Greenhouse gas emissions in US beef production can be reduced by up to 30% with the adoption of selected mitigation measures.

Greenhouse gas (GHG) emissions from beef production in the United States are unevenly distributed across the supply chain and production regions, complicating where and how to reduce emissions most effectively. Using spatially explicit life cycle assessment methods, we quantify the baseline GHG emissions and mitigation opportunities of 42 practices spanning the supply chain from crop and livestock production to processing. We find that the potential to reduce GHGs across the beef sector ranges up to 30% (20 million tonnes COe reduced and 58 million tonnes CO sequestered each year relative to the baseline) under ubiquitous adoption assumptions, largely driven by opportunities in the grazing stage.

Salivary and Serum Interleukin-6: A Credible Marker for Predicting Oral Leukoplakia and Oral Squamous Cell Carcinoma by Enzyme-Linked Immunosorbent Assay (ELISA).

Background: Oral squamous cell carcinoma (OSCC) is the most prevalent subtype of oral cancer. Detecting oral potentially malignant disorders (OPMDs) in their early stages is crucial to prevent their advancement into OSCC. One of the primary factors contributing to OSCC is tobacco use, which can lead to increased production of cytokines.

Improving ecosystem health in highly altered river basins: a generalized framework and its application to the Mississippi-Atchafalaya River Basin.

Continued large-scale public investment in declining ecosystems depends on demonstrations of "success". While the public conception of "success" often focuses on restoration to a pre-disturbance condition, the scientific community is more likely to measure success in terms of improved ecosystem health. Using a combination of literature review, workshops and expert solicitation we propose a generalized framework to improve ecosystem health in highly altered river basins by reducing ecosystem stressors, enhancing ecosystem processes and increasing ecosystem resilience.

Genetic synergy between undecaprenyl phosphate biosynthesis and the Mla system impacts cell envelope and antimicrobial resistance.

is a Gram-negative bacterial pathogen that poses a major health concern due to increasing multidrug resistance. The Gram-negative cell envelope is a key barrier to antimicrobial entry and includes an inner and outer membrane. The maintenance of lipid asymmetry (Mla) system is the main homeostatic mechanism by which Gram-negative bacteria maintain outer membrane asymmetry.

Genetic synergy in undecaprenyl biosynthesis and maintenance of lipid asymmetry impacts outer membrane and antimicrobial resistance.

is a Gram-negative healthcare-associated pathogen that poses a major health concern due to increasing multidrug resistance. The Gram-negative cell envelope is a key barrier to antimicrobial entry and includes an inner and outer membrane. The outer membrane has an asymmetric composition that is important for structural integrity and barrier to the environment.

The heterotrophic eubacterial and archaeal co-inhabitants of the halophilic in solar salterns fed by Bay of Bengal along south eastern coast of India.

Halophilic microbes are studied to understand the metabolic pathways adopted by organisms in such extreme environment and for their biotechnological exploitation. In thallosohaline environments worldwide, the autotrophic alga Teodoresco is omnipresent, but it is being recently realised that the heterotrophic components vary in different regions. The unexplored eastern coastline of India abutted by Bay of Bengal was investigated for the heterotrophic halophilic microbes in this region.

Distributed Newton Methods for Deep Neural Networks.

Deep learning involves a difficult nonconvex optimization problem with a large number of weights between any two adjacent layers of a deep structure. To handle large data sets or complicated networks, distributed training is needed, but the calculation of function, gradient, and Hessian is expensive. In particular, the communication and the synchronization cost may become a bottleneck.

Weekly injection of histaglobulin produces long-term remission in chronic urticaria: A prospective clinical study.

Objective: Treatment of chronic urticaria (CU) can be difficult in many patients. Achieving long-term remission and reducing the requirement of antihistamines are vital in CU. The objective of this study was to assess the effectiveness of injection histaglobulin, a complex of histamine and human immunoglobulin, in producing relief in patients with CU.

Infiltrating ductal carcinoma of the breast with coexisting lymphocytic mastitis in a non-diabetic adult female.

Lymphocytic mastitis, also known as diabetic mastopathy or sclerosing lymphocytic lobulitis, is a benign clinicopathological entity that, in earlier studies, has been described as an uncommon cause of breast mass in adult females with long-standing insulin-dependent diabetes mellitus. Further studies have suggested an autoimmune aetiology owing to its association with other autoimmune diseases such as Hashimoto's thyroiditis. On clinical examination, mammography and ultrasound, this lesion may mimic breast carcinoma.

Clinical and Laboratory Steps for Fabricating a Complete-Arch Fixed Prosthesis Using CAD/CAM.

The fabrication of a full-arch maxillary prosthesis has been associated with several prosthetic complications and difficulties. Even though it has been reported that phonetics, esthetics, and proper lip support are difficult to achieve, there is a scarcity in the literature regarding the clinical and laboratory procedures necessary to minimize these complications. This article provides clinical and laboratory steps that may enable the clinician to achieve more predictable restorative results when using computer-aided design/computer-assisted manufacture (CAD/CAM) to fabricate a full-arch maxillary implant-supported prosthesis.

Telmisartan aggravates pustular psoriasis.

Pustular psoriasis is characterized by abrupt onset of macroscopic pustules associated with erythema and symptoms of burning pain and constitutional symptoms. There are several precipitating factors, both physiological such as pregnancy and routinely prescribed drugs like antihypertensives, antifungals, corticosteroids and progesterone. We present a case of a 50-year-old male patient with pustular psoriasis, well controlled on oral methotrexate, who presented with sudden exacerbation of pustular psoriasis following the use of telmisartan.

Molecular characterization of outer membrane vesicles released from Acinetobacter radioresistens and their potential roles in pathogenesis.

Acinetobacter radioresistens is an important member of genus Acinetobacter from a clinical point of view. In the present study, we report that a clinical isolate of A. radioresistens releases outer membrane vesicles (OMVs) under in vitro growth conditions.

An Immature Type II Dens Invaginatus in a Mandibular Lateral Incisor with Talon's Cusp: A Clinical Dilemma to Confront.

Dens invaginatus (DI) is a malformation of teeth probably resulting from an infolding of the dental papilla during tooth development. DI is classified as type I, II, and III by Oehlers depending on the severity of malformation. The maxillary lateral incisor is the most commonly affected tooth.

Improvements to the SMO algorithm for SVM regression.

This paper points out an important source of inefficiency in Smola and Schölkopf's sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression. These modified algorithms perform significantly faster than the original SMO on the datasets tried.

A fast iterative nearest point algorithm for support vector machine classifier design.

In this paper we give a new fast iterative algorithm for support vector machine (SVM) classifier design. The basic problem treated is one that does not allow classification violations. The problem is converted to a problem of computing the nearest point between two convex polytopes.

A stochastic connectionist approach for global optimization with application to pattern clustering.

In this paper, a stochastic connectionist approach is proposed for solving function optimization problems with real-valued parameters. With the assumption of increased processing capability of a node in the connectionist network, we show how a broader class of problems can be solved. As the proposed approach is a stochastic search technique, it avoids getting stuck in local optima.

Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms.

The paper discusses implementation issues related to the tuning of the hyperparameters of a support vector machine (SVM) with L/sub 2/ soft margin, for which the radius/margin bound is taken as the index to be minimized, and iterative techniques are employed for computing radius and margin. The implementation is shown to be feasible and efficient, even for large problems having more than 10000 support vectors.

Support vector ordinal regression.

In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples.

Fast generalized cross-validation algorithm for sparse model learning.

We propose a fast, incremental algorithm for designing linear regression models. The proposed algorithm generates a sparse model by optimizing multiple smoothing parameters using the generalized cross-validation approach. The performances on synthetic and real-world data sets are compared with other incremental algorithms such as Tipping and Faul's fast relevance vector machine, Chen et al.

Parallel sequential minimal optimization for the training of support vector machines.

Sequential minimal optimization (SMO) is one popular algorithm for training support vector machine (SVM), but it still requires a large amount of computation time for solving large size problems. This paper proposes one parallel implementation of SMO for training SVM. The parallel SMO is developed using message passing interface (MPI).

An improved conjugate gradient scheme to the solution of least squares SVM.

The least square support vector machines (LS-SVM) formulation corresponds to the solution of a linear system of equations. Several approaches to its numerical solutions have been proposed in the literature. In this letter, we propose an improved method to the numerical solution of LS-SVM and show that the problem can be solved using one reduced system of linear equations.

Bayesian support vector regression using a unified loss function.

In this paper, we use a unified loss function, called the soft insensitive loss function, for Bayesian support vector regression. We follow standard Gaussian processes for regression to set up the Bayesian framework, in which the unified loss function is used in the likelihood evaluation. Under this framework, the maximum a posteriori estimate of the function values corresponds to the solution of an extended support vector regression problem.

An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels.

In this paper, we give an efficient method for computing the leave-one-out (LOO) error for support vector machines (SVMs) with Gaussian kernels quite accurately. It is particularly suitable for iterative decomposition methods of solving SVMs. The importance of various steps of the method is illustrated in detail by showing the performance on six benchmark datasets.