:1:1. The experimental results show that the accuracy of the multiscale localization network reaches 93.74%, and that the accuracy, specificity, and sensitivity of the classification network reach 86.34%, 81.29%, and 90.48%, respectively. Compared with the champion model of the TNSCUI 2020 classification competition, the accuracy rate is 1.52 points higher. Therefore, the network model proposed in this paper can effectively diagnose benign and malignant thyroid nodules. [ABSTRACT FROM AUTHOR]

Porcine epidemic diarrhea virus (PEDV) is one of the most devastating diseases affecting the pig industry globally. Due to the emergence of novel strains, no effective vaccines are available for prevention and control. Investigating the pathogenic mechanisms of PEDV may provide insights for creating clinical interventions. This study constructed and expressed eukaryotic expression vectors containing PEDV proteins (except NSP11) with a 3' HA tag in Vero cells. The subcellular localization of PEDV proteins was examined using endogenous protein antibodies to investigate their involvement in the viral life cycle, including endocytosis, intracellular trafficking, genome replication, energy metabolism, budding, and release. We systematically analyzed the potential roles of all PEDV viral proteins in the virus life cycle. We found that the endosome sorting complex required for transport (ESCRT) machinery may be involved in the replication and budding processes of PEDV. Our study provides insight into the molecular mechanisms underlying PEDV infection. The global swine industry has suffered immense losses due to the spread of PEDV. Currently, there are no effective vaccines available for clinical protection. Exploring the pathogenic mechanisms of PEDV may provide valuable insights for clinical interventions. This study investigated the involvement of viral proteins in various stages of the PEDV lifecycle in the state of viral infection and identified several previously unreported interactions between viral and host proteins. These findings contribute to a better understanding of the pathogenic mechanisms underlying PEDV infection and may serve as a basis for further research and development of therapeutic strategies. [ABSTRACT FROM AUTHOR]

The underground mine environment is dangerous and harsh, tracking and detecting humans based on computer vision is of great significance for mine safety monitoring, which will also greatly facilitate identification of humans using the symmetrical image features of human organs. However, existing methods have difficulty solving the problems of accurate identification of humans and background, unstable human appearance characteristics, and humans occluded or lost. For these reasons, an improved aberrance repressed correlation filter (IARCF) tracker for human tracking in underground mines based on infrared videos is proposed. Firstly, the preprocess operations of edge sharpening, contrast adjustment, and denoising are used to enhance the image features of original videos. Secondly, the response map characteristics of peak shape and peak to side lobe ratio (PSLR) are analyzed to identify abnormal human locations in each frame, and the method of calculating the image similarity by generating virtual tracking boxes is used to accurately relocate the human. Finally, using the value of PSLR and the highest peak point of the response map, the appearance model is adaptively updated to further improve the robustness of the tracker. Experimental results show that the average precision and success rate of the IARCF tracker in the five underground scenarios reach 0.8985 and 0.7183, respectively, and the improvement of human tracking in difficult scenes is excellent. The IARCF tracker can effectively track underground human targets, especially occluded humans in complex scenes. [ABSTRACT FROM AUTHOR]