5-MeO-DMT induces sleep-like LFP spectral signatures in the hippocampus and prefrontal cortex of awake rats.

5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) is a potent classical psychedelic known to induce changes in locomotion, behaviour, and sleep in rodents. However, there is limited knowledge regarding its acute neurophysiological effects. Local field potentials (LFPs) are commonly used as a proxy for neural activity, but previous studies investigating psychedelics have been hindered by confounding effects of behavioural changes and anaesthesia, which alter these signals. To address this gap, we investigated acute LFP changes in the hippocampus (HP) and medial prefrontal cortex (mPFC) of freely behaving rats, following 5-MeO-DMT administration. 5-MeO-DMT led to an increase of delta power and a decrease of theta power in the HP LFPs, which could not be accounted for by changes in locomotion. Furthermore, we observed a dose-dependent reduction in slow (20-50 Hz) and mid (50-100 Hz) gamma power, as well as in theta phase modulation, even after controlling for the effects of speed and theta power. State map analysis of the spectral profile of waking behaviour induced by 5-MeO-DMT revealed similarities to electrophysiological states observed during slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. Our findings suggest that the psychoactive effects of classical psychedelics are associated with the integration of waking behaviours with sleep-like spectral patterns in LFPs.

Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems.

As artificial intelligence systems gain traction, their trustworthiness becomes paramount to harness their benefits and mitigate risks. This study underscores the pressing need for an expectation management framework to align stakeholder anticipations before any system-related activities, such as data collection, modeling, or implementation. To this end, we introduce a comprehensive framework tailored to capture end-user expectations specifically for trustworthy artificial intelligence systems. To ensure its relevance and robustness, we validated the framework via semi-structured interviews, encompassing questions rooted in the framework's constructs and principles. These interviews engaged fourteen diverse end users across the healthcare and education sectors, including physicians, teachers, and students. Through a meticulous qualitative analysis of the interview transcripts, we unearthed pivotal themes and discerned varying perspectives among the interviewee groups. Ultimately, our framework stands as a pivotal tool, paving the way for in-depth discussions about user expectations, illuminating the significance of various system attributes, and spotlighting potential challenges that might jeopardize the system's efficacy.

Combinatorial optimization of pathway, process and media for the production of p-coumaric acid by Saccharomyces cerevisiae.

Microbial cell factories are instrumental in transitioning towards a sustainable bio-based economy, offering alternatives to conventional chemical processes. However, fulfilling their potential requires simultaneous screening for optimal media composition, process and genetic factors, acknowledging the complex interplay between the organism's genotype and its environment. This study employs statistical design of experiments to systematically explore these relationships and optimize the production of p-coumaric acid (pCA) in Saccharomyces cerevisiae. Two rounds of fractional factorial designs were used to identify factors with a significant effect on pCA production, which resulted in a 168-fold variation in pCA titre. Moreover, a significant interaction between the culture temperature and expression of ARO4 highlighted the importance of simultaneous process and strain optimization. The presented approach leverages the strengths of experimental design and statistical analysis and could be systematically applied during strain and bioprocess design efforts to unlock the full potential of microbial cell factories.

Pseudomonas putida as a platform for medium-chain length α,ω-diol production: Opportunities and challenges.

Medium-chain-length α,ω-diols (mcl-diols) play an important role in polymer production, traditionally depending on energy-intensive chemical processes. Microbial cell factories offer an alternative, but conventional strains like Escherichia coli and Saccharomyces cerevisiae face challenges in mcl-diol production due to the toxicity of intermediates such as alcohols and acids. Metabolic engineering and synthetic biology enable the engineering of non-model strains for such purposes with P. putida emerging as a promising microbial platform. This study reviews the advancement in diol production using P. putida and proposes a four-module approach for the sustainable production of diols. Despite progress, challenges persist, and this study discusses current obstacles and future opportunities for leveraging P. putida as a microbial cell factory for mcl-diol production. Furthermore, this study highlights the potential of using P. putida as an efficient chassis for diol synthesis.

Folk medicine, biological activity, and chemical profiles of Brazilian Acanthaceae (Lamiales) - A review.

INTRODUCTION: The botanical family Acanthaceae (order Lamiales) potentially comprises 4900 species in 191 genera with extensive morphological, habit and habitat diversity. The family is widely distributed throughout the world but is especially rich in tropical and subtropical regions. Many of its species have great ornamental importance and are broadly used for medicinal purposes in several countries of Asia and Africa. Brazil is a main center of diversity of the family, where they are distributed across all its biomes, mainly in the herbaceous-shrub stratum. Medicinal investigations about Brazilian species are scarce, the exception being a single native species, Justicia pectoralis Jacq., that is widely used and studied chemically. AIM OF THE REVIEW: This work compiled studies that indicated folk medicinal use, investigated biological activity, or evaluated the chemical composition of Brazilian species of Acanthaceae. MATERIAL AND METHODS: Medicinal uses, investigations of biological activities and chemical data were collected and summarized through bibliographic surveys. Tables were compiled to standardize the information and the appropriate references were gathered for each species. Registration of chemical components used in the treatment of ailments and in preserving health were emphasized with the aim of stimulating future investigations. RESULTS: The breadths of habitats and morphologies of the family are directly related to its chemical diversity, as confirmed here for Brazilian species. Although the investigated species represent less than 9% of the total richness of the family in Brazil, they encompass a great diversity of chemical substances. The data indicated folk medicinal uses for 26 species and biological tests for 23, while 30 species were investigated chemically. Ruellia and Justicia were the most researched genera with 12 and 11 species, representing approximately 14% and 7% of Brazilian species of each genus, respectively. Two species are native to other countries but become naturalized in Brazil. Studies of native species were carried out in different countries around the world, with many reports of medicinal uses and biological tests. Examples of uses include anticancer and antidepressant actions, as well as activities against respiratory problems and other diseases. CONCLUSIONS: This work highlights the chemical and biological diversity of the studied Brazilian species of Acanthaceae, which emphasizes the need to expand studies with native Brazilian species.

Machine Learning-Guided Optimization of p-Coumaric Acid Production in Yeast.

Industrial biotechnology uses Design-Build-Test-Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a biobased economy. To use them effectively, appropriate connections between the phases of the cycle are crucial. Using p-coumaric acid (pCA) production in Saccharomyces cerevisiae as a case study, we propose the use of one-pot library generation, random screening, targeted sequencing, and machine learning (ML) as links during DBTL cycles. We showed that the robustness and flexibility of the ML models strongly enable pathway optimization and propose feature importance and Shapley additive explanation values as a guide to expand the design space of original libraries. This approach allowed a 68% increased production of pCA within two DBTL cycles, leading to a 0.52 g/L titer and a 0.03 g/g yield on glucose.

Linking childhood trauma to the psychopathology of schizophrenia: the role of oxytocin.

Childhood trauma has been linked to schizophrenia, but underlying biological mechanisms remain elusive. This study explored the potential role of plasma oxytocin as a mediator in the relationship between childhood trauma and the psychopathology of schizophrenia. 160 patients with schizophrenia and 80 age- and sex-matched healthy controls were assessed for childhood trauma experiences using the Childhood Trauma Questionnaire and structured interviews. Psychopathology was evaluated using the Positive and Negative Syndrome Scale and plasma oxytocin levels were measured. Results showed that patients with schizophrenia had lower oxytocin levels and higher childhood trauma scores than healthy controls. There was a significant correlation between childhood trauma scores and psychopathology, with plasma oxytocin levels being inversely associated with psychopathology, except for positive symptoms. Hierarchical regression analysis indicated that both childhood trauma scores and plasma oxytocin levels significantly predicted psychopathology. Plasma oxytocin levels partially mediated the relationship between childhood trauma and schizophrenia psychopathology. This study underscores the potential role of oxytocin in bridging the gap between childhood trauma and schizophrenia.

Waveform-based classification of dentate spikes.

Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when memory consolidation is thought to take place. However, their functions in mnemonic processes are yet to be elucidated. The classification of DSs into types 1 or 2 is determined by their origin in the lateral or medial EC, as revealed by current source density (CSD) analysis, which requires recordings from linear probes with multiple electrodes spanning the DG layers. To allow the investigation of the functional role of each DS type in recordings obtained from single electrodes and tetrodes, which are abundant in the field, we developed an unsupervised method using Gaussian mixture models to classify such events based on their waveforms. Our classification approach achieved high accuracies (> 80%) when validated in 8 mice with DG laminar profiles. The average CSDs, waveforms, rates, and widths of the DS types obtained through our method closely resembled those derived from the CSD-based classification. As an example of application, we used the technique to analyze single-electrode LFPs from apolipoprotein (apo) E3 and apoE4 knock-in mice. We observed that the latter group, which is a model for Alzheimer's disease, exhibited wider DSs of both types from a young age, with a larger effect size for DS type 2, likely reflecting early pathophysiological alterations in the EC-DG network, such as hyperactivity. In addition to the applicability of the method in expanding the study of DS types, our results show that their waveforms carry information about their origins, suggesting different underlying network dynamics and roles in memory processing.

Coordinating brain-distributed network activities in memory resistant to extinction.

Certain memories resist extinction to continue invigorating maladaptive actions. The robustness of these memories could depend on their widely distributed implementation across populations of neurons in multiple brain regions. However, how dispersed neuronal activities are collectively organized to underpin a persistent memory-guided behavior remains unknown. To investigate this, we simultaneously monitored the prefrontal cortex, nucleus accumbens, amygdala, hippocampus, and ventral tegmental area (VTA) of the mouse brain from initial recall to post-extinction renewal of a memory involving cocaine experience. We uncover a higher-order pattern of short-lived beta-frequency (15-25 Hz) activities that are transiently coordinated across these networks during memory retrieval. The output of a divergent pathway from upstream VTA glutamatergic neurons, paced by a slower (4-Hz) oscillation, actuates this multi-network beta-band coactivation; its closed-loop phase-informed suppression prevents renewal of cocaine-biased behavior. Binding brain-distributed neural activities in this temporally structured manner may constitute an organizational principle of robust memory expression.

The interplay of childhood trauma, oxytocin, and impulsivity in predicting the onset of methamphetamine use.

BACKGROUND: Childhood trauma is associated with substance use disorders, including methamphetamine use disorder (MUD). Oxytocin, involved in social bonding, stress regulation, and reward processing, may influence addiction vulnerability and impulsivity in individuals with a history of childhood trauma. OBJECTIVE: To investigate the relationships among childhood trauma, oxytocin levels, impulsivity, and the age of first methamphetamine use in individuals with MUD. PARTICIPANTS AND SETTING: The study included 298 male participants (148 individuals with MUD and 150 healthy controls) from both probation offices and psychiatric clinics. METHODS: Childhood trauma was assessed using the Childhood Trauma Questionnaire-Short Form (CTQ-SF), impulsivity with the Barratt Impulsiveness Scale-11 (BIS-11), and plasma oxytocin levels were obtained. RESULTS: Individuals with MUD exhibited higher levels of childhood trauma, impulsivity, and lower plasma oxytocin levels compared to healthy controls. Childhood trauma was associated with a younger age of first methamphetamine use, higher impulsivity, and lower oxytocin levels among individuals with MUD. Plasma oxytocin levels partially mediated the relationship between childhood trauma and both the age of first methamphetamine use and impulsivity. Serial mediation analysis demonstrated that oxytocin levels and impulsivity sequentially mediated the relationship between childhood trauma and the age of first methamphetamine use. CONCLUSIONS: The findings reveal the complex interplay among childhood trauma, oxytocin, impulsivity, and methamphetamine use, emphasizing the importance of considering these factors in prevention and intervention strategies for MUD. Future research should explore oxytocin and impulsivity-focused interventions to mitigate the effects of childhood trauma and reduce MUD development risk.

Genes controlling hydrolysate toxin tolerance identified by QTL analysis of the natural Saccharomyces cerevisiae BCC39850.

Lignocellulosic material can be converted to valorized products such as fuels. Pretreatment is an essential step in conversion, which is needed to increase the digestibility of the raw material for microbial fermentation. However, pretreatment generates by-products (hydrolysate toxins) that are detrimental to microbial growth. In this study, natural Saccharomyces strains isolated from habitats in Thailand were screened for their tolerance to synthetic hydrolysate toxins (synHTs). The Saccharomyces cerevisiae natural strain BCC39850 (toxin-tolerant) was crossed with the laboratory strain CEN.PK2-1C (toxin-sensitive), and quantitative trait locus (QTL) analysis was performed on the segregants using phenotypic scores of growth (OD600) and glucose consumption. VMS1, DET1, KCS1, MRH1, YOS9, SYO1, and YDR042C were identified from QTLs as candidate genes associated with the tolerance trait. CEN.PK2-1C knockouts of the VMS1, YOS9, KCS1, and MRH1 genes exhibited significantly greater hydrolysate toxin sensitivity to growth, whereas CEN.PK2-1C knock-ins with replacement of VMS1 and MRH1 genes from the BCC39850 alleles showed significant increased ethanol production titers compared with the CEN.PK2-1C parental strain in the presence of synHTs. The discovery of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin tolerance in S. cerevisiae indicates the roles of the endoplasmic-reticulum-associated protein degradation pathway, plasma membrane protein association, and the phosphatidylinositol signaling system in this trait. KEY POINTS: • QTL analysis was conducted using a hydrolysate toxin-tolerant S. cerevisiae natural strain • Deletion of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin-sensitivity • Replacement of VMS1 and MRH1 with natural strain alleles increased ethanol production titers in the presence of hydrolysate toxins.

Metagenomics harvested genus-specific single-stranded DNA-annealing proteins improve and expand recombineering in Pseudomonas species.

The widespread Pseudomonas genus comprises a collection of related species with remarkable abilities to degrade plastics and polluted wastes and to produce a broad set of valuable compounds, ranging from bulk chemicals to pharmaceuticals. Pseudomonas possess characteristics of tolerance and stress resistance making them valuable hosts for industrial and environmental biotechnology. However, efficient and high-throughput genetic engineering tools have limited metabolic engineering efforts and applications. To improve their genome editing capabilities, we first employed a computational biology workflow to generate a genus-specific library of potential single-stranded DNA-annealing proteins (SSAPs). Assessment of the library was performed in different Pseudomonas using a high-throughput pooled recombinase screen followed by Oxford Nanopore NGS analysis. Among different active variants with variable levels of allelic replacement frequency (ARF), efficient SSAPs were found and characterized for mediating recombineering in the four tested species. New variants yielded higher ARFs than existing ones in Pseudomonas putida and Pseudomonas aeruginosa, and expanded the field of recombineering in Pseudomonas taiwanensisand Pseudomonas fluorescens. These findings will enhance the mutagenesis capabilities of these members of the Pseudomonas genus, increasing the possibilities for biotransformation and enhancing their potential for synthetic biology applications. .

Recognition of Grasping Patterns Using Deep Learning for Human-Robot Collaboration.

Recent advances in the field of collaborative robotics aim to endow industrial robots with prediction and anticipation abilities. In many shared tasks, the robot's ability to accurately perceive and recognize the objects being manipulated by the human operator is crucial to make predictions about the operator's intentions. In this context, this paper proposes a novel learning-based framework to enable an assistive robot to recognize the object grasped by the human operator based on the pattern of the hand and finger joints. The framework combines the strengths of the commonly available software MediaPipe in detecting hand landmarks in an RGB image with a deep multi-class classifier that predicts the manipulated object from the extracted keypoints. This study focuses on the comparison between two deep architectures, a convolutional neural network and a transformer, in terms of prediction accuracy, precision, recall and F1-score. We test the performance of the recognition system on a new dataset collected with different users and in different sessions. The results demonstrate the effectiveness of the proposed methods, while providing valuable insights into the factors that limit the generalization ability of the models.

Waveform-based classification of dentate spikes.

Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when memory consolidation is thought to take place. However, their functions in mnemonic processes are yet to be elucidated. The classification of DSs into types 1 or 2 is determined by their origin in the lateral or medial EC, as revealed by current source density (CSD) analysis, which requires recordings from linear probes with multiple electrodes spanning the DG layers. To allow the investigation of the functional role of each DS type in recordings obtained from single electrodes and tetrodes, which are abundant in the field, we developed an unsupervised method using Gaussian mixture models to classify such events based on their waveforms. Our classification approach achieved high accuracies (> 80%) when validated in 8 mice with DG laminar profiles. The average CSDs, waveforms, rates, and widths of the DS types obtained through our method closely resembled those derived from the CSD-based classification. As an example of application, we used the technique to analyze single-electrode LFPs from apolipoprotein (apo) E3 and apoE4 knock-in mice. We observed that the latter group, which is a model for Alzheimer's disease, exhibited wider DSs of both types from a young age, with a larger effect size for DS type 2, likely reflecting early pathophysiological alterations in the EC-DG network, such as hyperactivity. In addition to the applicability of the method in expanding the study of DS types, our results show that their waveforms carry information about their origins, suggesting different underlying network dynamics and roles in memory processing.

The medial septum controls hippocampal supra-theta oscillations.

Hippocampal theta oscillations orchestrate faster beta-to-gamma oscillations facilitating the segmentation of neural representations during navigation and episodic memory. Supra-theta rhythms of hippocampal CA1 are coordinated by local interactions as well as inputs from the entorhinal cortex (EC) and CA3 inputs. However, theta-nested gamma-band activity in the medial septum (MS) suggests that the MS may control supra-theta CA1 oscillations. To address this, we performed multi-electrode recordings of MS and CA1 activity in rodents and found that MS neuron firing showed strong phase-coupling to theta-nested supra-theta episodes and predicted changes in CA1 beta-to-gamma oscillations on a cycle-by-cycle basis. Unique coupling patterns of anatomically defined MS cell types suggested that indirect MS-to-CA1 pathways via the EC and CA3 mediate distinct CA1 gamma-band oscillations. Optogenetic activation of MS parvalbumin-expressing neurons elicited theta-nested beta-to-gamma oscillations in CA1. Thus, the MS orchestrates hippocampal network activity at multiple temporal scales to mediate memory encoding and retrieval.

The quest for the reliability of machine learning models in binary classification on tabular data.

In this paper we explore the reliability of contexts of machine learning (ML) models. There are several evaluation procedures commonly used to validate a model (precision, F1 Score and others); However, these procedures are not linked to the evaluation of learning itself, but only to the number of correct answers presented by the model. This characteristic makes it impossible to assess whether a model was able to learn through elements that make sense of the context in which it is inserted. Therefore, the model could achieves good results in the training stage but poor results when the model needs to be generalized. When there are many different models that achieve similar performance, the model that presented the highest number of hits in training does not mean that this model is the best. Therefore, we created a methodology based on Item Response Theory that allows us to identify whether an ML context is unreliable, providing an extra and different validation for ML models.

PortPred: Exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates.

The physiology of every living cell is regulated at some level by transporter proteins which constitute a relevant portion of membrane-bound proteins and are involved in the movement of ions, small and macromolecules across bio-membranes. The importance of transporter proteins is unquestionable. The prediction and study of previously unknown transporters can lead to the discovery of new biological pathways, drugs and treatments. Here we present PortPred, a tool to accurately identify transporter proteins and their substrate starting from the protein amino acid sequence. PortPred successfully combines pre-trained deep learning-based protein embeddings and machine learning classification approaches and outperforms other state-of-the-art methods. In addition, we present a comparison of the most promising protein sequence embeddings (Unirep, SeqVec, ProteinBERT, ESM-1b) and their performances for this specific task.

COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems.

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC = 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19.

DOES PARTIAL MEDIAL KNEE ARTHROPLATIES HAVE BETTER RESULTS THAN TOTAL ONES?

Objective: Compare the results of medial unicompartmental knee arthroplasty (UKA) using a mobile platform and total knee arthroplasty (TKA) in patients with isolated medial osteoarthritis. Methods: Retrospectivecross-sectional study. Preoperative radiographs of 602 patientswho underwent knee arthroplastybetween February 2017 and February 2020 were evaluated. Isolated medial osteoarthritis was found in 125 patients. Of these, 57 underwent UKA and 68 TKA. With chart analysis and telephone interviews, we compared patients' clinical outcomes and degree of satisfaction. The statistical analysis used a confidence level of 5%. Results: The group of UKA patients obtained 65.8% of favorable results against 79.1% of those undergoing TKA in the function questionnaire (p<0.0001). The complication rate was statistically similar between the groups(p>0.5). Most patients were satisfied or very satisfied in both groups (88.6% of UKA and 91.2% of TKA) (p>0.999). Conclusion: Patients submitted to UKA or TKA have presented the same degree of satisfaction and rate of postoperative complications when comparing patients with isolated medial osteoarthritis. UKA patients had less favorable results onthe clinical functional questionnaire than patients undergoing total arthroplasty. Level Of Evidence III;Retrospective Study.

Objetivo: Comparar os resultados da artroplastia unicompartimental do joelho (UKA) medial com plataforma móvel e artroplastia total do joelho (TKA) em pacientes com osteoartrose medial isolada. Métodos: Estudo transversal retrospectivo. Foram avaliadas radiografias pré-operatórias de 602 pacientes submetidos à artroplastia de joelho entre fevereiro de 2017 e fevereiro de 2020. A osteoartrose medial isolada foi encontrada em 125 pacientes, destes em 57 haviam sido submetidos a UKA e 68 TKA. Com análise de prontuários e entrevistas telefônicas comparamos os resultados clínicos e o grau de satisfação dos pacientes. A análise estatística utilizou nível de confiança de 5%. Resultados: O grupo de pacientes com UKA obteve 65,8% de resultados favoráveis contra 79,1% daqueles submetidos à TKA no questionário de função (p<0,0001). A taxa de complicações foi estatisticamente semelhante entre os grupos (p>0,5). A maioria dos pacientes estava satisfeita ou muito satisfeita em ambos os grupos (88,6% de UKA e 91,2% de TKA) (p>0,999). Conclusão: Pacientes submetidos a UKA ou TKA apresentaram o mesmo grau de satisfação e taxa de complicações pós-operatórias quando comparados pacientes com osteoartrite medial isolada. Os pacientes da UKA apresentaram resultados menos favoráveis ao questionário clínico funcional do que os pacientes submetidos à artroplastia total. Nível de Evidência III; Estudo Retrospectivo.