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From bacteria following simple chemical gradients.sup.1 to the brain distinguishing complex odour information.sup.2, the ability to recognize molecular patterns is essential for biological organisms. This type of information-processing function has been implemented using DNA-based neural networks.sup.3, but has been limited to the recognition of a set of no more than four patterns, each composed of four distinct DNA molecules. Winner-take-all computation.sup.4 has been suggested.sup.5,6 as a potential strategy for enhancing the capability of DNA-based neural networks. Compared to the linear-threshold circuits.sup.7 and Hopfield networks.sup.8 used previously.sup.3, winner-take-all circuits are computationally more powerful.sup.4, allow simpler molecular implementation and are not constrained by the number of patterns and their complexity, so both a large number of simple patterns and a small number of complex patterns can be recognized. Here we report a systematic implementation of winner-take-all neural networks based on DNA-strand-displacement.sup.9,10 reactions. We use a previously developed seesaw DNA gate motif.sup.3,11,12, extended to include a simple and robust component that facilitates the cooperative hybridization.sup.13 that is involved in the process of selecting a 'winner'. We show that with this extended seesaw motif DNA-based neural networks can classify patterns into up to nine categories. Each of these patterns consists of 20 distinct DNA molecules chosen from the set of 100 that represents the 100 bits in 10 × 10 patterns, with the 20 DNA molecules selected tracing one of the handwritten digits '1' to '9'. The network successfully classified test patterns with up to 30 of the 100 bits flipped relative to the digit patterns 'remembered' during training, suggesting that molecular circuits can robustly accomplish the sophisticated task of classifying highly complex and noisy information on the basis of similarity to a memory.DNA-strand-displacement reactions are used to implement a neural network that can distinguish complex and noisy molecular patterns from a set of nine possibilities--an improvement on previous demonstrations that distinguished only four simple patterns., Author(s): Kevin M. Cherry [sup.1] , Lulu Qian [sup.1] [sup.2] Author Affiliations:(1) Bioengineering, California Institute of Technology, Pasadena, USA(2) Computer Science, California Institute of Technology, Pasadena, USAMain Winner-take-all computation[sup.4] is [...]

Author(s): Grigory Tikhomirov [1]; Philip Petersen [2]; Lulu Qian (corresponding author) [1, 3] Self-assembled DNA nanostructures [1] enable nanometre-precise patterning that can be used to create programmable molecular machines [2, [...]

The impressive capabilities of the mammalian brain--ranging from perception, pattern recognition and memory formation to decision making and motor activity control--have inspired their re-creation in a wide range of artificial intelligence systems for applications such as face recognition, anomaly detection, medical diagnosis and robotic vehicle control (1). Yet before neuron-based brains evolved, complex biomolecular circuits provided individual cells with the 'intelligent' behaviour required for survival (2). However, the study of how molecules can 'think' has not produced an equal variety of computational models and applications of artificial chemical systems. Although biomolecular systems have been hypothesized to carry out neural-network-like computations in vivo (3,2,4) and the synthesis of artificial chemical analogues has been proposed theoretically (5-9), experimental work (10-13) has so far fallen short of fully implementing even a single neuron. Here, building on the richness of DNA computing (14) and strand displacement circuitry (15), we show how molecular systems can exhibit autonomous brain-like behaviours. Using a simple DNA gate architecture (16) that allows experimental scale-up of multilayer digital circuits (17), we systematically transform arbitrary linear threshold circuits (18) (an artificial neural network model) into DNA strand displacement cascades that function as small neural networks. Our approach even allows us to implement a Hopfield associative memory (19) with four fully connected artificial neurons that, after training insilico, remembers four single-stranded DNA patterns and recalls the most similar one when presented with an incomplete pattern. Our results suggest that DNA strand displacement cascades could be used to endow autonomous chemical systems with the capability of recognizing patterns of molecular events, making decisions and responding to the environment., The human brain is composed of ~[10.sup.11] neurons, and each has a few thousand synapses. Each synapse can receive signals from other neurons, raising or lowering the electrical potential inside [...]

The objective of this study was to evaluate and compare the effectiveness of three different types of bariatric surgeries, namely, sleeve gastrectomy (SG), one-anastomotic gastric bypass (OAGB), and single anastomosis sleeve ileal (SASI) bypass, in the treatment of metabolic syndrome (MS). The optimal approach for managing MS remains uncertain, and thus this study aimed to provide a recent analysis of the efficacy of these surgical procedures. This retrospective study evaluated data of individuals who underwent SG, OAGB, and SASI bypass. The primary outcome measures included weight, body mass index (BMI), glucolipid metabolic index, and the occurrence of treatment-related complications within 6 to 12 months post-surgery. A total of 324 patients were included in this study. Of these, 264 patients underwent SG, 30 underwent OAGB, and 30 underwent SASI bypass. A significant decrease in weight was observed at the 6-month and 12-month marks following all three surgical procedures. Of these, patients who underwent SASI bypass exhibited the greatest reduction in weight and BMI post-surgery. Furthermore, the SASI bypass was associated with a significantly higher percentage of total weight loss (%TWL) and excess body mass index loss (%EBMIL) compared to SG and OAGB. Patients who underwent OAGB and SASI bypass demonstrated notable improvements in type 2 diabetes mellitus (T2DM). Patients who underwent SASI bypass and OAGB experienced greater postoperative comfort and reported fewer complaints of discomfort compared to the other procedure. Based on the retrospective analysis of the data, SASI bypass was associated with greater reductions in weight and BMI, higher percentages of %TWL and %EBMIL, and better improvement in T2DM compared to SG and OAGB. Therefore, both SASI bypass and OAGB were found to be more effective than SG in the treatment of MS., (© 2024. The Author(s).)