Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of monocyte to lymphocyt ratio in predicting metastasis in rectal cancer

Aim: A lot of studies are being carried out to predict recurrence and metastasis in rectal cancer, which is a common type of cancer in general surgery practice. It is thought that MLR (monocyte to lymphocyte ratio) , an indice developed by division of monocyte count by lymphocyte count, may be a good marker to reflect the degree of tumor progression and predict prognosis. In our study, we investigated the role of MLR in predicting metastasis in rectal cancer. Materials and Methods: Fifty-seven patients diagnosed with rectal cancer in the Abant Izzet Baysal University general surgery clinic between 2012 and 2016 were retrospectively investigated. Albumin, CRP(C reactive protein), Platelet, Neutrophil, Lymphocyte, Monocyte values were obtained as the inflammatory markers in the blood count test of the patients before neoadjuvant treatment. Results: In this study, 25 patients who underwent neoadjuvant chemoradiotherapy and underwent full postoperative follow-up were evaluated. Eight of these 25 patients had metastasis until June 2019. There was a significant difference in monocyte and MLR values between groups with and without metastasis. Conclusion: This study is presented as a preliminary study. In our study, we showed that MLR value which measured before neoadjuvant therapy in rectum cancer patients may be useful in predicting metastasis development.
Progresses in Tensile, Torsional, and Multifunctional Soft Actuators

Soft actuators have received intensive attention in the fields of soft robots, sensors, intelligent control, artificial intelligence, and visual intelligence. By combination of tensile and torsional deformations, different types of motions can be realized, such as bending, rolling, and jumping. Soft robotics need soft actuators, such as artificial muscles to lift or move objects to perform some work. Additionally, actuation integrated with functions of sensing, signal transmission, and control is also needed in the development of advanced intelligent systems, which further stimulates the requirement of multifunctional actuators. Here different types of soft actuators that can perform tensile and torsional actuations are summarized, including twisted fiber artificial muscles, shape memory polymers, hydrogels, liquid crystal polymers, electrochemical actuators with conducting polymers, and some natural materials. Examples are also included regarding the bending or rolling deformations of the actuators for lifting objects. Then, recent interesting reports about multifunctional soft actuators combined with sensing and signal transmission performances, are summarized. Last, a summary of different ways to realize tensile and torsional actuations, different materials, and designs for lifting or moving objects, as well as construction of multifunctional actuators with actuation and sensing functions is provided.
Predicting Net Returns of Organic and Conventional Strawberry Following Soil Disinfestation with Steam or Steam Plus Additives

Pre-plant methods for managing soil-borne pests and diseases are an important priority for many agricultural production systems. This study investigates whether the application of steam is an economically sustainable pre-plant soil disinfestation technique for organic and conventional strawberry (Fragaria ananassa) production in California’s Central Coast region. We analyze net returns from field trials using steam and steam + mustard seed meal (MSM) as pre-plant soil disinfestation treatments. ANOVA tests identify statistically significant differences in net revenues by treatment and trial. Multivariate regressions estimate the magnitude of these effects. Predictive polynomial models identify relationships between net returns and two treatment characteristics: maximum temperature (°C) and time at ≥60 °C (minutes). For organic production, net returns are statistically similar for the steam and steam + MSM treatments. For conventional production, the steam + MSM treatment has significantly higher net returns than the steam treatment. Cross-validated polynomial models outperform the sample mean for prediction of net returns, except for the steam + MSM treatment in conventional production. The optimal degree of the polynomial ranges from 1–4 degrees, depending on the production system and treatment. Results from two of three organic models suggest that maximum soil temperatures of 62–63 °C achieved for 41–44 min maximizes net returns and may be a basis for further experiments.
Large eddy simulation and experimental analysis of a swirl-stabilized flame under preheated and/or stratified conditions

The ever increasing global energy consumption and environmental concerns combined with the lack of energy resources have put designers of combustion devices to a difficult test in order to come up with new technologies that are more energy efficient and less polluting. At the present time lean premixed (LPM) combustion is the most promising technology for environmentally friendly combustion systems since operating under fuel lean conditions can have low emissions and high efficiency. As mixing of fuel and air before combustion is not perfect in many practical premixed applications, the mixture is not spatially and temporally uniform and burns in partially premixed regime. Depending on the degree of mixture non-uniformity, the structure, amount of emissions and even flammability limits of a flame in this intermediate regime could be totally different from a premixed one. In order to investigate this effect on LPM combustion, a reduced scale swirl-stabilized LPM burner is built and analyzed experimentally and numerically in premixed and two different partially premixed configurations in this thesis. Experiments are done first visually using a digital camera for all configurations from stoichiometry to lean blow out and are repeated for an open and confined flame in a wide range of swirl numbers and two Reynolds numbers. Next, CO and NO/NOX measurements are carried out for the confined cases at the exit of the combustor using a Nondispersive Infrared (NDIR) and a Chemiluminescence analyzer (CLA) respectively. To further investigate the experimental results, the confined cases are, then, numerically analyzed by solving a system of partial differential equations including mass, momentum, species and enthalpy balance for turbulent reacting flows using finite rate chemistry combustion model and large eddy simulation. CFD and chemistry simulations are carried out using standard and customized solvers and libraries of two open source codes, OpenFOAM and Cantera respectively. Finally, the flowfield and flame structure of the premixed and stratified swirl-stabilized LPM flame is studied in a more practical situation and over a wide range of preheating
Deep Learning Algorithm for Classification of Cerebral Palsy from Functional Magnetic Resonance Imaging (fMRI)

Cerebral palsy is a disorder of neurology that may be caused by prenatal, perinatal or postnatal reasons that result in the failure of motor functioning in children besides mental well-being. Referring to the location of brain injury and the effect of it on the muscle tone, cerebral palsy is classified into subgroups namely spastic, non-spastic etc. Each type of palsy varies in symptoms and hence the therapy planning and rehabilitation are decided depending on the factors involved in each type. This urges the requirement of a suitable technique to classify the type of Palsy at the earlier stages to effectively plan therapy. Functional MRI of the neonatal brain helps in imaging and classification of cerebral palsy. The deep neural network is a subset of machine learning that is widely used in image classification applications. This technique is applied to the functional magnetic resonance brain images of infants to classify the type of cerebral palsy using a deep convolutional network of modified AlexNet architecture that helps the physician further in a planned rehabilitation to facilitate the lifestyle of the affected children.
A Comprehensive Review on Photodynamic Therapy (PDT) and Photothermal Therapy (PTT) for Cancer Treatment

Cancer is a group of diseases characterized by uncontrolled and abnormal cell growth, leading to serious health consequences. Although various approaches are available for treating cancer, including chemotherapy, surgery, radiation, and immunotherapy, the severe adverse effects of these approaches limit their clinical effectiveness. New cancer treatment strategies including phototherapy uses light to treat cancer, which has attracted wide interest in the oncology research community. There are two types of phototherapy: photodynamic therapy (PDT) and phototherapy (PTT). PDT requires the administration of a photosensitizing agent and light exposure at a particular wavelength. On the other hand, PTT uses a photothermal agent that activates and kills cancer cells at a longer wavelength of light; hence, it is less energetic and, therefore, less harmful to other cells and tissues. PTT is gaining tremendous popularity because of its limited side-effects. A significant downside of PDT is that the photosensitizing drug stays in the body for a long time, which renders the patients extremely sensitive to light exposure. PDT is useful for the treatment of lining organs as they are can be easily reached by the light source. Although PDT is helpful for treating lining organs, its potential side-effects have been reported in the treatment of skin mouth esophagus and lung cancer, among others. Therefore, PTT remains a good alternative for cancer treatment.
A Novel Collective User Web Behavior Simulation Method

A collective user web behavior simulation is an import means for generating a large-scale user network behavior in a network testbed or cyber range. Existing studies almost focus on individual web behavior analysis and prediction, which cannot simulate human dynamics that widely exist in large-scale users’ behaviors. To address these issues, we propose a novel collective user web behavior simulation method, in which an algorithm for constructing a connected virtual social network is proposed, and then a collective user web behavior simulation algorithm is designed on the virtual social network. In the simulation method, a new epidemic information dissemination algorithm based on the SIR model is proposed to drive the user web behavior with Breadth—First Search algorithm on the connected virtual social network. We specially build an experiment environment with 12 servers by using Docker container technology and then perform a wide range of experiments with different user scales to evaluate the method. The experimental results demonstrate that not only the degrees of the social network but also the time intervals of the collective users’ web behavior can be well fitted to a power-law distribution and show that our simulation method can well simulate a collective user web behavior.
A Novel Three-Phase Transformerless Cascaded Multilevel Inverter Topology for Grid-Connected Solar PV Applications

Nowadays, multilevel inverters (MLIs) are gaining huge popularity for high power transformerless PV applications. Among the traditional MLIs, the cascaded H-bridge (CHB) MLI accommodates lower voltage rated input dc sources, which reduce the voltage stress across the devices. However, the CHB MLI requires multiple PV sources as separate dc-link voltage sources, which create more paths for leakage currents. Therefore, it is a challenging task to deal with the leakage currents in the case of CHB MLIs. In this article, a novel three-phase transformerless inverter topology for grid-connected solar PV application is introduced. This proposed that the inverter topology has six switches per phase, and it has the combined advantages of dc-bypass and ac-bypass circuit configurations. A new modulation strategy is developed for the proposed topology; it is based on a sine triangle pulsewidth modulation technique combined with the dedicated logic functions. The gate pulses for all switches are provided by using these dedicated logic functions. The switching pulses obtained from the dedicated logic functions control all the inverter switches so that the variation of common-mode voltage (CMV) is constant during the inverter operation. This results in reduced leakage current throughout the operation of the inverter. The theoretical analysis, simulations, and experimental results are presented to validate the proposed topology concept for a 3-kVA grid-connected system. Both the simulation and experimental results show that the proposed solution can well attenuate the leakage current; all the results are presented in the article.
Information diffusion with network structures

Information diﬀusion refers to the process about passing certain information from one subject to another. It is a typical and critical phenomenon observed in large scale social networks. To statistically model such a phenomenon, a network diﬀusion model is proposed and studied. The diﬀu-sion process is then investigated under the modeling framework from both the short term and long term perspectives. To estimate the model, a maximum likelihood estimator and a moment estimator are proposed, whose asymptotic properties are further established. The resulting estimators are manifested to have a reliable ﬁnite sample performance through a number of numerical studies. Lastly, the diﬀusion of earthquake news on Sina Weibo is analyzed to illustrate the practical usefulness.
Look Up, Look Down: Articulating Inputs and Outputs of Social Media Social Comparison

Computer-mediated social comparisons have been identified as a threat to psychological well-being. Because online friends selectively self-present, social comparisons may be biased upward, producing feelings of inadequacy. However, earlier evidence consistently confounded social comparative thoughts with causes or outcomes. A cross-sectional survey (N = 163) tested how traits, motivations, selectivity, and mood management influence computer-mediated downward and upward social comparison, and how comparison influences affect, self-esteem, and peer misperceptions. Results indicated age, social comparison orientation, mood modification, selectivity, and Facebook intensity produced social comparisons. Younger, frequent users made more upward comparisons, while mood modifiers made more downward comparisons. Comparing upward boosted negative affect, harmed self-esteem, and produced pluralistic ignorance. Downward comparisons enhanced self-esteem and reduced pluralistic ignorance about offline friends.
A Knowledge-Enhanced Multi-View Framework for Drug-Target Interaction Prediction

Motivation: The prediction of drug-target interaction (DTI) from heterogeneous biological data is critical to predict drugs and therapeutic targets for known diseases such as tumor and bowel disease. The study of DTI based on drug representation learning can strengthen or integrate our knowledge of pharmacological and chemical phenomena. Therefore, there is a strong motivation to develop effective methods that can detect these potential drug-target interactions. Results: We have developed a novel Knowledge-Enhanced Multi-View framework (KEMV) to predict unknown DTIs from pharmacological data and chemical data on a large scale. The proposed method consists of two steps: (i) learning more comprehensive drug representations via the proposed multi-view attention mechanism, which bridges pharmacological and chemical information, and interactively summarizes the attention values depending on varying interactions between different pairs of drug features. (ii) predicting unknown drug-target interactions based on the drug and target representations. The method is tested on real-world dataset KEGG with three classes of important drug–target interactions involving enzymes, ion channels, and G-protein-coupled receptors. Our framework is proven to uncover potential DTIs with scientific evidences explaining the mechanism of the interactions through the processing of high-dimensional, heterogeneous, and sparse drug data. Availability: The originality of the proposed method lies in the attentive integration of pharmacological and chemical information for representation of drug candidate compounds and the prediction of drug-target interaction toward drug discovery in a unified framework. Our results are reproducible and and code is available at: https://github.com/YuanKQ/DTI-Prediction.
PREVALENCE AND ANTIBIOTIC SUSCEPTIBILITY OF ACINETOBACTERIN A TERTIARY CARE HOSPITAL IN CHENNAI, INDIA

Objective: The present study is to determine the prevalence and antibiotic susceptibility of Acinetobacter species in samples collected from patients in tertiary care hospital in Chennai. Methods: A total of17,827patient’s clinical samples were collected from various wards and ICUs of Saveetha Medical College and Hospital, Chennai, Tamilnadu over a period of 7 mo [between January 2020 and July 2020]. All samples were tested in the microbiology lab of Saveetha Medical College and Hospital using standard operating procedures. Results: Out of 17,827 samples, 2,816 were culture positive. 122 of the isolates tested positive for Acinetobacter spp.and 81.1% of the isolates belonged to Acinetobacterbaumannii. Most of the infection occurred in the age group of 21-40 y and predominantly in female patients (female, male ratio 1.9:1). General wards contributed to 54.9% of the Acinetobacter infection, followed by ICU(27%) and OPD(18%). Maximum isolates were recovered from urine(34.4%) and endotracheal secretions(29.5%). 60.7% of the Acinetobacterspp were multidrug-resistant(MDR)i.e. resistant to more than 3 antibiotic group.In our study, most Acinetobactersppwere resistant to penicillin(46-100%), third and fourth generation cephalosporin (36-61.5%), carbapenems (34.4-82.8%)and quinolones(39.3-46.7%). None of the isolates were resistant to colistin. 93.4% ofisolates were sensitive to tigecycline and 87.7% sensitive to amikacin. Conclusion: Our study observed a high incidence of MDR inAcinetobacterspp, which is in line with most of the research findings in recent times. Most of Acinetobacterspp were resistant to penicillin, third and fourth generation cephalosporins, quinolones, carbapenems,which is alarming as it leaves fewer options for the line of treatment. Some strains were sensitive to cefepime, ceftazidime, piperacillin-tazobactam, levofloxacin, imipenem and meropenem. Considering the increasing MDR nature of Acinetobacterspp a combination of the former along with colistin, tigecycline, amikacin(which have shown more than 85% sensitivity) would need to be studied.Also, strict measures to control the spread of Acinetobacter infection, better management of antibiotics usage and newer therapeutic option for treatment need to be looked at.
Exsolved Fe–Ni nano-particles from Sr2Fe1.3Ni0.2Mo0.5O6 perovskite oxide as a cathode for solid oxide steam electrolysis cells

A stable and catalytically active cathode consisting of homogeneously dispersed nano-socketed Fe–Ni particles has been elegantly fabricated in single-step treatment for solid oxide steam electrolysis cells via the in situ reduction of the Sr2Fe1.3Ni0. 2Mo0.5O6 (SFMNi) material in a humidified H2 (3 vol% H2O) atmosphere at 800 °C. The SFMNi–SDC/LCO/LSGM/SDC–LSCF electrolysis cell exhibits a high electrolysis current density of 1257 mA cm−2 and a high hydrogen production rate of 525 mL cm−2 h−1 at an applied voltage of 1.3 V at 850 °C. Moreover, the in situ grown nano-structured cathode shows a good short-term stability at a constant electrolysis current of −300 mA cm−2, resulting from the strong interface between the exsolved particles and the parent perovskite.
P53/miR-34a/SIRT1 Positive Feedback Loop regulates cell proliferation and promotes cell apoptosis in the termination stage of liver regeneration.

Background The capacity of the liver to restore its architecture and function assures good prognoses of patients who suffer serious hepatic injury or cancer resection. In our study, we found that the P53/miR-34a/SIRT1 positive feedback loop has a remarkable negative regulatory effect, which is related to the termination of liver regeneration. Here, we described how P53/miR-34a/SIRT1 positive feedback loop controls liver regeneration and its possible relationship with liver cancer.Method We performed partial hepatectomy (PH) in mice transfected with adenovirus (Ade) overexpressing P53 and adenovirus-associated virus (AAV) knock-downing miR-34a. LR was analyzed by liver weight/body weight, serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels and cell proliferation, and the related cellular signals were investigated. Bile acid (BA) levels during LR were analyzed by metabolomics of bile acids. Results We found that the P53/miR-34a/SIRT1 positive feedback loop was activated in the late phase of LR. Overexpression of P53 terminated LR early and enhanced P53/miR-34a/SIRT1 positive feedback loop expression and its proapoptotic effect. Mice from the Ade-P53 group showed smaller livers and higher levels of serum ALT and AST than control mice. While knock-down of miR-34a abolished P53/miR-34a/SIRT1 positive feedback loop during LR. Mice from anti-miR-34a group showed larger livers and lower levels of PCNA-positive cells than control mice. T-β-MCA increased gradually during LR and peaked at 7 days after PH. T-β-MCA inhibited cell proliferation and promoted cell apoptosis via facilitating the P53/miR-34a/SIRT1 positive feedback loop during LR by suppressing FXR/SHP. Conclusion The P53/miR-34a/SIRT1 positive feedback loop plays an important role in the termination of LR. Our findings shed light on the molecular and metabolic mechanisms of LR termination and provide a potential therapeutic alternative for treating P53-wild-type HCC patients.
The Effect of Tamarind Leaves (tamarindus indica linn) and Acarbose on Body Weight in Rats Type 2 Diabetes Mellitus Model

— Diabetes mellitus is a disease that causes an increase in blood glucose so that glucose in the calories is difficult to enter the cell and glycogenolysis process occurs then continues to the process of gluconeogenesis so that the body lacks energy and unloads food reserves in the muscles and adipose which results in the occurrence of weight loss in people with T2DM. Antioxidants are needed, namely tamarind leaves to help glucose enter cells. This study used 30 wistar rats that were 8 weeks old and weighed 150-200 g. Rats were induced with STZ as much as 45 mg / kg, then Na as much as 110 mg / kg. In this study, rats were grouped into 5 groups namely, KN groups which were DMT2 rats without treatment, KP group who were DMT2 rats given acarbose drugs by 1.8 mg / KgBB / day and Group P1, P3, P3 were given leaf treatment tamarind with a dose of 28.56, and 112 mg / 200gr / day for 14 days. All data were collected and analyzed using ANOVA and LSD post hoc with p value <0.05. In fasting blood glucose, the mean of all groups of rats was 264.83 ± 1.47 and the mean of body weight of rats after STZ induction was 180 ± 3.84. After 7 days of treatment, the body weight of rats in the treatment of KP (184.17 ± 3.18), P1 (182.50 ± 6.15), P2 (182.17 ± 5.77), and P3 (189.67) ± 2.65) increased significantly, whereas in the KN group (173.67 ± 4.59) weight loss occurred in the DMT2 model rats. Giving extract of tamarind leaves in P3 is better than KP which is an acarbose drug. Provision of tamarind leaves for 7 days, in the treatment group P3 increased body weight of rats and treatments P1 and P2 increased body weight the same as the treatment group KP (acarbose).