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This systematic realist review project encompasses both quantitative and qualitative research questions. By combining various analysis and synthesis methods, a comprehensive understanding of the mechanisms, contextual factors, and strategies influencing the implementation and transferability of working memory training (WMT) interventions will be achieved. Quantitative analysis, including effect size calculation, will be used to assess the overall impact of school based WMT on academic performance. Qualitative framework analysis will adopt the I-PARIHS framework (Harvey & Kitson, 2015) to represent suitable implementation science content.

Thin-film, plated-wire memory matrix for computer applications requires little power yet has higher speed and four times greater storage capacity than ferrite-core memories of the same size.

Episodic memories for naturalistic events take the form of a sequence of experience units (EUs)—each representing a moment of the past experience—that are separated by temporal discontinuities (i.e., moments that are not remembered; D’Argembeau et al., 2022). Because of these discontinuities, the time taken to mentally re-experience everyday activities is typically shorter than their actual duration (i.e., episodic memories are temporally compressed; Jeunehomme et al., 2018; Jeunehomme & D’Argembeau, 2019). The cognitive mechanisms leading to this structure of episodic memories are not fully understood, especially factors that might explain temporal discontinuities. A consistent body of research suggests that these discontinuities result from the way we perceive continuous events and integrate them in long term-memory. More precisely, temporal discontinuities could be a byproduct of the limit of working memory capacity in representing continuous events. We started to test this hypothesis in a recent study (https://osf.io/4hxzs) during which we measured the time taken by participants to mentally replay just-seen continuous events of varying duration (from 3 to 15 s). As hypothesised, short events were not temporally compressed during mental replay while longer events were. These results suggest that the temporal compression of events is not systematic but emerges when they are too long to be fully held in working memory. Nevertheless, unexpectedly, the average remembering duration for the 3-s events was significantly higher than 3 s. This could be explained by the fact that remembering durations (measured by asking participants to press a key when they started and finished their mental replay) could reflect the combined durations of two distinct mental processes: the time needed to mentally replay the unfolding of the event (what we wanted to investigate) and the time taken to access the initial memory trace (i.e., the time needed to represent the initial visual scene from which the participants had to mentally replay the subsequent unfolding of the event). In this study, our aim is to propose a new measure of remembering duration that controls for the time needed to access the initial memory trace in order to better characterize the impact of event duration on the temporal compression of events in memory. This measure will allow us to acquire better estimations of working memory temporal capacity for continuous events.

A recent upgrade of the TSRV research flight system at NASA Langley Research Center retained the original monochrome display system. However, the display memory loading equipment was replaced requiring design and development of new methods of performing this task. This paper describes the new techniques developed to load memory in the display system. An outdated paper tape method for loading the BOOTSTRAP control program was replaced by EPROM storage of the characters contained on the tape. Rather than move a tape past an optical reader, a counter was implemented which steps sequentially through EPROM addresses and presents the same data to the loader circuitry. A cumbersome cassette tape method for loading the applications software was replaced with a floppy disk method using a microprocessor terminal installed as part of the upgrade. The cassette memory image was transferred to disk and a specific software loader was written for the terminal which duplicates the function of the cassette loader.

Cloud is a new age technology used to connect to applications and data from anywhere round the globe via the internet. Anything and everything ranging from mobile phones to servers can be connected to the cloud. This has resulted into companies providing a large range of services to consumers. Upcoming businesses can now focus on its major purpose and not have to install their own servers. They can easily outsource it to these cloud providers saving them from a lot of capital investment. With the ever increasing devices on the cloud a major issue has emerged, something that needs immediate attention - Resource utilization. All the resources connected to the cloud need to function optimally for making cloud providers earn proper Return on Investment (RoI) and making the cloud users experience worthwhile. This paper proposes a new Resource Allocation Strategy for provisioning of virtual machines (VM). This work focuses on two phases, firstly the initial allocation of VMs using K-Mean Clustering technique and secondly on maximizing the optimal functioning of these VMs by migrating them from one physical machine (PM) to another. For this purpose the Load Balancing Algorithm, Skewness Algorithm and the Green Computing Algorithm are used. It also introduces a change in the Load Balancing Equation and the Skewness Equation which gives much better results.

In this paper, we propose a new technique to improve QRS complex detection. This technique consists of incorporating an autoencoder and bidirectional long short term memory (BiLSTM). The autoencoder used is a stacked autoencoder and functions as signal filtering. Meanwhile, BiLSTM is used as a detector. Exploration of the effect of hyperparameter in the autoencoder was also carried out to determine the effect on QRS complex detection. Furthermore, the dataset used in this study is the MIT-BIH arrhythmia database. Based on the experimental results, the hyperparameter in the autoencoder that gives a better effect on QRS complex detection is 16-8. Finally, the proposed method out-of-perform state of the art algorithm with accuracy 99.94%.

Trace analysis techniques are used to study memory referencing behavior for the purpose of designing local memories and determining how to allocate them for data and instructions. In an attempt to assess the inherent behavior of the source code, the trace analysis system described here reduced the effects of the compiler and host architecture on the trace by using a technical called flattening. The variables in the trace, their associated single-assignment values, and references are histogrammed on the basis of various parameters describing memory referencing behavior. Bounds are developed specifying the amount of memory space required to store all live values in a particular histogram class. The reduction achieved in main memory traffic by allocating local memory is specified for each class.

The traditional magnetic storage mechanisms (both analog and digital) apply an external field signal H(t) to a hysteretic magnetic material, and read the remanent magnetization M(t), which is (roughly) proportional to H(t). We propose a new analog method of recovering the signal from the magnetic material, making use of the shape of the hysteresis loop M(H). The field H, ``stored'' in a region with N domains or particles, can be recovered with fluctuations of order 1/N using the new method - much superior to the 1/sqrt{N} fluctuations in traditional analog storage.

Currently, Deep Learning (DL) with the Recurrent Neural Networks (RNN) variants is being applied successfully in many domains of Engineering for prediction. In view of the demand for precise forecasting and the aid of Artificial Intelligence Tools, time series prediction reveals a vital task in decision-making and risk assessment. However, the application of novel Recurrent DL models for obtaining an accurate prediction of time series is yet to be explored. Recent trends reveal that Hybrid Neural Networks and DL models are appropriate for time series forecasts. At the same time, the model's selection and the hyperparameter's tuning can greatly impact its performance. To address this problem, a parallel long-term memory (PLSTM) model integrated with Bayesian hyperparameter optimization (PLSTM-BO) is proposed for time series prediction. The model is tuned in terms of key parameters, including the number of neurons, dropout, learning rate, and optimization technique. The model's performance is assessed using the SARS-COVID-19 cumulative cases, deaths, recovery cases, and NIFTY 50 stock closing price time series dataset. The obtained results convey that the current model exhibits remarkable performance compared to existing models.

The purpose of this study is to assess the impact of a gamified vocabulary learning intervention of a new language (Spanish), on the cognitive functioning and overall well-being of older adults with subjective memory complaints.

The need for compact, solid-state actuation systems for use in the aerospace, automotive, and other transportation industries is currently motivating research in high-temperature shape-memory alloys (HTSMA) with transformation temperatures greater than 100 C. One of the basic high-temperature alloys investigated to fill this need is Ni(19.5)Ti(50.5)Pd30. Initial testing has indicated that this alloy, while having acceptable work characteristics, suffers from significant permanent deformation (or ratcheting) during thermal cycling under load. In an effort to overcome this deficiency, various solid-solution alloying and thermomechanical processing schemes were investigated. Solid-solution strengthening was achieved by substituting 5at% gold or platinum for palladium in Ni(19.5)Ti(50.5)Pd30, the so-called baseline alloy, to strengthen the martensite and austenite phases against slip processes and improve thermomechanical behavior. Tensile properties, work behavior, and dimensional stability during repeated thermal cycling under load for the ternary and quaternary alloys were compared. The relative difference in yield strength between the martensite and austenite phases and the dimensional stability of the alloy were improved by the quaternary additions, while work output was only minimally impacted. The three alloys were also thermomechanically processed by cycling repeatedly through the transformation range under a constant stress. This so-called training process dramatically improved the dimensional stability in these samples and also recovered the slight decrease in work output caused by quaternary alloying. An added benefit of the solid-solution strengthening was maintenance of enhanced dimensional stability of the trained material to higher temperatures compared to the baseline alloy, providing a greater measure of over-temperature capability.

Plated wire memory word drive line allows high storage density, good plated wire transmission and a simplified memory plane configuration. A half-turn word drive line with a magnetic keeper is used. The ground plane provides the return path for both the word current and the plated wire transmission line.

Memory and judgment are both affected by age-related cognitive decline. We test the hypothesis that these declines stem from a common mechanism. We adapted a latent learning task that measures one’s ability to learn and make judgments about associations among interconnected stimuli and evaluate performance across the lifespan.

When a program is loaded into memory for execution, the relative position of its basic blocks is crucial, since loading basic blocks that are unlikely to be executed first places them high in the instruction-memory hierarchy only to be dislodged as the execution goes on. In this paper we study the use of Bayesian networks as models of the input history of a program. The main point is the creation of a probabilistic model that persists as the program is run on different inputs and at each new input refines its own parameters in order to reflect the program's input history more accurately. As the model is thus tuned, it causes basic blocks to be reordered so that, upon arrival of the next input for execution, loading the basic blocks into memory automatically takes into account the input history of the program. We report on extensive experiments, whose results demonstrate the efficacy of the overall approach in progressively lowering the execution times of a program on identical inputs placed randomly in a sequence of varied inputs. We provide results on selected SPEC CINT2000 programs and also evaluate our approach as compared to the gcc level-3 optimization and to Pettis-Hansen reordering.

This article is from Nanoscale Research Letters , volume 8 . Abstract Resistive switching properties of a self-compliance resistive random access memory device in cross-point architecture with a simple stack structure of Ir/TaOx/W have been investigated. A transmission electron microscope and atomic force microscope were used to observe the film properties and morphology of the stack. The device has shown excellent switching cycle uniformity with a small operation of ±2.5 V and a resistance ratio of >100. The device requires neither any frorming-process nor current compliance limit for repeatable operation in contrast to conventional resistive random access memory devices. The effect of bottom electrode morphology and surface roughness is also studied. The improvement is due to the enhanced electric field at the nanotips in the bottom electrode and the defective TaOx switching layer which enable controlled filament formation/rupture. The device area dependence of the low resistance state indicates multifilament formation. The device has shown a robust alternating current endurance of >105 cycles and a data retention of >104 s.

This article is from F1000Research , volume 2 . Abstract Evidence suggests that excitatory transcranial direct current stimulation (tDCS) may improve performance on a wide variety of cognitive tasks. Due to the non-invasive and inexpensive nature of the method, harnessing its potential could be particularly useful for the treatment of neuropsychiatric illnesses involving cognitive dysfunction. However, questions remain regarding the efficacious stimulation parameters. Here, using a double-blind between-subjects design, we explored whether 1 mA excitatory (anodal) left dorsolateral prefrontal cortex stimulation with a contralateral extracerebral reference electrode, leads to enhanced working memory performance across two days, relative to sham stimulation. Participants performed the 3-back, a test of working memory, at baseline, and during and immediately following stimulation on two days, separated by 24-48 hours. Active stimulation did not significantly enhance performance versus sham over the course of the experiment. However, exploratory comparisons did reveal a significant effect of stimulation group on performance during the first stimulation phase only, with active stimulation recipients performing better than sham. While these results do not support the hypothesis that dorsolateral prefrontal cortex tDCS boosts working memory, they raise the possibility that its effects may be greatest during early learning stages.

Photochromic materials based on transition metal oxides were synthesized during this effort, and evaluated for potential use in future optical processing components and subsystems. This work grew out of a collaborative university/government discovery that Tungsten Oxides reversibly change physical state when exposed to certain combinations of optical radiation. Various formulations including different dopings, oxide enhancements, microstructure modifications, and deposition methods were investigated as well. Samples produced during these synthesis experiments were characterized via wavelength, power, and Raman characteristics of their photochromic/electrochromic response. Results show significant promise for this technology in high-density optical memory, fast all-optical switching and a number of advanced digital processing applications.

This article is from GigaScience , volume 1 . Abstract Background: There is a rapidly increasing amount of de novo genome assembly using next-generation sequencing (NGS) short reads; however, several big challenges remain to be overcome in order for this to be efficient and accurate. SOAPdenovo has been successfully applied to assemble many published genomes, but it still needs improvement in continuity, accuracy and coverage, especially in repeat regions. Findings: To overcome these challenges, we have developed its successor, SOAPdenovo2, which has the advantage of a new algorithm design that reduces memory consumption in graph construction, resolves more repeat regions in contig assembly, increases coverage and length in scaffold construction, improves gap closing, and optimizes for large genome. Conclusions: Benchmark using the Assemblathon1 and GAGE datasets showed that SOAPdenovo2 greatly surpasses its predecessor SOAPdenovo and is competitive to other assemblers on both assembly length and accuracy. We also provide an updated assembly version of the 2008 Asian (YH) genome using SOAPdenovo2. Here, the contig and scaffold N50 of the YH genome were ~20.9 kbp and ~22 Mbp, respectively, which is 3-fold and 50-fold longer than the first published version. The genome coverage increased from 81.16% to 93.91%, and memory consumption was ~2/3 lower during the point of largest memory consumption.

This article is from Nanoscale Research Letters , volume 7 . Abstract Improved resistive switching memory characteristics by controlling the formation polarity in an IrOx/Al2O3/IrOx-ND/Al2O3/WOx/W structure have been investigated. High density of 1 × 1013/cm2 and small size of 1.3 nm in diameter of the IrOx nano-dots (NDs) have been observed by high-resolution transmission electron microscopy. The IrOx-NDs, Al2O3, and WOx layers are confirmed by X-ray photo-electron spectroscopy. Capacitance-voltage hysteresis characteristics show higher charge-trapping density in the IrOx-ND memory as compared to the pure Al2O3 devices. This suggests that the IrOx-ND device has more defect sites than that of the pure Al2O3 devices. Stable resistive switching characteristics under positive formation polarity on the IrOx electrode are observed, and the conducting filament is controlled by oxygen ion migration toward the Al2O3/IrOx top electrode interface. The switching mechanism is explained schematically based on our resistive switching parameters. The resistive switching random access memory (ReRAM) devices under positive formation polarity have an applicable resistance ratio of > 10 after extrapolation of 10 years data retention at 85°C and a long read endurance of 105 cycles. A large memory size of > 60 Tbit/sq in. can be realized in future for ReRAM device application. This study is not only important for improving the resistive switching memory performance but also help design other nanoscale high-density nonvolatile memory in future.

This article is from Journal of clinical & experimental ophthalmology , volume 5 . Abstract Objective: An educational, interactive journal [Vision and Memory Stimulating (VMS) journal] was developed to boost patient confidence and promote long-term adherence with weekly vision self-monitoring in age-related macular degeneration (AMD) patients at risk for vision loss from new-onset neovascularization. Methods: In a multicenter randomized controlled trial, 198 subjects with intermediate stage, non-neovascular AMD received the VMS journal or followed usual care (e.g. their doctor’s instructions for vision monitoring; Amsler grid). At 6 and/or 12 months post-enrollment, 157 subjects completed a questionnaire on vision self-monitoring. Results: At 6 and 12 months, respectively, 85% and 80% of the VMS journal subjects reported vision monitoring at least weekly, which represent statistically significant 7.1 and 4.2 times greater odds than the 50% of controls who monitored weekly at both follow-up times (p

We demonstrate that the quantum communication between two parties can be significantly improved if the receiver is allowed to store the received signals in a quantum memory before decoding them. In the limit of an infinite memory, the transfer is perfect. We prove that this scheme allows the transfer of arbitrary multipartite states along Heisenberg chains of spin-1/2 particles with random coupling strengths.

This article is from Proteome Science , volume 10 . Abstract Background: Perilla (Perilla frutescens) oil is very rich in α-linolenic acid, an omega-3 fatty acid. As it is widely reported that omega-3 fatty acid supplementation improves cognitive function in children and adults, feeding rats with perilla diets followed by analysis of proteomic changes in the hippocampus can provide valuable information on the mechanism of learning and memory at the molecular level. To identify proteins playing roles in learning and memory, differentially expressed proteins in the hippocampus of the 5 week old rats fed perilla diets for 3 weeks or 3 months were identified by proteomic analysis and validated by immunological assays. Results: The perilla diet groups showed improved spatial learning and memory performances in a T-maze test. They also displayed elevated level of 22:6n-3 fatty acid, an omega-3 fatty acid (p

Cuckoo hashing [4] is a multiple choice hashing scheme in which each item can be placed in multiple locations, and collisions are resolved by moving items to their alternative locations. In the classical implementation of two-way cuckoo hashing, the memory is partitioned into contiguous disjoint fixed-size buckets. Each item is hashed to two buckets, and may be stored in any of the positions within those buckets. Ref. [2] analyzed a variation in which the buckets are contiguous and overlap. However, many systems retrieve data from secondary storage in same-size blocks called pages. Fetching a page is a relatively expensive process; but once a page is fetched, its contents can be accessed orders of magnitude faster. We utilize this property of memory retrieval, presenting a variant of cuckoo hashing incorporating the following constraint: each bucket must be fully contained in a single page, but buckets are not necessarily contiguous. Empirical results show that this modification increases memory utilization and decreases the number of iterations required to insert an item. If each item is hashed to two buckets of capacity two, the page size is 8, and each bucket is fully contained in a single page, the memory utilization equals 89.71% in the classical contiguous disjoint bucket variant, 93.78% in the contiguous overlapping bucket variant, and increases to 97.46% in our new non-contiguous bucket variant. When the memory utilization is 92% and we use breadth first search to look for a vacant position, the number of iterations required to insert a new item is dramatically reduced from 545 in the contiguous overlapping buckets variant to 52 in our new non-contiguous bucket variant. In addition to the empirical results, we present a theoretical lower bound on the memory utilization of our variation as a function of the page size.

Cuckoo hashing [4] is a multiple choice hashing scheme in which each item can be placed in multiple locations, and collisions are resolved by moving items to their alternative locations. In the classical implementation of two-way cuckoo hashing, the memory is partitioned into contiguous disjoint fixed-size buckets. Each item is hashed to two buckets, and may be stored in any of the positions within those buckets. Ref. [2] analyzed a variation in which the buckets are contiguous and overlap. However, many systems retrieve data from secondary storage in same-size blocks called pages. Fetching a page is a relatively expensive process; but once a page is fetched, its contents can be accessed orders of magnitude faster. We utilize this property of memory retrieval, presenting a variant of cuckoo hashing incorporating the following constraint: each bucket must be fully contained in a single page, but buckets are not necessarily contiguous. Empirical results show that this modification increases memory utilization and decreases the number of iterations required to insert an item. If each item is hashed to two buckets of capacity two, the page size is 8, and each bucket is fully contained in a single page, the memory utilization equals 89.71% in the classical contiguous disjoint bucket variant, 93.78% in the contiguous overlapping bucket variant, and increases to 97.46% in our new non-contiguous bucket variant. When the memory utilization is 92% and we use breadth first search to look for a vacant position, the number of iterations required to insert a new item is dramatically reduced from 545 in the contiguous overlapping buckets variant to 52 in our new non-contiguous bucket variant. In addition to the empirical results, we present a theoretical lower bound on the memory utilization of our variation as a function of the page size.

Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks. The only underlying LSTM structure that has been explored so far is a linear chain. However, natural language exhibits syntactic properties that would naturally combine words to phrases. We introduce the Tree-LSTM, a generalization of LSTMs to tree-structured network topologies. Tree-LSTMs outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task 1) and sentiment classification (Stanford Sentiment Treebank).

This article is from Iranian Journal of Basic Medical Sciences , volume 16 . Abstract Objective(s): Crocin influences many biological functions including memory and learning. The present study was aimed to investigate the effects of crocin on learning and memory impairments in streptozotocine-induced diabetic rats. Materials and Methods: Diabetes was induced by intraperitoneal (IP) injection of streptozotocin (STZ, 45 mg/kg). Transfer latency (TL) paradigm in elevated plus-maze (EPM) was used as an index of learning and memory. Plasma levels of total antioxidant capacity (TAC) and malondialdehyde (MDA), blood levels of glucose, and serum concentrations of insulin were measured. The number of hippocampal neurons was also counted. Results: STZ increased acquisition transfer latency (TL1) and retention transfer latency (TL2), and MDA, decreased transfer latency shortening (TLs) and TCA, produced hyperglycemia and hypoinsulinemia, and reduced the number of neurons in the hippocampus. Learning and memory impairments and blood TCA, MDA, glucose, and insulin changes induced by streptozotocin were improved with long-term IP injection of crocin at doses of 15 and 30 mg/kg. Crocin prevented hippocampal neurons number loss in diabetic rats. Conclusion: The results indicate that oxidative stress, hyperglycemia, hypoinsulinemia, and reduction of hippocampal neurons may be involved in learning and memory impairments in STZ-induced diabetic rats. Antioxidant, antihyperglycemic, antihypoinsulinemic, and neuroprotective activities of crocin might be involved in improving learning and memory impairments.

This article is from Iranian Journal of Basic Medical Sciences , volume 16 . Abstract Objective(s): Crocin influences many biological functions including memory and learning. The present study was aimed to investigate the effects of crocin on learning and memory impairments in streptozotocine-induced diabetic rats. Materials and Methods: Diabetes was induced by intraperitoneal (IP) injection of streptozotocin (STZ, 45 mg/kg). Transfer latency (TL) paradigm in elevated plus-maze (EPM) was used as an index of learning and memory. Plasma levels of total antioxidant capacity (TAC) and malondialdehyde (MDA), blood levels of glucose, and serum concentrations of insulin were measured. The number of hippocampal neurons was also counted. Results: STZ increased acquisition transfer latency (TL1) and retention transfer latency (TL2), and MDA, decreased transfer latency shortening (TLs) and TCA, produced hyperglycemia and hypoinsulinemia, and reduced the number of neurons in the hippocampus. Learning and memory impairments and blood TCA, MDA, glucose, and insulin changes induced by streptozotocin were improved with long-term IP injection of crocin at doses of 15 and 30 mg/kg. Crocin prevented hippocampal neurons number loss in diabetic rats. Conclusion: The results indicate that oxidative stress, hyperglycemia, hypoinsulinemia, and reduction of hippocampal neurons may be involved in learning and memory impairments in STZ-induced diabetic rats. Antioxidant, antihyperglycemic, antihypoinsulinemic, and neuroprotective activities of crocin might be involved in improving learning and memory impairments.

From the bitsavers.org collection, a scanned-in computer-related document. mit :: lincolnLaboratory :: division 6 :: 6M-3530 Improved Memory Cores Produced in Lincoln Laboratory Apr1955

Memoria technica; or, a new method of artificial memory: applied to, and exemplified in, chronology, history, geography, astronomy: also Jewish, Grecian, and Roman coins, weights, and measures, &c. With tables ... By Richard Grey ... - The fifth edition corrected and improved. 1778.. Digitized from IA40313314-85 . Previous issue: bim_eighteenth-century_a-short-historical-accou_dodd-ralph_1795 .

We consider a memory allocation problem that can be modeled as a version of bin packing where items may be split, but each bin may contain at most two (parts of) items. A 3/2-approximation algorithm and an NP-hardness proof for this problem was given by Chung et al. We give a simpler 3/2-approximation algorithm for it which is in fact an online algorithm. This algorithm also has good performance for the more general case where each bin may contain at most k parts of items. We show that this general case is also strongly NP-hard. Additionally, we give an efficient 7/5-approximation algorithm.

This article is from PLoS ONE , volume 8 . Abstract Soy (Glycine Max Merr, family Leguminosae) has been reported to possess anti-cancer, anti-lipidemic, estrogen-like, and memory-enhancing effects. We investigated the memory-enhancing effects and the underlying mechanisms of soyasaponin I (soya-I), a major constituent of soy. Impaired learning and memory were induced by injecting ibotenic acid into the entorhinal cortex of adult rat brains. The effects of soya-I were evaluated by measuring behavioral tasks and neuronal regeneration of memory-deficient rats. Oral administration of soya-I exhibited significant memory-enhancing effects in the passive avoidance, Y-maze, and Morris water maze tests. Soya-Ι also increased BrdU incorporation into the dentate gyrus and the number of cell types (GAD67, ChAT, and VGluT1) in the hippocampal region of memory-deficient rats, whereas the number of reactive microglia (OX42) decreased. The mechanism underlying memory improvement was assessed by detecting the differentiation and proliferation of neural precursor cells (NPCs) prepared from the embryonic hippocampus (E16) of timed-pregnant Sprague-Dawley rats using immunocytochemical staining and immunoblotting analysis. Addition of soya-Ι in the cultured NPCs significantly elevated the markers for cell proliferation (Ki-67) and neuronal differentiation (NeuN, TUJ1, and MAP2). Finally, soya-I increased neurite lengthening and the number of neurites during the differentiation of NPCs. Soya-Ι may improve hippocampal learning and memory impairment by promoting proliferation and differentiation of NPCs in the hippocampus through facilitation of neuronal regeneration and minimization of neuro-inflammation.

This article is from PLoS ONE , volume 8 . Abstract Soy (Glycine Max Merr, family Leguminosae) has been reported to possess anti-cancer, anti-lipidemic, estrogen-like, and memory-enhancing effects. We investigated the memory-enhancing effects and the underlying mechanisms of soyasaponin I (soya-I), a major constituent of soy. Impaired learning and memory were induced by injecting ibotenic acid into the entorhinal cortex of adult rat brains. The effects of soya-I were evaluated by measuring behavioral tasks and neuronal regeneration of memory-deficient rats. Oral administration of soya-I exhibited significant memory-enhancing effects in the passive avoidance, Y-maze, and Morris water maze tests. Soya-Ι also increased BrdU incorporation into the dentate gyrus and the number of cell types (GAD67, ChAT, and VGluT1) in the hippocampal region of memory-deficient rats, whereas the number of reactive microglia (OX42) decreased. The mechanism underlying memory improvement was assessed by detecting the differentiation and proliferation of neural precursor cells (NPCs) prepared from the embryonic hippocampus (E16) of timed-pregnant Sprague-Dawley rats using immunocytochemical staining and immunoblotting analysis. Addition of soya-Ι in the cultured NPCs significantly elevated the markers for cell proliferation (Ki-67) and neuronal differentiation (NeuN, TUJ1, and MAP2). Finally, soya-I increased neurite lengthening and the number of neurites during the differentiation of NPCs. Soya-Ι may improve hippocampal learning and memory impairment by promoting proliferation and differentiation of NPCs in the hippocampus through facilitation of neuronal regeneration and minimization of neuro-inflammation.

In Coles-Piani's recent remarkable version of the entropic uncertainty principle, the entropic sum is controlled by the first and second maximum overlaps between the two projective measurements. We generalize the entropic uncertainty relation in the presence of quantum memory and find the exact dependence on all $d$ largest overlaps between two measurements on any $d$-dimensional Hilbert space. The corresponding entropic uncertainty principle in the absence of quantum memory is also derived. Our bounds are strictly tighter than previous entropic bounds in the presence of quantum memory, which have potential applications to quantum cryptography with entanglement witnesses and quantum key distributions.

In Coles-Piani's recent remarkable version of the entropic uncertainty principle, the entropic sum is controlled by the first and second maximum overlaps between the two projective measurements. We generalize the entropic uncertainty relation in the presence of quantum memory and find the exact dependence on all $d$ largest overlaps between two measurements on any $d$-dimensional Hilbert space. The corresponding entropic uncertainty principle in the absence of quantum memory is also derived. Our bounds are strictly tighter than previous entropic bounds in the presence of quantum memory, which have potential applications to quantum cryptography with entanglement witnesses and quantum key distributions.

The paper proposes an improved quantum associative algorithm with distributed query based on model proposed by Ezhov et al. We introduce two modifications of the query that optimized data retrieval of correct multi-patterns simultaneously for any rate of the number of the recognition pattern on the total patterns. Simulation results are given.