With an incubation time of about 5 days, early diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical to control the spread of the coronavirus disease 2019 (COVID-19) ...that killed more than 3 million people in its first 1.5 years. Here, we report on the modification of the dopant density and the phononic energy of antibody-coupled graphene when it interfaces with SARS-CoV-2 spike protein. This graphene chemeo-phononic system was able to detect SARS-CoV-2 spike protein at the limit of detection of ∼3.75 and ∼1 fg/mL in artificial saliva and phosphate-buffered saline, respectively. It also exhibited selectivity over proteins in saliva and MERS-CoV spike protein. Since the change in graphene phononics is monitored instead of the phononic signature of the analyte, this optical platform can be replicated for other COVID variants and specific-binding-based biodetection applications.
A
bstract
We propose new 3d
N
= 2 Seiberg-like dualities by considering various monopole superpotential deformations on 3d
N
= 2 U(
N
c
) SQCDs with fundamental and adjoint matter fields. We provide ...nontrivial evidence of these new dualities by comparing the superconformal indices, from which we analyze the change of the moduli space due to the monopole deformation. In addition, we perform the
F
-maximization to check the relevance of the monopole deformation for some examples, one of which is found to exhibit nontrivial symmetry enhancement in the IR. We prove such enhancement of the global symmetry using the superconformal index.
A
bstract
We consider the analogue of Seiberg duality for two-dimensional
N
= (2, 2) gauge theory with orthogonal gauge groups and with fundamental chiral multiplets proposed by Hori. Following Hori, ...when we consider O(
k
) gauge group as the (semi)-direct product of SO(
k
) ⋉
Z
2
, we have to consider two kinds of the theories O
±
(
k
) depending on the orbifold action of
Z
2
. We give the evidences for the proposed dualities by working out the elliptic genus of dual pair. The matching of the elliptic genus is worked out perfectly for the proposed dualities.
A
bstract
We propose a novel procedure of assigning a pair of non-unitary topological quantum field theories (TQFTs), TFT
±
$$ \mathcal{T} $$
T
rank 0
, to a (2+1)D interacting
$$ \mathcal{N} $$
N
= ...4 superconformal field theory (SCFT)
$$ \mathcal{T} $$
T
rank 0
of rank 0, i.e. having no Coulomb and Higgs branches. The topological theories arise from particular degenerate limits of the SCFT. Modular data of the non-unitary TQFTs are extracted from the supersymmetric partition functions in the degenerate limits. As a non-trivial dictionary, we propose that
F
= max
α
(
−
log|
$$ {S}_{0\alpha}^{\left(+\right)} $$
S
0
α
+
|) = max
α
(
−
log|
$$ {S}_{0\alpha}^{\left(-\right)} $$
S
0
α
−
|), where
F
is the round three-sphere free energy of
$$ \mathcal{T} $$
T
rank 0
and
$$ {S}_{0\alpha}^{\left(\pm \right)} $$
S
0
α
±
is the first column in the modular S-matrix of TFT
±
. From the dictionary, we derive the lower bound on
F
,
F
≥
−
log
$$ \left(\sqrt{\frac{5-\sqrt{5}}{10}}\right) $$
5
−
5
10
≃ 0
.
642965, which holds for any rank 0 SCFT. The bound is saturated by the minimal
$$ \mathcal{N} $$
N
= 4 SCFT proposed by Gang-Yamazaki, whose associated topological theories are both the Lee-Yang TQFT. We explicitly work out the (rank 0 SCFT)/(non-unitary TQFTs) correspondence for infinitely many examples.
The synthesis, preclinical profile, and in vivo efficacy in rat xenograft models of the novel and selective anaplastic lymphoma kinase inhibitor 15b (LDK378) are described. In this initial report, ...preliminary structure–activity relationships (SARs) are described as well as the rational design strategy employed to overcome the development deficiencies of the first generation ALK inhibitor 4 (TAE684). Compound 15b is currently in phase 1 and phase 2 clinical trials with substantial antitumor activity being observed in ALK-positive cancer patients.
Magnetic sensors have great potential for biomedical applications, particularly, detection of magnetically-labeled biomolecules and cells. On the basis of the advantage of the planar Hall effect ...sensor, which consists of improved thermal stability as compared with other magnetic sensors, we have designed a portable biosensor platform that can detect magnetic labels without applying any external magnetic field. The trilayer sensor, with a composition of Ta (5 nm)/NiFe (10 nm)/Cu (
= 0 nm~1.2 nm)/IrMn (10 nm)/Ta (5 nm), was deposited on a silicon wafer using photolithography and a sputtering system, where the optimized sensor sensitivity was 6 μV/(Oe∙mA). The detection of the magnetic label was done by comparing the signals obtained in first harmonic AC mode (1f mode) using an external magnetic field and in the second harmonic AC mode (2f mode) with a self-field generated by current passing through the sensor. In addition, a technique for the β-amyloid biomarker-based antibody-antigen sandwich model was demonstrated for the detection of a series of concentrations of magnetic labels using the self-field mode method, where the signal-to-noise ratio (SNR) was high. The generated self-field was enough to detect an immobilized magnetic tag without an additional external magnetic field. Hence, it could be possible to reduce the device size to use the point-of-care testing using a portable circuit system.
To apply resistive random‐access memory (RRAM) to the neuromorphic system and improve performance, each cell in the array should be able to operate independently by reducing device variation. In ...addition, it is necessary to lower the operating current of the RRAM cell and enable gradual switching characteristics to mimic the low‐energy operations of biological. In most filamentary RRAMs, however, overshoot current occurs in the forming stage, and the RRAM shows large device variation, high operating current, and abrupt set and reset switching characteristics. Herein, the shortcomings occurring in the forming stage are overcome by introducing and optimizing an overshoot suppression layer. Consequently, the RRAM exhibits gradual switching characteristics both in the set and reset regions, thereby enabling implementation of 4‐bit multilevel operation. In addition, the forming step can be easily performed in a 16 × 16 crossbar array owing to its self‐compliance characteristics without disturbing neighboring cells in the array. The tuning and vector–matrix multiplication (VMM) operations are also experimentally verified in the array. Finally, classification performance with off‐chip training is compared in terms of accuracy and robustness to tuning tolerance depending on the number of bits of the implemented multiconductance levels.
Al2O3/TiOx based resistive switching devices with overshoot suppressed layer (OSL) are integrated in 16 × 16 crossbar array. Especially, tuning operation of 4‐bit states is demonstrated with incremental pulse programming and verifying scheme for off‐chip training of neuromorphic system. OSL effectively removes overshoot during the forming stage and enables gradual switching characteristics through weak filaments.
Accurate prediction of scoliotic curve progression is crucial for guiding treatment decisions in adolescent idiopathic scoliosis (AIS). Traditional methods of assessing the likelihood of AIS ...progression are limited by variability and rely on static measurements. This study developed and validated machine learning models for classifying progressive and non-progressive scoliotic curves based on gait analysis using wearable inertial sensors. Gait data from 38 AIS patients were collected using seven inertial measurement unit (IMU) sensors, and hip–knee (HK) cyclograms representing inter-joint coordination were generated. Various machine learning algorithms, including support vector machine (SVM), random forest (RF), and novel deep convolutional neural network (DCNN) models utilizing multi-plane HK cyclograms, were developed and evaluated using 10-fold cross-validation. The DCNN model incorporating multi-plane HK cyclograms and clinical factors achieved an accuracy of 92% in predicting curve progression, outperforming SVM (55% accuracy) and RF (52% accuracy) models using handcrafted gait features. Gradient-based class activation mapping revealed that the DCNN model focused on the swing phase of the gait cycle to make predictions. This study demonstrates the potential of deep learning techniques, and DCNNs in particular, in accurately classifying scoliotic curve progression using gait data from wearable IMU sensors.
In asynchronous Spiking Neural Networks (SNNs), the voltage division between passive resistive random-access memory (RRAM) arrays and neuron circuits presents a significant challenge, affecting the ...overall network accuracy and power efficiency. This study introduces the quantized-weight-splitting method (QWSM) as a novel solution to address this challenge. The QWSM optimizes and splits the quantized weights using the static read distortion owing to voltage division. The QWSM successfully guarantees inference accuracy and reduces power consumption. To validate the QWSM, the fabricated RRAM devices were measured, and a fitting model was carefully developed to describe their behavior, showing a strong correlation with measured data. In SNN simulations using the fitting model, the inference accuracy was improved across various weight quantization levels when the QWSM was applied. Moreover, the QWSM led to a substantial reduction in the average power consumption. Specifically, Compared to a network configured to have the smallest combined conductance of RRAMs for low-power operation, the network applying the QWSM showed a 12.56 -% reduction in average power per synapse. This power-saving feature, combined with improved accuracy, positions the QWSM as a valuable tool for efficient SNN design using passive RRAM arrays. Our findings highlight the potential of the QWSM in advancing neuromorphic computing with better energy efficiency and accuracy robustness.
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