427 research outputs found
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
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 -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
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 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
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
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
Timing and modulation of activity in the lower limb muscles during indoor rowing: What are the key muscles to target in FES-rowing protocols?
The transcutaneous stimulation of lower limb muscles during indoor rowing (FES Rowing) has led to a new sport and recreation and significantly increased health benefits in paraplegia. Stimulation is often delivered to quadriceps and hamstrings; this muscle selection seems based on intuition and not biomechanics and is likely suboptimal. Here, we sample surface EMGs from 20 elite rowers to assess which, when, and how muscles are activated during indoor rowing. From EMG amplitude we specifically quantified the onset of activation and silencing, the duration of activity and how similarly soleus, gastrocnemius medialis, tibialis anterior, rectus femoris, vastus lateralis and medialis, semitendinosus, and biceps femoris muscles were activated between limbs. Current results revealed that the eight muscles tested were recruited during rowing, at different instants and for different durations. Rectus and biceps femoris were respectively active for the longest and briefest periods. Tibialis anterior was the only muscle recruited within the recovery phase. No side differences in the timing of muscle activity were observed. Regression analysis further revealed similar, bilateral modulation of activity. The relevance of these results in determining which muscles to target during FES Rowing is discussed. Here, we suggest a new strategy based on the stimulation of vasti and soleus during drive and of tibialis anterior during recovery
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