Place Recognition Task Research Articles

Perinatal exposure to methadone or buprenorphine impairs hippocampal-dependent cognition and brain development in juvenile rats.

The opioid crisis continues to escalate, disproportionately affecting women of reproductive age. Traditionally the first line of treatment for pregnant women with opioid use disorder is the mu-opioid receptor agonist methadone. However, in recent years, the use of buprenorphine as a replacement therapy has increased as it has fewer side-effects and longer duration of action. Either drug significantly improves outcomes for the mother, but their impact on the developing infant is less certain. To this end, we directly compared the effects of perinatal methadone (MET; 9mg/kg/day starting dose) versus buprenorphine (BUP; 1mg/kg/day starting dose) delivered via mini osmotic pump on the long-term behavior of offspring and associated molecular changes in the brain. Opioid exposure across pregnancy resulted in reduced weight gain and smaller litters compared to sham controls, and female pups in particular gained weight at a slower rate across development. Opioid treatment delayed neuromuscular reflex development, with subtle differences observed between MET and BUP. As juveniles, pups with prenatal MET exposure showed poor object recognition, although both MET and BUP have led to deficits in place recognition task. Immunofluorescence studies found corresponding decreases in astrocytes and myelin-positive cells in the hippocampus in both MET and BUP pups. Overall, both MET and BUP were associated with significant developmental and cognitive delays and changes in markers of neuronal development and inflammation, particularly in the hippocampus. The majority of changes were similar between MET and BUP-treated pups, suggesting that gestational exposure to either drug has a similar long-term negative impact on offspring.

Visual place recognition for aerial imagery: A survey

Aerial imagery and its direct application to visual localization is an essential problem for many Robotics and Computer Vision tasks. While Global Navigation Satellite Systems (GNSS) are the standard default solution for solving the aerial localization problem, it is subject to a number of limitations, such as, signal instability or solution unreliability that make this option not so desirable. Consequently, visual geolocalization is emerging as a viable alternative. However, adapting Visual Place Recognition (VPR) task to aerial imagery presents significant challenges, including weather variations and repetitive patterns. Current VPR reviews largely neglect the specific context of aerial data. This paper introduces a methodology tailored for evaluating VPR techniques specifically in the domain of aerial imagery, providing a comprehensive assessment of various methods and their performance. However, we not only compare various VPR methods, but also demonstrate the importance of selecting appropriate zoom and overlap levels when constructing map tiles to achieve maximum efficiency of VPR algorithms in the case of aerial imagery. The code is available on our GitHub repository — https://github.com/prime-slam/aero-vloc.

Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception

Conventional cameras employed in autonomous vehicle (AV) systems support many perception tasks but are challenged by low-light or high dynamic range scenes, adverse weather, and fast motion. Novel sensors, such as event and thermal cameras, offer capabilities with the potential to address these scenarios, but they remain to be fully exploited. This paper introduces the Novel Sensors for Autonomous Vehicle Perception (NSAVP) dataset to facilitate future research on this topic. The dataset was captured with a platform including stereo event, thermal, monochrome, and RGB cameras as well as a high precision navigation system providing ground truth poses. The data was collected by repeatedly driving two ∼8 km routes and includes varied lighting conditions and opposing viewpoint perspectives. We provide benchmarking experiments on the task of place recognition to demonstrate challenges and opportunities for novel sensors to enhance critical AV perception tasks. To our knowledge, the NSAVP dataset is the first to include stereo thermal cameras together with stereo event and monochrome cameras. The dataset and supporting software suite is available at https://umautobots.github.io/nsavp .

LiDAR‐based place recognition for mobile robots in ground/water surface multiple scenes

AbstractLiDAR‐based 3D place recognition is an essential component of simultaneous localization and mapping systems in multi‐scene robotic applications. However, extracting discriminative and generalizable global descriptors of point clouds is still an open issue due to the insufficient use of the information contained in the LiDAR scans in existing approaches. In this paper, we propose a novel spatial‐temporal point cloud encoding network for multiple scenes, dubbed STM‐Net, to fully fuse the multi‐view spatial information and temporal information of LiDAR point clouds. Specifically, we first develop a spatial feature encoding module consisting of the single‐view transformer and multi‐view transformer. The module learns the correlation both within a single view and between two views by utilizing the multi‐layer range images generated by spherical projection and multi‐layer bird's eye view images generated by top‐down projection. Then in the temporal feature encoding module, we exploit the temporal transformer to mine the temporal information in the sequential point clouds, and a NetVLAD layer is applied to aggregate features and generate sub‐descriptors. Furthermore, we use a GeM pooling layer to fuse more information along the time dimension for the final global descriptors. Extensive experiments conducted on unmanned ground/surface vehicles with different LiDAR configurations indicate that our method (1) achieves superior place recognition performance than state‐of‐the‐art algorithms, (2) generalizes well to diverse sceneries, (3) is robust to viewpoint changes, (4) can operate in real‐time, demonstrating the effectiveness and satisfactory capability of the proposed approach and highlighting its promising applications in multi‐scene place recognition tasks.

Semantics-enhanced discriminative descriptor learning for LiDAR-based place recognition

LiDAR-based place recognition (LPR) aims to localize autonomous vehicles and mobile robots relative to pre-built maps or retrieve previously visited places. However, the complexity of real-world scenes and changes in viewpoint are significant challenges for place recognition. As high-level information, semantics makes it easier to distinguish geometrically similar scene situations. Unlike most existing methods that rely solely on a single type of information (geometric or semantic) to construct scene descriptors, we consider the complementary nature of the semantic and geometric information and propose a semantics-enhanced discriminative feature learning method for LPR. Specifically, we first develop a Multi-layer Fusion Feature Extraction Network (MFFEN) based on the transformer encoder to hierarchically fuse local geometric and semantic information and utilize contextual information for extracting discriminative local features. To obtain semantic information, we introduce the dynamic graph convolution network to extract local semantic features with local relations. In addition, to weaken the interference of redundancy and dynamic objects in the scene, we design a semantics-guided local attention network (SLAN) to focus on salient local features that are helpful for recognizing scenes, thereby enhancing the descriptive ability of the global descriptor. Extensive experiments on public datasets KITTI and KITTI-360 demonstrate that the proposed method performs better than recent LiDAR-based methods on the 3D place recognition task. For instance, it achieves the mean F1max score of 96.9% on the KITTI dataset, surpassing the strongest prior model by 2.7%.

CSPFormer: A cross-spatial pyramid transformer for visual place recognition

Recently, the Vision Transformer (ViT), which applied the Transformer structure to various visual detection tasks, has outperformed convolutional neural networks (CNNs). Nonetheless, due to the lack of scale representation ability of the Transformer, how to extract the local features of the scene to effectively form a global descriptor is still a challenging problem. In the paper, we propose a Cross-Spatial Pyramid Transformer (CSPFormer) to learn the discriminative global descriptors from multi-scale visual features for efficient visual place recognition. Specifically, we first develop a pyramid CNN module that can extract multi-scale visual feature representations. Then, the extracted feature representations of multi-scales are input to multiple connected spatial pyramid Transformer modules that adaptively learn the spatial relationship of the different scale descriptors, where the multiple self-attention is applied to learn a global descriptor from discriminative local descriptors. CNN pyramid features and Transformer multi-scale features are mutually weighted to perform cross-spatial feature representation. The multiple self-attention enhances the long-term dependencies of multi-scale visual descriptors and reduces the computational cost. To obtain the final place-matching result accurately, the cosine function is used to calculate the spatial similarity between the two scenes. Experimental results on public place datasets show that the proposed method achieves state-of-the-art on large-scale visual place recognition tasks. Our model has achieved 94.7%, 92.8%, 91.3%, and 95.7% average recall based on the top 1% candidate scenario on KITTI, Nordland, VPRICE, and EuRoc datasets, respectively.

MixedSCNet: LiDAR-Based Place Recognition Using Multi-Channel Scan Context Neural Network

In the realm of LiDAR-based place recognition tasks, three predominant methodologies have emerged: manually crafted feature descriptor-based methods, deep learning-based methods, and hybrid methods that combine the former two. Manually crafted feature descriptors often falter in reverse visits and confined indoor environments, while deep learning-based methods exhibit limitations in terms of generalization to distinct data domains. Hybrid methods tend to fix these problems, albeit at the cost of an expensive computational burden. In response to this, this paper introduces MixedSCNet, a novel hybrid approach designed to harness the strengths of manually crafted feature descriptors and deep learning models while keeping a relatively low computing overhead. MixedSCNet starts with constructing a BEV descriptor called MixedSC, which takes height, intensity, and smoothness into consideration simultaneously, thus offering a more comprehensive representation of the point cloud. Subsequently, MixedSC is fed into a compact Convolutional Neural Network (CNN), which further extracts high-level features, ultimately yielding a discriminative global point cloud descriptor. This descriptor is then employed for place retrieval, effectively bridging the gap between manually crafted feature descriptors and deep learning models. To substantiate the efficacy of this amalgamation, we undertake an extensive array of experiments on the KITTI and NCLT datasets. Results show that MixedSCNet stands out as the sole method showcasing state-of-the-art performance across both datasets, outperforming the other five methods while maintaining a relatively short runtime.

PointAttentionVLAD: A Two-Stage Self-Attention Network for Point Cloud-Based Place Recognition Task

PointAttentionVLAD: A Two-Stage Self-Attention Network for Point Cloud-Based Place Recognition Task

PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes

Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recall@top1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet.

Learning induces EPO/EPOr expression in memory relevant brain areas, whereas exogenously applied EPO promotes remote memory consolidation.

Memory and learning allow animals to appropriate certain properties of nature with which they can navigate in it successfully. Memory is acquired slowly and consists of two major phases, a fragile early phase (short-term memory, <4h) and a more robust and long-lasting late one (long-term memory, >4h). Erythropoietin (EPO) prolongs memory from 24 to 72h when animals are trained for 5min in a place recognition task but not when training lasted 3min (short-term memory). It is not known whether it promotes the formation of remote memory (≥21 days). We address whether the systemic administration of EPO can convert a short-term memory into a long-term remote memory, and the neural plasticity mechanisms involved. We evaluated the effect of training duration (3 or 5min) on the expression of endogenous EPO and its receptor to shed light on the role of EPO in coordinating mechanisms of neural plasticity using a single-trial spatial learning test. We administered EPO 10min post-training and evaluated memory after 24h, 96h, 15 days, or 21 days. We also determined the effect of EPO administered 10min after training on the expression of arc and bdnf during retrieval at 24h and 21 days. Data show that learning induces EPO/EPOr expression increase linked to memory extent, exogenous EPO prolongs memory up to 21 days; and prefrontal cortex bdnf expression at 24h and in the hippocampus at 21 days, whereas arc expression increases at 21 days in the hippocampus and prefrontal cortex.

Accelerating Maturation of Spatial Memory Systems by Experience: Evidence from Sleep Oscillation Signatures of Memory Processing.

During early development, memory systems gradually mature over time, in parallel with the gradual accumulation of knowledge. Yet, it is unknown whether and to what extent maturation is driven by discrete experience. Sleep is thought to contribute to the formation of long-term memory and knowledge through a systems consolidation process that is driven by specific sleep oscillations (i.e., ripples, spindles, and slow oscillations) in cortical and hippocampal networks. Based on these oscillatory signatures, we show here in rats that discrete spatial experience speeds the functional maturation of spatial memory systems during development. Juvenile male rats were exposed for 5 min periods to changes in the spatial configuration of two identical objects on postnatal day (PD)25, PD27, and PD29 (Spatial experience group), while a Control group was exposed on these occasions to the same two objects without changing their positions. On PD31, both groups were tested on a classical Object Place Recognition (OPR) task with a 3 h retention interval during which the sleep-associated EEG and hippocampal local field potentials were recorded. On PD31, consistent with forgoing studies, Control rats still did not express OPR memory. By contrast, rats with Spatial experience formed significant OPR memory and, in parallel, displayed an increased percentage of hippocampal ripples coupled to parietal slow oscillation-spindle complexes, and a stronger ripple-spindle phase-locking during the retention sleep. Our findings support the idea that experience promotes the maturation of memory systems during development by enhancing cortico-hippocampal information exchange and the formation of integrated knowledge representations during sleep.SIGNIFICANCE STATEMENT Cognitive and memory capabilities mature early in life. We show here that and how discrete spatial experience contributes to this process. Using a simple recognition paradigm in developing rats, we found that exposure of the rat pups to three short-lasting experiences enhances spatial memory capabilities to adult-like levels. The adult-like capability of building spatial memory was connected to a more precise coupling of ripples in the hippocampus with slow oscillation-spindle complexes in the thalamo-cortical system when the memory was formed during sleep. Our findings support the view that discrete experience accelerates maturation of cognitive and memory capabilities by enhancing the dialogue between hippocampus and cortex when these experiences are reprocessed during sleep.

Alternate-day fasting for the protection of cognitive impairment in c57BL/6J mice following whole-brain radiotherapy

NLRP3 inflammasome activation is implicated in irradiation-induced cognitive dysfunction. Alternate-day fasting (ADF) has been demonstrated to improve neuroinflammation as a non-pharmacological intervention. However, the exact mechanism and the anti-inflammatory effect in irradiation-induced cognitive dysfunction still need further in-depth study. The present study examined the effects of eight-week ADF on the cognitive functions of mice as well as inflammasome-mediated hippocampal neuronal loss following irradiation in mouse models of irradiation-induced cognitive deficits using seven-week-old male C57BL/6J mice. The behavioral results of novel place recognition and object recognition tasks revealed that ADF ameliorated cognitive functions in irradiation-induced cognitive dysfunction mice. ADF inhibited the expression of components of the NLRP3 inflammasome (NLRP3, ASC, and Cl.caspase-1), the downstream inflammatory factor (IL-1β and IL-18), and apoptosis-related proteins (caspase-3) via western blotting. Furthermore, an increased number of neurons and activated astrocytes were observed in the hippocampus using immunohistochemistry and Sholl analysis, which was jointly confirmed by western blotting. According to our study, this is the first time we found that ADF improved cognitive dysfunction induced by irradiation, and the anti-inflammatory effect of ADF could be due to inhibition in NLRP3-mediated hippocampal neuronal loss by suppressing astrocyte activation.

Place Recognition with Memorable and Stable Cues for Loop Closure of Visual SLAM Systems

Visual Place Recognition (VPR) is a fundamental yet challenging task in Visual Simultaneous Localization and Mapping (V-SLAM) problems. The VPR works as a subsystem of the V-SLAM. VPR is the task of retrieving images upon revisiting the same place in different conditions. The problem is even more difficult for agricultural and all-terrain autonomous mobile robots that work in different scenarios and weather conditions. Over the last few years, many state-of-the-art methods have been proposed to solve the limitations of existing VPR techniques. VPR using bag-of-words obtained from local features works well for a large-scale image retrieval problem. However, the aggregation of local features arbitrarily produces a large bag-of-words vector database, limits the capability of efficient feature learning, and aggregation and querying of candidate images. Moreover, aggregating arbitrary features is inefficient as not all local features equally contribute to long-term place recognition tasks. Therefore, a novel VPR architecture is proposed suitable for efficient place recognition with semantically meaningful local features and their 3D geometrical verifications. The proposed end-to-end architecture is fueled by a deep neural network, a bag-of-words database, and 3D geometrical verification for place recognition. This method is aware of meaningful and informative features of images for better scene understanding. Later, 3D geometrical information from the corresponding meaningful features is computed and utilised for verifying correct place recognition. The proposed method is tested on four well-known public datasets, and Micro Aerial Vehicle (MAV) recorded dataset for experimental validation from Victoria Park, Adelaide, Australia. The extensive experimental results considering standard evaluation metrics for VPR show that the proposed method produces superior performance than the available state-of-the-art methods.

How Many Events Do You Need? Event-Based Visual Place Recognition Using Sparse But Varying Pixels

Event cameras continue to attract interest due to desirable characteristics such as high dynamic range, low latency, virtually no motion blur, and high energy efficiency. One of the potential applications that would benefit from these characteristics lies in visual place recognition for robot localization, i.e. matching a query observation to the corresponding reference place in the database. In this letter, we explore the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distinctiveness of event streams</i> from a small subset of pixels (in the tens or hundreds). We demonstrate that the absolute difference in the number of events at those pixel locations accumulated into event frames can be sufficient for the place recognition task, when pixels that display large variations in the reference set are used. Using such <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sparse</i> (over image coordinates) but <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">varying</i> (variance over the number of events per pixel location) pixels enables frequent and computationally cheap updates of the location estimates. Furthermore, when event frames contain a constant number of events, our method takes full advantage of the event-driven nature of the sensory stream and displays promising robustness to changes in velocity. We evaluate our proposed approach on the Brisbane-Event-VPR dataset in an outdoor driving scenario, as well as the newly contributed indoor QCR-Event-VPR dataset that was captured with a DAVIS346 camera mounted on a mobile robotic platform. Our results show that our approach achieves competitive performance when compared to several baseline methods on those datasets, and is particularly well suited for compute- and energy-constrained platforms such as interplanetary rovers.

Investigating the Role of Image Retrieval for Visual Localization

Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two purposes: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially visible in a given query image. It is common practice to use state-of-the-art image retrieval algorithms for both of them. These algorithms are often trained for the goal of retrieving the same landmark under a large range of viewpoint changes which often differs from the requirements of visual localization. In order to investigate the consequences for visual localization, this paper focuses on understanding the role of image retrieval for multiple visual localization paradigms. First, we introduce a novel benchmark setup and compare state-of-the-art retrieval representations on multiple datasets using localization performance as metric. Second, we investigate several definitions of "ground truth" for image retrieval. Using these definitions as upper bounds for the visual localization paradigms, we show that there is still sgnificant room for improvement. Third, using these tools and in-depth analysis, we show that retrieval performance on classical landmark retrieval or place recognition tasks correlates only for some but not all paradigms to localization performance. Finally, we analyze the effects of blur and dynamic scenes in the images. We conclude that there is a need for retrieval approaches specifically designed for localization paradigms. Our benchmark and evaluation protocols are available at https://github.com/naver/kapture-localization.

Role of Type I Interferon Signaling and Microglia in the Abnormal Long-term Potentiation and Object Place Recognition Deficits of Male Mice With a Mutation of the Tuberous Sclerosis 2 Gene

BackgroundTuberous sclerosis complex is a genetic disorder associated with high rates of intellectual disability and autism. Mice with a heterozygous null mutation of the Tsc2 gene (Tsc2+/−) show deficits in hippocampal-dependent tasks and abnormal long-term potentiation (LTP) in the hippocampal CA1 region. Although previous studies focused on the role of neuronal deficits in the memory phenotypes of rodent models of tuberous sclerosis complex, the results presented here demonstrate a role for microglia in these deficits. MethodsTo test the possible role of microglia and type I interferon in abnormal hippocampal-dependent memory and LTP of Tsc2+/− mice, we used field recordings in CA1 and the object place recognition (OPR) task. We used the colony stimulating factor 1 receptor inhibitor PLX5622 to deplete microglia in Tsc2+/− mice and interferon alpha/beta receptor alpha chain null mutation (Ifnar1−/−) to manipulate a signaling pathway known to modulate microglia function. ResultsUnexpectedly, we demonstrate that male, but not female, Tsc2+/− mice show OPR deficits. These deficits can be rescued by depletion of microglia and by the Ifnar1−/− mutation. In addition to rescuing OPR deficits, depletion of microglia also reversed abnormal LTP of the Tsc2+/− mice. Altogether, our results suggest that altered IFNAR1 signaling in microglia causes the abnormal LTP and OPR deficits of male Tsc2+/− mice. ConclusionsMicroglia and IFNAR1 signaling have a key role in the hippocampal-dependent memory deficits and abnormal hippocampal LTP of Tsc2+/− male mice.

Spiking Neural Networks for Visual Place Recognition Via Weighted Neuronal Assignments

Spiking neural networks (SNNs) offer both compelling potential advantages, including energy efficiency and low latencies and challenges including the non-differentiable nature of event spikes. Much of the initial research in this area has converted deep neural networks to equivalent SNNs, but this conversion approach potentially negates some of the advantages of SNN-based approaches developed from scratch. One promising area for high-performance SNNs is template matching and image recognition. This research introduces the first high-performance SNN for the Visual Place Recognition (VPR) task: given a query image, the SNN has to find the closest match out of a list of reference images. At the core of this new system is a novel assignment scheme that implements a form of ambiguity-informed salience, by up-weighting single-place-encoding neurons and down-weighting "ambiguous" neurons that respond to multiple different reference places. In a range of experiments on the challenging Nordland, Oxford RobotCar, SPEDTest, Synthia, and St Lucia datasets, we show that our SNN achieves comparable VPR performance to state-of-the-art and classical techniques, and degrades gracefully in performance with an increasing number of reference places. Our results provide a significant milestone towards SNNs that can provide robust, energy-efficient, and low latency robot localization.

Fast and incremental loop closure detection with deep features and proximity graphs

AbstractIn recent years, the robotics community has extensively examined methods concerning the place recognition task within the scope of simultaneous localization and mapping applications. This article proposes an appearance‐based loop closure detection pipeline named “Fast and Incremental Loop closure Detection (FILD++). First, the system is fed by consecutive images and, via passing them twice through a single convolutional neural network, global and local deep features are extracted. Subsequently, a hierarchical navigable small‐world graph incrementally constructs a visual database representing the robot's traversed path based on the computed global features. Finally, a query image, grabbed each time step, is set to retrieve similar locations on the traversed route. An image‐to‐image pairing follows, which exploits local features to evaluate the spatial information. Thus, in the proposed article, we propose a single network for global and local feature extraction in contrast to our previous work (FILD), while an exhaustive search for the verification process is adopted over the generated deep local features avoiding the utilization of hash codes. Exhaustive experiments on eleven publicly available data sets exhibit the system's high performance (achieving the highest recall score on eight of them) and low execution times (22.05 ms on average in New College, which is the largest one containing 52,480 images) compared to other state‐of‐the‐art approaches.

NCMP-13. ID8 OVARIAN CANCER MOUSE MODEL MIMICS NEUROLOGICAL SEQUELAE OF OVARIAN CANCER IN WOMEN

Abstract OBJECTIVES Chemotherapy-related cognitive impairment (CRCI) and chemotherapy-induced peripheral neuropathy (CIPN) are neurological complications of cancer treatment. Cisplatin is used to treat ovarian malignancies, and over 70% of women experience CRCI/CIPN during and after platinum-based chemoTx. However, over 30% of non-CNS cancer patients experience cognitive impairment prior to chemoTx. To examine the contribution of cancer itself and additional neurological impairment with chemoTx, we used an ID8 syngeneic mouse model of ovarian cancer and assessed hyperalgesia and cognition +/- cisplatin treatment. METHODS C57BL/6 female mice were injected intraperitoneally with 107 ID8 ovarian cancer cells or 0.9% saline. After 10d of ID8 injections, mice received cisplatin (2.3 mg/kg/day, i.p.) or 0.9% saline (OvT+CIS, OvT+VEH, respectively) for 5d, followed by 5d of rest for 2 cycles. Mechanical and cold hyperalgesia were assessed longitudinally. Cognition was assessed 28d post-chemoTx by the open field test (OFT), novel object recognition (NOR), and novel place recognition (NPR) tasks. RESULTS OvT+VEH and OvT+CIS mice developed an increased sensitivity to mechanical ( &amp;gt;200%, p&amp;lt; 0.001) and thermal (cold) stimuli ( &amp;gt;78%, p&amp;lt; 0.004) starting 14d post-ID8 implantation, vs non-tumor controls (CON). In the OFT, OvT+CIS mice had increased anxiogenic behavior (55%, p&amp;lt; 0.001) vs CON, and (46%, p&amp;lt; 0.05) vs OvT+VEH. In NPR, OvT+CIS had reduced discrimination (37%, p&amp;lt; 0.05) vs CON. OvT+VEH and OvT+CIS showed impaired discrimination (25%, p&amp;lt; 0.05 &amp; 33%, p&amp;lt; 0.01, respectively) in NOR vs CON, with trending differences between OvT+CIS vs OvT+VEH in hyperalgesia and cognitive tasks. DISCUSSION: This is the first rodent model to demonstrate that ovarian cancer may evoke sensory and neurocognitive changes in the absence of chemotherapy. Future development of the model will address hyperalgesia and cognitive differences between OvT+VEH vs OvT+CIS. This model has potential for translational studies on the treatment of neurological sequelae of cancer and cisplatin-induced CRCI and CIPN.

Tau Cleavage Contributes to Cognitive Dysfunction in Strepto-Zotocin-Induced Sporadic Alzheimer’s Disease (sAD) Mouse Model

Tau cleavage plays a crucial role in the onset and progression of Alzheimer’s Disease (AD), a widespread neurodegenerative disease whose incidence is expected to increase in the next years. While genetic and familial forms of AD (fAD) occurring early in life represent less than 1%, the sporadic and late-onset ones (sAD) are the most common, with ageing being an important risk factor. Intracerebroventricular (ICV) infusion of streptozotocin (STZ)—a compound used in the systemic induction of diabetes due to its ability to damage the pancreatic β cells and to induce insulin resistance—mimics in rodents several behavioral, molecular and histopathological hallmarks of sAD, including memory/learning disturbance, amyloid-β (Aβ) accumulation, tau hyperphosphorylation, oxidative stress and brain glucose hypometabolism. We have demonstrated that pathological truncation of tau at its N-terminal domain occurs into hippocampi from two well-established transgenic lines of fAD animal models, such as Tg2576 and 3xTg mice, and that it’s in vivo neutralization via intravenous (i.v.) administration of the cleavage-specific anti-tau 12A12 monoclonal antibody (mAb) is strongly neuroprotective. Here, we report the therapeutic efficacy of 12A12mAb in STZ-infused mice after 14 days (short-term immunization, STIR) and 21 days (long-term immunization regimen, LTIR) of i.v. delivery. A virtually complete recovery was detected after three weeks of 12A12mAb immunization in both novel object recognition test (NORT) and object place recognition task (OPRT). Consistently, three weeks of this immunization regimen relieved in hippocampi from ICV-STZ mice the AD-like up-regulation of amyloid precursor protein (APP), the tau hyperphosphorylation and neuroinflammation, likely due to modulation of the PI3K/AKT/GSK3-β axis and the AMP-activated protein kinase (AMPK) activities. Cerebral oxidative stress, mitochondrial impairment, synaptic and histological alterations occurring in STZ-infused mice were also strongly attenuated by 12A12mAb delivery. These results further strengthen the causal role of N-terminal tau cleavage in AD pathogenesis and indicate that its specific neutralization by non-invasive administration of 12A12mAb can be a therapeutic option for both fAD and sAD patients, as well as for those showing type 2 diabetes as a comorbidity.