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This paper presents the results of an extensive numerical and analytical study to re-investigate the mechanism of load introduction into concrete-filled tubular column through shear connection. The new mechanism of load introduction revealed from this research indicates that the shear load from the connection is introduced through the column length above and within the connection. This is different from the currently assumed mechanism of load introduction from underneath the connection. In fact, a simple free-body diagram analysis would demonstrate that the currently assumed load introduction mechanism is not possible. The results of the numerical simulations also indicate that the load introduction length above the connection is unlimited and can be as long as the entire column height. Based on the new load introduction mechanism and the numerical simulation results, the maximum amount of load introduced into the concrete core can be calculated as the bond strength multiplying the entire steel tube/concrete core contact surface area for the entire column length above and including the connection. Under the new load introduction mechanism and using representative values of column dimensions and concrete cylinder strength, it has been demonstrated that complete load introduction can be achieved in almost all practical arrangements of concrete filled tubular construction. [ABSTRACT FROM AUTHOR]

Background: Obesity has been associated with increased mortality from hormone dependant cancers such as breast cancer which is the most prevalent cancer in women. The link between obesity and breast cancer can be attributed to excess estrogen produced through aromatization in adipose tissue. The role of steroid hormone receptors in breast cancer development is well studied but how obesity can affect the expression pattern of steroid hormones in patients with different grades of breast cancer was the aim of this study. Methods: In this case-control study, 70 women with breast cancer participated with different grades of obesity (36 none obese, BMI < 25 kg/m2 and 34 obese, BMI ≥ 25 kg/m2). The mean age of participants was 44.53 ± 1.79 yr (21-70 yr). The serum level of estrogen, progesterone and androgen determined by ELISA. Following quantitative expression of steroid hormone receptors mRNA in tumor tissues evaluated by Real-time PCR. Patients with previous history of radiotherapy or chemotherapy were excluded. SPSS 16 was used for data analysis and P < 0.05 considered statistically significant. Results: The difference in ERα, ERβ and PR mRNA level between normal and obese patients was significant (P < 0.001). In addition, the expression of AR mRNA was found to be higher than other steroid receptors. There was no significant relation between ERβ gene expression in two groups (P = 0.68). We observed a significant relationship between ERα and AR mRNA with tumor stage and tumor grade, respectively (P = 0.023, P = 0.015). Conclusion: According to the obtained results, it is speculated that obesity could paly a significant role in estrogen receptors gene expression and also could affect progression and proliferation of breast cancer cells. [ABSTRACT FROM AUTHOR]

Ballast plays an important role in the stability of railway track systems. The effectiveness of the ballast in maintaining the track stability is very much dependent on its mechanical conditions. The available ballast maintenance approaches are mainly based on only track geometry conditions (such as track profile) which do not sufficiently reflect the ballast mechanical behaviors. That is, the ballast potential of degradation (i.e., ballast long term behaviors) has been omitted. This makes the effectiveness of the current ballast maintenance approach questionable, indicating a need for a more comprehensive and effective ballast conditions assessment technique. In response to this need, two ballast condition indices based on ballast geometry degradation (BGI) and the level of ballast fouling (BFI) as the main indicators of ballast mechanical behavior were developed. The BGI is a function of the standard deviations of track alignment, unevenness and twist. The BFI was developed based on the data obtained from the ground penetration radar (GPR). Making use of the new indices, a more reliable maintenance algorithm was developed. Through illustrations of the applicability of the new maintenance algorithm in a railway line, it was shown that the new algorithm causes a considerable improvement in the maintenance effectiveness and an increase in the life cycle of railway tracks by making more effective allocation of resources and more accurate maintenance planning. [ABSTRACT FROM AUTHOR]

In this article, different s-substituted benzimidazole-thioquinoline derivatives were designed, synthesized, and evaluated for their possible α-glucosidase inhibitory activities. The most active compound in this series, 6j (X = 4-bromobenzyl) exhibited significant potency with an IC 50 value of 28.0 ± 0.6 µM compared to acarbose as the positive control with an IC 50 value of 750.0 µM. The kinetic study showed a competitive inhibition pattern against α-glucosidase for the 6j derivative. Also, the molecular dynamic simulations were performed to determine key interactions between compounds and the targeted enzyme. The in silico pharmacodynamics and ADMET properties were executed to illustrate the druggability of the novel derivatives. In general, it can be concluded that these derivatives can serve as promising leads to the design of potential α-glucosidase inhibitors., (© 2023. The Author(s).)

In the development of novel anti-α-glucosidase agents, we synthesized novel thieno[2,3-b]quinoline-hydrazones 9a-n by facile and efficient conventional chemical reactions. These compounds were characterized by IR, 1 H NMR, 13 C NMR, and elemental analysis. Inhibitory activities of the title compounds were evaluated against yeast α-glucosidase. In particular, compounds 9c, 9d, and 9h exhibited high anti-α-glucosidase activity. Representatively, compound 9c with IC 50  = 1.3 µM, was 576-times more potent than positive control acarbose. Molecular docking study of the most active compounds showed that these compounds formed important binding interactions at α-glucosidase active site. Molecular dynamics study of compound 9c was also performed and the obtained results were compared with acarbose. Compounds 9c, 9d, and 9h were also evaluated for in silico druglikeness properties and ADMET prediction. These studies showed that the title most potent compounds could be exploited as drug candidates., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: α-Glucosidase is a hydrolyzing enzyme that plays a crucial role in the degradation of carbohydrates and starch to glucose. Hence, α-glucosidase is an important target in carbohydrate mediated diseases such as diabetes mellitus., Objective: In this study, novel coumarin containing dithiocarbamate derivatives 4a-n were synthesized and evaluated against α-glucosidase in vitro and in silico., Methods: These compounds were obtained from the reaction between 4-(bromomethyl)-7- methoxy-2H-chromen-2-one 1, carbon disulfide 2, and primary or secondary amines 3a-n in the presence of potassium hydroxide and ethanol at room temperature. In vitro α-glucosidase inhibition and kinetic study of these compounds were performed. Furthermore, a docking study of the most potent compounds was also performed by Auto Dock Tools (version 1.5.6)., Results: Obtained results showed that all the synthesized compounds exhibited prominent inhibitory activities (IC50 = 85.0 ± 4.0-566.6 ± 8.6 μM) in comparison to acarbose as a standard inhibitor (IC50 = 750.0 ± 9.0 μM). Among them, the secondary amine derivative 4d with pendant indole group was the most potent inhibitor. Enzyme kinetic study of the compound 4d revealed that this compound competes with a substrate to connect to the active site of α-glucosidase and therefore is a competitive inhibitor. Moreover, a molecular docking study predicted that this compound interacted with the α-glucosidase active site pocket., Conclusion: Our results suggest that the coumarin-dithiocarbamate scaffold can be a promising lead structure for designing potent α-glucosidase inhibitors for the treatment of type 2 diabetes., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

A new series of benzyl pyridinium-2,4-dioxochroman derivatives 7a-o was synthesized and evaluated as new anti-Alzheimer agents. Among the synthesized compounds, the compounds 7f and 7i exhibited the most potent anti-AChE and anti-BuChE activities, respectively. The kinetic study of the compound 7f revealed that this compound inhibited AChE in a mixed-type inhibition mode. Furthermore, the docking study of the compounds 7f and 7i showed that these compounds bound to both the catalytic site (CS) and peripheral anionic site (PAS) of AChE and BuChE, respectively. The compound 7f also exhibited a greater self-induced Aβ peptide aggregation inhibitory activity in compare to donepezil. Furthermore, the neuroprotective activity of this compound at 20 μM was comparable to that of the standard neuroprotective agent (quercetin)., (Copyright © 2019 Elsevier Inc. All rights reserved.)

We present a bidirectional neural interface with a 4-channel biopotential analog-to-digital converter (bioADC) and a 4-channel current-mode stimulator in 180 nm CMOS. The bioADC directly transduces microvolt biopotentials into a digital representation without a voltage-amplification stage. Each bioADC channel comprises a continuous-time first-order ∆Σ modulator with a chopper-stabilized OTA input and current feedback, followed by a second-order comb-filter decimator with programmable oversampling ratio. Each stimulator channel contains two independent digital-to-analog converters for anodic and cathodic current generation. A shared calibration circuit matches the amplitude of the anodic and cathodic currents for charge balancing. Powered from a 1.5 V supply, the analog and digital circuits in each recording channel draw on average [Formula: see text] and [Formula: see text] of supply current, respectively. The bioADCs achieve an SNR of [Formula: see text] and a SFDR of [Formula: see text] , for better than 9-b ENOB. Intracranial EEG recordings from an anesthetized rat are shown and compared to simultaneous recordings from a commercial reference system to validate performance in-vivo . Additionally, we demonstrate bidirectional operation by recording cardiac modulation induced through vagus nerve stimulation, and closed-loop control of cardiac rhythm. The micropower operation, direct digital readout, and integration of electrical stimulation circuits make this interface ideally suited for closed-loop neuromodulation applications.

A few kinematic synergies identified by principal component analysis (PCA) account for most of the variance in the coordinated joint rotations of the fingers and wrist used for a wide variety of hand movements. To examine the possibility that motor cortex might control the hand through such synergies, we collected simultaneous kinematic and neurophysiological data from monkeys performing a reach-to-grasp task. We used PCA, jPCA and isomap to extract kinematic synergies from 18 joint angles in the fingers and wrist and analyzed the relationships of both single-unit and multiunit spike recordings, as well as local field potentials (LFPs), to these synergies. For most spike recordings, the maximal absolute cross-correlations of firing rates were somewhat stronger with an individual joint angle than with any principal component (PC), any jPC or any isomap dimension. In decoding analyses, where spikes and LFP power in the 100- to 170-Hz band each provided better decoding than other LFP-based signals, the first PC was decoded as well as the best decoded joint angle. But the remaining PCs and jPCs were predicted with lower accuracy than individual joint angles. Although PCs, jPCs or isomap dimensions might provide a more parsimonious description of kinematics, our findings indicate that the kinematic synergies identified with these techniques are not represented in motor cortex more strongly than the original joint angles. We suggest that the motor cortex might act to sculpt the synergies generated by subcortical centers, superimposing an ability to individuate finger movements and adapt the hand to grasp a wide variety of objects., (Copyright © 2014 the American Physiological Society.)

The performance of brain-machine interfaces (BMIs) that continuously control upper limb neuroprostheses may benefit from distinguishing periods of posture and movement so as to prevent inappropriate movement of the prosthesis. Few studies, however, have investigated how decoding behavioral states and detecting the transitions between posture and movement could be used autonomously to trigger a kinematic decoder. We recorded simultaneous neuronal ensemble and local field potential (LFP) activity from microelectrode arrays in primary motor cortex (M1) and dorsal (PMd) and ventral (PMv) premotor areas of two male rhesus monkeys performing a center-out reach-and-grasp task, while upper limb kinematics were tracked with a motion capture system with markers on the dorsal aspect of the forearm, hand, and fingers. A state decoder was trained to distinguish four behavioral states (baseline, reaction, movement, hold), while a kinematic decoder was trained to continuously decode hand end point position and 18 joint angles of the wrist and fingers. LFP amplitude most accurately predicted transition into the reaction (62%) and movement (73%) states, while spikes most accurately decoded arm, hand, and finger kinematics during movement. Using an LFP-based state decoder to trigger a spike-based kinematic decoder [r = 0.72, root mean squared error (RMSE) = 0.15] significantly improved decoding of reach-to-grasp movements from baseline to final hold, compared with either a spike-based state decoder combined with a spike-based kinematic decoder (r = 0.70, RMSE = 0.17) or a spike-based kinematic decoder alone (r = 0.67, RMSE = 0.17). Combining LFP-based state decoding with spike-based kinematic decoding may be a valuable step toward the realization of BMI control of a multifingered neuroprosthesis performing dexterous manipulation.

Background: Telomerase has been considered as an attractive molecular target for breast cancer therapy. The main objective of this work is to assess the inhibitory effects of silibinin and curcumin, two herbal substances, on telomerase gene expression in breast cancer cells., Materials and Methods: For determination of cell viability tetrazolium-based assays were conducted after 24, 48, and 72 h exposure times and expression of human telomerase reverse transcriptase gene was measured with real-time PCR., Results: Each compound exerted cytotoxic effects on T47D cells and inhibited telomerase gene expression, both in a time-and dose-dependent manner. The mixture of curcumin and silibinin showed relatively more inhibitory effect on growth of T47D cells and hTERT gene expression as compared with either agent alone., Conclusions: These findings suggest that cell viability along with hTERT gene expression in breast cancer cells could be reduced by curcumin and silibinin.

One of the most exciting and compelling areas of research and development is building brain machine interfaces (BMIs) for controlling prosthetic limbs. Prosthetic limb technology is advancing rapidly, and the modular prosthetic limb (MPL) of the Johns Hopkins University/ Applied Physics Laboratory (JHU/APL) permits actuation with 17 degrees of freedom in 26 articulating joints. There are many signals from the brain that can be leveraged, including the spiking rates of neurons in the cortex, electrocorticographic (ECoG) signals from the surface of the cortex, and electroencephalographic (EEG) signals from the scalp. Unlike microelectrodes that record spikes, ECoG does not penetrate the cortex and has a higher spatial specificity, signal-to-noise ratio, and bandwidth than EEG signals. We have implemented an ECoG-based system for controlling the MPL in the Johns Hopkins Hospital Epilepsy Monitoring Unit, where patients are implanted with ECoG electrode grids for clinical seizure mapping and asked to perform various recorded finger or grasp movements. We have shown that low-frequency local motor potentials (LMPs) and ECoG power in the high gamma frequency (70,150 Hz) range correlate well with grasping parameters, and they stand out as good candidate features for closed-loop control of the MPL.

To examine the spatiotemporal distribution of discriminable information about reach-to-grasp movements in the primary motor cortex upper extremity representation, we implanted four microelectrode arrays in the anterior bank and lip of the central sulcus in each of two monkeys. We used linear discriminant analysis to compare information, quantified as decoding accuracy, contained in various neurophysiological signals. For all signal types, decoding accuracy increased immediately after the movement cue, peaked around movement onset, and declined during the static hold. Spike recordings and local field potential (LFP) time domain amplitude provided more discriminable information than LFP frequency domain power. Discriminable information on movement type was distributed evenly across recording sites by LFP amplitude and 1-4 Hz power but unevenly by 100-170 Hz power and spike recordings. These latter two signal types provided higher decoding accuracies closer to the hemispheric surface than deep in the anterior bank and also provided accuracies that varied along the central sulcus. This variation in the distribution of movement-type information may be related to differences in the rostral versus caudal regions of the primary motor cortex and to its underlying somatotopic organization. The even distribution of information by LFP amplitude and 1-4 Hz power compared with the more localized distribution by 100-170 Hz power and spikes suggest that these different neurophysiological signals reflect different underlying processes that distribute information through the motor cortex during reach-to-grasp movements.

Long-term monitoring of neuronal activity in awake behaving subjects can provide fundamental information about brain dynamics for neuroscience and neuroengineering applications. Here, we present a miniature, lightweight, and low-power recording system for monitoring neural activity in awake behaving animals. The system integrates two custom designed very-large-scale integrated chips, a neural interface module fabricated in 0.5 μm complementary metal-oxide semiconductor technology and an ultra-wideband transmitter module fabricated in a 0.5 μm silicon-on-sapphire (SOS) technology. The system amplifies, filters, digitizes, and transmits 16 channels of neural data at a rate of 1 Mb/s. The entire system, which includes the VLSI circuits, a digital interface board, a battery, and a custom housing, is small and lightweight (24 g) and, thus, can be chronically mounted on small animals. The system consumes 4.8 mA and records continuously for up to 40 h powered by a 3.7-V, 200-mAh rechargeable lithium-ion battery. Experimental benchtop characterizations as well as in vivo multichannel neural recordings from awake behaving rats are presented here.

Recent studies in primate neurophysiology have focused on decoding multi-joint kinematics from single unit and local field potential recordings. However, the extent to which these results can be generalized to human subjects is not known. We have recorded simultaneous electrocorticographic (ECoG) and hand kinematics in a human subject performing reach-grasp-hold of objects varying in shape and size. All Spectral features in various gamma bands (30-50 Hz, 70-100 Hz and 100-150 Hz frequency bands) were able to predict the time course of grasp aperture with high correlation (max r = 0.80) using as few as one ECoG feature from a single electrode (max r for single feature = 0.75) in single trials without prior knowledge of task timing. These results suggest that the population activity captured with ECoG contains information about coordinated finger movements that potentially can be exploited to control advanced upper limb neuroprosthetics.

Background: Obesity has been associated with increased mortality from hormone dependant cancers such as breast cancer which is the most prevalent cancer in women. The link between obesity and breast cancer can be attributed to excess estrogen produced through aromatization in adipose tissue. The role of steroid hormone receptors in breast cancer development is well studied but how obesity can affect the expression pattern of steroid hormones in patients with different grades of breast cancer was the aim of this study., Methods: In this case-control study, 70 women with breast cancer participated with different grades of obesity (36 none obese, BMI < 25 kg/m(2) and 34 obese, BMI ≥ 25 kg/m(2)). The mean age of participants was 44.53 ± 1.79 yr (21-70 yr). The serum level of estrogen, progesterone and androgen determined by ELISA. Following quantitative expression of steroid hormone receptors mRNA in tumor tissues evaluated by Real-time PCR. Patients with previous history of radiotherapy or chemotherapy were excluded. SPSS 16 was used for data analysis and P < 0.05 considered statistically significant., Results: The difference in ERα, ERβ and PR mRNA level between normal and obese patients was significant (P < 0.001). In addition, the expression of AR mRNA was found to be higher than other steroid receptors. There was no significant relation between ERβ gene expression in two groups (P = 0.68). We observed a significant relationship between ERα and AR mRNA with tumor stage and tumor grade, respectively (P = 0.023, P = 0.015)., Conclusion: According to the obtained results, it is speculated that obesity could paly a significant role in estrogen receptors gene expression and also could affect progression and proliferation of breast cancer cells.

The intricate coupling between electrical and chemical activity in neural pathways of the central nervous system, and the implication of this coupling in neuropathologies, such as Parkinson's disease, motivates simultaneous monitoring of neurochemical and neuropotential signals. However, to date, neurochemical sensing has been lacking in integrated clinical instrumentation as well as in brain-computer interfaces (BCI). Here, we present an integrated system capable of continuous acquisition of data modalities in awake, behaving subjects. It features one channel each of a configurable neuropotential and a neurochemical acquisition system. The electrophysiological channel is comprised of a 40-dB gain, fully differential amplifier with tunable bandwidth from 140 Hz to 8.2 kHz. The amplifier offers input-referred noise below 2 muV rms for all bandwidth settings. The neurochemical module features a picoampere sensitivity potentiostat with a dynamic range spanning six decades from picoamperes to microamperes. Both systems have independent on-chip, configurable DeltaSigma analog-to-digital converters (ADCs) with programmable digital gain and resolution. The system was also interfaced to a wireless power harvesting and telemetry module capable of powering up the circuits, providing clocks for ADC operation, and telemetering out the data at up to 32 kb/s over 3.5 cm with a bit-error rate of less than 10(-5). Characterization and experimental results from the electrophysiological and neurochemical modules as well as the full system are presented.

Electrical activity in the brain spans a wide range of spatial and temporal scales, requiring simultaneous recording of multiple modalities of neurophysiological signals in order to capture various aspects of brain state dynamics. Here, we present a 16-channel neural interface integrated circuit fabricated in a 0.5 mum 3M2P CMOS process for selective digital acquisition of biopotentials across the spectrum of neural signal modalities in the brain, ranging from single spike action potentials to local field potentials (LFP), electrocorticograms (ECoG), and electroencephalograms (EEG). Each channel is composed of a tunable bandwidth, fixed gain front-end amplifier and a programmable gain/resolution continuous-time incremental DeltaSigma analog-to-digital converter (ADC). A two-stage topology for the front-end voltage amplifier with capacitive feedback offers independent tuning of the amplifier bandpass frequency corners, and attains a noise efficiency factor (NEF) of 2.9 at 8.2 kHz bandwidth for spike recording, and a NEF of 3.2 at 140 Hz bandwidth for EEG recording. The amplifier has a measured midband gain of 39.6 dB, frequency response from 0.2 Hz to 8.2 kHz, and an input-referred noise of 1.94 muV rms while drawing 12.2 muA of current from a 3.3 V supply. The lower and higher cutoff frequencies of the bandpass filter are adjustable from 0.2 to 94 Hz and 140 Hz to 8.2 kHz, respectively. At 10-bit resolution, the ADC has an SNDR of 56 dB while consuming 76 muW power. Time-modulation feedback in the ADC offers programmable digital gain (1-4096) for auto-ranging, further improving the dynamic range and linearity of the ADC. Experimental recordings with the system show spike signals in rat somatosensory cortex as well as alpha EEG activity in a human subject.

Electrical signals recorded from the brain cover a wide range of amplitudes, frequencies, and spatial scales, from spikes and local field potentials (LFP) inside the brain to electrocorticograms (ECoG) and electroencepalograms (EEG) outside. Each of these signal modalities represent different aspects of neural dynamics that can be combined to infer brain state and function in a broader context.We present a 16-channel interface circuit fabricated in a 0.5 mum CMOS process for the selective acquisition and digitization of any of the modalities. Each channel features a fixed gain bandpass amplifier with a tunable frequency response which allows isolation of the signal of interest without hardware modification and a programmable gain/resolution analog to digital converter (ADC). The bandpass amplifier analog front end has an input referred noise of 1.94 microV(rms) for a bandwidth of 8.2 kHz while drawing 12.2 microA of current from a 3.3 V supply. Experimental recordings with the system show spike signals in rat somatosensory cortex as well as alpha EEG activity in a human subject.

Recent studies have shown that cortical local field potentials (LFP) contain information about planning or executing hand movement. While earlier research has looked at gross motor movements, we investigate the spectral modulation of LFP activity and its dependence on recording location during dexterous motor actions. In this study, we recorded LFP activity from the primary motor cortex of a primate as it performed a fine finger manipulation task involving different switches. The event-related spectral perturbations (ERSP) in four different frequency bands were considered for the analysis; 4 Hz, 6-15 Hz, 17-40 Hz and 75-170 Hz. LFPs recorded from electrodes in the hand area showed the largest change in ERSP for the highest frequency band (75-170 Hz) (p 0.05), while LFPs recorded from electrodes placed more medially in the arm area showed the largest change in ERSP for the lowest frequency band (4 Hz) (p 0.05). Furthermore, the spectral information from the <4 Hz and 75-150 Hz frequency bands was used to successfully decode the three dexterous grasp movements with an average accuracy of up to 81%. Although previous research has shown that multi-unit neuronal activity can be used to decode fine motor movements, these results demonstrate that LFP activity can also be used to decode dexterous motor tasks. This has implications for future neuroprosthetic devices due to the robustness of LFP signals for chronic recording.

Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of principle component analysis, independent component analysis and dipole source localization to design a spatiotemporal multiple source tuning (SPAMSORT) filter bank, each channel of which was tuned to the activity of an underlying dipole source. Changes in the event-related spectral perturbation (ERSP) were measured and used to train a linear support vector machine to classify between four classes of motor imagery tasks (left hand, right hand, foot and tongue) for one subject. ERSP values were significantly (p<0.01) different across tasks and better (p<0.01) than conventional spatial filtering methods (large Laplacian and common average reference). Classification resulted in an average accuracy of 82.5%. This approach could lead to promising BCI applications such as control of a prosthesis with multiple degrees of freedom.