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Irritable bowel syndrome (IBS) is the most prevalent disorder of brain-gut interactions that affects between 5 and 10% of the general population worldwide. The current symptom criteria restrict the diagnosis to recurrent abdominal pain associated with altered bowel habits, but the majority of patients also report non-painful abdominal discomfort, associated psychiatric conditions (anxiety and depression), as well as other visceral and somatic pain-related symptoms. For decades, IBS was considered an intestinal motility disorder, and more recently a gut disorder. However, based on an extensive body of reported information about central, peripheral mechanisms and genetic factors involved in the pathophysiology of IBS symptoms, a comprehensive disease model of brain-gut-microbiome interactions has emerged, which can explain altered bowel habits, chronic abdominal pain, and psychiatric comorbidities. In this review, we will first describe novel insights into several key components of brain-gut microbiome interactions, starting with reported alterations in the gut connectome and enteric nervous system, and a list of distinct functional and structural brain signatures, and comparing them to the proposed brain alterations in anxiety disorders. We will then point out the emerging correlations between the brain networks with the genomic, gastrointestinal, immune, and gut microbiome-related parameters. We will incorporate this new information into a systems-based disease model of IBS. Finally, we will discuss the implications of such a model for the improved understanding of the disorder and the development of more effective treatment approaches in the future.

Carbon foot prints (CFs) studies based on life cycle assessment between sheep farming systems and green house gases (GHG) emissions is one of the best indicators to quantify the amount of GHG emissions per kg of product. Therefore, a life cycle assessment (LCA) study was conducted for three different sheep farming systems i.e. intensive system (stall fed only), semi-intensive (grazing with supplementation) and extensive system (grazing only) under semiarid region of India to assess the carbon cost of sheep rearing. The total CFs were estimated to be 16.9, 15.8 and 17.1 kg CO 2 -eq in intensive, semi-intensive and extensive system of grazing indicating semi-intensive system to be most carbon (C) efficient. For 1kg mutton production in semi-intensive and intensive system, around 30% and 24% CFs were contributed from enteric fermentation and feed respectively, whereas, in extensive system, the contribution of enteric fermentation increased up to 50%. The carbon foot prints analysis gives an insight of carbon inputs used but the amount of CO 2 sequestered in soil making LCA a holistic approach for estimating GHG emissions from livestock.

Renal cell carcinoma (RCC) is the most lethal of the common urological cancers, and is increasing in incidence globally. Bhatt and Finelli present a Timeline of landmarks in four centuries of research and treatment of RCC, the most common renal cancer, discussing current therapies and future prospects. The most common renal cancer is renal cell carcinoma (RCC), which arises from the renal parenchyma. The global incidence of RCC has increased over the past two decades by 2% per year. RCC is the most lethal of the common urological cancers: despite diagnostic advances, 20–30% of patients present with metastatic disease. A clearer understanding of the genetic basis of RCC has led to immune-based and targeted treatments for this chemoresistant cancer. Despite promising results in advanced disease, overall response rates and durable complete responses are rare. Surgery remains the main treatment modality, especially for organ-confined disease, with a selective role in advanced and metastatic disease. Smaller tumours are increasingly managed with biopsy, minimally invasive interventions and surveillance. The future promises multimodal, integrated and personalized care, with further understanding of the disease leading to new treatment options.

Our understanding of protein synthesis has been conceptualised around the structure and function of the bacterial ribosome. This complex macromolecular machine is the target of important antimicrobial drugs, an integral line of defence against infectious diseases. Here, we describe how open access to cryo-electron microscopy facilities combined with bespoke user support enabled structural determination of the translating ribosome from Escherichia coli at 1.55 Å resolution. The obtained structures allow for direct determination of the rRNA sequence to identify ribosome polymorphism sites in the E. coli strain used in this study and enable interpretation of the ribosomal active and peripheral sites at unprecedented resolution. This includes scarcely populated chimeric hybrid states of the ribosome engaged in several tRNA translocation steps resolved at ~2 Å resolution. The current map not only improves our understanding of protein synthesis but also allows for more precise structure-based drug design of antibiotics to tackle rising bacterial resistance. Developments in cryo-EM sample preparation and data collection are pivotal for structure determination. Fromm et al. present a 1.55 Å structure of the translating bacterial ribosome that provides new insights on its function and may allow for more precise structure-based drug design.

The optical properties of transition metal dichalcogenides (TMDs) are known to change dramatically with doping near their excitonic resonances. However, little is known about the effect of doping on the optical properties of TMDs at wavelengths far from these resonances, where the material is transparent and therefore could be leveraged in photonic circuits. We demonstrate the strong electrorefractive response of monolayer tungsten disulfide (WS2) at near-infrared wavelengths (deep in the transparency regime) by integrating it on silicon nitride photonic structures to enhance the light–matter interaction with the monolayer. We show that the doping-induced phase change relative to the change in absorption (|∆n/∆k|) is ~125, which is significantly higher than the |∆n/∆k| observed in materials commonly employed for silicon photonic modulators, including Si and III–V on Si, while accompanied by negligible insertion loss.Strong electrorefractive effects in semiconductor transition metal dichalcogenides (TMDs) at near-infrared wavelengths, where the TMDs are transparent, are observed and used to demonstrate photonic devices based on a composite SiN–TMD platform with large phase modulation, minimal induced loss and low electrical power consumption.

Energy transferred via thermal radiation between two surfaces separated by nanometer distances can be much larger than the blackbody limit. However, realizing a scalable platform that utilizes this near-field energy exchange mechanism to generate electricity remains a challenge. Here, we present a fully integrated, reconfigurable and scalable platform operating in the near-field regime that performs controlled heat extraction and energy recycling. Our platform relies on an integrated nano-electromechanical system that enables precise positioning of a thermal emitter within nanometer distances from a room-temperature germanium photodetector to form a thermo-photovoltaic cell. We demonstrate over an order of magnitude enhancement of power generation ( P gen  ~ 1.25 μWcm −2 ) in our thermo-photovoltaic cell by actively tuning the gap between a hot-emitter ( T E  ~ 880 K) and the cold photodetector ( T D  ~ 300 K) from ~ 500 nm down to ~ 100 nm. Our nano-electromechanical system consumes negligible tuning power ( P gen / P NEMS ~ 10 4 ) and relies on scalable silicon-based process technologies. Designing a scalable platform to generate electricity from the energy exchange mechanism between two surfaces separated by nanometer distances remains a challenge. Here, the authors demonstrate reconfigurable, scalable and fully integrated near-field thermo-photovoltaics for on-demand heat recycling.

Cervical intraepithelial neoplasia (CIN) and cervical cancer are major health problems faced by women worldwide. The conventional Papanicolaou (Pap) smear analysis is an effective method to diagnose cervical pre-malignant and malignant conditions by analyzing swab images. Various computer vision techniques can be explored to identify potential precancerous and cancerous lesions by analyzing the Pap smear image. The majority of existing work cover binary classification approaches using various classifiers and Convolution Neural Networks. However, they suffer from inherent challenges for minute feature extraction and precise classification. We propose a novel methodology to carry out the multiclass classification of cervical cells from Whole Slide Images (WSI) with optimum feature extraction. The actualization of Conv Net with Transfer Learning technique substantiates meaningful Metamorphic Diagnosis of neoplastic and pre-neoplastic lesions. As the Progressive Resizing technique (an advanced method for training ConvNet) incorporates prior knowledge of the feature hierarchy and can reuse old computations while learning new ones, the model can carry forward the extracted morphological cell features to subsequent Neural Network layers iteratively for elusive learning. The Progressive Resizing technique superimposition in consultation with the Transfer Learning technique while training the Conv Net models has shown a substantial performance increase. The proposed binary and multiclass classification methodology succored in achieving benchmark scores on the Herlev Dataset. We achieved singular multiclass classification scores for WSI images of the SIPaKMed dataset, that is, accuracy (99.70%), precision (99.70%), recall (99.72%), F-Beta (99.63%), and Kappa scores (99.31%), which supersede the scores obtained through principal methodologies. GradCam based feature interpretation extends enhanced assimilation of the generated results, highlighting the pre-malignant and malignant lesions by visual localization in the images.

Background The neutrophil-to-lymphocyte ratio (NLR) is a strong predictor of mortality in patients with colorectal, gastric, hepatocellular, pancreatic, and lung cancer. To date, the utility of NLR to predict mortality in breast cancer patients has not been studied. Therefore, the aim of our study was to determine whether the NLR is predictive of short- and long-term mortality in breast cancer patients. Methods Our observational study used an unselected cohort of breast cancer patients treated at the Staten Island University Hospital between January 2004 and December 2006. A total of 316 patients had a differential leukocyte count recorded prior to chemotherapy. Survival status was retrieved from our cancer registry and Social Security death index. Survival analysis, stratified by NLR quartiles, was used to evaluate the predictive value of NLR. Results Patients in the highest NLR quartile (NLR > 3.3) had higher 1-year (16% vs 0%) and 5-year (44% vs 13%) mortality rates compared with those in the lowest quartile (NLR < 1.8) ( P  < .0001). Those in the highest NLR quartile were statistically significantly older and had more advanced stages of cancer. After adjusting for the factors affecting the mortality and/or NLR (using two multivariate models), NLR level > 3.3 remained an independent significant predictor of mortality in both models (hazard ratio 3.13, P  = .01) (hazard ratio 4.09, P  = .002). Conclusion NLR is an independent predictor of short- and long-term mortality in breast cancer patients with NLR > 3.3. We suggest prospective studies to evaluate the NLR as a simple prognostic test for breast cancer.

Purpose The purpose of this paper is to study the effect of Malaysian Code on Corporate Governance (MCCG, 2007 and 2012) on the performance of the listed companies in Malaysia. The agency theory and resource dependency theories indicate that the firms with strong corporate governance outperform firms with weaker governance. This paper explores this relationship in a developing country like Malaysia having different institutional environment compared to western countries. Design/methodology/approach The study used a sample of 113 listed companies in Malaysia. The study incorporates the endogenous relationship between corporate governance, firm performance and leverage. Findings The study analyzes how the corporate governance framework affected firm performance in Malaysia with the help of self-developed corporate governance index (MCGI). The authors’ findings show that the performance of the firm is positively and significantly related with corporate governance measured by MCGI. Secondly, corporate governance of sample firms shows marked improvements after implementation of MCCG 2012 as compared to MCCG 2007. Originality/value The findings of this paper support the agency and the resource dependency theories. The study contributes to the understanding of the relationship between the corporate governance and firm performance in emerging economy and builds a case for enforcement of strong corporate governance code by government agencies.

Localized operating conditions inside boilers, heat recovery steam generators, or other large thermal systems have a huge impact on the efficiency, environmental performance, and lifetime of components. It is extremely difficult to measure species accurately within these systems due to the high temperatures and harsh environments, locally oxidizing or reducing atmospheres, ash, other particulates, and other damaging chemical species. Physical probes quickly suffer damage and are rendered nonfunctional. This work has attempted to adapt the measurement approach based on Scheimpflug light detection and ranging (S-LIDAR) for the remote sensing of gas species inside the high-temperature boiler environment. For a proof-of-concept, the detection of Raman signals of N2, O2, and CO2 and their behavior with increasing temperature have been presented.