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Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI), such as Erlotinib, have demonstrated remarkable efficacy in the treatment of non-small cell lung cancer (NSCLC) patients with mutated EGFR. However, the efficacy of EGFR-TKIs in wild-type (wt) EGFR tumours has been shown to be marginal. Methods that can sensitize Erlotinib to EGFR wild-type NSCLC remain rare. Herein, we developed a multifunctional superparamagnetic nanotheranostic agent as a novel strategy to potentiate Erlotinib to EGFR-wt NSCLCs. Our results demonstrate that the nanoparticles can co-escort Erlotinib and a vascular epithermal growth factor (VEGF) inhibitor, Bevacizumab (Bev), to EGFR-wt tumours. The nanotheranostic agent exhibits remarkable effects as an inhibitor of EGFR-wt tumour growth. Moreover, Bev normalizes the tumour embedded vessels, further promoting the therapeutic efficacy of Erlotinib. In addition, the tumour engagement of the nanoparticles and the vascular normalization could be tracked by magnetic resonance imaging (MRI). Collectively, our study, for the first time, demonstrated that elaborated nanoparticles could be employed as a robust tool to potentiate Erlotinib to EGFR-wt NSCLC, paving the way for imaging-guided nanotheranostics for refractory NSCLCs expressing EGFR wild-type genes.

Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the large storage overheads and the substantial computation cost of CNNs are problematic in hardware accelerators. Computing-in-memory (CIM) architecture has demonstrated great potential to effectively compute large-scale matrix-vector multiplication. However, the intensive multiply and accumulation (MAC) operations executed at the crossbar array and the limited capacity of CIM macros remain bottlenecks for further improvement of energy efficiency and throughput. To reduce computation costs, network pruning and quantization are two widely studied compression methods to shrink the model size. However, most of the model compression algorithms can only be implemented in digital-based CNN accelerators. For implementation in a static random access memory (SRAM) CIM-based accelerator, the model compression algorithm must consider the hardware limitations of CIM macros, such as the number of word lines and bit lines that can be turned on at the same time, as well as how to map the weight to the SRAM CIM macro. In this study, a software and hardware co-design approach is proposed to design an SRAM CIM-based CNN accelerator and an SRAM CIM-aware model compression algorithm. To lessen the high-precision MAC required by batch normalization (BN), a quantization algorithm that can fuse BN into the weights is proposed. Furthermore, to reduce the number of network parameters, a sparsity algorithm that considers a CIM architecture is proposed. Last, MARS, a CIM-based CNN accelerator that can utilize multiple SRAM CIM macros as processing units and support a sparsity neural network, is proposed., IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2021

Key Points Induction of Sbds deficiency in recipient BM niche cells impairs efficient engraftment of healthy donor HSC after transplantation.SBDS deficiency impairs multiple cellular signaling pathways in post-myeloablation marrow niches critical for restoration of hematopoiesis., Visual Abstract, Bone marrow (BM) niche-derived signals are critical for facilitating engraftment after hematopoietic stem cell (HSC) transplantation (HSCT). HSCT is required for restoration of hematopoiesis in patients with inherited BM failure syndromes (iBMFSs). Shwachman-Diamond syndrome (SDS) is a rare iBMFS associated with mutations in SBDS. Previous studies have demonstrated that SBDS deficiency in osteolineage niche cells causes BM dysfunction that promotes leukemia development. However, it is unknown whether BM niche defects caused by SBDS deficiency also impair efficient engraftment of healthy donor HSC after HSCT, a hypothesis that could explain morbidity noted after clinical HSCT for patients with SDS. Here, we report a mouse model with inducible Sbds deletion in hematopoietic and osteolineage cells. Primary and secondary BM transplantation (BMT) studies demonstrated that SBDS deficiency within BM niches caused poor donor hematopoietic recovery and specifically poor HSC engraftment after myeloablative BMT. We have also identified multiple molecular and cellular defects within niche populations that are driven by SBDS deficiency and are accentuated by or develop specifically after myeloablative conditioning. These abnormalities include altered frequencies of multiple niche cell subsets, including mesenchymal lineage cells, macrophages, and endothelial cells; disruption of growth factor signaling, chemokine pathway activation, and adhesion molecule expression; and p53 pathway activation and signals involved in cell cycle arrest. Taken together, this study demonstrates that SBDS deficiency profoundly impacts recipient hematopoietic niche function in the setting of HSCT, suggesting that novel therapeutic strategies targeting host niches could improve clinical HSCT outcomes for patients with SDS.

Computing-in-memory (CIM) architectures have gained importance in achieving high-throughput energy-efficient artificial intelligence (AI) systems. Resistive RAM (RRAM) is a promising candidate for CIM architectures due to a multiply-and-accumulate (MAC)-friendly structure, high bit density, compatibility with a CMOS process, and nonvolatility. Notwithstanding the advancement of RRAM technology, the reliability of an RRAM array hinders the spread of RRAM applications such that a circuit-technology joint approach is necessary to attain reliable RRAM-based CIM architectures. This article presents a 64-kb hybrid CIM/digital RRAM macro supporting: 1) active-feedback-based voltage-sensing read (RD) to enable 1-8-b programmable vector-matrix multiplication under a low-resistance ratio of the high-resistance state to the low-resistance state in an RRAM array; 2) iterative write with verification to secure a tight resistance distribution; and 3) online RD-disturb detection in the background during CIM. The test chip fabricated in a 40-nm CMOS and RRAM process achieves a peak energy efficiency of 56.67 TOPS/W while demonstrating the eight-bitline hybrid CIM/digital MAC operation with 1-8-b inputs and weights and 20-b outputs without quantization.

Although lung cancer screening with low-dose CT (LDCT) can reduce lung cancer mortality by 20%, without an appropriate eligibility criteria, it may result in a waste of medical resources and a degree of unnecessary damage to participants' health. This study aims to give the optimal screening strategy in China based on cost-effectiveness analysis on pros and cons of different situations. From the perspective of primary healthcare system, a Markov model was built to simulate LDCT screening of 100,000 heavy smokers (>30 pack years) aged 40 in different situations. Model parameters mainly came from screening programs conducted in China and other countries, official public data, and published literature. Two indicators of primary outcome, incremental cost-effectiveness ratio (ICER) and net health benefits (NHB), were compared with those of no screening. Sensitivity analysis was conducted to evaluate model uncertainties. We defined the optimal strategy as the one with both acceptable cost effectiveness and maximal NHB. Base-case analysis results showed that for all screening strategies, ICERs were less than three times of GDP per capita. As for NHB results, it showed that when the willingness to pay for screening was less than three times of GPD per capita, the largest NHB was obtained in the strategy which started screening at 50 years old and this strategy showed stable performance in univariate and probabilistic sensitivity as well. Prevention Relevance: LDCT screening is cost effective in heavy smokers in China, and the optimal age to start screening is suggested to be 50 years old.

Learning from a few examples (one/few-shot learning) on the fly is a key challenge for on-device machine intelligence. We present the first chip-level demonstration of one-shot learning with Stanford Associative memory for Programmable, Integrated Edge iNtelligence via life-long learning and Search (SAPIENS), a resistive random access memory (RRAM)-based non-volatile associative memory (AM) chip that serves as the backend for memory-augmented neural networks (MANNs). The 64-kb fully integrated RRAM-CMOS AM chip performs long-term feature embedding and retrieval, demonstrated on a 32-way one-shot learning task on the Omniglot dataset. Using only one example per class for 32 unseen classes during on-chip learning, SAPIENS achieves 79% measured inference accuracy on Omniglot, comparable to edge software model accuracy using five-level quantization (82%). It achieves an energy efficiency of 118 GOPS/W at 200 MHz for in-memory L1 distance computation and prediction. Multi-bank measurements on the same chip show that increasing the capacity from three banks (24 kb) to eight banks (64 kb) improves the chip accuracy from 73.5% to 79%, while minimizing the accuracy excursion due to bank-to-bank variability.

Background Cancer cachexia is a multifactorial metabolic syndrome in which bile acid (BA) metabolism might be involved. The aim of the present study was to clarify the contribution of liver and gut microbiota to BA metabolism disturbance in cancer cachexia and to check the possibility of targeting BA metabolism using agents such as tauroursodeoxycholic acid (TUDCA) for cancer cachexia therapy. Methods The BA profiles in liver, intestine, and serum of mice with cancer cachexia induced by inoculation of colon C26 tumour cells were analysed using metabolomics methods and compared with that of control mice. Proteomic analysis of liver protein expression profile and 16S rRNA gene sequencing analysis of gut microbiota composition in cancer cachexia mice were conducted. Expression levels of genes related to farnesoid X receptor (FXR) signalling pathway in the intestine and liver tissues were analysed using RT–PCR analysis. The BA profiles in serum of clinical colon cancer patients with or without cachexia were also analysed and compared with that of healthy volunteers. The effects of TUDCA in treating cancer cachexia mice were observed. Results In the liver of cancer cachexia mice, expression of BA synthesis enzymes was inhibited while the amount of total BAs increased (P

BACKGROUND Trauma is one of the leading causes of death in the pediatric population. Bronchial rupture is rare, but there are potentially severe complications. Establishing and maintaining a patent airway is the key issue in patients with bronchial rupture. Here we describe an innovative method for maintaining a patent airway. CASE SUMMARY A 3-year-old boy fell from the seventh floor. Oxygenation worsened rapidly with pulse oxygen saturation decreasing below 60%, as his heart rate dropped. Persistent pneumothorax was observed with insertion of the chest tube. Fiberoptic bronchoscopy was performed, which confirmed the diagnosis of bronchial rupture. A modified tracheal tube was inserted under the guidance of a fiberoptic bronchoscope. Pulse oxygen saturation improved from 60% to 90%. Twelve days after admission, right upper lobectomy was performed using bronchial stump suture by video-assisted thoracic surgery without complications. A follow-up chest radiograph showed good recovery. The child was discharged from hospital three months after admission. CONCLUSION A modified tracheal tube could be selected to ensure a patent airway and adequate ventilation in patients with bronchial rupture.

In this work, we present a four-transistor-two-resistor (4T2R) ternary content addressable memory (TCAM) bit cell based on the resistive memory (RRAM), comprising the conventional two-transistor-two-resistor (2T2R) cell with two additional comparison transistors. It can effectively amplify the match-line signal ratio (ML-ratio), lower the leakage current of the match cell ( ${I}_{{\mathrm {MATCH}}}$ ), and suppress the read disturbance. The proposed concept is silicon verified using the 180 nm CMOS technology with transition-metal-oxide (TMO) RRAM integrated at the back-end-of-line (BEOL). It achieves a considerable ML-ratio of 1860 and a low ${I}_{{\mathrm {MATCH}}}$ of 11.15 nA on average. In a typical search operation, it shows a negligible ML drop at the match case and a large ML swing range at the mismatch case. The SPICE simulation results further show it can support a long word-length (WDL) of 256 under a clock rate of 100 MHz for search operations, which demonstrates its promise for highly parallel nonvolatile TCAM.

Background Janus-activated kinase-1 (JAK1) plays a crucial role in many aspects of cell proliferation, differentiation, apoptosis and immune regulation. However, correlations of JAK1 with prognosis and immune infiltration in NSCLC have not been documented. Methods We analyzed the relationship between JAK1 expression and NSCLC prognosis and immune infiltration using multiple public databases. Results JAK1 expression was significantly decreased in NSCLC compared with that in paired normal tissues. JAK1 overexpression indicated a favourable prognosis in NSCLC. In subgroup analysis, high JAK1 expression was associated with a preferable prognosis in lung adenocarcinoma (OS: HR, 0.74, 95% CI from 0.58 to 0.95, log-rank P = 0.017), not squamous cell carcinoma. In addition, data from Kaplan–Meier plotter revealed that JAK1 overexpression was associated with a preferable prognosis in male and stage N2 patients and patients without distant metastasis. Notably, increased levels of JAK1 expression were associated with an undesirable prognosis in patients with stage 1 (OS: HR, 1.46, 95% CI from 1.06 to 2.00, P = 0.02) and without lymph node metastasis (PFS: HR, 2.18, 95% CI from 1.06 to 4.46, P = 0.029), which suggests that early-stage NSCLC patients with JAK1 overexpression may have a bleak prognosis. Moreover, multiple immune infiltration cells, including NK cells, CD8 + T and CD4 + T cells, B cells, macrophages, neutrophils, and dendritic cells (DCs), in NSCLC were positively correlated with JAK1 expression. Furthermore, diverse immune markers are associated with JAK1 expression. Conclusions JAK1 overexpression exhibited superior prognosis and immune infiltration in NSCLC.

This article presents a computing-in-memory (CIM) structure aimed at improving the energy efficiency of edge devices running multi-bit multiply-and-accumulate (MAC) operations. The proposed scheme includes a 6T SRAM-based CIM (SRAM-CIM) macro capable of: 1) weight-bitwise MAC (WbwMAC) operations to expand the sensing margin and improve the readout accuracy for high-precision MAC operations; 2) a compact 6T local computing cell to perform multiplication with suppressed sensitivity to process variation; 3) an algorithm-adaptive low MAC-aware readout scheme to improve energy efficiency; 4) a bitline header selection scheme to enlarge signal margin; and 5) a small-offset margin-enhanced sense amplifier for robust read operations against process variation. A fabricated 28-nm 64-kb SRAM-CIM macro achieved access times of 4.1–8.4 ns with energy efficiency of 11.5–68.4 TOPS/W, while performing MAC operations with 4- or 8-b input and weight precision.

Background According to a questionnaire sent to Designated Cancer Care Hospitals in Japan in 2013, only 39.4% of the institutes had medical oncology departments. Furthermore, most of these medical oncology departments were primarily responsible for the treatment of limited disease categories and the administration of newly developed therapeutic modalities, including molecular-targeted therapy. The aim of the present study was to update these previous findings and to clarify the changes over the intervening 7-year period. Methods The questionnaire was sent to all 393 Designated Cancer Care Hospitals on 13 March 2020. Similar to the previous questionnaires, questions were asked regarding the presence of a medical oncology department, the number of physicians in the department and the degrees of responsibility for drug therapies provided by medical oncologists to adult patients with solid cancers. Results In total, 270 institutions (68.7%) responded. Overall, 145 of these 270 institutions (53.7%) had medical oncology departments, representing a significant increase compared with the results of the previous study (P, The proportion of institutes with medical oncology departments in Japan has increased significantly and their responsibility has expanded to include newly emerging drugs.

As the growing demand on artificial intelligence (AI) Internet-of-Things (IoT) devices, smart vision sensors with energy-efficient computing capability are required. This article presents a low-power and low-voltage dual mode 0.5-V computational CMOS image sensor (C2IS) with array-parallel computing capability for feature extraction using convolution. In the feature extraction mode, by applying the pulsewidth modulation (PWM) pixel and switch-current integration (SCI) circuit, the in-sensor eight-directional matrix-parallel multiply–accumulate (MAC) operation is realized. Furthermore, the analog-domain convolution-on-readout (COR) operation, the programmable $3\times3$ kernel with ±3-bit weights, and the tunable-resolution column-parallel analog-to-digital converter (ADC) (1–8 bit) are implemented to achieve the real-time feature extraction without using additional memory and sacrificing frame rate. In the image capturing mode, the sensor provides the linear-response 8-bit raw image data. The C2IS prototype has been fabricated in the TSMC 0.18- $\mu \text{m}$ standard process technology and verified to demonstrate the raw and feature images at 480 frames/s with a power consumption of 77/ $117~\mu \text{W}$ and the resultant FoM of 9.8/14.8 pJ/pixel/frame, respectively. The prototype sensor is used as a real-time edge feature detection frond-end camera and accompanied with a simplified convolutional neural network (CNN) architecture to demonstrate the hand gesture recognition. The prototype system achieves more than 95% validation accuracy.

Objective We aim to determine the risk of acute ischemic stroke in patients with severe and non-severe coronavirus disease 2019 (COVID-19). Methods A literature search was conducted in the PubMed, Embase, Web of Science, and Cochrane Library databases until October 28, 2020. Studies covering COVID-19’s severity classification data and COVID-19 patients with acute ischemic stroke were included. Two independent evaluators extracted data, and the random effects model was used to calculate the risk ratios (RR) and 95% confidence interval (95% CI) of acute ischemic stroke associated with COVID-19’s severity. Results A total of 8 studies were included, involving 5266 patients. Among all COVID-19 patients, the total incidence of ischemic stroke was 1.76% (95% CI: 0.82–3.01). Severe patients have an increased risk of acute ischemic stroke compared with non-severe patients (RR = 3.53, 95% CI: 2.06–6.07, P < 0.0001; I2 = 12%). This association was also observed when COVID-19’s severity was defined by clinical parameters (RR 2.91, 95% CI: 1.17–7.26, P = 0.02; I2 = 29%) and the need for intensive care (RR 4.47, 95% CI: 2.40–8.31, P < 0.0001; I2 = 0%). Conclusions This meta-analysis shows that the severe course of COVID-19 is associated with an increased risk of acute ischemic stroke.

Background Cystic echinococcosis is one of the most severe helminth zoonosis with a drastic impact on human health and livestock industry. Investigating optimal control strategy and assessing the crucial factors are essential for developing countermeasures to mitigate this disease. Methods Two compartment models were formulated to study the dynamics of cystic echinococcosis transmission, to evaluate the effectiveness of various control measures, and to find the optimal control strategy. Sensitive analyses were conducted by obtaining PRCCs and contour plot was used to evaluate the effect of key parameters on the basic reproduction number. Based on forward–backward sweep method, numerical simulations were employed to investigate effects of key factors on the transmission of cystic echinococcosis and to obtain the optimal control strategy. Results The food resources of stray dog and invalid sheep vaccination rate, which are always neglected, were significant to the transmission and control of cystic echinococcosis. Numerical simulations suggest that, the implementation of optimal control strategy can significantly reduce the infections. Improving the cost of health education and domestic dog deworming could not decrease human infections. Conclusions Our study showed that only a long-term use of the optimal control measures can eliminate the disease. Meanwhile, during the intervention, sheep vaccination and stray dogs disposing should be emphasized ahead of domestic dogs deworming to minimize the control cost. Simultaneously reducing other wild intermediate hosts and strengthening the sheep vaccination as well as disposing the stray dogs would be most effective.

Objective: To study the relationship between syndrome elements and anterior communicating artery (ACoA) opening in patients with symptomatic severe carotid artery stenosis/occlusion. Methods: Thirty-six patients with symptomatic severe carotid stenosis/occlusion were collected, including 26 patients with cerebral infarction and 10 patients with transient ischemic attack (TIA). Syndrome elements at five time points were collected. Computer tomography angiography (CTA) combined with magenic resonance angiograp (MRA) was used to evaluate the primary collateral circulation, and the prognosis and syndrome elements were statistically analyzed according to whether the ACoA was open or not. Results: The ACoA was open more in the primary collateral circulation among patients with symptomatic severe carotid stenosis/occlusion. There was a statistically significant difference in national institute of health stroke scale (NIHSS) score improvement and good prognosis [the modified rankin scale (mRS) ≤ 2] between the ACoA open group and the ACoA non-open group on the 90th day (P < 0.05). The proportion of patients with internal wind syndrome, blood stasis syndrome, Qi deficiency syndrome, and Yin deficiency syndrome in the ACoA non-open group was higher than that in the open group. Conclusion: In the patients with severe carotid artery stenosis/ occlusion, the group with presence of anterior communicating artery had better prognosis. The syndrome elements are more complex in the group without the presence of anterior communicating artery. The proportion of Qi deficiency syndrome was positively correlated with the non-opening of anterior communicating artery. The imaging evaluation of collateral circulation can provide guidance for syndrome differentiation and treatment.

In recent years, the implantable cardiac monitors (ICM) have enhanced the recognition ability of atrial fibrillation (AF), which makes ICM have a new application in AF detection. We conducted a meta-analysis to determine the total incidence of newly found AF detected by ICM after cryptogenic stroke and to evaluate the factors related to the detection of AF. A literature search was conducted in the PubMed, EMBASE, Web of Science, and Cochrane library databases until March 1, 2020. Studies that reported the detection rate of AF using ICM in cryptogenic stroke patients with negative initial AF screening were analyzed. A total of 23 studies were included. The overall proportion of AF detected by ICM in cryptogenic stroke patients was 25% (95% confidence interval [CI], 22–29%). The rate of AF detected by ICM was independently related to both cardiac monitoring time (coefficient = 0.0003; 95% CI, 0.0001–0.0005; P = 0.0001) and CHA2DS2-VASc score (coefficient = 0.0834; 95% CI, 0.0339–0.1329; P = 0.001). In subgroup analysis, we found a significant difference in the detection rate of AF for monitoring duration ( 12 and ≤ 24 months: 23.6% [95% CI, 19.9–27.5%]; > 24 months and ≤ 36 months: 36.5% [95% CI, 24.2–49.9%]; P < 0.001), and continent (Europe: 26.5% [95% CI, 22.2–31.0%]; North America: 16.0% [95% CI, 10.3–22.6%]; Asia: 17.4% [95% CI, 12.4–23.0%]; P = 0.005). The longer the time of ICM monitoring after cryptogenic stroke, the higher the detection rate of AF. Further research is still needed to determine the optimal duration of long-term cardiac monitoring.

The development of small, energy-efficient artificial intelligence edge devices is limited in conventional computing architectures by the need to transfer data between the processor and memory. Non-volatile compute-in-memory (nvCIM) architectures have the potential to overcome such issues, but the development of high-bit-precision configurations required for dot-product operations remains challenging. In particular, input–output parallelism and cell-area limitations, as well as signal margin degradation, computing latency in multibit analogue readout operations and manufacturing challenges, still need to be addressed. Here we report a 2 Mb nvCIM macro (which combines memory cells and related peripheral circuitry) that is based on single-level cell resistive random-access memory devices and is fabricated in a 22 nm complementary metal–oxide–semiconductor foundry process. Compared with previous nvCIM schemes, our macro can perform multibit dot-product operations with increased input–output parallelism, reduced cell-array area, improved accuracy, and reduced computing latency and energy consumption. The macro can, in particular, achieve latencies between 9.2 and 18.3 ns, and energy efficiencies between 146.21 and 36.61 tera-operations per second per watt, for binary and multibit input–weight–output configurations, respectively. Commercial complementary metal–oxide–semiconductor and resistive random-access memory technologies can be used to create multibit compute-in-memory circuits capable of fast and energy-efficient inference for use in small artificial intelligence edge devices.

The present study aimed to investigate bariatric surgery’s effects on pregnancy in obese women. The source of the data was the Taiwan National Health Insurance Research Database. We extracted female patients aged 18 to 45 years who had been diagnosed with obesity from 2005 to 2010. Two groups were formed based on bariatric surgery, according to the medical procedure codes of ICD-9-2001. The propensity score match of 1:1 was coordinated, based on the age, previous delivery, and the existence of comorbidities in patients. The primary endpoint was regarded as a pregnancy, including abortion, ectopic pregnancy, and delivery. The follow-up was performed until the end of 2013, or death. There were 689 patients placed in each group, with a median of 64.8 follow-up months. Following multivariate analysis of the data, bariatric surgery resulted in significantly higher chances for pregnancy (aHR 2.886, p

Previous SRAM-based computing-in-memory (SRAM-CIM) macros suffer small read margins for high-precision operations, large cell array area overhead, and limited compatibility with many input and weight configurations. This work presents a 1-to-8-bit configurable SRAM CIM unit-macro using: 1) a hybrid structure combining 6T-SRAM based in-memory binary product-sum (PS) operations with digital near-memory-computing multibit PS accumulation to increase read accuracy and reduce area overhead; 2) column-based place-value-grouped weight mapping and a serial-bit input (SBIN) mapping scheme to facilitate reconfiguration and increase array efficiency under various input and weight configurations; 3) a self-reference multilevel reader (SRMLR) to reduce read-out energy and achieve a sensing margin 2 $\times $ that of the mid-point reference scheme; and 4) an input-aware bitline voltage compensation scheme to ensure successful read operations across various input-weight patterns. A 4-Kb configurable 6T-SRAM CIM unit-macro was fabricated using a 55-nm CMOS process with foundry 6T-SRAM cells. The resulting macro achieved access times of 3.5 ns per cycle (pipeline) and energy efficiency of 0.6–40.2 TOPS/W under binary to 8-b input/8-b weight precision.

Fano resonance is realized theoretically in a photonic crystal. The structure is composed of a cavity side coupled to a partially transmitting waveguide. By optimizing the structure parameters, asymmetric sharp Fano resonance transmit-tance spectrum is achieved with quality factor of 2 213, extinction ratio of 57 dB and peak loss of 0.2 dB. The sharp spectrum can be used in sensor applications. Such as pressure sensor, the pressure sensitivity is about 9.15 nm/GPa. and for refractive index sensing application, the sensitivity is about 800 nm/RIU, and the maximum of figure of merit can reach 1 000. Besides, this sharp Fano resonance based on photonic crystal has potential applications in optical switches, filters etc. And it can be integrated into optical communications and optical integration circuits.

Nonvolatile computing-in-memory (nvCIM) exhibits high potential for neuromorphic computing involving massive parallel computations and for achieving high energy efficiency. nvCIM is especially suitable for deep neural networks, which are required to perform large amounts of matrix-vector multiplications. However, a comprehensive quantization algorithm has yet to be developed, which overcomes the hardware limitations of resistive random access memory (ReRAM)-based nvCIM, such as the number of I/O, word lines (WLs), and ADC outputs. In this article, we propose a quantization training method for compressing deep models. The method comprises three steps: input and weight quantization, ReRAM convolution (ReConv), and ADC quantization. ADC quantization optimizes the error sampling problem by using the Gumbel-softmax trick. Under a 4-bit ADC of nvCIM, the accuracy only decreases by 0.05% and 1.31% for the MNIST and CIFAR-10, respectively, compared with the corresponding accuracies obtained under an ideal ADC. The experimental results indicate that the proposed method is effective for compensating the hardware limitations of nvCIM macros.

Hepatocellular carcinoma (HCC) remains a huge threat to human health even though the diagnosis and treatment strategies have improved rapidly in the past few decades. Increasing evidence has illustrated the critical role noncoding RNA and their regulatory network play in the pathology of HCC. Here, we identified a novel long noncoding RNA, RP5-1120P11.3, that is ectopically expressed in HCC. Further characterization of RP5-1120P11.3 revealed that it promoted proliferation and invasion of HCC cells while inhibiting apoptosis. Importantly, our data revealed that miR-196b-5p interacted with and was regulated by RP5-1120P11.3 via a sponging mechanism. Inhibition of miR-196b-5p attenuated the phenotypes resulting from RP5-1120P11.3 inhibition. Moreover, our data showed that miR-196b-5p inhibited the expression of WIPF2 in HCC, illustrating a regulatory axis of RP5-1120P11.3–miR-196b-5p–WIPF2 that facilitated the progression of HCC. In addition, our data showed that RP5-1120P11.3 contributed to xenograft generation in vivo by regulating miR-196b-5p and WIPF2. These findings suggested that the RP5-1120P11.3–miR-196b-5p–WIPF2 axis is a potential target for treatment of HCC.

Objective To investigate effect of double button plate fixation in the treatment of inferior patella fracture Methods Patients with inferior pole of patella fracture were treated with double button plate fixation. The perioperative and postoperative outcomes of MPIF with those of KWIF patients with metacarpal and phalangeal fractures were included. Results The average operation and hospital stay time was 55 min and 8 days respectively. Range of motion (ROM) and knee society scores(KSS) was related to a greater increase. No patients have nonunion, infection and revision. Conclusion In this study, double button plate fixation technology for the treatment of comminuted fracture of the inferior patellar pole has the advantages of simple operation, rapid recovery, early full weight bearing, better ROM and KSS, without incidence of nonunion, infection and revisions or reoperation.

Computing-in-memory (CIM) based on embedded nonvolatile memory is a promising candidate for energy-efficient multiply-and-accumulate (MAC) operations in artificial intelligence (AI) edge devices. However, circuit design for NVM-based CIM (nvCIM) imposes a number of challenges, including an area-latency-energy tradeoff for multibit MAC operations, pattern-dependent degradation in signal margin, and small read margin. To overcome these challenges, this article proposes the following: 1) a serial-input non-weighted product (SINWP) structure; 2) a down-scaling weighted current translator (DSWCT) and positive–negative current-subtractor (PN-ISUB); 3) a current-aware bitline clamper (CABLC) scheme; and 4) a triple-margin small-offset current-mode sense amplifier (TMCSA). A 55-nm 1-Mb ReRAM-CIM macro was fabricated to demonstrate the MAC operation of 2-b-input, 3-b-weight with 4-b-out. This nvCIM macro achieved $T_{\text {MAC}}= 14.6$ ns at 4-b-out with peak energy efficiency of 53.17 TOPS/W.

Computation-in-memory (CIM) is a promising candidate to improve the energy efficiency of multiply-and-accumulate (MAC) operations of artificial intelligence (AI) chips. This work presents an static random access memory (SRAM) CIM unit-macro using: 1) compact-rule compatible twin-8T (T8T) cells for weighted CIM MAC operations to reduce area overhead and vulnerability to process variation; 2) an even–odd dual-channel (EODC) input mapping scheme to extend input bandwidth; 3) a two’s complement weight mapping (C2WM) scheme to enable MAC operations using positive and negative weights within a cell array in order to reduce area overhead and computational latency; and 4) a configurable global–local reference voltage generation (CGLRVG) scheme for kernels of various sizes and bit precision. A 64 $\times $ 60 b T8T unit-macro with 1-, 2-, 4-b inputs, 1-, 2-, 5-b weights, and up to 7-b MAC-value (MACV) outputs was fabricated as a test chip using a foundry 55-nm process. The proposed SRAM-CIM unit-macro achieved access times of 5 ns and energy efficiency of 37.5–45.36 TOPS/W under 5-b MACV output.

Computing-in-memory (CIM) is a promising approach to reduce the latency and improve the energy efficiency of deep neural network (DNN) artificial intelligence (AI) edge processors. However, SRAM-based CIM (SRAM-CIM) faces practical challenges in terms of area overhead, performance, energy efficiency, and yield against variations in data patterns and transistor performance. This paper employed a circuit-system co-design methodology to develop a SRAM-CIM unit-macro for a binary-based fully connected neural network (FCNN) layer of the DNN AI edge processors. The proposed SRAM-CIM unit-macro supports two binarized neural network models: an XNOR neural network (XNORNN) and a modified binary neural network (MBNN). To achieve compact area, fast access time, robust operations, and high energy-efficiency, our proposed SRAM-CIM uses a split-wordline compact-rule 6T SRAM and circuit techniques, including a dynamic input-aware reference generation (DIARG) scheme, an algorithm-dependent asymmetric control (ADAC) scheme, a write disturb-free (WDF) scheme, and a common-mode-insensitive small offset voltage-mode sensing amplifier (CMI-VSA). A fabricated 65-nm 4-Kb SRAM-CIM unit-macro achieved 2.4- and 2.3-ns product-sum access times for a FCNN layer using XNORNN and MBNN, respectively. The measured maximum energy efficiency reached 30.49 TOPS/W for XNORNN and 55.8 TOPS/W for the MBNN modes.

BackgroundNon-small cell lung cancer (NSCLC) is the most prevalent type of lung carcinoma with an unfavorable prognosis. Ferroptosis is involved in the development of multiple cancers. Whereas, the prognostic value of ferroptosis-related lncRNAs in NSCLC remains uncertain.MethodsGene expression profiles and clinical information of NSCLC were retrieved from the TCGA database. Ferroptosis-related genes (FRGs) were explored in the FerrDb database and previous studies, ferroptosis-related lncRNAs (FRGs-lncRNAs) were identified by the correlation analysis and the LncTarD database. The differentially expressed FRGs-lncRNAs were screened and FRGs-lncRNAs associated with the prognosis were explored by univariate Cox regression analysis and Kaplan–Meier survival analysis. Then, an FRGs-lncRNAs signature was constructed and verified by the Lasso-penalized Cox analysis. Finally, the potential correlation between risk score, immune checkpoint genes, and chemotherapeutic sensitivity was further investigated.Results129 lncRNAs with a potential regulatory relationship with 59 differentially expressed FRGs were found in NSCLC, of which 10 were related to the prognosis of NSCLC (P P ConclusionA novel FRGs-lncRNAs signature was successfully constructed, which may contribute to improving the management strategies of NSCLC.

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease with unknown etiology and unfavorable prognosis. Ferroptosis is a form of regulated cell death with an iron-dependent way that is involved in the development of various diseases. Whereas the prognostic value of ferroptosis-related genes (FRGs) in IPF remains uncertain and needs to be further elucidated.Methods: The FerrDb database and the previous studies were screened to explore the FRGs. The data of patients with IPF were obtained from the GSE70866 dataset. Wilcoxon's test and univariate Cox regression analysis were applied to identify the FRGs that are differentially expressed between normal and patients with IPF and associated with prognosis. Next, a multigene signature was constructed by the least absolute shrinkage and selection operator (LASSO)-penalized Cox model in the training cohort and evaluated by using calibration and receiver operating characteristic (ROC) curves. Then, 30% of the dataset samples were randomly selected for internal validation. Finally, the potential function and pathways that might be affected by the risk score-related differently expressed genes (DEGs) were further explored.Results: A total of 183 FRGs were identified by the FerrDb database and the previous studies, and 19 of them were differentially expressed in bronchoalveolar lavage fluid (BALF) between IPF and healthy controls and associated with prognosis (p < 0.05). There were five FRGs (aconitase 1 [ACO1], neuroblastoma RAS viral (v-ras) oncogene homolog [NRAS], Ectonucleotide pyrophosphatase/phosphodiesterase 2 [ENPP2], Mucin 1 [MUC1], and ZFP36 ring finger protein [ZFP36]) identified as risk signatures and stratified patients with IPF into the two risk groups. The overall survival rate in patients with high risk was significantly lower than that in patients with low risk (p < 0.001). The calibration and ROC curve analysis confirmed the predictive capacity of this signature, and the results were further verified in the validation group. Risk score-related DEGs were found enriched in ECM-receptor interaction and focal adhesion pathways.Conclusion: The five FRGs in BALF can be used for prognostic prediction in IPF, which may contribute to improving the management strategies of IPF.

BackgroundPatients suffering from chronic kidney disease are at greater risk of perioperative and postoperative complications. There is no systematic review study demonstrating whether total joint arthroplasty can be safely performed in patients with chronic kidney disease.MethodsA literature search was performed in the PubMed, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang, and Cochrane Library databases for information from the earliest date of data collection to September of 2018. Studies comparing the perioperative and postoperative outcomes of no–chronic kidney dysfunction (CKD) patients with those of CKD patients were included. Statistical heterogeneity was quantitatively evaluated by means of the χ2test, with significance set atP< .10 orI2> 50%.ResultsThree articles consisting of 38,209 patients were included (35,363 no-CKD patients and 2,846 CKD patients). The results showed that CKD was related to a greater increase in postoperative infection rate, deep vein thrombosis, readmission, and mortality (P< .1). No differences in length of surgery, length of stay, pulmonary embolism, or revision were observed (P> .10).ConclusionsCompared with no-CKD patients, CKD patients demonstrated an increased risk of perioperative and postoperative complications.

To address the strategy of interface effects to achieve low-voltage operation in ferroelectric devices, this work presents a systematical study on Hf0.5Zr0.5O2 ferroelectric(FE) capacitors. Firstly, by detail electrical characterization of P-V curves under varied electrodes and ambient gas, two factors governed ferroelectric switching can be well distinguished. Then by combining the kinetical pulse measurements and first-principle calculation, it is found that, 1) the intrinsic dead-layer effect exits in nanocapacitors; 2) the ferroelectric domain switching speed and coherency can be boosted via lower thermal budget O2 treatment. Finally, a material with coherency and ultra-low access voltage as design guideline for sub-3nm technology eNVM is provided.

With the aging population, stroke has gradually become the leading cause of death and disability among adults. It is necessary to verify whether multi-delay pseudo-continuous arterial spin labeling (pCASL) MRI can be used as a standard neuroimaging protocol in the patients with ischemic stroke. We aimed to investigate the clinical utility of multi-delay pCASL for evaluating cerebral perfusion in ischemic stroke disease. Twenty-one ischemic stroke patients [18 men and 3 women; median age, 62 years (age range, 37–84 years)] were enrolled in this study. All patients underwent examinations, including the multi-delay pCASL protocol (using 6 PLDs between 1,000 and 3,500 ms) and computed tomography perfusion (CTP). The cerebral blood flow (CBF) and arterial transit time (ATT) maps were obtained by the multi-delay pCASL protocol, while CBF and mean transit time (MTT) maps were derived by CTP measurements. Based on the voxel level analysis, Pearson correlation coefficients were used to estimate the associations between the two modalities in the gray matter, white matter, and whole brain of each subject. Moderate to high positive associations between ASL-CBF and CTP-CBF were acquired by voxel-level-wise analysis in the gray matter, white matter, and whole brain of the enrolled patients (all P < 0.005), and the average Pearson correlation coefficients were 0.647, 0.585, and 0.646, respectively. Highly significant positive correlations between ASL-ATT and CTP-MTT were obtained by voxel-level-wise associations in the gray matter, white matter, and whole brain (all P < 0.005), and the average Pearson correlation coefficients were 0.787, 0.707, and 0.799, respectively. In addition, significant associations between ASL and CT perfusion were obtained in the gray, white matter and whole brain, according to the subgroup analyses of patient’s age and disease stage. There is a correlation between perfusion parameters from multi-delay pCASL and CT perfusion imaging in patients with ischemic stroke. Multi-delay pCASL is radiation-free and non-invasive, and could be an alternative method to CT scans for assessing perfusion in ischemic stroke disease.

Background: Gadolinium enhancement on high resolution magnetic resonance imaging (HR-MRI) has been considered a sign of instability and inflammation of intracranial atherosclerotic plaques. Our research objective was to explore the relationship between the extent of plaque enhancement (PE), the degree of intracranial artery stenosis, and acute ischemic stroke events.Methods: HR-MRI was performed in 91 patients with intracranial vascular stenosis to determine the existence and intensity of PE.Results: Among 91 patients enrolled in the trial, there were 43 patients in the acute/subacute group (≤1 month from ischemic stroke event), 15 patients in the chronic group (>1 month from ischemic stroke event), and 33 patients in the non-culprit plaques group (no ischemic stroke event). A total of 105 intracranial atherosclerotic plaques were detected in 91 patients. 14 (13.3%) were mild-stenosis plaques, 22 (21.0%) were moderate-stenosis plaques, and 69 (65.7%) were severe-stenosis plaques. There were 12 (11.4%), 18 (17.1%), and 75 (71.4%) plaques in the non-enhanced plaque group, the mild-enhancement group, and the significant-enhancement group, respectively. The degree of PE among the acute/subacute group, the chronic group, and the non-culprit plaque group had a significant difference (P = 0.005). Enhanced plaques were more often observed in culprit plaques (acute/subacute group and chronic group) than non-culprit plaques (96.7% vs 77.3%). Non-enhanced plaques were more often observed in non-culprit plaques than culprit plaques (acute/subacute group and chronic group) (22.7% vs 3.3%). And 36.6% of the enhanced plaques were non-culprit plaques. After performing univariate and multivariate logistic regression analysis, the results showed that strong plaque enhancement (P = 0.025, odds ratio [OR] 3.700, 95% confidence interval [95% CI] 1.182-11.583) and severe stenosis (P = 0.008, OR 4.393, 95%CI 1.481-13.030) were significantly associated with acute ischemic events.Conclusion: Enhanced plaques were more often observed in culprit plaques, and non-enhanced plaques were more often observed in non-culprit plaques. Moreover, significant plaque enhancement and severe ICAS were closely associated with acute ischemic events.

Background: Non-small cell lung cancer (NSCLC) is the most prevalent type of lung carcinoma with an unfavorable prognosis. Ferroptosis, a novel iron-dependent programmed cell death, is involved in the development of multiple cancers. Of note, the prognostic value of ferroptosis-related lncRNAs in NSCLC remains uncertain. Methods: Gene expression profiles and clinical information of NSCLC were retrieved from the TCGA database. Ferroptosis-related genes (FRGs) were explored in the FerrDb database and ferroptosis-related lncRNAs (FRGs-lncRNAs) were identified by the correlation analysis and the LncTarD database. Next, The differentially expressed FRGs-lncRNAs were screened and FRGs-lncRNAs associated with the prognosis were explored by univariate Cox regression analysis and Kaplan-Meier survival analysis. Then, an FRGs-lncRNAs signature was constructed by the Lasso-penalized Cox model in the training cohort and verified by internal and external validation. Finally, the potential correlation between risk score, immune response, and chemotherapeutic sensitivity was further investigated.Results: 129 lncRNAs with a potential regulatory relationship with 59 differentially expressed FRGs were found in NSCLC and 10 FRGs-lncRNAs associated with the prognosis of NSCLC were identified (PPConclusion: A novel FRGs-lncRNAs signature was successfully constructed, which may contribute to improving the management strategies of NSCLC.

CHIMERA is the first non-volatile deep neural network (DNN) chip for edge AI training and inference using foundry on-chip resistive RAM (RRAM) macros and no off-chip memory. CHIMERA achieves 0.92 TOPS peak performance and 2.2 TOPS/W. We scale inference to 6x larger DNNs by connecting 6 CHIMERAs with just 4% execution time and 5% energy costs, enabled by communication-sparse DNN mappings that exploit RRAM non-volatility through quick chip wakeup/shutdown (33 µs). We demonstrate the first incremental edge AI training which overcomes RRAM write energy, speed, and endurance challenges. Our training achieves the same accuracy as traditional algorithms with up to 283x fewer RRAM weight update steps and 340x better energy-delay product. We thus demonstrate 10 years of 20 samples/minute incremental edge AI training on CHIMERA.

Previous studies revealed that fine particulate matter (PM 2.5 ) exposure adversely affected blood pressure (BP), but factors that might attenuate this association were still unclear. Using a multicenter panel study among 277 hypertensive participants with intermediate-to-high risk of cardiovascular disease from 4 cities in China, we aimed to explore whether BP control status and antihypertensive medications were potential modifying factors. Each participant carried personal-portable monitors to record individual real-time PM 2.5 levels and 24-hour ambulatory BP up to 3× within 1 year. Generalized linear mixed model with individual-specific random intercept was used to assess effect sizes. We identified adverse impacts of short-term PM 2.5 exposure on BP. However, these impacts were attenuated among patients with controlled BP. For example, per interquartile range (43.78 µg/m 3 ) increment in the prior 10-hour moving average of PM 2.5 , systolic BP increased −0.20 (95% CI, −0.57 to 0.18) mm Hg and 0.85 (95% CI, 0.36–1.35) mm Hg among those with controlled and uncontrolled BP, respectively ( P interaction , 0.0009). Furthermore, among those with uncontrolled BP, treatment with angiotensin receptor blocker would potentially lower BP in responses to PM 2.5 , with systolic BP associated with per interquartile range increment in 10-hour moving average PM 2.5 of 0.32 (95% CI, −0.37 to 1.00) mm Hg and 1.53 (95% CI, 0.74–2.33) mm Hg among those taking angiotensin receptor blocker or not, respectively ( P interaction , 0.0229). In conclusion, although PM 2.5 exposure would increase BP, keeping well-controlled BP status and using angiotensin receptor blockers might attenuate these adverse impacts, which might provide supporting evidence for guiding hypertensive patients who live in areas with high level of PM 2.5 .

Computation-in-memory (CIM) is a feasible method to overcome "Von-Neumann bottleneck" with high throughput and energy efficiency. In this paper, we proposed a 1Mb Multi-Level (MLC) NOR Flash based CIM (MLFlash- CIM) structure with 40nm technology node. A multi-bit readout circuit was proposed to realize adaptive quantization, which comprises a current interface circuit, a multi-level analog shift amplifier (AS-Amp) and an 8-bit SAR-ADC. When applied to a modified VGG-16 Network with 16 layers, the proposed MLFlash-CIM can achieve 92.73% inference accuracy under CIFAR-10 dataset. This CIM structure also achieved a peak throughput of 3.277 TOPS and an energy efficiency of 35.6 TOPS/W with 4-bit multiplication and accumulation (MAC) operations.

This Special Issue of the IEEE Journal of Solid-State Circuits is dedicated to a collection of the best articles selected from the 2019 IEEE International Solid-State Circuits Conference (ISSCC) that took place on February 17–21, 2019, in San Francisco, CA, USA. This Special Issue covers articles from the Wireline, Digital Circuits, Digital Architectures and Systems (DASs), and Memory Committees.

Non-volatile computing-in-memory (nvCIM) could improve the energy efficiency of edge devices for artificial intelligence applications. The basic functionality of nvCIM has recently been demonstrated using small-capacity memristor crossbar arrays combined with peripheral readout circuits made from discrete components. However, the advantages of the approach in terms of energy efficiency and operating speeds, as well as its robustness against device variability and sneak currents, have yet to be demonstrated experimentally. Here, we report a fully integrated memristive nvCIM structure that offers high energy efficiency and low latency for Boolean logic and multiply-and-accumulation (MAC) operations. We fabricate a 1 Mb resistive random-access memory (ReRAM) nvCIM macro that integrates a one-transistor–one-resistor ReRAM array with control and readout circuits on the same chip using an established 65 nm foundry complementary metal–oxide–semiconductor (CMOS) process. The approach offers an access time of 4.9 ns for three-input Boolean logic operations, a MAC computing time of 14.8 ns and an energy efficiency of 16.95 tera operations per second per watt. Applied to a deep neural network using a split binary-input ternary-weighted model, the system can achieve an inference accuracy of 98.8% on the MNIST dataset. A 1 Mb non-volatile computing-in-memory system, which integrates a resistive memory array with control and readout circuits using an established 65 nm foundry CMOS process, can offer high energy efficiency and low latency for Boolean logic and multiply-and-accumulation operations.

We present a novel 3D-SRAM cells using a monolithic 3D integration technology for realizing both robustness of the cell and in-memory Boolean logic computing capability. The proposed two-layer cell designs make use of additional transistors over the SRAM layer to enable assist techniques as well as provide logic functions (such as AND/NAND, OR/NOR, and XNOR/XOR) or enable content addressability without degrading cell density. Through analysis, we provide insights into the benefits provided by three memory assist and two logic modes, and we evaluate the energy efficiency of our proposed design. We show that the assist techniques improve SRAM read stability by $2.2\times $ and increase the write margin by 17.6% while staying within the SRAM footprint. By the virtue of increased robustness, the cell enables seamless operation at lower supply voltages; and thereby, ensures energy efficiency. Energy delay product reduces by $1.6\times $ over standard 6T SRAM with a faster data access. When computing bulk In-memory operations, $6.5\times $ energy saving is achieved as compared to computing outside the memory system.

Lost circulation during well drilling and completion wastes productive time, and even kills the well in severe cases. Timely identifying lost circulation events and taking countermeasures has been the focus of related study. However, a real prediction of lost circulation risk before drilling would be an active response to the challenge. In this paper, a technical solution is proposed to evaluate geological lost-circulation risk in the field using 3D seismic data attributes and machine learning technique. First, four seismic attributes (variance, attenuation, sweetness, RMS amplitude) that are the most correlated with lost circulation incidents are recommended. Then a prediction model is built by conducting supervised learning that involves a majority voting algorithm. The performance of the model is illustrated by six unseen drilled wells and shows the ability and potential to forecast lost circulation probability both along well trajectory and in the region far away from the drilled wells. The prediction resolution in the lateral and vertical direction is about 25 m and 6 m (2 ms), respectively, which are distinct advantages over the traditional description of geological structures using seismic data. It shows that the lost circulation risk can be hardly recognized by interpreting one specific seismic attribute, which is a common practice. Finally, the challenges in predicting lost circulation risk using seismic data are summarized. Overall, the study suggests that machine learning would be a practical solution to predict various construction risks that are related to seismic-based geological issues. Knowing in advance the risks, people could avoid or at least minimize the losses by optimizing well deployment in the field and taking preventive measures.

Nonvolatile logic implemented by memristor devices is a potential candidate for inherent logic-in-memory architecture. Majority-inverter graph (MIG) logic is a novel logic-structure compared with conventional AND/OR/INV graphs logic. In this brief, MIG logic constructed by memristors is proposed to implement the stateful logic arithmetic. A design of full adders based on MIG logic is proposed, followed by a 4-bit Wallace tree multiplier based on the MIG logic to implement in-situ store. A test chip controlled by a FPGA is designed to verify its feasibility. Compared with multipliers implemented by conventional logic, this method has fewer steps and smaller area, making it suitable for frequent-off and instant-on circuits in IoT applications. The experimental results have proved its significance on the grounds that the 4-bit multiplier only needs 18 processing steps with 51 memristor cells to complete a multiplication arithmetic whereas, by contrast, one conventional 4-bit binary multiplier requires 648 MOSFETs and 124 steps while IMP based 4-bit multiplier logic requires 112 CRS units and 221 steps to operate a multiplying computation.

A 69-year-old woman was diagnosed as having non-small cell lung cancer (adenocarcinoma, T1aN3M1b). She had no history of surgery or abdominal trauma. She was treated with ramucirumab (10 mg/kg) plus docetaxel (60 mg/m(2)) intravenously (RAM + DTX) every 3 weeks. Although an enhanced CT examination showed a partial tumor response after eight courses of RAM + DTX, she gradually began to experience abdominal fullness with severe peripheral pitching edema. Her body weight increased by 18 kg in 2 months and RAM + DTX chemotherapy was discontinued. An enhanced CT examination showed a large amount of ascites and pleural effusion, with no obstructions of the central vein or lymphatic ducts. The ascites were white and milky in appearance and contained 527 mg/dL of triglyceride. In addition, her pleural effusion was also white and milky in appearance. No further increases in ascites and pleural effusion were observed thereafter. Four months after her last RAM + DTX chemotherapy, she continued to exhibit a partial response and no increases in ascites or pleural effusion were present. The chylous effusion might have been caused by the RAM + DTX chemotherapy.

Rhombohedral-like (R-like) BiFeO3 (BFO) thin films with a SrRuO3 (SRO) buffer layer have been deposited on a range of lattice mismatched substrates, including single crystalline (001)-oriented LaAlO3 (LAO), SrTiO3 (STO), MgO and TiN-buffered Si substrates. A wide range of strain states in BFO films have been achieved on these substrates. Ferroelectric measurements indicate that the film strain state affects the polarization of the BFO films. The bandgap of R-like BFO films follows the trend that highly compressive strain in BFO thin films leads to a larger bandgap. This study indicates that the physical properties including bandgap and ferroelectricity of BFO films, can be effectively tuned on large lattice mismatch substrates even with partial strain relaxation. Furthermore, the integration of ferroelectric BFO on SRO-buffered Si substrates suggests the possible application of strained BFO in Si-based electronic and optical devices.

Many cost-aware IoE devices require embedded nonvolatile memory (eNVM) to achieve high-speed read and low-power write operations for serving as code and data storage unit. Resistive random access memory (ReRAM) is a good candidate for eNVM of Internet-of-Everything (IoE) but suffers low read yield and require long read latency ( $T_{\text {CD}}$ ) against small $R$ -ratio, large cell-resistance variations, and device-mismatch induced input offset at current-mode sense amplifier (CSA). The wide distribution in write time also causes large wasted write power ( $E_{\text {W}}$ ) and long NVM-stress-time ( $T_{\text {STRS}}$ ). This paper proposes a bitline-current-aware small-offset CSA, using dynamic trip-point-mismatch sampling (DTPMS) scheme, to improve read yield and shorten $T_{\text {CD}}$ . This paper also proposes a low dc-current voltage-mode write termination (LDC-VWT) module, including SET and RESET termination circuits, to suppress $E_{\text {W}}$ and $T_{\text {STRS}}$ . A fabricated 65-nm 2-Mb ReRAM macro achieved $T_{\text {CD}}= 2.6$ ns and confirm the write-termination operations for reduction in $T_{\text {STRS}}$ and $E_{\text {W}}$ .

Publisher's version, Multiplex families with a high prevalence of a psychiatric disorder are often examined to identify rare genetic variants with large effect sizes. In the present study, we analysed whether the risk for bipolar disorder (BD) in BD multiplex families is influenced by common genetic variants. Furthermore, we investigated whether this risk is conferred mainly by BD-specific risk variants or by variants also associated with the susceptibility to schizophrenia or major depression. In total, 395 individuals from 33 Andalusian BD multiplex families (166 BD, 78 major depressive disorder, 151 unaffected) as well as 438 subjects from an independent, BD case/control cohort (161 unrelated BD, 277 unrelated controls) were analysed. Polygenic risk scores (PRS) for BD, schizophrenia (SCZ), and major depression were calculated and compared between the cohorts. Both the familial BD cases and unaffected family members had higher PRS for all three psychiatric disorders than the independent controls, with BD and SCZ being significant after correction for multiple testing, suggesting a high baseline risk for several psychiatric disorders in the families. Moreover, familial BD cases showed significantly higher BD PRS than unaffected family members and unrelated BD cases. A plausible hypothesis is that, in multiplex families with a general increase in risk for psychiatric disease, BD development is attributable to a high burden of common variants that confer a specific risk for BD. The present analyses demonstrated that common genetic risk variants for psychiatric disorders are likely to contribute to the high incidence of affective psychiatric disorders in the multiplex families. However, the PRS explained only part of the observed phenotypic variance, and rare variants might have also contributed to disease development., The study was supported by the German Federal Ministry of Education and Research (BMBF), through the Integrated Network IntegraMent, under the auspices of the e:Med programme (grants 01ZX1314A to MMN and SC; 01ZX1314G to MR; 01ZX1614J to BMM) through grants 01EE1406C to MR and 01EE1409C to MR and SHW, and through ERA-NET NEURON, “SynSchiz—Linking synaptic dysfunction to disease mechanisms in schizophrenia—a multilevel investigation” (01EW1810 to MR) and BMBF grants 01EE1409C and 01EE1406C to MR and SHW; by the German Research Foundation (DFG grants FOR2107; RI908/11-2 to MR; NO246/10-2 to MMN; MU1315/8-2 to BMM; WI 3439/3-2 to SHW), by the Andalusian regional Health and Innovation Government (grants PI-0060-2017, RC-0006-2015 the Nicolas Monarde Programme for YDO and CTS-546) and by the Swiss National Science Foundation (SNSF grant 156791 to SC). MMN is a member of the DFG-funded cluster of excellence ImmunoSensation. The PGC has received major funding from the US National Institute of Mental Health and the US National Institute of Drug Abuse (U01 MH109528 and U01 MH1095320). We thank the research participants and employees of 23andMe, Inc. for their contribution to the MDD meta-analysis published in [14]. We thank the International Genomics of Alzheimer's Project (IGAP) for providing summary results data for the present analyses. See the Supplementary Data for extended Acknowledgements.

High-performance memory production requires discovery of process anomalies that affect device performance. Optical metrology can monitor CDs of device structures, but has limited resolution for 3D NAND, especially towards the bottom of high aspect ratio (HAR) structures. Destructive, offline techniques (e.g., cross-sectional SEM, FIB, TEM) can be used for high resolution HAR profile measurement, but are time consuming, offering little statistical feedback for accurate process control. As an alternative, small angle X-ray scattering (CD-SAXS) can measure 3D HAR structures precisely and non-destructively. CD-SAXS has high sensitivity to 3D profiles, including profiles of CD, ellipticity and tilt through the entirety of the channel hole. CD-SAXS is non-destructive and used inline, providing fast time to results for efficient process characterization. In this paper, we will show data from a CD-SAXS system, including high-resolution CD, ellipticity and tilt profiles, and etch depth of 3D NAND channel holes with strong correlation to reference metrology. With this production-ready CD-SAXS system, flash memory CD profiles can be monitored inline for improved time to an optimized channel etch process.