UNASSIGNED: Pain can automatically interfere with ongoing cognitive processes such as attention and memory. The extent of pain\'s negative effects on cognitive functioning seems to depend on a balance between top-down and bottom-up factors.
UNASSIGNED: In this large, preregistered, pooled reanalysis of 8 studies, we investigated the robustness of the detrimental effect of acute pain on recognition memory and whether top-down mechanisms such as pain-related expectations or cognitions (pain-related fear, pain catastrophizing) modulate this effect.
UNASSIGNED: Two hundred forty-seven healthy participants underwent similar experimental paradigms, including a visual categorization task with images randomly paired with (or without) concomitant painful stimulation and a subsequent unannounced recognition task. Recognition memory (ie, d\', recollection, and familiarity) and categorization performance (ie, reaction time, accuracy) served as proxies for the effect of pain on cognitive performance.
UNASSIGNED: Acute painful stimulation significantly impaired recognition performance (d\', familiarity). However, recognition performance was not significantly modulated by participants\' expectations regarding the effect of pain on task performance or pain-related cognitions in this sample of healthy participants.
UNASSIGNED: Our results corroborate the negative effects of pain on (visual) memory encoding reported in previous studies and reports of \"memory problems\" from patients with chronic pain. To characterize the role of bottom-up and top-down factors for the detrimental effects of pain, large-scale studies with more nuanced study designs are necessary. Future studies in patient cohorts must unravel the interaction of maladaptive pain-related cognitions and the often-reported impaired cognitive performance in chronic pain patients.

We developed a surgical support system that visualises important microanatomies using artificial intelligence (AI). This study evaluated its accuracy in recognising the thoracic nerves during lung cancer surgery. Recognition models were created with deep learning using images precisely annotated for nerves. Computational evaluation was performed using the Dice index and the Jaccard index. Four general thoracic surgeons evaluated the accuracy of nerve recognition. Further, the differences in time lag, image quality and smoothness of movement between the AI system and surgical monitor were assessed. Ratings were made using a five-point scale. The computational evaluation was relatively favourable, with a Dice index of 0.56 and a Jaccard index of 0.39. The AI system was used for 10 thoracoscopic surgeries for lung cancer. The accuracy of thoracic nerve recognition was satisfactory, with a recall score of 4.5 ± 0.4 and a precision score of 4.0 ± 0.9. Though smoothness of motion (3.2 ± 0.4) differed slightly, nearly no difference in time lag (4.9 ± 0.3) and image quality (4.6 ± 0.5) between the AI system and the surgical monitor were observed. In conclusion, the AI surgical support system has a satisfactory accuracy in recognising the thoracic nerves.

Social animals need to keep track of other individuals in their group to be able to adjust their behaviour accordingly and facilitate group cohesion. This recognition ability varies across species and is influenced by cognitive capacities such as learning and memory. In reptiles, particularly Squamates (lizards, snakes, and worm lizards), chemical communication is pivotal for territoriality, reproduction, and other social interactions. However, the cognitive processes underlying these social interactions remain understudied. In our study, we examined the ability of male and female Tokay geckos (Gekko gecko) to chemically differentiate familiar and unfamiliar mating partners. Our findings suggest that both sexes can make this distinction, with males responding more to the odour of a familiar mate, and females responding more to unfamiliar mates. The lizards maintained their discriminatory abilities for two to three weeks but not up to six weeks after separation. This research highlights the efficacy of using odours as social stimuli for investigating social cognition in lizards, a promising avenue to better understand social cognition in these animals.

Cardiovascular diseases remain one of the main threats to human health, significantly affecting the quality and life expectancy. Effective and prompt recognition of these diseases is crucial. This research aims to develop an effective novel hybrid method for automatically detecting dangerous arrhythmias based on cardiac patients\' short electrocardiogram (ECG) fragments. This study suggests using a continuous wavelet transform (CWT) to convert ECG signals into images (scalograms) and examining the task of categorizing short 2-s segments of ECG signals into four groups of dangerous arrhythmias that are shockable, including ventricular flutter (C1), ventricular fibrillation (C2), ventricular tachycardia torsade de pointes (C3), and high-rate ventricular tachycardia (C4). We propose developing a novel hybrid neural network with a deep learning architecture to classify dangerous arrhythmias. This work utilizes actual electrocardiogram (ECG) data obtained from the PhysioNet database, alongside artificially generated ECG data produced by the Synthetic Minority Over-sampling Technique (SMOTE) approach, to address the issue of imbalanced class distribution for obtaining an accuracy-trained model. Experimental results demonstrate that the proposed approach achieves high accuracy, sensitivity, specificity, precision, and an F1-score of 97.75%, 97.75%, 99.25%, 97.75%, and 97.75%, respectively, in classifying all the four shockable classes of arrhythmias and are superior to traditional methods. Our work possesses significant clinical value in real-life scenarios since it has the potential to significantly enhance the diagnosis and treatment of life-threatening arrhythmias in individuals with cardiac disease. Furthermore, our model also has demonstrated adaptability and generality for two other datasets.

Episodic memory is accounted for with two processes: \'familiarity\' when generally recognizing an item and \'recollection\' when retrieving the full contextual details bound with the item. Paradoxically, people sometimes report contextual information as familiar but without recollecting details, which is not easily accounted for by existing theories. We tested a combination of item recognition confidence and source memory, focusing upon \'item-only hits with source unknown\' (\'item familiarity\'), \'low-confidence hits with correct source memory\' (\'context familiarity\'), and \'high-confidence hits with correct source memory\' (\'recollection\'). Results across multiple within-subjects (trial-wise) and between subjects (individual variability) levels indicated these were behaviorally and physiologically distinct. Behaviorally, a crossover interaction was evident in response times, with context familiarity being slower than each condition during item recognition, but faster during source memory. Electrophysiologically, a Condition x Time x Location triple dissociation was evident in event-related potentials (ERPs), which was then independently replicated. Context familiarity exhibited an independent negative central effect from 800-1200 ms, differentiated from positive ERPs for item-familiarity (400 to 600 ms) and recollection (600 to 900 ms). These three conditions thus reflect mutually exclusive, fundamentally different processes of episodic memory. Context familiarity is a third distinct process of episodic memory.

Morse code recognition plays a very important role in the application of human-machine interaction. In this paper, based on the carbon nanotube (CNT) and polyurethane sponge (PUS) composite material, a flexible tactile CNT/PUS sensor with great piezoresistive characteristic is developed for detecting Morse code precisely. Thirty-six types of Morse code, including 26 letters (A-Z) and 10 numbers (0-9), are applied to the sensor. Each Morse code was repeated 60 times, and 2160 (36 × 60) groups of voltage time-sequential signals were collected to construct the dataset. Then, smoothing and normalization methods are used to preprocess and optimize the raw data. Based on that, the long short-term memory (LSTM) model with excellent feature extraction and self-adaptive ability is constructed to precisely recognize different types of Morse code detected by the sensor. The recognition accuracies of the 10-number Morse code, the 26-letter Morse code, and the whole 36-type Morse code are 99.17%, 95.37%, and 93.98%, respectively. Meanwhile, the Gated Recurrent Unit (GRU), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) models are built to distinguish the 36-type Morse code (letters of A-Z and numbers of 0-9) based on the same dataset and achieve the accuracies of 91.37%, 88.88%, 87.04%, and 90.97%, respectively, which are all lower than the accuracy of 93.98% based on the LSTM model. All the experimental results show that the CNT/PUS sensor can detect the Morse code\'s tactile feature precisely, and the LSTM model has a very efficient property in recognizing Morse code detected by the CNT/PUS sensor.

Alternative recombinant sources of antivenoms have been successfully generated. The application of such strategies requires the characterization of the venoms for the development of specific neutralizing molecules against the toxic components. Five toxic peptides to mammals from the Mexican scorpion Centruroides villegasi were isolated by chromatographic procedures by means of gel filtration on Sephadex G-50, followed by ion-exchange columns on carboxy-methyl-cellulose (CMC) resins and finally purified by high-performance chromatography (HPLC) columns. Their primary structures were determined by Edman degradation. They contain 66 amino acids and are maintained well packed by four disulfide bridges, with molecular mass from 7511.3 to 7750.1 Da. They are all relatively toxic and deadly to mice and show high sequence identity with known peptides that are specific modifiers of the gating mechanisms of Na+ ion channels of type beta-toxin (β-ScTx). They were named Cv1 to Cv5 and used to test their recognition by single-chain variable fragments (scFv) of antibodies, using surface plasmon resonance. Three different scFvs generated in our laboratory (10FG2, HV, LR) were tested for recognizing the various new peptides described here, paving the way for the development of a novel type of scorpion antivenom.

The recognition of data matrix (DM) codes plays a crucial role in industrial production. Significant progress has been made with existing methods. However, for low-quality images with protrusions and interruptions on the L-shaped solid edge (finder pattern) and the dashed edge (timing pattern) of DM codes in industrial production environments, the recognition accuracy rate of existing methods sharply declines due to a lack of consideration for these interference issues. Therefore, ensuring recognition accuracy in the presence of these interference issues is a highly challenging task. To address such interference issues, unlike most existing methods focused on locating the L-shaped solid edge for DM code recognition, we in this paper propose a novel DM code recognition method based on locating the L-shaped dashed edge by incorporating the prior information of the center of the DM code. Specifically, we first use a deep learning-based object detection method to obtain the center of the DM code. Next, to enhance the accuracy of L-shaped dashed edge localization, we design a two-level screening strategy that combines the general constraints and central constraints. The central constraints fully exploit the prior information of the center of the DM code. Finally, we employ libdmtx to decode the content from the precise position image of the DM code. The image is generated by using the L-shaped dashed edge. Experimental results on various types of DM code datasets demonstrate that the proposed method outperforms the compared methods in terms of recognition accuracy rate and time consumption, thus holding significant practical value in an industrial production environment.

Bioengineering of plant immune receptors has emerged as a key strategy for generating novel disease resistance traits to counteract the expanding threat of plant pathogens to global food security. However, current approaches are limited by rapid evolution of plant pathogens in the field and may lack durability when deployed. Here, we show that the rice nucleotide-binding, leucine-rich repeat (NLR) immune receptor Pik-1 can be engineered to respond to a conserved family of effectors from the multihost blast fungus pathogen Magnaporthe oryzae. We switched the effector binding and response profile of the Pik NLR from its cognate rice blast effector AVR-Pik to the host-determining factor pathogenicity toward weeping lovegrass 2 (Pwl2) by installing a putative host target, OsHIPP43, in place of the native integrated heavy metal-associated domain (generating Pikm-1OsHIPP43). This chimeric receptor also responded to other PWL alleles from diverse blast isolates. The crystal structure of the Pwl2/OsHIPP43 complex revealed a multifaceted, robust interface that cannot be easily disrupted by mutagenesis, and may therefore provide durable, broad resistance to blast isolates carrying PWL effectors in the field. Our findings highlight how the host targets of pathogen effectors can be used to bioengineer recognition specificities that have more robust properties compared to naturally evolved disease resistance genes.

Sepsis accounts for a significant proportion of preventable deaths worldwide and early treatment has been found to be a mainstay of decreasing mortality. Early identification of sepsis in the first-aid setting is critical as this results in a shorter time to hospital presentation and management with antibiotics and initial resuscitation. Our aim was to explore the existing literature related to either sepsis recognition or awareness of sepsis by first-aid providers who are evaluating an adult suspected of an acute infection. Our scoping review was performed as part of the International Liaison Committee on Resuscitation\'s (ILCOR) continuous evidence evaluation process to update the 2024 ILCOR Consensus on Science with Treatment Recommendations. We searched Embase, Medline, and Cochrane databases from their inception to January 17, 2023, with updated searches performed on November 21, 2023, and December 2, 2023. The gray literature search was conducted on August 29, 2023. The population included adults presenting with an acute illness exhibiting signs and symptoms of a severe infection. Outcomes included sepsis recognition or awareness of sepsis by a lay first-aid provider. After reviewing 4380 potential sources, four reviews (three systematic reviews and one scoping review), 11 observational studies, and 27 websites met the inclusion criteria. No study directly addressed our PICOST (Population, Intervention, Comparator, Outcomes, Study Design, and Timeframe) question as none were performed in the first-aid setting. Three systematic reviews and nine observational studies that assessed the ability of early warning scores to detect sepsis and predict adverse outcomes secondary to sepsis had inconsistent results, but many found the screening tools to be useful. One scoping review and one observational study found public knowledge and awareness of sepsis to be variable and dependent upon healthcare employment, location, education level, ethnicity, sex, and age. Signs and symptoms associated with sepsis as listed by gray literature sources fell primarily under nine general categories as a means of educating the public on sepsis recognition. Although this scoping review did not identify any studies that directly addressed our outcomes, it highlights the need for future research to better understand the recognition of sepsis in first-aid settings.