Anti-Candida Activity of Bursera morelensis Ramirez Essential Oil and Two Compounds, α-Pinene and γ-Terpinene-An In Vitro Study.

The candidiasis caused by C. albicans is a public health problem. The abuse of antifungals has contributed to the development of resistance. B. morelensis has demonstrated antibacterial and antifungal activities. In this work the activity of the essential oil of B. morelensis was evaluated and for its two pure compounds with analysis of the different mechanisms of pathogenesis important for C. albicans. The essential oil was obtained by the hydro-distillation method and analyzed using GC-MS. The anti-Candida activity was compared between to essential oil, α-Pinene and γ-Terpinene. GC-MS of the essential oil demonstrated the presence of 13 compounds. The essential oil showed antifungal activity against four C. albicans strains. The most sensitive strain was C. albicans 14065 (MFC 2.0 mg/mL and MIC50 0.125 mg/mL) with α-Pinene and γ-Terpinene having MFCs of 4.0 and 16.0 mg/mL respectively. The essential oil inhibited the growth of the germ tube in 87.94% (8.0 mg/mL). Furthermore, it was observed that the essential oil diminishes the transcription of the gene INT1. This work provides evidence that confirms the anti-Candida activity of the B. morelensis essential oil and its effect on the growth of the germ tube and transcription of the gene INT1.

Early Diagnosis of Hemophagocytic Lymphohistiocytosis in an Adult Presenting With Sickle Cell Disease: A Case Report.

Hemophagocytic lymphohistiocytosis (HLH) is a rare but potentially life-threatening syndrome characterized by excessive immune activation and tissue inflammation. This case report describes the early diagnosis of HLH in an adult patient who initially presented with a febrile syndrome associated with low back pain. The patient, a 33-year-old male, exhibited bicytopenia, hepatosplenomegaly, and hyperferritinemia without a previous diagnosis of sickle cell disease (SCD). Diagnostic challenges arose due to the overlapping clinical manifestations of SCD and HLH and their uncommon association. However, timely recognition and intervention were achieved through comprehensive diagnostic evaluations, including a bone marrow biopsy. The patient was promptly started on an appropriate therapeutic regimen, which led to significant clinical improvement. This case underscores the importance of considering HLH in the differential diagnosis of adults presenting with hematologic abnormalities and systemic inflammation. Early diagnosis and treatment are critical to improving outcomes for patients with this complex and severe disorder.

Local delivery of sirolimus nanoparticles for the treatment of in-stent restenosis.

OBJECTIVES: To test the local delivery of sirolimus nanoparticles following percutaneous transluminal coronary angioplasty (PTCA) to treat in-stent restenosis (ISR) in a swine model. BACKGROUND: Coronary bare-metal stent (BMS) implantation reduces major adverse cardiac events when compared with PTCA; however, ISR rates remain high. METHODS: Eighteen swine underwent BMS deployment guided by intravascular ultrasound (IVUS). Of these, 16 developed ISR (1 stent/swine) and underwent angioplasty with a noncompliant balloon (PTCA-NC). The animals were then randomized into four groups for local infusion of sirolimus nanoparticles through a porous balloon catheter, as follows: (1) PTCA-NC alone (control); (2) PTCA-NC + (polylactic acid)-based nanoparticle formulation (anionic 1); (3) PTCA-NC + (polylactic-co-glycolic acid)-based nanoparticle formulation (anionic 2); and (4) PTCA-NC + Eudragit RS nanoparticle formulation (cationic). Coronary angiography and IVUS follow-up were performed 28 days after ISR treatment. RESULTS: There was one episode of acute coronary occlusion with the cationic formulation. Late area loss was similar in all groups at 28 days according to IVUS. However, luminal volume loss (control = 20.7%, anionic 1 = 4.0%, anionic 2 = 6.7%, cationic = 9.6%; P = 0.01) and neointimal volume gain (control = 68.7%, anionic 1 = 17.4%, anionic 2 = 29.5%, cationic = 31.2%; P = 0.019) were significantly reduced in all treatment groups, especially in anionic 1. CONCLUSIONS: PTCA-NC followed by local infusion of sirolimus nanoparticles was safe and efficacious to reduce neointima in this model, and this strategy may be a promising treatment for BMS ISR. Further studies are required to validate this method in humans.

Compositional Learning of Human Activities With a Self-Organizing Neural Architecture.

An important step for assistive systems and robot companions operating in human environments is to learn the compositionality of human activities, i.e., recognize both activities and their comprising actions. Most existing approaches address action and activity recognition as separate tasks, i.e., actions need to be inferred before the activity labels, and are thus highly sensitive to the correct temporal segmentation of the activity sequences. In this paper, we present a novel learning approach that jointly learns human activities on two levels of semantic and temporal complexity: (1) transitive actions such as reaching and opening, e.g., a cereal box, and (2) high-level activities such as having breakfast. Our model consists of a hierarchy of GWR networks which process and learn inherent spatiotemporal dependencies of multiple visual cues extracted from the human body skeletal representation and the interaction with objects. The neural architecture learns and semantically segments input RGB-D sequences of high-level activities into their composing actions, without supervision. We investigate the performance of our architecture with a set of experiments on a publicly available benchmark dataset. The experimental results show that our approach outperforms the state of the art with respect to the classification of the high-level activities. Additionally, we introduce a novel top-down modulation mechanism to the architecture which uses the actions and activity labels as constraints during the learning phase. In our experiments, we show how this mechanism can be used to control the network's neural growth without decreasing the overall performance.

Continual lifelong learning with neural networks: A review.

Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to long-term memory consolidation and retrieval. Consequently, lifelong learning capabilities are crucial for computational learning systems and autonomous agents interacting in the real world and processing continuous streams of information. However, lifelong learning remains a long-standing challenge for machine learning and neural network models since the continual acquisition of incrementally available information from non-stationary data distributions generally leads to catastrophic forgetting or interference. This limitation represents a major drawback for state-of-the-art deep neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which information becomes incrementally available over time. In this review, we critically summarize the main challenges linked to lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic forgetting. Although significant advances have been made in domain-specific learning with neural networks, extensive research efforts are required for the development of robust lifelong learning on autonomous agents and robots. We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as structural plasticity, memory replay, curriculum and transfer learning, intrinsic motivation, and multisensory integration.

Expectation Learning for Stimulus Prediction Across Modalities Improves Unisensory Classification.

Expectation learning is a unsupervised learning process which uses multisensory bindings to enhance unisensory perception. For instance, as humans, we learn to associate a barking sound with the visual appearance of a dog, and we continuously fine-tune this association over time, as we learn, e.g., to associate high-pitched barking with small dogs. In this work, we address the problem of developing a computational model that addresses important properties of expectation learning, in particular focusing on the lack of explicit external supervision other than temporal co-occurrence. To this end, we present a novel hybrid neural model based on audio-visual autoencoders and a recurrent self-organizing network for multisensory bindings that facilitate stimulus reconstructions across different sensory modalities. We refer to this mechanism as stimulus prediction across modalities and demonstrate that the proposed model is capable of learning concept bindings by evaluating it on unisensory classification tasks for audio-visual stimuli using the 43,500 Youtube videos from the animal subset of the AudioSet corpus.

Inducing hardening and healability in poly(ethylene-co-acrylic acid) via blending with complementary low molecular weight additives.

The design and synthesis of low molecular weight additives based on self-assembling nitroarylurea units, and their compatibility with poly(ethylene-co-acrylic acid) copolymers are reported. The self-assembly properties of the low molecular weight additives have been demonstrated in a series of gelation studies. Upon blending at low percentage weights (≤5%) with poly(ethylene-co-acrylic acid) the additives were capable of increasing the stress and strain to failure when compared to the parent copolymer. By varying the percentage weight of the additive as well as the type of additive the mechanical properties of poly(ethylene-co-acrylic acid) could be tailored. Finally, the healability characteristics of the blends were improved when compared to the original polymer via the introduction of a supramolecular 'network within a network'.

Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization.

Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability to learn from continuous streams of information is referred to as lifelong learning and represents a long-standing challenge for neural network models due to catastrophic forgetting in which novel sensory experience interferes with existing representations and leads to abrupt decreases in the performance on previously acquired knowledge. Computational models of lifelong learning typically alleviate catastrophic forgetting in experimental scenarios with given datasets of static images and limited complexity, thereby differing significantly from the conditions artificial agents are exposed to. In more natural settings, sequential information may become progressively available over time and access to previous experience may be restricted. Therefore, specialized neural network mechanisms are required that adapt to novel sequential experience while preventing disruptive interference with existing representations. In this paper, we propose a dual-memory self-organizing architecture for lifelong learning scenarios. The architecture comprises two growing recurrent networks with the complementary tasks of learning object instances (episodic memory) and categories (semantic memory). Both growing networks can expand in response to novel sensory experience: the episodic memory learns fine-grained spatiotemporal representations of object instances in an unsupervised fashion while the semantic memory uses task-relevant signals to regulate structural plasticity levels and develop more compact representations from episodic experience. For the consolidation of knowledge in the absence of external sensory input, the episodic memory periodically replays trajectories of neural reactivations. We evaluate the proposed model on the CORe50 benchmark dataset for continuous object recognition, showing that we significantly outperform current methods of lifelong learning in three different incremental learning scenarios.

Lifelong learning of human actions with deep neural network self-organization.

Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather learn a batch of training data with a predefined number of action classes and samples. Thus, there is the need to develop learning systems with the ability to incrementally process available perceptual cues and to adapt their responses over time. We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences. The architecture comprises growing self-organizing networks equipped with recurrent neurons for processing time-varying patterns. We use a set of hierarchically arranged recurrent networks for the unsupervised learning of action representations with increasingly large spatiotemporal receptive fields. Lifelong learning is achieved in terms of prediction-driven neural dynamics in which the growth and the adaptation of the recurrent networks are driven by their capability to reconstruct temporally ordered input sequences. Experimental results on a classification task using two action benchmark datasets show that our model is competitive with state-of-the-art methods for batch learning also when a significant number of sample labels are missing or corrupted during training sessions. Additional experiments show the ability of our model to adapt to non-stationary input avoiding catastrophic interference.

A new species of Liolaemus related to L. nigroviridis from the Andean highlands of Central Chile (Iguania, Liolaemidae).

The Liolaemus nigroviridis group is a clade of highland lizards endemic to Chile. These species are distributed from northern to central Chile, and currently there are no cases of sympatric distribution. This study describes a new species, Liolaemus uniformis sp. n., from this group, and provides a detailed morphological characterization and mitochondrial phylogeny using cytochrome-b. Liolaemus uniformis was found in sympatry with Liolaemus nigroviridis but noticeably differed in size, scalation, and markedly in the color pattern, without sexual dichromatism. This new species has probably been confused with Liolaemus monticola and Liolaemus bellii, both of which do not belong to the nigroviridis group. The taxonomic issues of this group that remain uncertain are also discussed.

Two new Liolaemus lizards from the Andean highlands of Southern Chile (Squamata, Iguania, Liolaemidae).

Liolaemus is a diverse genus of lizards, subdivided into two subgenera: Liolaemus (sensu stricto) and Eulaemus, distributed mainly in Chile and Argentina. The Liolaemus elongatus-kriegi complex is the most diverse group within Liolaemus (sensu stricto), especially the species closely related to Liolaemus elongatus, which form a clade currently comprising nine species. Several Chilean species of this group have been recently described, mainly from volcanoes and poorly explored mountains. Here molecular and morphological evidence are provided for a new species of the Liolaemus elongatus clade, which is characterized by its small size and lack of dorsal pattern, unusual features for the species of this group of lizards. Additionally, the lack of precloacal pores in males of Liolaemus (sensu stricto) is a trait found in few species, which do not constitute a monophyletic group. A second new southern Chilean species is also described, without precloacal pores and supported by molecular phylogenetics to be related to Liolaemus villaricensis. Both new species were found in the same locality, near a lake located in a pre-Andean zone with Araucaria and Nothofagus forest. The two species are dedicated to prominent Lonkos (tribal chiefs) of the Mapuche and Pehuenche people: Janequeo and Leftraru. Additionally, the phylogenetic results suggest that Liolaemus lonquimayensis is a synonym of Liolaemus elongatus.

Non-cytotoxic copper overload boosts mitochondrial energy metabolism to modulate cell proliferation and differentiation in the human erythroleukemic cell line K562.

Copper is integral to the mitochondrial respiratory complex IV and contributes to proliferation and differentiation, metabolic reprogramming and mitochondrial function. The K562 cell line was exposed to a non-cytotoxic copper overload to evaluate mitochondrial dynamics, function and cell fate. This induced higher rates of mitochondrial turnover given by an increase in mitochondrial fusion and fission events and in the autophagic flux. The appearance of smaller and condensed mitochondria was also observed. Bioenergetics activity included more respiratory complexes, higher oxygen consumption rate, superoxide production and ATP synthesis, with no decrease in membrane potential. Increased cell proliferation and inhibited differentiation also occurred. Non-cytotoxic copper levels can modify mitochondrial metabolism and cell fate, which could be used in cancer biology and regenerative medicine.

Physicochemical property profile for brain permeability: comparative study by different approaches.

A comparative study of classification models of brain penetration by different approaches was carried out on a training set of 1000 chemicals and drugs, and an external test set of 100 drugs. Ten approaches were applied in this work: seven medicinal chemistry approaches (including "rule of 5" and multiparameter optimization) and also three SAR techniques: logistic regression (LR), random forest (RF) and support vector machine (SVM). Forty-one different medicinal chemistry descriptors representing diverse physicochemical properties were used in this work. Medicinal chemistry approaches based on the intuitive estimation of preference zones of CNS or non-CNS chemicals, with different rules and scoring functions, yield unbalanced models with poor classification accuracy. RF and SVM methods yielded 82% and 84% classification accuracy respectively for the external test set. LR was also successful in CNS/non-CNS (denoted in this study as CNS+/CNS-) classification and yielded an overall accuracy equivalent to that of SVM and RF. At the same time, LR is especially valuable for medicinal chemists because of its simplicity and the possibility of clear mechanistic interpretation.

Informational suppression to investigate structural functional and evolutionary aspects of the Erwinia chrysanthemi cellulase EGZ.

The cellulase EGZ produced by the plant pathogen Erwinia chrysanthemi belongs to family 5 of the beta-glycohydrolases (also referred to as cellulase family A), which contains over 40 members from Gram-negative and Gram-positive bacteria and fungi. Amber mutations were introduced into 16 codons of the celZ gene encoding EGZ. Targeted residues included: (1) two Glu, two His and one Arg residue, strictly conserved throughout family 5; (2) one Arg and one His residue conserved in sub-family 5-2; and (3) one His and six Arg residues not conserved at all. Each amber allele was introduced into 13 Escherichia coli strains each carrying a different suppressor tRNA that inserts an amino acid at the mutated position. In vivo stability of the mutated forms of EGZ and their cellulase activity were analysed as well as suppression efficiency. For some positions of particular interest, missense mutations were introduced into the celZ gene either to confirm the effect of the suppressor-mediated amino acid substitution or to broaden the spectrum of mutations available. The substitution patterns of the two Glu positions were interpretable in the light of the stereospecificity of the reaction catalysed by EGZ: Glu133 and Glu220 are proposed to act as a proton donor and as a nucleophile, respectively, forming the glycosyl-enzyme intermediate. Substitution at His-occupied positions, including two non-conserved positions, yielded proteins affected in their catalytic activity but not their in vivo stability. In particular, evidence was obtained for His at position 98 to be involved in interactions with the substrate. The view that Arg residues are important in stabilizing proteins was supported by the identification of three Arg residues, whose substitution yielded thermosensitive forms of EGZ. In addition, Pro substitutions of any of the six Arg residues altered protein stability in vivo but the substitutions scored almost neutral for activity. Five positions, predicted to be within alpha-helices, were found to be susceptible to Pro substitutions (but not to Ala) with respect to stability in vivo. Overall, the systematic alteration of all His and Arg residues coupled with the simultaneous analysis of activity and in vivo stability allowed us to demonstrate that substitution matrices vary at each position and for each biological property considered. Ideally, therefore, substitution matrices used in sequence alignment procedures should be reconsidered as position-specific and as property-specific.(ABSTRACT TRUNCATED AT 400 WORDS)

Self-organizing neural integration of pose-motion features for human action recognition.

The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented toward human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR) networks that obtain progressively generalized representations of sensory inputs and learn inherent spatio-temporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best results for a public benchmark of domestic daily actions.

Development of human B cells and antibodies following human hematopoietic stem cell transplantation to Rag2(-/-)γc(-/-) mice.

Humanized mice represent a valuable model system to study the development and functionality of the human immune system. In the RAG-hu mouse model highly immunodeficient Rag2(-/-)γc(-/-) mice are transplanted with human CD34(+) hematopoietic stem cells, resulting in human hematopoiesis and a predominant production of B and T lymphocytes. Human adaptive immune responses have been detected towards a variety of antigens in humanized mice but both cellular and humoral immune responses tend to be weak and sporadically detected. The underlying mechanisms for inconsistent responses are poorly understood. Here, we analyzed the kinetics of human B cell development and antibody production in RAG-hu mice to better understand the lack of effective antibody responses. We found that T cell levels in blood did not significantly change from 8 to 28 weeks post-engraftment, while B cells reached a peak at 14 weeks. Concentrations of 3 antibody classes (IgM, IgG, IgA) were found to be at levels about 0.1% or less of normal human levels, but human antibodies were still detected up to 32 weeks after engraftment. Human IgM was detected in 92.5% of animals while IgG and IgA were detected in about half of animals. We performed flow cytometric analysis of human B cells in bone marrow, spleen, and blood to examine the presence of precursor B cells, immature B cells, naïve B cells, and plasma B cells. We detected high levels of surface IgM(+) B cells (immature and naïve B cells) and low levels of plasma B cells in these organs, suggesting that B cells do not mature properly in this model. Low levels of human T cells in the spleen were observed, and we suggest that the lack of T cell help may explain poor B cell development and antibody responses. We conclude that human B cells that develop in humanized mice do not receive the signals necessary to undergo class-switching or to secrete antibody effectively, and we discuss strategies to potentially overcome these barriers.

Stereochemistry of the hydrolysis reaction catalyzed by endoglucanase Z from Erwinia chrysanthemi.

Endoglucanase Z from the phytopathogenic bacterium Erwinia chrysanthemi (strain 3937) was purified by affinity chromatography on microcrystalline cellulose Avicel PH101. A kinetic characterization using p-nitrophenyl beta-D-cellobioside and p-nitrophenyl beta-D-lactosde as substrates was conducted: endoglucanase Z exhibited Km values of 3 mM and 7.5 mM and Vm values of 129 and 40 nmol.min-1.mg-1 towards p-nitrophenyl beta-D-cellobioside (kcat = 0.1 s-1) and p-nitrophenyl beta-D-lactoside (kcat = 0.03 s-1), respectively). The hydrolysis of cellotetraitol by endoglucanase Z was followed by HPLC and 1H NMR. Results show that cellobiitol and beta-cellobiose are initially formed, demonstrating that the enzyme is acting by a molecular mechanism retaining the anomeric configuration. This suggests the involvement of a glycosyl-enzyme intermediate.

Primary malignant lymphoma of the prostate--a report of three cases.

We report the clinical, morphological and immunohistochemical findings in 3 cases of primary non-Hodgkins malignant lymphoma of the prostate. After treatment with doxorubicin-based chemotherapy, two patients achieved a complete remission, and 1 died of infective endocarditis three months after diagnosis. Until a consensus has been reached regarding the optimal treatment of prostatic lymphoma, therapy should be determined by the histologic type diagnosed and stage of the lymphoma.

[Mutagenic activity of the pesticide hexachlorobutadiene]. / O mutagennoi aktivnosti pestitsida geksakhlorbutadiena.

Pesticide hexachlorobutadiene (HCBD) has been studied for its mutagenic activity in two test systems: in vivo and in vitro. The above pesticide manifested a mutagenic activity in the bone marrow cells under peroral and inhalation effect. No clastogenic action in the human peripheral lymphocyte culture was observed. Results of this study and data available in literature permit concluding that HCBD is an indirect mutagen.

[Primary evaluation of the cytogenetic activity and potential mutagenic hazard of 22 pesticides]. / Pervichnaia otsenka tsitogeneticheskoi aktovnosti i potentsial'noi mutagennoi opasnosti 22 pestitsidov.

The paper deals with the primary principle of potential mutagenic pesticides hazard evaluation from the results of study on their cytogenetic activity in experimental animals. This evaluation may be based on two main parameters characterizing a mutagenic property of pesticides--the degree of maximum cytogenetic effect expression and the level of minimum effective dose. According to the suggested principle 22 pesticides under study are distributed into some groups in conformity with the degree of their potential mutagenic hazard.