Search Results (89)

This study aimed to evaluate the microbiological quality of colostrum on three dairy farms with different colostrum management hygiene practices and to compare it with the current colostrum quality guidelines. On farm A, colostrum was fed raw, while on farms B and C it was heat treated. On farms A and B, the feeding equipment was cleaned manually, while on farm C, an automated cleaning system was used. Samples were collected from the calf-feeding equipment and submitted for microbial culture: total plate count (TPC); total coliform count (TCC); and E. coli, enterobacteria (ENTB), staphylococci (STAP), and lactic acid bacteria counts. In addition, pH, water activity (a_W), and Brix were analyzed. Colostrum quality was defined as follows: good quality (GQ)—TPC < 100,000, TCC < 10,000, STAP < 50,000 cfu/mL, and Brix ≥ 22%; excellent quality (EQ)—TPC < 20,000, TCC < 100, STAP < 5000 cfu/mL, and Brix ≥ 25%. Mean concentrations were as follows: TPC was 3.99 × 10⁵ cfu/mL (min: 40.00, max: 1.32 × 10⁷ cfu/mL); TCC was 1.17 × 10⁴ cfu/mL (min: <detection limit, max: 6.37 × 10⁵ cfu/mL); and STAP was 1.77 × 10⁴ cfu/mL (min: <detection limit, max: 3.50 × 10⁵ cfu/mL). Approximately 54% (GQ) and 32% (EQ) of samples met the defined criteria. Farm C consistently showed lower microbial counts across all culture types. Colostrum from farm B had lower TCC, LAB, and E. coli counts than farm A but not TPC, STAP, and ENTB. These results showed that a considerable proportion of calves were fed colostrum with suboptimal quality, especially when less rigorous hygiene practices were implemented. Full article

Sparse recovery space–time adaptive processing (STAP) has an off-grid feature and high computational complexity. To address these shortcomings, this study proposes a self-calibrating STAP algorithm based on sparse Bayesian learning (SBL). The proposed algorithm constructs a dimensionality reduction dictionary by selecting the steering vectors corresponding to atoms with high power values. Then, a small-scale auxiliary dictionary is constructed with a stepwise search approach to calibrate the uniformly discretized dictionary. In this way, the atoms of the auxiliary dictionary can converge to the clutter ridge adaptively when off-grid occurs. The clutter plus noise covariance matrix is estimated via SBL combined with the updated dictionary. The experimental results demonstrate that the proposed algorithm can effectively suppress the clutter ridge expansion caused by the off-grid problem while reducing the computation burden significantly compared with the existing methods. Full article

This paper proposes a new carrier domain approach to suppress spreading first-order sea clutter in shipborne passive radar systems using Digital Radio Mondiale (DRM) signals as illuminators. The DRM signal is a broadcast signal that operates in the high-frequency (HF) band and employs orthogonal frequency-division multiplexing (OFDM) modulation. In shipborne DRM-based passive radar, sea clutter sidelobes elevate the noise level of the clutter-plus-noise covariance matrix, thereby degrading the target signal-to-interference-plus-noise ratio (SINR) in traditional space–time adaptive processing (STAP). Moreover, the limited number of space–time snapshots in traditional STAP algorithms further degrades clutter suppression performance. By exploiting the multi-carrier characteristics of OFDM, this paper proposes a novel algorithm, termed Space Time Adaptive Processing by Carrier (STAP-C), to enhance clutter suppression performance. The proposed method improves the clutter suppression performance from two aspects. The first is removing the transmitted symbol information from the space–time snapshots, which significantly reduces the effect of the sea clutter sidelobes. The other is using the space–time snapshots obtained from all subcarriers, which substantially increases the number of available snapshots and thereby improves the clutter suppression performance. In addition, we combine the proposed algorithm with the dimensionality reduction algorithm to develop the Joint Domain Localized-Space Time Adaptive Processing by Carrier (JDL-STAP-C) algorithm. JDL-STAP-C algorithm transforms space–time data into the angle–Doppler domain for clutter suppression, which reduces the computational complexity. Simulation results show the effectiveness of the proposed algorithm in providing a high improvement factor (IF) and less computational time. Full article

STap2Go is the first purely digital strategy-based method for the training of executive functions, making its evaluation relevant. This study assesses the effectiveness of this intervention in children with (at risk) and without (no-risk) psychosocial risk, which refers to socio-educational vulnerability, and examines whether its impact differs between groups. A total of 124 children (9–12 years old) were randomly assigned to either an experimental or an active control group. Individual assessments and family questionnaires were administered (FDT, WISC-V, RIST, BRIEF-2). Both groups received a 12-week intervention. The experimental group showed significant improvements in executive functions, processing speed, IQ, academic performance, and emotional and behavioral regulation compared to the controls. Notably, IQ, metacognition, and working memory continued improving at follow-up, suggesting lasting effects. While both groups benefited, the effects were more pronounced in at-risk children, particularly in BRIEF-2 (Inhibition, Metacognition, Behavioral Regulation) and academic performance in mathematics and language. Moreover, the psychosocial risk control group showed a trend toward deterioration over time. The far transfer achieved thanks to digital strategy-based training seems to have a greater effect on at-risk children, and can be used to compensate for their difficulties. Full article

Introduction and Objectives: Familial hypercholesterolemia (FH) is an autosomal dominant genetic disorder of lipid metabolism that is characterized by elevated low-density lipoprotein cholesterol (LDL-C) levels and a high risk of atherosclerotic cardiovascular disease. Familial hypercholesterolemia is typically caused by mutations in the LDL receptor gene (LDLR), although other alterations may be found. The aim of this study was to perform a genetic study on a population identified through a new population-based diagnostic screen program for FH. Methods: Genetic variants in LDLR, apolipoprotein B (APOB), apolipoprotein E (APOE), proprotein convertase subtilisin/kexin type 9 (PCSK9), signal transducing Adaptor Family Member 1 (STAP1), low density lipoprotein receptor adaptor protein 1 (LDLRAP1) and lipase A, and lysosomal acid type lipase A (LIPA), as well as a genetic risk score, were evaluated in 84 individuals with a clinical diagnosis of FH based on the Dutch Lipid Clinics Network criteria (DLCN ≥ 6). These individuals were selected from a cohort of 752 patients with an abnormal lipid profile, obtained by screening existing centralized analytics. Results: A clinical diagnosis of FH was established in 17.9% of the patients evaluated, with mean LDL-C levels of 305.7 mg/dL (95% CI 250.4–360.9). Genetic variants were detected in 70.2% of these patients, with 50 different mutations identified, mainly in the LDLR. The most frequent pathogenic variants were c.1342C>T and c.313+1G>C. Null variants exhibited a more severe phenotype, and the risk score indicates that patients carrying genetic alterations have a 42% higher risk of developing cardiovascular disease. Conclusions: A high rate of genetic alterations was detected in patients with severe FH. In most cases, the phenotypic findings did not predict the genetic results, which provide important information regarding the cardiovascular risk of patients. Full article

To tackle the challenge of enhancing moving target detection performance in environments characterized by small sample sizes and non-uniformity, methods rooted in sparse signal reconstruction have been incorporated into Space-Time Adaptive Processing (STAP) algorithms. Given the prominent sparse nature of clutter spectra in the angle-Doppler domain, adopting sparse recovery algorithms has proven to be a feasible approach for accurately estimating high-resolution spatio-temporal two-dimensional clutter spectra. Sparse Bayesian Learning (SBL) is a pivotal tool in sparse signal reconstruction and has been previously utilized, yet it has demonstrated limited success in enhancing sparsity, resulting in insufficient robustness in local fitting. To significantly improve sparsity, this paper introduces a hierarchical Bayesian prior framework and derives iterative parameter update formulas through variational inference techniques. However, this algorithm encounters significant computational hurdles during the parameter update process. To overcome this obstacle, the paper proposes an enhanced Variational Bayesian Inference (VBI) method that leverages prior information on the rank of the temporal clutter covariance matrix to refine the parameter update formulas, thereby significantly reducing computational complexity. Furthermore, this method fully exploits the joint sparsity of the Multiple Measurement Vector (MMV) model to achieve greater sparsity without compromising accuracy, and employs a first-order Taylor expansion to eliminate grid mismatch in the dictionary. The research presented in this paper enhances the moving target detection capabilities of STAP algorithms in complex environments and provides new perspectives and methodologies for the application of sparse signal reconstruction in related fields. Full article

Sepsis represents a serious disease burden that lacks effective treatment. Drug development for sepsis requires laboratory models that adequately represent sepsis patients. Simultaneous Transcriptome-based Activity Profiling of Signal Transduction Pathway (STAP-STP) technology quantitatively infers STP activity from mRNA levels of target genes of the STP-associated transcription factor. Here, we used STAP-STP technology to compare STP activities between sepsis patients and lipopolysaccharide (LPS)-based models. Activity scores of Androgen Receptor (AR), TGFβ, NFκB, JAK-STAT1/2, and JAK-STAT3 STPs were calculated based on publicly available transcriptome data. Peripheral blood mononuclear cells (PBMCs) from patients with Gram-negative sepsis, nor PBMCs stimulated with LPS in vitro, showed altered STP activity. Increased NFκB, JAK-STAT1/2, and JAK-STAT3 STP activity was found in whole blood stimulated with LPS in vitro, and in whole blood obtained after intravenous injection of LPS in humans in vivo; AR and TGFβ STP activity only increased in the in vivo LPS model. These results resembled previously reported STP activity in whole blood of sepsis patients. We provide the first comparison of STP activity between patients with sepsis and laboratory model systems. Results are of use for the refinement of sepsis model systems for rational drug development. Full article

In the realm of poultry production, viral superinfections pose significant challenges, causing substantial economic losses worldwide. Among these, avian leukosis virus subgroup J (ALV-J) and infectious bursal disease virus (IBDV) are particularly concerning as they frequently lead to superinfections in chicken, further exacerbating production losses and health complications. Our previous research delved into the pathogenicity and immunosuppressive effects of these superinfections through in vitro and in vivo analyses. Yet, the underlying key genes and pathways governing this phenomenon remained elusive. In this study, we randomly selected three chickens at 21 days post infection from each treatment group (ALV-J, IBDV, ALV-J+IBDV, and control group) to collect the bursa of Fabricius samples for full transcriptome analysis. Utilizing these data, we constructed a comprehensive circRNA/lncRNA-miRNA-mRNA network which elucidated both synergistic and specific activations during the superinfection. Notably, three pivotal genes (FILIP1L, DCX, and MYPN) were pinpointed in datasets reflecting synergistic activations. Conversely, four other genes (STAP, HKR6, XKR4, and TLR5) emerged in datasets associated with specific activations. Further exploration revealed diverse significant GO terms and pathways associated with both synergistic and distinct activation processes. These ceRNA network and core genes potentially wield substantial influence over the synergistic or specific activation of tumorigenesis and pathogenesis induced by ALV-J and IBDV. These findings could help develop targeted therapies and improve disease control in poultry, reducing economic losses. Full article

Sub-array-level digital arrays effectively diminish the computational complexity and sample demand of space-time adaptive processing (STAP), thus finding extensive applications in many airborne platforms. Nonetheless, airborne sub-array-level digital array radar still encounters pronounced performance deterioration in highly heterogeneous clutter environments due to inadequate training samples. To address this issue, a clutter-sensing-driven STAP approach for airborne sub-array-level digital arrays is proposed in this paper. Firstly, we derive a signal model of sub-array-level clutter sensing in detail and then further analyze the influence of the sidelobe characteristics of the conventional sub-array joint beam on clutter sensing. Secondly, a sub-array joint beam optimization model is proposed, which optimizes the sub-array joint beam into a wide beam with flat-top characteristics to improve the clutter-sensing performance in the beam sidelobe region. Finally, we decompose the complex optimization problem into two subproblems and then relax them into the low sidelobe-shaped beam pattern synthesisproblem and second-order cone programming problem, which can be effectively solved. The effectiveness of the proposed approach is validated in a real clutter environment through numerical experiments. Full article

Airborne Frequency Diversity Array (FDA) radar operating at a high pulse repetition frequency encounters severe range-ambiguous clutter. The slight frequency increments introduced by the FDA result in angle and range coupling. Under these conditions, conventional space-time adaptive processing (STAP) often exhibits diminished performance or fails, complicating target detection. This paper proposes a method combining elevation oblique subspace projection with azimuth-Doppler STAP to suppress range-ambiguous clutter. The method compensates for the quadratic range dependence by analyzing the relationship between elevation frequency and range. It uses an elevation oblique subspace projection technique to construct an elevation adaptive filter, which separates clutter from ambiguous regions. Finally, residual clutter suppression is achieved through azimuth-Doppler STAP, enhancing target detection performance. Simulation results demonstrate that the proposed method effectively addresses range dependence and ambiguity issues, improving target detection performance in complex airborne FDA radar environments. Full article

Bistatic radar exhibits spatial isomerism and diverse configurations, leading to unique clutter characteristics distinct from those of monostatic radar. The clutter rank serves as a pivotal indicator of clutter characteristics, enabling the quantification of clutter severity. Space-time adaptive processing (STAP) is a critical technique to detect moving targets, and clutter rank determines the number of independent and identically distributed (IID) training samples and the degree of freedom (DOF) for effective suppression of clutter that STAP requires. Therefore, the accurate estimation of clutter rank for bistatic radar can provide a crucial indicator for designing and constructing STAP processors, thereby facilitating fast and efficient clutter suppression in bistatic radar systems. This study is based on the idea that clutter rank is the number of prolate spheroidal wave function (PSWF) orthogonal bases utilized for approximating the clutter signal. Firstly, the challenge of utilizing PSWF orthogonal bases for approximating the clutter signal in bistatic radar is elucidated. This pertains to the fact that, unlike monostatic radar clutter, bistatic radar clutter is not capable of being expressed as a single-frequency signal. The clutter rank estimation for bistatic radar is thus derived as the frequency bandwidth estimation. Secondly, to achieve this estimation, the frequency distribution of each individual scattering unit is investigated, thereby determining their extending frequency broadening (EFB) as compared to that of single-frequency. Subsequently, the integral average of EFB across the entire range bin is computed, ultimately enabling the acquisition of bistatic radar’s frequency bandwidth. Finally, the estimation method is extended to non-side-looking mode and limited observation areas with pattern modulation. Simulation experiments confirm that our proposed method provides accurate clutter rank estimations, surpassing 99% proportions of large eigenvalues across various bistatic configurations, observation modes, and areas. Full article

The traditional sparse recovery (SR) space-time adaptive processing (STAP) algorithms are greatly affected by grid mismatch, leading to poor performance in airborne bistatic radar clutter suppression. In order to address this issue, this paper proposes an SR STAP algorithm for airborne bistatic radars based on atomic selection under the Bayesian framework. This method adopts the idea of atomic selection for the process of Bayesian inference, continuously evaluating the contribution of atoms to the likelihood function to add or remove atoms, and then using the selected atoms to estimate the clutter support subspace and perform sparse recovery in the clutter support subspace. Due to the inherent sparsity of clutter signals, performing sparse recovery in the clutter support subspace avoids using a massive number of atoms from an overcomplete space-time dictionary, thereby greatly improving computational efficiency. In airborne bistatic radar scenarios where significant grid mismatch exists, this method can mitigate the performance degradation caused by grid mismatch by encrypting grid points. Since the sparse recovery is performed in the clutter support subspace, encrypting grid points does not lead to excessive computational burden. Additionally, this method integrates out the noise term under a new hierarchical Bayesian model, preventing the adverse effects caused by inaccurate noise power estimation during iterations in the traditional SR STAP algorithms, further enhancing its performance. Our simulation results demonstrate the high efficiency and superior clutter suppression performance and target detection performance of this method. Full article

Space–time adaptive processing (STAP) based on sparse recovery achieves excellent clutter suppression and target detection performance, even with a limited number of available training samples. However, most of these methods face performance degradation due to grid mismatch, which impedes their application in bistatic clutter suppression. Some gridless methods, such as atomic norm minimization (ANM), can effectively address grid mismatch issues, yet they are sensitive to parameter settings and array errors. In this article, the authors propose a data and model-driven algorithm that unfolds the iterative process of atomic norm minimization into a deep network. This approach establishes a concrete and systematic link between iterative algorithms, extensively utilized in signal processing, and deep neural networks. This methodology not only addresses the challenges associated with parameter settings in traditional optimization algorithms, but also mitigates the lack of interpretability issues commonly found in deep neural networks. Moreover, due to more rational parameter settings, the proposed algorithm achieves effective clutter suppression with fewer iterations, thereby reducing computational time. Finally, extensive simulation experiments demonstrate the effectiveness of the proposed algorithm in clutter suppression for airborne bistatic radar. Full article

In space-time adaptive processing (STAP), the coprime sampling structure can obtain better clutter suppression capabilities at a lower hardware cost than the uniform linear sampling structure. However, in practical applications, the performance of the algorithm is often limited by the number of training samples. To solve this problem, this paper proposes a fast iterative coprime STAP algorithm based on truncated kernel norm minimization (TKNM). This method establishes a virtual clutter covariance matrix (CCM), introduces truncated kernel norm regularization technology to ensure the low rank of the CCM, and transforms the non-convex problem into a convex optimization problem. Finally, a fast iterative solution method based on the alternating direction method is presented. The effectiveness and accuracy of the proposed algorithm are verified through simulation experiments. Full article

This article examines the structure and functions of the plant synthetic promoters frequently used to precisely regulate complex regulatory routes. It details the composition of native promoters and their interacting proteins to provide a better understanding of the tasks associated with synthetic promoter development. The production of synthetic promoters is performed by relatively small libraries produced generally by basic molecular or genetic engineering methods such as cis-element shuffling or domain swapping. The article also describes the preparation of large-scale libraries supported by synthetic DNA fragments, directed evolution, and machine or deep-learning methodologies. The broader application of novel, synthetic promoters reduces the prevalence of homology-based gene silencing or improves the stability of transgenes. A particularly interesting group of synthetic promoters are bidirectional forms, which can enable the expression of up to eight genes by one regulatory element. The introduction and controlled expression of several genes after one transgenic event strongly decreases the frequency of such problems as complex segregation patterns and the random integration of multiple transgenes. These complications are commonly observed during the transgenic crop development enabled by traditional, multistep transformation using genetic constructs containing a single gene. As previously tested DNA promoter fragments demonstrate low complexity and homology, their abundance can be increased by using orthogonal expression systems composed of synthetic promoters and trans-factors that do not occur in nature or arise from different species. Their structure, functions, and applications are rendered in the article. Among them are presented orthogonal systems based on transcription activator-like effectors (dTALEs), synthetic dTALE activated promoters (STAPs) and dCas9-dependent artificial trans-factors (ATFs). Synthetic plant promoters are valuable tools for providing precise spatiotemporal regulation and introducing logic gates into the complex genetic traits that are important for basic research studies and their application in crop plant development. Precisely regulated metabolic routes are less prone to undesirable feedback regulation and energy waste, thus improving the efficiency of transgenic crops. Full article