General Features Research Articles

New Perspectives on Delocalization Pathways in Aromatic Molecular Chameleons.

This study comprehensively analyzes the magnetically induced current density of polycyclic compounds labeled as "aromatic chameleons" since they can arrange their π-electrons to exhibit aromaticity in both the ground and the lowest triplet state. These compounds comprise benzenoid moieties fused to a central skeleton with 4n π-electrons and traditional magnetic descriptors are biased due to the superposition of local magnetic responses. In the S0 state, our analysis reveals that the molecular constituent fragments preserve their (anti)aromatic features in agreement with two types of resonant structures: one associated with aromatic benzenoids and the other with a central antiaromatic ring. Regarding the T1 state, a global and diatropic ring current is revealed. Our aromaticity study is complemented with advanced electronic and geometric descriptors to consider different aspects of aromaticity, particularly important in the evaluation of excited state aromaticity. Remarkably, these descriptors consistently align with the general features on the main delocalization pathways in polycyclic hydrocarbons consisting of fused 4n π-electron rings. Moreover, our study demonstrates an inverse correlation between the singlet-triplet energy difference and the antiaromatic character of the central ring in S0.

Utilizing developmental dynamics for evolutionary prediction and control

Understanding, predicting, and controlling the phenotypic consequences of genetic and environmental change is essential to many areas of fundamental and applied biology. In evolutionary biology, the generative process of development is a major source of organismal evolvability that constrains or facilitates adaptive change by shaping the distribution of phenotypic variation that selection can act upon. While the complex interactions between genetic and environmental factors during development may appear to make it impossible to infer the consequences of perturbations, the persistent observation that many perturbations result in similar phenotypes indicates that there is a logic to what variation is generated. Here, we show that a general representation of development as a dynamical system can reveal this logic. We build a framework that allows predicting the phenotypic effects of perturbations, and conditions for when the effects of perturbations of different origins are concordant. We find that this concordance is explained by two generic features of development, namely the dynamical dependence of the phenotype on itself and the fact that all perturbations must affect the developmental process to have an effect on the phenotype. We apply our theoretical framework to classical models of development and show that it can be used to predict the evolutionary response to selection using information of plasticity and to accelerate evolution in a desired direction. The framework we introduce provides a way to quantitatively interchange perturbations, opening an avenue of perturbation design to control the generation of variation.

A Hybrid Expert System for Estimation of the Manufacturability of a Notional Design

The more “manufacturable” a product is, the “easier” it is to manufacture. For two different product designs targeting the same role, one may be more manufacturable than the other. Evaluating manufacturability requires experts in the processes of manufacturing, “manufacturing process engineers” (MPEs). Human experts are expensive to train and employ, while a well-designed expert system (ES) could be quicker, more reliable, and provide higher performance and superior accuracy. In this work, a group of MPEs (“Team A”) externalized a portion of their expertise into a rule-based expert system in cooperation with a group of ES knowledge engineers and developers. We produced a large ES with 113 total rules and 94 variables. The ES comprises a crisp ES which constructs a Fuzzy ES, thus producing a two-stage ES. Team A then used the ES and a derivation of it (the “MAKE A”) to conduct assessments of the manufacturability of several “notional” designs, providing a sanity check of the rule-base. A provisional assessment used a first draft of the rule-base, and MAKE A, and was of notional wing designs. The primary assessment, using an updated rule-base and MAKE A, was of notional rotor blade designs. We describe the process by which this ES was made and the assessments that were conducted and conclude with insights gained from constructing the ES. These insights can be summarized as follows: build a bridge between expert and user, move from general features to specific features, do not make the user do a lot of work, and only ask the user for objective observations. We add the product of our work to the growing library of tools and methodologies at the disposal of the U.S. Army Engineer Research and Development Center (ERDC). The primary findings of the present work are (1) an ES that satisfied the experts, according to their expressed performance expectations, and (2) the insights gained on how such a system might best be constructed.

Offloading the computational complexity of transfer learning with generic features.

Deep learning approaches are generally complex, requiring extensive computational resources and having high time complexity. Transfer learning is a state-of-the-art approach to reducing the requirements of high computational resources by using pre-trained models without compromising accuracy and performance. In conventional studies, pre-trained models are trained on datasets from different but similar domains with many domain-specific features. The computational requirements of transfer learning are directly dependent on the number of features that include the domain-specific and the generic features. This article investigates the prospects of reducing the computational requirements of the transfer learning models by discarding domain-specific features from a pre-trained model. The approach is applied to breast cancer detection using the dataset curated breast imaging subset of the digital database for screening mammography and various performance metrics such as precision, accuracy, recall, F1-score, and computational requirements. It is seen that discarding the domain-specific features to a specific limit provides significant performance improvements as well as minimizes the computational requirements in terms of training time (reduced by approx. 12%), processor utilization (reduced approx. 25%), and memory usage (reduced approx. 22%). The proposed transfer learning strategy increases accuracy (approx. 7%) and offloads computational complexity expeditiously.

The subject and non-subject agreements in the Yemsa relative clauses

This study aims to elucidate the subject and non-subject agreement mechanisms within Yemsa relative clauses, an area not comprehensively covered by existing research. By analysing these agreement forms, the study seeks to bridge the identified gap in the understanding of Yemsa’s syntactic structures. The research questions guiding this study focus on the mechanisms through which subject agreements are manifested in Yemsa's relative clauses and the morphological markers they employ, alongside an examination of how non-subject agreements within these clauses diverge from subject agreements. The data were collected through the elicitation technique through informant interviews about subject and non-subject agreements in the Yemsa relative clauses. The data were analysed using a descriptive approach. The person-marker inventories are suffixes. Siewierska’s Prominence Hierarchy works in Yemsa. The order of the suffixes is modifier > head. Person markers simultaneously indicate masculine and feminine referents and numbers. Siewierska’s Predicate Hierarchy works on Yemsa. The suffix element -nà appears in the relative verbs. The word order in the relative clause is (O)VS. The nominative case is unmarked, whereas the accusative case is marked. The subject argument is expressed in the same manner as an independent clause. The description of the subject and the non-subject agreement in the Yemsa relative clauses has significant implications for developing the general features of Omotic and Afroasiatic languages. The study will serve as an input for the preparation of pedagogical materials in the language.

Weakly-Supervised Mirror Detection via Scribble Annotations

Mirror detection is of great significance for avoiding false recognition of reflected objects in computer vision tasks. Existing mirror detection frameworks usually follow a supervised setting, which relies heavily on high quality labels and suffers from poor generalization. To resolve this, we instead propose the first weakly-supervised mirror detection framework and also provide the first scribble-based mirror dataset. Specifically, we relabel 10,158 images, most of which have a labeled pixel ratio of less than 0.01 and take only about 8 seconds to label. Considering that the mirror regions usually show great scale variation, and also irregular and occluded, thus leading to issues of incomplete or over detection, we propose a local-global feature enhancement (LGFE) module to fully capture the context and details. Moreover, it is difficult to obtain basic mirror structure using scribble annotation, and the distinction between foreground (mirror) and background (non-mirror) features is not emphasized caused by mirror reflections. Therefore, we propose a foreground-aware mask attention (FAMA), integrating mirror edges and semantic features to complete mirror regions and suppressing the influence of backgrounds. Finally, to improve the robustness of the network, we propose a prototype contrast loss (PCL) to learn more general foreground features across images. Extensive experiments show that our network outperforms relevant state-of-the-art weakly supervised methods, and even some fully supervised methods. The dataset and codes are available at https://github.com/winter-flow/WSMD.

SECap: Speech Emotion Captioning with Large Language Model

Speech emotions are crucial in human communication and are extensively used in fields like speech synthesis and natural language understanding. Most prior studies, such as speech emotion recognition, have categorized speech emotions into a fixed set of classes. Yet, emotions expressed in human speech are often complex, and categorizing them into predefined groups can be insufficient to adequately represent speech emotions. On the contrary, describing speech emotions directly by means of natural language may be a more effective approach. Regrettably, there are not many studies available that have focused on this direction. Therefore, this paper proposes a speech emotion captioning framework named SECap, aiming at effectively describing speech emotions using natural language. Owing to the impressive capabilities of large language models in language comprehension and text generation, SECap employs LLaMA as the text decoder to allow the production of coherent speech emotion captions. In addition, SECap leverages HuBERT as the audio encoder to extract general speech features and Q-Former as the Bridge-Net to provide LLaMA with emotion-related speech features. To accomplish this, Q-Former utilizes mutual information learning to disentangle emotion-related speech features and speech contents, while implementing contrastive learning to extract more emotion-related speech features. The results of objective and subjective evaluations demonstrate that: 1) the SECap framework outperforms the HTSAT-BART baseline in all objective evaluations; 2) SECap can generate high-quality speech emotion captions that attain performance on par with human annotators in subjective mean opinion score tests.

Automated linguistic analysis in speech samples of Turkish-speaking patients with schizophrenia-spectrum disorders

Modern natural language processing (NLP) methods provide ways to objectively quantify language disturbances for potential use in diagnostic classification. We performed computerized language analysis in speech samples of 82 Turkish-speaking subjects, including 44 patients with schizophrenia spectrum disorders (SSD) and 38 healthy controls (HC). Exploratory analysis of speech samples involved 16 sentence-level semantic similarity features using SBERT (Sentence Bidirectional Encoder Representation from Text) as well as 8 generic and 8 part-of-speech (POS) features. The random forest classifier using SBERT-derived semantic similarity features achieved a mean accuracy of 85.6 % for the classification of SSD and HC. When semantic similarity features were combined with generic and POS features, the classifier's mean accuracy reached to 86.8 %. Our analysis reflected increased sentence-level semantic similarity scores in SSD. Generic and POS analyses revealed an increase in the use of verbs, proper nouns and pronouns in SSD while our results showed a decrease in the utilization of conjunctions, determiners, and both average and maximum sentence length in SSD compared to HC. Quantitative language features were correlated with the expressive deficit domain of BNSS (Brief Negative Symptom Scale) as well as with the duration of illness. These findings from Turkish-speaking interviews contribute to the growing evidence-based NLP-derived assessments in non-English-speaking patients.

Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma

BackgroundRecently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited in lung adenocarcinoma (LUAD).MethodsSingle-cell RNA sequencing and bulk RNA sequencing data are integrated to identify TIME-related lncRNAs. A total of 1368 LUAD patients are enrolled from 6 independent datasets. An integrative machine learning framework is introduced to develop a TIME-related lncRNA signature (TRLS).ResultsThis study identified TIME-related lncRNAs from integrated analysis of single‑cell and bulk RNA sequencing data. According to these lncRNAs, a TIME-related lncRNA signature was developed and validated from an integrative procedure in six independent cohorts. TRLS exhibited a robust and reliable performance in predicting overall survival. Superior prediction performance barged TRLS to the forefront from comparison with general clinical features, molecular characters, and published signatures. Moreover, patients with low TRLS displayed abundant immune cell infiltration and active lipid metabolism, while patients with high TRLS harbored significant genomic alterations, high PD-L1 expression, and elevated DNA damage repair (DDR) relevance. Notably, subclass mapping analysis of nine immunotherapeutic cohorts demonstrated that patients with high TRLS were more sensitive to immunotherapy.ConclusionsThis study developed a promising tool based on TIME-related lncRNAs, which might contribute to tailored treatment and prognosis management of LUAD patients.

The Algorithms for Managing a Matrix-Based Warehouse Utilizing Standardized Transport and Storage Cells

The article explores the principles of organizing warehouse systems based on standardized transport and storage cells. It discusses the general hardware features of transport and storage cells that enable the movement of stored goods between them. The article presents a method for constructing a warehouse graph that takes into account possible directions for transferring containers with cargo between the transport and storage cells integrated into the warehouse structure, along with their common hardware characteristics. Key criteria used in determining the edge weights of the graph are described, including the basic cost of movement for each axis, cell wear and tear, cargo weight, fragility of the cargo, distance to the nearest available cells, and repairability. Algorithms are presented, the primary task of which is to determine the sequence of container movements between warehouse cells to facilitate loading and unloading operations. Simulation modeling of a warehouse with dimensions of 5×5×5 was conducted using the proposed algorithms, both with and without considering cell wear and tear parameters. The results of the simulation highlighted the significance of this criterion, allowing for extended warehouse servicing intervals and maximizing the time until the first failure.The study also investigates the possibility of optimizing the structure of such warehouse systems to meet various requirements. As part of this investigation, the structure was optimized for a warehouse with dimensions of 4×3×3.

The dualistic legal nature of the contract for the performance of development work

The research is conducted in order to develop practical recommendations for the further development and improvement of various forms or sources of law, individual legal acts, norms and institutions. The main objective of the article is to highlight the problems of legal regulation in the theory of legal science and in law enforcement practice, the elaboration of legal mechanisms and recommendations for improving current legislation, overcoming legal gaps.The subject of the research is the norms of Russian legislation regulating relations in the field of development work, legal positions reflected in judicial practice. The object of research is public relations related to the legal institution of a contract for the performance of development work. These agreements belong to the group of consensual, retaliatory and mutual, that is, bilaterally binding agreements that are considered concluded from the moment the parties reach all essential conditions.This type of contract has the features characteristic of both contracts for the performance of work and contracts for the provision of services. The practice of using this legal institution shows the dualistic legal nature of contracts of this type and the need to separate it into a separate independent type of contract – a contract of design activities and special legal regulation. Today in Russia, the contract for the implementation of the R&D is considered adjacent to the contract for the performance of work and is regulated by Chapter 38 of the Civil Code of the Russian Federation.The purpose of this article is to study the main features of the contract for the performance of experimental design work, generalization or isolation of its general and specific features, analysis of various points of view on the issue of its legal nature, review of Russian legislation, as well as Russian judicial practice, their positive and negative aspects.Methodology. In order to best solve the task, a systematic approach was used in combination with a logical method. This allowed us to study the theoretical, factual and legal foundations of the phenomenon of the contract for the implementation of R&D. The comparative legal method was also used by comparing single legal concepts and processes, contrasting legal norms and identifying similarities and differences between them, using conclusions by analogy, relying on the similarity of the signs of the facts being studied, which will allow transferring the signs from one phenomenon being studied to another.Main results and scope of application. The article outlines the main features of the contract for the implementation of R&D, different points of view on the definition of the legal nature of this type of contract. The results of the study contribute to the identification of problems of legal regulation in the theory of legal science and in law enforcement practice, the elaboration of legal mechanisms and recommendations for improving current legislation, overcoming legal gaps.

Abstract 4916: Enhancing multi-organ frozen section cancer discrimination model by sharing cancer discrimination and organ classification task

Abstract Introduction: Frozen section(FS) testing is an important method that removes part or all of a patient's tissue of a suspected tumor during surgery and microscopic examination to determine a diagnosis and surgical method that requires a rapid and accurate diagnosis. In this study, we developed a deep learning model to determine the presence of cancer and the type of organ in H&E FS whole slide images(WSIs) of 5 organs such as Breast(BR), Lung(LU), Stomach(ST), Breast Sentinel Lymph Node(LN), and Prostate(PR). Design: In this study, we utilized a dataset comprising 19,881 H&E FS WSIs and 11,985 H&E-stained formalin-fixed paraffin-embedded (FFPE) WSIs to enhance generalization. The dataset included FS and FFPE WSIs from 33 different organs sourced from The Cancer Genome Atlas (TCGA) and additional FS and FFPE WSIs from a domestic hospital and the Camelyon 16 dataset, respectively. For the FS dataset, 20% was set aside for performance evaluation, while the remaining was randomly split into a 3:1 ratio for train and validation. In our methodology, we utilized instance-based multi-instance learning (MIL) to identify patches (1024*1024 pixels, 20x mag) with a high likelihood of cancer presence in each WSI. Then, we implemented multi-task learning (MTL) to predict the presence of cancer and the organ simultaneously. Result: The table shows the WSI level performance through the 5 organs. The top 5 rows show results of MIL that only discriminate cancer. In comparison, the bottom 5 rows show results of MIL with MTL that share features from the cancer discrimination and organ classification. MIL with MTL make higher roc/auc than MIL only. Conclusion: This study explores the automated analysis of H&E-stained FS WSIs using a deep-learning to discriminate cancer and classify organs. We could figure out that training cancer discrimination and organ classification simultaneously can make general features, and it leads to increased diagnostic performance. Performance Table Between MIL vs MIL + MTL Method Organ F1-score Sensitivity Specificity ROC/AUC Organ Accuracy MIL LN 0.9358 0.9669 0.6170 0.7919 NaN ST 0.9458 0.9583 0.8302 0.8943 LU 0.9577 0.9457 0.9275 0.9366 BR 0.9902 0.9895 0.9757 0.9826 PR 0.9346 0.9328 0.8786 0.9057 MIL + MTL LN 0.9448 0.9448 0.7872 0.8660 0.8816 ST 0.9771 0.9697 0.9623 0.9660 0.7595 LU 0.9700 0.9721 0.9203 0.9462 0.9509 BR 0.9932 0.9910 0.9877 0.9894 0.8979 PR 0.9480 0.9515 0.8913 0.9214 0.9803 Citation Format: Joonho Lee, Joonyoung Cho, Junho Lee, Yoon-La Choi, Kyungsoo Jung, Tae-Yeong Kwak, Sun Woo Kim, HyeYoon Chang. Enhancing multi-organ frozen section cancer discrimination model by sharing cancer discrimination and organ classification task [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4916.

Human-Fungal Pathogen Interactions from the Perspective of Immunoproteomics Analyses.

Antibody immunity is now known to play a critical role in combating mycotic infections. The identification of molecules that can elicit an antibody response against fungal pathogens is the first step in developing antibody-based therapeutic strategies. Antigenic proteins are molecules recognized by the immune system that can stimulate antibody production and, therefore, can be a direct target for studying human-fungal pathogen interactions. Advances in recent immunoproteomic approaches have substantially aided in determining the key antigenic proteins on a large scale. In this review, we present a collection of antigenic proteins identified in yeast, dimorphic, and filamentous fungal pathogens to date. The general features of antigenic proteins are summarized and reveal that the proteins could commonly function in antistress responses, protein synthesis, and metabolism. The antigenic proteins listed here could serve as starting materials for developing species-specific or broad-spectrum diagnostic tests, therapeutic antibodies, and even vaccines against fungal infections.

Innovative agricultural ontology construction using NLP methodologies and graph neural network

Advancements in technology brought various innovations to agricultural practices. As a part of the development, establishing an agricultural ontology would unleash the growth of cross-domain agriculture and Natural Language Processing (NLP). For constructing such domain-based ontology, semantic and syntactic understanding of the domain data is needed. In agriculture, the availability of pre-determined domain-based data is not sufficient hence, a standard methodology with syntactic and general semantic features are required for processing the data. In this research work, Agricultural Domain based Ontology Construction (ADOC) is proposed and the overall framework has three approaches for establishing the agriculture domain based ontologies. The input text documents undergo anaphora resolution phase utilizing the semantic-based method. In the first method of ADOC the ontology is developed using the terms and relationships that are extracted from the NLP techniques. The second method of ADOC uses pretrained BERT model and Hearst patterns while the third model of ADOC is based on pretrained BERT with regular expressions and unsupervised Graph Neural Network (GNN) for creating the agricultural ontology. The efficacy of the proposed ADOC utilizing BERT with regular expressions and GNN method shows an outstanding result when compared to other proposed and prevailing systems, with a precision and recall of 96.67% and 98.31%.

Ceramic metal oxide coatings formation by hybrid plasma electrolytic treatment: Interaction scenarios of ceramic NPs with oxide layers

Understanding the formation of protective ceramic metal oxide (CMO) coatings (layers) on the surface of light alloys remains a pressing task in light of products operating in extreme conditions. Plasma electrolytic oxidation (PEO) of light alloy surfaces has recently been modified by introducing various ceramic nanoparticles (NPs) into the electrolyte to perform hybrid plasma electrolytic treatment (HPET). In this work, the general and distinctive features of the interaction mechanisms of TiC, SiO2 and ZrO2 nanoparticles (NPs) with the CMO coating formed by HPET of Al–Si and Mg-based alloys were summarized and theoretically substantiated. The interaction of charged ceramic NPs accelerated by an electric field with the oxide layer is determined by transformation of the NPs kinetic energy into heating and plastic deformation, into phase transformations, as well as into the creation of new surface defects. One of the controlling factors is the ratio of the particle and the oxide layer hardnesses. In the case of SiO2 in Al–Si alloys, the formed coating is harder than the ceramic NPs while in other cases, the NPs are harder than the CMO coating, which determines the different scenarios for the interaction of NPs with the coating under HPET. In summary 7 different scenarios for the interaction of ceramic NPs added to the electrolyte with the СМO layer during its formation put forward, which depending on the ratio of the hardness of the CMO layer and the added particles, temperature/pressure conditions of the phase transformations in the NP (including its melting), the particles size distribution and the base PEO process parameters.

Shortcuts of Freely Relaxing Systems Using Equilibrium Physical Observables.

Many systems, when initially placed far from equilibrium, exhibit surprising behavior in their attempt toequilibrate. Striking examples are the Mpemba effect and the cooling-heating asymmetry. These anomalous behaviors can be exploited to shorten the time needed to cool down (or heat up) a system. Though, a strategy to design these effects in mesoscopic systems is missing. We bring forward a description that allows us to formulate such strategies, and, along the way, makes natural these paradoxical behaviors. In particular, we study the evolution of macroscopic physical observables of systems freely relaxing under the influence of one or two instantaneous thermal quenches. The two crucial ingredients in our approach are timescale separation and a nonmonotonic temperature evolution of an important state function. We argue that both are generic features near a first-order transition. Our theory is exemplified with the one-dimensional Ising model in a magnetic field using analytic results and numerical experiments.

A novel feature-level fusion scheme with multimodal attention CNN for heart sound classification

Background and objectiveMost of the existing machine learning-based heart sound classification methods achieve limited accuracy. Since they primarily depend on single domain feature information and tend to focus equally on each part of the signal rather than employing a selective attention mechanism. In addition, they fail to exploit convolutional neural network (CNN) - based features with an effective fusion strategy. MethodsIn order to overcome these limitations, a novel multimodal attention convolutional neural network (MACNN) with a feature-level fusion strategy, in which Mel-cepstral domain as well as general frequency domain features are incorporated to increase the diversity of the features, is proposed in this paper. In the proposed method, DilationAttenNet is first utilized to construct attention-based CNN feature extractors and then these feature extractors are jointly optimized in MACNN at the feature-level. The attention mechanism aims to suppress irrelevant information and focus on crucial diverse features extracted from the CNN. ResultsExtensive experiments are carried out to study the efficacy of the feature level fusion in comparison to that with early fusion. The results show that the proposed MACNN method significantly outperforms the state-of-the-art approaches in terms of accuracy and score for the two publicly available Github and Physionet datasets. ConclusionThe findings of our experiments demonstrated the high performance for heart sound classification based on the proposed MACNN, and hence have potential clinical usefulness in the identification of heart diseases. This technique can assist cardiologists and researchers in the design and development of heart sound classification methods.

Issues of accounting for intangible assets under the new standard at the stage of their identification

Subject. Since 2024, the new Federal Accounting Standard (FSBU) 14/2022 – Intangible Assets is mandatory. This article examines the categorical framework of this Standard, as well as the past and international standards in comparison with legal norms. Objectives. The article aims to identify the types of intangible assets to be accounted for in accordance with the new Standard, based on the essence and generic features, the legal content of the types of intangible assets mentioned in the accounting standards in order to identify them, based on their importance in generating economic benefits and participating in the processes of the organization. Methods. For the study, we used the general scientific methods of cognition, a systems approach, logical generalization, analysis and synthesis, and analogy. Results. The study shows that the new Standard is not much different from the old one in terms of defining categories. In contrast to the international similar standard, the domestic standard lacks detailed explanations that are essential for identifying costs as expenses of the period or intangible assets. Conclusions and Relevance. For the reasonable recognition of intangible assets in accounting, it is necessary that the Standard takes into account the norms of the Civil Code of the Russian Federation and other laws that make it possible to distinguish between generic and essential features of intangible assets, which are the results of intellectual activity and means of individualization. The presented results are intended to further develop the methodology and practice of accounting for intangible assets of organizations.

Minimum intensity projection of embossed quadrant-detection images for improved photoreceptor mosaic visualisation.

Non-confocal split-detection imaging reveals the cone photoreceptor inner segment mosaic in a plethora of retinal conditions, with the potential of providing insight to ageing, disease, and response to treatment processes, in vivo, and allows the screening of candidates for cell rescue therapies. This imaging modality complements confocal reflectance adaptive optics scanning light ophthalmoscopy, which relies on the waveguiding properties of cones, as well as their orientation toward the pupil. Split-detection contrast, however, is directional, with each cone inner segment appearing as opposite dark and bright semicircles, presenting a challenge for either manual or automated cell identification. Quadrant-detection imaging, an evolution of split detection, could be used to generate images without directional dependence. Here, we demonstrate how the embossed-filtered quadrant-detection images, originally proposed by Migacz etal. for visualising hyalocytes, can also be used to generate photoreceptor mosaic images with better and non-directional contrast for improved visualisation. As a surrogate of visualisation improvement between legacy split-detection images and the images resulting from the method described herein, we provide preliminary results of simple image processing routines that may enable the automated identification of generic image features, as opposed to complex algorithms developed specifically for photoreceptor identification, in pathological retinas.

Formation mechanisms of persistent extreme precipitation events over the eastern periphery of the Tibetan Plateau: Synoptic conditions, moisture transport and the effect of steep terrain

The eastern periphery of the Tibetan Plateau (EPTP) is prone to frequent and severe Persistent Extreme precipitation (PEP) events in summer. Given the complexity of weather systems and the intricate nature of terrain over this region, the generation and development mechanisms of the PEP in the EPTP remain to be determined. In this study, the formation and persistence mechanisms are further explored from the perspective of the general features of large-scale circulations, moisture source diagnosis and the influence of steep topography by using a thermal-dynamical diagnosis method, a mesoscale numerical simulation and a Lagrangian identification of the main moisture sources. The results show that during PEP events, the corresponding atmospheric circulation systems are characterized by an anomalous Rossby wave train at middle levels, an intensified westerly jet, an eastward extension of the South Asian high and a westward extension of the western Pacific subtropical high, all of which are possible to facilitate the development of potential instability and anticyclonic convergence of water vapor transport. The evaporative moisture sources from the Southeastern Asia (SEA), Bay of Bengal (BOB), and Southern China Sea (SCS) are remarkably enhanced during PEP events, with a contribution of 56.44%, which is 28.5% higher than the climatic mean. The further sensitive experiment indicates that the steep terrain could enhance the continuous transport of positive potential vorticity and the downward propagation of upper-level isentropic potential vorticity disturbances, thereby playing an essential role in the triggering and development of PEP events.