Maladaptive plasticity is linked to the chronification of diseases such as pain, but the transition from acute to chronic pain is not well understood mechanistically. Neuroplasticity in the central nucleus of the amygdala (CeA) has emerged as a mechanism for sensory and emotional-affective aspects of injury-induced pain, although evidence comes from studies conducted almost exclusively in acute pain conditions and agnostic to cell type specificity. Here, we report time-dependent changes in genetically distinct and projection-specific CeA neurons in neuropathic pain. Hyperexcitability of CRF projection neurons and synaptic plasticity of parabrachial (PB) input at the acute stage shifted to hyperexcitability without synaptic plasticity in non-CRF neurons at the chronic phase. Accordingly, chemogenetic inhibition of the PB→CeA pathway mitigated pain-related behaviors in acute, but not chronic, neuropathic pain. Cell-type-specific temporal changes in neuroplasticity provide neurobiological evidence for the clinical observation that chronic pain is not simply the prolonged persistence of acute pain.

UNASSIGNED: The mesolimbic reward system is associated with the promotion and rewarding benefits of social relationships. In the socially monogamous prairie vole (Microtus ochrogaster), the establishment of a pair bond can be displayed by a robust preference for a breeding partner and aggressive rejection of unfamiliar conspecifics. Mesolimbic dopamine signaling influences bond-related behaviors within the vole through dopamine transmission and receptor activity in the nucleus accumbens. However, only one experiment has examined how the ventral tegmental area (VTA), a region that produces much of the fore- and mid-brain dopamine, regulates these social behaviors. Specifically, inhibition of either glutamate or GABA neurons in the VTA during a brief courtship promoted a partner preference formation in male prairie voles. The VTA is a heterogeneous structure that contains dopamine, GABA, and glutamate neurons as well as receives a variety of projections including corticotropin-releasing factor (CRF) suggested to modulate dopamine release.
UNASSIGNED: We used pharmacological manipulation to examine how GABA and CRF signaling in the VTA modulate partner preference formation in male and female prairie voles. Specifically, we used a 3 h partner preference test, a social choice test, to assess the formation of a partner preference following an infused bicuculline and CRF during a 1 h cohabitation and muscimol and CP154526, a CRFR1 antagonist, during a 24 h cohabitation with an opposite-sex conspecific.
UNASSIGNED: Our study demonstrated that bicuculline, a GABA A receptor antagonist, and CRF in the VTA promoted a partner preference, whereas low-dose muscimol, a GABA A receptor agonist, and CP154526, a CRFR1 antagonist, inhibited a partner preference in both male and female prairie voles.
UNASSIGNED: This study demonstrated that GABA and CRF inputs into the VTA is necessary for the formation of a partner preference in male and female prairie voles.

UNASSIGNED: The diversity of edible fungus species and the extent of mycological knowledge pose significant challenges to the research, cultivation, and popularization of edible fungus. To tackle this challenge, there is an urgent need for a rapid and accurate method of acquiring relevant information. The emergence of question and answer (Q&A) systems has the potential to solve this problem. Named entity recognition (NER) provides the basis for building an intelligent Q&A system for edible fungus. In the field of edible fungus, there is a lack of a publicly available Chinese corpus suitable for use in NER, and conventional methods struggle to capture long-distance dependencies in the NER process.
UNASSIGNED: This paper describes the establishment of a Chinese corpus in the field of edible fungus and introduces an NER method for edible fungus information based on XLNet and conditional random fields (CRFs). Our approach combines an iterated dilated convolutional neural network (IDCNN) with a CRF. First, leveraging the XLNet model as the foundation, an IDCNN layer is introduced. This layer addresses the limited capacity to capture features across utterances by extending the receptive field of the convolutional kernel. The output of the IDCNN layer is input to the CRF layer, which mitigates any labeling logic errors, resulting in the globally optimal labels for the NER task relating to edible fungus.
UNASSIGNED: Experimental results show that the precision achieved by the proposed model reaches 0.971, with a recall of 0.986 and an F1-score of 0.979.
UNASSIGNED: The proposed model outperforms existing approaches in terms of these evaluation metrics, effectively recognizing entities related to edible fungus information and offering methodological support for the construction of knowledge graphs.

HIV-1 is characterized by remarkable genetic diversity resulting from its high replication rate, error-prone reverse transcriptase enzyme and recombination events. In Uganda, HIV-1 subtype diversity is mostly dominated by subtypes A, D, and A1/D Unique Recombinant Forms (URFs). In this study, deep sequences of HIV from patients with known antiretroviral therapy (ART) status were analyzed to determine the subtypes and to identify drug-resistance mutations circulating in the study population. Of the 187 participant samples processed for next-generation sequencing (NGS), 137 (73%) were successfully classified. The majority of HIV-1 strains were classified as subtype A (75, 55%), D (43, 31%), with other subtypes including C (3, 2%), A1/D (9, 7%) and CRF10_CD (1, <1%). Recombinant analysis of nine complete A1/D HIV genomes identified novel recombination patterns described herein. Furthermore, we report for the first time in Uganda, an HIV-1 CRF10_CD strain from a fisherfolk in a Lake Victoria Island fishing community.

It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets.
This study\'s objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials.
We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs.
Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date.
The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element\'s (variable\'s) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by \"wrapping\" them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium\'s Clinical Data Acquisition Standards Harmonization Model.

Mild traumatic brain injury (mTBI) is a significant health burden due to mTBI-related chronic debilitating cognitive and psychiatric morbidities. Recent evidence from our laboratory suggests a possible dysregulation within reward/motivational circuit function at the level of a subcortical structure, the lateral habenula (LHb), where we demonstrated a causal role for hyperactive LHb in mTBI-induced motivational deficits in self-care grooming behavior in young adult male mice when exposed to mTBI injury during late adolescence (at ~8 weeks old). Here we extended this observation by further characterizing neurobehavioral effects of this repetitive closed head injury model of mTBI in both young adult male and female mice on LHb excitability, corticotropin releasing factor (CRF) modulation of LHb activity, and behavioral responses of motivation to self-care behavior, and approach versus avoidance behavior in the presence of a social- or threat-related stimulus. We show that mTBI increases LHb spontaneous tonic activity in female mice similar to what we previously observed in male mice as well as promoting LHb neuronal hyperexcitability and hyperpolarization-induced LHb bursting in both male and female mice. Interestingly, mTBI only increases LHb intrinsic excitability in male mice coincident with higher levels of the hyperpolarization-activated cation currents (HCN/Ih) and reduces levels of the M-type potassium currents while potentiating M-currents without altering intrinsic excitability in LHb neurons of female mice. Since persistent dysregulation of brain CRF systems is suggested to contribute to chronic psychiatric morbidities and that LHb neurons are highly responsive to CRF, we then tested whether LHb CRF subsystem becomes engaged following mTBI. We found that in vitro inhibition of CRF receptor type 1 (CRFR1) within the LHb normalizes mTBI-induced enhancement of LHb tonic activity and hyperexcitability in both sexes, suggesting that an augmented intra-LHb CRF-CRFR1-mediated signaling contributes to the overall LHb hyperactivity following mTBI. Behaviorally, mTBI diminishes motivation for self-care grooming in female mice as in male mice. mTBI also alters defensive behaviors in the looming shadow task by shifting the innate defensive behaviors towards more passive action-locking rather than escape behaviors in response to an aerial threat in both male and female mice as well as prolonging the latency to escape responses in female mice. While, this model of mTBI reduces social preference in male mice, it induces higher social novelty seeking during the novel social encounters in both male and female mice. Overall, our study provides further translational validity for the use of this preclinical model of mTBI for investigation of mTBI-related reward circuit dysfunction and mood/motivation-related behavioral deficits in both sexes while uncovering a few sexually dimorphic neurobehavioral effects of this model that may differentially affect young males and females when exposed to this type of mTBI injury during late adolescence.

To address the problem of poor entity recognition performance caused by the lack of Chinese annotation in clinical electronic medical records, this paper proposes a multi-medical entity recognition method F-MNER using a fusion technique combining BART, Bi-LSTM, and CRF. First, after cleaning, encoding, and segmenting the electronic medical records, the obtained semantic representations are dynamically fused using a bidirectional autoregressive transformer (BART) model. Then, sequential information is captured using a bidirectional long short-term memory (Bi-LSTM) network. Finally, the conditional random field (CRF) is used to decode and output multi-task entity recognition. Experiments are performed on the CCKS2019 dataset, with micro avg Precision, macro avg Recall, weighted avg Precision reaching 0.880, 0.887, and 0.883, and micro avg F1-score, macro avg F1-score, weighted avg F1-score reaching 0.875, 0.876, and 0.876 respectively. Compared with existing models, our method outperforms the existing literature in three evaluation metrics (micro average, macro average, weighted average) under the same dataset conditions. In the case of weighted average, the Precision, Recall, and F1-score are 19.64%, 15.67%, and 17.58% higher than the existing BERT-BiLSTM-CRF model respectively. Experiments are performed on the actual clinical dataset with our MF-MNER, the Precision, Recall, and F1-score are 0.638, 0.825, and 0.719 under the micro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.685, 0.800, and 0.733 under the macro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.647, 0.825, and 0.722 under the weighted avg evaluation mechanism. The above results show that our method MF-MNER can integrate the advantages of BART, Bi-LSTM, and CRF layers, significantly improving the performance of downstream named entity recognition tasks with a small amount of annotation, and achieving excellent performance in terms of recall score, which has certain practical significance. Source code and datasets to reproduce the results in this paper are available at https://github.com/xfwang1969/MF-MNER .

Ovarian high-grade serous carcinoma (OHGSC) is the most common type of ovarian cancer worldwide. Genome sequencing has identified mutations in chromatin remodeling factors (CRFs) in gynecological cancer, such as clear cell carcinoma, endometrioid carcinoma and endometrial serous carcinoma. However, to the best of our knowledge, the association between CRFs and OHGSC remains unexplored. The present study aimed to investigate the clinicopathological and molecular characteristics of CRF dysfunction in OHGSC. CRF alterations were analyzed through numerous methods, including the analysis of public next-generation sequencing (NGS) data from 585 ovarian serous carcinoma cases from The Cancer Genome Atlas (TCGA), immunohistochemistry (IHC), and DNA copy number assays, which were performed on 203 surgically resected OHGSC samples. In the public NGS dataset, the most frequent genetic alteration was actin-like protein 6A (ACTL6A) amplification at 19.5%. Switch/sucrose non-fermentable related, matrix associated, actin dependent regulator of chromatin subfamily c member 2 (SMARCC2) amplification (3.1%) was associated with significantly decreased overall survival (OS). In addition, chromodomain-helicase-DNA-binding protein 4 (CHD4) amplification (5.7%) exhibited unfavorable outcome trends, although not statistically significant. IHC revealed the protein expression loss of ARID1A (2.5%), SMARCA2 (2.5%) and SMARCA4 (3.9%). The protein expression levels of ACTL6A, SMARCC2 and CHD4 were evaluated using H-score. Patients with low protein expression levels of ACTL6A showed a significantly decreased OS. Copy number gain or gene amplification was demonstrated in ACTL6A (66.2%) and SMARCC2 (33.5%), while shallow deletion or deep deletion was demonstrated in CHD4 (70.7%). However, there was no statistically significant difference in protein levels of these CRFs, between the different copy number alterations (CNAs). Overall, OHGSC exhibited CNAs and protein loss, indicating possible gene alterations in CRFs. Moreover, there was a significant association between the protein expression levels of ACTL6A and poor prognosis. Based on these findings, it is suggested that CRFs could serve as prognostic markers for OHGSC.

Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with extensive data volumes. Smart grids provide a solution by enabling two-way electricity flow, thereby facilitating the detection, analysis, and implementation of new measures to address data flow issues. The key objective is to provide a deep learning-based amalgamated model to detect electricity theft and secure the smart grid. This research introduces an innovative approach to overcome the limitations of current electricity theft detection systems, which predominantly rely on analyzing one-dimensional (1-D) electric data. These approaches often exhibit insufficient accuracy when identifying instances of theft. To address this challenge, the article proposes an ensemble model known as the RNN-BiLSTM-CRF model. This model amalgamates the strengths of recurrent neural network (RNN) and bidirectional long short-term memory (BiLSTM) architectures. Notably, the proposed model harnesses both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data, thereby enhancing the effectiveness of the theft detection process. The experimental results showcase an impressive accuracy rate of 93.05% in detecting electricity theft, surpassing the performance of existing models in this domain.

Cardiovascular diseases and cancer are the leading causes of death in the Western world and share common risk factors. Reduced cardiorespiratory fitness (CRF) is a major determinant of cardiovascular morbidity and cancer survival. In this review we discuss cancer- induced disturbances of parenchymal, cellular, and mitochondrial function, which limit CRF and may be antagonized and attenuated through exercise training. We show the impact of CRF on cancer survival and its attenuating effects on cardiotoxicity of cancer-related treatment. Tailored exercise programs are not yet available for each tumor entity as several trials were performed in heterogeneous populations without adequate cardiopulmonary exercise testing (CPET) prior to exercise prescription and with a wide variation of exercise modalities. There is emerging evidence that exercise may be a crucial pillar in cancer treatment and a tool to mitigate cardiotoxic treatment effects. We discuss modalities of aerobic exercise and resistance training and their potential to improve CRF in cancer patients and provide an example of a periodization model for exercise training in cancer.