Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells.

Neurons are frequently classified into distinct types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 4,200 mouse visual cortical GABAergic interneurons and reconstructed the local morphologies of 517 of those neurons. We find that most transcriptomic types (t-types) occupy specific laminar positions within visual cortex, and, for most types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties. These properties display both discrete and continuous variation among t-types. Through multimodal integrated analysis, we define 28 met-types that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability. We identify layer-specific axon innervation pattern as a defining feature distinguishing different met-types. These met-types represent a unified definition of cortical GABAergic interneuron types, providing a systematic framework to capture existing knowledge and bridge future analyses across different modalities.

Multimodal Analysis of Cell Types in a Hypothalamic Node Controlling Social Behavior.

The ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) contains ∼4,000 neurons that project to multiple targets and control innate social behaviors including aggression and mounting. However, the number of cell types in VMHvl and their relationship to connectivity and behavioral function are unknown. We performed single-cell RNA sequencing using two independent platforms-SMART-seq (∼4,500 neurons) and 10x (∼78,000 neurons)-and investigated correspondence between transcriptomic identity and axonal projections or behavioral activation, respectively. Canonical correlation analysis (CCA) identified 17 transcriptomic types (T-types), including several sexually dimorphic clusters, the majority of which were validated by seqFISH. Immediate early gene analysis identified T-types exhibiting preferential responses to intruder males versus females but only rare examples of behavior-specific activation. Unexpectedly, many VMHvl T-types comprise a mixed population of neurons with different projection target preferences. Overall our analysis revealed that, surprisingly, few VMHvl T-types exhibit a clear correspondence with behavior-specific activation and connectivity.

Architectural and Functional Commonalities between Enhancers and Promoters.

With the explosion of genome-wide studies of regulated transcription, it has become clear that traditional definitions of enhancers and promoters need to be revisited. These control elements can now be characterized in terms of their local and regional architecture, their regulatory components, including histone modifications and associated binding factors, and their functional contribution to transcription. This Review discusses unifying themes between promoters and enhancers in transcriptional regulatory mechanisms.

ETV5 promotes lupus pathogenesis and follicular helper T cell differentiation by inducing osteopontin expression.

Follicular helper T (TFH) cells mediate germinal center reactions to generate high affinity antibodies against specific pathogens, and their excessive production is associated with the pathogenesis of systemic autoimmune diseases such as systemic lupus erythematosus (SLE). ETV5, a member of the ETS transcription factor family, promotes TFH cell differentiation in mice. In this study, we examined the role of ETV5 in the pathogenesis of lupus in mice and humans. T cell-specific deletion of Etv5 alleles ameliorated TFH cell differentiation and autoimmune phenotypes in lupus mouse models. Further, we identified SPP1 as an ETV5 target that promotes TFH cell differentiation in both mice and humans. Notably, extracellular osteopontin (OPN) encoded by SPP1 enhances TFH cell differentiation by activating the CD44-AKT signaling pathway. Furthermore, ETV5 and SPP1 levels were increased in CD4+ T cells from patients with SLE and were positively correlated with disease activity. Taken together, our findings demonstrate that ETV5 is a lupus-promoting transcription factor, and secreted OPN promotes TFH cell differentiation.

The Angelman Syndrome protein Ube3A regulates synapse development by ubiquitinating arc.

Angelman Syndrome is a debilitating neurological disorder caused by mutation of the E3 ubiquitin ligase Ube3A, a gene whose mutation has also recently been associated with autism spectrum disorders (ASDs). The function of Ube3A during nervous system development and how Ube3A mutations give rise to cognitive impairment in individuals with Angleman Syndrome and ASDs are not clear. We report here that experience-driven neuronal activity induces Ube3A transcription and that Ube3A then regulates excitatory synapse development by controlling the degradation of Arc, a synaptic protein that promotes the internalization of the AMPA subtype of glutamate receptors. We find that disruption of Ube3A function in neurons leads to an increase in Arc expression and a concomitant decrease in the number of AMPA receptors at excitatory synapses. We propose that this deregulation of AMPA receptor expression at synapses may contribute to the cognitive dysfunction that occurs in Angelman Syndrome and possibly other ASDs.

Shared and distinct transcriptomic cell types across neocortical areas.

The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.

L-Fucose-Rich Sulfated Glycans from Edible Brown Seaweed: A Promising Functional Food for Obesity and Energy Expenditure Improvement.

The global obesity epidemic, exacerbated by the sedentary lifestyle fostered by the COVID-19 pandemic, presents a growing socioeconomic burden due to decreased physical activity and increased morbidity. Current obesity treatments show promise, but they often come with expensive medications, frequent injections, and potential side effects, with limited success in improving obesity through increased energy expenditure. This study explores the potential of a refined sulfated polysaccharide (SPSL), derived from the brown seaweed Scytosiphon lomentaria (SL), as a safe and effective anti-obesity treatment by promoting energy expenditure. Chemical characterization revealed that SPSL, rich in sulfate and L-fucose content, comprises nine distinct sulfated glycan structures. In vitro analysis demonstrated potent anti-lipogenic properties in adipocytes, mediated by the downregulation of key adipogenic modulators, including 5' adenosine monophosphate-activated protein kinase (AMPK) and peroxisome proliferator-activated receptor γ (PPARγ) pathways. Inhibiting AMPK attenuated the anti-adipogenic effects of SPSL, confirming its involvement in the mechanism of action. Furthermore, in vivo studies using zebrafish models showed that SPSL increased energy expenditure and reduced lipid accumulation. These findings collectively highlight the therapeutic potential of SPSL as a functional food ingredient for mitigating obesity-related metabolic dysregulation by promoting energy expenditure. Further mechanistic and preclinical investigations are warranted to fully elucidate its mode of action and evaluate its efficacy in obesity management, potentially offering a novel, natural therapeutic avenue for this global health concern.

Distribution of 35 Polycyclic Aromatic Hydrocarbons in Pine Needle Samples from Selected Locations in the Republic of Korea.

Polycyclic aromatic hydrocarbons (PAHs), dust, and wax were measured in pine needles, and PAHs were also measured in surface soil. Pearson correlation analysis was performed between the analytical values. The main compounds responsible for the increase in total PAHs were non-carcinogenic phenanthrene and fluoranthene. Therefore, the % content of carcinogenic PAHs decreased with a slope = -0.037 (r = 0.47, p < 0.01), as the total PAH concentration in pine needles increased. Correlations between individual PAHs in pine needles and surface soil were very high when only low-number ring PAHs (2R- and 3R-PAHs) were statistically analyzed and significant when only high-number ring PAHs were statistically analyzed. Low-number ring PAH mainly moves in the gas phase and diffuses into the wax layer, so it was found to be statistically significant with the wax content of pine needles. High-number ring PAHs showed a high correlation with the amount of dust in pine needles because they mainly attached to dust particles and accumulated on the surface of pine needles. The ratios of fluoranthene/pyrene and methylphenanthrene/phenanthrene for predicting the origin of atmospheric PAHs have also been proven valid for pine needles.

Hippocampal subfield transcriptome analysis in schizophrenia psychosis.

We have previously demonstrated functional and molecular changes in hippocampal subfields in individuals with schizophrenia (SZ) psychosis associated with hippocampal excitability. In this study, we use RNA-seq and assess global transcriptome changes in the hippocampal subfields, DG, CA3, and CA1 from individuals with SZ psychosis and controls to elucidate subfield-relevant molecular changes. We also examine changes in gene expression due to antipsychotic medication in the hippocampal subfields from our SZ ON- and OFF-antipsychotic medication cohort. We identify unique subfield-specific molecular profiles in schizophrenia postmortem samples compared with controls, implicating astrocytes in DG, immune mechanisms in CA3, and synaptic scaling in CA1. We show a unique pattern of subfield-specific effects by antipsychotic medication on gene expression levels with scant overlap of genes differentially expressed by SZ disease effect versus medication effect. These hippocampal subfield changes serve to confirm and extend our previous model of SZ and can explain the lack of full efficacy of conventional antipsychotic medication on SZ symptomatology. With future characterization using single-cell studies, the identified distinct molecular profiles of the DG, CA3, and CA1 in SZ psychosis may serve to identify further potential hippocampal-based therapeutic targets.

Enhancer RNA facilitates NELF release from immediate early genes.

Enhancer RNAs (eRNAs) are a class of long noncoding RNAs (lncRNA) expressed from active enhancers, whose function and action mechanism are yet to be firmly established. Here we show that eRNAs facilitate the transition of paused RNA polymerase II (RNAPII) into productive elongation by acting as a decoy for the negative elongation factor (NELF) complex upon induction of immediate early genes (IEGs) in neurons. eRNAs are synthesized prior to the culmination of target gene transcription and interact with the NELF complex. Knockdown of eRNAs expressed at neuronal enhancers impairs transient release of NELF from the specific target promoters during transcriptional activation, coinciding with a decrease in target mRNA induction. The enhancer-promoter interaction was unaffected by eRNA knockdown. Instead, chromatin looping might enable eRNAs to act locally at a specific promoter. Our findings highlight the spatiotemporally regulated action mechanism of eRNAs during early transcriptional elongation.

Radiology "forensics": determination of age and sex from chest radiographs using deep learning.

PURPOSE: To develop and test the performance of deep convolutional neural networks (DCNNs) for automated classification of age and sex on chest radiographs (CXR). METHODS: We obtained 112,120 frontal CXRs from the NIH ChestX-ray14 database performed in 48,780 females (44%) and 63,340 males (56%) ranging from 1 to 95 years old. The dataset was split into training (70%), validation (10%), and test (20%) datasets, and used to fine-tune ResNet-18 DCNNs pretrained on ImageNet for (1) determination of sex (using entire dataset and only pediatric CXRs); (2) determination of age < 18 years old or ≥ 18 years old (using entire dataset); and (3) determination of age < 11 years old or 11-18 years old (using only pediatric CXRs). External testing was performed on 662 CXRs from China. Area under the receiver operating characteristic curve (AUC) was used to evaluate DCNN test performance. RESULTS: DCNNs trained to determine sex on the entire dataset and pediatric CXRs only had AUCs of 1.0 and 0.91, respectively (p < 0.0001). DCNNs trained to determine age < or ≥ 18 years old and < 11 vs. 11-18 years old had AUCs of 0.99 and 0.96 (p < 0.0001), respectively. External testing showed AUC of 0.98 for sex (p = 0.01) and 0.91 for determining age < or ≥ 18 years old (p < 0.001). CONCLUSION: DCNNs can accurately predict sex from CXRs and distinguish between adult and pediatric patients in both American and Chinese populations. The ability to glean demographic information from CXRs may aid forensic investigations, as well as help identify novel anatomic landmarks for sex and age.

Quality of reduced-fat meat emulsion: effect of pre-emulsified duck skin and hydrocolloids.

This study explored the impact of reduced-fat meat emulsion with pre-emulsified duck skin and hydrocolloids on physicochemical properties such as cooking loss, emulsion stability, apparent viscosity, protein solubility, and texture profile analysis. Six different reduced-fat meat emulsions were produced: control (pork back fat), T1 (duck skin, DS), T2 (pre-emulsified with duck skin, PDS), T3 (PDS + 2% carrageenan), T4 (PDS + 2% alginate), T5 (PDS + 2% pectin), and T6 (PDS + 2% guar gum). Moisture content, protein content, yellowness, and apparent viscosity of reduced-fat emulsion with PDS and hydrocolloids were all higher (P < 0.05) than control. Cooking loss and emulsion stability of T4 and T6 were lower (P < 0.05) than the control values. Cooking loss and total fluid separation were greatest (P < 0.05) for T5. Fat content of reduced-fat emulsion with PDS was lower (P < 0.05) than that of the control. Meat emulsion comprising PDS with alginate resulted in superior physicochemical properties compared to the other reduced-fat meat emulsion.

Infection-related hospitalization following ureteroscopic stone treatment: results from a surgical collaborative.

BACKGROUND: Unplanned hospitalization following ureteroscopy (URS) for urinary stone disease is associated with patient morbidity and increased healthcare costs. To this effect, AUA guidelines recommend at least a urinalysis in patients prior to URS. We examined risk factors for infection-related hospitalization following URS for urinary stones in a surgical collaborative. METHODS: Reducing Operative Complications from Kidney Stones (ROCKS) is a quality improvement (QI) initiative from the Michigan Urological Surgery Improvement Collaborative (MUSIC) consisting of academic and community practices in the State of Michigan. Trained abstractors prospectively record standardized data elements from the health record in a web-based registry including patient characteristics, surgical details and complications. Using the ROCKS registry, we identified all patients undergoing primary URS for urinary stones between June 2016 and October 2017, and determined the proportion hospitalized within 30 days with an infection-related complication. These patients underwent chart review to obtain clinical data related to the hospitalization. Multivariable logistic regression analysis was performed to determine risk factors for hospitalization. RESULTS: 1817 URS procedures from 11 practices were analyzed. 43 (2.4%) patients were hospitalized with an infection-related complication, and the mortality rate was 0.2%. Median time to admission and length of stay was 4 and 3 days, respectively. Nine (20.9%) patients did not have a pre-procedure urinalysis or urine culture, which was not different in the non-hospitalized cohort (20.5%). In hospitalized patients, pathogens included gram-negative (61.5%), gram-positive (19.2%), yeast (15.4%), and mixed (3.8%) organisms. Significant factors associated with infection-related hospitalization included higher Charlson comorbidity index, history of recurrent UTI, stone size, intra-operative complication, and procedures where fragments were left in-situ. CONCLUSIONS: One in 40 patients are hospitalized with an infection-related complication following URS. Awareness of risk factors may allow for individualized counselling and management to reduce these events. Approximately 20% of patients did not have a pre-operative urine analysis or culture, and these findings demonstrate the need for further study to improve urine testing and compliance.

Automated detection and classification of shoulder arthroplasty models using deep learning.

OBJECTIVE: To develop and evaluate the performance of deep convolutional neural networks (DCNN) to detect and identify specific total shoulder arthroplasty (TSA) models. MATERIALS AND METHODS: We included 482 radiography studies obtained from publicly available image repositories with native shoulders, reverse TSA (RTSA) implants, and five different TSA models. We trained separate ResNet DCNN-based binary classifiers to (1) detect the presence of shoulder arthroplasty implants, (2) differentiate between TSA and RTSA, and (3) differentiate between the five TSA models, using five individual classifiers for each model, respectively. Datasets were divided into training, validation, and test datasets. Training and validation datasets were 20-fold augmented. Test performances were assessed with area under the receiver-operating characteristic curves (AUC-ROC) analyses. Class activation mapping was used to identify distinguishing imaging features used for DCNN classification decisions. RESULTS: The DCNN for the detection of the presence of shoulder arthroplasty implants achieved an AUC-ROC of 1.0, whereas the AUC-ROC for differentiation between TSA and RTSA was 0.97. Class activation map analysis demonstrated the emphasis on the characteristic arthroplasty components in decision-making. DCNNs trained to distinguish between the five TSA models achieved AUC-ROCs ranging from 0.86 for Stryker Solar to 1.0 for Zimmer Bigliani-Flatow with class activation map analysis demonstrating an emphasis on unique implant design features. CONCLUSION: DCNNs can accurately identify the presence of and distinguish between TSA & RTSA, and classify five specific TSA models with high accuracy. The proof of concept of these DCNNs may set the foundation for an automated arthroplasty atlas for rapid and comprehensive model identification.

Can AI outperform a junior resident? Comparison of deep neural network to first-year radiology residents for identification of pneumothorax.

PURPOSE: To (1) develop a deep learning system (DLS) using a deep convolutional neural network (DCNN) for identification of pneumothorax, (2) compare its performance to first-year radiology residents, and (3) evaluate the ability of a DLS to augment radiology residents by detecting missed pneumothoraces. METHODS: This was a retrospective study performed in September 2018. We obtained 112,120 chest radiographs (CXRs) from the NIH ChestXray14 database, of which 4360 cases (4%) were labeled as pneumothorax by natural language processing. We utilized 111,518 CXRs to train and validate the ResNet-152 DCNN pretrained on ImageNet to identify pneumothorax. DCNN testing was performed on a hold-out set of 602 CXRs, whose groundtruth was determined by a cardiothoracic radiologist. Two first-year radiology residents evaluated the test CXRs for presence of pneumothorax. Receiver operating characteristic (ROC) curves were generated for each evaluator with area under the curve (AUC) compared using the DeLong parametric method. RESULTS: The DCNN achieved AUC of 0.841 for identification of pneumothorax at a rate of 1980 images/min. In contrast, both first-year residents achieved significantly higher AUCs of 0.942 and 0.905 (p < 0.01 for both compared to DCNN), but at a slower rate of two images/min. The DCNN identified 3 of 31 (9.7%) additional pneumothoraces missed by at least one of the residents. CONCLUSION: A DLS for pneumothorax identification had lower AUC than 1st-year radiology residents, but interpreted images > 1000× as fast and identified 3 additional pneumothoraces missed by the residents. Our findings suggest that DLS could augment radiologists-in-training to identify potential urgent findings.

Effects of red glasswort as sodium chloride substitute on the physicochemical properties of pork loin ham.

OBJECTIVE: This study was conducted to evaluate the effect of red glasswort (RG) (Salicornia herbacea L.) curing on the physicochemical, textural and sensory properties of cooked pork loin ham (M. longissimus thoracis et lumborum). METHODS: All treatments were cured with different salt and RG powder levels. RG0 treatment was prepared with only 4% NaCl (w/w) as a control, and RG25, 3% NaCl:1% RG (w/w); RG50, 2% NaCl:2% RG (w/w); RG75, 1% NaCl:3% RG (w/w); RG100, 0% NaCl:4% RG (w/w) treatments were prepared sequentially. All samples were individually vacuum packaged in polyethylene bags and stored for 7 d at 3°C±1°C. RESULTS: The results showed that as the rate of RG substitution increased, pH value, redness, myofibrillar protein solubility, and myofibrillar fragmentation index increased (p<0.05), but salt concentration and shear force decreased (p<0.05). However, there were no significant differences in cooking loss and moisture content. In terms of sensory evaluation, RG100 exhibited higher scores in tenderness and juiciness than RG0 (p<0.05). CONCLUSION: The partial substitution of NaCl by RG could improve the physicochemical properties, textural and sensory characteristics of cooked pork loin. Therefore, it is suggested that RG as a natural salt replacer could be an effective ingredient for developing low-sodium cured hams.

Comparative effects of dry-aging and wet-aging on physicochemical properties and digestibility of Hanwoo beef.

OBJECTIVE: The purpose of this study was to investigate the effects of aging methods (AM) i.e. dry-aging (DA) and wet-aging (WA) on the physicochemical properties and in vitro digestibility of proteins in beef short loin. METHODS: Short loins (M. longissmus lumborum), were trimmed and boned-out on the fifth day postmortem, from a total of 18 Hanwoo, which were purchased from a commercial slaughterhouse. Short loins were separated randomly grouped into one of the three treatments: control, WA (1°C, 7 days), and DA (1°C, 0.5 m/s, 85% relative humidity [RH], 30 days). RESULTS: Dry-aged beef (DAB) exhibited higher pH, water holding capacity (WHC), myofibrillar fragmentation index (MFI), and digestibility, however lower lightness, redness, and yellowness values, cooking loss, and shear force (SF), than those of wet-aged beef (WAB) (p<0.05). The myosin light chain band intensity of DAB was higher than that of control and WAB in sodium dodecyl sulfate-polyacrylamide gel electrophoresis. The in vitro digestibility of aged beef was highly (p<0.001) correlated to physicochemical properties except WHC. The correlation coefficient between AMs and WHC was higher than that between AM and SF (p<0.05) or MFI (p<0.001). A high correlation was observed between SF and MFI (p<0.001). CONCLUSION: Thus, we believe that DAB is more advantageous than WAB owing to its high digestibility and WHC and low SF.

Emerging themes in neuronal activity-dependent gene expression.

In this review, we attempt to discuss emerging themes in the regulation of neuronal activity-regulated genes, focusing primarily on an important subset of immediate-early genes. We first discuss earlier studies that have illuminated the role of cis-acting elements within the promoters of immediate-early genes, the corresponding transcription factors that bind these elements, and the roles of major activity-regulated signaling pathways. However, our emphasis is on new studies that have revealed an important role for epigenetic and topological mechanisms, including enhancer-promoter interactions, enhancer RNAs (eRNAs), and activity-induced DNA breaks, that have emerged as important factors that govern the temporal dynamics of activity-induced gene transcription.

Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning.

BACKGROUND: An automated method for identifying the anatomical region of an image independent of metadata labels could improve radiologist workflow (e.g., automated hanging protocols) and help facilitate the automated curation of large medical imaging data sets for machine learning purposes. Deep learning is a potential tool for this purpose. OBJECTIVE: To develop and test the performance of deep convolutional neural networks (DCNN) for the automated classification of pediatric musculoskeletal radiographs by anatomical area. MATERIALS AND METHODS: We utilized a database of 250 pediatric bone radiographs (50 each of the shoulder, elbow, hand, pelvis and knee) to train 5 DCNNs, one to detect each anatomical region amongst the others, based on ResNet-18 pretrained on ImageNet (transfer learning). For each DCNN, the radiographs were randomly split into training (64%), validation (12%) and test (24%) data sets. The training and validation data sets were augmented 30 times using standard preprocessing methods. We also tested our DCNNs on a separate test set of 100 radiographs from a single institution. Receiver operating characteristics (ROC) with area under the curve (AUC) were used to evaluate DCNN performances. RESULTS: All five DCNN trained for classification of the radiographs into anatomical region achieved ROC AUC of 1, respectively, for both test sets. Classification of the test radiographs occurred at a rate of 33 radiographs per s. CONCLUSION: DCNNs trained on a small set of images with 30 times augmentation through standard processing techniques are able to automatically classify pediatric musculoskeletal radiographs into anatomical region with near-perfect to perfect accuracy at superhuman speeds. This concept may apply to other body parts and radiographic views with the potential to create an all-encompassing semantic-labeling DCNN.

Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs.

Ensuring correct radiograph view labeling is important for machine learning algorithm development and quality control of studies obtained from multiple facilities. The purpose of this study was to develop and test the performance of a deep convolutional neural network (DCNN) for the automated classification of frontal chest radiographs (CXRs) into anteroposterior (AP) or posteroanterior (PA) views. We obtained 112,120 CXRs from the NIH ChestX-ray14 database, a publicly available CXR database performed in adult (106,179 (95%)) and pediatric (5941 (5%)) patients consisting of 44,810 (40%) AP and 67,310 (60%) PA views. CXRs were used to train, validate, and test the ResNet-18 DCNN for classification of radiographs into anteroposterior and posteroanterior views. A second DCNN was developed in the same manner using only the pediatric CXRs (2885 (49%) AP and 3056 (51%) PA). Receiver operating characteristic (ROC) curves with area under the curve (AUC) and standard diagnostic measures were used to evaluate the DCNN's performance on the test dataset. The DCNNs trained on the entire CXR dataset and pediatric CXR dataset had AUCs of 1.0 and 0.997, respectively, and accuracy of 99.6% and 98%, respectively, for distinguishing between AP and PA CXR. Sensitivity and specificity were 99.6% and 99.5%, respectively, for the DCNN trained on the entire dataset and 98% for both sensitivity and specificity for the DCNN trained on the pediatric dataset. The observed difference in performance between the two algorithms was not statistically significant (p = 0.17). Our DCNNs have high accuracy for classifying AP/PA orientation of frontal CXRs, with only slight reduction in performance when the training dataset was reduced by 95%. Rapid classification of CXRs by the DCNN can facilitate annotation of large image datasets for machine learning and quality assurance purposes.