In total, around 631 million broilers were slaughtered in Germany in 2022. This evaluation included data of approx. 198 million broilers of different ages and breeds that were transported in Germany in 2022 (31% of all cases of broiler chicken transport in 2022). The aim of this study was to analyze German broiler chicken transport (n = 14,054) to the slaughterhouse between January 2022 and May 2023 with regard to the dead-on-arrival (DOA) rate and the possible influencing factors. Therefore, the relation between the total amount of animals per transport, the duration and distance of the transport, the planned stocking density in the transport cages, the average daily temperature and time of day and season of the transport as well as the DOA rate were statistically evaluated. The results showed a mean DOA rate of 0.09% (SD 0.09). Transport conducted at midday (11:00 to 17:00) showed higher DOA rates (p < 0.05) than transport at other times (day split into 6 h intervals). The highest mean DOA rate (0.10%) was found in the fall, followed by the winter, while transport in the spring and summer resulted in the lowest DOA rate (p < 0.05). All in all, the relatively low DOA rate (%) in Germany indicates the good standard of their broiler transport compared to available data from research in other European countries.

Hereditary optic neuropathies (HONs) such as dominant optic atrophy (DOA) and Leber Hereditary Optic Neuropathy (LHON) are mitochondrial diseases characterized by a degenerative loss of retinal ganglion cells (RGCs) and are a cause of blindness worldwide. To date, there are only limited disease-modifying treatments for these disorders. The discovery of induced pluripotent stem cell (iPSC) technology has opened several promising opportunities in the field of HON research and the search for therapeutic approaches. This systematic review is focused on the two most frequent HONs (LHON and DOA) and on the recent studies related to the application of human iPSC technology in combination with biomaterials technology for their potential use in the development of RGC replacement therapies with the final aim of the improvement or even the restoration of the vision of HON patients. To this purpose, the combination of natural and synthetic biomaterials modified with peptides, neurotrophic factors, and other low- to medium-molecular weight compounds, mimicking the ocular extracellular matrices, with human iPSC or iPSC-derived cell retinal progenitors holds enormous potential to be exploited in the near future for the generation of transplantable RGC populations.

The direction estimation of the coherent source in a uniform circular array is an essential part of the signal processing area of the array, but the traditional uniform circular array algorithm has a low localization accuracy and a poor localization effect on the coherent source. To solve this problem, this paper proposes a two-dimensional direction of arrival (DOA) estimation for the coherent source in broadband. Firstly, the central frequency of the coherent sound source is estimated using the frequency estimation method of the delayed data, and a real-valued beamformer is constructed using the concept of the multiloop phase mode. Then, the cost function in the beam space is obtained. Finally, the cost function is searched in two dimensions to locate the sound source. In this paper, we simulate the DOA of the sound source at different frequencies and signal-to-noise ratios and analyze the resolution of the circular array. The simulation results show that the proposed algorithm can estimate the direction of arrival with high precision and achieve the desired results.

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier\'s performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier\'s performance emphasizes the role of feature selection methods.

Dominant optic atrophy (DOA) is an inherited disease that leads to the loss of retinal ganglion cells (RGCs), the projection neurons that relay visual information from the retina to the brain through the optic nerve. The majority of DOA cases can be attributed to mutations in optic atrophy 1 (OPA1), a nuclear gene encoding a mitochondrial-targeted protein that plays important roles in maintaining mitochondrial structure, dynamics, and bioenergetics. Although OPA1 is ubiquitously expressed in all human tissues, RGCs appear to be the primary cell type affected by OPA1 mutations. DOA has not been extensively studied in human RGCs due to the general unavailability of retinal tissues. However, recent advances in stem cell biology have made it possible to produce human RGCs from pluripotent stem cells (PSCs). To aid in establishing DOA disease models based on human PSC-derived RGCs, we have generated iPSC lines from two DOA patients who carry distinct OPA1 mutations and present very different disease symptoms. Studies using these OPA1 mutant RGCs can be correlated with clinical features in the patients to provide insights into DOA disease mechanisms.

Direction of arrival (DOA) estimation is an effective method for detecting various active interference signals during the satellite navigation process. It can be utilized for both interference detection and anti-interference applications. This paper proposes a DOA estimation algorithm for satellite interference sources based on frequency domain covariance matrix reconstruction (FDCMR) to address various types of active interference that may occur in the satellite navigation positioning process. This algorithm can estimate the DOA of coherent signals from multiple frequency points under low signal-to-noise ratio (SNR) conditions. The signals received from the array are transformed from the time domain to the frequency domain using a fast Fourier transform (FFT). The data corresponding to the frequency point of the target signal is extracted from the signal in the frequency domain. The frequency domain covariance matrix of the received array signals is reconstructed by utilizing its covariance matrix property. The spatial spectrum search method is used for the final DOA estimation. Simulation experiments have shown that the proposed algorithm performs well in the DOA estimation under low SNR conditions and also resolves coherency. Moreover, the algorithm\'s effectiveness is verified through comparison with three other algorithms. Finally, the algorithm\'s applicability is validated through simulations of various interference scenarios.

In Structural Health Monitoring (SHM) applications, the Direction of Arrival (DoA) estimation of Guided Waves (GW) on sensor arrays is often used as a fundamental means to locate Acoustic Sources (AS) generated by damages growth or undesired impacts in thin-wall structures (e.g., plates or shells). In this paper, we consider the problem of designing the arrangement and shape of piezo-sensors in planar clusters in order to optimize the DoA estimation performance in noise-affected measurements. We assume that: (i) the wave propagation velocity is unknown, (ii) the DoA is estimated via the time delays of wavefronts between sensors, and (iii) the maximum value of the time delays is limited. The optimality criterion is derived basing on the Theory of Measurements. The sensor array design is so that the DoA variance is minimized in an average sense by exploiting the Calculus of Variations. In this way, considering a three-sensor cluster and a monitored angles sector of 90°, the optimal time delays-DoA relations are derived. A suitable re-shaping procedure is used to impose such relations and, at the same time, to induce the same spatial filtering effect between sensors so that the sensor acquired signals are equal except for a time-shift. In order to achieve the last aim, the sensors shape is realized by exploiting a technique called Error Diffusion, which is able to emulate piezo-load functions with continuously modulated values. In this way, the Shaped Sensors Optimal Cluster (SS-OC) is derived. A numerical assessment via Green\'s functions simulations shows improved performance in DoA estimation by means of the SS-OC when compared to clusters realized with conventional piezo-disk transducers.

BACKGROUND: Meat inspection data is commonly used to monitor health and welfare in commercial broiler production; however, less used in layers. Slaughterhouse records can provide insight into animal and herd health and identify important health and welfare challenges. To gain knowledge of health issues in commercial aviary housed laying hens, the aim of this repeated cross-sectional study was to describe the occurrence and causes of carcass condemnation, including dead-on-arrivals (DOA), in commercial aviary housed layers in Norway, and to explore seasonal patterns and correlation between DOA and number of carcass condemnations.
RESULTS: Data from January 2018 to December 2020 were collected from one poultry abattoir in Norway. In total, 759,584 layers were slaughtered during this period in 101 slaughter batches from 98 flocks and 56 farms. In total, 33,754 (4.4%) layers were condemned, including the DOA. The most common carcass condemnation causes were (percent of all slaughtered layers): abscess/cellulitis (2.03%), peritonitis (0.38%), DOA (0.22%), emaciation (0.22%), discoloration/smell (0.21%), acute skin lesions (0.21%) and ascites (0.17%). Regression analysis showed an estimated higher prevalence of total carcass condemnation during winter compared to the other seasons.
CONCLUSIONS: The three most common condemnation causes found in the present study were: abscess/cellulitis, peritonitis, and DOA. We found a large between-batch variation in causes of condemnation and DOA indicating that prevention might be possible. The results can be used to inform and guide further studies on layer health and welfare.

Mitochondrial optic neuropathies have a leading role in the field of mitochondrial medicine ever since 1988, when the first mutation in mitochondrial DNA was associated with Leber\'s hereditary optic neuropathy (LHON). Autosomal dominant optic atrophy (DOA) was subsequently associated in 2000 with mutations in the nuclear DNA affecting the OPA1 gene. LHON and DOA are both characterized by selective neurodegeneration of retinal ganglion cells (RGCs) triggered by mitochondrial dysfunction. This is centered on respiratory complex I impairment in LHON and defective mitochondrial dynamics in OPA1-related DOA, leading to distinct clinical phenotypes. LHON is a subacute, rapid, severe loss of central vision involving both eyes within weeks or months, with age of onset between 15 and 35 years old. DOA is a more slowly progressive optic neuropathy, usually apparent in early childhood. LHON is characterized by marked incomplete penetrance and a clear male predilection. The introduction of next-generation sequencing has greatly expanded the genetic causes for other rare forms of mitochondrial optic neuropathies, including recessive and X-linked, further emphasizing the exquisite sensitivity of RGCs to compromised mitochondrial function. All forms of mitochondrial optic neuropathies, including LHON and DOA, can manifest either as pure optic atrophy or as a more severe multisystemic syndrome. Mitochondrial optic neuropathies are currently at the forefront of a number of therapeutic programs, including gene therapy, with idebenone being the only approved drug for a mitochondrial disorder.

The direction of arrival (DOA) and number of sound sources is usually estimated by short-time Fourier transform and the conjugate cross-spectrum. However, the ability of a single AVS to distinguish between multiple sources will decrease as the number of sources increases. To solve this problem, this paper presents a multimodal fusion method based on a single acoustic vector sensor (AVS). First, the output of the AVS is decomposed into multiple modes by intrinsic time-scale decomposition (ITD). The number of sources in each mode decreases after decomposition. Then, the DOAs and source number in each mode are estimated by density peak clustering (DPC). Finally, the density-based spatial clustering of applications with the noise (DBSCAN) algorithm is employed to obtain the final source counting results from the DOAs of all modes. Experiments showed that the multimodal fusion method could significantly improve the ability of a single AVS to distinguish multiple sources when compared to methods without multimodal fusion.