Instrumental learning

Punishment learning is learning of the causal relationship between responses and their adverse or undesirable consequences. Here, we review our translational approach for understanding whether, when, and how individuals differ in what they learn during punishment, and how these differences in learning may drive persistent poor or maladaptive decisions. We show that individual differences in punishment insensitivity can emerge from differences between individuals in what they learn about punishment (instrumental contingency knowledge), rather than differences in aversive valuation, reward valuation, general (impulsivity), or specific (habit) behavioral control...

BACKGROUND: Cardiovascular disorders (CVDs) are the leading cause of death worldwide. Lower- and middle-income countries (LMICs), such as Bangladesh, are also affected by several types of CVDs, such as heart failure and stroke. The leading cause of death in Bangladesh has recently switched from severe infections and parasitic illnesses to CVDs. MATERIALS AND METHODS: The study dataset comprised a random sample of 391 CVD patients' medical records collected between August 2022 and April 2023 using simple random sampling...

BACKGROUND: Social media is essential in the lives of adolescents, with 97% of US teenagers engaging daily. While it facilitates communication, learning, and identity development, it also poses risks like harmful content exposure and psychological distress, particularly for adolescents in their critical developmental stage. Teaching digital life skills innovatively counters these risks, adapting traditional competencies such as decision-making, problem-solving, creative and critical thinking, communication, interpersonal skills, self-awareness, empathy, and emotional and stress management to digital challenges...

BACKGROUND: Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection...

Behavioral plasticity is subjected to various sensory stimuli, experiences, and physiological states, representing the temporal and spatial patterns of neural circuit dynamics. Elucidation of how genes and neural circuits in our brain actuate behavioral plasticity requires functional imaging during behavioral assays to manifest temporal and spatial neural regulation in behaviors. The exploration of the nervous systems of Caenorhabditis elegans has catalyzed substantial scientific advancements in elucidating the mechanistic link between circuit dynamics and behavioral plasticity...

Ozone (O3 ) profiles are crucial for comprehending the intricate interplay among O3 sources, sinks, and transport. However, conventional O3 monitoring approaches often suffer from limitations such as low spatiotemporal resolution, high cost, and cumbersome procedures. Here, we propose a novel approach that combines multiaxis differential optical absorption spectroscopy (MAX-DOAS) and machine learning (ML) technology. This approach allows the retrieval of O3 profiles with exceptionally high temporal resolution at the minute level and vertical resolution reaching the hundred-meter scale...

The long-term monitoring stability of electronic current transformers is crucial for accurately obtaining the current signal of the power grid. However, it is difficult to accurately distinguish between the fluctuation of non-stationary random signals on the primary side of the power grid and the gradual error of the transformers themselves. A current transformer error prediction model, CNN-MHA-BiLSTM, based on the golden jackal optimization (GJO) algorithm, which is used to obtain the optimal parameter values, bidirectional long short-term memory (BiLSTM) network, convolutional neural networks (CNNs), and multi-head attention (MHA), is proposed to address the difficulty of measuring error evaluation...

Transanal minimally invasive surgery (TAMIS) is an effective procedure that plays an important role in the care of patients with significant rectal neoplasia and polyps including early-stage cancers. However, it is perhaps underutilised and under threat from both advanced flexible endoscopic procedures and proceduralists (who often act as gatekeepers for referral to colorectal surgeons), as well as from robotic surgery proponents. TAMIS advocates can learn and adopt practice insights from both these fields and incorporate available technological innovations building on the huge accomplishments already delivered in this area...

AIMS: In an era of evolving diagnostic possibilities, existing diagnostic systems are not fully sufficient to promptly recognize patients with early-stage hypertrophic cardiomyopathy (HCM) without symptomatic and instrumental features. Considering the sudden death of HCM, developing a novel diagnostic model to clarify the patients with early-stage HCM and the immunological characteristics can avoid misdiagnosis and attenuate disease progression. METHODS AND RESULTS: Three hundred eighty-five samples from four independent cohorts were systematically retrieved...

The strategies that enable musicians to adapt their behaviors so that they can break through, feel energized, and perform well collectively distinguish what it is to be a self-regulated learner. These strategies range from one's ability to monitor thoughts and actions to being able to navigate and control one's emotions, especially when feeling frustrated or anxious. Given the challenges of the music profession, it becomes imperative for teachers to equip their students with the necessary skills to self-regulate their own actions, feelings, and thinking so that they are eventually able to cope with the demands required of a contemporary professional musical career...

BACKGROUND: Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease's spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission...

The impacts of climate change present numerous risks to the present and future state of teaching and learning. Natural disasters such as hurricanes, heat waves, flooding, blizzards, wildfires, sea level rise, and droughts threaten our ability to produce the learning outcomes promised to our pupils. Taking action to adapt to imminent climate-related challenges and mitigating measures that provoke and prolong ecological challenges is critical to the survival of these cultural institutions. Paradoxically, centers of teaching and learning can be seen as both victims of climate change as well as an instrumental part of the solution...

UNLABELLED: We investigate the productivity spillovers from the UK government's decision to use extensive property tax reductions as a key instrument to stimulate innovation in smaller businesses and drive local growth. To capture the complex interaction and clustering of hierarchical effects, we apply non-parametric Random Effects Expectation Maximisation algorithm that complements more standard econometric estimators, namely matching to control for endogeneity and control functions to estimate total factor productivity...

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings...

Closed-loop sleep modulation is an emerging research paradigm to treat sleep disorders and enhance sleep benefits. However, two major barriers hinder the widespread application of this research paradigm. First, subjects often need to be wire-connected to rack-mount instrumentation for data acquisition, which negatively affects sleep quality. Second, conventional real-time sleep stage classification algorithms give limited performance. In this work, we conquer these two limitations by developing a sleep modulation system that supports closed-loop operations on the device...

This article is dedicated to the development of a model for competencies within an educational program and its implementation through the use of semantic technologies. The model proposed by the authors is distinctive in that competencies are organized into a hierarchical data structure with arbitrary levels of nesting. Furthermore, the article presents an original solution for modelling the input requirements for studying a course, which is defined in the form of dependencies between the competencies generated by the course and the competencies of other courses...

OBJECTIVE: Real-time measurement of biological joint moment could enhance clinical assessments and generalize exoskeleton control. Accessing joint moments outside clinical and laboratory settings requires harnessing non-invasive wearable sensor data for indirect estimation. Previous approaches have been primarily validated during cyclic tasks, such as walking, but these methods are likely limited when translating to non-cyclic tasks where the mapping from kinematics to moments is not unique...

BACKGROUND: Robotic assistance in spine surgery is emerging as an accurate, effective and enabling technology utilized in the treatment of patients with surgical spinal pathology. The safety and reproducibility of robotic assistance in the placement of pedicle screw instrumentation is still being investigated. The objective of this study was to present our experience of instrumented spinal fusion utilizing an intraoperative robotic guidance system. METHODS: We retrospectively reviewed all cases of spinal instrumentation of the thoracic and lumbo-sacral spine using the Mazor X robotic system (Medtronic Inc, Minneapolis, MN, USA), performed at our institution by one surgeon between July 2017 and June 2020...

PURPOSE: The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos. METHODS: A systematic review of the literature about intra-operative analysis of cataract surgery videos with machine learning techniques was performed. Cataract diagnosis and detection algorithms were excluded. Resulting algorithms were compared, descriptively analyzed, and metrics summarized or visually reported...

Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies in optimizing laparoscopic surgery, offering innovative solutions to enhance surgical precision, efficiency, and safety. This editorial explores the potential role of AI/ML across the surgical continuum, including preoperative optimization, intraoperative assistance, and postoperative care. It outlines the benefits of laparoscopic surgery compared to traditional open procedures and identifies current challenges such as technical difficulty and human error...