Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Naming the Precious Child: New Evidence of Intentional Family Planning in Historical Populations.

In Demography

Can the names parents gave their children give us insights into how parents in historical times planned their families? In this study, we explore whether the names given to the firstborn child can be used as indicators of family-size preferences and, if so, what this reveals about the emergence of intentional family planning over the course of the demographic transition. We analyze historical populations from 1850 to 1940 in the United States, where early fertility control and large sample sizes allow separate analyses of the White and Black populations. We also analyze Norway from 1800 to 1910, where there was a much later fertility transition. A split-sample method allows automated scoring of each name in terms of predicted family size. We find a strong relationship between naming and family size in the U.S. White population as early as 1850, for the Black population beginning in 1940, and for the Norwegian population in 1910. These results provide new evidence of the emergence of "conscious calculation" during the fertility transition. Our methods may also be applicable to modern high-fertility populations in the midst of fertility decline.

Goldstein Joshua R, Stecklov Guy

2023-Mar-14

Fertility decline, Fertility transition, Ideational change, Naming patterns, Textual machine learning

General General

The accuracy of artificial intelligence-based virtual assistants in responding to routinely asked questions about orthodontics.

In The Angle orthodontist ; h5-index 34.0

OBJECTIVES : To evaluate the utility and efficiency of four voice-activated, artificial intelligence-based virtual assistants (Alexa, Google Assistant, Siri, and Cortana) in addressing commonly asked patient questions in orthodontic offices.

MATERIALS AND METHODS : Two orthodontists, an orthodontic resident, an oral and maxillofacial radiologist, and a dental student used a standardized list of 12 questions to query and evaluate the four most common commercial virtual assistant devices. A modified Likert scale was used to evaluate their performance.

RESULTS : Google Assistant had the lowest (best) mean score, followed by Siri, Alexa, and Cortana. The score of Google Assistant was significantly lower than Alexa and Cortana. There was significant variablity in virtual assistant response scores among the evaluators, with the exception of Amazon Alexa. Lower scores indicated superior efficiency and utility.

CONCLUSIONS : The common commercially available virtual assistants tested in this study showed significant differences in how they responded to users. There were also significant differences in their performance when responding to common orthodontic queries. An intelligent virtual assistant with evidence-based responses specifically curated for orthodontics may be a good solution to address this issue. The investigators in this study agreed that such a device would provide value to patients and clinicians.

Perez-Pino Anthony, Yadav Sumit, Upadhyay Madhur, Cardarelli Lauren, Tadinada Aditya

2023-Mar-14

Artificial intelligence, Virtual assistants

General General

Opportunistic Screening for Atrial Fibrillation on Routine Chest Computed Tomography.

In Journal of thoracic imaging

PURPOSE : Quantitative biomarkers from chest computed tomography (CT) can facilitate the incidental detection of important diseases. Atrial fibrillation (AFib) substantially increases the risk for comorbid conditions including stroke. This study investigated the relationship between AFib status and left atrial enlargement (LAE) on CT.

MATERIALS AND METHODS : A total of 500 consecutive patients who had undergone nongated chest CTs were included, and left atrium maximal axial cross-sectional area (LA-MACSA), left atrium anterior-posterior dimension (LA-AP), and vertebral body cross-sectional area (VB-Area) were measured. Height, weight, age, sex, and diagnosis of AFib were obtained from the medical record. Parametric statistical analyses and receiver operating characteristic curves were performed. Machine learning classifiers were run with clinical risk factors and LA measurements to predict patients with AFib.

RESULTS : Eighty-five patients with a diagnosis of AFib were identified. Mean LA-MACSA and LA-AP were significantly larger in patients with AFib than in patients without AFib (28.63 vs. 20.53 cm2, P<0.000001; 4.34 vs. 3.5 cm, P<0.000001, respectively), both with area under the curves (AUCs) of 0.73. Multivariable logistic regression analysis including age, sex, and VB-Area with LA-MACSA improved the AUC for predicting AFib (AUC=0.77). An LA-MACSA threshold of 30 cm2 demonstrated high specificity for AFib diagnosis at 92% and sensitivity of 48%, and LA-AP threshold at 4.5 cm demonstrated 90% specificity and 42% sensitivity. A Bayesian machine learning model using age, sex, height, body surface area, and LA-MACSA predicted AFib with an AUC of 0.743.

CONCLUSIONS : LA-MACSA or LA-AP can be rapidly measured from routine chest CT, and when >30 cm2 and >4.5 cm, respectively, are specific indicators to predict patients at increased risk for AFib.

Parker William A, Vigneault Davis M, Yang Issac, Bratt Alex, Marquardt Alizee C, Sharifi Husham, Guo Haiwei Henry

2023-Mar-06

General General

Cicada-Wing-Inspired Highly Sensitive Tactile Sensors Based on Elastic Carbon Foam with Nanotextured Surfaces.

In ACS applied materials & interfaces ; h5-index 147.0

Electronic devices with tactile and pressure-sensing capabilities are becoming increasingly popular in the automatic industry, human motion/health monitoring, and artificial intelligence applications. Inspired by the natural nanotopography of the cicada wing, we propose here a straightforward strategy to fabricate a highly sensitive tactile sensor through nanotexturing of erected polyaniline (PANI) nanoneedles on a conductive and elastic three-dimensional (3D) carbon skeleton. The robust and compressible carbon networks offer a resilient and conducting matrix to catering complex scenarios; the biomimetic PANI nanoneedles firmly and densely anchored on a 3D carbon skeleton provide intimate electrical contact under subtle deformation. As a result, a piezoresistive tactile sensor with ultrahigh sensitivity (33.52 kPa-1), fast response/recovery abilities (97/111 ms), and reproducible sensing performance (2500 cycles) is developed, which is capable of distinguishing motions in a wide pressure range from 4.66 Pa to 60 kPa, detecting spatial pressure distribution, and monitoring various gestures in a wireless manner. These excellent performances demonstrate the great potential of nature-inspired tactile sensors for practical human motion monitoring and artificial intelligence applications.

Chang Kangqi, Wu Zhenzhong, Meng Jian, Guo Minhao, Yan Xiu-Ping, Qian Hai-Long, Ma Piming, Zhao Jianhua, Wang Fangneng, Huang Yunpeng, Liu Tianxi

2023-Mar-14

biomimetic nanostructures, elastic carbon foams, human motion detection, polyaniline, tactile sensors

General General

DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences.

In Briefings in bioinformatics

Identifying the function of DNA sequences accurately is an essential and challenging task in the genomic field. Until now, deep learning has been widely used in the functional analysis of DNA sequences, including DeepSEA, DanQ, DeepATT and TBiNet. However, these methods have the problems of high computational complexity and not fully considering the distant interactions among chromatin features, thus affecting the prediction accuracy. In this work, we propose a hybrid deep neural network model, called DeepFormer, based on convolutional neural network (CNN) and flow-attention mechanism for DNA sequence function prediction. In DeepFormer, the CNN is used to capture the local features of DNA sequences as well as important motifs. Based on the conservation law of flow network, the flow-attention mechanism can capture more distal interactions among sequence features with linear time complexity. We compare DeepFormer with the above four kinds of classical methods using the commonly used dataset of 919 chromatin features of nearly 4.9 million noncoding DNA sequences. Experimental results show that DeepFormer significantly outperforms four kinds of methods, with an average recall rate at least 7.058% higher than other methods. Furthermore, we confirmed the effectiveness of DeepFormer in capturing functional variation using Alzheimer's disease, pathogenic mutations in alpha-thalassemia and modification in CCCTC-binding factor (CTCF) activity. We further predicted the maize chromatin accessibility of five tissues and validated the generalization of DeepFormer. The average recall rate of DeepFormer exceeds the classical methods by at least 1.54%, demonstrating strong robustness.

Yao Zhou, Zhang Wenjing, Song Peng, Hu Yuxue, Liu Jianxiao

2023-Mar-14

DNA function prediction, convolutional neural network, flow-attention mechanism, linear attention mechanism, motif

Dermatology Dermatology

Clinical Application of Artificial Intelligence for Non-melanoma Skin Cancer.

In Current treatment options in oncology

The development and implementation of artificial intelligence is beginning to impact the care of dermatology patients. Although the clinical application of AI in dermatology to date has largely focused on melanoma, the prevalence of non-melanoma skin cancers, including basal cell and squamous cell cancers, is a critical application for this technology. The need for a timely diagnosis and treatment of skin cancers makes finding more time efficient diagnostic methods a top priority, and AI may help improve dermatologists' performance and facilitate care in the absence of dermatology expertise. Beyond diagnosis, for more severe cases, AI may help in predicting therapeutic response and replacing or reinforcing input from multidisciplinary teams. AI may also help in designing novel therapeutics. Despite this potential, enthusiasm in AI must be tempered by realistic expectations regarding performance. AI can only perform as well as the information that is used to train it, and development and implementation of new guidelines to improve transparency around training and performance of algorithms is key for promoting confidence in new systems. Special emphasis should be placed on the role of dermatologists in curating high-quality datasets that reflect a range of skin tones, diagnoses, and clinical scenarios. For ultimate success, dermatologists must not be wary of AI as a potential replacement for their expertise, but as a new tool to complement their diagnostic acumen and extend patient care.

Sanchez Katherine, Kamal Kanika, Manjaly Priya, Ly Sophia, Mostaghimi Arash

2023-Mar-14

Artificial intelligence, Artificial intelligence guidelines, Dermatology, Diversity and inclusion, Non-melanoma skin cancer