Desaturases : structural and mechanistic insights into the biosynthesis of unsaturated fatty acids

This review highlights the key role of fatty acid desaturases in the synthesis of naturally occurring, more common and not, unsaturated fatty acids. The three major classes of fatty acid desaturases, such as acyl-lipid, acyl-acyl carrier protein and acyl-coenzyme A (CoA), are described in details, with particular attention to the cellular localisation, the structure, the substrate and products specificity, and the expression and regulation of desaturases' genes. The review also gives an insight into the biocatalytic reaction of fatty acid desaturation by covering the general and the more class-specific mechanistic studies around the synthesis of unsaturated fatty acids Finally, we conclude the review looking at the numerous novel applications for desaturases in order to meet a very high demand for polyunsaturated fatty acids, taking into account the opportunity for the development of new, more efficient, easily reproducible, sustainable bioengineering advances in the field.Publisher PDFPeer reviewe

Enhancing low-rank solutions in semidefinite relaxations of Boolean quadratic problems

Boolean quadratic optimization problems occur in a number of applications. Their mixed integer-continuous nature is challenging, since it is inherently NP-hard. For this motivation, semidefinite programming relaxations (SDR's) are proposed in the literature to approximate the solution, which recasts the problem into convex optimization. Nevertheless, SDR's do not guarantee the extraction of the correct binary minimizer. In this paper, we present a novel approach to enhance the binary solution recovery. The key of the proposed method is the exploitation of known information on the eigenvalues of the desired solution. As the proposed approach yields a non-convex program, we develop and analyze an iterative descent strategy, whose practical effectiveness is shown via numerical results

Sparse linear regression with compressed and low-precision data via concave quadratic programming

We consider the problem of the recovery of a k-sparse vector from compressed linear measurements when data are corrupted by a quantization noise. When the number of measurements is not sufficiently large, different $k$-sparse solutions may be present in the feasible set, and the classical l1 approach may be unsuccessful. For this motivation, we propose a non-convex quadratic programming method, which exploits prior information on the magnitude of the non-zero parameters. This results in a more efficient support recovery. We provide sufficient conditions for successful recovery and numerical simulations to illustrate the practical feasibility of the proposed method

Sparse linear regression from perturbed data

The problem of sparse linear regression is relevant in the context of linear system identification from large datasets. When data are collected from real-world experiments, measurements are always affected by perturbations or low-precision representations. However, the problem of sparse linear regression from fully-perturbed data is scarcely studied in the literature, due to its mathematical complexity. In this paper, we show that, by assuming bounded perturbations, this problem can be tackled by solving low-complex l2 and l1 minimization problems. Both theoretical guarantees and numerical results are illustrated in the paper

Design and Characterization of a Textile Electrode System for the Detection of High-Density sEMG

Muscle activity monitoring in dynamic conditions is a crucial need in different scenarios, ranging from sport to rehabilitation science and applied physiology. The acquisition of surface electromyographic (sEMG) signals by means of grids of electrodes (High-Density sEMG, HD-sEMG) allows obtaining relevant information on muscle function and recruitment strategies. During dynamic conditions, this possibility demands both a wearable and miniaturized acquisition system and a system of electrodes easy to wear, assuring a stable electrode-skin interface. While recent advancements have been made on the former issue, detection systems specifically designed for dynamic conditions are at best incipient. The aim of this work is to design, characterize, and test a wearable, HD-sEMG detection system based on textile technology. A 32-electrodes, 15 mm inter-electrode distance textile grid was designed and prototyped. The electrical properties of the material constituting the detection system and of the electrode-skin interface were characterized. The quality of sEMG signals was assessed in both static and dynamic contractions. The performance of the textile detection system was comparable to that of conventional systems in terms of stability of the traces, properties of the electrode-skin interface and quality of the collected sEMG signals during quasi-isometric and highly dynamic tasks

Post Activation Potentiation of Back Squat and Trap Bar Deadlift on Acute Sprint Performance

Please refer to the pdf version of the abstract located adjacent to the title

On the Semantics of Snapshot Isolation

Snapshot isolation (SI) is a standard transactional consistency model used in databases, distributed systems and software transactional memory (STM). Its semantics is formally defined both declaratively as an acyclicity axiom, and operationally as a concurrent algorithm with memory bearing timestamps. We develop two simpler equivalent operational definitions of SI as lock-based reference implementations that do not use timestamps. Our first locking implementation is prescient in that requires a priori knowledge of the data accessed by a transaction and carries out transactional writes eagerly (in-place). Our second implementation is non-prescient and performs transactional writes lazily by recording them in a local log and propagating them to memory at commit time. Whilst our first implementation is simpler and may be better suited for developing a program logic for SI transactions, our second implementation is more practical due to its non-prescience. We show that both implementations are sound and complete against the declarative SI specification and thus yield equivalent operational definitions for SI. We further consider, for the first time formally, the use of SI in a context with racy non-transactional accesses, as can arise in STM implementations of SI. We introduce robust snapshot isolation (RSI), an adaptation of SI with similar semantics and guarantees in this mixed setting. We present a declarative specification of RSI as an acyclicity axiom and analogously develop two operational models as lock-based reference implementations (one eager, one lazy). We show that these operational models are both sound and complete against the declarative RSI model

Design and validation of a wireless Body Sensor Network for integrated EEG and HD-sEMG acquisitions

Sensorimotor integration is the process through which the human brain plans the motor program execution according to external sources. Within this context, corticomuscular and corticokinematic coherence analyses are common methods to investigate the mechanism underlying the central control of muscle activation. This requires the synchronous acquisition of several physiological signals, including EEG and sEMG. Nevertheless, physical constraints of the current, mostly wired, technologies limit their application in dynamic and naturalistic contexts. In fact, although many efforts were made in the development of biomedical instrumentation for EEG and HD-sEMG signal acquisition, the need for an integrated wireless system is emerging. We hereby describe the design and validation of a new fully wireless body sensor network for the integrated acquisition of EEG and HD-sEMG signals. This Body Sensor Network is composed of wireless bio-signal acquisition modules, named sensor units, and a set of synchronization modules used as a general-purpose system for time-locked recordings. The system was characterized in terms of accuracy of the synchronization and quality of the collected signals. An in-depth characterization of the entire system and an end-to-end comparison of the wireless EEG sensor unit with a wired benchmark EEG device were performed. The proposed device represents an advancement of the State-of-the-Art technology allowing the integrated acquisition of EEG and HD-sEMG signals for the study of sensorimotor integration

Quantification of cortical proprioceptive processing through a wireless and miniaturized EEG amplifier

Corticokinematic coherence (CKC) is computed between limb kinematics and cortical activity (e.g. MEG, EEG), and it can be used to detect, quantify and localize the cortical processing of proprioceptive afference arising from the body. EEG-based studies on CKC have been limited to lab environments due to bulky, non-portable instrumentations. We recently proposed a wireless and miniaturized EEG acquisition system aimed at enabling EEG studies outside the laboratory. The purpose of this work is to compare the EEG-based CKC values obtained with this device with a conventional wired-EEG acquisition system to validate its use in the quantification of cortical proprioceptive processing. Eleven healthy right-handed participants were recruited (six males, four females, age range: 24-40 yr). A pneumatic-movement actuator was used to evoke right index-finger flexion-extension movement at 3 Hz for 4 min. The task was repeated both with the wireless-EEG and wired-EEG devices using the same 30-channel EEG cap preparation. CKC was computed between the EEG and finger acceleration. CKC peaked at the movement frequency and its harmonics, being statistically significant (p < 0.05) in 8-10 out of 11 participants. No statistically significant differences (p < 0.05) were found in CKC strength between wireless-EEG (range 0.03-0.22) and wired-EEG (0.02-0.33) systems, that showed a good agreement between the recording systems (3 Hz: r = 0.57, p = 0.071, 6 Hz: r = 0.82, p = 0.003). As expected, CKC peaked in sensors above the left primary sensorimotor cortex contralateral to the moved right index finger. As the wired-EEG device, the tested wireless-EEG system has proven feasible to quantify CKC, and thus can be used as a tool to study proprioception in the human neocortex. Thanks to its portability, the wireless-EEG used in this study has the potential to enable the examination of cortical proprioception in more naturalistic conditions outside the laboratory environment. Clinical Relevance - Our study will contribute to provide innovative technological foundations for future unobtrusive EEG recordings in naturalistic conditions to examine human sensorimotor system