Enhancing Within-Person Estimation of Neurocognition and the Prediction of Externalizing Behaviors in Adolescents.

Decades of research document an association between neurocognitive dysfunction and externalizing behaviors, including rule-breaking, aggression, and impulsivity. However, there has been very little work that examines how multiple neurocognitive functions co-occur within individuals and which combinations of neurocognitive functions are most relevant for externalizing behaviors. Moreover, Latent Profile Analysis (LPA), a widely used method for grouping individuals in person-centered analysis, often struggles to balance the tradeoff between good model fit (splitting participants into many latent profiles) and model interpretability (using only a few, highly distinct latent profiles).

Temporal segmentation and "look ahead" simulation: Physical events structure visual perception of intuitive physics.

How we perceive the physical world is not only organized in terms of objects, but also structured in time as sequences of events. This is especially evident in intuitive physics, with temporally bounded dynamics such as falling, occlusion, and bouncing demarcating the continuous flow of sensory inputs. While the spatial structure and attentional consequences of physical objects have been well-studied, much less is known about the temporal structure and attentional consequences of physical events in visual perception.

Images with harder-to-reconstruct visual representations leave stronger memory traces.

Much of what we remember is not because of intentional selection, but simply a by-product of perceiving. This raises a foundational question about the architecture of the mind: how does perception interface with and influence memory? Here, inspired by a classic proposal relating perceptual processing to memory durability, the level-of-processing theory, we present a sparse coding model for compressing feature embeddings of images, and show that the reconstruction residuals from this model predict how well images are encoded into memory. In an open memorability dataset of scene images, we show that reconstruction error not only explains memory accuracy, but also response latencies during retrieval, subsuming, in the latter case, all of the variance explained by powerful vision-only models.

Is Mean Platelet Volume a Predictive Marker for the Development of Thrombosis in Patients with COVID-19 Infection?

Mean platelet volume (MPV) can provide important information about the course and prognosis of many diseases. MPV is an early indicator of platelet activation, which has an important role in the pathogenesis of thrombosis. In this study, we aimed to investigate whether MPV was a predictive marker for the development of thrombosis in hospitalized patients with COVID-19 infection.

Three-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation.

Ensuring robustness of image classifiers against adversarial attacks and spurious correlation has been challenging. One of the most effective methods for adversarial robustness is a type of data augmentation that uses adversarial examples during training. Here, inspired by computational models of human vision, we explore a synthesis of this approach by leveraging a structured prior over image formation: the 3D geometry of objects and how it projects to images.

From task structures to world models: what do LLMs know?

In what sense does a large language model (LLM) have knowledge? We answer by granting LLMs 'instrumental knowledge': knowledge gained by using next-word generation as an instrument. We then ask how instrumental knowledge is related to the ordinary, 'worldly knowledge' exhibited by humans, and explore this question in terms of the degree to which instrumental knowledge can be said to incorporate the structured world models of cognitive science. We discuss ways LLMs could recover degrees of worldly knowledge and suggest that such recovery will be governed by an implicit, resource-rational tradeoff between world models and tasks.

How does the primate brain combine generative and discriminative computations in vision?

Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vision. The first emphasizes bottom-up signal flow, describing vision as a largely feedforward, discriminative inference process that filters and transforms the visual information to remove irrelevant variation and represent behaviorally relevant information in a format suitable for downstream functions of cognition and behavioral control.

Impact of lipodystrophy on health-related quality of life: the QuaLip study.

Background: Lipodystrophy is a rare disease characterized by loss of adipose tissue. Natural history studies have demonstrated significant burden of disease; however, there is limited data on the impact of lipodystrophy on quality of life (QoL) and psychoemotional well-being. The QuaLip study is a prospective observational real-world study that aims to determine the impact of lipodystrophy on QoL and psychoemotional well-being and explore subjective burden of the disease.

The pH of Drinking Water and Its Effect on the pH of Urine.

Aim: The aim was to determine whether urine pH changed or not with different pH values of drinking water. With the results obtained from animal studies, comments can be made about the effect of water with different pH levels that people drink on kidney stones.

Method: A total of 24 Wistar Albino rats were divided into three groups containing eight rats each: the first group was given water with pH 5.

Perception of 3D shape integrates intuitive physics and analysis-by-synthesis.

Many surface cues support three-dimensional shape perception, but humans can sometimes still see shape when these features are missing-such as when an object is covered with a draped cloth. Here we propose a framework for three-dimensional shape perception that explains perception in both typical and atypical cases as analysis-by-synthesis, or inference in a generative model of image formation. The model integrates intuitive physics to explain how shape can be inferred from the deformations it causes to other objects, as in cloth draping.

Evaluation of Patients with COVID-19 Followed Up in Intensive Care Units in the Second Year of the Pandemic: A Multicenter Point Prevalence Study.

Objective: A 1-day point prevalence study was planned to obtain country data by determining the clinical characteristics, follow-up and treatment methods of coronavirus disease 2019 (COVID-19) cases that required intensive care unit (ICU) treatment in the second year of the pandemic.

Material And Methods: All patients who were hospitalized in the ICUs due to COVID-19 between March 11, 2022, 08.00 am, and March 12, 2022, 08.

Seeing Soft Materials Draped Over Objects: A Case Study of Intuitive Physics in Perception, Attention, and Memory.

We typically think of intuitive physics in terms of high-level cognition, but might aspects of physics also be extracted during lower-level visual processing? Might we not only think about physics, but also see it? We explored this using multiple tasks in online adult samples with objects covered by soft materials-as when you see a chair with a blanket draped over it-where you must account for the physical interactions between cloth, gravity, and object. In multiple change-detection experiments ( = 200), observers from an online testing marketplace were better at detecting image changes involving underlying object structure versus those involving only the superficial folds of cloths-even when the latter were more extreme along several dimensions. And in probe-comparison experiments ( = 100), performance was worse when both probes (vs.

Patients with severe coronavirus disease 2019 have high frequency of factor 5 Leiden and prothrombin gene mutations.

We investigated the frequency of factor 5 Leiden (FVL) and prothrombin gene (PTG) mutations in patients with severe coronavirus disease 2019 (COVID-19). Our primary aim is to reveal whether these mutations are associated with severity of disease and mortality. A total of 249 patients were included in this cross-sectional study.

A 5-year surveillance of healthcare-associated infections in a university hospital: A retrospective analysis.

Objectives: "Nosocomial infections" or "healthcare-associated infections" are a significant public health problem around the world. This study aimed to assess the rate of laboratory-confirmed healthcare-associated infections, frequency of nosocomial pathogens, and the antimicrobial resistance patterns of bacterial isolates in a University Hospital.

Methods: A retrospective evaluation of healthcare-associated infections in a University Hospital, between the years 2015 and 2019 in Tekirdag, Turkey.

The incidence of acute kidney injury and its association with mortality in patients diagnosed with COVID-19 followed up in intensive care unit.

Introduction: The kidneys are some of the most frequently affected organs during coronavirus disease 2019 (COVID-19). This multicenter study evaluated the incidence of and risk factors for acute kidney injury (AKI) in COVID-19 patients followed up in intensive care unit (ICU) and its association with mortality.

Methods: Three hundred twenty-eight patients diagnosed with COVID-19 and hospitalized in ICU were included.

The place of infectious markers in predicting culture positivity in patients with renal failure hospitalized with suspected infection.

Infectious diseases are an important cause of mortality in patients with renal failure. The markers used to diagnose infection in patients with renal failure have various limitations. Culture positivity is an objective guide in that context.

Relationship between serum soluble endothelial protein C receptor level and COVID-19 findings.

Coronavirus-related disease-2019 (COVID-19)-associated coagulopathy presents predominantly with thrombosis and leads to complications in close association with inflammatory process. Soluble endothelial protein C receptor (sEPCR), which is the soluble form of EPCR, reduces the anticoagulant and anti-inflammatory activity of activated protein C. The purpose of this study is to investigate the relationship between sEPCR and the laboratory parameters and thorax computed tomography (CT) findings in the course of COVID-19.

Analysis of the coracoid morphology with multiplanar 2D CT and its effects on the graft size in the Latarjet procedure.

Purpose: In this study, we aimed to reveal the individual differences regarding the size of the coracoid and their effects on the classical and modified Latarjet procedures.

Methods: Computed tomography images of 120 patients (mean age: 41.18 ± 12.

Efficient inverse graphics in biological face processing.

Vision not only detects and recognizes objects, but performs rich inferences about the underlying scene structure that causes the patterns of light we see. Inverting generative models, or "analysis-by-synthesis", presents a possible solution, but its mechanistic implementations have typically been too slow for online perception, and their mapping to neural circuits remains unclear. Here we present a neurally plausible efficient inverse graphics model and test it in the domain of face recognition.

Modeling human intuitions about liquid flow with particle-based simulation.

Humans can easily describe, imagine, and, crucially, predict a wide variety of behaviors of liquids-splashing, squirting, gushing, sloshing, soaking, dripping, draining, trickling, pooling, and pouring-despite tremendous variability in their material and dynamical properties. Here we propose and test a computational model of how people perceive and predict these liquid dynamics, based on coarse approximate simulations of fluids as collections of interacting particles. Our model is analogous to a "game engine in the head", drawing on techniques for interactive simulations (as in video games) that optimize for efficiency and natural appearance rather than physical accuracy.

An integrative computational architecture for object-driven cortex.

Computational architecture for object-driven cortex Objects in motion activate multiple cortical regions in every lobe of the human brain. Do these regions represent a collection of independent systems, or is there an overarching functional architecture spanning all of object-driven cortex? Inspired by recent work in artificial intelligence (AI), machine learning, and cognitive science, we consider the hypothesis that these regions can be understood as a coherent network implementing an integrative computational system that unifies the functions needed to perceive, predict, reason about, and plan with physical objects-as in the paradigmatic case of using or making tools. Our proposal draws on a modeling framework that combines multiple AI methods, including causal generative models, hybrid symbolic-continuous planning algorithms, and neural recognition networks, with object-centric, physics-based representations.

Determining the Efficiency of Different Preoperative Difficult Intubation Tests on Patients Undergoing Caesarean Section.

Background: Pregnancy-induced anatomical and physiological changes in the airway make airway management difficult in obstetric patients; thus, preoperative evaluation of the airway is important for obstetric patients.

Aims: To determine the effectiveness of the modified Mallampati test; the interincisor, sternomental and thyromental distances and the upper limb bite test. The second aim was to assess the effectiveness of the combination of the upper limb bite test with the other tests in obstetric patients.

[The prognostic value of cerebral oxygen saturation measurement for assessing prognosis after cardiopulmonary resuscitation].

Background: Despite new improvements on cardiopulmonary resuscitation (CPR), brain damage is very often after resuscitation.

Objective: To assess the prognostic value of cerebral oxygen saturation measurement (rSO) for assessing prognosis on patients after cardiopulmonary resuscitation.

Design: Retrospective analysis.