Male Accuracy Research Articles

Convolutional Neural Network-Based Deep Learning Methods for Skeletal Growth Prediction in Dental Patients

This study aimed to predict the skeletal growth maturation using convolutional neural network-based deep learning methods using cervical vertebral maturation and the lower 2nd molar calcification level so that skeletal maturation can be detected from orthopantomography using multiclass classification. About 1200 cephalometric radiographs and 1200 OPGs were selected from patients seeking treatment in dental centers. The level of skeletal maturation was detected by CNN using the multiclass classification method, and each image was identified as a cervical vertebral maturation index (CVMI); meanwhile, the chronological age was estimated from the level of the 2nd molar calcification. The model’s final result demonstrates a high degree of accuracy with which each stage and gender can be predicted. Cervical vertebral maturation reported high accuracy in males (98%), while females showed high accuracy of 2nd molar calcification. CNN multiclass classification is an accurate method to detect the level of maturation, whether from cervical maturation or the calcification of the lower 2nd molar, and the calcification level of the lower 2nd molar is a reliable method to trust in the growth level, so the traditional OPG is enough for this purpose.

Retrospective evaluation of medical information for predicting tazobactam/piperacillin-induced liver injury.

Tazobactam/piperacillin is a first-line treatment option for hospital-acquired pneumonia; however, drug-induced liver injury (DILI) is relatively frequently observed with tazobactam/piperacillin in clinical practice. This study aimed to verify the usefulness of available patient data for predicting DILI prior to tazobactam/piperacillin administration. Tazobactam/piperacillin-treated patients were retrospectively selected and divided into patients with and without DILI. Comparative analysis was performed regarding age, gender, dose, duration of treatment, clinical laboratory values prior to treatment initiation, and the types of organ-specific infections in both groups. Multiple logistic regression analyses indicated that elevated C-reactive protein (odds ratio (OR), 1.284; 95% confidence interval (CI), 1.172 - 1.406; p < 0.001) and high hemoglobin (OR, 1.697; 95% CI, 1.259 - 2.286; p < 0.001) levels prior to the administration of tazobactam/piperacillin were risk factors for DILI in males who received a 4.5-g dose. A predictive model for DILI risk was constructed by combining these test values and analyzed using receiver operating characteristic curves, obtaining 0.910 for the model construction set and 0.845 for the validation set. The development of DILI was predicted with good accuracy in males who received a 4.5-g dose with elevated C-reactive protein and hemoglobin.

Accuracy of geometric morphometrics for age estimation using frontal face photographs of children and adolescents: A promising method for forensic practice

Age estimation is crucial in legal and humanitarian contexts. Forensic professionals may use various procedures to estimate age, including dental analysis, bone density tests, evaluation of physical characteristics including facial bone structure and development, and image-based methods. Although images are often the only material available, visual observation of photographic material is an imprecise method in age estimation, which can compromise judicial decision-making. Analyzing 4000 photographs from the Brazilian Federal Police database, representing four age groups (6, 10, 14, and 18 years), the study employed automated analysis by marking 28 photogrammetric points. Data were used to establish facial patterns by age and sex using the facial geometric morphometrics method. Performance was assessed through a Multinomial Logistic Regression model, evaluating accuracy, sensitivity, and specificity across the categorical age groups. Analyses were conducted using R software, with a 5 % significance level. The study found that facial geometric morphometrics achieved an overall accuracy of 69.3 % in age discrimination, with higher accuracy in males (74.7 %) compared to females (65.8 %) (p < 0.001). The method excelled at predicting the age of 6-year-olds with 87.3 % sensitivity and 95.6 % specificity but had lower performance at 14 years. It showed greater accuracy in distinguishing age groups with larger age gaps, achieving up to 99.5 % accuracy between certain groups, and was particularly effective in differentiating ages of 6 and 10 years in females and 10, 14, and 18 years in males. The facial geometric morphometrics emerges as a promising approach for age estimation among children and adolescents in forensic settings.

A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure.

Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur. We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling. We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value < 0.001). The area under the receiver operating characteristic curve (AUC) using PC1 (0.776) was higher than that using all FE parameters combined (0.737) in the males (p-value < 0.001). The global FEA-computed fracture risk index increased hip fracture risk prediction accuracy in males.

Advancing age grading techniques for Glossina morsitans morsitans, vectors of African trypanosomiasis, through mid-infrared spectroscopy and machine learning.

Tsetse are the insects responsible for transmitting African trypanosomes, which cause sleeping sickness in humans and animal trypanosomiasis in wildlife and livestock. Knowing the age of these flies is important when assessing the effectiveness of vector control programs and modelling disease risk. Current methods to assess fly age are, however, labour-intensive, slow, and often inaccurate as skilled personnel are in short supply. Mid-infrared spectroscopy (MIRS), a fast and cost-effective tool to accurately estimate several biological traits of insects, offers a promising alternative. This is achieved by characterising the biochemical composition of the insect cuticle using infrared light coupled with machine-learning (ML) algorithms to estimate the traits of interest. We tested the performance of MIRS in estimating tsetse sex and age for the first-time using spectra obtained from their cuticle. We used 541 insectary-reared Glossina m. morsitans of two different age groups for males (5 and 7 weeks) and three age groups for females (3 days, 5 weeks, and 7 weeks). Spectra were collected from the head, thorax, and abdomen of each sample. ML models differentiated between male and female flies with a 96% accuracy and predicted the age group with 94% and 87% accuracy for males and females, respectively. The key infrared regions important for discriminating sex and age classification were characteristic of lipid and protein content. Our results support the use of MIRS as a rapid and accurate way to identify tsetse sex and age with minimal pre-processing. Further validation using wild-caught tsetse could pave the way for this technique to be implemented as a routine surveillance tool in vector control programmes.

Adiposity and insulin resistance moderate the links between neuroelectrophysiology and working and episodic memory functions in young adult males but not females

IntroductionObesity and insulin resistance negatively influence neural activity and cognitive function, but electrophysiological mechanisms underlying these interrelationships remain unclear. This study investigated whether adiposity and insulin resistance moderated neural activity and underlying cognitive functions in young adults. MethodsReal-time electroencephalography (EEG) was recorded in 38 lean (n = 12) and obese (n = 26) young adults with (n = 15) and without (n = 23) insulin resistance (18–38 years, 55.3% female) as participants completed three neurocognitive tasks in working memory (Operation Span), inhibitory control (Stroop), and episodic memory (Visual Association Test). Body fat percentage was quantified by a dual-energy X-ray absorptiometry scan (DEXA/DXA). Fasting serum insulin and glucose were quantified to calculate Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) values, for which a higher value indicates more insulin resistance. Hierarchical moderated regression analysis tested these interrelationships. ResultsIn males, greater frontal negative slow wave (fNSW) and positive slow wave (PSW) amplitudes were linked to higher working memory accuracy in participants with low, but not high, body fat percentage and HOMA-IR levels. In contrast, body fat percentage and HOMA-IR did not moderate these associations in females. Furthermore, body fat percentage and HOMA-IR values moderated the relationship between greater fNSW amplitudes and better episodic memory accuracy in males, but not females. Finally, body fat percentage and insulin resistance did not moderate the link between neural activity and inhibitory control for either sex. ConclusionYoung adult males, but not females, with higher body adiposity and insulin resistance showed reduced neural activity and worse underlying working and episodic memory functions.

Sex Estimation of Medial Aspect of the Ischiopubic Ramus in Adults Based on Deep Learning.

To investigate the reliability and accuracy of deep learning technology in automatic sex estimation using the 3D reconstructed images of the computed tomography (CT) from the Chinese Han population. The pelvic CT images of 700 individuals (350 males and 350 females) of the Chinese Han population aged 20 to 85 years were collected and reconstructed into 3D virtual skeletal models. The feature region images of the medial aspect of the ischiopubic ramus (MIPR) were intercepted. The Inception v4 was adopted as the image recognition model, and two methods of initial learning and transfer learning were used for training. Eighty percent of the individuals' images were randomly selected as the training and validation dataset, and the remaining were used as the test dataset. The left and right sides of the MIPR images were trained separately and combinedly. Subsequently, the models' performance was evaluated by overall accuracy, female accuracy, male accuracy, etc. When both sides of the MIPR images were trained separately with initial learning, the overall accuracy of the right model was 95.7%, the female accuracy and male accuracy were both 95.7%; the overall accuracy of the left model was 92.1%, the female accuracy was 88.6% and the male accuracy was 95.7%. When the left and right MIPR images were combined to train with initial learning, the overall accuracy of the model was 94.6%, the female accuracy was 92.1% and the male accuracy was 97.1%. When the left and right MIPR images were combined to train with transfer learning, the model achieved an overall accuracy of 95.7%, and the female and male accuracies were both 95.7%. The use of deep learning model of Inception v4 and transfer learning algorithm to construct a sex estimation model for pelvic MIPR images of Chinese Han population has high accuracy and well generalizability in human remains, which can effectively estimate the sex in adults.

A Geometric Morphometrics Approach for Sex Estimation Based on the Orbital Region of Human Skulls from Bosnian Population.

Understanding the anatomy and morphological variability of the orbital region is of great importance in clinical practice, forensic medicine, and biological anthropology. Several methods are used to estimate sex based on the skeleton or parts of the skeleton: classic methods and the geometric morphometric method. The objective of this research was to analyse sex estimation of the orbital region on a sample of skulls from a Bosnian population using the geometric morphometric method. The research was conducted on three-dimensional models of 211 human adult skulls (139 males and 72 females) from the Osteological Collection at the Faculty of Medicine in Sarajevo. The skulls were recorded using a laser scanner to obtain skull 3D models. We marked 12 landmarks on each model to analyse sexual dimorphism. Landmarks were marked using the program Landmark Editor. After marking the landmarks, we used the MorphoJ program to analyse the morphological variability between male and female orbital regions. After Procrustes superimposition, generating a covariant matrix, and introducing sex as a variable for classification, a discriminant functional analysis (DFA) was applied which determined the estimation for males with 86.33% accuracy and for females with 88.89% based on the form of the orbital region. The results of regression analysis showed that the size of the orbital region has a statistically significant effect on its shape's sexual dimorphism. After excluding the influence of size and providing DFA, we concluded that sex estimation was possible with 82.01% accuracy for males and 80.55% accuracy for females based on the shape of the orbital region in the examined sample. Sex estimation based on the orbital region was possible with more than 80% accuracy for both sexes, which is a high percentage of correct estimation. Therefore, we recommend using the orbital region of the skull for sex estimation.

Evaluation of Metabolic Syndrome Using Lipid Accumulation Products, Visceral Adiposity Index and Body Mass Index in Apparently Healthy Students of University of Maiduguri

Introduction: Metabolic Syndrome (MetS) is a group of conditions that together raise your risk of cardiovascular disease, diabetes mellitus, dyslipidemia and hypertension. The diagnosis of MetS was established according to the revised criteria of the NCEP/ATP III (MS-NCEP/ ATP III). Although BMI is the most common screening measures to identify people who are at relatively high risk of MetS, this tool is not particularly the most effective tool. Thus, we seek to find single parameter with the strongest diagnostic accuracy for MetS in a sample of healthy, unrelated adults. Materials &amp; Methods: The study was conducted on 200 apparently healthy male and female students of university of Maiduguri. Random sampling techniques were used to recruit the subjects aged 18–41 years with mean age of 25.65±5.56 years for males and 24.11±4.60 years for females. Examination of subjects consisted of physical examination with measurement of anthropometric and clinical parameters, filling out a questionnaire, and evaluation of serum lipid levels. Result: The result shows the mean±SD for the components of MetS includes BP (SBP=105.67±9.82; DBP=70.43±6.16); TG=1.69±0.72; HDL=1.10±0.37; FBG=3.88±0.72; WC=87.76±8.41 for males and BP (SBP=104.69±9.93; DBP=69.93±6.44), TG=1.82±0.69; HDL=1.13±0.39; FBG=3.64±0.79; WC=86.93±9.89 for females. The result shows prevalence of MetS in males was 8.1% and 21.5% in females. The result also shows that LAP has the highest diagnostic accuracy among the study population (AUC=0.856) than VAI (AUC=0.820) and BMI (AUC=0.523). LAP was also found to have highest diagnostic accuracy in males (AUC=0.908) than VAI (AUC=0.850) and BMI (AUC=0.498) while in females VAI was found to have highest diagnostic accuracy with AUC (0.865) than LAP (AUC=0.721) and BMI (AUC=0.436). Conclusion: This study therefore shows LAP has the highest diagnostic accuracy and particularly the most effective tool in identifying males who are at the risk of MetS. While VAI has ...........

Application of pretrained model based on electronic medical record in recognition of acute respiratory infection

Objective: To evaluate the recognition of acute respiratory infection (ARI) by a pretrained model based on electronic medical records (EMRs). Methods: 38 581 EMRs were obtained from Chongqing University Three Gorges Hospital in December 2021. Bidirectional encoder representation from transformers (BERT) pretrained model was used to identify ARI in EMRs. The results of medical professionals were considered as the gold standard to calculate the sensitivity, specificity, Kappa value, and area under the curve of the receiver operating characteristic (AUC). Results: There were 3 817 EMRs in the test set, with 1 200 ARIs. A total of 1 205 cases were determined as ARI by the model, with a sensitivity of 92.67% (1 112/1 200) and a specificity of 96.45% (2 524/2 617). The model identified ARI with similar accuracy in males and females (AUCs 0.95 and 0.94, respectively), and was more accurate in identifying ARI cases in those aged less than 18 than in adults 18-59 and adults 60 and older (AUCs 0.94, 0.89 and 0.94, respectively). The current model had a better identification of ARIs in outpatient patients than that in hospitalized patients, with AUCs of 0.74 and 0.95, respectively. Conclusion: The use of the BERT pretrained model based on EMRs has a good performance in the recognition of ARI cases, especially for the outpatients and juveniles. It shows a great potential to be applied to the monitoring of ARI cases in medical institutions.

Midshaft diameter of humerus and its accuracy in sex determination of south Indian population

Background: Humerus is one of the long bones generally favoured in osteometric studies as it appears to yield highest level of classification accuracy in sex determination. Aim: To study the midshaft diameter of humerus and its accuracy factor in sex determination. Methods: A total of 100 (Male - 72, Female - 28) dry human humeri were collected from the Department of Anatomy as well as from the under graduate students of Santhiram Medical College, Nandyal. The maximum midshaft diameter of humerus was measured from absolute greatest diameter without regard to the vertical or transverse plane. The minimum midshaft diameter of humerus was measured from absolute least diameter in the middle without regard to any plane or maximum diameter Results: The mean maximum mid shaft diameter of humerus in male bones was 1.953±0.027 and in female bones was 1.75±0.030. The mean minimum midshaft diameter of humerus in male was 1.657±0.0211 and in female was 1.485±0.026 in the present study Conclusion: The mid shaft diameter can be considered as one of the sex determinations factors due to its higher accuracy in males than females with other predictive variables of humerus.

Midshaft diameter of humerus and its accuracy in sex determination of south Indian population

Background: Humerus is one of the long bones generally favoured in osteometric studies as it appears to yield highest level of classification accuracy in sex determination. Aim: To study the midshaft diameter of humerus and its accuracy factor in sex determination. Methods: A total of 100 (Male - 72, Female - 28) dry human humeri were collected from the Department of Anatomy as well as from the under graduate students of Santhiram Medical College, Nandyal. The maximum midshaft diameter of humerus was measured from absolute greatest diameter without regard to the vertical or transverse plane. The minimum midshaft diameter of humerus was measured from absolute least diameter in the middle without regard to any plane or maximum diameter Results: The mean maximum mid shaft diameter of humerus in male bones was 1.953±0.027 and in female bones was 1.75±0.030. The mean minimum midshaft diameter of humerus in male was 1.657±0.0211 and in female was 1.485±0.026 in the present study Conclusion: The mid shaft diameter can be considered as one of the sex determinations factors due to its higher accuracy in males than females with other predictive variables of humerus.

Comparison of Aboul-Azm and Fouda’s approach of mixed dentition analysis with Moyers technique

Introduction: The prediction of mesiodistal widths of unerupted canines and premolars are an important aspect of analysis of the developing permanent dentition. Various radiographic as well as non radiographic methods have been tested and researched to predict the width of these teeth. The Moyers mixed dentition analysis is a universally accepted technique. Whereas Aboul-Azm and Fouda’s approach of mixed dentition analysis is a concept that derives the measurement from equation based on the buccolingual width of the permanent first molars. It does not require any table for the prediction. Objective: The present study compares the Aboul-Azm and Fouda’s approach of mixed dentition analysis with Moyers technique. Methodology: Estimations of the widths of the unerupted permanent canines, first and second premolars were performed for maxillary and mandibular arches using Aboul-Azm and Fouda’s and Moyers prediction methods. The predicted values were then compared with the measurements of the actual teeth on 224 study models of males and females. The study was conducted from October to December 2021. Results: The study was conducted on 112 male and female samples each. For males, statistically significant underestimation were found for Aboul-Azm and Fouda’s method in both arches whereas Moyers method showed better accuracy in males. In females Moyers method showed a significant overestimation. In the total sample the Moyers method showed accuracy for maxillary arch whereas Aboul-Azm and Fouda’s method was more accurate for the mandibular arch. Conclusion: Moyers method showed a good accuracy in the maxillary arch while in the mandibular arch Aboul-Azm and Fouda’s method was more accurate.

Age estimation of epiphyseal union around wrist joint and its correlation with chronological age: A radiological study in Qassim population, Saudi Arabia

ABSTRACT In forensic anthropology, age estimation is one of the most important aspects of analysis. The use of radiographic data for age estimation based on the epiphyseal union is a scientifically approved and widely accepted method. X-ray images of the right wrist joint of 281 patients (143 males and 138 females) aged 12–22 years were retrospectively examined and the stages of union at the distal epiphyses of radius and ulna were assessed, to estimate age in a Qassim population, Saudi Arabia. The distal epiphyses of radius and ulna fused completely at 18–19 years in females and 19–20 years in males. Mean age gradually increased and varied with each stage of union, with a statistically significant difference of the females’ and males’ comparable means. Females experienced epiphyseal union one year earlier than males. The ROC curve showed 90.9% accuracy in males and 90.6% in females. The findings of this study indicate that radiographic analysis of the union at distal epiphyses of radius and ulna is a helpful technique for estimation of chronological age. When compared to other studies, the results showed limited variance among different population groups.

Impacts of a perinatal exposure to manganese coupled with maternal stress in rats: Learning, memory and attentional function in exposed offspring

The developmental effects of chemicals that co-occur in vulnerable populations with elevated psychological stress are of increasing concern to the public. To investigate these concerns, we developed a rodent model of co-occurring perinatal manipulations and conducted a series of cognitive assessments in male and female offspring. Manganese (Mn), a neurodevelopmental toxicant when exceeding physiological requirements, was delivered in the drinking water (0, 2, or 4 mg Mn/mL) of rats from gestational day (GD) 7 to postnatal day (PND) 22. A variable perinatal stress paradigm was applied to half of the animals from GD13 to PND9. Novel object recognition (NOR), Morris water maze (MWM), differential reinforcement of low-rates procedure (DRL) and cued and uncued choice reaction time (CRT) tests were used to assess cognitive functions in offspring. Mn (4 mg/mL) and stress impaired NOR in adolescent males but facilitated NOR performance in females. However, when stress and Mn were combined these effects were attenuated in both sexes. During training for the DRL, Mn (2 mg/mL) facilitated, while stress impaired, lever press learning in both sexes. Few effects related to the treatments were found on DRL or MWM. During cued CRT, Mn (2 and 4 mg/mL) and stress reduced accuracy in males, while stress and Mn (2 mg/mL) increased anticipatory responding and slowed decision time in both sexes. Stress combined with Mn (2 mg/mL) improved cued accuracy and decision time, and Mn attenuated the effect of stress on anticipatory responding in both sexes. Stress slowed female movement time but when combined with Mn (4 mg/mL) the effect of stress was attenuated. During uncued CRT, except for decision time (which replicated effects observed with the cued task), no other effects of Mn or its combination with stress occurred. Females remained negatively affected by stress in most uncued CRT performance measures, while stressed improved male uncued accuracy. Taken together these data do not support increased cognitive impairment produced by Mn when combined with stress. However, the effects of perinatal stress alone, on these cognitive functions may hinder the detection of effects due to chemical exposures and underscores the need to consider the psychological health and wellbeing of the mother and her environment in risk assessment for developmental neurotoxicity of chemicals.

Real-Time Nanoscopic Rider Safety System for Smart and Green Mobility Based upon Varied Infrastructure Parameters

To create a safe bicycle infrastructure system, this article develops an intelligent embedded learning system using a combination of deep neural networks. The learning system is used as a case study in the Northumbria region in England’s northeast. It is made up of three components: (a) input data unit, (b) knowledge processing unit, and (c) output unit. It is demonstrated that various infrastructure characteristics influence bikers’ safe interactions, which is used to estimate the riskiest age and gender rider groups. Two accurate prediction models are built, with a male accuracy of 88 per cent and a female accuracy of 95 per cent. The findings concluded that different infrastructures pose varying levels of risk to users of different ages and genders. Certain aspects of the infrastructure are hazardous to all bikers. However, the cyclist’s characteristics determine the level of risk that any infrastructure feature presents. Following validation, the built learning system is interoperable under various scenarios, including current heterogeneous and future semi-autonomous and autonomous transportation systems. The results contribute towards understanding the risk variation of various infrastructure types. The study’s findings will help to improve safety and lead to the construction of a sustainable integrated cycling transportation system.

DEVELOPMENT OF NEW PREDICTIVE EQUATIONS FOR ESTIMATING RESTING ENERGY EXPENDITURE IN ADULTS WITH CROHN'S DISEASE

Increased resting energy expenditure (REE) has been hypothesized to be a potential cause of weight loss in individuals with Crohn's Disease (CD), mainly due to inflammatory response. This study aimed to develop and validate new predictive equations for REE in adults with CD. Adult patients aged between 18-65 years and with CD were recruited. Anthropometry, indirect calorimetry and bio-impedance analysis (BIA) were performed in all patients. New predictive equations were generated using two models: Model 1 with age, weight, height and CDAI as predictors, and Model 2 in which both bioimpedance-index (BI-index) and phase angle were added. Finally, accuracy prediction within ±10% was assessed and then compared with several published equations. A total of 270 CD patients (159 males, 111 females) were included and assigned to the calibration (n=180; age: 37.9±13years; BMI: 22.2±3.4kg/m2) and the validation groups (n=90; age: 38.5±13years; BMI: 22.7±3.7kg/m2). REE was directly correlated with weight (r=0.733, p=0.000) and BI-index (r=0.762, p=0.000). Models were both suitable for estimating REE at population level (bias: -0.2, bias: -0.3; respectively). Individual accuracy was high in both models (∼80%; 83%;respectively), unrelated to gender. Similarly, the Harris and Benedict, FAO and Schofield equations showed a good accuracy in males and females. Therefore, new equations specifically derived from CD patients provided a very good prediction of REE at population level. Interestingly, the formula based on raw-BIA variables was as accurate as those based on anthropometry, unrelated to gender. However, further studies are required to verify the application of those formulas and the role of raw-BIA variables in REE prediction.

Norepinephrine enhances song responsiveness and encoding in the auditory forebrain of male zebra finches

Norepinephrine (NE) can dynamically modulate excitability and functional connectivity of neural circuits in response to changes in external and internal states. Regulation by NE has been demonstrated extensively in mammalian sensory cortices, but whether NE-dependent modulation in sensory cortex alters response properties in downstream sensorimotor regions is less clear. Here we examine this question in male zebra finches, a songbird species with complex vocalizations and a well-defined neural network for auditory processing of those vocalizations. We test the hypothesis that NE modulates auditory processing and encoding, using paired extracellular electrophysiology recordings and pattern classifier analyses. We report that a NE infusion into the auditory cortical region NCM (caudomedial nidopallium; analogous to mammalian secondary auditory cortex) enhances the auditory responses, burst firing, and coding properties of single NCM neurons. Furthermore, we report that NE-dependent changes in NCM coding properties, but not auditory response strength, are transmitted downstream to the sensorimotor nucleus HVC. Finally, NE modulation in the NCM of males is qualitatively similar to that observed in females: in both sexes, NE increases auditory response strengths. However, we observed a sex difference in the mechanism of enhancement: whereas NE increases response strength in females by decreasing baseline firing rates, NE increases response strength in males by increasing auditory-evoked activity. Therefore, NE signaling exhibits a compensatory sex difference to achieve a similar, state-dependent enhancement in signal-to-noise ratio and coding accuracy in males and females. In summary, our results provide further evidence for adrenergic regulation of sensory processing and modulation of auditory/sensorimotor functional connectivity. NEW & NOTEWORTHY This study documents that the catecholamine norepinephrine (also known as noradrenaline) acts in the auditory cortex to shape local processing of complex sound stimuli. Moreover, it also enhances the coding accuracy of neurons in the auditory cortex as well as in the downstream sensorimotor cortex. Finally, this study shows that while the sensory-enhancing effects of norepinephrine are similar in males and females, there are sex differences in the mode of action.

Anxiety and Gender Influence Reward-Related Processes in Children and Adolescents.

This study examined the effects of pediatric anxiety and its interaction with gender on reward processes. Based on the purported greater sensitivity to risk in females than males and the propensity for risk aversion in anxiety, clinical anxiety and female gender were hypothesized to act synergistically in reducing reward sensitivity and increasing risk aversion in a pediatric population. This hypothesis was tested in two separate experiments using two independent samples. Both experiments compared clinically anxious with typically developing (TD) youth, 8-18 years. Experiment 1 used a decision-making task, the Wheel of Fortune task (WOF), to examine risk taking as a function of varying levels of risk and reward in 36 anxious and 61 TD youths. Experiment 2 used an incentive delay task, the Piñata task, to examine sensitivity to reward and motivation to work for a reward in 38 anxious and 30 TD youth. Percent bet, reaction time, and accuracy were analyzed as a function of gender and diagnostic group. As hypothesized, anxiety was associated with reduced risk taking and sensitivity to reward. However, contrary to prediction, this effect was seen in males and not in females. These findings are consistent across both experiments. In experiment 1 (WOF), betting rate (i.e., risk taking) was significantly lower in anxious than in TD males (F[1;53] = 7.07, p = 0.01), whereas anxious females did not differ from TD females (F[1,42] = 1.2, p = 0.28). In experiment 2 (Piñata), anxiety impaired performance accuracy in males (F[1;36] = 8.39; p < 0.01) but not females (F[1;28] = 0.6; p = 0.445). Anxiety affected reward function differently in males and females. Contrary to hypothesis, anxious females behaved similarly to TD females on both tasks. However, anxious males were significantly more risk averse and less accurate than TD males. These findings suggest that therapeutic interventions for anxiety, which use manipulations of reward processes, should consider gender for optimal outcome.

Vocal individuality of Little Spotted Kiwi (Apteryx owenii)

The ability to identify individuals by call is particularly important for monitoring cryptic or nocturnal species. It provides key benefits in conservation management, such as allowing correction of biases in censuses and monitoring individual survival. Call surveys are a key tool in conservation of kiwi (Apterygidae), yet individuality of calls has previously been assessed in just one of the five species, the Brown Kiwi (formerly North Island Brown Kiwi, Apteryx mantelli). We have made the first test of whether Little Spotted Kiwi (Apteryx owenii; LSK) can be individually identified by call. Three classification methods (discriminant function analysis, support vector machines and statistical classifiers) were used to assign identity from either call or syllable variables. Using syllables, classification was significantly better than expected by chance for both males and females. Individual LSK can therefore be identified acoustically, suggesting that vocalisations may be used for conspecific identification. Classifier performance was worse using call variables, with no better than chance assignment for females and low accuracy for males. Significant differences in classification ability between the sexes support the hypothesis that the function of calls of male and female LSK differ. Furthermore, identification by call was much less reliable in LSK than in the more genetically diverse Brown Kiwi. This indicates that call diversity may be related to genetic diversity in kiwi, which, if confirmed, could provide a powerful conservation tool for rapid assessment of genetic differences among populations of these threatened species.