Using an evidence-based online module to improve parents’ ability to manage their child with Developmental Coordination Disorder

Background : Developmental coordination disorder (DCD) is a prevalent neurodevelopmental disorder. Best practices include raising parents’ awareness and building capacity but few interventions incorporating these best practices are documented. Objective : To examine whether an evidence-based online module can increase the perceived knowledge and skills of parents of children with DCD, and lead to behavioural changes when managing their child’s health condition. Methods : A mixed-methods, before-after-follow-up design guided by the theory of planned behaviour was employed. Data about the knowledge, skills and behaviours of parents of children with DCD were collected using questionnaires prior to completing the module, immediately after, and three months later. One-way repeated measures ANOVAs and thematic analyses were performed on data as appropriate. Results : Fifty-eight participants completed all questionnaires. There was a significant effect of time on self-reported knowledge [F(2.00,114.00)=16.37, p=0.00] and skills [F(1.81,103.03)=51.37, p=0.00] with higher post- and follow-up scores than pre-intervention scores. Thirty-seven (65%) participants reported an intention to change behaviour postintervention; 29 (50%) participants had tried recommended strategies at follow-up. Three themes emerged to describe parents’ behavioural change: sharing information, trialing strategies and changing attitudes. Factors influencing parents’ ability to implement these behavioural changes included clear recommendations, time, and ‘right’ attitude. Perceived outcomes associated with the parental behavioural changes involved improvement in well-being for the children at school, at home, and for the family as a whole. Conclusions : The online module increased parents’ self-reported knowledge and skills in DCD management. Future research should explore its impacts on children’s outcomes long-term.

Using an evidence-based online module to improve parents’ ability to manage their child with Developmental Coordination Disorder

Background : Developmental coordination disorder (DCD) is a prevalent neurodevelopmental disorder. Best practices include raising parents’ awareness and building capacity but few interventions incorporating these best practices are documented. Objective : To examine whether an evidence-based online module can increase the perceived knowledge and skills of parents of children with DCD, and lead to behavioral changes when managing their child’s health condition. Methods : A mixed-methods, before-after design guided by the theory of planned behavior was employed. Data about the knowledge, skills and behaviors of parents of children with DCD were collected using questionnaires prior to completing the module, immediately after, and three months later. Paired T-tests, sensitivity analyses and thematic analyses were performed on data as appropriate. Results: One hundred-sixteen, 81 and 58 participants respectively completed the three questionnaires. For knowledge and skills, post- and follow-up scores were significantly higher than baseline scores (p<0.01). Fifty-two (64%) participants reported an intention to change behavior post-intervention and 29 (50%) participants had tried recommended strategies at follow-up. Three themes emerged to describe parents’ behavioral change: sharing information, trialing strategies and changing attitudes. Factors influencing parents’ ability to implement these behavioral changes included clear recommendations, time, and ‘right’ attitude. Perceived outcomes associated with the parental behavioral changes involved improvement in well-being for the children at school, at home, and for the family as a whole. Conclusions : The online module increased parents’ self-reported knowledge and skills in DCD management. Future research should explore its impacts on children’s long-term outcomes.

Assessment of the elderly’s functional ability to manage theirmedication: a systematic literature review

Background The evaluation of the elderly’s ability to manage medication through the use of a validated tool can be a significant step in identifying inabilities and needs, with the objective of increasing their self-care skills, and promoting successful aging. Aim of the review To identify studies assessing the elderly’s functional ability to manage their own medication. Method For the search strategy, the PICO method was used: P—Population(elderly), I—Instruments (tools for assessing medication management ability), C—Context (community) and O—Outcomes (functional ability to manage medication). Thefinal search query was run in MEDLINE/PubMed,CINAHL Plus, ISI Web of Science and Scopus. The whole process was developed according to the PRISMA statement. Results The search retrieved 8051 records. In each screening stage, the selection criteria were applied to eliminate records where at least one of the exclusion criteria was verified. At the end of this selection, we obtained a total of 18 papers (17 studies). The results allow the conclusion to be drawn that studies use several different instruments, most of them not validated. The authors agree that medication management abilities decrease as cognitive impairment increases, even if a lot of studies assess only the physical dimension. DRUGS was the instrument most often used. Conclusion Older adults’ ability to manage their medication should be assessed using tools specifically built and validate for the purpose. DRUGS (which uses the real regimen taken by the elderly) was the most widely used assessment instrument in the screened studies.

Assessing the ability of rural agrarian areas to provide cultural ecosystem services (CES): A multi scale social indicator framework (MSIF)

Assessing the ways in which rural agrarian areas provide Cultural Ecosystem Services (CES) is proving difficult to achieve. This research has developed an innovative methodological approach named as Multi Scale Indicator Framework (MSIF) for capturing the CES embedded into the rural agrarian areas. This framework reconciles a literature review with a transdisciplinary participatory workshop. Both of these sources reveal that societal preferences diverge upon judgemental criteria which in turn relate to different visual concepts that can be drawn from analyzing attributes, elements, features and characteristics of rural areas. We contend that it is now possible to list a group of possible multi scale indicators for stewardship, diversity and aesthetics. These results might also be of use for improving any existing European indicators frameworks by also including CES. This research carries major implications for policy at different levels of governance, as it makes possible to target and monitor policy instruments to the physical rural settings so that cultural dimensions are adequately considered. There is still work to be developed on regional specific values and thresholds for each criteria and its indicator set. In practical terms, by developing the conceptual design within a common framework as described in this paper, a considerable step forward toward the inclusion of the cultural dimension in European wide assessments can be made

“Biomarkers in a One Health perspective: current knowledge of their ability to merge human and animal health

Biomarkers are nowadays essential tools to be one step ahead for fighting disease, enabling an enhanced focus on disease prevention and on the probability of its occurrence. Research in a multidisciplinary approach has been an important step towards the repeated discovery of new biomarkers. Biomarkers are defined as biochemical measurable indicators of the presence of disease or as indicators for monitoring disease progression. Currently, biomarkers have been used in several domains such as oncology, neurology, cardiovascular, inflammatory and respiratory disease, and several endocrinopathies. Bridging biomarkers in a One Health perspective has been proven useful in almost all of these domains. In oncology, humans and animals are found to be subject to the same environmental and genetic predisposing factors: examples include the existence of mutations in BR-CA1 gene predisposing to breast cancer, both in human and dogs, with increased prevalence in certain dog breeds and human ethnic groups. Also, breast feeding frequency and duration has been related to a decreased risk of breast cancer in women and bitches. When it comes to infectious diseases, this parallelism is prone to be even more important, for as much as 75% of all emerging diseases are believed to be zoonotic. Examples of successful use of biomarkers have been found in several zoonotic diseases such as Ebola, dengue, leptospirosis or West Nile virus infections. Acute Phase Proteins (APPs) have been used for quite some time as biomarkers of inflammatory conditions. These have been used in human health but also in the veterinary field such as in mastitis evaluation and PRRS (porcine respiratory and reproductive syndrome) diagnosis. Advantages rely on the fact that these biomarkers can be much easier to assess than other conventional disease diagnostic approaches (example: measured in easy to collect saliva samples). Another domain in which biomarkers have been essential is food safety: the possibility to measure exposure to chemical contaminants or other biohazards present in the food chain, which are sometimes analytical challenges due to their low bioavailability in body fluids, is nowadays a major breakthrough. Finally, biomarkers are considered the key to provide more personalized therapies, with more efficient outcomes and fewer side effects. This approach is expected to be the correct path to follow also in veterinary medicine, in the near future.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.

Fusarium head blight: characterization of secondary Fusarium species, evaluation of their pathogenetic role and ability to produce mycotoxins

Fusarium head blight (FHB) is a worldwide cereal disease caused by a complex of Fusarium species resulting in high yield losses, reduction in quality and mycotoxin contamination of grain. A shift in Fusarium head blight community has been observed worldwide. The present work aimed to analyze the evolution of Italian FHB community focusing the attention on species considered “secondary” in the past years such as members of Fusarium tricinctum species complex (FTSC) and F. proliferatum. The first goal of the study was to analyze the fungal community associated with Italian durum wheat in two different years. F. poae, F. avenaceum and F. proliferatum were the main species detected on Italian durum kernels. A variable mycotoxins contamination was observed in the analyzed samples. Considering, the increased incidence of F. avenaceum and other members of FTSC in Italian FHB, the second aim was to investigate genetic diversity among the FTSC and estimate the mycotoxin risk related to these species. Phylogenetic analyses revealed that F. avenaceum (FTSC 4) was the most common species in Italy, followed by an unnamed Fusarium sp., F. tricinctum and F. acuminatum. In addition to these four phylospecies, five other F. tricinctum clade species were sampled. These included strains of four newly discovered species (Fusarium spp. FTSC 11, 13, 14, 15) and F. iranicum (FTSC 6). Most isolates tested for mycotoxin production on rice cultures were able to produce quantitative levels of enniatins and moniliformin. In addition, a preliminary study was conducted to evaluate the ability of a selected F. proliferatum isolate to produce fumonisins on wheat in open field and under natural climatic conditions. The three analogues (FB1, FB2 and FB3) were quantified by HPLC-FLD analysis on kernels, chaff and rachis. Fumonisins were detected in all the three investigated fractions without significant differences.

Variational Quantum Splines: Moving Beyond Linearity for Quantum Activation Functions

Activation functions within neural networks play a crucial role in Deep Learning since they allow to learn complex and non-trivial patterns in the data. However, the ability to approximate non-linear functions is a significant limitation when implementing neural networks in a quantum computer to solve typical machine learning tasks. The main burden lies in the unitarity constraint of quantum operators, which forbids non-linearity and poses a considerable obstacle to developing such non-linear functions in a quantum setting. Nevertheless, several attempts have been made to tackle the realization of the quantum activation function in the literature. Recently, the idea of the QSplines has been proposed to approximate a non-linear activation function by implementing the quantum version of the spline functions. Yet, QSplines suffers from various drawbacks. Firstly, the final function estimation requires a post-processing step; thus, the value of the activation function is not available directly as a quantum state. Secondly, QSplines need many error-corrected qubits and a very long quantum circuits to be executed. These constraints do not allow the adoption of the QSplines on near-term quantum devices and limit their generalization capabilities. This thesis aims to overcome these limitations by leveraging hybrid quantum-classical computation. In particular, a few different methods for Variational Quantum Splines are proposed and implemented, to pave the way for the development of complete quantum activation functions and unlock the full potential of quantum neural networks in the field of quantum machine learning.

Continual learning on nano drones

Miniaturized flying robotic platforms, called nano-drones, have the potential to revolutionize the autonomous robots industry sector thanks to their very small form factor. The nano-drones’ limited payload only allows for a sub-100mW microcontroller unit for the on-board computations. Therefore, traditional computer vision and control algorithms are too computationally expensive to be executed on board these palm-sized robots, and we are forced to rely on artificial intelligence to trade off accuracy in favor of lightweight pipelines for autonomous tasks. However, relying on deep learning exposes us to the problem of generalization since the deployment scenario of a convolutional neural network (CNN) is often composed by different visual cues and different features from those learned during training, leading to poor inference performances. Our objective is to develop and deploy and adaptation algorithm, based on the concept of latent replays, that would allow us to fine-tune a CNN to work in new and diverse deployment scenarios. To do so we start from an existing model for visual human pose estimation, called PULPFrontnet, which is used to identify the pose of a human subject in space through its 4 output variables, and we present the design of our novel adaptation algorithm, which features automatic data gathering and labeling and on-device deployment. We therefore showcase the ability of our algorithm to adapt PULP-Frontnet to new deployment scenarios, improving the R2 scores of the four network outputs, with respect to an unknown environment, from approximately [−0.2, 0.4, 0.0,−0.7] to [0.25, 0.45, 0.2, 0.1]. Finally we demonstrate how it is possible to fine-tune our neural network in real time (i.e., under 76 seconds), using the target parallel ultra-low power GAP 8 System-on-Chip on board the nano-drone, and we show how all adaptation operations can take place using less than 2mWh of energy, a small fraction of the available battery power.

An Isoflavone From Dipteryx Alata Vogel Is Active Against The In Vitro Neuromuscular Paralysis Of Bothrops Jararacussu Snake Venom And Bothropstoxin I, And Prevents Venom-induced Myonecrosis.

Snakebite is a neglected disease and serious health problem in Brazil, with most bites being caused by snakes of the genus Bothrops. Although serum therapy is the primary treatment for systemic envenomation, it is generally ineffective in neutralizing the local effects of these venoms. In this work, we examined the ability of 7,8,3'-trihydroxy-4'-methoxyisoflavone (TM), an isoflavone from Dipteryx alata, to neutralize the neurotoxicity (in mouse phrenic nerve-diaphragm preparations) and myotoxicity (assessed by light microscopy) of Bothrops jararacussu snake venom in vitro. The toxicity of TM was assessed using the Salmonella microsome assay (Ames test). Incubation with TM alone (200 μg/mL) did not alter the muscle twitch tension whereas incubation with venom (40 μg/mL) caused irreversible paralysis. Preincubation of TM (200 μg/mL) with venom attenuated the venom-induced neuromuscular blockade by 84% ± 5% (mean ± SEM; n = 4). The neuromuscular blockade caused by bothropstoxin-I (BthTX-I), the major myotoxic PLA2 of this venom, was also attenuated by TM. Histological analysis of diaphragm muscle incubated with TM showed that most fibers were preserved (only 9.2% ± 1.7% were damaged; n = 4) compared to venom alone (50.3% ± 5.4% of fibers damaged; n = 3), and preincubation of TM with venom significantly attenuated the venom-induced damage (only 17% ± 3.4% of fibers damaged; n = 3; p < 0.05 compared to venom alone). TM showed no mutagenicity in the Ames test using Salmonella strains TA98 and TA97a with (+S9) and without (-S9) metabolic activation. These findings indicate that TM is a potentially useful compound for antagonizing the neuromuscular effects (neurotoxicity and myotoxicity) of B. jararacussu venom.

The Effect Of Pelvic Floor Muscle Training Alone Or In Combination With Electrostimulation In The Treatment Of Sexual Dysfunction In Women With Multiple Sclerosis.

Sexual dysfunction (SD) affects up to 80% of multiple sclerosis (MS) patients and pelvic floor muscles (PFMs) play an important role in the sexual function of these patients. The objective of this paper is to evaluate the impact of a rehabilitation program to treat lower urinary tract symptoms on SD of women with MS. Thirty MS women were randomly allocated to one of three groups: pelvic floor muscle training (PFMT) with electromyographic (EMG) biofeedback and sham neuromuscular electrostimulation (NMES) (Group I), PFMT with EMG biofeedback and intravaginal NMES (Group II), and PFMT with EMG biofeedback and transcutaneous tibial nerve stimulation (TTNS) (Group III). Assessments, before and after the treatment, included: PFM function, PFM tone, flexibility of the vaginal opening and ability to relax the PFMs, and the Female Sexual Function Index (FSFI) questionnaire. After treatment, all groups showed improvements in all domains of the PERFECT scheme. PFM tone and flexibility of the vaginal opening was lower after the intervention only for Group II. All groups improved in arousal, lubrication, satisfaction and total score domains of the FSFI questionnaire. This study indicates that PFMT alone or in combination with intravaginal NMES or TTNS contributes to the improvement of SD.

Antioxidant And Antiproliferative Activities Of Methanolic Extract From A Neglected Agricultural Product: Corn Cobs.

Neglected agricultural products (NAPs) are defined as discarded material in agricultural production. Corn cobs are a major waste of agriculture maize. Here, a methanolic extract from corn cobs (MEC) was obtained. MEC contains phenolic compounds, protein, carbohydrates (1.4:0.001:0.001). We evaluated the in vitro and in vivo antioxidant potential of MEC. Furthermore, its antiproliferative property against tumor cells was assessed through MTT assays and proteins related to apoptosis in tumor cells were examined by western blot. MEC showed no hydroxyl radical scavenger capacity, but it showed antioxidant activity in Total Antioxidant Capacity and DPPH scavenger ability assays. MEC showed higher Reducing Power than ascorbic acid and exhibited high Superoxide Scavenging activity. In tumor cell culture, MEC increased catalase, metallothionein and superoxide dismutase expression in accordance with the antioxidant tests. In vivo antioxidant test, MEC restored SOD and CAT, decreased malondialdehyde activities and showed high Trolox Equivalent Antioxidant Capacity in animals treated with CCl4. Furthermore, MEC decreased HeLa cells viability by apoptosis due an increase of Bax/Bcl-2 ratio, caspase 3 active. Protein kinase C expression increased was also detected in treated tumor cells. Thus, our findings pointed out the biotechnological potential of corn cobs as a source of molecules with pharmacological activity.

Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.

Phylogenetic Analysis Of The Microbial Community In Hypersaline Petroleum Produced Water From The Campos Basin.

In this work the archaea and eubacteria community of a hypersaline produced water from the Campos Basin that had been transported and discharged to an onshore storage facility was evaluated by 16S recombinant RNA (rRNA) gene sequence analysis. The produced water had a hypersaline salt content of 10 (w/v), had a carbon oxygen demand (COD) of 4,300 mg/l and contains phenol and other aromatic compounds. The high salt and COD content and the presence of toxic phenolic compounds present a problem for conventional discharge to open seawater. In previous studies, we demonstrated that the COD and phenolic content could be largely removed under aerobic conditions, without dilution, by either addition of phenol degrading Haloarchaea or the addition of nutrients alone. In this study our goal was to characterize the microbial community to gain further insight into the persistence of reservoir community members in the produced water and the potential for bioremediation of COD and toxic contaminants. Members of the archaea community were consistent with previously identified communities from mesothermic reservoirs. All identified archaea were located within the phylum Euryarchaeota, with 98 % being identified as methanogens while 2 % could not be affiliated with any known genus. Of the identified archaea, 37 % were identified as members of the strictly carbon-dioxide-reducing genus Methanoplanus and 59 % as members of the acetoclastic genus Methanosaeta. No Haloarchaea were detected, consistent with the need to add these organisms for COD and aromatic removal. Marinobacter and Halomonas dominated the eubacterial community. The presence of these genera is consistent with the ability to stimulate COD and aromatic removal with nutrient addition. In addition, anaerobic members of the phyla Thermotogae, Firmicutes, and unclassified eubacteria were identified and may represent reservoir organisms associated with the conversion hydrocarbons to methane.

The Effect Of Celastrol, A Triterpene With Antitumorigenic Activity, On Conformational And Functional Aspects Of The Human 90kda Heat Shock Protein Hsp90α, A Chaperone Implicated In The Stabilization Of The Tumor Phenotype.

Hsp90 is a molecular chaperone essential for cell viability in eukaryotes that is associated with the maturation of proteins involved in important cell functions and implicated in the stabilization of the tumor phenotype of various cancers, making this chaperone a notably interesting therapeutic target. Celastrol is a plant-derived pentacyclic triterpenoid compound with potent antioxidant, anti-inflammatory and anticancer activities; however, celastrol's action mode is still elusive. In this work, we investigated the effect of celastrol on the conformational and functional aspects of Hsp90α. Interestingly, celastrol appeared to target Hsp90α directly as the compound induced the oligomerization of the chaperone via the C-terminal domain as demonstrated by experiments using a deletion mutant. The nature of the oligomers was investigated by biophysical tools demonstrating that a two-fold excess of celastrol induced the formation of a decameric Hsp90α bound throughout the C-terminal domain. When bound, celastrol destabilized the C-terminal domain. Surprisingly, standard chaperone functional investigations demonstrated that neither the in vitro chaperone activity of protecting against aggregation nor the ability to bind a TPR co-chaperone, which binds to the C-terminus of Hsp90α, were affected by celastrol. Celastrol interferes with specific biological functions of Hsp90α. Our results suggest a model in which celastrol binds directly to the C-terminal domain of Hsp90α causing oligomerization. However, the ability to protect against protein aggregation (supported by our results) and to bind to TPR co-chaperones are not affected by celastrol. Therefore celastrol may act primarily by inducing specific oligomerization that affects some, but not all, of the functions of Hsp90α. To the best of our knowledge, this study is the first work to use multiple probes to investigate the effect that celastrol has on the stability and oligomerization of Hsp90α and on the binding of this chaperone to Tom70. This work provides a novel mechanism by which celastrol binds Hsp90α.