Mitochondrial phospholipid transport: Role of contact sites and lipid transport proteins.

One of the major constituents of mitochondrial membranes is the phospholipids, which play a key role in maintaining the structure and the functions of the mitochondria. However, mitochondria do not synthesize most of the phospholipids in situ, necessitating the presence of phospholipid import pathways. Even for the phospholipids, which are synthesized within the inner mitochondrial membrane (IMM), the phospholipid precursors must be imported from outside the mitochondria. Therefore, the mitochondria heavily rely on the phospholipid transport pathways for its proper functioning. Since, mitochondria are not part of a vesicular trafficking network, the molecular mechanisms of how mitochondria receive its phospholipids remain a relevant question. One of the major ways that hydrophobic phospholipids can cross the aqueous barrier of inter or intraorganellar spaces is by apposing membranes, thereby decreasing the distance of transport, or by being sequestered by lipid transport proteins (LTPs). Therefore, with the discovery of LTPs and membrane contact sites (MCSs), we are beginning to understand the molecular mechanisms of phospholipid transport pathways in the mitochondria. In this review, we will present a brief overview of the recent findings on the molecular architecture and the importance of the MCSs, both the intraorganellar and interorganellar contact sites, in facilitating the mitochondrial phospholipid transport. In addition, we will also discuss the role of LTPs for trafficking phospholipids through the intermembrane space (IMS) of the mitochondria. Mechanistic insights into different phospholipid transport pathways of mitochondria could be exploited to vary the composition of membrane phospholipids and gain a better understanding of their precise role in membrane homeostasis and mitochondrial bioenergetics.

PHLPP1 promotes neutral lipid accumulation through AMPK/ChREBP-dependent lipid uptake and fatty acid synthesis pathways.

Infiltration of arterial intima by foamy macrophages is a hallmark of early atherosclerotic lesions. Here, we investigated the potential role of Ser/Thr phosphatase PHLPP1 in foam cell development. PHLPP1 levels were elevated in OxLDL-exposed macrophages and high-fat diet (HFD)-fed zebrafish larvae. Using overexpression and knockdown approaches, we show that PHLPP1 promotes the accumulation of neutral lipids, and augments cellular total cholesterol and free fatty acid (FFA) levels. RNA-Seq analysis uncovered PHLPP1 role in lipid metabolism pathways. PHLPP1 interacted with and modestly increased ChREBP recruitment to Fasn promoter. PHLPP1-mediated lipid accumulation was attenuated by AMPK activation. Pharmacological inhibition or CRISPR/Cas9-mediated disruption of PHLPP1 resulted in lower lipid accumulation in the intersegmental vessels of HFD-fed zebrafish larvae along with a reduction in total cholesterol and triglyceride levels. Deficiency of phlp-2, C. elegans PHLPP1/2 ortholog, abolished lipid accumulation in high cholesterol-fed worms. We conclude that PHLPP1 exerts a significant effect on lipid buildup.

A Review of Current and Future Antithrombotic Strategies in Surgical Patients-Leaving the Graduated Compression Stockings Behind?

Venous thromboembolism (VTE) remains an important consideration within surgery, with recent evidence looking to refine clinical guidance. This review provides a contemporary update of existing clinical evidence for antithrombotic regimens for surgical patients, providing future directions for prophylaxis regimens and research. For moderate to high VTE risk patients, existing evidence supports the use of heparins for prophylaxis. Direct oral anticoagulants (DOACs) have been validated within orthopaedic surgery, although there remain few completed randomised controlled trials in other surgical specialties. Recent trials have also cast doubt on the efficacy of mechanical prophylaxis, especially when adjuvant to pharmacological prophylaxis. Despite the ongoing uncertainty in higher VTE risk patients, there remains a lack of evidence for mechanical prophylaxis in low VTE risk patients, with a recent systematic search failing to identify high-quality evidence. Future research on rigorously developed and validated risk assessment models will allow the better stratification of patients for clinical and academic use. Mechanical prophylaxis' role in modern practice remains uncertain, requiring high-quality trials to investigate select populations in which it may hold benefit and to explore whether intermittent pneumatic compression is more effective. The validation of DOACs and aspirin in wider specialties may permit pharmacological thromboprophylactic regimens that are easier to administer.

Overcoming Long-Term Variability in Local Field Potentials Using an Adaptive Decoder.

Long-term variability remains one of the major hurdles in using intracortical recordings like local field potentials for brain computer interfaces (BCI). Practical neural decoders need to overcome time instability of neural signals to estimate subject behavior accurately and faithfully over the long term. This paper presents a novel decoder that 1) characterizes each behavioral task (i.e., different movement directions under different force conditions) with multiple neural patterns and 2) adapts to the long-term variations in neural features by identifying the stable neural patterns. This adaptation can be performed in both an unsupervised and a semisupervised learning framework requiring minimal feedback from the user. To achieve generalization over time, the proposed decoder uses redundant sparse regression models that adapt to day-to-day variations in neural patterns. While this update requires no explicit feedback from the BCI user, any feedback (explicit or derived) to the BCI improves its performance. With this adaptive decoder, we investigated the effects of long-term neural modulation especially when subjects encountered new external forces against movement. The proposed decoder predicted eight hand-movement directions with an accuracy of 95% over two weeks (when there was no external forces); and 85% in later acquisition sessions spanning up to 42 days (when the monkeys countered external field forces). Since the decoder can operate with or without manual intervention, it could alleviate user frustration associated with BCI.

Block design enhances classification of 3D reach targets from electroencephalographic signals.

To date, decoding accuracy of actual or imagined pointing movements to targets in 3D space from electroencephalographic (EEG) signals has remained modest. The reason may pertain to the fact that these movements activate essentially the same neural networks. In this study, we aimed at testing whether repetitive pointing movements to each of the targets promotes the development of segregated neural patterns, resulting in enhanced decoding accuracy. Six human subjects generated slow or fast repetitive pointing movements with their right dominant arm to one of five targets distributed in 3D space, followed by repetitive imagery of movements to the same target or to a different target. Nine naive subjects generated both repetitive and non-repetitive slow actual movements to each of the five targets to test the effect of block design on decoding accuracy. In order to assure that base line drift and low frequency motion artifacts do not contaminate the data, the data were high-pass filtered in 4-30Hz, leaving out the delta and gamma band. For the repetitive trials, the model decoded target location with 81% accuracy, which is significantly higher than chance level. The average decoding rate of target location was only 30% for the non-repetitive trials, which is not significantly different than chance level. A subset of electrodes, mainly over the contralateral sensorimotor areas, was found to provide most of the discriminative features for all tested conditions. Time proximity between trained and tested blocks was found to enhance decoding accuracy of target location both by target non-specific and specific mechanisms. Our findings suggest that movement repetition promotes the development of distinct neural patterns, presumably by the formation of target-specific kinesthetic memory.

Long-term decoding of arm movement using Spatial Distribution of Neural Patterns.

Day to day variability and non-stationarity caused by changes in subject motivation, learning and behavior pose a challenge in using local field potentials (LFP) for practical Brain Computer Interfaces. Pattern recognition algorithms require that the features possess little to no variation from the training to test data. As such models developed on one day fail to represent the characteristics on the other day. This paper provides a solution in the form of adaptive spatial features. We propose an algorithm to capture the local spatial variability of LFP patterns and provide accurate long-term decoding. This algorithm achieved more than 95% decoding of eight movement directions two weeks after its initial training.

Source localization techniques for direction decoding from local field potentials.

Local Field Potential (LFP) recordings are one type of intracortical recordings, (besides Single Unit Activity) that can help decode movement direction successfully. In the longterm however, using LFPs for decoding presents some major challenges like inherent instability and non-stationarity. Our approach to overcome this challenge bases around the hypothesis that each task has a signature source-location pattern. The methodology involves introduction of source localization, and tracking of sources over a period of time that enables us to decode movement direction in an eight-direction center-out-reach-task. We establish that such tracking can be used for long term decoding, with preliminary results indicating consistent patterns. In fact, tracking task related source locations render up to 66% accuracy in decoding movement direction one week after the decoding model was learnt.

Robust movement direction decoders from local field potentials using spatio-temporal qualitative patterns.

A major drawback of using Local Field Potentials (LFP) for Brain Computer Interface (BCI) is their inherent instability and non-stationarity. Specifically, even when a well-trained subject performs the same task over a period of time, the neural data observed are unstable. To overcome this problem in decoding movement direction, this paper proposes the use of qualitative information in the form of spatial patterns of inter-channel ranking of multi-channel LFP recordings. The quality of the decoding was further refined by concentrating on the statistical distributions of the top powered channels. Decoding of movement direction was performed using Support Vector Machines (SVM) to construct decoders, instead of the traditional spatial patterns. Our algorithm provides a decoding power of up to 74% on average over a period of two weeks, compared with the state-of-the-art methods in the literature that yield only 33%. Furthermore, it provides 62.5% direction decoding in novel motor environments, compared with 29.5% with conventional methods. Finally, a comparison with the traditional methods and other surveyed literature is presented.

Time robust movement direction decoding in Local Field Potentials using channel ranking.

Movement direction for Brain Machine Interface (BMI) can be decoded successfully using Local Field Potentials (LFP) and Single Unit Activity (SUA). A major challenge when dealing with the intra-cortical recordings is to develop decoders that are robust in time. In this paper we present for the first time a technique that uses the qualitative information derived from multiple LFP channels rather than the absolute power of the recorded signals. In this novel method, we use a power based inter-channel ranking system to define the quality of a channel in multi-channel LFP. This representation enables us to bypass the problems associated with the dynamic ranges of absolute power. We also introduce a parameter based ranking system that provides the same rank to channels that have comparable powers. We show that using our algorithms, we can develop models that provide stable decoding of eight movement directions with an average efficiency of above 56% over a period of two weeks. Moreover, the decoding power using this method is 46% at the end of two weeks versus the 13% using the traditional approaches. We also applied these models to decoding movements performed in a force field and again achieved significantly higher decoding power than the existing methods.

Neural oscillations associated with the primacy and recency effects of verbal working memory.

For sequential information, the first (primacy) and last (recency) items are better remembered than items in the middle of the sequence. The cognitive operations and neural correlates for the primacy and recency effects are unclear. In this paper, we investigate brain oscillations associated with these effects. MEG recordings were obtained on 19 subjects performing a modified Sternberg paradigm. Correlation analyses were performed between brain oscillatory activity and primacy and recency indices. Oscillatory activity during information maintenance, not encoding, was correlated with the primacy and recency effects. The primacy effect was associated with occipital post-desynchrony, and temporal post-synchrony. The recency effect was associated with parietal and temporal desynchrony. Differences were also observed according to the maintenance strategy. These data indicate that the primacy and recency effects are related to different neural, and likely cognitive, operations that are dependant on the strategy for information maintenance.

Denoising of multiscale/multiresolution structural feature dictionaries for rapid training of a brain computer interface.

Multichannel neural activities such as EEG or ECoG in a brain computer interface can be classified with subset selection algorithms running on large feature dictionaries describing subject specific features in spectral, temporal and spatial domain. While providing high accuracies in classification, the subset selection techniques are associated with long training times due to the large feature set constructed from multichannel neural recordings. In this paper we study a novel denoising technique for reducing the dimensionality of the feature space which decreases the computational complexity of the subset selection step radically without causing any degradation in the final classification accuracy. The denoising procedure was based on the comparison of the energy in a particular time segment and in a given scale/level to the energy of the raw data. By setting denoising threshold a priori the algorithm removes those nodes which fail to capture the energy in the raw data in a given scale. We provide experimental studies towards the classification of motor imagery related multichannel ECoG recordings for a brain computer interface. The denoising procedure was able to reach the same classification accuracy without denoising and a computational complexity around 5 times smaller. We also note that in some cases the denoised procedure performed better classification.

Classification of EEG with structural feature dictionaries in a brain computer interface.

We present a new method for the classification of EEG in a brain computer interface by adapting subject specific features in spectral, temporal and spatial domain. For this particular purpose we extend our previous work on ECoG classification based on structural feature dictionary and apply it to extract the spectro-temporal patterns of multichannel EEG recordings related to a motor imagery task. The construction of the feature dictionary based on undecimated wavelet packet transform is extended to block FFT. We evaluate several subset selection algorithms to select a small number of features for final classification. We tested our proposed approach on five subjects of BCI Competition 2005 dataset- IVa. By adapting the wavelet filter for each subject, the algorithm achieved an average classification accuracy of 91.4% The classification results and characteristic of selected features indicate that the proposed algorithm can jointly adapt to EEG patterns in spectro-spatio-temporal domain and provide classification accuracies as good as existing methods used in the literature.