Processing Mechanisms Research Articles

Implications of depressive mood in OSAHS patients: insights from event-related potential

BackgroundObstructive sleep apnea-hypopnea syndrome (OSAHS) is a chronic breathing disorder characterized by recurrent upper airway obstruction during sleep. Although previous studies have shown a link between OSAHS and depressive mood, the neurobiological mechanisms underlying mood disorders in OSAHS patients remain poorly understood. This study aims to investigate the emotion processing mechanism in OSAHS patients with depressive mood using event-related potentials (ERPs).MethodsSeventy-four OSAHS patients were divided into the depressive mood and non-depressive mood groups according to their Self-rating Depression Scale (SDS) scores. Patients underwent overnight polysomnography and completed various cognitive and emotional questionnaires. The patients were shown facial images displaying positive, neutral, and negative emotions and tasked to identify the emotion category, while their visual evoked potential was simultaneously recorded.ResultsThe two groups did not differ significantly in age, BMI, and years of education, but showed significant differences in their slow wave sleep ratio (P = 0.039), ESS (P = 0.006), MMSE (P < 0.001), and MOCA scores (P = 0.043). No significant difference was found in accuracy and response time on emotional face recognition between the two groups. N170 latency in the depressive group was significantly longer than the non-depressive group (P = 0.014 and 0.007) at the bilateral parieto-occipital lobe, while no significant difference in N170 amplitude was found. No significant difference in P300 amplitude or latency between the two groups. Furthermore, N170 amplitude at PO7 was positively correlated with the arousal index and negatively with MOCA scores (both P < 0.01).ConclusionOSAHS patients with depressive mood exhibit increased N170 latency and impaired facial emotion recognition ability. Special attention towards the depressive mood among OSAHS patients is warranted for its implications for patient care.

Ultrafast processing of zirconia ceramics by transient and selective laser absorption

Ultrashort-pulse lasers (USPLs) are used in the machining of zirconia ceramics (ZrO2) because of their extremely high peak intensities. However, highly efficient and high precision microprocessing of ZrO2 remains challenging. In this study, we applied a transient and selective laser (TSL) processing technique by combining a USPL and a continuous-wave (CW) laser to realize ultrafast drilling of opaque ZrO2. Ultra-high-efficiency TSL drilling of ZrO2 was achieved with a processing efficiency 1800 times higher than that of USPL drilling. The material removal mechanism, hole formation process, and parameter dependence of TSL drilling were revealed by direct observation of the internal processing phenomenon on a timescale of microseconds. The variations in the processed surface characteristics, including surface morphology, elemental composition, and phase composition, are also presented. Subsequently, the effect of different excited electron regions occurring in the picosecond timescale induced by the USPL on the TSL drilling performance is discussed. Finally, the repeatability and stability of the TSL processing were verified by fabricating microarrays of different sizes. This study will contribute to revealing the ultrafast processing mechanisms of TSL processing in ZrO2 microfabrication. Furthermore, this technique can significantly broaden the applications of ZrO2 in the advanced electronic industry by greatly improving the microprocessing efficiency.

Design of novel differential power processing scheme for small-scale PV array application operating under partial shading conditions: modelling and experimental validation

In this study, an innovative approach known as Series and Parallel Differential Power Processing (SP-DPP) converters has been introduced and applied to a small-scale 2 × 2 PV array configuration. Unlike conventional methods, this approach employs Bidirectional Ćuk DC-DC Converters (BCC) in lieu of bypass diodes within serially connected PV modules, facilitating precise adjustment of module currents. Additionally, the Differential Power Processing (DPP) mechanism, utilising an Inverted-Buck Converter (IBC) topology, replaces traditional blocking diodes across parallel string connections. The paper develops mathematical models for these converters based on their transfer functions in the Continuous Conduction Mode (CCM), establishing new design criteria for SP-DPP systems. This approach ensures a well-behaved transient response and mitigates non-minimum phase response effects. Empirical testing demonstrates the efficacy of the proposed system under various static and dynamic partial shading conditions (PSCs). Furthermore, a model-based control strategy employing P + I controllers are formulated and experimentally validated to optimise the operating conditions of PV modules, enabling them to operate at their Maximum Power Points (MPPs) in varying lighting conditions. Through simulation and experimental results, the study affirms a substantial increase in the total output power of the SP-DPP system (approximately 37–41 %) compared to conventional Tied-Crossties (TCT) array configuration relying solely on bypass and blocking diodes under various static shading conditions, such as Short-Narrow (SN), Short-Wide (SW), Long-Narrow (LN) and Long-Wide (LW). Ultimately, this research highlights the novelty of the proposed SP-DPP scheme over other existing DPP schemes in the literature, in terms of maximum power extraction, cost and complexity under various lighting conditions. This research extends to explore the potential economic viability and global implications of large-scale DPP system implementation.

Neural Generative Models and the Parallel Architecture of Language: A Critical Review and Outlook.

According to the parallel architecture, syntactic and semantic information processing are two separate streams that interact selectively during language comprehension. While considerable effort is put into psycho- and neurolinguistics to understand the interchange of processing mechanisms in human comprehension, the nature of this interaction in recent neural Large Language Models remains elusive. In this article, we revisit influential linguistic and behavioral experiments and evaluate the ability of a large language model, GPT-3, to perform these tasks. The model can solve semantic tasks autonomously from syntactic realization in a manner that resembles human behavior. However, the outcomes present a complex and variegated picture, leaving open the question of how Language Models could learn structured conceptual representations.

Analysis of the Operating Energy Variables Involved in Mechanized Khoa Production

Significant portion of the total milk production of India has been utilized for the manufacture of the Traditional Indian Dairy Products (TIDP). Khoa based products were traditionally produced in the Indian sub-continent since ancient times. Khoa produced by the mechanized methods can overcome the demerits of traditional manufacturing methods like insufficient use of energy, poor hygiene and sanitation, non-uniform product quality, etc. In the present study, steam jacketed open type hemispherical kettle equipped with spring loaded Teflon edged scraper blade assembly was utilized for the mechanized production of khoa. A standardized mixed milk having (6.0±0.1%) fat and (9.0±0.2%) SNF, was evaporated to the different level of concentration using a batch type vacuum pan. The performance of the mechanized manufacture of khoa was evaluated at different steam pressures (P1= 98.06 kPa, P2=147.1 kPa, P3=171.61 kPa), scraper speed (R1=0.67rps), and different level of milk concentration (C1=35 %TS, C2=40 %TS, C3=45 %TS) for first stage. During the second stage, effect of operating variables on process mechanization were evaluated at different steam pressures (S1= 49.03 kPa and S2= 78.45 kPa) and scraper speed (R2=0.33rps). The primary objective of present study is to carry out thermal and electrical energy analysis associated with mechanized khoa production, which has a significant role in process optimization, scale-up of the operation and to design and fabricate the mechanized system. The overall heat transfer coefficients (U-values) of the mechanized system obtained at various processing parameters ranged from 156.87 to 234.16 W/m2K during khoa manufacturing. The treatment C1 P1 S2 gave the highest U-values for sensible and latent heating. The mean U-values of sensible heating and latent heating were increased with an increase in steam pressure during khoa making in the mechanized system. The mean value of specific steam consumption (kg steam/kg water evaporated) for the optimized operating conditions was 1.950 (kg steam/kg water evaporated) for manufacture of khoa in mechanized system. The total heat losses of mechanized system during manufacture of khoa was 17.15 to 28.02 % at different operating conditions studied. The mean values of electrical power consumption ranged from 141 to 203 Wh under different operating conditions. Considering the energy consumption data, we can narrate that existing system is energy efficient with lower electrical energy consumption and can reduce the labour cost due to mechanization.

Triple alignment-enhanced complex question answering over knowledge bases

Program induction is a crucial paradigm for complex question answering over knowledge bases. In the existing learning framework, the predicted program is required to strictly align (word-by-word) with a gold program, which could cause over penalization for minor deviations. Meanwhile, due to the existence of synonyms, program induction question answering often fails to retrieve answer from the knowledge base because individual function argument cannot perfectly replicate the target argument. To address above misalignment problems, we propose a triple alignment-enhanced complex question answering (TACQA) method by incorporating global token alignment, function alignment, and argument alignment. First, apart from the classical global token alignment, the predicted functions are extracted and aligned separately with the gold functions, enabling efficient learning of implicit structural information related to the query framework of program from input questions. Second, an argument alignment is introduced to correct the ambiguous function arguments, which enhances the disambiguation processing efficiency of multi-argument by optimizing candidate pool construction and similarity calculation. The experiments on KQA Pro show that our method consistently outperforms the SOTA methods, demonstrating the effectiveness of triple alignment processing mechanism for simultaneously addressing function misalignment and argument ambiguity in program induction and further improving the model performance.

Cross-Study of English Education and Cognitive Science: Language Processing and Learning Mechanisms

With the acceleration of globalization and the rapid development of information technology, English, as the main language of international communication, has increasingly prominent importance in education and learning. English education, as a discipline that studies the theory and practice of English education, has been exploring more effective teaching methods and learning strategies. At the same time, cognitive science, as a discipline that studies cognitive processes such as human thinking, learning, and memory, provides a profound theoretical foundation for understanding language processing and learning mechanisms. The cross-study of English education and cognitive science helps to build a more complete theoretical framework for language processing and learning mechanisms. By integrating the theoretical achievements of the two disciplines, we can more fully understand the cognitive processes and laws of English learning, revealing the intrinsic connection and essential characteristics of language processing and learning mechanisms. This can not only enrich and develop the theoretical systems of English education and cognitive science but also provide new ideas and perspectives for research in other related fields. In addition, with the continuous development of artificial intelligence and big data technology, research on language processing and learning mechanisms can also provide technical support and innovative ideas for fields such as natural language processing and intelligent education. Therefore, the cross-study of English education and cognitive science not only has far-reaching academic value but also has broad application prospects and social significance.

Application of Femtosecond Laser Processing Method in the Sustainable Conservation of Stone Cultural Relics: An Example of Green Schist in Wudang Mountain, China

The ancient building complex in Wudang Mountain, China, is known as the “Museum of Ancient Chinese Architectural Accomplishments”. However, the valuable stone components are preserved in open or semi-open environments and environmental factors such as rain seriously threaten its sustainable conservation. In this context, a femtosecond laser processing method has been demonstrated to be able to prepare hierarchical micro-nano structures on the stone surface to regulate its wettability, achieving the purpose of sustainable conservation. In this paper, the processing mechanism and performance of the femtosecond laser on green schist, a local stone material in the Wudang Mountain, are systematically investigated. It is found that green schist, as a typical non-homogeneous material, exhibits significant differences in its absorption of femtosecond laser with different compositions. Among them, quartz, chlorite, and muscovite are the three main compositions, and they are mainly characterized by cold ablation, thermal melting, and expansion under the irradiation of the femtosecond laser (238 fs, 100 kHz, 40 μJ, 33 μm, 500–40,000 pulses), respectively, and it is difficult to achieve a uniform and stable surface structure. Based on this, we prepared grooves with a spacing of 100–400 μm by scanning the femtosecond laser. Through the characterization of surface morphology, elemental composition, and three-dimensional structure, the processing mechanism of the hierarchical micro-nano structures of green schist under the irradiation of the femtosecond laser is comprehensively revealed. Finally, the wettability modulation result of water contact angle up to 147° is achieved by processing the grooves with an optimal spacing of 400 μm. The results of this research are of guiding significance for the sustainable conservation of ancient buildings and cultural relics.

Inhibiting presynaptic calcium channel motility in the auditory cortex suppresses synchronized input processing.

The emergent coherent population activity from thousands of stochastic neurons in the brain is believed to constitute a key neuronal mechanism for salient processing of external stimuli and its link to internal states like attention and perception. In the sensory cortex, functional cell assemblies are formed by recurrent excitation and inhibitory influences. The stochastic dynamics of each cell involved is largely orchestrated by presynaptic CAV2.1 voltage-gated calcium channels (VGCCs). Cav2.1 VGCCs initiate the release of neurotransmitters from the presynaptic compartment and are therefore able to add variability into synaptic transmission which can be partly explained by their mobile organization around docked vesicles. To investigate the relevance of Cav2.1 channel surface motility for the input processing in the primary auditory cortex (A1) in vivo, we make use of a new optogenetic system which allows for acute, reversable cross-linking Cav2.1 VGCCs via a photo-cross-linkable cryptochrome mutant, CRY2olig. In order to map neuronal activity across all cortical layers of the A1, we performed laminar current-source density (CSD) recordings with varying auditory stimulus sets in transgenic mice with a citrine tag on the N-terminus of the VGCCs. Clustering VGCCs suppresses overall sensory-evoked population activity, particularly when stimuli lead to a highly synchronized distribution of synaptic inputs. Our findings reveal the importance of membrane dynamics of presynaptic calcium channels for sensory encoding by dynamically adjusting network activity across a wide range of synaptic input strength.

Diminished differentiation of rewards in individuals at clinical high-risk for psychosis.

Reward processing is impaired in people with schizophrenia, which may begin in the clinical high-risk (CHR) for psychosis period. The Monetary Incentive Delay (MID) task has been important in understanding the neural correlates of reward processing deficits in various psychiatric disorders. Previous research has found that CHR individuals have an imprecise mental representation of rewards, which leads to a diminished differentiation between rewards, though this has not been observed behaviorally. A total of 19 CHR individuals and 20 controls were given a novel variant of the MID task, designed to examine how modulating reward context may impact responses to reward cues, a process often referred to as "adaptive coding." Both groups appeared to update their behavior in response to the rewards available in this adaptive task. However, when compared to controls who showed a more graded decrease in response time to increasing reward contexts, CHR individuals appeared to have a sharp decrease in response time in the low reward context that is nearly stable across higher reward contexts. This is largely driven by the exponential component of the response time distribution, which is often interpreted to be more cognitively or effortfully influenced. Response times are related to negative symptoms, but not positive symptoms, disorganized symptoms, or estimated intelligence. Although an adaptive coding effect was not observed, these results provide novel insight into the reward processing mechanisms and volitional processes in the CHR population, as this was the first study to observe the diminished differentiation of rewards behaviorally.

The Effect of Electronic Data Interchange (EDI) on Improving Tax Compliance Rates

Kosovo's tax administration plays a fundamental role in the state's functioning, carrying out many duties and obligations. In addition to these duties, TAK also acts as a stabilizing element for the macro-fiscal economy. In addition to the importance of human resources, the function of information technology is unquestionably crucial in achieving TAK's objectives. The implementation of information technology has resulted in the mechanization of several processes, simplifying the gathering of data and the distribution of tax responsibilities. Advanced information technology systems have improved communication channels with taxpayers and other individuals or groups involved, promoting increased transparency in tax information management. The adoption of the Electronic Data Exchange (EDI) application has resulted in advantages not just for TAK but also for taxpayers and the general public. There are several benefits to using TAK, such as increased efficiency in processing tax return data, less dependence on people specifically assigned to tax return processing, and a lower likelihood of errors in tax returns. The benefits for taxpayers and citizens include the convenience of filing and paying taxes, the option to do so online at any time, and the ability to access online services without the need for in-person engagement with tax authorities. Another vital part of TAK's employment of information technology is the facilitation of online declarations and payments, as well as digital connections with taxpayers. This progress has led to higher adherence to tax obligations and a decreased incidence of tax evasion.

Study on the components changes of polysaccharides and saponins during nine steaming and drying of Polygonatum sibiricum.

In this study, the content and structure of Polygonatum sibiricum polysaccharides and saponins during different processing stages were determined. After processing of Polygonatum, the content of polysaccharide and glucose decreased, and the content of galactose, glucuronic acid and sugar substitution gradually increased. The content of total saponins increased significantly. Only 18 compounds were found in raw Polygonatum and 17 new compounds were presented in processed Polygonatum. During the processing of Polygonatum, the polysaccharide was partially degraded into oligosaccharides, the molecular weight gradually decreased, and the neutral sugar was converted into uronic acid, resulting in a decrease in polysaccharide content. The saponins were partially degraded into sapogenins or modified. This study clarifies the changes in the content and structure of polysaccharides and saponins in processed Polygonatum, which will pave the way for elucidating the processing mechanism. © 2024 Society of Chemical Industry.

Liver kinase B-1 modulates the activity of dopamine neurons in the ventral tegmental area and regulates social memory formation.

Social memory is the ability to discriminate between familiar and unknown conspecifics. It is an important component of social cognition and is therefore essential for the establishment of social relationships. Although the neural circuit mechanisms underlying social memory encoding have been well investigated, little focus has been placed on the regulatory mechanisms of social memory processing. The dopaminergic system, originating from the midbrain ventral tegmental area (VTA), is a key modulator of cognitive function. This study aimed to illustrate its role in modulating social memory and explore the possible molecular mechanisms. Here, we show that the activation of VTA dopamine (DA) neurons is required for the formation, but not the retrieval, of social memory. Inhibition of VTA DA neurons before social interaction, but not 24 h after social interaction, significantly impaired social discrimination the following day. In addition, we showed that the activation of VTA DA neurons was regulated by the serine/threonine protein kinase liver kinase B1 (Lkb1). Deletion of Lkb1 in VTA DA neurons reduced the frequency of burst firing of dopaminergic neurons. Furthermore, Lkb1 plays an important role in regulating social behaviors. Both genetic and virus-mediated deletions of Lkb1 in the VTA of adult mice impaired social memory and subsequently attenuated social familiarization. Altogether, our results provide direct evidence linking social memory formation to the activation of VTA DA neurons in mice and illustrate the crucial role of Lkb1 in regulating VTA DA neuron function.

Stagnation point flow in a Maxwell fluid with a slip condition and the role of viscous dissipation and radiation

This research aims to examine the impact of viscous dissipation, slip conditions, and radiation on the stagnation point flow when a stretching/shrinking sheet is immersed in a Maxwell fluid. The significance of viscous dissipation has great importance in an extensive range of engineering applications, including heat exchangers, lubrication systems, polymer processing, and biofluid mechanics. In heat and mass transfer fluid flow problems, viscous dissipation can have significant effects, particularly in situations involving high fluid velocities or high viscosity fluids. In this computational study, the governing equations, namely the momentum, and energy equations, are formulated and non-dimensionalized using appropriate similarity parameters and the Runge-Kutta-Fehlberg method is then employed to resolve the problem. The obtained results provide detailed insights into the velocity, temperature, skin friction and wall temperature distributions in the flow domain. Additionally, the role of viscous dissipation in altering the heat transfer characteristics of the Maxwell fluid is examined. The results indicate that the existence of viscous dissipation significantly impacts the flow behaviour of the Maxwell fluid. It alters the boundary layer thickness, temperature distribution, and the overall heat transfer rate. Additionally, it is observed that the values of the Prandtl number strongly affect the influence of viscous dissipation on the velocity and temperature distribution. The outcomes of this study correlate with those that have been previously reported.

Kinetics of Intermediate Release Enhances P450 11B2-Catalyzed Aldosterone Synthesis.

The mitochondrial enzyme cytochrome P450 11B2 (aldosterone synthase) catalyzes the 3 terminal transformations in the biosynthesis of aldosterone from 11-deoxycorticosterone (DOC): 11β-hydroxylation to corticosterone, 18-hydroxylation, and 18-oxidation. Prior studies have shown that P450 11B2 produces more aldosterone from DOC than from the intermediate corticosterone and that the reaction sequence is processive, with intermediates remaining bound to the active site between oxygenation reactions. In contrast, P450 11B1 (11β-hydroxylase), which catalyzes the terminal step in cortisol biosynthesis, shares a 93% amino acid sequence identity with P450 11B2, converts DOC to corticosterone, but cannot synthesize aldosterone from DOC. The biochemical and biophysical properties of P450 11B2, which enable its unique 18-oxygenation activity and processivity, yet are not also represented in P450 11B1, remain unknown. To understand the mechanism of aldosterone biosynthesis, we introduced point mutations at residue 320, which partially exchange the activities of P450 11B1 and P450 11B2 (V320A and A320V, respectively). We then investigated NADPH coupling efficiencies, binding kinetics and affinities, and product formation of purified P450 11B1 and P450 11B2, wild-type, and residue 320 mutations in phospholipid vesicles and nanodiscs. Coupling efficiencies for the 18-hydroxylase reaction with corticosterone as the substrate failed to correlate with aldosterone synthesis, ruling out uncoupling as a relevant mechanism. Conversely, corticosterone dissociation rates correlated inversely with aldosterone production. We conclude that intermediate dissociation kinetics, not coupling efficiency, enable P450 11B2 to synthesize aldosterone via a processive mechanism. Our kinetic data also suggest that the binding of DOC to P450 11B enzymes occurs in at least two distinct steps, favoring an induced-fit mechanism.

IN-MEMORY INTELLIGENT COMPUTING

Context. Processed big data has social significance for the development of society and industry. Intelligent processing of big data is a condition for creating a collective mind of a social group, company, state and the planet as a whole. At the same time, the economy of big data (Data Economy) takes first place in the evaluation of processing mechanisms, since two parameters are very important: speed of data processing and energy consumption. Therefore, mechanisms focused on parallel processing of large data within the data storage center will always be in demand on the IT market.&#x0D; Objective. The goal of the investigation is to increase the economy of big data (Data Economy) thanks to the analysis of data as truth table addresses for the identification of patterns of production functionalities based on the similarity-difference metric.&#x0D; Method. Intelligent computing architectures are proposed for managing cyber-social processes based on monitoring and analysis of big data. It is proposed to process big data as truth table addresses to solve the problems of identification, clustering, and classification of patterns of social and production processes. A family of automata is offered for the analysis of big data, such as addresses. The truth table is considered as a reasonable form of explicit data structures that have a useful constant – a standard address routing order. The goal of processing big data is to make it structured using a truth table for further identification before making actuator decisions. The truth table is considered as a mechanism for parallel structuring and packing of large data in its column to determine their similarity-difference and to equate data at the same addresses. Representation of data as addresses is associated with unitary encoding of patterns by binary vectors on the found universe of primitive data. The mechanism is focused on processorless data processing based on read-write transactions using in-memory computing technology with significant time and energy savings. The metric of truth table big data processing is parallelism, technological simplicity, and linear computational complexity. The price for such advantages is the exponential memory costs of storing explicit structured data.&#x0D; Results. Parallel algorithms of in-memory computing are proposed for economic mechanisms of transformation of large unstructured data, such as addresses, into useful structured data. An in-memory computing architecture with global feedback and an algorithm for matrix parallel processing of large data such as addresses are proposed. It includes a framework for matrix analysis of big data to determine the similarity between vectors that are input to the matrix sequencer. Vector data analysis is transformed into matrix computing for big data processing. The speed of the parallel algorithm for the analysis of big data on the MDV matrix of deductive vectors is linearly dependent on the number of bits of the input vectors or the power of the universe of primitives. A method of identifying patterns using key words has been developed. It is characterized by the use of unitary coded data components for the synthesis of the truth table of the business process. This allows you to use read-write transactions for parallel processing of large data such as addresses.&#x0D; Conclusions. The scientific novelty consists in the development of the following innovative solutions: 1) a new vector-matrix technology for parallel processing of large data, such as addresses, is proposed, characterized by the use of read-write transactions on matrix memory without the use of processor logic; 2) an in-memory computing architecture with global feedback and an algorithm for matrix parallel processing of large data such as addresses are proposed; 3) a method of identifying patterns using keywords is proposed, which is characterized by the use of unitary coded data components for the synthesis of the truth table of the business process, which makes it possible to use the read-write transaction for parallel processing of large data such as addresses. The practical significance of the study is that any task of artificial intelligence (similarity-difference, classification-clustering and recognition, pattern identification) can be solved technologically simply and efficiently with the help of a truth table (or its derivatives) and unitarily coded big data . Research prospects are related to the implementation of this digital modeling technology devices on the EDA market. KEYWORDS: Intelligent

Dissociable Neural Mechanisms for Human Inference Processing Predicted by Static and Contextual Language Models.

Language models (LMs) continue to reveal non-trivial relations to human language performance and the underlying neurophysiology. Recent research has characterized how word embeddings from an LM can be used to generate integrated discourse representations in order to perform inference on events. The current research investigates how such event knowledge may be coded in distinct manners in different classes of LMs and how this maps onto different forms of human inference processing. To do so, we investigate inference on events using two well-documented human experimental protocols from Metusalem et al. (2012) and McKoon and Ratcliff (1986), compared with two protocols for simpler semantic processing. Interestingly, this reveals a dissociation in the relation between local semantics versus event-inference depending on the LM. In a series of experiments, we observed that for the static LMs (word2vec/GloVe), there was a clear dissociation in the relation between semantics and inference for the two inference tasks. In contrast, for the contextual LMs (BERT/RoBERTa), we observed a correlation between semantic and inference processing for both inference tasks. The experimental results suggest that inference as measured by Metusalem and McKoon rely on dissociable processes. While the static models are able to perform Metusalem inference, only the contextual models succeed in McKoon inference. Interestingly, these dissociable processes may be linked to well-characterized automatic versus strategic inference processes in the psychological literature. This allows us to make predictions about dissociable neurophysiological markers that should be found during human inference processing with these tasks.

A simplified classification computational model of opinion mining using deep learning

Opinion and attempts to develop an automated system to determine people's viewpoints towards various units such as events, topics, products, services, organizations, individuals, and issues. Opinion analysis from the natural text can be regarded as a text and sequence classification problem which poses high feature space due to the involvement of dynamic information that needs to be addressed precisely. This paper introduces effective modelling of human opinion analysis from social media data subjected to complex and dynamic content. Firstly, a customized preprocessing operation based on natural language processing mechanisms as an effective data treatment process towards building quality-aware input data. On the other hand, a suitable deep learning technique, bidirectional long short term-memory (Bi-LSTM), is implemented for the opinion classification, followed by a data modelling process where truncating and padding is performed manually to achieve better data generalization in the training phase. The design and development of the model are carried on the MATLAB tool. The performance analysis has shown that the proposed system offers a significant advantage in terms of classification accuracy and less training time due to a reduction in the feature space by the data treatment operation.

Revealing the mechanism of post-harvest processing on rose quality based on dynamic changes in water content, enzyme activity, volatile and non-volatile metabolites

Existing studies on post-harvest processing of edible roses have mainly focused on processing techniques and physicochemical properties of the final dried products, with limited studies on how changes in metabolites during processing affect the quality of these products. This study investigated changes in water content and status, enzyme activity, phenolic compounds, and volatile and non-volatile compounds during processing and revealed the mechanisms by which post-harvest processing (drying without blanching (WBD) and drying with blanching (BD)) affects the quality of dried roses by establishing their correlations. Results showed that the blanching reduced the relative content of free water and water activity, thus reducing the subsequent drying time and enzyme activity. The BD method caused higher levels of phenolic compounds than the WBD method in terms of gallic acid, ellagic acid, epicatechin, and quercetin. The OPLS-DA analysis identified 6 differential volatiles out of 72 detected volatiles, contributing to the unique aroma of dried roses by activating olfactory receptors through hydrogen bonding and hydrophobic interactions. 58 differential metabolites were screened from 964 non-volatile metabolites. KEGG pathway analysis revealed that the changes in volatile and non-volatile metabolites induced by different processing methods were due to the effect of blanching on glutathione and fatty acid metabolism. These findings provide a comprehensive understanding of how post-harvest processing affects the quality of dried roses.

Mechanical pain sensitivity is associated with hippocampal structural integrity.

Rodents and human studies indicate that the hippocampus, a brain region necessary for memory processing, responds to noxious stimuli. However, the hippocampus has yet to be considered a key brain region directly involved in the human pain experience. One approach to answer this question is to perform quantitative sensory testing on patients with hippocampal damage-ie, medial temporal lobe epilepsy. Some case studies and case series have performed such tests in a handful of patients with various types of epilepsy and have reported mixed results. Here, we aimed to determine whether mechanical pain sensitivity was altered in patients diagnosed with temporal lobe epilepsy. We first investigated whether mechanical pain sensitivity in patients with temporal lobe epilepsy differs from that of healthy individuals. Next, in patients with temporal lobe epilepsy, we evaluated whether the degree of pain sensitivity is associated with the degree of hippocampal integrity. Structural integrity was based on hippocampal volume, and functional integrity was based on verbal and visuospatial memory scores. Our findings show that patients with temporal lobe epilepsy have lower mechanical pain sensitivity than healthy individuals. Only left hippocampal volume was positively associated with mechanical pain sensitivity-the greater the hippocampal damage, the lower the sensitivity to mechanical pain. Hippocampal measures of functional integrity were not significantly associated with mechanical pain sensitivity, suggesting that the mechanisms of hippocampal pain processing may be different than its memory functions. Future studies are necessary to determine the mechanisms of pain processing in the hippocampus.