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In weak-lensing cosmological studies, peak statistics is sensitive to nonlinear structures and thus complementary to cosmic shear two-point correlations. In this paper, we explore a new approach, namely, the peak steepness statistics, with the overall goal to understand the cosmological information embedded there in comparison with the commonly used peak height statistics. We perform the analyses with ray-tracing simulations considering different sets of cosmological parameters $\Omega_{\rm m}$ and $\sigma_8$. A theoretical model to calculate the abundance of high peaks based on steepness is also presented, which can well describe the main trend of the peak distribution from simulations. We employ $\Delta\chi^2$ and Fisher analyses to study the cosmological dependence of the two peak statistics using our limited sets of simulations as well as our theoretical model. Within our considerations without including potential systematic effects, the results show that the steepness statistics tends to have higher sensitivities to the cosmological parameters than the peak height statistics and this advantage is diluted with the increase of the shape noise. Using the theoretical model, we investigate the physical reasons accounting for the different cosmological information embedded in the two statistics. Our analyses indicate that the projection effect from large-scale structures plays an important role to enhance the gain from the steepness statistics. The redshift and cosmology dependence of dark matter halo density profiles also contributes to the differences between the two statistics., Comment: 20 pages, 15 figures. Accepted for publication in MNRAS

In this paper, we demonstrate the design of FiND, a novel neighbor discovery protocol that accelerates BLE neighbor discovery via Wi-Fi fingerprints without any hardware modifications. The design rationale of FiND is that the two modes of Wi-Fi and BLE show complementarity in both wireless interference and discovery pattern. When abstracting the neighbor discovery problem, this demonstration provides validation for the approach of reasoning-based presence detection in the real world. © 2023 IEEE.

Dang W, Xing B, Jia X, et al. Int J Nanomedicine. 2023;18:3047-3068. The authors have advised Figure 4 on page 3059 is incorrect. Due to an error at the time of figure assembly parts of Figure 4B and Figure 4C were inadvertently duplicated. The correct Figure 4 is shown below. Figure 4 (A) The wound healing of different formulations. (B) The pictures of migration and invasion. (C) Wound healing rate, migration rate and invasion rate. ***P < 0.001, *P < 0.05, results were expressed as mean ± SD, n = 3. The authors sincerely apologize for these errors and guarantee that this correction has no impact to the interpretation of the presented results and conclusions of this work.

Wenli Dang,1– 3 Bin Xing,1– 3 Xintao Jia,1– 3 Ying Zhang,1– 3 Bei Jia,1– 3 Changxiang Yu,1– 3 Jiachen He,1– 3 Ziwei Li,1– 3 Huihui Li,1– 3 Zhidong Liu1– 3 1State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 2Engineering Research Center of Modern Chinese Medicine Discovery and Preparation Technique, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 3Haihe Laboratory of Modern Chinese Medicine, Tianjin, People’s Republic of ChinaCorrespondence: Zhidong Liu, Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, 301617, Tuanbo New Town, Jinghai District, Tianjin, People’s Republic of China, Tel +86 22-59596170, Email lonerliuzd@163.comBackground: Subcellular organelle targeted nano-formulations for cancer treatment are receiving increasing attention owing to their benefits of precise drug delivery, maximized therapeutic index, and reduced off-target side effects. The nucleus and mitochondria, as the main subcellular organelles, are the significant organelles responsible for maintaining cell operation and metabolism. They can be involved in many essential physiological and pathological processes such as cell proliferation, organism metabolism, intracellular transportation, and play a critical role in regulating cell biology. Meanwhile, breast cancer metastasis is one of the leading causes of death in breast cancer patients. With the development of nanotechnology, nanomaterials have been widely used in tumor therapy.Methods: We designed a subcellular organelle targeted nanostructured lipid carriers (NLC) to deliver paclitaxel (PTX) and gambogic acid (GA) to tumor tissues.Results: Due to the surface of NLC being modified by subcellular organelle targeted peptide, the PTX and GA co-loaded NLC can accurately release PTX and GA in tumor cells. This property makes NLC abl

With the spread of the Internet of Things in the construction industry, non-invasive thermal adaptive behavior recognition provides a new method for the real-time assessment of the indoor thermal environment. Pioneering research on non-invasive measurements of thermal discomfort for thermal preference prediction based on oc-cupants' adaptive behavior recognition was undertaken. Questionnaire surveys on the thermal adaptive behavior of office building users in five climatic regions of China were undertaken. From 654 valid questionnaires, 14 thermal adaptive behaviors related to thermal discomfort were clustered. Then experiments were conducted to collect thermal discomfort adaptive behavioral video data from 8 views (front, rear, left, right, left-front, right -front, left-rear, and right-rear) at two heights (1.5 m and 2 m). A large-scale dataset for thermal adaptive behavior recognition with 27,504 video samples was developed. Two adaptive behavior recognition models were constructed based on the neural network model Two-Stream Inflated 3D ConvNet (I3D) and SlowFast. The results show that there is no significant difference in the types of indoor thermal adaptive behaviors among office users in different climate zones of China. The established I3D and SlowFast recognition models adopt an end-to-end approach for recognition, which overcomes dependence on human key point recognition and enables the models to utilize more environmental information. The average prediction accuracy of both models reaches 95% or above, even when complex background and human key points are partially obscured. Additionally, the recognition time of the two models is at the millisecond level, which can support real-time thermal adaptive behavior recognition.

Optical microscopy, owing to its noninvasiveness and subcellular resolution, enables in vivo visualization of neuronal structure and function in the physiological context. Optical-sectioning structured illumination microscopy (OS-SIM) is a widefield fluorescence imaging technique that uses structured illumination patterns to encode in-focus structures and optically sections 3D samples. However, its application to in vivo imaging has been limited. In this study, we optimized OS-SIM for in vivo neural imaging. We modified OS-SIM reconstruction algorithms to improve signal-to-noise ratio and correct motion-induced artifacts in live samples. Incorporating an adaptive optics (AO) module to OS-SIM, we found that correcting sample-induced optical aberrations was essential for achieving accurate structural and functional characterizations in vivo. With AO OS-SIM, we demonstrated fast, high-resolution in vivo imaging with optical sectioning for structural imaging of mouse cortical neurons and zebrafish larval motor neurons, and functional imaging of quantal synaptic transmission at Drosophila larval neuromuscular junctions.

Optical microscopy, owing to its noninvasiveness and subcellular resolution, enables in vivo visualization of neuronal structure and function in the physiological context. Optical-sectioning structured illumination microscopy (OS-SIM) is a widefield fluorescence imaging technique that uses structured illumination patterns to encode in-focus structures and optically sections 3D samples. However, its application to in vivo imaging has been limited. In this study, we optimized OS-SIM for in vivo neural imaging. We modified OS-SIM reconstruction algorithms to improve signal-to-noise ratio and correct motion-induced artifacts in live samples. Incorporating an adaptive optics (AO) module to OS-SIM, we found that correcting sample-induced optical aberrations was essential for achieving accurate structural and functional characterizations in vivo. With AO OS-SIM, we demonstrated fast, high-resolution in vivo imaging with optical sectioning for structural imaging of mouse cortical neurons and zebrafish larval motor neurons, and functional imaging of quantal synaptic transmission at Drosophila larval neuromuscular junctions.

RNA polymerase II (Pol II) elongation is a critical step in gene expression. Here we found that NDF, which was identified as a bilaterian nucleosome-destabilizing factor, is also a Pol II transcription factor that stimulates elongation with plain DNA templates in the absence of nucleosomes. NDF binds directly to Pol II and enhances elongation by a different mechanism than that used by transcription factor TFIIS. Moreover, yeast Pdp3, which is related to NDF, binds to Pol II and stimulates elongation. Thus, NDF is a Pol II binding transcription elongation factor that is localized over gene bodies and is conserved from yeast to humans.

RNA polymerase II (Pol II) elongation is a critical step in gene expression. Here we found that NDF, which was identified as a bilaterian nucleosome-destabilizing factor, is also a Pol II transcription factor that stimulates elongation with plain DNA templates in the absence of nucleosomes. NDF binds directly to Pol II and enhances elongation by a different mechanism than that used by transcription factor TFIIS. Moreover, yeast Pdp3, which is related to NDF, binds to Pol II and stimulates elongation. Thus, NDF is a Pol II binding transcription elongation factor that is localized over gene bodies and is conserved from yeast to humans.

Remote sensing of the Earth's surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by 1) the spatial resolution of public satellite imagery, 2) classification schemes that operate at the pixel level, and 3) the need for multiple spectral bands. We advance the state-of-the-art by 1) using commercial imagery with panchromatic and multispectral resolutions of 30 cm and 1.2 m, respectively, 2) developing multiple fully convolutional neural networks (FCN) that can learn the morphological features of water bodies in addition to their spectral properties, and 3) FCN that can classify water even from panchromatic imagery. This study focuses on rivers in the Arctic, using images from the Quickbird, WorldView, and GeoEye satellites. Because no training data are available at such high resolutions, we construct those manually. First, we use the RGB, and NIR bands of the 8-band multispectral sensors. Those trained models all achieve excellent precision and recall over 90% on validation data, aided by on-the-fly preprocessing of the training data specific to satellite imagery. In a novel approach, we then use results from the multispectral model to generate training data for FCN that only require panchromatic imagery, of which considerably more is available. Despite the smaller feature space, these models still achieve a precision and recall of over 85%. We provide our open-source codes and trained model parameters to the remote sensing community, which paves the way to a wide range of environmental hydrology applications at vastly superior accuracies and 2 orders of magnitude higher spatial resolution than previously possible., Comment: 21 pages, 10 figures, 3 tables

Precipitation extremes intensify in most regions in climate model projections. Changes in vertical velocities contribute to the changes in intensity of precipitation extremes but remain poorly understood. Here, we find that midtropospheric vertical velocities in extratropical precipitation extremes strengthen overall in simulations of twenty-first-century climate change. For each extreme event, we solve the quasigeostrophic omega equation to decompose this strengthening into different physical contributions. We first consider a dry decomposition in which latent heating is treated as an external forcing of upward motion. Much of the positive contribution to upward motion from increased latent heating is offset by negative contributions from increases in dry static stability and changes in the horizontal length scale of vertical velocities. However, taking changes in latent heating as given is a limitation when the aim is to understand changes in precipitation, since latent heating and precipitation are closely linked. Therefore, we also perform a moist decomposition of the changes in vertical velocities in which latent heating is represented through a moist static stability. In the moist decomposition, changes in moist static stability play a key role and contributions from other factors such as changes in the depth of the upward motion increase in importance. While both dry and moist decompositions are self-consistent, the moist dynamical perspective has greater potential to give insights into the causes of the dynamical contributions to changes in precipitation extremes in different regions.

Pre-training is prevalent in nowadays deep learning to improve the learned model's performance. However, in the literature on federated learning (FL), neural networks are mostly initialized with random weights. These attract our interest in conducting a systematic study to explore pre-training for FL. Across multiple visual recognition benchmarks, we found that pre-training can not only improve FL, but also close its accuracy gap to the counterpart centralized learning, especially in the challenging cases of non-IID clients' data. To make our findings applicable to situations where pre-trained models are not directly available, we explore pre-training with synthetic data or even with clients' data in a decentralized manner, and found that they can already improve FL notably. Interestingly, many of the techniques we explore are complementary to each other to further boost the performance, and we view this as a critical result toward scaling up deep FL for real-world applications. We conclude our paper with an attempt to understand the effect of pre-training on FL. We found that pre-training enables the learned global models under different clients' data conditions to converge to the same loss basin, and makes global aggregation in FL more stable. Nevertheless, pre-training seems to not alleviate local model drifting, a fundamental problem in FL under non-IID data., Comment: Accepted to ICLR 2023

While an E3 ubiquitin ligase, RNF8, was initially reported to be required for histone-to-protamine exchange in spermiogenesis, we subsequently demonstrated that RNF8 is not involved in this process. Nevertheless, reflecting a lingering misunderstanding in the field, a growing number of studies have continued to postulate a requirement for RNF8 in the histone-to-protamine exchange. For example, a recent study claimed that a mouse PIWI protein, MIWI, controls RNF8-mediated histone-to-protamine exchange. Here, confirming our earlier conclusions, we show that RNF8 is required neither for the establishment of histone H4K16 acetylation, which is an initial step in histone removal during spermiogenesis, nor for the incorporation of two protamine proteins, PRM1 and PRM2. Thus, whereas RNF8 mediates ubiquitination of H2A on the sex chromosomes in meiosis, during the prior stage of spermatogenesis, our genetic evidence underscores that RNF8 is not involved in histone-to-protamine exchange.

Precipitation extremes intensify in most regions in climate model projections. Changes in vertical velocities contribute to the changes in intensity of precipitation extremes but remain poorly understood. Here, we find that midtropospheric vertical velocities in extratropical precipitation extremes strengthen overall in simulations of twenty-first-century climate change. For each extreme event, we solve the quasigeostrophic omega equation to decompose this strengthening into different physical contributions. We first consider a dry decomposition in which latent heating is treated as an external forcing of upward motion. Much of the positive contribution to upward motion from increased latent heating is offset by negative contributions from increases in dry static stability and changes in the horizontal length scale of vertical velocities. However, taking changes in latent heating as given is a limitation when the aim is to understand changes in precipitation, since latent heating and precipitation are closely linked. Therefore, we also perform a moist decomposition of the changes in vertical velocities in which latent heating is represented through a moist static stability. In the moist decomposition, changes in moist static stability play a key role and contributions from other factors such as changes in the depth of the upward motion increase in importance. While both dry and moist decompositions are self-consistent, the moist dynamical perspective has greater potential to give insights into the causes of the dynamical contributions to changes in precipitation extremes in different regions.

The optimal design of neural networks is a critical problem in many applications. Here, we investigate how dynamical systems with polynomial nonlinearities can inform the design of neural systems that seek to emulate them. We propose a Learnability metric and its associated features to quantify the near-equilibrium behavior of learning dynamics. Equating the Learnability of neural systems with equivalent parameter estimation metric of the reference system establishes bounds on network structure. In this way, norms from theory provide a good first guess for neural structure, which may then further adapt with data. The proposed approach neither requires training nor training data. It reveals exact sizing for a class of neural networks with multiplicative nodes that mimic continuous- or discrete-time polynomial dynamics. It also provides relatively tight lower size bounds for classical feed-forward networks that is consistent with simulated assessments., Comment: 7 pages

The Super Tau-Charm Facility (STCF) is a future electron-positron collider proposed in China with a peak luminosity of above 0.5$\times$10$^{35}$ cm$^{-2}$s$^{-1}$ and center-of-mass energy ranging from 2 to 7 GeV. An excellent particle identification (PID) capability is one of the most important requirements for the detector at the STCF. A 3$\sigma$ $\pi$/K separation power at the momentum of up to 2 GeV/c is required within the detector acceptance. A DIRC-like time-of-flight (DTOF) detector is proposed to meet the PID requirement for the endcap region of the STCF. The conceptual design of the DTOF detector and its geometry optimization is herein presented. The PID performance of the detector is studied using Geant4 simulation. With a proper reconstruction algorithm, an overall time resolution of ~50 ps is achieved for the detector with an optimum geometry when convoluting contributions from all other sources, including the transit time spread (TTS) of the photodetector, electronic timing accuracy, and an assumed precision (~40 ps) of the event start time. A $\pi$/K separation power of better than 4$\sigma$ at the momentum of 2 GeV/c is achieved over the entire sensitive area of the DTOF detector, thereby fulfilling the physics requirement of the PID detector for the experiment at the STCF., Comment: accepted for Journal of Instrumentation

Qiang Hou,1,2,* Zhenzhen Jiang,2,* Ziwei Li,2 Mingfeng Jiang1,2 1Department of Clinical Laboratory, Hangzhou Cancer Hospital, Hangzhou, Zhejiang, 320000, People’s Republic of China; 2Cancer Research Institute, Hangzhou Cancer Hospital, Hangzhou, Zhejiang, 320000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Mingfeng JiangDepartment of Clinical Laboratory, Hangzhou Cancer Hospital, 34# Yan-Guan Street, Shang-Cheng District, Hangzhou, Zhejiang, 320000, People’s Republic of ChinaEmail wounding110@126.comIntroduction: Esophageal squamous cell carcinoma (ESCC) is often resistant to radiotherapy, likely due to sub-clones that survive and repopulate in the tumor. The analysis of genomic sequencing data related to radiotherapy will provide a better understanding of the intratumoral heterogeneity and genetic evolution of ESCC during radiotherapy.Methods: We analyzed whole-exome sequencing data from pre- and post-irradiation ESCC patients at single-cell and bulk levels in public datasets. We investigated the gene functions involving radioresistance in ESCC cell lines. Furthermore, we established gene knockdown cell lines and explored the transcriptional alterations induced by RNA interference (RNAi) of these genes in KYSE-150 ESCC cell line.Results: We identified three candidate genes related to radioresistance: AHNAK2, EVPL and LAMA5. Knockdown of AHNAK2 and EVPL genes led to increased radioresistance in ESCC cell lines, but not LAMA5. The transcriptome analysis indicated that these genes may regulate the expression of interleukins, interleukin receptors and chemokines by inhibiting the NF-κB and TNF signaling pathways in radioresistant ESCC cells, thereby suppressing their immune response.Conclusion: These data may provide new therapeutic strategies by targeting general ESCC radioresistance-related genes, which may eventually help the development of targeted therapies.Keywords: esophageal sq

Different thermal demands and preferences between individuals lead to a low occupant satisfaction rate, despite the high energy consumption by HVAC system. This study aims to quantify the difference in thermal demands, and to compare the influential factors which might lead to those differences. With the recently released ASHRAE Database, we quantitatively answered the following two research questions: which factors would lead to marked individual difference, and what the magnitude of this difference is. Linear regression has been applied to describe the macro-trend of how people feel thermally under different temperatures. Three types of factors which might lead to different thermal demands have been studied and compared in this study, i.e. individual factors, building characteristics and geographical factors. It was found that the local climate has the most marked impact on the neutral temperature, with an effect size of 3.5 °C; followed by country, HVAC operation mode and body built, which lead to a difference of more than 1 °C. In terms of the thermal sensitivity, building type and local climate are the most influential factors. Subjects in residential buildings or coming from Dry climate zone could accept 2.5 °C wider temperature range than those in office, education buildings or from Continental climate zone. The findings of this research could help thermal comfort researchers and designers to identify influential factors that might lead to individual difference, and could shed light on the feature selection for the development of personal comfort models. © 2020

Concerning first-order unstable processes with time delays that are typical in chemical processes, a modified 2-degree-of-freedom proportional–integral–derivative control method is put forward. The system presents a two-loop structure: inner loop and outer loop. The inner loop is in a classical feedback control structure with a proportional controller intended for implementing stable control of the unstable process; the outer loop is in a 2-degree-of-freedom structure with feedforward control of set points, where the system’s tracking response of set points is separated from its disturbance response. To be specific, the system has a feedforward controller that is designed based on the controlled object models and mainly used for regulating the system’s set point tracking characteristics; besides, it has a feedback controller that is designed on the ground of direct synthesis of disturbance suppression characteristics to improve the system’s disturbance rejection. To verify the effectiveness, the system is put into a theoretical analysis and simulated comparison with other methods. Simulation results show that the system has good set point tracking characteristics and disturbance suppression characteristics. © The Author(s) 2020.

Convolutional Neutral Networks have been successfully applied in searching for strong lensing systems, leading to discoveries of new candidates from large surveys. On the other hand, systematic investigations about their robustness are still lacking. In this paper, we first construct a neutral network, and apply it to $r$-band images of Luminous Red Galaxies (LRGs) of the Kilo Degree Survey (KiDS) Data Release 3 to search for strong lensing systems. We build two sets of training samples, one fully from simulations, and the other one using the LRG stamps from KiDS observations as the foreground lens images. With the former training sample, we find 48 high probability candidates after human-inspection, and among them, 27 are newly identified. Using the latter training set, about 67\% of the aforementioned 48 candidates are also found, and there are 11 more new strong lensing candidates identified. We then carry out tests on the robustness of the network performance with respect to the variation of PSF. With the testing samples constructed using PSF in the range of 0.4 to 2 times of the median PSF of the training sample, we find that our network performs rather stable, and the degradation is small. We also investigate how the volume of the training set can affect our network performance by varying it from 0.1 millions to 0.8 millions. The output results are rather stable showing that within the considered range, our network performance is not very sensitive to the volume size., Comment: 17 pages, 13 figures. Accepted by MNRAS. Comments welcome

Different thermal demands and preferences between individuals lead to a low occupant satisfaction rate, despite the high energy consumption by HVAC system. This study aims to quantify the difference in thermal demands, and to compare the influential factors which might lead to those differences. With the recently released ASHRAE Database, we quantitatively answered the following two research questions: which factors would lead to marked individual difference, and what the magnitude of this difference is. Linear regression has been applied to describe the macro-trend of how people feel thermally under different temperatures. Three types of factors which might lead to different thermal demands have been studied and compared in this study, i.e. individual factors, building characteristics and geographical factors. It was found that the local climate has the most marked impact on the neutral temperature, with an effect size of 3.5 °C; followed by country, HVAC operation mode and body built, which lead to a difference of more than 1 °C. In terms of the thermal sensitivity, building type and local climate are the most influential factors. Subjects in residential buildings or coming from Dry climate zone could accept 2.5 °C wider temperature range than those in office, education buildings or from Continental climate zone. The findings of this research could help thermal comfort researchers and designers to identify influential factors that might lead to individual difference, and could shed light on the feature selection for the development of personal comfort models. © 2020

Concerning first-order unstable processes with time delays that are typical in chemical processes, a modified 2-degree-of-freedom proportional–integral–derivative control method is put forward. The system presents a two-loop structure: inner loop and outer loop. The inner loop is in a classical feedback control structure with a proportional controller intended for implementing stable control of the unstable process; the outer loop is in a 2-degree-of-freedom structure with feedforward control of set points, where the system’s tracking response of set points is separated from its disturbance response. To be specific, the system has a feedforward controller that is designed based on the controlled object models and mainly used for regulating the system’s set point tracking characteristics; besides, it has a feedback controller that is designed on the ground of direct synthesis of disturbance suppression characteristics to improve the system’s disturbance rejection. To verify the effectiveness, the system is put into a theoretical analysis and simulated comparison with other methods. Simulation results show that the system has good set point tracking characteristics and disturbance suppression characteristics. © The Author(s) 2020.

Convolutional Neutral Networks have been successfully applied in searching for strong lensing systems, leading to discoveries of new candidates from large surveys. On the other hand, systematic investigations about their robustness are still lacking. In this paper, we first construct a neutral network, and apply it to $r$-band images of Luminous Red Galaxies (LRGs) of the Kilo Degree Survey (KiDS) Data Release 3 to search for strong lensing systems. We build two sets of training samples, one fully from simulations, and the other one using the LRG stamps from KiDS observations as the foreground lens images. With the former training sample, we find 48 high probability candidates after human-inspection, and among them, 27 are newly identified. Using the latter training set, about 67\% of the aforementioned 48 candidates are also found, and there are 11 more new strong lensing candidates identified. We then carry out tests on the robustness of the network performance with respect to the variation of PSF. With the testing samples constructed using PSF in the range of 0.4 to 2 times of the median PSF of the training sample, we find that our network performs rather stable, and the degradation is small. We also investigate how the volume of the training set can affect our network performance by varying it from 0.1 millions to 0.8 millions. The output results are rather stable showing that within the considered range, our network performance is not very sensitive to the volume size., Comment: 17 pages, 13 figures. Accepted by MNRAS. Comments welcome

Different thermal demands and preferences between individuals lead to a low occupant satisfaction rate, despite the high energy consumption by HVAC system. This study aims to quantify the difference in thermal demands, and to compare the influential factors which might lead to those differences. With the recently released ASHRAE Database, we quantitatively answered the following two research questions: which factors would lead to marked individual difference, and what the magnitude of this difference is. Linear regression has been applied to describe the macro-trend of how people feel thermally under different temperatures. Three types of factors which might lead to different thermal demands have been studied and compared in this study, i.e. individual factors, building characteristics and geographical factors. It was found that the local climate has the most marked impact on the neutral temperature, with an effect size of 3.5 °C; followed by country, HVAC operation mode and body built, which lead to a difference of more than 1 °C. In terms of the thermal sensitivity, building type and local climate are the most influential factors. Subjects in residential buildings or coming from Dry climate zone could accept 2.5 °C wider temperature range than those in office, education buildings or from Continental climate zone. The findings of this research could help thermal comfort researchers and designers to identify influential factors that might lead to individual difference, and could shed light on the feature selection for the development of personal comfort models. © 2020

Concerning first-order unstable processes with time delays that are typical in chemical processes, a modified 2-degree-of-freedom proportional–integral–derivative control method is put forward. The system presents a two-loop structure: inner loop and outer loop. The inner loop is in a classical feedback control structure with a proportional controller intended for implementing stable control of the unstable process; the outer loop is in a 2-degree-of-freedom structure with feedforward control of set points, where the system’s tracking response of set points is separated from its disturbance response. To be specific, the system has a feedforward controller that is designed based on the controlled object models and mainly used for regulating the system’s set point tracking characteristics; besides, it has a feedback controller that is designed on the ground of direct synthesis of disturbance suppression characteristics to improve the system’s disturbance rejection. To verify the effectiveness, the system is put into a theoretical analysis and simulated comparison with other methods. Simulation results show that the system has good set point tracking characteristics and disturbance suppression characteristics. © The Author(s) 2020.

Ziwei Li,1,* Bin Li,1,* Li Li,1 Guanying Wang,1 Yuanyuan Li,1 Ruoqiu Fu,1 Yue Ming,1 Rui Ni,1 Jiming Wang,2 George Ye,3 Jianhong Chen1 1Department of Pharmacy, Daping Hospital, Army Medical University, Chongqing 400042, People’s Republic of China; 2Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA; 3Yes Biotech Laboratories Ltd, Mississauga, ON L5S 1V6, Canada*These authors contributed equally to this workCorrespondence: Jianhong ChenDepartment of Pharmacy, Daping Hospital, Army Medical University, 10# Changjiangzhilu, Chongqing 400042, People’s Republic of ChinaTel +86 23 6875 7098Fax +86 23 6889 8867Email chenjhlab@163.comBackground: Monoclonal antibodies (mAbs) that target the programmed cell death-1 (PD-1)/programmed death-ligand 1 (PD-L1) immune checkpoint have demonstrated substantial clinical benefit for a variety of solid tumors. However, their applications in patients with hepatocellular carcinoma (HCC) are reported with unclear molecular mechanisms. Here, we report a novel mouse anti-human PD-1 mAb that can reverse the immunosuppressive effect of HePG2 cells on Jurkat cells.Materials and Methods: HepG2 liver cancer cells, which were induced to overexpress PD-L1 by IFN-γ, were co-cultured with PHA-activated Jurkat lymphocytic cells to investigate the immunostimulative effect and mechanisms of the 14 newly generated PD-1 mAbs. Multiple cellular and molecular biology experiments were performed in this study, such as CCK-8, ELISA, flow cytometry, immunofluorescence and Western blot.Results: We found that mAb B1C4 significantly enhanced the tumor-killing cytokine secretion level by Jurkat cells in the co-culture system and increased the killing ability of Jurkat cells on HepG2 cells. Co-culture with HePG2 cells led to Jurkat cell cycle delay in S phase, and B1C4 promoted cell cycle progression from S to G2/M. Co-culture with HePG2 cells also caused apoptosis in Jurkat cells, which was inhibited by B1C4

The Large High-Altitude Air Shower Observatory (LHAASO) is being built at Haizi Mountain, Sichuan province of China at an altitude of 4410 meters. One of its main goals is to survey the northern sky for very-high-energy gamma ray sources via its ground-based water Cherenkov detector array (WCDA). 900 8-inch photomultiplier tubes (PMTs) CR365-02-1 from Beijing Hamamatsu Photon Techniques INC. (BHP) are installed in the WCDA, collecting Cherenkov photons produced by air shower particles crossing water. The design of the PMT base with a high dynamic range for CR365-02-1, the PMT batch test system, and the test results of 997 PMTs are presented in this paper., Comment: 23 pages, 17 figures

Cells in the brain act as components of extended networks. Therefore, to understand neurobiological processes in a physiological context, it is essential to study them in vivo. Super-resolution microscopy has spatial resolution beyond the diffraction limit, thus promising to provide structural and functional insights that are not accessible with conventional microscopy. However, to apply it to in vivo brain imaging, we must address the challenges of 3D imaging in an optically heterogeneous tissue that is constantly in motion. We optimized image acquisition and reconstruction to combat sample motion and applied adaptive optics to correcting sample-induced optical aberrations in super-resolution structured illumination microscopy (SIM) in vivo. We imaged the brains of live zebrafish larvae and mice and observed the dynamics of dendrites and dendritic spines at nanoscale resolution.

Bathroom cleaning is one of the troublesome daily issues. All kinds of contaminants on the bathroom floor take people a lot of time and effort to deal with. With the development of computational technologies, smart products are gradually entering into our lives to solve these troublesome issues. Therefore, there is a chance to use new technologies to solve the bathroom floor cleaning problem and to make life convenient. Obviously, different people have different attitudes towards smart products, which influence their preferences on interacting with smart products. Based on my personal experience, Chinese and Dutch young adults have this difference, and are chosen as the target groups. To sum up, this project aims to explore the interaction with a smart bathroom floor cleaner in Chinese and Dutch cultural contexts. The main challenge is to collect and analyze information from different aspects, and then integrated into a complex system, namely a smart bathroom floor cleaner system. The related aspects include context, user experience, smart product technologies, etc. We analyzed the bathroom environment conditions, the cleaning related concerns, the expected cleaning experience, promising technologies, Cyber-physical systems principles, and the opportunities in Chinese and Dutch smart product markets. The results show that the two groups have no significant differences in bathroom usage and cleaning habits. The expected experience for both groups is trustworthy and effortless. However, their attitudes towards smart products are different. The Chinese group has higher interest, while the Dutch group is more conservative. This difference is also consistent with the difference in the smart product markets. Therefore, we designed different interaction concepts based on the device in different intelligence levels in the conceptualization phase. In order to design an interaction concept, at first we need a device concept that can meet the requirements of the bathroom clean, Design for Interaction

Cells in the brain act as components of extended networks. Therefore, to understand neurobiological processes in a physiological context, it is essential to study them in vivo. Super-resolution microscopy has spatial resolution beyond the diffraction limit, thus promising to provide structural and functional insights that are not accessible with conventional microscopy. However, to apply it to in vivo brain imaging, we must address the challenges of 3D imaging in an optically heterogeneous tissue that is constantly in motion. We optimized image acquisition and reconstruction to combat sample motion and applied adaptive optics to correcting sample-induced optical aberrations in super-resolution structured illumination microscopy (SIM) in vivo. We imaged the brains of live zebrafish larvae and mice and observed the dynamics of dendrites and dendritic spines at nanoscale resolution.

Bathroom cleaning is one of the troublesome daily issues. All kinds of contaminants on the bathroom floor take people a lot of time and effort to deal with. With the development of computational technologies, smart products are gradually entering into our lives to solve these troublesome issues. Therefore, there is a chance to use new technologies to solve the bathroom floor cleaning problem and to make life convenient. Obviously, different people have different attitudes towards smart products, which influence their preferences on interacting with smart products. Based on my personal experience, Chinese and Dutch young adults have this difference, and are chosen as the target groups. To sum up, this project aims to explore the interaction with a smart bathroom floor cleaner in Chinese and Dutch cultural contexts. The main challenge is to collect and analyze information from different aspects, and then integrated into a complex system, namely a smart bathroom floor cleaner system. The related aspects include context, user experience, smart product technologies, etc. We analyzed the bathroom environment conditions, the cleaning related concerns, the expected cleaning experience, promising technologies, Cyber-physical systems principles, and the opportunities in Chinese and Dutch smart product markets. The results show that the two groups have no significant differences in bathroom usage and cleaning habits. The expected experience for both groups is trustworthy and effortless. However, their attitudes towards smart products are different. The Chinese group has higher interest, while the Dutch group is more conservative. This difference is also consistent with the difference in the smart product markets. Therefore, we designed different interaction concepts based on the device in different intelligence levels in the conceptualization phase. In order to design an interaction concept, at first we need a device concept that can meet the requirements of the bathroom clean, Design for Interaction

The use of artificial neural networks as models of chaotic dynamics has been rapidly expanding. Still, a theoretical understanding of how neural networks learn chaos is lacking. Here, we employ a geometric perspective to show that neural networks can efficiently model chaotic dynamics by becoming structurally chaotic themselves. We first confirm neural network's efficiency in emulating chaos by showing that a parsimonious neural network trained only on few data points can reconstruct strange attractors, extrapolate outside training data boundaries, and accurately predict local divergence rates. We then posit that the trained network's map comprises sequential geometric stretching, rotation, and compression operations. These geometric operations indicate topological mixing and chaos, explaining why neural networks are naturally suitable to emulate chaotic dynamics., Comment: in IEEE Transactions on Neural Networks and Learning Systems

PRMT5 is a type II protein arginine methyltransferase with roles in stem cell biology, reprograming, cancer and neurogenesis. During embryogenesis in the mouse, it was hypothesized that PRMT5 functions with the master germline determinant BLIMP1 to promote primordial germ cell (PGC) specification. Using a Blimp1-Cre germline conditional knockout, we discovered that Prmt5 has no major role in murine germline specification, or the first global epigenetic reprograming event involving depletion of cytosine methylation from DNA and histone H3 lysine 9 dimethylation from chromatin. Instead, we discovered that PRMT5 functions at the conclusion of PGC reprograming I to promote proliferation, survival and expression of the gonadal germline program as marked by MVH. We show that PRMT5 regulates gene expression by promoting methylation of the Sm spliceosomal proteins and significantly altering the spliced repertoire of RNAs in mammalian embryonic cells and primordial cells.

Our ancestors and our children are linked by a single, special cell lineage called the germline. Germ cells are tasked with the role to accurately pass DNA from one generation to the next. In today's society infertility is an important health concern as it is estimated to affect 10% of the reproductive age population. In many cases infertility can be traced to abnormal germline cell development. The early events of germline formation are difficult to study because of limited materials, especially low germ cell numbers in the process of embryogenesis.To overcome the challenge of limited materials, an in vitro method to derive cell types faithfully reporting in vivo phenotypes needs to be devised to facilitate the research process. The key results from the in vitro model should then be evaluated carefully in vivo to show relevance in development. My thesis first focused on establishing an in vitro model to recapitulate early development of germline, by differentiating embryonic stem cells (ESCs) to form in vitro primordial germ cells (iPGCs), in a dish. This method allows good number of germ cells to be produced for molecular and biochemical studies. Using the in vitro model, key germline modifiers for ESC maintenance and germ cell formation were identified, one of which is protein arginine methyltransferase 5 (PRMT5). PRMT5 is a type II arginine methyltransferase (PRMT) that modifies symmetrical dimethylated arginines (SDMAs) on proteins, which substrates include histone H2A and H4, as well as the Sm proteins involved in RNA splicing. However, the molecular function of PRMT5 in ground state ESCs remains unknown. In our study, for the first time, we generated an inducible knock out of Prmt5 in ESCs cultured in ground state pluripotency (cultured with 2i inhibitors and leukemia inhibitory factor). We characterized PRMT5's role in ESCs through the method of paired-end RNA sequencing to show that PRMT5 affects cell survival, proliferation and chromatin organization etc.

Our ancestors and our children are linked by a single, special cell lineage called the germline. Germ cells are tasked with the role to accurately pass DNA from one generation to the next. In today's society infertility is an important health concern as it is estimated to affect 10% of the reproductive age population. In many cases infertility can be traced to abnormal germline cell development. The early events of germline formation are difficult to study because of limited materials, especially low germ cell numbers in the process of embryogenesis.To overcome the challenge of limited materials, an in vitro method to derive cell types faithfully reporting in vivo phenotypes needs to be devised to facilitate the research process. The key results from the in vitro model should then be evaluated carefully in vivo to show relevance in development. My thesis first focused on establishing an in vitro model to recapitulate early development of germline, by differentiating embryonic stem cells (ESCs) to form in vitro primordial germ cells (iPGCs), in a dish. This method allows good number of germ cells to be produced for molecular and biochemical studies. Using the in vitro model, key germline modifiers for ESC maintenance and germ cell formation were identified, one of which is protein arginine methyltransferase 5 (PRMT5). PRMT5 is a type II arginine methyltransferase (PRMT) that modifies symmetrical dimethylated arginines (SDMAs) on proteins, which substrates include histone H2A and H4, as well as the Sm proteins involved in RNA splicing. However, the molecular function of PRMT5 in ground state ESCs remains unknown. In our study, for the first time, we generated an inducible knock out of Prmt5 in ESCs cultured in ground state pluripotency (cultured with 2i inhibitors and leukemia inhibitory factor). We characterized PRMT5's role in ESCs through the method of paired-end RNA sequencing to show that PRMT5 affects cell survival, proliferation and chromatin organization etc.

This paper presents a method to reconstruct dense semantic trajectory stream of human interactions in 3D from synchronized multiple videos. The interactions inherently introduce self-occlusion and illumination/appearance/shape changes, resulting in highly fragmented trajectory reconstruction with noisy and coarse semantic labels. Our conjecture is that among many views, there exists a set of views that can confidently recognize the visual semantic label of a 3D trajectory. We introduce a new representation called 3D semantic map---a probability distribution over the semantic labels per trajectory. We construct the 3D semantic map by reasoning about visibility and 2D recognition confidence based on view-pooling, i.e., finding the view that best represents the semantics of the trajectory. Using the 3D semantic map, we precisely infer all trajectory labels jointly by considering the affinity between long range trajectories via estimating their local rigid transformations. This inference quantitatively outperforms the baseline approaches in terms of predictive validity, representation robustness, and affinity effectiveness. We demonstrate that our algorithm can robustly compute the semantic labels of a large scale trajectory set involving real-world human interactions with object, scenes, and people.

This paper introduces a new eddy detection method based on object segmentation strategies. It incorporates the advantages of the Okubo-Weiss (OW) method, and improves the algorithm robustness by using three different segmentation methods. First, by intersecting the OW mask with the positive and negative SLA masks, two separate initial eddy candidate segment masks for anticyclonic and cyclonic eddies are produced. Then the number of extrema inside the individual segments is calculated. If the number is two/greater than two, a histogram threshold/watershed method is applied to further divide the segments into subsegments. If the number is one, the segment is tested by predefined eddy criteria. If the criteria are not fulfilled, the eddy boundary is repeatedly shrunk by one pixel inward until the segment meets all the criteria. In this case it is saved as an eddy. A segment is discarded if the number of extrema is zero. The eddy detection results of this method, of the OW method and of a geometric eddy detection method are displayed and analyzed statistically. The seasonal cycle of eddy number from the three eddy detection methods is discussed in comparison to the EKE.

This paper introduces a new eddy detection method based on object segmentation strategies. It incorporates the advantages of the Okubo-Weiss (OW) method, and improves the algorithm robustness by using three different segmentation methods. First, by intersecting the OW mask with the positive and negative SLA masks, two separate initial eddy candidate segment masks for anticyclonic and cyclonic eddies are produced. Then the number of extrema inside the individual segments is calculated. If the number is two/greater than two, a histogram threshold/watershed method is applied to further divide the segments into subsegments. If the number is one, the segment is tested by predefined eddy criteria. If the criteria are not fulfilled, the eddy boundary is repeatedly shrunk by one pixel inward until the segment meets all the criteria. In this case it is saved as an eddy. A segment is discarded if the number of extrema is zero. The eddy detection results of this method, of the OW method and of a geometric eddy detection method are displayed and analyzed statistically. The seasonal cycle of eddy number from the three eddy detection methods is discussed in comparison to the EKE.

This paper introduces a new eddy detection method based on object segmentation strategies. It incorporates the advantages of the Okubo-Weiss (OW) method, and improves the algorithm robustness by using three different segmentation methods. First, by intersecting the OW mask with the positive and negative SLA masks, two separate initial eddy candidate segment masks for anticyclonic and cyclonic eddies are produced. Then the number of extrema inside the individual segments is calculated. If the number is two/greater than two, a histogram threshold/watershed method is applied to further divide the segments into subsegments. If the number is one, the segment is tested by predefined eddy criteria. If the criteria are not fulfilled, the eddy boundary is repeatedly shrunk by one pixel inward until the segment meets all the criteria. In this case it is saved as an eddy. A segment is discarded if the number of extrema is zero. The eddy detection results of this method, of the OW method and of a geometric eddy detection method are displayed and analyzed statistically. The seasonal cycle of eddy number from the three eddy detection methods is discussed in comparison to the EKE.

Primordial germ cells (PGCs) are fate restricted to differentiate into gametes in vivo. However, when removed from their embryonic niche, PGCs undergo reversion to pluripotent embryonic germ cells (EGCs) in vitro. One of the major differences between EGCs and embryonic stem cells (ESCs) is variable methylation at imprinting control centers (ICCs), a phenomenon that is poorly understood. Here we show that reverting PGCs to EGCs involved stable ICC methylation erasure at Snrpn, Igf2r, and Kcnqot1. In contrast, the H19/Igf2 ICC undergoes erasure followed by de novo re-methylation. PGCs differentiated in vitro from ESCs completed Snrpn ICC erasure. However, the hypomethylated state is highly unstable. We also discovered that when the H19/Igf2 ICC was abnormally hypermethylated in ESCs, this is not erased in PGCs differentiated from ESCs. Therefore, launching PGC differentiation from ESC lines with appropriately methylated ICCs is critical to the generation of germline cells that recapitulate endogenous ICC erasure.

Primordial germ cells (PGCs) are fate restricted to differentiate into gametes in vivo. However, when removed from their embryonic niche, PGCs undergo reversion to pluripotent embryonic germ cells (EGCs) in vitro. One of the major differences between EGCs and embryonic stem cells (ESCs) is variable methylation at imprinting control centers (ICCs), a phenomenon that is poorly understood. Here we show that reverting PGCs to EGCs involved stable ICC methylation erasure at Snrpn, Igf2r, and Kcnqot1. In contrast, the H19/Igf2 ICC undergoes erasure followed by de novo re-methylation. PGCs differentiated in vitro from ESCs completed Snrpn ICC erasure. However, the hypomethylated state is highly unstable. We also discovered that when the H19/Igf2 ICC was abnormally hypermethylated in ESCs, this is not erased in PGCs differentiated from ESCs. Therefore, launching PGC differentiation from ESC lines with appropriately methylated ICCs is critical to the generation of germline cells that recapitulate endogenous ICC erasure.

Human pluripotent stem cells (PSCs) are critical in vitro tools for understanding mechanisms that regulate lineage differentiation in the human embryo as well as a potentially unlimited supply of stem cells for regenerative medicine. Pluripotent human and mouse embryonic stem cells (ESCs) derived from the inner cell mass of blastocysts share a similar transcription factor network to maintain pluripotency and self-renewal, yet there are considerable molecular differences reflecting the diverse environments in which mouse and human ESCs are derived. In the current study we evaluated the role of Protein arginine methyltransferase 5 (PRMT5) in human ESC (hESC) self-renewal and pluripotency given its critical role in safeguarding mouse ESC pluripotency. Unlike the mouse, we discovered that PRMT5 has no role in hESC pluripotency. Using microarray analysis we discovered that a significant depletion in PRMT5 RNA and protein from hESCs changed the expression of only 78 genes, with the majority being repressed. Functionally, we discovered that depletion of PRMT5 had no effect on expression of OCT4, NANOG or SOX2, and did not prevent teratoma formation. Instead, we show that PRMT5 functions in hESCs to regulate proliferation in the self-renewing state by regulating the fraction of cells in Gap 1 (G1) of the cell cycle and increasing expression of the G1 cell cycle inhibitor P57. Taken together our data unveils a distinct role for PRMT5 in hESCs and identifies P57 as new target.