RMBase v3.0: decode the landscape, mechanisms and functions of RNA modifications.

Although over 170 chemical modifications have been identified, their prevalence, mechanism and function remain largely unknown. To enable integrated analysis of diverse RNA modification profiles, we have developed RMBase v3.0 (http://bioinformaticsscience.cn/rmbase/), a comprehensive platform consisting of eight modules. These modules facilitate the exploration of transcriptome-wide landscape, biogenesis, interactome and functions of RNA modifications. By mining thousands of epitranscriptome datasets with novel pipelines, the 'RNA Modifications' module reveals the map of 73 RNA modifications of 62 species. the 'Genes' module allows to retrieve RNA modification profiles and clusters by gene and transcript. The 'Mechanisms' module explores 23 382 enzyme-catalyzed or snoRNA-guided modified sites to elucidate their biogenesis mechanisms. The 'Co-localization' module systematically formulates potential correlations between 14 histone modifications and 6 RNA modifications in various cell-lines. The 'RMP' module investigates the differential expression profiles of 146 RNA-modifying proteins (RMPs) in 18 types of cancers. The 'Interactome' integrates the interactional relationships between 73 RNA modifications with RBP binding events, miRNA targets and SNPs. The 'Motif' illuminates the enriched motifs for 11 types of RNA modifications identified from epitranscriptome datasets. The 'Tools' introduces a novel web-based 'modGeneTool' for annotating modifications. Overall, RMBase v3.0 provides various resources and tools for studying RNA modifications.

Goliath induces inflammation in obese mice by linking fatty acid ß-oxidation to glycolysis.

Obesity is associated with metabolic disorders and chronic inflammation. However, the obesity-associated metabolic contribution to inflammatory induction remains elusive. Here, we show that, compared with lean mice, CD4+ T cells from obese mice exhibit elevated basal levels of fatty acid ß-oxidation (FAO), which promote T cell glycolysis and thus hyperactivation, leading to enhanced induction of inflammation. Mechanistically, the FAO rate-limiting enzyme carnitine palmitoyltransferase 1a (Cpt1a) stabilizes the mitochondrial E3 ubiquitin ligase Goliath, which mediates deubiquitination of calcineurin and thus enhances activation of NF-AT signaling, thereby promoting glycolysis and hyperactivation of CD4+ T cells in obesity. We also report the specific GOLIATH inhibitor DC-Gonib32, which blocks this FAO-glycolysis metabolic axis in CD4+ T cells of obese mice and reduces the induction of inflammation. Overall, these findings establish a role of a Goliath-bridged FAO-glycolysis axis in mediating CD4+ T cell hyperactivation and thus inflammation in obese mice.

Exosomal circPVT1 promotes angiogenesis in laryngeal cancer by activating the Rap1b-VEGFR2 signaling pathway.

Laryngeal cancer (LC) is the second most common head and neck cancer and has a decreasing 5-year survival rate worldwide. Circular RNAs regulate cancer development in diverse ways based on their distinct biogenesis mechanisms and expansive regulatory roles. However, currently, there is little research on how exosomal circular RNAs are involved in the development of laryngeal cancer. Here, we demonstrated that circPVT1, a circular RNA derived from the well-studied long noncoding RNA PVT1, is correlated with disease progression in LC and promotes angiogenesis both in vivo and in vitro. Mechanistically, circPVT1 is loaded into LC cell-secreted exosomes and taken up by vascular epithelium cells. By sponging miR-30c-5p, exosomal circPVT1 promotes Rap1b expression, which dramatically enhances VEGFR2 and PI3K/AKT pathway activation, ultimately resulting in the induction of angiogenesis. Furthermore, our xenograft models demonstrated that the combination of shRNA-circPVT1 and cetuximab showed high efficacy in inhibiting tumor growth and angiogenesis. Collectively, these findings uncover a novel mechanism of exosomal circular RNA-mediated angiogenesis modulation and provide a preclinical rationale for testing this analogous combination in patients with LC.

Flexible Synergistic MoS2 Quantum Dots/PEDOT: PSS Film Sensor for Acetaldehyde Sensing at Room Temperature.

Acetaldehyde (CH3CHO) is known as a primary carcinogen, and the development of wearable gas sensors for its detection at room temperature has rarely been rarely reported. Herein, MoS2 quantum dots (MoS2 QDs) have been employed to dope with poly(3,4-ethylenedioxythiophene): polystyrenesulfonate (PEDOT: PSS) via a simple in situ polymerization technique, and the CH3CHO gas-sensing properties of the resultant flexible and transparent film were investigated. MoS2 QDs had been evenly dispersed into the polymer, and it was shown that PEDOT: PSS doped with the 20 wt % MoS2 QDs sensor exhibited the highest response value of 78.8% against 100 ppm CH3CHO and its detection limit reached 1 ppm. Moreover, the sensor response remained stable for more than 3 months. In particular, the different bending angles (from 60 to 240°) had little effect on the sensor response to CH3CHO. The possible reason for the enhanced sensing properties was attributed to the large number of reaction sites on the MoS2 QDs and the direct charge transfer between the MoS2 QDs and PEDOT: PSS. This work suggested a platform to inspire MoS2 QDs-doping PEDOT: PSS materials as wearable gas sensors for highly sensitive chemoresistive sensors to detect CH3CHO at room temperature.

Multipartite entanglement generation with high-order non-Hermitian exceptional points from dressing-controlled atomic nonlinearity.

Multipartite entanglement has emerged as a valuable quantum resource for constructing large-scale quantum networks. However, the presence of non-Hermitian features induced by natural microscopic quantum systems significantly modifies the overall response of nonlinear parametric processes, thereby enabling direct manipulation of multipartite entanglement properties. In this study, we demonstrate the generation of multimode entanglement through atomic four-wave mixing (FWM) and analyze the properties of exceptional points (EP) under dressing control in non-Hermitian systems. By leveraging dressing-controlled atomic nonlinearity, we achieve versatile EPs and higher-order EPs by carefully tuning the atomic multi-parameter in the cascading FWM system. Additionally, we investigate the entanglement properties of various permutations of the output signal modes using the positive partial transpose (PPT) criterion. Notably, under non-Hermitian control, the application of single-, double-, and N-dressing splits leads to coherent multichannel control and further extends the scale of quantum entanglement. The outcomes of our research offer a novel approach to actively control non-Hermitian quantum phenomena without relying on artificial photonic structures. Furthermore, this paves the way for the realization of complex quantum information tasks by exploiting the non-Hermitian characteristics of the light-matter interaction.

Discovery and characterization of a novel cGAS covalent inhibitor for the treatment of inflammatory bowel disease.

Cyclic GMP-AMP synthase (cGAS), a cytosolic DNA sensor, acts as a nucleotidyl transferase that catalyzes ATP and GTP to form cyclic GMP-AMP (cGAMP) and plays a critical role in innate immunity. Hyperactivation of cGAS-STING signaling contributes to hyperinflammatory responses. Therefore, cGAS is considered a promising target for the treatment of inflammatory diseases. Herein, we report the discovery and identification of several novel types of cGAS inhibitors by pyrophosphatase (PPiase)-coupled activity assays. Among these inhibitors, 1-(1-phenyl-3,4-dihydro-1H-pyrrolo[1,2-a]pyrazin-2-yl)prop-2-yn-1-one (compound 3) displayed the highest potency and selectivity at the cellular level. Compound 3 exhibited better inhibitory activity and pathway selectivity than RU.521, which is a selective cGAS inhibitor with anti-inflammatory effects in vitro and in vivo. Thermostability analysis, nuclear magnetic resonance and isothermal titration calorimetry assays confirmed that compound 3 directly binds to the cGAS protein. Mass spectrometry and mutation analysis revealed that compound 3 covalently binds to Cys419 of cGAS. Notably, compound 3 demonstrated promising therapeutic efficacy in a dextran sulfate sodium (DSS)-induced mouse colitis model. These results collectively suggest that compound 3 will be useful for understanding the biological function of cGAS and has the potential to be further developed for inflammatory disease therapies.

Drug repurposing against breast cancer by integrating drug-exposure expression profiles and drug-drug links based on graph neural network.

MOTIVATION: Breast cancer is one of the leading causes of cancer deaths among women worldwide. It is necessary to develop new breast cancer drugs because of the shortcomings of existing therapies. The traditional discovery process is time-consuming and expensive. Repositioning of clinically approved drugs has emerged as a novel approach for breast cancer therapy. However, serendipitous or experiential repurposing cannot be used as a routine method. RESULTS: In this study, we proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer. GraphRepur integrated two major classes of computational methods, drug network-based and drug signature-based. The differentially expressed genes of disease, drug-exposure gene expression data and the drug-drug links information were collected. By extracting the drug signatures and topological structure information contained in the drug relationships, GraphRepur can predict new drugs for breast cancer, outperforming previous state-of-the-art approaches and some classic machine learning methods. The high-ranked drugs have indeed been reported as new uses for breast cancer treatment recently. AVAILABILITYAND IMPLEMENTATION: The source code of our model and datasets are available at: https://github.com/cckamy/GraphRepur and https://figshare.com/articles/software/GraphRepur_Breast_Cancer_Drug_Repurposing/14220050. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Plant community-mediated effects of grazing on plant diseases.

Grazing is one of the most important management practices for grasslands. To date, most studies on how grazing affects plant diseases have focused on a single plant species, ignoring plant community characteristics and phylogeny. We used data from a 6-year yak grazing experiment (0, 1, 2, and 3 yak(s) ha - 1 treatment) in an alpine meadow ecosystem of Qinghai-Tibetan Plateau, from which we tested grazing effects on foliar fungal diseases at both population and community levels. By measuring plant community variables (including richness, evenness, phylogenetic diversity, and composition) and disease severity, we evaluated the relative importance of plant community-mediated effects of yak grazing on community pathogen load with a multi-model inference approach. We found significant differences in pathogen load among different grazing treatments; we recorded the highest and lowest pathogen loads in the 1 yak ha - 1 treatment and in the 3 yaks ha - 1 treatment, respectively. Pielou's evenness index and community proneness (i.e., an estimate of the capacity of plant communities to support diseases) best explained variation in pathogen load, indicating that plant community-mediated effects (through evenness and proneness) of yak grazing determined pathogen load. Our study provides empirical evidence that grazing influences foliar fungal disease prevalence through plant community evenness and composition, which demonstrates the necessity of incorporating host plant community characteristics into disease load prediction frameworks.

IT-business alignment, big data analytics capability, and strategic decision-making: Moderating roles of event criticality and disruption of COVID-19.

Prior research has confirmed the importance of IT-business alignment (ITBA) and big data analytics capability (BDAC) in supporting firms' strategic decision-making under normal circumstances. However, the global outbreak of COVID-19 has significantly changed firms' strategic decision-making landscapes and raised questions regarding the effects of ITBA and BDAC on strategic decision-making as conditioned by COVID-19 characteristics. In this study, we contextualize two important event impact factors (i.e., event criticality and event disruption) in the context of COVID-19 and examine their contingent roles in the effects of ITBA and BDAC on strategic decision-making. Our analyses, based on two-round, multi-respondents matched survey data collected from 175 Chinese firms to elucidate the differential moderating roles of event criticality and disruption of COVID-19 in the impact of ITBA and BDAC on strategic decision speed and quality. The results indicate the event criticality of COVID-19 strengthens the effects of ITBA on decision speed and quality but weakens the influence of BDAC on decision quality. Meanwhile, the event disruption of COVID-19 weakens the influence of ITBA on decision speed and quality but strengthens the effect of BDAC on decision speed and quality. These findings have important theoretical and practical implications, which we discuss in the conclusion.

TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments.

MOTIVATION: Identifying compound-protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional structure account for a large part of potential biological targets, which requires developing methods using only protein sequence information to predict CPI. However, sequence-based CPI models may face some specific pitfalls, including using inappropriate datasets, hidden ligand bias and splitting datasets inappropriately, resulting in overestimation of their prediction performance. RESULTS: To address these issues, we here constructed new datasets specific for CPI prediction, proposed a novel transformer neural network named TransformerCPI, and introduced a more rigorous label reversal experiment to test whether a model learns true interaction features. TransformerCPI achieved much improved performance on the new experiments, and it can be deconvolved to highlight important interacting regions of protein sequences and compound atoms, which may contribute chemical biology studies with useful guidance for further ligand structural optimization. AVAILABILITY AND IMPLEMENTATION: https://github.com/lifanchen-simm/transformerCPI.

Structural and functional insights into macrophage migration inhibitory factor from Oncomelania hupensis, the intermediate host of Schistosoma japonicum.

Oncomelania hupensis is the unique intermediate host of Schistosoma japonicum. As an irreplaceable prerequisite in the transmission and prevalence of schistosomiasis japonica, an in-depth study of this obligate host-parasite interaction can provide glimpse into the molecular events in the competition between schistosome infectivity and snail immune resistance. In previous studies, we identified a macrophage migration inhibitory factor (MIF) from O. hupensis (OhMIF), and showed that it was involved in the snail host immune response to the parasite S. japonicum. Here, we determined the crystal structure of OhMIF and revealed that there were distinct structural differences between the mammalian and O. hupensis MIFs. Noticeably, there was a projecting and structured C-terminus in OhMIF, which not only regulated the MIF's thermostability but was also critical in the activation of its tautomerase activity. Comparative studies between OhMIF and human MIF (hMIF) by analyzing the tautomerase activity, oxidoreductase activity, thermostability, interaction with the receptor CD74 and activation of the ERK signaling pathway demonstrated the functional differences between hMIF and OhMIF. Our data shed a species-specific light on structural, functional, and immunological characteristics of OhMIF and enrich the knowledge on the MIF family.

Study on Purification and Characterization of Polyphenol Oxidase from Acetes chinensis.

Acetes chinensis (belonging to the Decapoda Sergestidae genus) is widely distributed in East Asian waters and is extremely widespread and present in the shallow coastal areas of China. Polyphenol oxidase (PPO), which was extracted from Acetes chinensis, was purified in a four-step procedure involving phosphate-buffered saline treatment, ammonium sulphate precipitation, DEAE-Cellulose chromatography, and Phenyl-Sepharose HP chromatography, and then, its biochemical characterization was measured. The specific activity of the purified enzyme was increased to 643.4 U/mg, which is a 30.35 times increase in purification, and the recovery rate was 17.9%. L-dopa was used as the substrate, the enzymatic reactions catalyzed by PPO conformed to the Michaelis equation, the maximum reaction velocity was 769.23 U/mL, and the Michaelis constant Km was 0.846 mmol/L. The optimal pH of PPO from Acetes chinensis was 7.5, and the optimal temperature was 35 °C. The metal ions experiment showed that Mn2+ and K+ could enhance the activity of PPO; that Ba2+ and Ca2+ could inhibit the activity of PPO; and that Cu2+ had a double effect on PPO, increasing the PPO activity at low concentrations and inhibiting the PPO activity at high concentrations. The inhibitor experiment showed that the inhibitory effects of EDTA and kojic acid were weak and that ascorbic acid and sodium pyrophosphate had good inhibitory effects. The purification and characterization of Acetes chinensis serve as guidelines for the prediction of enzyme behavior, leading to effective prevention of enzymatic browning during processing.

Surface water areas significantly impacted 2014 dengue outbreaks in Guangzhou, China.

Dengue transmission in urban areas is strongly influenced by a range of biological and environmental factors, yet the key drivers still need further exploration. To better understand mechanisms of environment-mosquito-urban dengue transmission, we propose an empirical model parameterized and cross-validated from a unique dataset including viral gene sequences, vector dynamics and human dengue cases in Guangzhou, China, together with a 36-year urban environmental change maps investigated by spatiotemporal satellite image fusion. The dengue epidemics in Guangzhou are highly episodic and were not associated with annual rainfall over time. Our results indicate that urban environmental changes, especially variations in surface area covered by water in urban areas, can substantially alter the virus population and dengue transmission. The recent severe dengue outbreaks in Guangzhou may be due to the surge in an artificial lake construction, which could increase infection force between vector (mainly Aedes albopictus) and host when urban water area significantly increased. Impacts of urban environmental change on dengue dynamics may not have been thoroughly investigated in the past studies and more work needs to be done to better understand the consequences of urbanization processes in our changing world.

A Combination of Species Evenness and Functional Diversity Is the Best Predictor of Disease Risk in Multihost Communities.

Although accumulated evidence has shown that biodiversity can play an important role in disease transmission and prevalence, it remains unclear how different measures of diversity based on taxonomy or function perform in predicting disease risk. In this article, we assess the relative ability of species richness, Shannon's evenness index, single functional traits, and several functional diversity metrics and their interactions to predict disease risk in both nonequilibrium and equilibrium communities simulated by a multihost epidemiological model. On the basis of generalized linear models and Akaike's information criterion, we found that Shannon's evenness index outperforms species richness as a single variable in explaining variation in disease risk, while the best combination consists of Shannon's evenness index and functional diversity. This study is, to our knowledge, the first to demonstrate the important role played by species evenness and functional diversity in accounting for variation in disease risk in multihost communities.

Repetitive hyperbaric oxygen therapy provides better effects on brain inflammation and oxidative damage in rats with focal cerebral ischemia.

BACKGROUND/PURPOSE: Repetitive hyperbaric oxygen (HBO2) therapy may cause excessive generation of reactive oxygen species. This study assessed whether repetitive or 2-4-day trials of HBO2 therapy (2 treatments daily for 2-4 consecutive days) provides better effects in reducing brain inflammation and oxidative stress caused by middle cerebral artery occlusion (MCAO) in rats than did a 1-day trial of HBO2 therapy (2 treatments for 1 day). METHODS: Rats were randomly divided into four groups: sham; MCAO without HBO2 treatment; MCAO treated with 1-day trial of HBO2; and MCAO treated with 2-4-day trials of HBO2. One treatment of HBO2 (100% O2 at 253 kPa) lasted for 1 hour in a hyperbaric chamber. RESULTS: Therapy with the 2-4-day trials of HBO2 significantly and dose-dependently attenuated the MCAO-induced cerebral infarction and neurological deficits more than the 1-day trial of HBO2 therapy. The beneficial effects of repetitive HBO2 therapy were associated with: (1) reduced inflammatory status in ischemic brain tissues (evidenced by decreased levels of tumor necrosis factor-α, interleukin-1ß, and myeloperoxidase activity); (2) decreased oxidative damage in ischemic brain tissues (evidenced by decreased levels of reactive oxygen and nitrogen species, lipid peroxidation, and enzymatic pro-oxidants, but increased levels of enzymatic antioxidant defenses); and (3) increased production of an anti-inflammatory cytokine, interleukin-10. CONCLUSION: The results provide the apparently contradictory finding that heightened oxygen tension reduced oxidative stress (and inflammation), which was reflected by increased antioxidant and decreased oxidant contents under focal cerebral ischemia.

Survival benefits of gastrectomy in patients with metastatic gastric cancer: A meta-analysis.

BACKGROUND: Individuals with metastatic gastric cancer (MGC) are incurable and have a poor prognosis. To date, surgical resection with curative intent is the only treatment providing hope for a cure, but the role of surgical resection is still controversial. OBJECTIVES: To assess the effects of gastrectomy compared to non-resection on MGC patient survival. MATERIAL AND METHODS: PubMed, Embase, Cochrane Library, and Web of Science databases were searched up to October 10, 2023. Primary outcomes were 1-, 2-, 3-, and 5-year overall survival (OS), OS, and OS time. RESULTS: Forty-six studies with 7,152 MGC patients were included. Compared to MGC patients receiving no resection, MGC patients with gastrectomy had significantly improved 1-year OS (pooled relative risk (RR):1.90, 95% confidence intervals (95% CIs): 1.50, 2.41), 2-year OS (pooled RR: 2.23, 95% CI: 1.40, 3.53), 3-year OS (pooled RR: 6.09, 95% CI: 3.12, 11.87), 5-year OS (pooled RR: 4.30, 95% CI: 1.35, 13.74), and reduced risk of death (pooled hazard ratio (HR): 0.49, 95% CI: 0.37, 0.65). Gastrectomy combined with metastasectomy or not also revealed similar results regarding OS and risk of death. Additionally, OS time was significantly longer in patients receiving gastrectomy than patients not receiving resection (pooled weighted mean difference (WMD): 6.06, 95% CI: 1.36, 10.760). No significant difference in postoperative morbidity was detected between the patients receiving gastrectomy and patients not receiving resection (pooled RR: 2.54, 95% CI: 0.13, 51.39). CONCLUSION: Gastrectomy, with or metastasectomy, may provide MGC patients with survival benefits.

Impact of vector richness on the risk of vector-borne disease: The role of vector competence.

A central goal of disease ecology is to identify the factors that drive the spread of infectious diseases. Changes in vector richness can have complex effects on disease risk, but little is known about the role of vector competence in the relationship between vector richness and disease risk. In this study, we firstly investigated the combined effects of vector competence, interspecific competition, and feeding interference on disease risk through a two-vector, one-host SIR-SI model, and obtained threshold conditions for the occurrence of dilution and amplification effects. Secondly, we extended the above model to the case of N vectors and assumed that all vectors were homogeneous to obtain analytic expressions for disease risk. It was found that in the two-vector model, disease risk declined more rapidly as interspecific competition of the high-competence vector increased. When vector richness increases, the positive effects of adding a high-competence vector species on disease transmission may outweigh the negative effects of feeding interference due to increased vector richness, making an amplification effect more likely to occur. While the addition of a highly competitive vector species may exacerbate the negative effects of feeding interference, making a dilution effect more likely to occur. In the N-vector model, the effect of increased vector richness on disease risk was fully driven by the strength of feeding interference and interspecific competition, and changes in vector competence only quantitatively but not qualitatively altered the vector richness-disease risk relationship. This work clarifies the role of vector competence in the relationship between vector richness and disease risk and provides a new perspective for studying the diversity-disease relationship. It also provides theoretical guidance for vector management and disease prevention strategies.

Learning protein fitness landscapes with deep mutational scanning data from multiple sources.

One of the key points of machine learning-assisted directed evolution (MLDE) is the accurate learning of the fitness landscape, a conceptual mapping from sequence variants to the desired function. Here, we describe a multi-protein training scheme that leverages the existing deep mutational scanning data from diverse proteins to aid in understanding the fitness landscape of a new protein. Proof-of-concept trials are designed to validate this training scheme in three aspects: random and positional extrapolation for single-variant effects, zero-shot fitness predictions for new proteins, and extrapolation for higher-order variant effects from single-variant effects. Moreover, our study identified previously overlooked strong baselines, and their unexpectedly good performance brings our attention to the pitfalls of MLDE. Overall, these results may improve our understanding of the association between different protein fitness profiles and shed light on developing better machine learning-assisted approaches to the directed evolution of proteins. A record of this paper's transparent peer review process is included in the supplemental information.

Single-base resolution mapping of 2'-O-methylation sites by an exoribonuclease-enriched chemical method.

2'-O-methylation (Nm) is one of the most abundant RNA epigenetic modifications and plays a vital role in the post-transcriptional regulation of gene expression. Current Nm mapping approaches are normally limited to highly abundant RNAs and have significant technical hurdles in mRNAs or relatively rare non-coding RNAs (ncRNAs). Here, we developed a new method for enriching Nm sites by using RNA exoribonuclease and periodate oxidation reactivity to eliminate 2'-hydroxylated (2'-OH) nucleosides, coupled with sequencing (Nm-REP-seq). We revealed several novel classes of Nm-containing ncRNAs as well as mRNAs in humans, mice, and drosophila. We found that some novel Nm sites are present at fixed positions in different tRNAs and are potential substrates of fibrillarin (FBL) methyltransferase mediated by snoRNAs. Importantly, we discovered, for the first time, that Nm located at the 3'-end of various types of ncRNAs and fragments derived from them. Our approach precisely redefines the genome-wide distribution of Nm and provides new technologies for functional studies of Nm-mediated gene regulation.

Using less annotation workload to establish a pathological auxiliary diagnosis system for gastric cancer.

Pathological diagnosis of gastric cancer requires pathologists to have extensive clinical experience. To help pathologists improve diagnostic accuracy and efficiency, we collected 1,514 cases of stomach H&E-stained specimens with complete diagnostic information to establish a pathological auxiliary diagnosis system based on deep learning. At the slide level, our system achieves a specificity of 0.8878 while maintaining a high sensitivity close to 1.0 on 269 biopsy specimens (147 malignancies) and 163 surgical specimens (80 malignancies). The classified accuracy of our system is 0.9034 at the slide level for 352 biopsy specimens (201 malignancies) from 50 medical centers. With the help of our system, the pathologists' average false-negative rate and average false-positive rate on 100 biopsy specimens (50 malignancies) are reduced to 1/5 and 1/2 of the original rates, respectively. At the same time, the average uncertainty rate and the average diagnosis time are reduced by approximately 22% and 20%, respectively.