Weight Balance Research Articles

Gut-brain axis: The role of gut microbiota in energy balance and body weight regulation

Obesity currently represents a major societal and health problem worldwide. Its prevalence has reached epidemic levels, and trends continue to increase; This, in turn, reflects the need for more effective preventive measures. Dietary composition is one of the main factors that modulate the structure and function of the gut microbiota. Therefore, abnormal dietary patterns or unhealthy diets can alter gut microbiota-diet interactions and alter nutrient availability and/or microbial ligands that transmit information from the gut to the brain in response to nutrient intake, thereby disrupting energy homeostasis. Accordingly, this review aims to examine how dietary composition modulates the gut microbiota and thus the potential effects of these biological products on energy homeostasis through gut-brain based mechanisms. It also assesses the knowledge gaps and advances needed to clinically implement microbiome-based strategies to improve gut-brain axis function and therefore combat obesity.

The GIP receptor activates futile calcium cycling in white adipose tissue to increase energy expenditure and drive weight loss in mice.

Obesity is a chronic disease that contributes to the development of insulin resistance, type 2 diabetes (T2D), and cardiovascular risk. Glucose-dependent insulinotropic polypeptide (GIP) receptor (GIPR) and glucagon-like peptide-1 (GLP-1) receptor (GLP-1R) co-agonism provide an improved therapeutic profile in individuals with T2D and obesity when compared with selective GLP-1R agonism. Although the metabolic benefits of GLP-1R agonism are established, whether GIPR activation impacts weight loss through peripheral mechanisms is yet to be fully defined. Here, we generated a mouse model of GIPR induction exclusively in the adipocyte. We show that GIPR induction in the fat cell protects mice from diet-induced obesity and triggers profound weight loss (∼35%) in an obese setting. Adipose GIPR further increases lipid oxidation, thermogenesis, and energy expenditure. Mechanistically, we demonstrate that GIPR induction activates SERCA-mediated futile calcium cycling in the adipocyte. GIPR activation further triggers a metabolic memory effect, which maintains weight loss after the transgene has been switched off, highlighting a unique aspect in adipocyte biology. Collectively, we present a mechanism of peripheral GIPR action in adipose tissue, which exerts beneficial metabolic effects on body weight and energy balance.

Goffin's cockatoos use object mass but not balance cues when making object transport decisions.

Utilising weight cues can improve the efficiency of foraging behaviours by providing information on nutritional value, material strength, and tool functionality. Attending to weight cues may also facilitate the optimisation of object transport. Though some animals' ability to assess weight cues has been determined, research into whether they can apply weight assessment during practical decision making is limited. In this study, we investigate whether Goffin's cockatoos (Cacatua goffiniana) account for relative weight and unequal versus equal weight distribution when making object transport decisions, and whether sensitivity to these cues varies depending on transport mode. We conducted a series of binary choice experiments in which birds could choose to transport one of two identical, non-functional, equally rewarded objects differing only in overall weight (experiment 1) or weight balance (experiment 2) over a short distance. We found that in experiment 1, Goffin's cockatoos preferred to transport light objects over heavy objects and seemed to rely more on weight cues to inform decisions over time, whereas in experiment 2, weight balance cues were ignored. Contrary to our predictions, Goffin's cockatoos did not show increased preference for lighter or more balanced objects when employing higher energy transport modes (flight) compared to lower energy modes (walking). We suggest that this may be due to an insufficient difference in physical effort between transport modes due to the short distance travelled. These findings provide the first evidence of weight cues being considered to optimise object transport in birds.

Femoral Artery Infusion of αMSH Increases Muscle Thermogenesis and Promotes Glucose Uptake in Ovariectomized Ewes.

The melanocortin system is fundamental to neural control of energy balance and long-term weight regulation. Recent evidence shows that melanocortins also act at peripheral tissues to regulate metabolism, independent of the brain or the sympathetic nervous system (SNS). One such target is skeletal muscle, which contributes to energy expenditure through changes in adaptive thermogenesis. We aimed to determine 1) whether direct femoral infusion of α-melanocyte-stimulating hormone (αMSH) could increase muscle heat production independent of SNS activation and 2) if αMSH-induced skeletal muscle heat production was associated with altered mitochondrial function. Dataloggers were implanted into one hind leg of ovariectomized ewes and set to record vastus lateralis temperature every 15 minutes. A cannula was inserted into one femoral artery for infusion of either αMSH (0.1 µg/h) or saline. Femoral infusion of αMSH increased (P < .0001) skeletal muscle heat production, without effect on food intake. State 4 respiration increased (P < .05) and the respiratory control ratio decreased (P < .05) in mitochondria isolated from αMSH-treated animals. In addition, femoral infusion of αMSH reduced plasma glucose concentration in the femoral, but not the jugular vein; there was no effect of αMSH treatment on nonesterified fatty acid concentrations. These data suggest that αMSH can act locally to increase glucose uptake. We further show that blockade of the α- and β-adrenergic limbs of the SNS with either phentolamine or propranolol infusion had no effect on αMSH-induced skeletal muscle heat production. Overall, we show that αMSH acts directly at skeletal muscle to promote glucose uptake and increase energy expenditure via mitochondrial thermogenesis.

Pneumonia Detection from Chest X-Ray Images Using Deep Learning and Transfer Learning for Imbalanced Datasets.

Pneumonia remains a significant global health challenge, necessitating timely and accurate diagnosis for effective treatment. In recent years, deep learning techniques have emerged as powerful tools for automating pneumonia detection from chest X-ray images. This paper provides a comprehensive investigation into the application of deep learning for pneumonia detection, with an emphasis on overcoming the challenges posed by imbalanced datasets. The study evaluates the performance of various deep learning architectures, including visual geometry group (VGG), residual networks (ResNet), and Vision Transformers (ViT) along with strategies to mitigate the impact of imbalanced dataset, on publicly available datasets such as the Chest X-Ray Images (Pneumonia) dataset, BRAX dataset, and CheXpert dataset. Additionally, transfer learning from pre-trained models, such as ImageNet, is investigated to leverage prior knowledge for improved performance on pneumonia detection tasks. Our investigation extends to zero-shot and few-shot learning experiments on different geographical regions. The study also explores semi-supervised learning methods, including the Mean Teacher algorithm, to utilize unlabeled data effectively. Experimental results demonstrate the efficacy of transfer learning, data augmentation, and balanced weight in addressing imbalanced datasets, leading to improved accuracy and performance in pneumonia detection. Our findings emphasize the importance of selecting appropriate strategies based on dataset characteristics, with semi-supervised learning showing particular promise in leveraging unlabeled data. The findings highlight the potential of deep learning techniques in revolutionizing pneumonia diagnosis and treatment, paving the way for more efficient and accurate clinical workflows in the future.

Greek, Roman, and Byzantine Weights from the Ephesus Museum

This article, part of the Corpus Ponderum Antiquorum et Islamicorum (= CPAI ) project, introduces 302 balance weights from the collection of the Ephesus Museum, located in Selçuk (Efes), İzmir. The majority of the weights are made of lead, with a smaller number made of bronze. Among these, 115 weights follow the Greek system, while 187 belong to the Roman system, of which some are classified as Byzantine weights. The find spots of these weights are primarily in and around Selçuk and nearby archaeological sites, providing important data for their provenance or attribution. Although some weights bear ethnics, most only feature unit marks; however, their find spots enable accurate attribution. The Roman system weights, especially the librae, range from the theoretical 12 unciae to as much as 25 unciae, indicating adjustments over time due to inflation and devaluation. Among the Byzantine weights, the glass ones, dating from the 5th to 7th centuries AD, are also notable for the monograms they bear. Additionally, a weight made from a coin dates to the 10th-11th centuries AD.

Parallel Force Feedback Device Design With Constant Jacobian Matrix and Net Weight Fully Balanced

Abstract Force feedback devices offer realistic and intuitive feedback to operators, and achieving higher accuracy and transparency has been a prominent research focus in this field. However, errors in real-time force or position calculations are inevitable, which can have adverse effects on the accuracy of the equipment. This article presents the design of a parallel mechanism that incorporates a constant force and velocity mapping relationship based on the type synthesis of the constant Jacobian matrix. Specifically, the mechanism maintains a fixed direction of force applied to the operating platform by the drive pair, which is mounted on the fixed platform in each limb, irrespective of position changes. This unique feature enables the mechanism to achieve a complete net weight balance. The implementation method is detailed in this article, which involves adding appropriate counterweights to achieve a balance between the weight of the connecting rod and the overall gravity of the operating platform. Furthermore, the optimization of structural parameters helps to improve the performance of the developed prototype. The proposed design scheme not only addresses the fundamental reduction of errors caused by real-time force and position mapping solving but also enhances operational transparency. Finally, simulation and experimental tests are conducted to validate the proposed theory.

Research on small target detection algorithm for water surface in complex weather environment

Abstract Detecting small targets on water surfaces in varying weather conditions presents considerable challenges, primarily due to changing lighting and precipitation such as rain or fog. To mitigate these issues, an enhanced YOLOv5 algorithm for the recognition of small targets on water surfaces is proposed. This approach leverages optical flow to trace ship movements and create synthesized frames, thereby improving image clarity via histogram equalization. The algorithm integrates CBAM attention mechanisms and feature pyramid structures, which significantly boost the detection of small targets. Additionally, this method incorporates data augmentation to enlarge the training dataset, coupled with class weight balancing and the SIOU loss function to improve target localization accuracy. Experimental results demonstrate a marked improvement in mIoU, increasing from 0.7538 to 0.8392, thus effectively recognizing small targets in complex environments.

Automation of conceptual design and modification of aircraft type unmanned aerial vehicles using multidisciplinary optimization and evolutionary algorithms. Part 1: Methods and models

This paper proposes a method for selecting rational parameters for large-size aircraft-type unmanned aerial vehicles at the initial design stages using an optimization algorithm of differential evolution and numerical mathematical modeling of aerodynamic problems. The method assumes implementation of weight and aerodynamic balance in the main flight modes, it can consider aircraft-type unmanned aerial vehicles with one or two lifting surfaces, applies parallel calculations, and automatically generates a three-dimensional geometric model of the aircraft appearance based on the optimization results. A method for accelerating by more than three times the process of solving the problem of optimizing aircraft takeoff weight parameters by introducing the target function into the set of design variables is proposed and demonstrated. The results of assessing the reliability of the mathematical models used for aerodynamics and the correct calculation of the target function are presented, taking into account various constraints. A comprehensive check of the operability and effectiveness of the method were considered by solving demonstration problems by optimizing more than ten main design parameters of the appearance of two existing heavy-class unmanned aerial vehicles with known characteristics from open sources. Examples of using the optimization results to modify prototypes are provided.

Research on faults diagnosis of dynamic balance in scroll compressor based on CWT-MViTV2

Abstract The dynamic balancing of rotor systems is a critical factor affecting the vibration of scroll compressors. Traditionally, the detection and correction of rotor dynamic balancing systems have relied on time-domain and frequency-domain analysis of vibration signals, as well as validation using rotor dynamic balancing test platforms. This paper proposes a novel fault diagnosis method for the dynamic balancing of scroll compressor rotor systems. This method acquires the time-domain vibration signals of the scroll compressor through online detection experiments. These signals are then transformed into time-frequency maps using continuous wavelet transform (CWT). By integrating the MViTV2 neural network, this approach enables effective identification of dynamic balancing fault types without requiring disassembly or shutdown of the machine. The fault types, corresponding to the offset of the balancing weight’s centroid, will provide direct data support for subsequent validation work. This study compares various neural network models combined with time-frequency maps and demonstrates that the proposed model achieves the highest accuracy of 99.749% compared to other models, and the method’s generalizability is validated in the public dataset. Furthermore, the proposed model maintains a high accuracy of 94.872% in high-noise environments. After improvement, the accuracy of the model has been increased to 95.641%, the training time and the diagnosis time has been reduced to 0.240 iter/s and 0.0277 s.

Measurement-Noise Filtering for Automatic Discovery of Flow Splitting Ratios in ISP Networks

Network telemetry and analytics is essential for providing highly dependable services in modern computer networks. In particular, network flow analytics for ISP networks allows operators to inspect and reason about traffic patterns in their networks in order to react to anomalies. High performance network analytics systems are designed with scalability in mind, and can consequently only observe partial information about the network traffic. Still, they need to provide a holistic view of the traffic, including the distribution of different traffic flows on each link. It is impractical to monitor such fine-grained telemetry, and in large, heterogeneous networks it is often too complex and error-prone, if not impossible, to access and maintain all technical specifications and router-specific configurations needed to determine e.g. the load balancing weights used when traffic is split onto multiple paths. The ratios by which flows are split on the possible paths must be derived indirectly from the measured flow demands and link utilizations. Motivated by a case study provided by a major European ISP, we suggest an efficient method to estimate the flow splitting ratios. Our approach, based on quadratic linear programming, is scalable and achieves robustness to the measurement noise found in a typical network analytics deployment by filtering out certain constraints in the linear program. Finally, we implement an automated tool for estimating the flow splitting ratios and document its applicability on real data from the ISP.

Acetyl-11-keto-beta-boswellic Acid (AKBA) Ameliorates Histopathological and Oxidative Stress Brain Disorders in the Experimental Model of Cuprizone-induced Demyelination

Background: Multiple sclerosis is an inflammatory autoimmune disease that destroys the myelin sheath (demyelination) and neurons of the central nervous system (CNS), with several factors contributing to its development. Objectives: This study investigated the impact of administering acetyl 11-keto-beta boswellic acid (AKBA) in an animal model of cuprizone-induced demyelination. Methods: Thirty-two C57BL/6 female mice (WT; 20 - 25 g, 8 - 10 weeks old) were randomly divided into four groups: (1) Control (CO), (2) Cuprizone-treated (CPZ, 0.3%), (3) AKBA + CPZ (50 mg/kg, pre-treatment), and (4) CPZ + AKBA (50 mg/kg, post-treatment). At the end of the study, the weight and movement balance of the animals were assessed using the rotarod test. After sampling the brain, the extent of the demyelination area in the corpus callosum was measured with Luxol Fast Blue staining, the number of oligodendrocyte cells was determined by immunohistochemistry, and the levels of malondialdehyde (MDA) and total antioxidant capacity (TAC) as oxidative stress factors in the brain tissue were measured. Results: The use of acetyl-11-keto-beta boswellic acid in the cuprizone-induced demyelination model prevented weight loss, increased animal resistance in the rotarod test, and reduced the demyelination rate in the corpus callosum. Additionally, AKBA elevated the number of oligodendrocytes and total antioxidant capacity levels while reducing malondialdehyde levels in brain tissue. Pretreatment with AKBA exhibited a more significant effect. Conclusions: This study showed that acetyl-11-keto-beta boswellic acid, an important active substance of the frankincense plant, likely protects the nervous system and neurons in the cuprizone-induced demyelination model by preventing the decrease in the number of oligodendrocytes, reducing oxidative stress, and preventing the destruction of myelin.

Identification of an ionic mechanism for ERα-mediated rapid excitation in neurons.

The major female ovarian hormone, 17β-estradiol (E2), can alter neuronal excitability within milliseconds to regulate a variety of physiological processes. Estrogen receptor-α (ERα), classically known as a nuclear receptor, exists as a membrane-bound receptor to mediate this rapid action of E2, but the ionic mechanisms remain unclear. Here, we show that a membrane channel protein, chloride intracellular channel protein-1 (Clic1), can physically interact with ERα with a preference to the membrane-bound ERα. Clic1-mediated currents can be enhanced by E2 and reduced by its depletion. In addition, Clic1 currents are required to mediate the E2-induced rapid excitations in multiple brain ERα populations. Further, genetic disruption of Clic1 in hypothalamic ERα neurons blunts the regulations of E2 on female body weight balance. In conclusion, we identified the Clic1 chloride channel as a key mediator for E2-induced rapid neuronal excitation, which may have a broad impact on multiple neurobiological processes regulated by E2.

Expression of Intelectin-1, also known as Omentin-1, is related to clinical phenotypes such as overweight, obesity, insulin resistance, and changes after bariatric surgery.

Intelectin-1 (ITLN1; also Omentin-1, OMNT1) is secreted by adipose tissue (AT) and plays an important role in glucose metabolism regulation, with links to obesity-associated diseases. ITLN1 activity so far has rarely been investigated using RNA-sequencing and in larger cohorts. We evaluated ITLN1 expression among three clinical cohorts of the Leipzig Obesity BioBank-a cross-sectional cohort comprising of 1480 people, a cohort of people with metabolically healthy or unhealthy obesity (31 insulin-sensitive, 42 insulin-resistant individuals with obesity), and a longitudinal two-step bariatric surgery cohort (n = 65). We hypothesized that AT ITLN1 expression is associated with serum omentin-1, clinical parameters associated with obesity, and with weight loss after bariatric surgery. We also investigated the correlation of AT ITLN1 expression with genes related to inflammatory response, lipid metabolism, obesity, and regulation of energy balance. Likewise, we inspected gene group expression and metabolic pathways associated with ITLN1 expression using gene set enrichment and gene correlation analysis. We show that ITLN1 expression differs in VAT and SAT, and should therefore be analyzed separately. Furthermore, ITLN1 expression increases with VAT tissue mass, but is negatively affected by AT tissue dysfunction among individuals with unhealthy obesity, corroborated by interplay with genes related to tissue inflammation. Gene set enrichment and gene correlation analysis of ITLN1 expression suggest that AT ITLN1 expression is related to local inflammatory processes in AT, but also in processes such as regulation of appetite, energy balance, and maintenance of body weight.

Financial risk control model based on multi-swarm multi-objective root system growth algorithms

With the globalization of the economy, the challenges of financial risk management continue to grow, and the current traditional algorithms are often limited by the lack of search capability and diversity maintenance, which makes it difficult to predict as well as manage financial risks. Therefore, a multi-population multi-objective root system growth algorithm is proposed. The algorithm uses the plant root tip position and growth state as heuristic information to guide the search process. It also introduces adaptive search space to adjust the parameters, a multi-swarm strategies to enhance the exploration ability, and multi-objective optimization to adjust the weight balance among the objectives. The experimental results showed that in the single objective optimization function, the mean value of RSGA model was 5.80E-20, the standard deviation was 1.29E-19, the best position was 2.90E-26, and the worst position was 2.89E-19. In the biobjective optimization function, the average IGD of RSGA model was 2.28E-3. In the three-objective optimization function, the average IGD and HV of RSGA model were 1.05E-1 and 6.53E-1 respectively. In financial risk prediction, the best risk of RSGA model in small-scale investment was 0.1961, the worst risk was 0.2483, and the average risk was 0.2236. The best risk of medium-scale investment was 0.3057, the worst risk was 0.3387, and the average risk was 0.3194. In large-scale investment, the best risk was 0.191, the worst risk was 1.8795, and the standard deviation was 0.3769. Under MV portfolio, the maximum HV value of RSGA model was 1.13E-1, the minimum HV value was 4.20E-1, the average value was 8.74E-1, and the standard deviation was 5.46E-1. Under the RRC portfolio, the maximum HV of RSGA model was 1.49E-0, the minimum was 3.63E-1, the average was 8.17E-1, and the standard deviation was 3.95E-1.

BM-FL: A Balanced Weight Strategy for Multi-Stage Federated Learning Against Multi-Client Data Skewing

BM-FL: A Balanced Weight Strategy for Multi-Stage Federated Learning Against Multi-Client Data Skewing

Influence of strut angle and radius on the energy absorption and failure mechanisms in 3-strut, 4-strut and 6-strut lattice structures

Abstract 3D-printed truss structures have remarkable potential in the aerospace and weight-critical technologies fields. However, design parameters need to be carefully considered. A low overhang angle or diameter can result in discrepancies. This research presents an approach by examining the effect of strut overhang angle and radius on the mechanical properties of 3-, 4- and 6-strut lattice structures under compressive loading. 1.6- and 2.4-mm diameter struts were designed with 45°, 50°, 55° and 60° overhang angle strut lattices. Experiments were simulated and compared with test results for each parameter. Even if joint regions have little effect on specimens’ density, it has a remarkable effect on mechanical properties of the specimen. To simulate this, many studies were investigated to simulate joint regions. The study’s goal is to deepen the understanding of how design variations in strut lattice structures influence their energy-absorbing characteristic and mechanical behavior, using a combination of static tests and finite element analysis for validation. This insight is crucial for optimizing lattice design to balance weight, strength, and energy-absorbing capacity effectively. The experimental test result and numerical result showed rather good agreement. It is observed that joint regions, overhang angle, and diameters were the main parameters affecting specimens’ mechanical behavior.

Minimally invasive colorectal cancer surgery: an observational study of medicare advantage and fee-for-service beneficiaries

BackgroundEnrollment of Medicare beneficiaries in medicare advantage (MA) plans has been steadily increasing. Prior research has shown differences in healthcare access and outcomes based on Medicare enrollment status. This study sought to compare utilization of minimally invasive colorectal cancer (CRC) surgery and postoperative outcomes between MA and Fee-for-Service (FFS) beneficiaries.MethodsA retrospective cohort study of beneficiaries ≥ 65.5 years of age enrolled in FFS and MA plans was performed of patients undergoing a CRC resection from 2016 to 2019. The primary outcome was operative approach, defined as minimally invasive (laparoscopic) or open. Secondary outcomes included robotic assistance, hospital length-of-stay, mortality, discharge disposition, and hospital readmission. Using balancing weights, we performed a tapered analysis to examine outcomes with adjustment for potential confounders.ResultsMA beneficiaries were less likely to have lymph node (12.9 vs 14.4%, p < 0.001) or distant metastases (15.5% vs 17.0%, p < 0.001), and less likely to receive chemotherapy (6.2% vs 6.7%, p < 0.001), compared to FFS beneficiaries. MA beneficiaries had a higher risk-adjusted likelihood of undergoing laparoscopic CRC resection (OR 1.12 (1.10–1.15), p < 0.001), and similar rates of robotic assistance (OR 1.00 (0.97–1.03), p = 0.912), compared to FFS beneficiaries. There were no differences in risk-adjusted length-of-stay (β coefficient 0.03 (− 0.05–0.10), p = 0.461) or mortality at 30-60-and 90-days (OR 0.99 (0.95–1.04), p = 0.787; OR 1.00 (0.96–1.04), p = 0.815; OR 0.98 (0.95–1.02), p = 0.380). MA beneficiaries had a lower likelihood of non-routine disposition (OR 0.77 (0.75–0.78), p < 0.001) and readmission at 30-60-and 90-days (OR 0.76 (0.73–0.80), p < 0.001; OR 0.78 (0.75–0.81), p < 0.001; OR 0.79 (0.76–0.81), p < 0.001).ConclusionsMA beneficiaries had less advanced disease at the time of CRC resection and a greater likelihood of undergoing a laparoscopic procedure. MA enrollment is associated with improved health outcomes for elderly beneficiaries undergoing operative treatment for CRC.

Utilizing neural networks and genetic algorithms in AI-assisted CFD for optimizing PCM-based thermal energy storage units with extended surfaces

Phase Change Materials (PCMs) offer significant benefits for applications such as electronic cooling and Thermal Energy Storage (TES) due to their high energy storage capacity and stability. However, their limited thermal conductivity restricts widespread utilization. To address this limitation, the use of fins has become a common technique to enhance heat transfer. Optimizing fin lengths and locations is challenging, as they affect natural convection. This study aims to develop a model using a feed-forward back-propagation network, trained on simulation data, for designing a TES unit. A design matrix, incorporating factors such as fin lengths and heights, is obtained through response surface methodology. Single- and multi-objective Genetic Algorithms (GAs) are employed to seek optimal solutions, considering Complete Melting Time (CMT) and total fin length. The results demonstrate the excellent performance of the network model, with an error of less than 2.98%. The single-objective GA yields a minimum CMT. In a finless enclosure, the CMT is 338.5% higher compared to the single-objective configuration. The multi-objective GA, using Euclidean distance specified from the Pareto front to balance weight and material costs, results in a CMT that is 192% higher compared to the single-objective optimization but with a total fin length that is 286% shorter. While these improvements are significant, it is important to note that fins can increase material and manufacturing costs, as well as the overall unit weight. This artificial intelligence-computational fluid dynamics method not only predicts TES unit performance but also reduces computational costs in designing an optimal TES unit.

Analyzing Runs of Homozygosity Reveals Patterns of Selection in German Brown Cattle.

An increasing trend in ancestral and classical inbreeding coefficients as well as inbreeding depression for longevity were found in the German Brown population. In addition, the proportion of US Brown Swiss genes is steadily increasing in German Browns. Therefore, the aim of the present study was to analyze the presence and genomic localization of runs of homozygosity (ROH) in order to evaluate their associations with the proportion of US Brown Swiss genes and survival rates of cows to higher lactations. Genotype data were sampled in 2364 German Browns from 258 herds. The final data set included 49,693 autosomal SNPs. We identified on average 35.996 ± 7.498 ROH per individual with a mean length of 8.323 ± 1.181 Mb. The genomic inbreeding coefficient FROH was 0.122 ± 0.032 and it decreased to 0.074, 0.031 and 0.006, when genomic homozygous segments > 8 Mb (FROH>8), >16 Mb (FROH>16) and >32 Mb (FROH>32) were considered. New inbreeding showed the highest correlation with FROH>32, whereas ancestral inbreeding coefficients had the lowest correlations with FROH>32. The correlation between the classical inbreeding coefficient and FROH was 0.572. We found significantly lower FROH, FROH>4, FROH>8 and FIS for US Brown Swiss proportions <60% compared to >80%. Cows surviving to the 2nd, 4th, 6th, 8th, and 10th lactation had lower genomic inbreeding for FROH and up to FROH>32, which was due to a lower number of ROH and a shorter average length of ROH. The strongest ROH island and consensus ROH shared by 50% of the animals was found on BTA 6 at 85-88 Mb. The genes located in this genomic region were associated with longevity (NPFFR2 and ADAMTS3), udder health and morphology (SLC4A4, NPFFR2, GC and RASSF6), milk production, milk protein percentage, coagulation properties of milk and milking speed (CSN3). On BTA 2, a ROH island was detected only in animals with <60% US Brown Swiss genes. Genes within this region are predominantly important for dual-purpose cattle breeds including Original Browns. For cows reaching more than 9 lactations, an exclusive ROH island was identified on BTA 7 with genes assumed to be associated with longevity. The analysis indicated that genomic homozygous regions important for Original Browns are still present and also ROH containing genes affecting longevity may have been identified. The breeding of German Browns should prevent any further increase in genomic inbreeding and run a breeding program with balanced weights on production, robustness and longevity.