Abigail Marsh has found that many psychopaths don't want to be cruel and uncaring, and argues that they deserve support to help them get there Think of a psychopath and you probably picture someone dangerous, someone whose ruthless self-interest leads to great harm for others and considerable success for themselves. Perhaps unsurprisingly, while only around 1 per cent of people in the general population have psychopathy, roughly 1 in 5 men in prison show signs of it, and research has also found a link between corporate leadership and psychopathic traits . But just as it is painful to know a psychopath, it isn't necessarily fun to be one either. Abigail Marsh, a professor of psychology and neuroscience at Georgetown University in Washington DC, studies those with psychopathic traits who largely lead ordinary lives among us. She has uncovered something surprising: many don't want to be psychopathic at all. Researchers are still honing the precise definition, but psychopathy is characterised by callousness, a lack of empathy, glib social charm and impulsivity.

Insulin resistance (IR) is a key precursor to diabetes and a significant risk factor for cardiovascular disease. Traditional IR assessment methods require multiple blood tests. We developed a simple AI model using only fasting blood glucose to predict IR in non-diabetic populations. Data from the NHANES (1999-2020) and CHARLS (2015) studies were used for model training and validation. Input features included age, gender, height, weight, blood pressure, waist circumference, and fasting blood glucose. The CatBoost algorithm achieved AUC values of 0.8596 (HOMA-IR) and 0.7777 (TyG index) in NHANES, with an external AUC of 0.7442 for TyG. For METS-IR prediction, the model achieved AUC values of 0.9731 (internal) and 0.9591 (external), with RMSE values of 3.2643 (internal) and 3.057 (external). SHAP analysis highlighted waist circumference as a key predictor of IR. This AI model offers a minimally invasive and effective tool for IR prediction, supporting early diabetes and cardiovascular disease prevention.

Based on limited observations, machine learning discerns a dependence which is expected to hold in the future. What makes it possible? Statistical learning theory imagines indefinitely increasing training sample to justify its approach. In reality, there is no infinite time or even infinite general population for learning. Here I argue that practical machine learning is based on an implicit assumption that underlying dependence is relatively ``smooth" : likely, there are no abrupt differences in feedback between cases with close data points. From this point of view learning shall involve selection of the hypothesis ``smoothly" approximating the training set. I formalize this as Practical learning paradigm. The paradigm includes terminology and rules for description of learners. Popular learners (local smoothing, k-NN, decision trees, Naive Bayes, SVM for classification and for regression) are shown here to be implementations of this paradigm.

The work demonstrates that brain might reflect the external world causal relationships in the form of a logically consistent and prognostic model of reality, which shows up as consciousness. The paper analyses and solves the problem of statistical ambiguity and provides a formal model of causal relationships as probabilistic maximally specific rules. We suppose that brain makes all possible inferences from causal relationships. We prove that the suggested formal model has a property of an unambiguous inference: from consistent premises we infer a consistent conclusion. It enables a set of all inferences to form a consistent model of the perceived world. Causal relationships may create fixed points of cyclic inter-predictable properties. We consider the "natural" classification introduced by John St. Mill and demonstrate that a variety of fixed points of the objects' attributes forms a "natural" classification of the external world. Then we consider notions of "natural" categories and causal models of categories, introduced by Eleanor Rosch and Bob Rehder and demonstrate that fixed points of causal relationships between objects attributes, which we perceive, formalize these notions. If the "natural" classification describes the objects of the external world, and "natural" concepts the perception of these objects, then the theory of integrated information, introduced by G. Tononi, describes the information processes of the brain for "natural" concepts formation that reflects the "natural" classification. We argue that integrated information provides high accuracy of the objects identification. A computer-based experiment is provided that illustrates fixed points formation for coded digits.

Ashley Madison chief strategy officer Paul Keable insists people would be cheating whether or not the controversial'dating' site existed. Gen Z appears to be more comfortable with the concept of non-monogamy than previous generations, according to controversial online "dating" service Ashley Madison. The polarizing Ashley Madison, which caters to people looking to cheat on their partners and uses the slogan "Life is short. Have an affair," said that Gen Z is the top age group to sign up for their scandalous product and accounted for 40% of new members in 2022. To understand why so many members of Gen Z, defined as those 18-29 years old, are joining the pro-adultery site, the company surveyed their Gen Z members as well as those ages in the general population across 10 countries via YouGov.

The COVID-19 pandemic has disproportionately impacted the lives of minorities, such as members of the LGBTQ community (lesbian, gay, bisexual, transgender, and queer) due to pre-existing social disadvantages and health disparities. Although extensive research has been carried out on the impact of the COVID-19 pandemic on different aspects of the general population's lives, few studies are focused on the LGBTQ population. In this paper, we develop and evaluate two sets of machine learning classifiers using a pre-pandemic and a during-pandemic dataset to identify Twitter posts exhibiting minority stress, which is a unique pressure faced by the members of the LGBTQ population due to their sexual and gender identities. We demonstrate that our best pre- and during-pandemic models show strong and stable performance for detecting posts that contain minority stress. We investigate the linguistic differences in minority stress posts across pre- and during-pandemic periods. We find that anger words are strongly associated with minority stress during the COVID-19 pandemic. We explore the impact of the pandemic on the emotional states of the LGBTQ population by adopting propensity score-based matching to perform a causal analysis. The results show that the LGBTQ population have a greater increase in the usage of cognitive words and worsened observable attribute in the usage of positive emotion words than the group of the general population with similar pre-pandemic behavioral attributes. Our findings have implications for the public health domain and policy-makers to provide adequate support, especially with respect to mental health, to the LGBTQ population during future crises.

Recent work has reported that respiratory audio-trained AI classifiers can accurately predict SARS-CoV-2 infection status. Here, we undertake a large-scale study of audio-based AI classifiers, as part of the UK government's pandemic response. We collect a dataset of audio recordings from 67,842 individuals, with linked metadata, of whom 23,514 had positive PCR tests for SARS-CoV-2. In an unadjusted analysis, similar to that in previous works, AI classifiers predict SARS-CoV-2 infection status with high accuracy (ROC-AUC=0.846 However, after matching on measured confounders, such as selfreported symptoms, performance is much weaker (ROC-AUC=0.619 Upon quantifying the utility of audio-based classifiers in practical settings, we find them to be outperformed by predictions based on user-reported symptoms. We make best-practice recommendations for handling recruitment bias, and for assessing audio-based classifiers by their utility in relevant practical settings. Our work provides novel insights into the value of AI audio analysis and the importance of study design and treatment of confounders in AI-enabled diagnostics. The coronavirus disease 2019 (COVID-19) pandemic has been estimated by the World Health Organization (WHO) to have caused 14.9 million excess deaths over the 2020-2021 period (link). Table S1 summarises nine highly cited datasets and corresponding classification performance. Here, we analyse the largest PCR-validated dataset collected to date in the field of audio-based COVID-19 screening (ABCS). We design and specify an analysis plan in advance, to investigate whether using audio-based classifiers can improve the accuracy of COVID-19 screening over using self-reported symptoms. Our contribution is as follows: - We collect a respiratory acoustic dataset of 67,842 individuals with linked PCR test outcomes, including 23,514 who tested positive for COVID-19.

For many years, artificial intelligence (AI) technology has pledged the promise of vastly improving the healthcare industry. Whether through the promise of increasing access to and the understanding of data, providing ways to better navigate patient care, or better deciphering new research and development efforts, healthcare pundits have eagerly looked forward to the mainstream use of AI. Many companies have invested billions of dollars with the hopes of improving the quality and usable viability of AI in their respective domains. And, rightly so, these efforts have certainly provided a lot of useful results, much of which has been the bedrock for continued building and innovation in this space. Nonetheless, the technology still has a long way to go.

Have you ever spotted someone in a crowd that you thought was a friend, only to discover it was simply someone who looks remarkably similar? Many of us have seen so-called doppelgängers of loved ones, family members and even ourselves. Now, a study has revealed that these doppelgängers don't just look alike – they also likely have very similar DNA, and even share personality traits. Researchers from the Josep Carreras Leukaemia Research Institute in Barcelona have revealed that strong facial similarity is associated with shared genetic variants. 'These results will have future implications in forensic medicine - reconstructing the criminal's face from DNA - and in genetic diagnosis - the photo of the patient's face will already give you clues as to which genome he or she has,' said Dr Manel Esteller, senior author of the study.

Over 12 billion doses of COVID-19 vaccines have been administered at the time of writing. However, public perceptions of vaccines have been complex. We analyzed COVID-19 vaccine-related tweets to understand the evolving perceptions of COVID-19 vaccines. We finetuned a deep learning classifier using a state-of-the-art model, XLNet, to detect each tweet's sentiment automatically. We employed validated methods to extract the users' race or ethnicity, gender, age, and geographical locations from user profiles. Incorporating multiple data sources, we assessed the sentiment patterns among subpopulations and juxtaposed them against vaccine uptake data to unravel their interactive patterns. 11,211,672 COVID-19 vaccine-related tweets corresponding to 2,203,681 users over two years were analyzed. The finetuned model for sentiment classification yielded an accuracy of 0.92 on testing set. Users from various demographic groups demonstrated distinct patterns in sentiments towards COVID-19 vaccines. User sentiments became more positive over time, upon which we observed subsequent upswing in the population-level vaccine uptake. Surrounding dates where positive sentiments crest, we detected encouraging news or events regarding vaccine development and distribution. Positive sentiments in pregnancy-related tweets demonstrated a delayed pattern compared with trends in general population, with postponed vaccine uptake trends. Distinctive patterns across subpopulations suggest the need of tailored strategies. Global news and events profoundly involved in shaping users' thoughts on social media. Populations with additional concerns, such as pregnancy, demonstrated more substantial hesitancy since lack of timely recommendations. Feature analysis revealed hesitancies of various subpopulations stemmed from clinical trial logics, risks and complications, and urgency of scientific evidence.