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Liquid biopsy provides a convenient and safer procedure for the diagnosis and genomic profiling of tumors that are inaccessible to biopsy by analyzing exfoliated tumor cells (ETCs) or tumor-derived cell-free DNA (cfDNA). However, its primary challenge lies in its limited accuracy in comparison to tissue-based approaches. We report a parallel single-ETC genomic sequencing (Past-Seq) method for the accurate diagnosis and genomic profiling of hard-to-biopsy tumors such as cholangiocarcinoma (CCA) and upper tract urothelial carcinoma (UTUC). For CCA, a prospective cohort of patients with suspicious biliary strictures (n= 36) was studied. Parallel single-cell whole genome sequencing and whole exome sequencing were performed on bile ETCs for CCA diagnosis and resolving mutational profiles, respectively, along with bile cfDNA sequenced for comparison. Concordant single-cell copy number alteration (CNA) profiles in multiple ETCs provided compelling evidence for generating a malignant diagnosis. Past-Seq yielded bile-based accurate CCA diagnosis (96% sensitivity, 100% specificity, and positive predictive value), surpassing pathological evaluation (56% sensitivity) and bile cfDNA CNA analysis (13% sensitivity), and generated the best performance in the retrieval tissue mutations. To further explore the applicability of Past-Seq, 10 suspicious UTUC patients were investigated with urine specimens, and Past-Seq exhibited 90% sensitivity in diagnosing UTUC, demonstrating its broad applicability across various liquid biopsies and cancer types.

The expansion of photovoltaic power generation makes photovoltaic power forecasting an essential requirement. With the development of deep learning, more accurate predictions have become possible. This paper proposes an efficient end-to-end model for solar power generation that allows for long-sequence time series forecasting. Two modules comprise the forecasting model: the anomaly detection module and the forecasting module. Singular values are detected and corrected by the anomaly detection module. And in the forecasting module, the series is first divided into different intervals. After that, the divided data is passed through a decomposition module embedded in the neural network, and each interval is encoded as high-dimension vectors. These vectors are fed into the mixing layer, which is used to learn the relationship between intervals and channels after the encoding process. Every mixing layer comprises two different layers: the channel-mixing layer allows communication between different channels, and the interval-mixing layer allows communication between different intervals. This neural network is purely dependent on the multilayer perceptron, resulting in a network with low system latency and low training cost. The experiments results not only show the model proposed in this paper can make accurate long-sequence time series forecasts for photovoltaic power generation at three sites but also show that the model can defeat the state-of-the-art model in long-sequence photovoltaic power generation time series forecasting work.

Bisabolene is a bioactive sesquiterpene with a wide range of applications in food, cosmetics, medicine, and aviation fuels. Microbial production offers a green, efficient, and sustainable alternative. In this study, we focused on improving the titers of α-bisabolene in Yarrowia lipolyticaby applying two strategies, (i) optimizing the metabolic flux of α-bisabolene biosynthetic pathway and (ii) sequestering α-bisabolene in lipid droplet, thus alleviating its inherent toxicity to host cells. We showed that overexpression of DGA1and OLE1to increase lipid content and unsaturated fatty acid levels was essential for boosting the α-bisabolene synthesis when supplemented with auxiliary carbon sources. The final engineered strain Po1gαB10 produced 1954.3 mg/L α-bisabolene from the waste cooking oil under shake flask fermentation, which was 96-fold higher than the control strain Po1gαB0. At the time of writing, our study represents the highest reported α-bisabolene titer in the engineered Y. lipolyticacell factory. This work describes novel strategies to improve the bioproduction of α-bisabolene that potentially may be applicable for other high-value terpene products.

With the emergence of innovative applications in vertical industries such as smart home and industrial automation, machine communication has shown a spurt of development. Different from the traditional human-oriented cellular communication, machine communication is characterized by strong uncertainty and abruptness, large-scale concurrent device connection as well as uneven and unsaturated data traffic. This article investigates the dynamic multiple-devices multiple-channels access for unsaturated traffic with retransmission mechanism, which is aimed at reducing the long-term data packet loss resulting from buffer overflows and transmission failure. The instant channel selection will lead to a non-negligible impact on the future decision, motivating us to model this problem as a Markov decision process. Limited by the unknown environment knowledge, we proposed a dynamic access policy based on deep reinforcement learning algorithm to optimally select the channel for transmission or keep silent for Internet-of-Things devices. Simulation results confirm that our proposed channel access strategy can reduce the collision and the packet loss of network. Furthermore, it can also work well when coexisting with the devices that adopt time division multiple access (TDMA) or ALOHA protocol.

This review aims at providing an extensive discussion of modern constraints relevant for dense and hot strongly interacting matter. It includes theoretical first-principle results from lattice and perturbative QCD, as well as chiral effective field theory results. From the experimental side, it includes heavy-ion collision and low-energy nuclear physics results, as well as observations from neutron stars and their mergers. The validity of different constraints, concerning specific conditions and ranges of applicability, is also provided.

•In the direct coal liquefaction residue (DCLR) composite modified asphalt (C-DCLR-A) prepared in this study, the content of DCLR could reach 11.5% of the asphalt mass, which can significantly accelerate the recycling process of DCLR.•The high-temperature rheological properties and fatigue properties of C-DCLR-A were much higher than the base asphalt, and the low-temperature rheological properties of C-DCLR-A were close to the base asphalt.•The effect of DCLR on asphalt viscoelastic properties can be alleviated by the benzaldehyde and dioctyl phthalate.

As one of the prime applications of liquid biopsy, the detection of tumor-derived whole cells and molecular markers is enabled in a noninvasive means before symptoms or hints from imaging procedures used for cancer screening. However, liquid biopsy is not a diagnostic test of malignant diseases per sebecause it fails to establish a definitive cancer diagnosis. Although single-cell genomics provides a genome-wide genetic alternation landscape, it is technologically challenging to confirm cell malignancy of a suspicious cell in body fluids due to unknown technical noise of single-cell sequencing and genomic variation among cancer cells, especially when tumor tissues are unavailable for sequencing as the reference. To address this challenge, we report a molecular algorithm, named scCancerDx, for confirming cell malignancy based on single-cell copy number alternation profiles of suspicious cells from body fluids, leading to a definitive cancer diagnosis. The scCancerDx algorithm has been trained with normal cells and cancer cell lines and validated with single tumor cells disassociated from clinical samples. The established scCancerDx algorithm then validates hexokinase 2 (HK2) as an efficient metabolic function-associated marker of identifying disseminated tumor cells in different body fluids across many cancer types. The HK2-based test, together with scCancerDx, has been investigated for the early detection of bladder cancer (BC) at a preclinical phase by detecting high glycolytic HK2hightumor cells in urine. Early BC detection improves patient prognosis and avoids radical resection for enhancing life quality.

To linearize the multi-band PAs/ transmitters, a serial of multi-band predistortion models based on multi-dimensional architecture have been proposed. However, most of these models work properly only for the signals whose harmonic and intermodulation products of carriers' non-overlap with the interested fundamental bands. In this paper, the non-overlapping conditions for dual-band and tri-band signals are derived and denoted in the form of closed-form expression. It can be used to verify whether a given dual-band/ multi-band signals can be linearized properly by these multi-dimensional behavioral models. Also the conditions can be used to plan the frequency spacing and maximum bandwidth of a multi-band or non-continuous carrier aggregation signal. Several dual-band and triband signals were tested on the same PA, by employing 2-D DPD and 3-D DPD behavioral models. The measurement results show that the signals which don’t satisfy the non-overlapping conditions cannot be linearized well by the multi-dimensional behavioral models which does not take the harmonic and intermodulation products of carriers' into account.

In daily life, people make decisions not only for themselves but also on behalf of others. There may be differences in terms of the endowment effect when making decisions in these two situations. In Study 1, this question was investigated with an existing dataset exploring the traditional endowment effect, in which 86 students (Mage = 20.8 years, SD= 5.0) at Harvard University were asked to make a decision on selling or buying a coffee mug for themselves or for others as brokers. When making decisions for the self, the average price demanded by sellers was much higher than that offered by buyers; while making decisions for others, the price discrepancy disappeared. In Study 2, a similar study was conducted with a sample of 42 Chinese university students (Mage = 22.3 years, SD= 2.5), and a similar pattern of results was obtained. Further analysis indicated that when making decisions for others, only buyers increased their valuations, therefore mitigating the seller–buyer price discrepancy. Finally, the findings were interpreted from the perspective of Construal Level Theory.

Wearable elastic electronic devices have attracted tremendous attention due to their monitoring capabilities for human motion detection. In this work, a hydrogen bond acceptor quaternary ammonium salt, choline chloride (ChCl), has been used to fabricate deep eutectic polymer (DEP) blends with polyvinyl alcohol (PVA). The miscibility, molecular interaction, and physical properties of PVA/ChCl DEP blends were investigated systematically. It is demonstrated that the deep eutectic of PVA/ChCl can be obtained by simple solution blending, and the melting points of both PVA and ChCl are reduced respectively due to the strong hydrogen bond between PVA and ChCl. Due to the elasticity of the PVA/ChCl elastomer and the response of ChCl ions to temperature and humidity, the fabricated sensor showed stable and repeatable resistance changes upon strain, temperature, and humidity variations. We hypothesize that the DEP blend system has potential applications in functional composites and the final PVA/ChCl elastomer composites exhibited high transparent, antifreeze, and recyclable capability, which may be promising for applications in soft/flexible devices.