Transcription Factor Theft-PU.1 Caught Red-Handed.

The mechanistic understanding of gene-expression regulation is still evolving. In this issue of Immunity, Hosokawa et al. (2018) reveal that PU.1 represses transcription indirectly during early T cell development by "stealing" other regulators such as Runx1 and Satb1 from their DNA binding sites.

Unexpected employee location is associated with injury during robberies.

Millions of employees are victims of violent crimes at work every year, particularly those in the retail industry, who are frequent targets of robbery. Why are some employees injured while others escape from these incidents physically unharmed? Departing from prevailing models of workplace violence, which focus on the static characteristics of perpetrators, victims, and work environments, we examine why and when injuries during robberies occur. Our multimethod investigation of convenience-store robberies sought evidence from detailed coding of surveillance videos and matched archival data, preregistered experiments with formerly incarcerated individuals and customer service personnel, and a 3-y longitudinal intervention study in the field. While standard retail-industry safety protocols encourage employees to be out from behind the cash register area to be safer, we find that robbers are significantly more likely to injure or kill employees who are located there (versus behind the cash register area) when a robbery begins. A 3-y field study demonstrates that changing the safety training protocol-through providing employees with a behavioral script to follow should a robbery begin when they are on the sales floor-was associated with a significantly lower rate of injury during these robberies. Our research establishes the importance of understanding the interactive dynamics of workplace violence, crime, and conflict.

Emergent Resource Exchange and Tolerated Theft Behavior Using Multiagent Reinforcement Learning.

For decades, the evolution of cooperation has piqued interest in numerous academic disciplines, such as game theory, economics, biology, and computer science. In this work, we demonstrate the emergence of a novel and effective resource exchange protocol formed by dropping and picking up resources in a foraging environment. This form of cooperation is made possible by the introduction of a campfire, which adds an extended period of congregation and downtime for agents to explore otherwise unlikely interactions. We find that the agents learn to avoid getting cheated by their exchange partners, but not always from a third party. We also observe the emergence of behavior analogous to tolerated theft, despite the lack of any punishment, combat, or larceny mechanism in the environment.

Trophy, souvenir, or simple theft? Taking items from the victim in sexual homicide.

Although most people have heard the terms 'souvenirs', 'trophies', and 'mementos', discussed in books and movies on the true crimes of sexual murderers, limited research has delved into the phenomenon of theft in sexual homicide (SH). Using a sample of 762 SH cases coming from the Sexual Homicide International Database, the current study examines the crime-commission process of the pre-crime, crime, and post-crime phases of sexual homicide offenders (SHOs) who engaged in theft during a SH. Additionally, this study seeks to determine if a specific type of SHO engages in this behaviour over others. Results from the sequential logistic regression indicate that victims who are 16 years or older, were strangers to the SHO, and were sex workers were more likely to be victims of theft. Additionally, results indicate that the presence of sadism made it more likely the SHO would engage in theft from the victim and/or crime scene. Findings suggest there is a group of SHOs who engage in theft not for monetary purposes but due to the paraphilia of the offender. These findings can inform the police investigation of these crimes.

Trends and Sources of Crime Guns in California: 2010-2021.

Firearm-related interpersonal violence is a leading cause of death and injury in cities across the United States, and understanding the movement of firearms from on-the-books sales to criminal end-user is critical to the formulation of gun violence prevention policy. In this study, we assemble a unique dataset that combines records for over 380,000 crime guns recovered by law enforcement in California (2010-2021), and more than 126,000 guns reported stolen, linked to in-state legal handgun transactions (1996-2021), to describe local and statewide crime gun trends and investigate several potentially important sources of guns to criminals, including privately manufactured firearms (PMFs), theft, and "dirty" dealers. We document a dramatic increase over the decade in firearms recovered shortly after purchase (7% were recovered within a year in 2010, up to 33% in 2021). This corresponds with a substantial rise in handgun purchasing over the decade, suggesting some fraction of newly and legally acquired firearms are likely diverted from the legal market for criminal use. We document the rapid growth of PMFs over the past 2-3 years and find theft plays some, though possibly diminishing, role as a crime gun source. Finally, we find evidence that some retailers contribute disproportionately to the supply of crime guns, though there appear to be fewer problematic dealers now than there were a decade ago. Overall, our study points to temporal shifts in the dynamics of criminal firearms commerce as well as significant city variation in the channels by which criminals acquire crime guns.

Vacant Building Removals Associated with Relative Reductions in Violent and Property Crimes in Baltimore, MD 2014-2019.

Vacant and abandoned buildings are common features in many post-industrial US cities, and are consistent predictors of violence. Demolition programs are regularly employed as an urban land use policy to stabilize housing markets and mitigate public health problems including violence. The objective of this research was to examine the effect of vacant building removals on violent and property crimes in Baltimore, MD from 2014 to 2019. We conducted a difference-in-differences analysis using spatio-temporal Bayesian mixed models on six crime types on block faces with and without building removals, before compared with after removal. There were significant reductions in total, violent crimes (with and without assaults), thefts, and burglaries on block faces with building removals relative to their controls. Total crimes decreased 1.4% per mi2 (CrI: 0.5 - 2.3%), which translates to a relative reduction ~ 2.6 total crimes per mi2 per year. The largest relative decreases in crime were found among assaults (4.9%; CrI: 3.4 - 6.3%) and violent crimes (3.0%; CrI: 1.9 - 4.1%). Building removals were associated with relative reductions in crime in Baltimore City. The relative reductions in crime, at building removals compared to at control vacant lots, were found among assaults and violent crimes, the crimes of greatest public health concern. Building removals provide co-benefits to their communities, and may be considered part of a crime reduction strategy compatible with other approaches. A systematic effort to understand the role of care for remaining vacant lots could further inform our findings, and efforts to further decrease violence and improve community health.

Determinants of Stock Theft and Its Implication on Household Dietary Diversity in Semiarid Regions of Zimbabwe: Case of Gwanda District.

Stock theft is a major threat to livestock production in Africa and has been on the rise in recent years. Zimbabwe is no exception. The purpose of this study was to analyze factors that contribute to stock theft in rural areas. The study used a mixed research design. The study was limited to wards 20 and 24 of Gwanda district of Matabeleland South Province. The linear regression model was used to analyze the factors that affected stock theft in rural areas. The majority (57.1%) of the interviewed household heads were males and aged below 50 years (55.8%) with an average household size of 5 members. On average, each household owned 5 cattle, 2 sheep, 17 goats, 4 donkeys, and 5 chicken. The participants kept livestock mainly for income generation, source of school fees, draught power, meat, milk, manure, and eggs. The major causes of livestock loss apart from stock theft were drought, disease outbreaks, trapped in mine holes, and predators. All the respondents practiced livestock identification which includes branding, ear notching, and the use of ear tags. The most vulnerable livestock species to stock theft were goats, cattle, donkeys, sheep, and chicken. Stock theft mostly takes place before midday and on Mondays and Wednesdays. It is at its peak levels in January and November. Goats and donkeys were the main stolen livestock species. The stolen livestock is mostly sold to meat processors. The distance from the border, the use of livestock identification tags, the total number of livestock units owned by the household, and the day of the week were significant in influencing the intensity of stock theft (p < 0.10). Stock theft does not directly affect household dietary diversity (p > 0.05) because rural households do not use livestock for their nutritional benefit, particularly goats, sheep, and cattle. Thus, if dietary diversity is taken as a proxy for food security, it can be concluded that stock theft does not significantly affect the household's food security status. Working in groups through neighborhood watch committees, livestock branding, tending livestock which reduces the time that the livestock roam freely unattended, and assisting the police with investigations whenever there is a case of stock theft were identified as important mitigation strategies. At the service provider level, it was suggested that the law enforcing needed to increase its efficiency to mitigate stock theft.

Grand Theft Auto-Based Cycling Simulator for Cognitive Enhancement Technologies in Dangerous Traffic Situations.

While developing traffic-based cognitive enhancement technology (CET), such as bike accident prevention systems, it can be challenging to test and evaluate them properly. After all, the real-world scenario could endanger the subjects' health and safety. Therefore, a simulator is needed, preferably one that is realistic yet low cost. This paper introduces a way to use the video game Grand Theft Auto V (GTA V) and its sophisticated traffic system as a base to create such a simulator, allowing for the safe and realistic testing of dangerous traffic situations involving cyclists, cars, and trucks. The open world of GTA V, which can be explored on foot and via various vehicles, serves as an immersive stand-in for the real world. Custom modification scripts of the game give the researchers control over the experiment scenario and the output data to be evaluated. An off-the-shelf bicycle equipped with three sensors serves as a realistic input device for the subject's movement direction and speed. The simulator was used to test two early-stage CET concepts enabling cyclists to sense dangerous traffic situations, such as trucks approaching from behind the cyclist. Thus, this paper also presents the user evaluation of the cycling simulator and the CET used by the subjects to sense dangerous traffic situations. With the knowledge of the first iteration of the user-centered design (UCD) process, this paper concludes by naming improvements for the cycling simulator and discussing further research directions for CET that enable users to sense dangerous situations better.

Armed robbery and the meanings of money.

In this study, we investigate the meanings active armed robbers give to money before, during, and after their crimes and how these meanings shape their offending. We do so by examining interviews undertaken from 1994 to 1995 with robbers in St. Louis, Missouri, USA. Prior to their robberies, the interviewees' desperation leads them to define money as essential to survival. Immediately following robberies and in looking back on them, they come to view this essential money in other ways as well-as too time-consuming to get, as "easy," or as guilt-free. These meanings facilitate the contradictory way robbers see money as "fast" after offences. We discuss how these shifting meanings of money shape and are shaped by robbers' structural positions, cultural outlooks, and social relations. In doing so, we also help to explain how the shifting meanings of money play into criminogenic cycles of predatory offending.

Bias in Stroke Evaluation: Rethinking the Cookie Theft Picture.

Despite a current emphasis on equity in stroke care, one of the most common stroke assessment tools that is used both nationally and internationally, includes an anachronistic image that projects cultural, linguistic, and socioeconomic bias. This image, titled The Cookie Theft picture, is included in the National Institutes of Health Stroke Scale and was originally developed in 1972. Now, 50 years later, it does not reflect our current diverse, linguistically rich, and multicultural patient population.

Emotional Harm in the Radiology Department: Analysis of an Underrecognized Preventable Error.

Background Emotional harm incidents in health care may result in lost trust and adverse outcomes. However, investigations of emotional harm in radiology departments remain lacking. Purpose To better understand contributors and clinical scenarios in which emotional harm can occur in radiology, to document incidences, and to develop preventative countermeasures. Materials and Methods A large tertiary hospital adverse event reporting system was retrospectively searched for submissions under the category of dignity and respect in radiology between December 2014 and December 2020. Submissions were assigned to one of 14 categories per a previously developed classification system. Root-cause analysis of events was performed with a focus on countermeasures for future prevention. The person experiencing emotional harm (patient or staff) was noted. Results Of all radiology-related submissions, 37 of 3032 (1.2%) identified 43 dignity and respect incidents: failure to be patient centered (n = 23; 54%), disrespectful communication (n = 16; 37%), privacy violation (n = 2; 5%), minimization of patient concerns (n = 1; 2%), and loss of property (n = 1; 2%). Failure to be patient centered (n = 23) was subcategorized into disregard for patient preference (12 of 23; 52%), delay in care (eight of 23; 35%), and ineffective communication (three of 23; 13%). Of the 43 incidents, 32 involved patients (74%) and 11 involved staff (26%). Emotional harm in staff was because of disrespectful communication from other staff (eight of 11; 73%). Seventy-three countermeasures were identified: staff communication training (n = 32; 44%), individual feedback (n = 18; 25%), system innovation (n = 16; 22%), improvement of existing communication processes (n = 3; 4%), process reminders (n = 3; 4%), and unclear (n = 1; 1%). Individual feedback and staff communication training that focused on active listening, asking for the patient's preferences, and closed-loop communication addressed 34 of the 43 incidents (79%). Conclusion Most emotional harm incidents were from disrespectful communication and failure to be patient centered. Providing training focused on active listening, asking for patient's preferences, and closed-loop communication would potentially prevent most of these incidents. © RSNA, 2021 See also the editorial by Bruno in this issue.

Neighborhood Racial and Economic Segregation and Disparities in Violence During the COVID-19 Pandemic.

Objectives. To describe associations between neighborhood racial and economic segregation and violence during the COVID-19 pandemic. Methods. For 13 US cities, we obtained zip code-level data on 5 violence outcomes from March through July 2018 through 2020. Using negative binomial regressions and marginal contrasts, we estimated differences between quintiles of racial, economic, and racialized economic segregation using the Index of Concentration at the Extremes as a measure of neighborhood privilege (1) in 2020 and (2) relative to 2018 through 2019 (difference-in-differences). Results. In 2020, violence was higher in less-privileged neighborhoods than in the most privileged. For example, if all zip codes were in the least privileged versus most privileged quintile of racialized economic segregation, we estimated 146.2 additional aggravated assaults (95% confidence interval = 112.4, 205.8) per zip code on average across cities. Differences over time in less-privileged zip codes were greater than differences over time in the most privileged for firearm violence, aggravated assault, and homicide. Conclusions. Marginalized communities endure endemically high levels of violence. The events of 2020 exacerbated disparities in several forms of violence. Public Health Implications. To reduce violence and related disparities, immediate and long-term investments in low-income neighborhoods of color are warranted. (Am J Public Health. 2022;112(1):144-153. https://doi.org/10.2105/AJPH.2021.306540).

Wage theft and life expectancy inequities in the United States: A simulation study.

Wage theft - employers not paying workers their legally entitled wages and benefits - costs workers billions of dollars annually. We tested whether preventing wage theft could increase U.S. life expectancy and decrease inequities therein. We obtained nationally representative estimates of the 2001-2014 association between income and expected age at death for 40-year-olds (40 plus life expectancy at age 40) compiled from tax and Social Security Administration records, and estimates of the burden of wage theft from several sources, including estimates regarding minimum-wage violations (not paying workers the minimum wage) developed from Current Population Survey data. After modeling the relationship between income and expected age at death, we simulated the effects of scenarios preventing wage theft on mean expected age at death, assuming a causal effect of income on expected age at death. We simulated several scenarios, including one using data suggesting minimum-wage violations constituted 38% of all wage theft and caused 58% of affected workers' losses. Among women in the lowest income decile, mean expected age at death was 0.17 years longer in the counterfactual scenario than observed (95% confidence interval [CI]: 0.11-0.22), corresponding to 528,685 (95% CI: 346,018-711,353) years extended in the total 2001-2014 age-40 population. Among men in the lowest decile, the estimates were 0.12 (95% CI: 0.07-0.17) and 380,502 (95% CI: 229,630-531,374). Moreover, among women, mean expected age at death in the counterfactual scenario increased 0.16 (95% CI: 0.06-0.27) years more among the lowest decile than among the highest decile; among men, the estimate was 0.12 (95% CI: 0.03-0.21).

Assessing the risk of robbery in bank branches to reduce impact on personnel.

According to existing literature, bank robberies can have a considerable impact on the people involved (employees, customers, and police officers), even if the direct economic losses are negligible. Consequently, this article presents a model to assess the risk of bank robbery, with the aim of reducing the impact on the people and prioritizing the investments in security measures. It is based on the MIVES (Spanish acronym for the Integrated Value Model for Sustainability Assessment) method and it was combined with Monte Carlo simulation as a way of taking into account the uncertainty. Correlations were also modeled, for simulation purposes. Indicators for addressing issues related to security features, employees, operational procedures, and physical and social environment were defined. The model was applied to two fictitious but realistic sets of cases. The first simulation provides a quick overview of the risk level of a fictitious bank, before collecting the full set of data from hundreds or thousands of branches. The second simulation analyzes the risk variation of a specific bank branch over time. The model was also used to assess the risk index of 636 real branches belonging to a Spanish bank. All the results are presented and discussed in depth. The model allows the user to identify the weak points of a branch, so that corrective measures can be taken.

Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU-BiLSTM Model with Feature Engineering-Based Preprocessing.

In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples' nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model's efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors.

CamNuvem: A Robbery Dataset for Video Anomaly Detection.

(1) Background: The research area of video surveillance anomaly detection aims to automatically detect the moment when a video surveillance camera captures something that does not fit the normal pattern. This is a difficult task, but it is important to automate, improve, and lower the cost of the detection of crimes and other accidents. The UCF-Crime dataset is currently the most realistic crime dataset, and it contains hundreds of videos distributed in several categories; it includes a robbery category, which contains videos of people stealing material goods using violence, but this category only includes a few videos. (2) Methods: This work focuses only on the robbery category, presenting a new weakly labelled dataset that contains 486 new real-world robbery surveillance videos acquired from public sources. (3) Results: We have modified and applied three state-of-the-art video surveillance anomaly detection methods to create a benchmark for future studies. We showed that in the best scenario, taking into account only the anomaly videos in our dataset, the best method achieved an AUC of 66.35%. When all anomaly and normal videos were taken into account, the best method achieved an AUC of 88.75%. (4) Conclusion: This result shows that there is a huge research opportunity to create new methods and approaches that can improve robbery detection in video surveillance.

GrAb: A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection.

Integrating information and communication technology (ICT) and energy grid infrastructures introduces smart grids (SG) to simplify energy generation, transmission, and distribution. The ICT is embedded in selected parts of the grid network, which partially deploys SG and raises various issues such as energy losses, either technical or non-technical (i.e., energy theft). Therefore, energy theft detection plays a crucial role in reducing the energy generation burden on the SG and meeting the consumer demand for energy. Motivated by these facts, in this paper, we propose a deep learning (DL)-based energy theft detection scheme, referred to as GrAb, which uses a data-driven analytics approach. GrAb uses a DL-based long short-term memory (LSTM) model to predict the energy consumption using smart meter data. Then, a threshold calculator is used to calculate the energy consumption. Both the predicted energy consumption and the threshold value are passed to the support vector machine (SVM)-based classifier to categorize the energy losses into technical, non-technical (energy theft), and normal consumption. The proposed data-driven theft detection scheme identifies various forms of energy theft (e.g., smart meter data manipulation or clandestine connections). Experimental results show that the proposed scheme (GrAb) identifies energy theft more accurately compared to the state-of-the-art approaches.

Attitudes of Sport Fans Toward the Electronic Sign-Stealing Scandal in Major League Baseball: Differing Associations With Perfectionism and Excellencism.

The winners of the 2017 World Series were found guilty of illegally using electronic devices to steal the signs of their opponents. Many but not all sport fans negatively reacted to this cheating incident. We relied on the model of excellencism and perfectionism to determine if perfection strivers are less unfavorable toward electronic sign stealing (cheating) compared with excellence strivers. Sport fans (N = 321) completed a measure of excellencism and perfectionism. We used three different approaches to measure attitudes toward electronic sign stealing in baseball. Results of a multivariate multiple regression showed that sport fans who are perfection strivers held more favorable attitudes toward electronic sign stealing compared with excellence strivers. Perfection strivers also reported higher moral disengagement and winning-at-all-cost mentality. These findings are insightful because they indicate that perfectionistic standards significantly relate to sport cheating-related attitudes once we separate excellencism from perfectionism.