A Theoretical Analysis of NDCG Type Ranking Measures

A central problem in ranking is to design a ranking measure for evaluation of ranking functions. In this paper we study, from a theoretical perspective, the widely used Normalized Discounted Cumulative Gain (NDCG)-type ranking measures. Although there are extensive empirical studies of NDCG, little is known about its theoretical properties. We first show that, whatever the ranking function is, the standard NDCG which adopts a logarithmic discount, converges to 1 as the number of items to rank goes to infinity. On the first sight, this result is very surprising. It seems to imply that NDCG cannot differentiate good and bad ranking functions, contradicting to the empirical success of NDCG in many applications. In order to have a deeper understanding of ranking measures in general, we propose a notion referred to as consistent distinguishability. This notion captures the intuition that a ranking measure should have such a property: For every pair of substantially different ranking functions, the ranking measure can decide which one is better in a consistent manner on almost all datasets. We show that NDCG with logarithmic discount has consistent distinguishability although it converges to the same limit for all ranking functions. We next characterize the set of all feasible discount functions for NDCG according to the concept of consistent distinguishability. Specifically we show that whether NDCG has consistent distinguishability depends on how fast the discount decays, and 1/r is a critical point. We then turn to the cut-off version of NDCG, i.e., NDCG@k. We analyze the distinguishability of NDCG@k for various choices of k and the discount functions. Experimental results on real Web search datasets agree well with the theory.Comment: COLT 201

Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice.

To gain insight into how mutant huntingtin (mHtt) CAG repeat length modifies Huntington's disease (HD) pathogenesis, we profiled mRNA in over 600 brain and peripheral tissue samples from HD knock-in mice with increasing CAG repeat lengths. We found repeat length-dependent transcriptional signatures to be prominent in the striatum, less so in cortex, and minimal in the liver. Coexpression network analyses revealed 13 striatal and 5 cortical modules that correlated highly with CAG length and age, and that were preserved in HD models and sometimes in patients. Top striatal modules implicated mHtt CAG length and age in graded impairment in the expression of identity genes for striatal medium spiny neurons and in dysregulation of cyclic AMP signaling, cell death and protocadherin genes. We used proteomics to confirm 790 genes and 5 striatal modules with CAG length-dependent dysregulation at the protein level, and validated 22 striatal module genes as modifiers of mHtt toxicities in vivo

Complete genome sequence of biocontrol strain Bacillus velezensis YC89 and its biocontrol potential against sugarcane red rot

IntroductionSugarcane is one of the most important sugar crops worldwide, however, sugarcane production is seriously limited by sugarcane red rot, a soil-borne disease caused by Colletotrichum falcatum. Bacillus velezensis YC89 was isolated from sugarcane leaves and can significantly inhibited red rot disease caused by C. falcatum.MethodsIn this study, the genome of YC89 strain was sequenced, its genome structure and function were analyzed using various bioinformatics software, and its genome was compared with those of other homologous strains. In addition, the effectiveness of YC89 against sugarcane red rot and the evaluation of sugarcane plant growth promotion were also investigated by pot experiments.ResultsHere, we present the complete genome sequence of YC89, which consists of a 3.95 Mb circular chromosome with an average GC content of 46.62%. The phylogenetic tree indicated that YC89 is closely related to B. velezensis GS-1. Comparative genome analysis of YC89 with other published strains (B. velezensis FZB42, B. velezensis CC09, B. velezensis SQR9, B. velezensis GS-1, and B. amyloliquefaciens DSM7) revealed that the strains had a part common coding sequences (CDS) in whereas 42 coding were unique of strain YC89. Whole-genome sequencing revealed 547 carbohydrate-active enzymes and identified 12 gene clusters encoding secondary metabolites. Additionally, functional analysis of the genome revealed numerous gene/gene clusters involved in plant growth promotion, antibiotic resistance, and resistance inducer synthesis. In vitro pot tests indicated that YC89 strain controlled sugarcane red rot and promoted the growth of sugarcane plants. Additionally, it increased the activity of enzymes involved in plant defense, such as superoxide dismutase, peroxidase, polyphenol oxidase, chitinase, and β-1,3-glucanase.DiscussionThese findings will be helpful for further studies on the mechanisms of plant growth promotion and biocontrol by B. velezensis and provide an effective strategy for controlling red rot in sugarcane plants

Research on fuzzy comprehensive evaluation of fire risk in ancient buildings

The study of the distribution pattern of fire risk in ancient buildings can provide a starting point for emergency measures to be taken in case of possible fires. Based on the fire characteristics, system complexity, and geographic information attributes of large palace ancient building complexes, this paper adopts a fuzzy comprehensive evaluation method combining fuzzy mathematics and analytic hierarchy process to construct a fire risk evaluation system for ancient buildings, which includes six criteria and 28 indicators such as the value index, and the fire likelihood. For the evaluation method of this system, the expert scoring and Analytic Hierarchy Process are used to determine the weights of various indicators. Then, the multiple rounds of expert analysis with a review of relative literature, the membership degree of each indicator is analyzed one-by-one, and the final risk model, and risk factor are determined. By combining MHMapGIS technology, this comprehensive evaluation method was applied as an example to the Imperial Palace (large Ming and Qing ancient architectural buildings) in Beijing (China) for grid and visual analysis, and the rationality of the results were verified. The evaluation results can intuitively and reasonably show the distribution of fire risk, indicating that the constructed evaluation system and its model method display a certain of feasibility

Risk stratification and outcomes in diabetes mellitus patients with preserved ejection fraction: a cardiac MRI study

Background: Patients with diabetes mellitus (DM) have a significantly increased risk of developing heart failure (HF), which exacerbates adverse cardiovascular outcomes. Limited data are available on the prognostic value of cardiac MRI in DM. We aimed to evaluate the association between MRI-derived strain analysis and adverse outcomes in DM patients at different heart failure (HF) stages. Methods: In this prospective study, DM participants with preserved ejection fraction (EF) underwent MRI examination between January 2019 and December 2021 were evaluated. Feature tracking strain parameters were measured using cine MRI. The primary outcome was a composite outcome including HF hospitalization or cardiovascular death. Cox proportional regression was used to assess the association between risk factors and outcomes. Results: A total of 581 DM participants (mean age, 56 years ± 13; 401 men) including 390 asymptomatic patients (stage A/B HF) and 191 heart failure with preserved EF were evaluated. After a median follow-up of 34.3 months, 74 DM patients reached the primary outcome; 13(2.2%) had cardiovascular mortality and 61(10.5%) had heart failure hospitalization. Kaplan–Meier survival curves showed that patients with global longitudinal strain (GLS) greater than or equal to -13.76% and patients with global early diastolic longitudinal strain rate (eGLSR) less than or equal to 0.51/s were more likely to experience the primary outcome (log-rank P < 0.001). In multivariable analysis, eGLSR was independently associated with an increased risk of the primary endpoint(per SD, adjusted HR: 2.038; 95% CI: 1.384–3.002; P < 0.001), but GLS was not. When risk stratification was based on GLS and eGLSR, Kaplan–Meier survival curves showed that patients with abnormal eGLSR had a significantly higher risk of adverse outcomes, regardless of GLS status. In addition, eGLSR provided incremental predictive power over clinical and imaging variables, achieving the largest C-statistic of 0.744. Of note, the association between eGLSR and outcomes was consistent in stage A/B HF patients and patients with HFpEF. Subgroup analysis showed non-ischemic LGE assessed by MRI was also independently associated with outcomes in patients with DM. Conclusions: In DM patients with preserved ejection fraction, left ventricular eGLSR measured by cardiac MRI was an independent predictor of adverse outcomes and offered incremental prognostic value over conventional clinical and imaging indices. Graphical Abstract: Comparison of outcomes in groups defined by eGLSR and GLS. Based on GLS and eGLSR, three different risk categories were constructed. Kaplan-Meier curves showed the relationship of risk categories and proportion of surviving patients. The representative strain and strain rate curves from three groups were also shown with four-chamber view images. eGLSR = global early diastolic longitudinal strain rate, GLS = global longitudinal strain

Prognosis and Risk Stratification in Dilated Cardiomyopathy With LVEF≤35%: Cardiac MRI Insights for Better Outcomes

BackgroundCurrent guidelines recommend implantable cardioverter defibrillators for the primary prevention of sudden cardiac death (SCD) in patients with dilated cardiomyopathy with left ventricular ejection fraction (LVEF)≤35%. However, its effectiveness is hindered by the inability to reliably discriminate between the risk of SCD and competing death of heart failure deterioration, thereby limiting its clinical utility. We aimed to refine the SCD risk stratification model based on cardiac magnetic resonance imaging for patients with dilated cardiomyopathy with LVEF≤35%.MethodsA total of 1272 patients with dilated cardiomyopathy with LVEF≤35% who underwent cardiac magnetic resonance imaging were consecutively enrolled in this study. The primary end point is a composite of SCD or aborted SCD and the second end point is a composite of heart failure death and heart transplantation.ResultsOver a median follow-up of 86.3 months, 101 patients reached the primary end point. In the adjusted analysis, age (hazard ratio [HR], 1.02 [95% CI, 1.01-1.04]; P=0.006) years, a family history of SCD (HR, 2.00 [95% CI, 1.01-3.98]; P=0.05), NT-proBNP (N-terminal pro-B-type natriuretic peptide) (HR, 2.02 [95% CI, 1.18-3.44]; P=0.01), LVEF (per 5% HR, 0.79 [95% CI, 0.66-0.95]; P=0.01), and late gadolinium enhancement≥7.5% (HR, 4.11[95% CI, 2.72-6.21]; P2 was an independent predictor of the secondary end point (adjusted HR, 1.65 [95% CI, 1.13-2.40]; P=0.009). Compared with late gadolinium enhancementConclusionsPatients with dilated cardiomyopathy with late gadolinium enhancement≥7.5% were at heightened risk of SCD events, which can be used for risk assessment. Risk stratifications for SCD, combining clinical and cardiac magnetic resonance imaging may potentially guide decision-making for implantable cardioverter defibrillator therapy

Discovery of Distinctin-Like-Peptide-PH (DLP-PH) from the Skin Secretion of Phyllomedusa hypochondrialis, a Prototype of a Novel Family of Antimicrobial Peptide

Amphibian skin secretions are an important treasure house of bioactive antimicrobial peptides (AMPs). Despite having been the focus of decades of research in this context, investigations of phyllomedusine frogs continue to identify new AMPs from their skin secretions. In this study, the prototype of a novel family of AMP distinctin-like-peptide-PH (DLP-PH) was identified from the skin secretion of the otherwise well-studied Tiger-Legged Tree Frog Phyllomedusa hypochondrialis through cloning of its precursor-encoding cDNA from a skin secretion-derived cDNA library by a 3′-rapid amplification of cDNA ends (RACE) strategy. Subsequently, the mature peptide was isolated and characterized using reverse-phase HPLC and MS/MS fragmentation sequencing. DLP-PH adopted an α-helical conformation in membrane mimetic solution and demonstrated unique structural features with two distinct domains that differed markedly in their physiochemical properties. Chemically synthesized replicates of DLP-PH showed antimicrobial activity against planktonic bacterial and yeast cells, but more potent against Escherichia coli at 32 μg/mL. However, DLP-PH showed much weaker inhibitory activity against the growth of sessile cells in biofilms. In addition, DLP-PH exhibited anti-proliferative activity against human cancer cell lines, H157, and PC3, but with no major toxicity against normal human cell, HMEC-1. These combined properties make DLP-PH deserving further study as an antimicrobial agent and further investigations of its structure-activity relationship could provide valuable new insights into drug lead candidates for antimicrobial and/or anti-cancer purposes

Data-Driven Modeling of Spatial-temporal Dynamics and Multi-modal Interactions in Urban Mobility

Urban mobility networks are inherently dynamic, shaped by the interplay of spatial-temporal heterogeneity and multi-modal interactions. These systems exhibit complex patterns, including intra-modal and inter-modal dependencies, as well as varied responses to system perturbations. Such dynamics are influenced by localized demand fluctuations, operational inefficiencies, and the evolving behaviors of participants. A comprehensive understanding of the challenges in multi-modal transportation systems is essential, beginning with intra-modal complexities, progressing to inter-modal interactions, and ultimately addressing system-wide dynamics and resilience. This understanding is crucial for enabling the effective management of these systems through equitable policy design and resilient infrastructure planning. To bridge these gaps, this thesis proposes a data-driven framework built upon three interconnected and progressive studies. The first study addresses the spatial-temporal complexities within single-modal systems by analyzing the continuous transportation behaviors of individual participants. Focusing on ride-sourcing drivers, we examine their sequential order-taking activities named as work slices (WSs)that capture spatiotemporal continuity. By analyzing WS characteristics and their combinations, we uncover how geographic proximity and temporal scheduling influence supply-side efficiency. For example, suburban WSs exhibit fragmented demand with lower fare-income ratios, while rural WSs face systemic inefficiencies. Heterogeneous WS patterns reveal trade-offs between income stability and operational flexibility, highlighting the need for context-aware platform governance. These findings establish a foundation for understanding how localized and temporal dynamics of single-modal patterns influence broader multi-modal interactions. Building on this, the second study quantifies partial demand dependencies and heterogeneous interactions in multi-modal transportation systems. We propose a copula-based framework to model probabilistic correlations between transportation modes under conditions of competition and complementarity, illustrated on a Bike-and-Ride (BnR) system formed by bike-sharing and subway services. The analysis identifies localized demand asymmetries, such as stronger first-mile correlations during low-demand periods compared to weaker last-mile linkages. These insights underscore the necessity of mode-specific interventions, such as rebalancing bike resources near transit hubs during off-peak hours, to mitigate spatial inequities. The methodology advances the understanding of inter-modal dependencies by capturing dynamic interactions under varying conditions of competition and complementarity. Finally, to address the dynamic nature of multi-modal systems and their response to perturbations, the third study introduces the Fluctuation-Aware Dynamic Graph Neural Network (FDGNN). This method integrates spatial-temporal heterogeneity and network topology uncovered in earlier studies to predict system evolution under both steady and extreme conditions. By dynamically adapting graph topologies to real-time anomalies, FDGNN achieves robust long-term demand predictions, bridging micro-level behavioral insights with macro-level system resilience. Evaluated on a multi-modal system formed by demand-responsive taxis and bike-sharing, FDGNN outperforms state-of-the-art baselines in capturing abrupt demand shifts, particularly during traffic incidents or weather disruptions. This demonstrates its potential for scalable, adaptive multi-modal management. Together, these contributions advance a holistic understanding of urban mobility, from granular operational patterns to scalable predictive frameworks. By integrating spatial-temporal continuity, multi-modal heterogeneity, and adaptive machine learning, this work provides actionable tools for enhancing equity, reducing congestion, and improving sustainability in rapidly evolving urban environments.</p