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Abstract Background The fibular free flap is considered one of the most valuable options for mandible reconstruction. A perforator flap has gained widespread acceptance in oral and maxillofacial reconstruction. Typically, the fibula flap is obtained primarily with the distal perforator due to its reliable blood supply, with less attention given to the proximal perforators during the harvesting process. Normally, the distal perforator of the fibula exhibits stability and shows limited anatomical variations. However, there have been reported cases in which the distal perforator is absent. At times, these vascular abnormalities remain undetectable through Doppler examination or preoperative angiography evaluation. Therefore, this case details the experience of encountering the rare event of vascular abnormality in oral cancer surgery. Case presentation This article reports the case of a patient who presented with a congenital absence of the distal perforator in the peroneal artery, attributed to a vascular abnormality. Additionally, we provide a review of the concept of utilizing the proximal perforator as an alternative approach in the flap harvesting process. Conclusions While the distal perforator of the peroneal artery is typically utilized for fibula free flap procedures, surgeons must remain cognizant of the potential for its absence due to aberrant anatomy. Recognizing an alternative approach in such cases can be pivotal for precise surgical planning and favorable outcomes in oral and maxillofacial reconstruction

Recent work in crowd counting focuses on counting over detected individuals rather than estimating the number of people in the image. However, existing crowd localization methods directly detect the head point or region of individuals, which may entail non-responsibility of the outputs that fall outside the grid. Our proposed Pseudo Square Label Network (PSL-Net) presents a novel method for crowd counting and localization, which takes advantage of the anchor-free detection in which PSL-Net predicts the probability of the center point that fall into the responsible grid, while indirectly detecting an individual outside of the responsible grid through box regression and centerness estimation. This study proposes to supervise with pseudo square label(PSL), which is generated around point annotation with fixed size. Furthermore, we design a partial many-to-one matching algorithm to assign precise labels by only matching within PSL during the training phase, and associate the predicted points with their responsible grids through centerness during the inference phase. As a result, not only PSL-Net achieves state-of-the-art on ShanghaiTech Part A and B, which are the most popular datasets in crowd counting, but also achieves state-of-the-art among the point detection-based methods in crowd localization.

The control that have the greatest influence on comfortable in the office occupants are the heating, ventilation, and air conditioning (HVAC) system operation and the thermal environment. However, comfortable HVAC operation is difficult in the office space characterized by a recommended standard thermal environment or a centralized HVAC system. To consider the occupant's thermal comfort to the greatest possible extent, must establish a method to quantify the variables related to the occupant’s thermal comfort. This study aims to group occupants in Thermal sensation vote (TSV) clusters and perform sensitivity analysis (SA) on the relationship between thermal environmental factors in an office building and each cluster’s TSV to establish the typology of the control indicators for each cluster. A total of 10 field experiments were conducted in the same office. This field study was carried out 2022. The indoor thermal environmental parameters, the subjective evaluation of the thermal comfort of the resident and the operation pattern of the heating system were monitored at the same time. A total of 4,200 datasets related to indoor thermal environmental parameters and a total of 1,680 datasets related to occupants’ thermal comfort were collected and analyzed. The results of this study show that people have different levels of adaptability and sensitivity to a given thermal environment. This study founded distinguishable similarities in their thermal sensation traits and grouped similar TSV values into five clusters that responded differently to the same thermal environment. Each cluster showed different TSV and Thermal comfort vote (TCV) patterns, which allowed us to classify the groups that had sensitive responses to the thermal environment and those that did not. This study was determined different control indicators and guidelines for the divided groups according to thermal sensitivity.

This study aims to determine whether it can distinguish odontogenic keratocyst (OKC) and simple bone cyst (SBC) based solely on preoperative panoramic radiographs through a deep learning algorithm. (1) Methods: We conducted a retrospective analysis of patient data from January 2018 to December 2022 at Pusan National University Dental Hospital. This study included 63 cases of OKC confirmed by histological examination after surgical excision and 125 cases of SBC that underwent surgical curettage. All panoramic radiographs were obtained utilizing the Proline XC system (Planmeca Co., Helsinki, Finland), which already had diagnostic data on them. The panoramic images were cut into 299 × 299 cropped sizes and divided into 80% training and 20% validation data sets for 5-fold cross-validation. Inception-ResNet-V2 system was adopted to train for OKC and SBC discrimination. (2) Results: The classification network for diagnostic performance evaluation achieved 0.829 accuracy, 0.800 precision, 0.615 recall, and a 0.695 F1 score. (4) Conclusions: The deep learning algorithm demonstrated notable accuracy in distinguishing OKC from SBC, facilitated by CAM visualization. This progress is expected to become an essential resource for clinicians, improving diagnostic and treatment outcomes.

Accurate gene expression is fundamental for sustaining life, enabling adaptive responses to routine tasks and management of urgent cellular environments. RNA polymerases (RNAP I, RNAP II, and RNAP III) and ribosomal proteins (RPs) play pivotal roles in the precise synthesis of proteins from DNA sequences. In this review, we briefly examined the structure and function of their constituent proteins and explored to characterize these proteins and the genes encoding them, particularly in terms of their expression quantitative trait loci (eQTL) associated with complex human traits. We gathered a comprehensive set of 4007 genome-wide association study (GWAS) signal–eQTL pairs, aligning GWAS Catalog signals with eQTLs across various tissues for the genes involved. These pairs spanned 16 experimental factor ontology (EFO) parent terms defined in European Bioinformatics Institute (EBI). A substantial majority (83.4%) of the pairs were attributed to the genes encoding RPs, especially RPS26 (32.9%). This large proportion was consistent across all tissues (15.5~81.9%), underscoring its extensive impact on complex human traits. Notably, these proportions of EFO terms differed significantly (p < 0.0031) from those for RNAPs. Brain-specific pairs for POLR3H, a component of RNAP III, were implicated in neurological disorders. The largest number of pairs in RNAP I was found for POLR1H, encoding RPA12, a built-in transcription factor essential for high transcriptional efficiency of RNAP I. RNAP II-related pairs were less abundant, with unique structural organization featuring minimal subunits for flexible transcription of a diverse range of genes with customized dissociable subunits. For instance, RPB4 encoded by POLR2D, the RNAP II gene with the most pairs, forms its dissociable stalk module with RPB7. This study provides insightful genetic characteristics of RPs and RNAPs, with a priority emphasis on RPS26, POLR1H, POLR2D, and POLR3H, for future studies on the impact of individual genetic variation on complex human traits.

In this study, genetic variations and characteristics of Listeria monocytogenes isolates from enoki mushrooms (23), smoked ducks (7), and processed ground meat products (30) were examined with respect to hemolysis, virulence genes, growth patterns, and heat resistance. The isolates that showed the highest pathogenicity and the lowest pathogenicity were analyzed to obtain the whole-genome sequence, and the sequences were further analyzed to identify genetic variations in virulence, low-temperature growth-related, and heat resistance-related factors. All isolates had β-hemolysis and virulence genes (actA, hlyA, inlA, inlB, and plcB). At low temperatures, isolates with high growth (L. monocytogenes strains SMFM 201803 SD 1-1, SMFM 201803 SD 4-2, and SMFM 201804 SD 5-3) and low growth (L. monocytogenes strains SMFM 2019-FV43, SMFM 2019-FV42, and SMFM 2020-BT30) were selected. Among them, L. monocytogenes SMFM 201804 SD 5-3 showed the highest resistance at 60°C and 70°C. The strains SMFM 201804 SD 5-3 (high-risk) and SMFM 2019-FV43 (low-risk) harbored 45 virulence genes; 41 single nucleotide variants (SNVs) were identified between these two isolates. A comparison of 26 genes related to low-temperature growth revealed 18 SNVs between these two isolates; a comparison of the 21 genes related to heat resistance revealed 16 SNVs. These results indicate that the differences in the pathogenicity of L. monocytogenes SMFM 201804 SD 5-3 and L. monocytogenes SMFM 2019-FV43 are associated with the SNVs identified in virulence genes, low-temperature growth-related genes, and heat resistance-related genes.

We propose a technique that allows designers to develop energy-efficient buildings focused on occupants from the early design stage. The technique integrates the physiological responses of occupants and the energy performance of buildings. Among the architectural design elements, we considered the aspect ratio, ceiling height, and window-to-wall ratio as design variables and created 30 design alternatives for a single-occupancy room in a postpartum care center. These design alternatives were recreated in virtual reality, allowing 33 female participants to immerse themselves in the designed rooms. During the experiment, we collected electroencephalography (EEG) data from the participants. Furthermore, we used DesignBuilder to simulate 30 design alternatives and calculated the primary energy consumption per unit area for each alternative. By integrating the EEG data and energy performance analysis, we identified the design alternative among the 30 options that positively influenced the physiological responses of occupants while also being energy efficient. The selected alternative was designed with an aspect ratio of 1:1.6, a ceiling height of 2.3 m, and a window-to-wall ratio of 60%. This research represents a creative exploration that demonstrates how studies combining human physiological responses and architecture can evolve through integration with other subjects. Our findings provide a robust framework to explore the relationship between physiological responses and energy optimization for detailed architectural design elements.

Abstract Traditional clinical approaches diagnose disorders of the nervous system using standardized observational criteria. Although aiming for homogeneity of symptoms, this method often results in highly heterogeneous disorders. A standing question thus is how to automatically stratify a given random cohort of the population, such that treatment can be better tailored to each cluster’s symptoms, and severity of any given group forecasted to provide neuroprotective therapies. In this work we introduce new methods to automatically stratify a random cohort of the population composed of healthy controls of different ages and patients with different disorders of the nervous systems. Using a simple walking task and measuring micro-fluctuations in their biorhythmic motions, we combine non-linear causal network connectivity analyses in the temporal and frequency domains with stochastic mapping. The methods define a new type of internal motor timings. These are amenable to create personalized clinical interventions tailored to self-emerging clusters signaling fundamentally different types of gait pathologies. We frame our results using the principle of reafference and operationalize them using causal prediction, thus renovating the theory of internal models for the study of neuromotor control.

(1) Background: The accurate diagnosis of periodontal disease typically involves complex clinical and radiologic examination. However, recent studies have demonstrated the potential of deep learning in improving diagnostic accuracy and reliability through the development of computer-aided detection and diagnosis algorithms for dental problems using various radiographic sources. This study focuses on the use of panoramic radiographs, which are preferred due to their ability to assess the entire dentition with a single radiation dose. The objective is to evaluate whether panoramic radiographs are a reliable source for the detection of periodontal bone loss using deep learning, and to assess its potential for practical use on a large dataset. (2) Methods: A total of 4083 anonymized digital panoramic radiographs were collected using a Proline XC machine (Planmeca Co., Helsinki, Finland) in accordance with the research ethics protocol. These images were used to train the Faster R-CNN object detection method for detecting periodontally compromised teeth on panoramic radiographs. (3) Results: This study demonstrated a high level of consistency and reproducibility among examiners, with overall inter- and intra-examiner correlation coefficient (ICC) values of 0.94. The Area Under the Curve (AUC) for detecting periodontally compromised and healthy teeth was 0.88 each, and the overall AUC for the entire jaw, including edentulous regions, was 0.91. (4) Conclusions: The regional grouping of teeth exhibited reliable detection performance for periodontal bone loss using a large dataset, indicating the possibility of automating the diagnosis of periodontitis using panoramic radiographs.

Multimodal digital data registered with wearable biosensors have emerged as highly complementary of clinical pencil-and-paper criteria, offering new insights in ways to detect and diagnose various aspects of Parkinson’s disease (PD). A pressing question is how to combine both the clinical knowledge of PD and the new technology to create interpretable digital biomarkers easily obtainable with off-the-shelf technology. Several challenges concerning disparity in biophysical units, anatomical differences across participants, sensor positioning, and sampling resolution are addressed in this work, along with identification of optimal parameters to automatically differentiate patients with PD from controls. We combine data from a multitude of biosensors registering signals from the central (electroencephalography) and peripheral (magnetometry, kinematics) nervous systems, inclusive of the autonomic nervous system (electrocardiogram), as the participants perform natural tasks requiring different levels of intentional planning and automatic control. We find that magnetometer data during walking, across a variety of amplitude and timing signals, provide optimal separation of PD from neurotypical controls. We conclude that using multimodal signals within the context of actions that bear different levels of intent, can be revealing of features of PD that would scape the naked eye. Further, we add that clinical criteria combined with such optimal digital parameter spaces offer a far more complete picture of PD than using either one of these pieces of data alone.

In services such as metaverse, which should provide a constant quality of service (QoS) regardless of the user’s physical location, the end-to-end (E2E) latency must be fairly distributed over any flow in the network. To this end, we propose a reinforcement learning (RL)-based scheduler for minimizing the maximum network E2E latency. The RL model used the double deep Q-network (DDQN) with the prioritized experience replay (PER). In order to see the performance change according to the type of RL agent, we implemented a single-agent environment where the controller is an agent and a multi-agent environment where each node is an agent. Since the agents were unable to identify E2E latencies in the multi-agent environment, the state and reward were formulated using the estimated E2E latencies. To precisely evaluate the RL-based scheduler, we set out benchmark algorithms to compare with which a network-arrival-time-based heuristic algorithm (NAT-HA) and a maximum-estimated-delay-based heuristic algorithm (MED-HA). The RL-based scheduler, first-in-first-out (FIFO), round-robin (RR), NAT-HA, and MED-HA were compared through large-scale simulations on four network topologies. The simulation results in fixed-packet generation scenarios showed that our proposal, the RL-based scheduler, achieved the minimization of maximum E2E latency in all the topologies. In other scenarios with random flow generation, the RL-based scheduler and MED-HA showed the lowest maximum E2E latency for all topologies. Depending on the topology, the maximum E2E latency of NAT-HA was equal to or larger than that of the RL-based scheduler. In terms of fairness, the RL-based scheduler showed a higher level of fairness than that of FIFO and RR. NAT-HA had similar or lower fairness than the RL-based scheduler depending on the topology, and MED-HA had the same level of fairness as the RL-based scheduler.

Since the coronavirus disease outbreak in 2019, several antibody therapeutics have been developed to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Antibody therapeutics are effective in neutralizing the virus and reducing hospitalization in patients with mild and moderate infections. These therapeutics target the spike protein of SARS-CoV-2; however, emerging mutations in this protein reduce their efficiency. In this study, we developed a universal SARS-CoV-2 neutralizing antibody. We generated a humanized monoclonal antibody, MG1141A, against the receptor-binding domain of the spike protein through traditional mouse immunization. We confirmed that MG1141A could effectively neutralize live viruses, with an EC50 of 92 pM, and that it exhibited effective Fc-mediated functions. Additionally, it retained its neutralizing activity against the alpha (UK), beta (South Africa), and gamma (Brazil) variants of SARS-CoV-2. Taken together, our study contributes to the development of a novel antibody therapeutic approach, which can effectively combat emerging SARS-CoV-2 mutations.

Although interest in using wearable sensors to characterize movement disorders is growing, there is a lack of methodology for developing clinically interpretable biomarkers. Such digital biomarkers would provide a more objective diagnosis, capturing finer degrees of motor deficits, while retaining the information of traditional clinical tests. We aim at digitizing traditional tests of cognitive and memory performance to derive motor biometrics of pen-strokes and voice, thereby complementing clinical tests with objective criteria, while enhancing the overall characterization of Parkinson’s disease (PD). 35 participants including patients with PD, healthy young and age-matched controls performed a series of drawing and memory tasks, while their pen movement and voice were digitized. We examined the moment-to-moment variability of time series reflecting the pen speed and voice amplitude. The stochastic signatures of the fluctuations in pen drawing speed and voice amplitude of patients with PD show a higher signal-to-noise ratio compared to those of neurotypical controls. It appears that contact motions of the pen strokes on a tablet evoke sensory feedback for more immediate and predictable control in PD, while voice amplitude loses its neurotypical richness. We offer new standardized data types and analytics to discover the hidden motor aspects within the cognitive and memory clinical assays.

Ribosomal proteins (RPs) are essential components that translate genetic information from mRNA templates into proteins. Their expressional dysregulation adversely affects the survival and growth of human cells. Nevertheless, little is known about the nucleotide sequences regulating the expression of RPs. Genome-wide associations for expression level of 70 RP genes were conducted across expression stages. Eighteen expression regulatory quantitative trait loci (erQTLs) were identified for protein abundances of 21 RPs (P < 1 × 10–5), but not for their mRNA expression and ribosome occupancy (P > 1 × 10–5). These erQTLs for protein abundance (pQTLs) were all trans-acting. Three of the pQTLs were associated with the expression of long noncoding RNAs (lncRNAs). Target genes of these lncRNAs may produce ribosomal components or may control the metabolic cues for ribosome synthesis. mRNAs of the RP genes extensively interact with miRNAs. The protein-specific erQTLs may become engendered by intensive miRNA controls at the translational stage, which in turn can produce RPs efficient in handling instantaneous cell requirements. This study suggests that the expression levels of RPs may be greatly influenced by trans-acting regulation, presumably via interference of miRNAs and target genes of lncRNAs. Further studies are warranted to examine the molecular functions of pQTLs presented in this study and to understand the underlying regulatory mechanisms of gene expression of RPs.

Buckwheat contains more essential proteins, dietary fiber, vitamins, minerals, and diverse phytochemicals than wheat and rice. The aims of this study are to develop the descriptive sensory attributes and evaluate the consumer acceptability of six buckwheat jellies (memilmuk) with added mung bean starch and to analyze the relationship between their descriptive sensory attributes and consumer acceptability. Statistical analyses were performed by one-way analysis of variance (ANOVA), principal component analysis (PCA), and partial least squares regression (PLSR). A total of 18 sensory attributes of buckwheat jelly, including appearance (brown, brightness, and roughness), odor/aroma (soymilk smell, grain smell, red bean porridge smell, and buckwheat tea smell), flavor or taste (savory flavor, plain taste, buckwheat taste, sweet taste, salty taste, and umami), and texture (squashed, dry, smooth, elasticity and stickiness) were developed. Consumer acceptability tests of six buckwheat jellies were conducted by 93 consumers evaluating for color, smell, savory taste, aftertaste, harmony with the sauce, overall liking, and would recommend or try again. Buckwheat jelly with 25% of mung bean starch (BJ_916) was the most favorable jelly sample among the six samples. All attributes except color, smell, and the savory taste of samples showed a significant difference (p < 0.001). BJ_916 showed a close relationship with a grain smell, elasticity, red bean porridge smell, and sweet taste of descriptive attributes and also all attributes of consumer acceptability. The determination of sensory attributes and consumer acceptability of buckwheat jelly will help to improve sensory characteristics to fulfill consumer needs and desires. Furthermore, this current study will help facilitate the expansion of the buckwheat consumption market.

The purpose of this study was to investigate the accuracy of the airway volume measurement by a Regression Neural Network-based deep-learning model. A set of manually outlined airway data was set to build the algorithm for fully automatic segmentation of a deep learning process. Manual landmarks of the airway were determined by one examiner using a mid-sagittal plane of cone-beam computed tomography (CBCT) images of 315 patients. Clinical dataset-based training with data augmentation was conducted. Based on the annotated landmarks, the airway passage was measured and segmented. The accuracy of our model was confirmed by measuring the following between the examiner and the program: (1) a difference in volume of nasopharynx, oropharynx, and hypopharynx, and (2) the Euclidean distance. For the agreement analysis, 61 samples were extracted and compared. The correlation test showed a range of good to excellent reliability. A difference between volumes were analyzed using regression analysis. The slope of the two measurements was close to 1 and showed a linear regression correlation (r2 = 0.975, slope = 1.02, p < 0.001). These results indicate that fully automatic segmentation of the airway is possible by training via deep learning of artificial intelligence. Additionally, a high correlation between manual data and deep learning data was estimated.

The study aimed to examine the 20-year trends in fruit and non-starch/unsalted vegetable intake among the Korean elderly aged 65 years or older based on the Korea National Health and Nutrition Examination Survey (KNHANES) data. A total of 3722 elderly citizens aged 65 years or older who participated in the dietary survey (24-h recall of dietary intake) of the 1998, 2008, and 2018 NHANES were selected as the subjects of this study. Fruit and non-starchy/unsalted vegetable intake increased by approximately 86.53 g over the past 20 years, from 268.27 g in 1998 to 355.8 g in 2018. In particular, 65–74-year-olds had an increased intake by approximately 130.38 g over the past 20 years, from 277.34 g in 1998 to 407.72 g in 2018. In addition, snacks intake significantly increased over the past 20 years (p for trend < 0.001). Intake according to daily meal cooking location increased by approximately 130 g over the past 20 years, from 64.50 g in 1998 to 123.39 g in 2008, and to 198.01 g in 2018. The annual proportion of the total elderly population who meet the amount of vegetable food intake recommended by the World Health Organization (WHO)/World Cancer Research Fund (WCRF) (400 g or more fruits and non-starchy vegetables) increased by approximately 11.28%p (percentage points) over the past 20 years, from 21.78% in 1998 to 24.63% in 2008, and to 33.06% in 2018. The results of this study suggest that more fundamental measures are required to increase the fruit and non-starchy vegetable intake among the elderly. Furthermore, it is thought that the results of this study can be used as basic data in establishing dietary policy. In addition, it is thought that it can be used in developing nutrition education and dietary guidelines for enhancing fruit and vegetable intake.

The field of enacted/embodied cognition has emerged as a contemporary attempt to connect the mind and body in the study of cognition. However, there has been a paucity of methods that enable a multi-layered approach tapping into different levels of functionality within the nervous systems (e.g., continuously capturing in tandem multi-modal biophysical signals in naturalistic settings). The present study introduces a new theoretical and statistical framework to characterize the influences of cognitive demands on biophysical rhythmic signals harnessed from deliberate, spontaneous and autonomic activities. In this study, nine participants performed a basic pointing task to communicate a decision while they were exposed to different levels of cognitive load. Within these decision-making contexts, we examined the moment-by-moment fluctuations in the peak amplitude and timing of the biophysical time series data (e.g., continuous waveforms extracted from hand kinematics and heart signals). These spike-trains data offered high statistical power for personalized empirical statistical estimation and were well-characterized by a Gamma process. Our approach enabled the identification of different empirically estimated families of probability distributions to facilitate inference regarding the continuous physiological phenomena underlying cognitively driven decision-making. We found that the same pointing task revealed shifts in the probability distribution functions (PDFs) of the hand kinematic signals under study and were accompanied by shifts in the signatures of the heart inter-beat-interval timings. Within the time scale of an experimental session, marked changes in skewness and dispersion of the distributions were tracked on the Gamma parameter plane with 95% confidence. The results suggest that traditional theoretical assumptions of stationarity and normality in biophysical data from the nervous systems are incongruent with the true statistical nature of empirical data. This work offers a unifying platform for personalized statistical inference that goes far beyond those used in conventional studies, often assuming a “one size fits all model” on data drawn from discrete events such as mouse clicks, and observations that leave out continuously co-occurring spontaneous activity taking place largely beneath awareness.

Transmembrane 4 L six family member 5 (TM4SF5) can form tetraspanin-enriched microdomains (TERMs) on the cell’s surface. TERMs contain protein-protein complexes comprised of tetraspanins, growth factor receptors, and integrins. These complexes regulate communication between extracellular and intracellular spaces to control diverse cellular functions. TM4SF5 influences the epithelial-mesenchymal transition (EMT), aberrant multilayer cellular growth, drug resistance, enhanced migration and invasion, circulation through the bloodstream, tumor-initiation property, metastasis, and muscle development in zebrafish. Here, current data on TM4SF5’s roles in the development of fibrotic phenotypes are reviewed. TM4SF5 is induced by transforming growth factor β1 (TGFβ1) signaling via a collaboration with epidermal growth factor receptor (EGFR) activation. TM4SF5, by itself or in concert with other receptors, transduces signals intracellularly. In hepatocytes, TM4SF5 expression regulates cell cycle progression, migration, and expression of extracellular matrix components. In CCl4-treated mice, TM4SF5, α-smooth muscle actin (α-SMA), and collagen I expression are observed together along the fibrotic septa regions of the liver. These fibrotic phenotypes are diminished by anti-TM4SF5 reagents, such as a specific small compound [TSAHC, 4′-(p-toluenesulfonylamido)-4-hydroxychalcone] or a chimeric antibody. This review discusses the antifibrotic strategies that target TM4SF5 and its associated protein networks that regulate the intracellular signaling necessary for fibrotic functions of hepatocytes.

This study investigated heritability for bovine growth estimated with genomewide single nucleotide polymorphism (SNP) information obtained from a DNA microarray chip. Three hundred sixty seven Korean cattle were genotyped with the Illumina BovineSNP50 BeadChip, and 39,112 SNPs of 364 animals filtered by quality assurance were analyzed to estimate heritability of body weights at 6, 9, 12, 15, 18, 21, and 24 months of age. Restricted maximum likelihood estimate of heritability was obtained using covariance structure of genomic relationships among animals in a mixed model framework. Heritability estimates ranged from 0.58 to 0.76 for body weights at different ages. The heritability estimates using genomic information in this study were larger than those which had been estimated previously using pedigree information. The results revealed a trend that the heritability for body weight increased at a younger age (6 months). This suggests an early genetic evaluation for bovine growth using genomic information to increase genetic merits of animals.

Transmembrane 4 L6 family member 5 (TM4SF5) is overexpressed during CCl4-mediated murine liver fibrosis and in human hepatocellular carcinomas. The tetraspanins form tetraspanin-enriched microdomains (TEMs) consisting of large membrane protein complexes on the cell surface. Thus, TM4SF5 may be involved in the signal coordination that controls liver malignancy. We investigated the relationship between TM4SF5-positive TEMs with liver fibrosis and tumorigenesis, using normal Chang hepatocytes that lack TM4SF5 expression and chronically TGFβ1-treated Chang cells that express TM4SF5. TM4SF5 expression is positively correlated with tumorigenic CD151 expression, but is negatively correlated with tumor-suppressive CD63 expression in mouse fibrotic and human hepatic carcinoma tissues, indicating cooperative roles of the tetraspanins in liver malignancies. Although CD151 did not control the expression of TM4SF5, TM4SF5 appeared to control the expression levels of CD151 and CD63. TM4SF5 interacted with CD151, and caused the internalization of CD63 from the cell surface into late lysosomal membranes, presumably leading to terminating the tumor-suppressive functions of CD63. TM4SF5 could overcome the tumorigenic effects of CD151, especially cell migration and extracellular matrix (ECM)-degradation. Taken together, TM4SF5 appears to play a role in liver malignancy by controlling the levels of tetraspanins on the cell surface, and could provide a promising therapeutic target for the treatment of liver malignancies.