Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications. Abstract Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.
Depression in post-traumatic stress disorder

Abstract Major depressive disorder (MDD) symptoms commonly occur after trauma-exposure, both alone and in combination with post-traumatic stress disorder (PTSD). This article reviews recent research on comorbidity between these disorders, including its implications for symptom severity and response to treatment. Despite considerable symptom overlap, the two disorders represent distinct constructs and depend, at least in part, on separate biological mechanisms. Both, however, are also clearly related to stress psychopathology. We recommend that more research focus specifically on the study of individual differences in symptom expression in order to identify distinct subgroups of individuals and develop targeted treatments. However, a barrier to this line of inquiry is the trend of excluding particular patients from clinical trials of new interventions based on symptom severity or comorbidity. Another obstacle is the overreliance on self-report measures in human research. We argue that developing computer-based behavioral measures in order to supplement self-report can help address this challenge. Furthermore, we propose that these measures can help tie findings from human and non-human animal research. A number of paradigms have been used to model MDD-and PTSD-like behavior in animals. These models remain valuable for understanding the biological basis of these disorders in humans and for identifying potential interventions, but they have been underused for the study of comorbidity. Although the interpretation of animal behavior remains a concern, we propose that this can also be overcome through the development of close human analogs to animal paradigms.
Torsion points, Pell’s equation, and integration in elementary terms

The main results of this paper involve general algebraic differentials $\omega$ on a general pencil of algebraic curves. We show how to determine if $\omega$ is integrable in elementary terms for infinitely many members of the pencil. In particular, this corrects an assertion of James Davenport from 1981 and provides the first proof, even in rather strengthened form. We also indicate analogies with work of Andre and Hrushovski and with the Grothendieck-Katz Conjecture. To reach this goal, we first provide proofs of independent results which extend conclusions of relative Manin-Mumford type allied to the Zilber-Pink conjectures: we characterize torsion points lying on a general curve in a general abelian scheme of arbitrary relative dimension at least 2. In turn, we present yet another application of the latter results to a rather general pencil of Pell equations $A^2-DB^2=1$ over a polynomial ring. We determine whether the Pell equation (with squarefree $D$) is solvable for infinitely many members of the pencil.
The Relationship of Substance Abuse with Depression and Stress in Diabetic Adolescents Mediated by Conflict Resolution and Problem Solving Skills

Allahyar A & Zeinali A. Substance Abuse Reduction Model. JAMS. 2020; 23(2):126-137. Background and Aim Diabetes is one of the most common chronic diseases in children and adolescents. Due to its specific problems, it can sometimes lead to substance abuse in adolescents through reduced health and increased depression and stress. The present study aimed to investigate the relationship of substance abuse with depression and stress in diabetic adolescents mediated by conflict resolution and problem solving skills. Methods & Materials This is a descriptive/correlational study. The study population consisted of all diabetic adolescent members of Urmia Diabetes Association and those referred to Imam Khomeini Hospital and diabetes specialists in Urmia city in Spring 2019. Of these, 206 were selected using a purposive sampling method. data collection tools were Substance Abuse Subtle Screening Inventory Adolescents Version (Miller & Lazowski, 2001), Beck’s Depression Inventory (Beck et al, 1988), The Perceived Stress Scale (Cohen et al, 1983), Problem Solving Inventory (Heppner & Petersen, 1982) and Conflict Resolution Style Inventory (Weeks, 1994). Collected data were analyzed by using Pearson correlation test and structural equation modeling in SPSS and LISREL applications. Ethical Considerations All ethical principles were considered in this study. Results Depression and stress directly had a negative and significant effect on conflict resolution and problem solving skills, but had no significant effect on substance abuse. Conflict resolution and problem solving skills directly had a negative and significant effect on substance abuse. Moreover, depression and stress indirectly had a significant effect on substance abuse through medication by conflict resolution and problem solving skills (P<0.05). Conclusion Depression and stress can lead to increased substance abuse in diabetic adolescents by decreasing conflict resolution and problem solving skills. Therefore, in order to reduce substance abuse in these patients, it is recommended to hold training workshops to reduce their depression and stress and improve conflict resolution and problem solving skills.
Sensor-network-based navigation of delivery robot for baggage handling in international airport

Automated guided vehicles (AGVs) have been regarded as a promising means for the future delivery industry by many logistic companies. Several AGV-based delivery systems have been proposed, but they generally have drawbacks in delivering and locating baggage by magnet line, such as the high maintenance cost, and it is hard to change the trajectory of AGV. This article considers using multi-AGVs as delivery robots to coordinate and sort baggage in the large international airport. This system has the merit of enlarging the accuracy of baggage sorting and delivering. Due to the inaccurate transportation efficiency, a time-dependent stochastic baggage delivery system is proposed and a stochastic model is constructed to characterize the running priority and optimal path planning for multi-AGVs according to the flight information. In the proposed system, ultra-wideband technology is applied to realize precisely positioning and navigation for multi-AGVs in the baggage distribution center. Furthermore, the optimal path planning algorithm based on time-window rules and rapidly exploring random tree algorithm is considered to avoid collision and maneuverability constraints and to determine whether the running path for each AGV is feasible and optimal. Computer simulations are conducted to demonstrate the performance of the proposed method.
Haste Makes Waste: Banking Organization Growth and Operational Risk

This study shows that banking organization growth is associated with higher operational losses per dollar of total assets and incidence of tail risks. Event studies using M&A activity and instrumental variable regressions provide consistent evidence. The relationship between banking organization growth and operational risk varies by loss event types and balance sheet categories. We demonstrate that higher growth predicts worse operational risk realizations during the global financial crisis. These findings have implications for bank performance, risk management and supervision in a continually consolidating banking industry.
Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach

BACKGROUND: Bedside monitors in the ICU routinely measure and collect patients' physiologic data in real time to continuously assess the health status of patients who are critically ill. With the advent of increased computational power and the ability to store and rapidly process big data sets in recent years, these physiologic data show promise in identifying specific outcomes and/or events during patients' ICU hospitalization. METHODS: We introduced a methodology designed to automatically extract information from continuous-in-time vital sign data collected from bedside monitors to predict if a patient will experience a prolonged stay (length of stay) on mechanical ventilation, defined as >4 d, in a pediatric ICU. RESULTS: Continuous-in-time vital signs information and clinical history data were retrospectively collected for 284 ICU subjects from their first 24 h on mechanical ventilation from a medical-surgical pediatric ICU at Boston Children's Hospital. Multiple machine learning models were trained on multiple subsets of these subjects to predict the likelihood that each of these subjects would experience a long stay. We evaluated the predictive power of our models strictly on unseen hold-out validation sets of subjects. Our methodology achieved model performance of >83% (area under the curve) by using only vital sign information as input, and performances of 90% (area under the curve) by combining vital sign information with subjects' static clinical data readily available in electronic health records. We implemented this approach on 300 independently trained experiments with different choices of training and hold-out validation sets to ensure the consistency and robustness of our results in our study sample. The predictive power of our approach outperformed recent efforts that used deep learning to predict a similar task. CONCLUSIONS: Our proposed workflow may prove useful in the design of scalable approaches for real-time predictive systems in ICU environments, exploiting real-time vital sign information from bedside monitors. (ClinicalTrials.gov registration NCT02184208.)
Control of trotting gait for load-carrying quadruped walking vehicle with eccentric torso

Aiming at the problems of common methods in trotting gait control of a load-carrying quadruped walking vehicle, a control method, combining virtual model and centroidal dynamics, is proposed. The control of the walking vehicle is divided into two parts, meaning the motion control of the vehicle body and the motion control of the swing leg. The virtual model control method is used to work out the accelerations of the vehicle body, while the centroidal dynamics approach is used to obtain the resultant forces acting on the vehicle. Next, quadratic programming is used to distribute the resultant forces to the foot-ends of the supporting legs. Last, combining the Jacobian matrices of supporting legs, the vehicle body’s motion control is achieved. The virtual forces, acting on the swing leg foot-end, are obtained using the virtual model control method. Combining the swing leg’s Jacobian matrix, joint torques of swing leg are worked out. Simulink and Adams are adopted to jointly simulate omnidirectional trotting of the vehicle, under the condition of fixed and shifting position of eccentric weight. The effects of the virtual model and centroidal dynamics control method are compared with that of the virtual model control method. The results show that the errors of roll angle and pitch angle are reduced by 50%, 89% and 50%, 80%, respectively, as derived by virtual model and centroidal dynamic control method, under the two conditions. The proposed control algorithm is proved effective.
Geometry-based finger kinematic models for joint rotation configuration and parameter estimation

In this work, geometry-based finger kinematic models for joint rotation configuration are proposed. The purpose of the work is to provide an effective means of describing an individual-specific finger motion during flexion or extension movements as precisely as possible. Based on the finger’s geometric postures that are observed when fingers naturally grasp a cylindrical object with a circular cross-section, its geometric relation between each phalanx of a finger and the object is extracted, and forms of contact between them are taken into consideration to secure more degrees of freedom for representing finger motions and are parameterized in the model development. A parameter identification approach is adopted to find model parameters that can be used to describe an individual-specific grasping style. For the validation of the proposed models, a set of optical motion capture experiments is performed. From the simulation study, one can see that the models provide one of the feasible and viable solutions to imitate the human finger’s flexion and/or extension movements.
Limonoids From Choerospondias axillaris

Eight limonoids (1-8), including 1 new compound named as axillariol A (1), were isolated from the dichloromethane extract of Choerospondias axillaris branches and leaves using various chromatographic separations. Their structures were confirmed by detailed analysis of the 1-dimensional (1D), 2D nuclear magnetic resonance, and high-resolution electrospray ionization quadrupole time-of-flight mass spectra. In addition, toonaciliatins N (6) and toonaciliatins O (7) showed selective and significant cytotoxicity on the rhabdomyosarcoma human cancer cell line with half-maximal inhibitory concentration values of 9.12 and 7.79 µM, respectively.
Continuous shared control in prosthetic hand grasp tasks by Deep Deterministic Policy Gradient with Hindsight Experience Replay

Grasp using a prosthetic hand in real life can be a difficult task. The amputee users are often capable of planning the reaching trajectory and hand grasp location selection, however, failed in precise finger movements, such as adapting the fingers to the surface of the object without excessive force. It is much efficient to leave that part to the machine autonomy. In order to combine the intention and planning ability of users with robotic control, the shared control is introduced in which users’ inputs and robot control methods are combined to achieve a goal. The shared control problem can be formulated as a Partially Observable Markov Decision Process. To find the optimal control policy, we adopt an adaptive dynamic programming and reinforcement learning-based control algorithm-Deep Deterministic Policy Gradient combined with Hindsight Experience Replay. We proposed the algorithm with a prediction layer using the reparameterization technique. The system was tested in a modified simulation environment for the ability to follow the user’s intention and keep the contact force in boundary for safety.
Embodied tactile perception and learning

Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large‐scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.
Mechanisms of autoregulation of C3G, activator of the GTPase Rap1, and its catalytic deregulation in lymphomas

Cancer-associated mutations in the GEF C3G may promote lymphoma progression by activating the GTPase Rap1. Activating Rap1 with mutant C3G in lymphoma The GTPase Rap1 promotes integrin-mediated cell adhesion and is associated with the development of various cancers, including B cell malignancies. Carabias et al. found two missense mutations in the Rap1 activator C3G in non-Hodgkin’s lymphomas (NHLs) from a database of cancer-associated mutations. Examination of the structure of C3G revealed that the basal autoinhibitory structure of the protein is disrupted by these mutations, enabling constitutive activity that promoted Rap1-integrin signaling independently of the normal activation mechanisms for C3G. Developing ways to target C3G in NHL cells may lead to targeted therapeutics for patients with NHL and possibly other cancers. C3G is a guanine nucleotide exchange factor (GEF) that regulates cell adhesion and migration by activating the GTPase Rap1. The GEF activity of C3G is stimulated by the adaptor proteins Crk and CrkL and by tyrosine phosphorylation. Here, we uncovered mechanisms of C3G autoinhibition and activation. Specifically, we found that two intramolecular interactions regulate the activity of C3G. First, an autoinhibitory region (AIR) within the central domain of C3G binds to and blocks the catalytic Cdc25H domain. Second, the binding of the protein’s N-terminal domain to its Ras exchanger motif (REM) is required for its GEF activity. CrkL activated C3G by displacing the AIR/Cdc25HD interaction. Two missense mutations in the AIR found in non-Hodgkin’s lymphomas, Y554H and M555K, disrupted the autoinhibitory mechanism. Expression of C3G-Y554H or C3G-M555K in Ba/F3 pro–B cells caused constitutive activation of Rap1 and, consequently, the integrin LFA-1. Our findings suggest that sustained Rap1 activation by deregulated C3G might promote progression of lymphomas and that designing therapeutics to target C3G might treat these malignancies.
Cidofovir Intralesional Injection for Recalcitrant Common Warts: A Comparison with Sodium Tetradecyl Sulfate Intralesional Injection

Background A novel treatment method is required for recalcitrant common warts. Objective This study aimed to compare the complete wart removal rate of cidofovir, a broad-spectrum antiviral agent, intralesional injection and sodium tetradecyl sulfate intralesional injection. Methods This retrospective study included 45 patients with recalcitrant common warts on the hands and/or feet, treated with cidofovir or sodium tetradecyl sulfate intralesional injection. Results The treatment results were evaluated in three groups as follows: (1) failure - recalcitrant common warts remaining despite three or more injections, (2) success - free from warts for more than 6 months after the injection, and (3) recurrence. The cidofovir group (n=22) showed significantly higher treatment success rates than the sodium tetradecyl sulfate group (n=23) (90.91% vs. 26.09%, p<0.001). Two immunosuppressed patients in the cidofovir group had recurrent lesions after 2 months of being declared free from warts. Considering adverse effects, two patients in the cidofovir group complained of bulla formation with severe pain requiring narcotic painkillers. Conclusion Although this study has the limitations of a small sample size and retrospective design, patients with recalcitrant common warts showed a dramatic response to the treatment with cidofovir intralesional injection, with minimal complications.
Title Observing atmospheric clouds through stereo reconstruction

Observing cloud lifecycles and obtaining measurements on cloud features are significant problems in atmospheric cloud research. Scanning radars have been the most capable instruments to provide such measurements, but they have shortcomings when it comes to spatial and temporal resolution. High spatial and temporal resolution is particularly important to capture the variations in developing convections. Stereo photogrammetry can complement scanning radars with the potential to observe clouds as distant as tens of kilometers and to provide high temporal and spatial resolution, although it comes with the calibration challenges peculiar to various outdoor settings required to collect measurements on atmospheric clouds. This work explores the use of stereo photogrammetry in atmospheric cloud research, focusing on tracking vertical motion in developing convections. Calibration challenges and strategies to overcome these challenges are addressed within two different stereo settings in Miami, Florida and in the plains of Oklahoma. A feature extraction and matching algorithm is developed and implemented to identify cloud features of interest. A two-level resolution hierarchy is exploited in feature extraction and matching. 3D positions of cloud features are reconstructed from matched pixel pairs, and cloud tops of developing turrets in shallow to deep convection are tracked in time to estimate vertical accelerations. Results show that stereophotogrammetry provides a useful tool to observe cloud lifecycles and track the vertical acceleration of turrets exceeding 10 km height.