Neighbor Distance Research Articles

Random forests with spatial proxies for environmental modelling: opportunities and pitfalls

Abstract. Spatial proxies, such as coordinates and distance fields, are often added as predictors in random forest (RF) models without any modifications being made to the algorithm to account for residual autocorrelation and improve predictions. However, their suitability under different predictive conditions encountered in environmental applications has not yet been assessed. We investigate (1) the suitability of spatial proxies depending on the modelling objective (interpolation vs. extrapolation), the strength of the residual spatial autocorrelation, and the sampling pattern; (2) which validation methods can be used as a model selection tool to empirically assess the suitability of spatial proxies; and (3) the effect of using spatial proxies in real-world environmental applications. We designed a simulation study to assess the suitability of RF regression models using three different types of spatial proxies: coordinates, Euclidean distance fields (EDFs), and random forest spatial prediction (RFsp). We also tested the ability of probability sampling test points, random k-fold cross-validation (CV), and k-fold nearest neighbour distance matching (kNNDM) CV to reflect the true prediction performance and correctly rank models. As real-world case studies, we modelled annual average air temperature and fine particulate air pollution for continental Spain. In the simulation study, we found that RFs with spatial proxies were poorly suited for spatial extrapolation to new areas due to significant feature extrapolation. For spatial interpolation, proxies were beneficial when both strong residual autocorrelation and regularly or randomly distributed training samples were present. In all other cases, proxies were neutral or counterproductive. Random k-fold cross-validation generally favoured models with spatial proxies even when it was not appropriate, whereas probability test samples and kNNDM CV correctly ranked models. In the case studies, air temperature stations were well spread within the prediction area, and measurements exhibited strong spatial autocorrelation, leading to an effective use of spatial proxies. Air pollution stations were clustered and autocorrelation was weaker and thus spatial proxies were not beneficial. As the benefits of spatial proxies are not universal, we recommend using spatial exploratory and validation analyses to determine their suitability, as well as considering alternative inherently spatial modelling approaches.

Quantitative dispersion characterization of cement particles in hardened cement matrix

Optimizing the cement particle distribution in hardened cement matrix is one of the effective means to utilize cement efficiently, and obtaining information about cement distribution is a prerequisite for this. In view of this, this paper investigates the quantitative dispersion of cement particles in hardened cement paste from an experimental perspective. Cement pastes with different water-binder (W/B) ratios were prepared as the objects. BSE images of these samples were acquired for further analysis, including Delaunay triangulation algorithm, nearest neighbor distance function G(r) and fractal theory. Results showed that when the W/B ratio increases from 0.36 to 0.42, the average cement particle spacing rises from 32.7 μm to 34.9 μm and the proportion of particles with spacing between particle centroids <20 μm decreases from 41.96 % to 25.98 %. Similarly, the increasing W/B ratio causes the nearest neighbor distance function G(r) to shift towards larger distances. When the W/B ratio is 0.34, the median distance between particles (R50) exhibits the most significant change. Additionally, the intricate details of cement particle spacing information can be accurately described by fractal theory. Parameters such as R50 and the proportion of particles with spacing between centroids <20 μm demonstrate strong fits with various fractal parameters. Notably, they exhibit optimal fitting with the spectral width of the generalized fractal spectrum, with an R2 value of 0.99.

Experience-dependent modulation of collective behavior in larval zebrafish.

Complex group behavior can emerge from simple inter-individual interactions. Commonly, these interactions are considered static and hardwired and little is known about how experience and learning affect collective group behavior. Young larvae use well described visuomotor transformations to guide interindividual interactions and collective group structure. Here, we use naturalistic and virtual-reality (VR) experiments to impose persistent changes in population density and measure their effects on future visually evoked turning behavior and the resulting changes in group structure. We find that neighbor distances decrease after exposure to higher population densities, and increase after the experience of lower densities. These adaptations develop slowly and gradually, over tens of minutes and remain stable over many hours. Mechanistically, we find that larvae estimate their current group density by tracking the frequency of neighbor-evoked looming events on the retina and couple the strength of their future interactions to that estimate. A time-varying state-space model that modulates agents' social interactions based on their previous visual-social experiences, accurately describes our behavioral observations and predicts novel aspects of behavior. These findings provide concrete evidence that inter-individual interactions are not static, but rather continuously evolve based on past experience and current environmental demands. The underlying neurobiological mechanisms of experience dependent modulation can now be explored in this small and transparent model organism.

DNA mismatch repair defect and intratumor heterogeneous deficiency differently impact immune responses in diffuse large B-cell lymphoma

ABSTRACT Deficient (d) DNA mismatch repair (MMR) is a biomarker predictive of better response to PD-1 blockade immunotherapy in solid tumors. dMMR can be caused by mutations in MMR genes or by protein inactivation, which can be detected by sequencing and immunohistochemistry, respectively. To investigate the role of dMMR in diffuse large B-cell lymphoma (DLBCL), MMR gene mutations and expression of MSH6, MSH2, MLH1, and PMS2 proteins were evaluated by targeted next-generation sequencing and immunohistochemistry in a large cohort of DLBCL patients treated with standard chemoimmunotherapy, and correlated with the tumor immune microenvironment characteristics quantified by fluorescent multiplex immunohistochemistry and gene-expression profiling. The results showed that genetic dMMR was infrequent in DLBCL and was significantly associated with increased cancer gene mutations and favorable immune microenvironment, but not prognostic impact. Phenotypic dMMR was also infrequent, and MMR proteins were commonly expressed in DLBCL. However, intratumor heterogeneity existed, and increased DLBCL cells with phenotypic dMMR correlated with significantly increased T cells and PD-1+ T cells, higher average nearest neighbor distance between T cells and PAX5+ cells, upregulated immune gene signatures, LE4 and LE7 ecotypes and their underlying Ecotyper-defined cell states, suggesting the possibility that increased T cells targeted only tumor cell subsets with dMMR. Only in patients with MYC¯ DLBCL, high MSH6/PMS2 expression showed significant adverse prognostic effects. This study shows the immunologic and prognostic effects of genetic/phenotypic dMMR in DLBCL, and raises a question on whether DLBCL-infiltrating PD-1+ T cells target only tumor subclones, relevant for the efficacy of PD-1 blockade immunotherapy in DLBCL.

Scale effect of landscape characteristics on undergrowth vegetation variance with different ecological traits

Herbaceous understory vegetation plays a significant role in temperate forested ecosystems, with its diversity strongly dependent on both local and landscape conditions. However, the contribution of these factors across different spatial scales (e.g., scale effect) remains inadequately understood. The objective of this study was to determine the most important landscape and local characteristics influencing the species richness of the herb layer. We surveyed and sampled 53 sites in a mountainous forest within the Baihuashan National Nature Reserve of Beijing. Using generalized linear models, we investigated the simultaneous effect of local (e.g. tree density, average tree height, and cover of tree, shrub, and ground vegetation) and landscape (e.g. Area-weighted Mean Patch Size, Area-weighted Mean Euclidean Nearest Neighbor Distance, Area-weighted Mean Fractal Dimension Index, forest area, and shrubbery area) variables on the richness of species in the understory herb layer. Variation partitioning was used to examine the individual contribution of local and landscape variables. Our results identified 366 plant species from 95 families across 53 sampling sites. Significant differences in species richness were found among life forms, seed dispersal types, and habitat preferences (P < 0.001). Parsimonious models indicated that combining local and landscape factors at a 2 km scale explained the most variation in phanerophytes (R2 = 0.658). Locally, average tree height significantly influenced the species richness of biotic dispersal, phanerophytes, and geophytes. At the landscape level, variables such as the Area-weighted Mean Fractal Dimension Index and farmland area significantly impacted plant community traits across different scales. Understanding these effects is crucial for developing effective sustainable forest ecosystem management strategies and for establishing robust ecological conservation policies.

Lymphocyte Infiltration Score and Spatial Characteristics Refined the Prognosis and Denosumab Treatment Responsiveness Indicators for Giant Cell Tumor of Bone.

The prognostic value of lymphocyte infiltration score (LIS) and its nearest neighbor distance to tumor cells (NNDTC) in giant cell tumor of bone (GCTB) is currently not well established. This study aims to characterize LIS and NNDTC and examine their correlation with denosumab treatment responsiveness, clinicopathologic features, and patient prognosis. Using multiplexed quantitative immunofluorescence, LIS was evaluated in 253 tumor specimens, whereas NNDTC was computed using HALO software. Subsequently, we analyzed the association of these parameters with patient outcomes (progression-free survival [PFS] and overall survival [OS]), clinicopathologic features, and denosumab treatment responsiveness. Low LIS was indicative of both poor PFS and OS (both P < .001). In addition, LIS was significantly associated with sex (P = .046), Enneking staging (P < .001), Ki-67 expression (P = .007), and denosumab treatment responsiveness (P = .005). Lower CD8+ (tumor interior [TI]) NNDTC, and CD3+ (TI) NNDTC were associated with worse PFS (P = .003 and .038, respectively), whereas lower CD8+ (TI) NNDTC was associated with worse OS (P = .001), but CD8+ (tumor infiltrating margin) NNDTC had the opposite effect (P = .002). Moreover, NNDTC showed a correlation with several clinicopathologic features. Importantly, LIS outperformed Enneking and Campanacci staging systems in predicting the clinical outcomes of GCTB. These findings suggest that LIS is a reliable predictive tool for clinically relevant outcomes and response to denosumab therapy in patients with GCTB. These parameters may prove to be useful in guiding prognostic risk stratification and therapeutic optimization for patients.

Photocatalytic and electrochemical investigation of Mn and Sn co-doped ZnO nanoparticles: Effect of doping concentration

The chemical precipitation method is employed to prepare the Mn and Sn co-doped ZnO nanoparticles with various doping concentrations. Debye Scherrer’s formula, Williamson-Hall equation and Halder-Wagner methods are used to calculate the crystallite size of the prepared nanoparticles. The addition of dopants enhances the periodicity of the atoms. The bond lengths, second nearest neighbor distances and bond angles are examined. The purity of the samples is confirmed using energy dispersive X-ray analysis technique. The process of doping reduces the maximum optical absorption. The dopants reduce the band gap of the prepared nanoparticles and the band gap ranges from 3.07 eV to 3.03 eV. The charge carrier concentrations are calculated from the optical band gap. The Hall coefficient of the prepared nanoparticles increases due to the addition of the dopants. The refractive index of the material is also examined. The quenching is observed in the photoluminescence spectrum due to the dopants. The dielectric properties and electric modulus depend on doping concentrations of Mn and Sn. The degradation of the Congo red and methylene blue dyes with prepared nanoparticles as catalysis is reported. The specific charge of the Mn and Sn co-doped ZnO-based electrode is analyzed using cyclic voltammetric study and galvanostatic charge–discharge technique.

Melting curves of MgSiO3 and CaSiO3 perovskites with cubic structure at extreme conditions

In this work, we derive the analytical expression of Helmholtz free energy, mean nearest neighbor distance between two atoms, lattice constant, and molar volume using statistical moment method (SMM). Then, we present how to determine the melting temperature of cubic perovskite at high pressures by combining SMM and work-heat equivalence principle. The results of the melting theory of cubic perovskite are applied to two perovskites MgSiO3 and CaSiO3 under extreme conditions of the Earth’s lower mantle. Our obtained melting curves of MgSiO3 up to 1400 GPa and CaSiO3 up to 136 GPa were compared with available experiment and other calculations and were in good agreement. The present study provides an effective theoretical tool for finding the melting curves of strongly anharmonic materials under extreme conditions. Our theoretical calculations would be helpful to clarify geological formation process at the Earth’s lower mantle.

Correlation between bioluminescent blinks and swimming behavior in the splitfin flashlight fish Anomalops katoptron

BackgroundThe light organs of the splitfin flashlight fish Anomalops katoptron are necessary for schooling behavior, to determine nearest neighbor distance, and to feed on zooplankton under dim light conditions. Each behavior is coupled to context-dependent blink frequencies and can be regulated via mechanical occlusion of light organs. During shoaling in the laboratory individuals show moderate blink frequencies around 100 blinks per minute. In this study, we correlated bioluminescent blinks with the spatio-temporal dynamics of swimming profiles in three dimensions, using a stereoscopic, infrared camera system.ResultsGroups of flashlight fish showed intermediate levels of polarization and distances to the group centroid. Individuals showed higher swimming speeds and curved swimming profiles during light organ occlusion. The largest changes in swimming direction occurred when darkening the light organs. Before A. katoptron exposed light organs again, they adapted a nearly straight movement direction.ConclusionsWe conclude that a change in movement direction coupled to light organ occlusion in A. katoptron is an important behavioral trait in shoaling of flashlight fish.

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.

Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.

Nearest neighbour analysis as a new probe for fuzzy dark matter

ABSTRACT Fuzzy dark matter (FDM) is a promising candidate for dark matter (DM), characterized by its ultra-light mass, which gives rise to wave effects at astrophysical scales. These effects offer potential solutions to the small-scale issues encountered within the standard cold dark matter (CDM) paradigm. In this paper, we investigate the large-scale structure of the cosmic web using FDM simulations, comparing them to CDM-only simulations and a simulation incorporating baryonic effects. Our study employs the nearest neighbour (NN) analysis as a new statistical tool for examining the structure and statistics of the cosmic web in an FDM universe. This analysis could capture the information absent in the two-point correlation functions. In particular, we analyse data related to the spherical contact, nearest neighbour distances (NND), and the angle between the first and second nearest neighbours of haloes (NNA). Specifically, we utilize probability distribution functions, statistical moments, and fitting parameters, as well as G(x), F(x), and J(x) functions to analyse the above data. Remarkably, the results from the FDM simulations differ significantly from the others across these analyses, while no noticeable distinction is observed between the baryonic and CDM-only simulations. Moreover, the lower FDM mass leads to more significant deviations from the CDM simulations. These compelling results highlight the efficiency of the NN analysis – mainly through the use of the J(x) function, $s_3$, $l_{3}$, and $a_4$ parameters – as a prominent new tool for investigating FDM on large scales and making observational predictions.

Nest site preferences of the Eurasian Goshawk Accipiter gentilis, an indicator species of conservation concern, in hemiboreal mixed forests

The Goshawk has been recognized as an effective bioindicator sensitive to forest management. We used a comprehensive data set on 75 Goshawk nest sites from Estonia to explore the species’ preferences in relation with forest and landscape characteristics. Goshawk nest sites were regularly distributed with a mean nearest neighbour distance of 6.9 km. The hawks showed a preference for nesting in old (mean age 101 years) coniferous Oxalis-type stands with medium tree density, although the preferred nest tree species was deciduous birch. In surroundings of nest sites, there was a relatively high proportion of old-growth forest (64 %), and nests were closer to mires (1319 m) than expected, while being at a moderate distance from the forest edge (135 m). However, we did not detect any significant effect of the proximity of buildings and roads. Our results suggest that territoriality sets the upper limit to the nesting population, and the ultimate choice of nest location is constrained by the availability of suitable nest sites. We recommend that foresters planning timber harvesting near Goshawk nests avoid clear cutting and maintain sufficiently mature forest nearby. Furthermore, keeping suitable forest patches across forest land support a healthy Goshawk population.

Isoline extraction based on a global hexagonal grid

ABSTRACT Isoline extraction is influenced by numerical differences between grid cell neighbors. Due to varying neighborhood distances and directions of a traditional square grid cell across geographical space, numerical differences fail to precisely reflect the trends of terrain features. Alternatively, hexagonal grid spatial discretization has been proven to be superior to the square grid, attributed to its isotropic, consistent connectivity, uniform neighborhood. Taking these advantages into consideration, this paper explores the advantages of hexagonal grid discretization for isoline extraction in comparison with square grid. The comparisons show that a hexagonal grid performs better than a square grid, and it can extract finer isoline based on the same resolution. Result show that hexagonal grid can express the numerical differences between neighboring grid cells consistently and correctly, leading to improvements in isoline extraction. This paper demonstrates the significant impact of grid cell geometry on the quality of isoline obtained from grid-based geographical spatial data, providing new insights for spatial analysis of geographical data.

Обстоятельства развития внутренней и внешней экспансии горцев Северного Кавказа в начале XIX века

The article substantiates the socio-cultural and other conditions, causes and essence of mountain internal and external expansion, which in the local interpretation took the form of raiding practice. The author lists the problems that the mountaineers of the North Caucasus sought to solve with the help of raids on close or distant neighbors. This phenomenon had a long history and reflected the peculiarities and specifics of the everyday life of mountain societies that maintained a militarized way of life. Together with traditional agricultural activities, equestrianism played an important role in their sociocultural development, contributed to the formation of new social relations in the mountain environment, built on status stratification, and established the institution of private property and inequality among the mountaineers. These processes, however, should not be taken into account as absolutes, since mountain communities had well-developed mechanisms to prevent the decomposition of the established social system. To the greatest extent by the beginning of the 19th century the practice of raiding was developed among the Circassians and the peoples of Dagestan, although all other ethnic groups of the North Caucasus used raids at the first opportunity, due to the fact that it was an attribute of their everyday upbringing and manifestation of the spirit of belligerence.

Local and global expansivity in water.

The supra-molecular structure of a liquid is strongly connected to its dynamics, which in turn control macroscopic properties such as viscosity. Consequently, detailed knowledge about how this structure changes with temperature is essential to understand the thermal evolution of the dynamics ranging from the liquid to the glass. Here, we combine infrared spectroscopy (IR) measurements of the hydrogen (H) bond stretching vibration of water with molecular dynamics simulations and employ a quantitative analysis to extract the inter-molecular H-bond length in a wide temperature range of the liquid. The extracted expansivity of this H-bond differs strongly from that of the average nearest neighbor distance of oxygen atoms obtained through a common conversion of mass density. However, both properties can be connected through a simple model based on a random loose packing of spheres with a variable coordination number, which demonstrates the relevance of supra-molecular arrangement. Furthermore, the exclusion of the expansivity of the inter-molecular H-bonds reveals that the most compact molecular arrangement is formed in the range of ∼316-331K (i.e., above the density maximum) close to the temperature of several pressure-related anomalies, which indicates a characteristic point in the supra-molecular arrangement. These results confirm our earlier approach to deduce inter-molecular H-bond lengths via IR in polyalcohols [Gabriel et al. J. Chem. Phys. 154, 024503 (2021)] quantitatively and open a new alley to investigate the role of inter-molecular expansion as a precursor of molecular fluctuations on a bond-specific level.

Individual and group behavioral responses to nutritional state and context in a social fish

Understanding how animal collectives and societies form and function is a fundamental goal in animal biology. To date, research examining fish shoaling behavior has focused mostly on the general principles and ecological relevance of the phenomeon, while the ways in which physiological state (e.g., nutrition) affects collective behavior remain overlooked. Here, we investigated the shoaling behavior of common minnows (Phoxinus phoxinus) with three different nutritional states (control treatment: fasting for 24 h, fasting treatment: fasting for 7 days, and digestion treatment: 1 h after satiation feeding) across two ecological contexts (i.e., without and with food). No effects of either nutritional state or context were found on swimming speed, but the acceleration was greater in the digestion group than in the control group, with that in the fasting group being intermediate. Similar to change tendency in group length and group width of shoals, both interindividual distance and nearest neighbor distance were also greater in the fasting group than in the digestion group, suggesting that fasting and digestion may exert opposite driving forces on group cohesion. However, neither nutritional state nor context influenced the group area, group speed, group percent time moving, or group polarization. Both the foraging efficiency and the percentage of food items consumed by the fish shoals were greater in the fasting and control groups than in the digestion group. Our study suggested that one week of hunger and the energetically demanding stage of food digestion tend to have opposite influences on group shape, while the social foraging context does not influence the individual and group behavior of fish.

Multivariate anomaly detection models enhance identification of errors in routine clinical chemistry testing.

Conventional autoverification rules evaluate analytes independently, potentially missing unusual patterns of results indicative of errors such as serum contamination by collection tube additives. This study assessed whether multivariate anomaly detection algorithms could enhance the detection of such errors. Multivariate Gaussian, k-nearest neighbours (KNN) distance, and one-class support vector machine (SVM) anomaly detection models, along with conventional limit checks, were developed using a training dataset of 127,451 electrolyte, urea, and creatinine (EUC) results, with a 5 % flagging rate targeted for all approaches. The models were compared with limit checks for their ability to detect atypical EUC results from samples spiked with additives from collection tubes: EDTA, fluoride, sodium citrate, or acid citrate dextrose (n=200 per contaminant). The study additionally assessed the ability of the models to identify 127,449 single-analyte errors, a potential weakness of multivariate models. The KNN distance and SVM models outperformed limit checks for detecting all contaminants (p-values <0.05). The multivariate Gaussian model did not surpass limit checks for detecting EDTA contamination but was superior for detecting the other additives. All models surpassed limit checks for identifying single-analyte errors, with the KNN distance model demonstrating the highest overall sensitivity. Multivariate anomaly detection models, particularly the KNN distance model, were superior to the conventional approach for detecting serum contamination and single-analyte errors. Developing multivariate approaches to autoverification is warranted to optimise error detection and improve patient safety.

Urban 3D building morphology and energy consumption: empirical evidence from 53 cities in China

The impact of building morphology on building energy consumption has been extensively studied. However, research on how 3D building morphology affects energy consumption at a macroscopic scale is lacking. In this study, we measured the mean building height (BH), mean building volume (BV), and mean European nearest neighbor distance (MENN) of the city to quantify the 3D building morphology. We then used a spatial regression model to analyze the quantitative impact of urban 3D building morphology on per capita electricity consumption (PCEC). Results indicate that at the macroscopic scale of the city, the BH and the MENN have a significant positive impact on the PCEC, while the BV has a significant negative impact on the PCEC. Moreover, the inclusion of the 3D building morphology greatly improves the model’s ability to explain building energy efficiency, surpassing the impact of traditional economic factors. Considering the 3D building morphology indicators together, buildings with a lower height, a larger volume, and a more compact 3D morphology have greater potential for energy savings and are more conducive to electricity conservation. This study offers valuable insights for the energy-efficient arrangement of buildings.

Intensity-based optoretinography reveals sub-clinical deficits in cone function in retinitis pigmentosa.

Clinical tools have been widely used in the diagnosis, description, and monitoring the progression of retinitis pigmentosa (RP); however, many of these methods have inherently low sensitivity and specificity, and significant photoreceptor disruption can occur before RP progression has clinically manifest. Adaptive optics scanning light ophthalmoscopy (AOSLO) has shown promise as a powerful tool for assessing photoreceptor disruption both structurally and functionally due to its increased resolution. Here we assess photoreceptor structure and function at the cellular level through AOSLO by acquiring intensity based optoretinography (iORG) in 15 individuals with no reported retinal pathology and 7 individuals with a prior clinical diagnosis of RP. Photoreceptor structure was quantified by calculating cone nearest neighbor distance (NND) across different retinal eccentricities from the AOSLO images. Cone outer segment length was measured across different retinal eccentricities using optical coherence tomography (OCT) derived longitudinal reflectivity profiles (LRPs). Finally, iORG measures of photoreceptor function were compared to retinal sensitivity as measured using the macular integrity assessment (MAIA) microperimeter. Broadly, participants with RP exhibited increasing cone nearest neighbor distances and decreasing cone outer segment length as a function of retinal eccentricity, consistent with prior reports for both controls and individuals with RP. Nearly all individuals with RP had reduced iORG amplitudes for all retinal eccentricities when compared to the control cohort, and the reduction was greater in eccentricities further from the fovea. Comparing iORG amplitudes to MAIA retinal sensitivity, we found that the iORG was more sensitive to early changes in photoreceptor function whereas MAIA was more sensitive to later stages of disease. This highlights the utility of iORG as a method to detect sub-clinical deficits in cone function in all stages of disease progression and supports the future use of iORG for identifying cells that are candidates for cellular based therapies.

Cobitis feroniae, a new spined loach from southern Latium, Italy (Teleostei: Cobitidae)

Cobitis feroniae, new species, is described from central Italy. It is distinguished from C. zanandreai, its putatively closest relative, by having several, small, black dots below Z4; minute, black spot at the upper caudal peduncle, and the pigmentation in Z2 separated from pigmentation in Z1 anterior to the dorsal-fin origin. It is further distinguished from C. zanandreai by having 13 diagnostic nucleotide substitutions in the mtDNA COI barcode region, and a K2P nearest–neighbour distance of 2.9%.